to development of roads, flyovers, routes, traffic signals, etc. It also offers the root .... Kolkata, Chennai, Bangalore and Delhi are classic examples. It has been the ...
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According to the Global Status Report on Road Safety by the World Health Organisation [WHO, 2013], India has the highest absolute number of recorded road deaths (105,725), followed by China (96,611), the US (42,642) and Russia (35,972). In comparison, the UK has 3,298 recorded road deaths In the context of road deaths proportional to a country's population, the Cook Islands comes out on top (45.0 road deaths per 100,000 people), followed by Libya (34.7), South Africa (33.2) and Iran (32.2). According to [43] and [PTV Group, 2004], reasons for road accidents are manifold and so are the approaches for improvement.
There are three main areas of action regarding measures for improvement of the road safety situation. These are human, vehicles and the road infrastructure. Researchers have explored factors contributing to accidents in both urban and regional landscapes. The prime human contributing factor to accident is speed [JOB. S, 2009] [FLEITER ET AL, 2010] [MORTON. R & WHITE, 2013] [RoSPA, 2013].
ϯϭ
Other driver related accident factors include alcohol, social components and driver precipitating attributes [FLEITER ET AL, 2010] [MOSEDALE, 2004] while [OBST, P L ET AL., 2011] noted age and gender issues while tired. Age is also identified by [WHO, 2014] for the demographic class of drivers aged 15-29 years. Some of these components are mitigated with the introduction of fixed speed cameras, resting area provisions and training programs [HAQUE ET AL., 2009]. Car manufacturing standards were raised by [WHO, 2009] as a consideration into accidents. However, successful vehicular enhancements have already taken place with the introduction of passive and active safety restraints systems [LAUFER ET AL., 2013] [PTV Group, 2004].
This leaves the road infrastructure with significant potential for improvements. Elements relating to road infrastructure safety issues are extensively covered including alignment, gradient, surface, drainage [HAQUE ET AL., 2009] and the impact of variable speeds [ABDUL HANAN ET AL., 2011]. [JOB. S, 2009] identifies solutions such as wire caEOLQJRQPRWRUZD\PHGLDQVUHGXFHVDFFLGHQWUDWHVZLWKIHZHU³KHDGRQ´DQG³UXQRII URDG´FUDVKHV$QXPEHURIDXWKRUVDOVRRXWOLQHWKHEHQHILWVRI,QWHOOLJHQW6SHHG$GDSWLRQ to communicate with drivers and manage top end of speeds without affecting average speeds [CARSTEN, O, 2009] as well as Pay-As-You-Speed disincentives [AGERHOLM, N, 2009]. However, the application potential of smart approaches based on integrated computer assisted mapping, analysis, predictions of accidents and implications of infrastructure improvement with traffic planning is less explored. A number of authors have developed Accident Prediction Models (APM) to facilitate such safety impact assessment for future. These models operate in a similar manner to demand forecasting models ± each are different but often contain common variables. [EENINK, R and REURINGS, M, 2007] examined APM in the context of traffic volume, and segment lengths as well as turn pocket allocation. [SCHUELLER, H, 2011] examined 20,000 accidents to determine a speed behavior model that included speed ranges (free flow, mean and 85th percentile), components of land uses, intersection number and road sections, median demarcation, public transport and on street parking issues.
ϯϮ
Figure 2.4. Combining Transportation Planning with Road Safety Source: [PTV Group, 2004]
2.2.8. Summary In the review of literature, various researches done in the field of traffic planning have been studied. Traffic planning focuses upon the public provision and financing of transportation assets, particularly roads and public transit systems. There are different methods adopted in the traffic survey and traffic volume counts. The Four-Step Model provides an effective way for analyzing the traffic and helps in traffic planning process. Traffic forecasting have been considering an important measure to predict the future development in road used and hence taken an important factor for traffic planning. Parking analysis provides the optimization of parking problems because, parking is the major issue for traffic congestion. Traffic accident analysis provides the information about the effect of accident on road user and traffic conditions. Traffic accident should be considering at prior level because safety is the prime factor consider for traffic planning.
ϯϯ
CHAPTER - 3 CASE STUDY
ϯϰ
CASE STUDY
3.1. General Madhubani District (Fig.2) is one of the thirty-eight districts of Bihar state (Fig.1), India, and Madhubani town is the administrative headquarters of this district. Madhubani district is a part of Darbhanga Division. The district occupies an area of 3501 km² and has a population of 3,570,651 (as of 2001). This is the centre of Mithila, a region where the main language is Maithili. This mid-sized city has high traffic operations through-out the city area. There are lots of road connecting the different junctions, intersections, crossings, etc. The city has huge participation of public transport traffic for in/out through city to different destinations.
3.2. About Mid-Sized City: Madhubani
Fig.3.1. Map of Bihar
ϯϱ
Fig.3.2. Map of Madhubani
The district of Madhubani was carved out of the old Darbhanga district in the year 1972 as a result of re-organization of the districts in the State. This was formerly the northern subdivision of Darbhanga district. It consists of 21 Development Blocks. Bounded on the north by a hill region of Nepal and extending to the border of its parent district Darbhanga in the south, Sitamarhi in the west and Supaul in the east, Madhubani fairly represents the centre of the territory once known as Mithila and the district has maintained a distinct individuality of its own. It is located at a Longitude of 25º-59' to 26º-39' East and the Latitude is 85º-43' to 86º-42' North. The Madhubani district is situated at height of 80 meters from Sea.
3.2.1. Area and Geography Table.3.1. Area & Geography
Items
Units
Total Area
3501 sq.kms.
Main Rivers
Kamla,
Kareh,
Bhutahi
Balan,
ϯϲ
Balan, Gehuan,
Supen, Trishula, Jeevachh, Koshi & Adhwara Group. Highest Flood Level
54.017 m
Earthquake Zone
V
Total Cropped Area
218381 Hectare
Barren/Uncultivated Land
1456.5 Hectare
Land under Non-agricultural use
51273.24 Hectare
Cultivable Barren Land
333.32 Hectare
Permanent Pasture
1372.71 Hectare
Miscellaneous Trees
8835.90 Hectare
Cultivable Land
232724 Hectare
Cropping Intensity
134.23 %
Rainfall Varies Between
900 mm and 1300 mm
Average Rainfall
1273.2 mm
3.2.2. Demograph According to the 2011 census Madhubani district has a population of 4,487,379 roughly equal to the nation of Croatia or the US state of Louisiana. This gives it a ranking of 37th in India (out of a total of 640). Population comprises of male and female as 2,329,313 and 2,158,066 respectively. The district has a population density of 1,282 inhabitants per square kilo-metre (3,310/sq mi). Its population growth rate over the decade 2001-2011 was 25.19%. Madhubani has a sex ratio of 926 females for every 1000 males and a literacy rate of 58.62%. Table.3.2. Administrative Units
Administrative
Numbers
Units
Administrative Units
Sub-Divisions
5
Outposts
5
Blocks
21
Town Outposts
4
Circles
20
Villages
1111
Panchayats
399
MP Constituencies
2
ϯϳ
Numbers
Police Stations
18
MLA Constituencies
11
Assisting Thana
13
Zila Parishad Members
56
As per the Census of Madhubani in 2001 and 2011, the following data was observed: Table.3.3. Census data of Madhubani
Description
2011
2001
Actual Population
4,487,379
3,575,281
Male
2,329,313
1,840,997
Female
2,158,066
1,734,284
Population Growth
25.51%
26.08%
Area (Sq. Km.)
3,501
3,501
Density/Km2
1,282
1,021
Proportion to Bihar Population
4.31%
4.31%
Sex Ratio per 1000
926
942
Average Literacy
58.62
41.97
Literates
2,155,338
1,195,776
Male Literates
1,340,085
832,849
Female Literates
815,253
362,927
Table.3.4. Rural & Urban Population
Description
Rural
Urban
Population %
96.40 %
3.60 %
Total Population
4,325,884
161,495
Male Population
2,244,287
85,026
Female Population
2,081,597
76,469
Sex Ratio
928
899
Literates
2,058,895
96,443
Male Literates
1,283,625
56,460
Female Literates
775,270
39,983
Average Literacy
58.14 %
71.06 %
ϯϴ
3.2.3. Madhubani District Urban Population Out of the total Madhubani population for 2011 census, 3.60 percent lives in urban regions of district. In total 161,495 people lives in urban areas of which males are 85,026 and females are 76,469. Sex Ratio in urban region of Madhubani district is 899 as per 2011 census data. Similarly, child sex ratio in Madhubani district was 925 in 2011 census. Child population (0-6) in urban region was 25,775 of which males and females were 13,388 and 12,387. This child population figure of Madhubani district is 15.75 % of total urban population. Average literacy rate in Madhubani district as per census 2011 is 71.06 % of which males and females are 78.81 % and 62.39 % literates respectively.
3.3. Roads of High Traffic Operation Though the Madhubani city is the key place for major marketing and employment of the district, there is always a huge traffic moving throughout the city. There are lots of road network on which the traffic condition is very serious, but there are certain roads with high traffic operations through-out the city. There are following certain major roads having high traffic operations: a. Thana Chowk Road b. Neelam Chowk Road c. Bata Chowk Road d. Churi Bazaar Road e. Mahila College Road f. Railway Station Road g. Ganga Sagar Chowk Road h. Bus Stand Road i. Bara Bazaar Road j. Chavaccha Mor
So, to understand the road networks of high traffic operation of the city, the following figures shows the pattern of road intersections.
ϯϵ
Fig.3.3. Intersection Drawing of Thana Chowk Road
Fig.3.4. Intersection Drawing of Neelam Chowk Road
ϰϬ
Fig.3.5. Intersection Drawing of Bata Chowk Road
Fig.3.6. Intersection Drawing of Churi Bazaar Road
ϰϭ
Fig.3.7. Intersection Drawing of Mahila College Road
Fig.3.8. Intersection Drawing of Railway Station Road
ϰϮ
Fig.3.9. Intersection Drawing of Ganga Sagar Chowk Road
Fig.3.10. Intersection Drawing of Bus Stand Road
ϰϯ
Fig.3.11. Intersection Drawing of Bara Bazaar Road
Fig.3.12. Intersection Drawing of Chavaccha Mor Road
ϰϰ
3.4. Present Situation of Road & Traffic Traffic and transportation plays an important role in the overall functioning of the city. It is an integral part of urban planning and is responsible for the smooth functioning of the city. It is also responsible, besides other factors, for the spatial growth of the city by increasing the accessibility of sites on the periphery of the city.
A study of the transport infrastructure for Madhubani is crucial for the understanding and analyzing micro level (within the different zones of the city ± linkages between the different parts of city and the market).
Total length of the roads within the city area is to be measured, which constitute roads maintained by state PWD department and other roads maintained by MCM. Out of total length of the roads, MCM maintains approximately 70% roads, which are internal arterial roads & narrow streets in the old town area are as shown in Table 3.5. Table.3.5. Road Length
Sr.No.
Route
Length in Kms.
1
Kotwali Chowk-Thana Chowk-Station Road-Ganga Sagar
3.78
Chowk-Bus Stand Road-Shankar Chowk-Chavaccha Mor 2
Kotwali Chowk-Thana Chowk-Mahila College Road-
3.58
Ganga Sagar Chowk Road-Shankar Chowk RoadChavaccha Mor 3
Kotwali
Chowk-Thana
Chowk-Neelam
Chowk-Bata
Chowk-Churi Bazaar-Shankar Chowk-Chavaccha Mor
ϰϱ
4.58
Fig.3.13. Traffic congestion at Bata
Fig.3.15. Unauthorized Bus Stop
chowk
Fig.3.14. Unauthorized parking at Thana
Fig.3.16. Roadside Parking
Chowk
3.5. Issues Regarding Traffic in Madhubani Madhubani have mixed traffic conditions. The major traffic problem in the city is traffic congestion & traffic jam. This cause the traffic delay, fuel wastage, physiological & psychological impact to the motorists & passengers. There are following factors which can focus on the traffic related issues in the city: a. Unavailability of traffic signal b. Improper road marking c. Unavailability of bus stop ϰϲ
d. Unavailability of parking facilities e. Improper traffic signs f. Single lane road g. Worst drainage system h. Improper planning of vehicle inspection in respect of driving license, helmet, shoes, etc. i. Unawareness of traffic rules j. Improper planning for enforcement of traffic police
3.6. Study Done in this Research In this research, study of traffic condition of Madhubani city have been done to enhance and provide a better traffic planning approach to improve the traffic problems of the city and reduce the traffic congestion, accidents, etc. For this purpose, the major traffic operated roads have been observed. These roads have been surveyed to understand the traffic volume, AADT, road use pattern and traffic flow characteristics of the city. The previous accident data have been obtained from the city police station to understand, analyze & predict the accident rate for the city in the present condition of traffic in future & in improved case of traffic in future. Spot speed study have been carried out to analyze the speed limits. The number of vehicle running on the roads of Madhubani have been obtained from the District Transport Office to analyze the vehicle growth in past decades and to forecast the vehicle growth in the future decade. This will be considered a major factor for planning the traffic system of the city. On the basics of results obtained, improvement methodologies and recommendations have been made.
3.7. Summary The case study of Madhubani city have been used just for observing, analyzing and formulation a good traffic planning system for a mid-sized city. As Madhubani is a midsized city having no any traffic planning system, it had huge growth in traffic in last
ϰϳ
GHFDGH,WGRHVQ¶Wmatter whether the city is small or large, where there is extreme traffic growth, there must need a traffic planning approach to maintain the proper traffic system of city. Lots of deficiencies are found in small cities regarding traffic. So, to overcome on the deficiencies of traffic, lots of parameters have been considered to formulate a better traffic plan. Traffic survey is very useful for understanding the traffic conditions. Accident record also gives the traffic & road condition. DTO vehicle record helps us to analyze or forecast the traffic of the city. These all the parameters have been focused for making the traffic plan for a mid-sized city.
ϰϴ
CHAPTER - 4 RESEARCH METHODOLOGY
ϰϵ
RESEARCH METHODOLOGY
4.1.
General
There are lots of parameter to be consider for making a traffic plan for a mid ± sized city. The set of actions to be followed for getting a result on traffic state have been practiced by some certain methods with mathematical analysis of the data obtained by different methods. As traffic planning will be used to implement by road administration will utilize the data as follow: ¾ Analyzing the problem. ¾ Defining the problem. ¾ Formulating the goals. ¾ Formulating the methodologies to meet the goals. ¾ Defining the benefits of the system. ¾ Economic evaluation of system. ¾ Level of utilization. ¾ Policies & measures.
4.2.
Data Collection Methods
There are several set of methods have been used to formulate a method to meet the objective of research. All the methods will be formulated to collect the necessary data and make a method to analyze the data to go through a useful result. The major methods used to make a traffic plan for a mid ± sized city are explained below. 4.2.1. Field Study/Survey Method The very first step of traffic planning is the field study. It means to analyze the current status of traffic in the city. So, field study can be carried out by survey method. Traffic survey gives us several parameters to carry out the research work such as traffic volume, traffic flow characteristics, traffic capacity, parking analysis, traffic flop or accident studies, etc. Two major survey useful to analyze the current traffic conditions are: a. Traffic Survey
ϱϬ
The main purpose of traffic survey is to get the observations for traffic volume, traffic flow characteristics, traffic capacity, etc. Traffic volume is termed as the number of vehicles crossing a section of the road per unit time at any selected period of time. It is used as a quantity measure of flow. Traffic volume study may have classified the volume study by recording the volume of various types and class of vehicles. The daily traffic volume may vary in weeks as well in seasons. So, the true picture can be obtained by knowing the hourly traffic volume along with the pattern of hourly, daily and seasonal variations. For the classified traffic volume study, the traffic is classified under several categories such as buses, passenger cars, truck, bycycle, rickshaw, NMV-3 wheeler, tongas, bullock carts, pedestrians, etc. and the volume of each class should be noted separately. Generally, traffic stream flow and counter flow along a common route, unless the stream is separated into pair of one-way flows by proper design and regulation. Talking about the traffic capacity, it is the ability of a roadway to accommodate traffic volume. It is the maximum number of vehicle in a lane or on a road that can pass a given point in unit time. Traffic capacity and traffic volume are measures of traffic flow and have the same units. Volume represents an actual rate of flow and responds to variations in traffic demand, while capacity indicates a capability or maximum rate of flow with a certain level of service characteristics that can be carried by the roadway. Video photography mode and data sheet method had been adopted for traffic survey because in less manpower, it can be setup easily and survey can be easily done. Video photography mode includes a high quality digital camera, a tripod stands, adequate power supply at-least for half an hour. The camera has been set-up on the side of the road and video mode started. After recording the video for the desired time, the camera video transferred into the laptop. In laptop, video have been used to analyze the traffic data. The different class of the vehicles have been counted from the video and filled in the data sheet at the given time interval in the data sheet. Further, the filled data sheet has been used to analyze the data for the required result. The survey time have been taken from 9 am to 11 am as 1st session and 4 pm to 6 pm as 2nd session for continuous three days of the same sight fir the uniformity of data. The reason for selecting the specific survey time is as because at this time, there is very more traffic due to school time, office time, market
ϱϭ
time, etc. As we have very much site, I had surveyed some selected site where traffic is at its peak level. The performa used to record the traffic survey data is given in Table. 4.1 and 4.2. Table.4.1. Data Sheet for Traffic Survey in 1st Session Time
PCU
2-
3-
Ca
Jeep/Va
Min
Bu
LC
Truc
Tracto
Bicycl
Tricycl
Tota
PC
Wheele
Wheele
r
n
i
s
V
k
r
e
e
l
U
r
r
0.5
1.2
2.2
1.4
2.2
4
0.4
1.5
Bus 1
1
1.4
09:00 09:15 09:15 09:30 09:30 09:45 09:45 10:00 10:00 10:15 10:15 10:30 10:30 10:45 10:45 11:00 Total PCU
Traffic survey had been carried out on the ten major junctions of the road in session wise. i.e., morning peak hours and evening peak hours.
The detailed traffic survey data
with different classes of vehicles in respect to the time have been presented in chapter ± 5. The detailed traffic survey data have been expressed in tabular form shown in Table.5.1., Table.5.2., table.5.3., Table.5.4., Table.5.5., table.5.6., Table.5.7., Table.5.8., table.5.9., Table.5.10., Table.511., table.5.12., Table.5.13., Table.5.14., table.5.15., Table.5.16., Table.5.17., table.5.18., Table.5.19. and Table.5.20. ϱϮ
Table.4.2. Data Sheet for Traffic Survey in 2nd Session Time
PCU
2-
3-
Ca
Jeep/Va
Min
Bu
LC
Truc
Tracto
Bicycl
Tricycl
Tota
PC
Wheele
Wheele
r
n
i
s
V
k
r
e
e
l
U
r
r
0.5
1.2
2.2
1.4
2.2
4
0.4
1.5
Bus 1
1
1.4
04:00 04:15 04:15 04:30 04:30 04:45 04:45 05:00 05:00 05:15 05:15 05:30 05:30 05:45 05:45 06:00 Total PCU
b. Parking Survey Parking space demand by the automobile user is a major problem of highway transportation, especially in metropolitan city but now a day, parking demand is also at peak level in new growing cities i.e. mid-sized cities. Parking facilities should be given priority while planning a traffic system of a city and will be considers as a prime importance because it will help the city by making the traffic congestion free. Various aspects are investigated during parking study, such as:
ϱϯ
a. Parking demand b. Parking characteristics c. Parking space inventory So, the traffic survey should be focused in such a way that in the busy market, there should be proper survey for parking facilities. If at any place, there is an existing parking facilities exists, there should be survey for the existing parking facility in order to improve the parking facilities and to make the city area free from congestion. The same method should be used for parking survey which have been used for traffic survey and the data sheet performa will also be same.
4.2.2. Spot Speed Method The spot speeds measurement is performed at any particular location will depend upon lots of factor such as traffic composition, road condition, geometric layout, traffic volume, environmental factors, human factors, vehicular characteristics, etc. By using this method in this research work, the current different speeds limit is analyzed and the improvement if needed will be formulated. By using percentile speeds, the different categories of speeds such as design speed, speed limit, lower speed limit, etc. are analyzed. Stopwatch spot speed study method have been adopted because of the small sample size taken over a relatively short period of time. The stopwatch method is quick and inexpensive method for collecting spot speed data. A stopwatch spot speed study includes five key steps: 1. Obtain appropriate study length. 2. Select proper location and layout. 3. Record observations on stopwatch spot speed study data form. 4. Calculate vehicle speeds. 5. Generate frequency distribution table and determine speed percentiles.
Fig.4.1. Stopwatch spot speed study layout ϱϰ
The following is the generalized table used to collect the data for speed. Table.4.3. Performa of Survey for Spot Speed
Route:
Date/Time:
Speed Range (KMPH)
No. of vehicles observed
0 ± 10 10 ± 20 20 ± 30 30 ± 40 40 ± 50 50 ± 60 60 ± 70 70 ± 80 80 ± 90 90 ± 100 Spot speed data for the different road sections are shown in Table.5.22, Table.5.23 and Table.5.24.
4.2.3. Accident Study Method Safe traffic movement is the main objective of traffic engineering. So, in order to plan a safe traffic system, traffic engineering will have to consider the systematic accident studies to investigate the cause of accidents and to take preventive measures in terms of design and control. The statistical analysis of accidents carried out periodically at critical locations or road stretches or zones will help to arrive at suitable measures to effectively decrease the rate of accidents. Accident study will help in measuring the black spot. The accident data were collected throughout the city area of Madhubani. The police stations have the fir records of accident of several years. Last ten \HDUV¶ data were extracted from the data record from record number. A sample copy of data record performa is shown in the table.
ϱϱ
Table.4.4. Performa for accident data from FIR record
Year
Number of accidents
Fatal Injury
Major Injury
Minor Injury
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Total
To understand the accident in more precise way, the following table gives us some more detail of some major accidents held. Table.4.5. Performa for major accident data from FIR record
Date/Day/Time
Location Accident
of Details Accident
of Vehicle(s) Involved
Possible Reasons
During 2006-2015, accident details in the city area are shown in Table No. 5.25. Year wise accident data were collected from police station record and then sorted out year wise. Major accidents in the city is shown in Table.5.26.
ϱϲ
4.2.4. DTO Data Method
District Transport Office is the only governmental authority which provides the UHJLVWUDWLRQWRWKHYHKLFOH9HKLFOHXVHUJHWWKHLUYHKLFOH¶VUHJLVWUDWLRQDQGYHKLFOHQXPEHU from DTO. The number of vehicles purchased from the district will have to register their vehicle from DTO. So, from DTO, with prior permission to the responsible officer, we get the number of different class of vehicles registered in the district. This number of vehicle will give an idea of number of vehicles moving in the city. This yearly DTO vehicle record will helps in future forecasting of number of vehicles. The future forecasting of vehicles will help us to understand the future traffic demand and to make the traffic plan more precise for maximum period of time. A sample copy of DTO vehicle data record performa is given in Table.
Table.4.6. Performa for DTO vehicle data record Year
Two
Three
Wheelers Wheelers
Cars, Jeeps, Tractors
LMV
Bus/Truck
Total
Vans
2011 2012 2013 2014 2015 Total
Previous ten years DTO vehicle data have been consider from 2011 ± 2015 for the present and future analysis of vehicle & traffic. Year wise DTO vehicle data were collected from DTO office and then sorted out month wise. Yearly variation of categorized vehicles is shown in Table.5.27.
ϱϳ
4.2.5. Public Questionnaire Method The actual status of traffic and its deficiencies can easily be understanding by asking the public/motorist about their problem faced by them while driving the vehicles, their experience about the present traffic system available. So, I had introduced a method called Public Questionnaire Method in which I had made some set of questions to monitor the present traffic status, difficulties and traffic awareness among the motorist. This will help in setting traffic enforcement, awareness measures, need for the development in the present traffic conditions. It will have a great impact for the development of traffic systems and strictness in traffic rules as it gives us lots of real information about the present conditions. It will justify the deficiencies in the present traffic system and in motorist too. Table.4.7. shows the performa of Questionnaire. Table.4.7. Public Questionnaire Form Date:
Name:
Vehicle Reg.
Place:
No.:
Sr.No. Questions
Response
1.
Are you regularly travelling through this route?
2.
Do you have driving license?
3.
Do you wear helmet and shoes regularly while travelling?
4.
On an average, how much time you lose due to congestion
5.
Do you think about the need of traffic signals in our city?
6.
Do you park the vehicles at proper parking place?
7.
Does the traffic police work efficiently to remove
every day?
congestions and conflicts? 8.
'R\RXWKLQNWRKDYH&&79¶VLQVWDOOHGRQURDGWRPRQLWRU traffic and crimes?
9.
Any Complaint?
10.
Any Suggestion?
Signature ϱϴ
4.3.
Data Administration
For all advantageous administration frameworks, the vitality of compelling information organization can't be over accentuated. The association between the information, the responsibility for information and a point by point depiction of the information must be effectively settled and characterized at the beginning and kept up for the duration of the life of the framework. It is the obligation of the administration inside an association to advertise the imperativeness of powerful information organization and to guarantee that staff is generally prepared and have a proper order for the acknowledgment of this assignment. Specific consideration is obliged where information hails from sources outside the association. Administration must make clear what data is needed, which associations are mindful and what information are to be supplied. The appropriation of an organized methodology will distinguish any crevices in the data and will highlight any information that are of deficient quality.
4.4.
Data Analysis Methods
Data collection method explains several methods needed to execute the research. Data analysis for making a traffic plan for a mid-sized city includes some methods which can provide an analysis methodology for the data collected and necessary result needed for the research. Traffic planning for a mid-sized city includes the following data analysis: ¾ Identification of present traffic condition. ¾ Identification of previous traffic status. ¾ Identification of accident vulnerable roads. ¾ Interpretation of gathered traffic data. ¾ Traffic forecasting of the city. Basically, there are two major types of data analysis: a. Technical Analysis
ϱϵ
Technical analysis deals with the technical aspects of research. It includes the analysis of data collected through the method of data collection methodology. Basically, it deals with the accident analysis, present traffic analysis, traffic volume analysis, traffic forecasting analysis, etc. In accident analysis, accident rate and frequency will be analyzed by its formula. Annual, monthly, hourly variations of accidents are analyzed. Vehicles involved, accident related to the traffic volumes, trends of injuries, traffic road side features impacting accidents are monitored and analyzed. All the traffic survey data collected needs uniformity in the sense of unit. So, all the vehicular counts are converted into passenger car unit (PCU). By PWD traffic data, it helps to analyze and compare the traffic data from past with respect to present data. DTO traffic data will be used to analyze the vehicle growth factor and vehicle future forecasting. It gives analysis with the average daily traffic, hourly daily traffic, daily PCU. Different mathematical methods will be used to analyze the data for technical analysis such as Multiple Linear Regression method, accident rate & frequency method, etc. Brief of technical analysis is executed in chapter ± 6. b. Economic Analysis Economic analysis deals with the economic evaluation for execution and implementation of this research. Economic analysis involves the economic evaluation of the research in the sense of project implementation, monitoring, maintenance, etc. cost. It will also consider the cost of HTXLSPHQW¶V, machineries installed for the traffic planning purpose. The estimated data will be compared to the existing economic value of same project of specific implementation/construction/installing costs. The economic analysis focuses on the motto of less economical and more valuable work as no any government will willing to invest much more for the traffic development of a mid-sized or small city. Table.4.8. Types of analysis
Type of analysis
Analysis
Technical Analysis
Vehicle Growth Rate Vehicular/Traffic volume study Road Use Pattern Traffic Count Analysis
ϲϬ
Traffic Forecasting Capacity and Level of Service Parking Demand Road construction demand Accident Rate Accident Forecasting Spot Speed Analysis Motorist security
4.5.
Work Plan with Timeline
Different part of the research work has been completed with making research objective, reading research papers, preparing introduction, literature review, research methodology, data collection & analysis, etc. with a different time frame. Following table shows the progress of this research work with the timeline.
Table.4.9. Work Plan with Timeline
Sr.
Work
Time
No. 01
Motivation towards research, Problem
January 2015
Finding. 02
Problem Identification and Research
February 2015
03
Introduction of research.
March 2015
04
Reviewing Literatures
April 2015
05
Case study preparation & Institutional
May 2015
formulation.
Evaluation 06
Formulation of Methodologies & Traffic
June 2015
Survey 07
Traffic Survey
July 2015
ϲϭ
08
Traffic Survey & Data Presentation
August 2015
09
Data Presentation & Methodology
September 2015
Improvement 10
Data Presentation & Report Drafting
October 2015
11
Data Presentation & Report Drafting
November 2015
12
Report Drafting & Submission for Dissertation
December 2015
±I 13
Data Presentation & Analysis
January 2016
14
Data Analysis & Drafting
February 2016
15
Improvement Methodology, Limitations
March 2016
16
Result & Discussion & Final Report
April 2016
Preparation Conference and Publication of Research
4.6.
Summary
Several methodologies have been developed to carry out this research work and the methodologies to analyze the data have been also developed to make this research worth. All the parameters needed to make a traffic plans have been studied. All the elements of data collection have been kept very accurate in order to analyze the data in a result oriented manner. The data comparison with the previous data makes the project report more precise. Traffic survey kept in the sense of improvement of traffic state of the city as well as making a good parking facilities for the vehicles. The technical analysis makes the research into a decisional result. The methodologies are developed in such a way that it can be easily make understand and easily implemented for any small or mid-sized city.
ϲϮ
CHAPTER - 5 DATA COLLECTION
ϲϯ
DATA COLLECTION
5.1.
General
Data collection is the stage of research work where we can justify our methodologies and make a decisional strategy for the result of research. The methodologies explained in the chapter ± 4, based on that, the data are collected from different sources for all the individual methodologies. Traffic survey have been carried out electro-manually to study the number of movements of vehicle with respect to time. Accident data have been extracted from police records. The yearly vehicle record has been provided by District Transport Office. Manually, spot speed study survey have been performed. Feb data records have been obtained from Right to Information Act of Bihar State for the actual verification of our survey data record from the past records of the data. Public questionnaire data have been collected by making a camp on a particular route for few hours and data have been recorded from motorists manually. This is all about data collection performed for the research work. On the basics of the obtained data, the different methodologies have been adopted to analyze these data in chapter ± 6.
5.2.
Traffic Survey Data
Electro-manually, present traffic data have been recorded on every major intersection of road. At every site, the data were collected in two sessions, i.e., morning session & evening session. For each and every survey, the data analysis has been carried out accordingly and the methodologies used to analyze the data have been explained and justified. Initially, at one site, the data have been collected form morning of 8 am to 12 pm and in evening from 3 pm to 7 pm. This has been done to find the peak hour of traffic i.e., the time when there is the maximum movement of vehicles on the road. So, by this, peak time have been obtained and according to the peak time, traffic survey has been carried out. The traffic survey video has been captured in DSLR Camera and have been analyzed to find the number of vehicles moving on the road at different time. The traffic survey has been carried out only for vehicular count at different time. Pedestrian movement have not been considering for traffic survey. Traffic survey is a very beneficial
ϲϰ
tool for analyzing the current movement of traffic and the current vehicle movement record is the best tool for the planning and development of traffic related parameters. 5.2.1. Thana Chowk Road In order to justify the session time, this site have been surveyed from 8 am to 12 pm and 3pm to 7 pm on 02nd June 2015. Table.5.1. Data for Traffic Survey at Thana Chowk Road in 1st Session Mi ni Bu s
Bus
LC V
Tru ck
Tract or
5
2
3
2
3
3
2
4
3
2
1
1
3
1
5
1
3
0
49
6
4
6
4
2
09:0 009:1 5
32
5
5
6
5
09:1 509:3 0
42
3
3
5
09:3 009:4 5
42
5
2
09:4 510:0 0
53
6
10:0 010:1 5
42
5
Tim e
2Whee ler
3Whee ler
C ar
08:0 008:1 5
28
3
3
08:1 508:3 0
21
4
08:3 008:4 5
19
08:4 509:0 0
Tricy cle
Tot al
PCU
43
3
98
78.1
1
34
0
73
51.1
2
0
44
3
81
53.6
0
1
2
23
2
99
74.1
1
2
2
3
33
4
98
80.6
4
5
1
2
2
34
3
104
81.1
7
6
0
2
1
2
43
1
111
76.1
4
8
5
1
3
0
3
42
2
127
90.9
4
5
2
3
2
1
1
52
1
118
76.7
Jeep/ Van
ϲϱ
Bicy cle
10:1 510:3 0
36
5
3
4
3
5
1
0
1
38
4
100
72.8
10:3 010:4 5
31
4
5
6
2
6
2
2
3
36
3
100
85.4
10:4 511:0 0
29
6
6
5
4
4
1
1
2
32
6
96
80.5
11:0 011:1 5
32
6
5
4
3
3
0
3
2
37
4
99
78.4
11:1 511:3 0
28
7
5
3
2
4
1
2
4
29
1
86
76.9
11:3 011:4 5
21
4
3
5
2
5
2
0
1
26
2
71
57.3
11:4 512:0 0
19
6
2
6
1
4
0
2
2
32
3
77
64.6
524
78
57
84
49
51
20
23
32
578
42
153 8
262
93.6
57
84
68. 6
112 .2
28
50. 6
128
231. 2
63
Tota l PC U
1178 .2
Wh
Variation of PCU with Time ϭϬϬ ϵϬ ϴϬ ϳϬ ϲϬ ϱϬ ϰϬ ϯϬ ϮϬ ϭϬ Ϭ
Wh >ŝŶĞĂƌ;WhͿ
dŝŵĞ Fig.5.1. Variation of PCU with time on Thana Chowk Road in 1st Session ϲϲ
04:1 504:3 0 04:3 004:4 5 04:4 505:0 0 05:0 005:1 5 05:1 505:3 0 05:3 005:4 5 05:4 506:0 0 06:0 006:1 5 06:1 506:3 0 06:3 006:4 5 06:4 507:0 0 Tota l PC U
31
1
3
2
3
1
1
0
0
52
2
96
53.3
42
2
2
2
2
4
0
2
0
56
4
116
71.8
58
3
4
1
3
2
2
1
1
48
1
124
75.9
56
3
2
0
2
3
2
0
1
62
3
134
79.1
52
2
3
0
0
1
0
3
2
60
6
129
81.2
57
2
1
2
2
0
0
1
1
54
4
124
70.5
68
1
1
1
1
2
1
2
2
52
5
136
85.1
53
2
2
0
3
3
3
2
1
42
6
117
80.1
42
1
2
1
2
1
1
1
2
38
5
96
64.5
35
0
3
1
0
4
2
0
0
36
4
85
53.5
32
1
1
0
1
2
0
1
2
31
2
73
49.6
649
32
36
20
30
35
18
17
19
705
60
162 1
324.5
38.4
36
20
42
77
25. 2
37.4
76
282
90
ϲϴ
1048 .5
5.2.2. Neelam Chowk Road Date: 08th June 2015 Table.5.3. Data for Traffic Survey at Neelam Chowk Road in 1st Session
Jeep/V an
Mi ni Bu s
Bu s
LC V
2
1
0
0
2
1
0
62
8
111
64. 4
2
3
0
0
0
1
0
1
56
6
105
60. 2
34
3
2
0
0
0
2
0
0
52
8
101
58. 2
09:4 510:0 0
38
1
1
1
0
0
1
1
0
66
5
114
59. 7
10:0 010:1 5
34
0
3
1
0
0
0
0
1
64
4
107
56. 6
10:1 510:3 0
32
2
2
0
0
0
1
0
0
48
6
91
50
10:3 010:4 5
29
0
3
1
0
0
0
0
0
46
3
82
41. 4
10:4 511:0 0
36
2
1
0
0
0
0
0
0
52
6
97
51. 2
Tota l
271
13
17
4
0
0
7
2
2
446
46
808
PCU
135.5
15.6
17
4
0
0
9.8
4.4
8
178.4
69
Tim e
2Whee ler
3Whee ler
09:0 009:1 5
32
3
09:1 509:3 0
36
09:3 009:4 5
C ar
ϳϬ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
441 .7
Table.5.4. Data for Traffic Survey at Neelam Chowk Road in 2nd Session
Jeep/V an
Mi ni Bu s
Bu s
LC V
1
0
0
0
2
0
1
68
7
115
64. 9
1
2
1
0
0
0
1
0
54
12
113
67
30
2
1
2
0
0
1
0
2
58
7
103
63. 5
04:4 505:0 0
36
2
1
1
0
0
0
0
0
62
8
110
59. 2
05:0 005:1 5
42
0
0
0
0
0
0
0
1
59
7
109
59. 1
05:1 505:3 0
48
1
1
2
0
0
0
0
0
44
6
102
54. 8
05:3 005:4 5
42
0
0
1
0
0
0
0
1
56
5
105
55. 9
05:4 506:0 0
38
0
1
2
0
0
0
0
0
48
8
97
53. 2
Tota l
312
8
7
9
0
0
3
1
5
449
60
854
PCU
156
9.6
7
9
0
0
4.2
2.2
20
179.6
90
Tim e
2Whee ler
3Whee ler
04:0 004:1 5
34
2
04:1 504:3 0
42
04:3 004:4 5
C ar
ϳϮ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
477 .6
5.2.3. Bata Chowk Road Date: 15th June 2015 Table.5.5. Data for Traffic Survey at Bata Chowk Road in 1st Session Jeep/V an
Mi ni Bu s
Bu s
LC V
0
2
0
0
0
0
0
32
2
71
38. 1
2
1
1
0
0
1
0
0
38
4
79
43
35
1
0
0
0
0
0
0
0
42
5
83
43
09:4 510:0 0
29
0
1
2
0
0
1
0
0
32
2
67
34. 7
10:0 010:1 5
21
0
0
1
0
0
0
0
0
31
3
56
28. 4
10:1 510:3 0
26
1
1
2
0
0
1
0
0
35
4
70
38. 6
10:3 010:4 5
28
1
2
1
0
0
0
0
0
36
3
71
37. 1
10:4 511:0 0
32
0
1
0
0
0
0
0
0
44
4
81
40. 6
Tota l
234
9
6
9
0
0
3
0
0
290
27
578
PCU
117
10.8
6
9
0
0
4.2
0
0
116
40.5
Tim e
2Whee ler
3Whee ler
09:0 009:1 5
31
4
09:1 509:3 0
32
09:3 009:4 5
C ar
ϳϰ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
303 .5
Table.5.6. Data for Traffic Survey at Bata Chowk Road in 2nd Stretch
Jeep/V an
Mi ni Bu s
Bu s
LC V
0
2
0
0
0
0
0
36
8
90
51. 8
1
1
1
0
0
1
0
0
34
6
88
49. 2
43
0
2
1
0
0
0
0
0
38
5
89
47. 2
04:4 505:0 0
41
0
1
2
0
0
0
0
0
32
7
83
46. 8
05:0 005:1 5
38
0
0
2
0
0
0
0
0
45
5
90
46. 5
05:1 505:3 0
40
1
1
3
0
0
1
0
0
46
8
100
57
05:3 005:4 5
36
0
2
1
0
0
0
0
0
44
7
90
49. 1
05:4 506:0 0
32
0
1
0
0
0
0
0
0
48
8
89
48. 2
Tota l
316
4
8
12
0
0
2
0
0
323
54
719
PCU
158
4.8
8
12
0
0
2.8
0
0
129.2
81
Tim e
2Whee ler
3Whee ler
04:0 004:1 5
42
2
04:1 504:3 0
44
04:3 004:4 5
C ar
ϳϲ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
395 .8
5.2.4. Churi Bazaar Road Date: 22nd June 2015 Table.5.7. Data for Traffic Survey at Churi Bazaar Road in 1st Session Jeep/V an
Mi ni Bu s
Bu s
LC V
2
1
0
0
2
0
0
44
3
89
47. 8
2
1
0
0
0
1
0
0
38
4
84
45
37
1
2
0
0
0
0
0
1
41
5
87
49. 6
09:4 510:0 0
52
3
1
2
0
0
1
0
0
42
4
105
56. 8
10:0 010:1 5
47
0
1
1
0
0
0
0
0
41
5
95
49. 4
10:1 510:3 0
44
2
1
2
0
0
1
0
1
36
3
90
51. 7
10:3 010:4 5
48
1
2
1
0
0
1
0
0
38
4
95
50. 8
10:4 511:0 0
48
3
1
1
0
0
0
0
0
46
4
103
54
Tota l
349
14
11
8
0
0
6
0
2
326
32
748
PCU
174.5
16.8
11
8
0
0
8.4
0
8
130.4
48
Tim e
2Whee ler
3Whee ler
09:0 009:1 5
35
2
09:1 509:3 0
38
09:3 009:4 5
C ar
ϳϴ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
405 .1
Table.5.8. Data for Traffic Survey at Churi Bazaar Road in 2nd Session
Jeep/V an
Mi ni Bu s
Bu s
LC V
2
1
0
0
1
0
0
48
4
104
55
2
1
0
0
0
1
0
0
43
6
95
52
47
2
1
0
0
0
0
0
0
45
5
100
52. 4
04:4 505:0 0
53
3
1
2
0
0
0
0
0
47
6
112
60. 9
05:0 005:1 5
49
0
0
2
0
0
1
0
0
45
5
102
53. 4
05:1 505:3 0
48
2
1
2
0
0
0
0
0
41
4
98
51. 8
05:3 005:4 5
53
0
2
0
0
0
1
0
0
42
5
103
54. 2
05:4 506:0 0
49
3
1
1
0
0
0
0
0
47
3
104
53. 4
Tota l
387
14
9
8
0
0
4
0
0
358
38
818
PCU
193.5
16.8
9
8
0
0
5.6
0
0
143.2
57
Tim e
2Whee ler
3Whee ler
04:0 004:1 5
46
2
04:1 504:3 0
42
04:3 004:4 5
C ar
ϴϬ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
433 .1
5.2.5. Mahila College Road Date: 29th June 2015 Table.5.9. Data for Traffic Survey at Mahila College Road in 1st Session
Jeep/V an
Mi ni Bu s
Bu s
LC V
3
1
0
0
0
0
1
46
4
89
50. 1
3
1
0
0
0
2
0
2
48
3
94
56. 6
32
1
3
2
0
0
0
0
1
52
5
96
54. 5
09:4 510:0 0
36
2
1
2
0
0
1
0
0
44
4
90
48. 4
10:0 010:1 5
27
0
3
1
1
0
0
0
2
43
5
82
51. 6
10:1 510:3 0
33
2
1
2
0
0
1
0
1
51
3
94
52. 2
10:3 010:4 5
35
2
2
1
0
0
1
0
2
48
2
93
54. 5
10:4 511:0 0
31
2
2
2
2
0
0
0
0
47
4
90
49. 5
Tota l
262
13
16
11
3
0
5
0
9
379
30
728
PCU
131
15.6
16
11
4.2
0
7
0
36
151.6
45
Tim e
2Whee ler
3Whee ler
09:0 009:1 5
33
1
09:1 509:3 0
35
09:3 009:4 5
C ar
ϴϮ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
417 .4
Table.5.10. Data for Traffic Survey at Mahila College Road in 2nd Session
Jeep/V an
Mi ni Bu s
Bu s
LC V
2
2
2
0
0
0
0
52
5
102
55. 3
2
1
0
0
0
0
0
1
51
6
94
53. 3
35
1
2
1
0
0
1
0
0
48
8
96
54. 3
04:4 505:0 0
34
1
1
0
0
0
0
0
0
46
6
88
46. 6
05:0 005:1 5
31
0
3
2
0
0
0
0
0
49
5
90
47. 6
05:1 505:3 0
36
1
2
1
0
0
1
0
1
42
7
91
54. 9
05:3 005:4 5
38
2
1
2
0
0
0
0
0
43
4
90
47. 6
05:4 506:0 0
34
1
2
1
0
0
0
0
0
48
5
91
47. 9
Tota l
279
9
14
9
2
0
2
0
2
379
46
742
PCU
139.5
10.8
14
9
2.8
0
2.8
0
8
151.6
69
Tim e
2Whee ler
3Whee ler
04:0 004:1 5
38
1
04:1 504:3 0
33
04:3 004:4 5
C ar
ϴϰ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
407 .5
5.2.6. Railway Station Road Date: 06th July 2015 Table.5.11. Data for Traffic Survey at Railway Station Road in 1st Session Tim e
2Whee ler
3Whee ler
C ar
Jeep/V an
Mi ni Bu s
Bu s
LC V
09:0 009:1 5
46
4
4
4
5
3
4
3
2
56
3
134
96. 5
09:1 509:3 0
44
5
5
6
4
4
3
2
0
59
5
137
93. 1
09:3 009:4 5
48
4
6
5
5
2
2
0
3
57
3
135
93. 3
09:4 510:0 0
59
3
5
6
3
3
3
1
1
52
6
142
95. 1
10:0 010:1 5
52
4
3
7
4
2
2
2
3
55
4
138
98
10:1 510:3 0
46
3
6
5
5
4
1
2
2
49
5
128
94. 3
10:3 010:4 5
45
5
7
6
4
3
3
1
2
58
3
137
95. 8
10:4 511:0 0
41
4
8
6
5
3
2
1
1
53
6
130
92. 1
Tota l
381
32
44
45
35
24
20
12
14
439
35
108 1
PCU
190.5
38.4
44
45
49
52. 8
28
26.4
56
175.6
52.5
ϴϲ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
758 .2
Table.5.12. Data for Traffic Survey at Railway Station Road in 2nd Session
Tim e
2Whee ler
3Whee ler
C ar
Jeep/V an
Mi ni Bu s
Bu s
LC V
04:0 004:1 5
44
3
5
5
3
4
3
2
1
52
5
127
89. 5
04:1 504:3 0
48
4
3
4
2
2
3
1
2
54
4
127
85
04:3 004:4 5
51
3
4
6
4
4
1
0
2
55
6
136
93. 9
04:4 505:0 0
52
5
6
5
6
3
3
1
0
53
5
139
93. 1
05:0 005:1 5
55
4
4
4
5
1
2
2
2
56
3
138
91. 6
05:1 505:3 0
41
4
5
5
4
3
1
1
3
58
5
130
93. 8
05:3 005:4 5
43
3
6
3
5
4
2
2
2
57
4
131
93. 9
05:4 506:0 0
42
5
4
5
3
3
2
1
3
53
5
126
92. 5
Tota l
376
31
37
37
32
24
17
10
15
438
37
105 4
PCU
188
37.2
37
37
44. 8
52. 8
23. 8
22
60
175.2
55.5
ϴϴ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
733 .3
5.2.7. Ganga Sagar Chowk Road Date: 13th July 2015 Table.5.13. Data for Traffic Survey at Ganga Sagar Chowk Road in 1st Session Tim e
2Whee ler
3Whee ler
C ar
Jeep/V an
Mi ni Bu s
Bu s
LC V
09:0 009:1 5
36
3
4
3
5
4
4
2
3
55
3
122
92. 9
09:1 509:3 0
38
4
5
4
4
3
2
1
2
58
6
127
90. 2
09:3 009:4 5
42
4
5
3
3
4
4
0
3
54
5
127
93. 5
09:4 510:0 0
44
5
4
5
5
5
3
1
1
55
7
135
97. 9
10:0 010:1 5
42
3
4
4
4
3
2
1
0
57
5
125
80. 1
10:1 510:3 0
45
4
3
2
3
5
2
0
1
54
4
123
81. 9
10:3 010:4 5
43
2
5
4
4
4
1
1
0
53
6
123
81. 1
10:4 511:0 0
41
3
6
3
2
3
2
1
2
55
6
124
86. 5
Tota l
331
28
36
28
30
31
20
7
12
441
42
100 6
PCU
165.5
33.6
36
28
42
68. 2
28
15.4
48
176.4
63
ϵϬ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
704 .1
Table.5.14. Data for Traffic Survey at Ganga Sagar Chowk Road in 2nd Session
Tim e
2Whee ler
3Whee ler
C ar
Jeep/V an
Mi ni Bu s
Bu s
LC V
04:0 004:1 5
42
4
5
2
5
5
3
1
2
62
5
136
97. 5
04:1 504:3 0
44
3
4
4
4
4
4
1
1
61
4
134
90. 2
04:3 004:4 5
43
4
2
3
3
6
2
0
3
58
6
130
95. 7
04:4 505:0 0
41
5
4
2
5
5
1
1
2
56
5
127
92
05:0 005:1 5
45
3
3
3
3
4
2
0
1
61
7
132
86. 8
05:1 505:3 0
52
4
2
1
2
3
2
0
1
59
8
134
85. 6
05:3 005:4 5
51
2
3
2
3
5
3
0
1
53
5
128
85
05:4 506:0 0
43
1
4
3
1
4
1
1
0
56
6
120
74. 9
Tota l
361
26
27
20
26
36
18
4
11
466
46
104 1
PCU
180.5
31.2
27
20
36. 4
79. 2
25. 2
8.8
44
186.4
69
ϵϮ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
707 .7
5.2.8. Bus Stand Road Date: 20th July 2015 Table.5.15. Data for Traffic Survey at Bus Stand Road in 1st Session Jeep/V an
Mi ni Bu s
Bu s
LC V
3
3
5
5
2
0
2
48
3
102
76. 1
2
4
2
4
4
0
1
3
44
4
102
77. 6
32
3
3
4
6
5
3
0
2
46
3
107
81. 1
09:4 510:0 0
38
4
5
3
5
3
1
0
1
47
2
109
72. 6
10:0 010:1 5
42
2
4
2
4
5
3
0
1
46
5
114
80. 1
10:1 510:3 0
39
3
5
3
6
4
2
0
1
49
4
116
80. 7
10:3 010:4 5
43
4
3
3
3
4
1
0
2
46
5
114
80. 6
10:4 511:0 0
39
2
4
2
5
5
3
1
2
39
4
106
81. 9
Tota l
295
23
31
22
38
35
15
2
14
365
30
870
PCU
147.5
27.6
31
22
53. 2
77
21
4.4
56
146
45
Tim e
2Whee ler
3Whee ler
09:0 009:1 5
28
3
09:1 509:3 0
34
09:3 009:4 5
C ar
ϵϰ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
630 .7
Table.5.16. Data for Traffic Survey at Bus Stand Road in 2nd Session
Jeep/V an
Mi ni Bu s
Bu s
LC V
4
2
4
4
1
0
2
41
3
96
71
2
3
4
3
5
0
0
1
46
4
100
69
29
4
4
3
4
4
1
0
2
44
3
98
72. 2
04:4 505:0 0
34
3
3
3
3
5
1
0
3
46
2
103
76. 6
05:0 005:1 5
36
2
3
3
5
5
2
0
1
42
5
104
75. 5
05:1 505:3 0
48
2
2
2
4
3
0
0
0
52
6
110
72. 4
05:3 005:4 5
38
4
3
2
5
5
1
0
1
46
5
110
78. 1
05:4 506:0 0
37
2
2
2
4
4
1
0
2
39
4
97
70. 3
Tota l
285
23
24
21
32
35
7
0
12
356
32
818
PCU
142.5
27.6
24
21
44. 8
77
9.8
0
48
142.4
48
Tim e
2Whee ler
3Whee ler
04:0 004:1 5
31
4
04:1 504:3 0
32
04:3 004:4 5
C ar
ϵϲ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
585 .1
5.2.9. Bara Bazaar Road Date: 27th July 2015 Table.5.17. Data for Traffic Survey at Bara Bazaar Road in 1st Session Jeep/V an
Mi ni Bu s
Bu s
LC V
2
1
2
2
1
1
3
42
5
84
65. 4
3
2
3
1
1
0
1
1
54
5
99
61. 5
34
2
3
0
0
3
2
0
3
38
4
89
65
09:4 510:0 0
32
2
2
1
1
1
2
1
2
44
5
93
63. 1
10:0 010:1 5
33
4
3
2
3
2
1
0
1
47
3
99
63. 6
10:1 510:3 0
35
3
1
2
2
1
1
0
2
48
4
99
63. 7
10:3 010:4 5
31
2
1
1
1
3
0
0
2
46
5
92
61. 8
10:4 511:0 0
34
3
2
1
4
2
2
1
1
48
3
101
66. 3
Tota l
248
23
16
11
14
15
9
4
15
367
34
756
PCU
124
27.6
16
11
19. 6
33
12. 6
8.8
60
146.8
51
Tim e
2Whee ler
3Whee ler
09:0 009:1 5
21
4
09:1 509:3 0
28
09:3 009:4 5
C ar
ϵϴ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
510 .4
Table.5.18. Data for Traffic Survey at Bara Bazaar Road in 2nd Session
Tim e
2Whee ler
3Whee ler
C ar
Jeep/V an
Mi ni Bu s
Bu s
LC V
04:0 004:1 5
26
2
2
1
1
3
0
2
1
45
5
88
60. 3
04:1 504:3 0
35
3
3
2
1
1
0
0
0
48
8
101
60. 9
04:3 004:4 5
34
3
3
0
0
2
1
0
2
46
6
97
64. 8
04:4 505:0 0
32
2
1
1
1
1
2
0
2
43
6
91
61
05:0 005:1 5
38
2
2
1
1
3
1
0
1
44
5
98
62. 9
05:1 505:3 0
35
2
1
2
2
1
1
0
1
47
7
99
62. 6
05:3 005:4 5
33
2
1
0
1
2
0
0
2
42
8
91
62. 5
05:4 506:0 0
35
2
1
1
2
1
2
1
1
48
5
99
62. 6
Tota l
268
18
14
8
9
14
7
3
10
363
50
764
PCU
134
21.6
14
8
12. 6
30. 8
9.8
6.6
40
145.2
75
ϭϬϬ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
497 .6
5.2.10. Chavaccha Mor Date: 03rd August 2015 Table.5.19. Data for Traffic Survey at Chavaccha Mor in 1st Session Tim e
2Whee ler
3Whee ler
C ar
Jeep/V an
Mi ni Bu s
Bu s
LC V
09:0 009:1 5
38
4
0
2
2
2
2
1
2
46
3
102
68. 9
09:1 509:3 0
36
4
1
3
2
2
1
1
2
46
2
100
67
09:3 009:4 5
33
2
2
1
3
1
1
0
3
47
3
96
65
09:4 510:0 0
48
4
1
2
1
0
0
1
1
44
2
104
60
10:0 010:1 5
42
3
1
2
2
2
1
0
1
46
1
101
60. 1
10:1 510:3 0
41
3
2
2
1
2
1
0
2
46
2
102
64. 7
10:3 010:4 5
42
3
2
1
1
1
0
0
2
49
3
104
63. 3
10:4 511:0 0
44
3
1
1
2
2
1
0
1
46
1
102
60. 1
Tota l
324
26
10
14
14
12
7
3
14
370
17
811
PCU
162
31.2
10
14
19. 6
26. 4
9.8
6.6
56
148
25.5
ϭϬϮ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
509 .1
Table.5.20. Data for Traffic Survey at Chavaccha Mor in 2nd Session
Tim e
2Whee ler
3Whee ler
C ar
Jeep/V an
Mi ni Bu s
Bu s
LC V
04:0 004:1 5
42
4
1
3
2
1
1
1
3
43
2
103
70. 6
04:1 504:3 0
44
2
1
3
2
2
1
0
2
48
2
107
67. 2
04:3 004:4 5
45
3
2
1
2
1
2
0
2
44
3
105
67
04:4 505:0 0
46
4
3
2
3
1
0
0
0
52
4
115
66
05:0 005:1 5
49
5
1
3
2
2
1
0
1
46
1
111
67
05:1 505:3 0
44
4
2
2
3
1
1
0
1
46
3
107
65. 5
05:3 005:4 5
45
3
2
3
2
1
0
0
2
47
3
108
67. 4
05:4 506:0 0
48
4
2
1
2
2
1
0
1
46
2
109
65. 8
Tota l
363
29
14
18
18
11
7
1
12
372
20
865
PCU
181.5
34.8
14
18
25. 2
24. 2
9.8
2.2
48
148.8
30
ϭϬϰ
Tru ck
Tract or
Bicy cle
Tricy cle
Tot al
PC U
536 .5
5.3.
Spot Speed Data
The three most traffic demand road have been analyzed for speed study. They are Kotwali Chowk ± Thana Chowk road, Thana Chowk ± Bus Stand road and Bus Stand road ± Chavaccha Mor road. These routes are the main route for the major traffic movements. The speed of vehicles on the different road sections have been observed by stopwatch method. The Spot Speed survey have been carried out for 30 minutes of the peak hour session obtained from traffic survey. This survey has been carried out by the help of two peoples using cellphone for giving the information of the vehicles to record the responses. Speed had been obtained by time taken by the vehicle to cross the specific stretch distance. Table.5.21. Spot Speed road section with distance
Sr.No.
Road Stretch
Distance (KM)
1
Kotwali Chowk ± Thana Chowk Road
1.7
2
Thana Chowk ± Bus Stand Road
1.25
3
Bus Stand ± Chavaccha Mor Road
0.83
a. Kotwali Chowk ± Thana Chowk Road Table.5.22. Data for Spot Speed on Kotwali Chowk ± Thana Chowk road
Route: Kotwali Chowk ± Thana Chowk
Date/Time: 4th June 2015/10:30 ± 11:00 am
Speed Range (KMPH)
No. of vehicles observed
0 ± 10
8
10 ± 20
10
20 ± 30
38
30 ± 40
62
40 ± 50
66
50 ± 60
46
60 ± 70
5
ϭϬϲ
EŽ͘ŽĨsĞŚŝĐůĞƐ
^ƉĞĞĚǀͬƐEŽ͘ŽĨsĞŚŝĐůĞƐ ϳϬ ϲϬ ϱϬ ϰϬ ϯϬ ϮϬ ϭϬ Ϭ
EŽ͘ŽĨsĞŚŝĐůĞƐ
Ϭʹ ϭϬ
ϭϬʹ ϮϬ
ϮϬʹ ϯϬ
ϯϬʹ ϰϬ
ϰϬʹ ϱϬ
ϱϬʹ ϲϬ
ϲϬʹ ϳϬ
^ƉĞĞĚ;ŬŵƉŚͿ
Fig.5.41. Speed v/s no. of vehicles on Kotwali Chowk ± Thana Chowk Road
b. Thana Chowk ± Bus Stand Road Table.5.23. Data for Spot Speed on Thana Chowk ± Bus Stand road
Route: Thana Chowk ± Bus Stand
Date/Time: 8th July 2015/10:30 ± 11:00
Speed Range (KMPH)
No. of vehicles observed
0 ± 10
10
10 ± 20
16
20 ± 30
42
30 ± 40
51
40 ± 50
49
50 ± 60
33
60 ± 70
2
am
^ƉĞĞĚǀͬƐEŽ͘ŽĨsĞŚŝĐůĞƐ EŽ͘ŽĨsĞŚŝĐůĞƐ
ϲϬ ϱϬ ϰϬ ϯϬ ϮϬ
EŽ͘ŽĨsĞŚŝĐůĞƐ
ϭϬ Ϭ Ϭʹ ϭϬ
ϭϬʹ ϮϬ
ϮϬʹ ϯϬ
ϯϬʹ ϰϬ
ϰϬʹ ϱϬ
ϱϬʹ ϲϬ
ϲϬʹ ϳϬ
^ƉĞĞĚ;ŬŵƉŚͿ
Fig.5.42. Speed v/s no. of vehicles on Thana Chowk ± Bus Stand Road ϭϬϳ
c. Bus Stand ± Chavaccha Mor Road Table.5.24. Data for Spot Speed on Bus Stand ± Chavaccha Mor road
Route: Bus Stand ± Chavaccha Mor
Date/Time: 29th July 2015/10:30 ± 11:00 am
Speed Range (KMPH)
No. of vehicles observed
0 ± 10
6
10 ± 20
8
20 ± 30
41
30 ± 40
58
40 ± 50
61
50 ± 60
42
60 ± 70
6
^ƉĞĞĚǀͬƐEŽ͘ŽĨsĞŚŝĐůĞƐ ϳϬ ϲϬ
EŽ͘ŽĨsĞŚŝĐůĞƐ
ϱϬ ϰϬ ϯϬ
EŽ͘ŽĨsĞŚŝĐůĞƐ
ϮϬ ϭϬ Ϭ Ϭʹ ϭϬ
ϭϬʹ ϮϬ
ϮϬʹ ϯϬ
ϯϬʹ ϰϬ
ϰϬʹ ϱϬ
ϱϬʹ ϲϬ
ϲϬʹ ϳϬ
^ƉĞĞĚ;ŬŵƉŚͿ
Fig.5.43. Speed v/s no. of vehicles on Bus Stand ± Chavaccha Mor Road
5.4.
Accident Record Data
Accident records have been obtained from the city police station from 2006 ± 2015 with the total number of accidents and types of accidents. Another table shows the description of major accident record in more proper way.
ϭϬϴ
Table.5.25. Yearly Accident Data
Year
Fatal Injury
Major Injury
2006
Number of accidents 35
0
13
Minor Injury 22
2007
37
0
16
21
2008
37
0
14
23
2009
41
0
16
25
2010
46
0
14
32
2011
49
0
15
34
2012
45
0
13
32
2013
50
0
17
33
2014
52
0
16
36
2015
49
0
15
34
Total
441
0
149
292
ϰϬ ϯϱ
ĐĐŝĚĞŶƚ^ƚƵĚLJ
LJсϬ͘ϮϲϬϲdžнϭϯ͘ϲϲϳ
ϯϲ ϯϰ
ϯϮ
ϯϰ
ϯϯ
ϯϮ
ϯϬ Ϯϱ
ĐĐŝĚĞŶƚƐ
Ϯϱ
Ϯϯ
ϮϮ
Ϯϭ
ϮϬ ϭϲ ϭϱ
ϭϳ
ϭϲ ϭϰ
ϭϯ
ϭϳ
ϭϲ
ϭϱ
ϭϰ
ϭϯ
ϭϬ ϱ Ϭ Ϭ
Ϭ
Ϭ
Ϭ
Ϭ
Ϭ
Ϭ
Ϭ
Ϭ
ϮϬϬϳ
ϮϬϬϴ
ϮϬϬϵ
ϮϬϭϬ
ϮϬϭϭ
ϮϬϭϮ
ϮϬϭϯ
ϮϬϭϰ
&ĂƚĂů
Ϭ
Ϭ
Ϭ
Ϭ
Ϭ
Ϭ
Ϭ
Ϭ
Ϭ
Ϭ
DĂũŽƌ
ϭϯ
ϭϲ
ϭϰ
ϭϲ
ϭϰ
ϭϱ
ϭϯ
ϭϳ
ϭϲ
ϭϳ
DŝŶŽƌ
ϮϮ
Ϯϭ
Ϯϯ
Ϯϱ
ϯϮ
ϯϰ
ϯϮ
ϯϯ
ϯϲ
ϯϰ
zĞĂƌ
Fig.5.44. Accident Data Presentation
ϭϬϵ
Ϭ
ϮϬϬϲ
ϮϬϭϱ
Table.5.26. Major Accident Data
Date/Day/Time Location of Type Accident
of Nature
Vehicle(s)
Possible
of
Involved
Reasons
Accident
Accident 12-06-2010
Kotwali
Major
Right
Chowk
2-W
In-proper
Angled
sight
Collision
distance
21-04-2011
Thana
06-03-2013
Chowk-P.O.
Turn
enforcement
Mor
Collision
measure
03-09-2009
P.O. Colony Major
Right
09-02-2013
Mor
Turn
limit
Collision
enforcement
Minor
Right
03-04-2-14
2-W & 4-W
2-W & 4-W
No
No
traffic
speed
measure 08-12-2012
05-04-2013
Station
Minor
Rear End 2-W & 4-W
Road
Collision
Ganga Sagar Minor
Right
Chowk
Turn
Congestion
2-W
Congestion
2-W
Complex
Collision 05-08-2010
Chavaccha
21-03-214
Mor
Minor
Right Turn
road pattern
Collision
& Congestion
05-01-2016
Maharajgunj Fatal
Side
Truck-
Reduction
Road
Swipe
Bicycle
in
road
width due to illegal throw JDUEDJH¶V on road
ϭϭϬ
of
5.5.
DTO Vehicle Record Data
District Transport Office is the only governmental authority which provides the registration to the vehicle. From DTO, with prior permission to the responsible officer, we get the number of different class of vehicles registered in the district. This number of vehicle will give an idea of number of vehicles moving in the city. Table.5.27. DTO Vehicle Data Record
Year Two
Three
Cars,
Tractors LMV Bus/Truck Total
Wheelers Wheelers Jeeps, Vans 2011
4534
12
00
29
00
00
4575
2012
5988
23
11
37
09
00
6068
2013
7855
28
19
48
11
00
7961
2014
9767
37
26
52
15
00
9897
2015
11322
42
32
63
21
00
11480
142
88
229
56
00
39981
Total 39466
DTO Vehicle Record ϭϮϬϬϬ ϭϬϬϬϬ
Vehicle No.
ϴϬϬϬ ϲϬϬϬ dǁŽtŚĞĞůĞƌ
ϰϬϬϬ
dŚƌĞĞtŚĞĞůĞƌ ϮϬϬϬ Ϭ
Ăƌͬ:ĞĞƉƐͬsĂŶƐ dƌĂĐƚŽƌƐ ϮϬϭϭ
ϮϬϭϮ
ϮϬϭϯ
ϮϬϭϰ
ϮϬϭϱ
ϰϱϯϰ
ϱϵϴϴ
ϳϴϱϱ
ϵϳϲϳ
ϭϭϯϮϮ
dŚƌĞĞtŚĞĞůĞƌ
ϭϮ
Ϯϯ
Ϯϴ
ϯϳ
ϰϮ
Ăƌͬ:ĞĞƉƐͬsĂŶƐ
Ϭ
ϭϭ
ϭϵ
Ϯϲ
ϯϮ
dƌĂĐƚŽƌƐ
Ϯϵ
ϯϳ
ϰϴ
ϱϮ
ϲϯ
ƵƐͬdƌƵĐŬ
Ϭ
Ϭ
Ϭ
Ϭ
Ϭ
dǁŽtŚĞĞůĞƌ
Year
Fig.5.45. DTO Vehicle Record
ϭϭϭ
ƵƐͬdƌƵĐŬ
5.6.
Public Questionnaire Record
The actual status of traffic and its deficiencies can easily be understanding by asking the public/motorist about their problem faced by them while driving the vehicles, their experience about the present traffic system available. The following table showing is the maximum similar answer given by the motorist as 200 motorists have been interviewed. The maximum similar responses have been considered for the analysis of the interview record. Table.5.28. Public Questionnaire Form Date:
Name:
Vehicle
Place:
No.:
Sr. No.
Questions
Response
1.
Are you regularly travelling through this route?
Yes
2.
Do you have driving license?
Yes
3.
Do you wear helmet and shoes regularly while travelling?
No
4.
On an average, how much time you lose due to congestion 30 min every day?
5.
Do you think about the need of traffic signals in our city?
Yes
6.
Do you park the vehicles at proper parking place?
No
7.
Does the traffic police work efficiently to remove No congestions and conflicts?
8.
'R\RXWKLQNWRKDYH&&79¶VLQVWDOOHGRQURDGWRPRQLWRU Yes traffic and crimes?
9.
Any Complaint?
NA
10.
Any Suggestion?
NA
Signature
ϭϭϮ
Reg.
5.7.
Summary
Data collection have a great importance for this research. For the validation of the research methodologies formulated for the research, data have been collected for the analysis. The different types of data which have been collected have its own importance such as the traffic survey data gives the daily movement of traffics on the road and helps in the traffic volume study. Same as DTO vehicle record gives the vehicular growth in the city and helps in analyzing the vehicular growth rate. Accident data gives the accident incidents occurs in the city. It helps in the identification of black spot and formulation preventive measures. Spot speed study gives the data for the speed limiting determination for the traffic movement. Public Questionnaire data gives the public opinion about the existing traffic conditions and their desires for improving the same.
ϭϭϯ
CHAPTER - 6 DATA ANALYSIS
ϭϭϰ
DATA ANALYSIS
6.1.
General
The analysis of data means the extraction or analysis of all the collected data with some particular methodology for result orientation. For each and every data collection method, the different analysis methodologies have been used. As data collected in the previous chapter, we have traffic survey data, spot speed data, accident record data, DTO vehicle registration data, etc. Traffic survey data helps in analyzing the several aspects of traffic planning such as volume study, road use pattern, traffic counts, future traffic growth, road capacity, level of service etc. DTO vehicle record data gives vehicle growth rate, accident data gives accident forecasting, rate & frequency. Spot study data gives the analysis of speed control status.
6.2.
Traffic Volume Study
Traffic volume study have been carried out by traffic survey. It is the maneuverability of vehicles on the road with respect to time. Traffic volume have been counted as total vehicles and PCU. Following are the motor vehicle volume statics observed during traffic survey. a. Thana Chowk Road in 1st Session Table.6.1. Motor Vehicle Volume for Thana Chowk Road in 1st Session
Time
09:00-
09:15-
09:30-
09:45-
10:00-
10:15-
10:30-
10:45-
09:15
09:30
09:45
10:00
10:15
10:30
10:45
11:00
Volume 98
104
111
127
118
100
100
96
PCU
81.1
76.1
90.9
76.7
72.8
85.4
80.5
80.6
b. Thana Chowk Road in 2nd Session Table.6.2. Motor Vehicle Volume for Thana Chowk Road in 2nd Session
Time
04:00-
04:15-
04:30-
04:45-
05:00-
05:15-
05:30-
05:45-
04:15
04:30
04:45
05:00
05:15
05:30
05:45
06:00
96
116
124
134
129
124
136
Volume 96
ϭϭϱ
PCU
61.9
53.3
71.8
75.9
79.1
81.2
70.5
85.1
sZ/d/KEK&sK>hD ΘWhK& /&& Z Ed^ ^^/KEtZdd/D sŽůƵŵĞϭƐƚ^ĞƐƐŝŽŶ
WhϭƐƚ^ĞƐƐŝŽŶ
ϳϱ͘ϵ
sŽůƵŵĞϮŶĚ^ĞƐƐŝŽŶ
ϳϵ͘ϭ
ϳϭ͘ϴ ϲϭ͘ϵ
ϭϮϰ
ϱϯ͘ϯ
ϵϲ
ϴϬ͘ϲ
ϴϭ͘ϭ
ϳϲ͘ϭ
ϵϴ
ϭϬϰ
ϭϭϭ
ϵϬ͘ϵ
ϬͲϭϱ
ϭϱͲϯϬ
ϯϬͲϰϱ
ϭϮϳ
ϰϱͲϲϬ
ϳϲ͘ϳ
ϳϬ͘ϱ
ϭϮϵ
ϭϮϰ
ϭϯϲ
ϴϱ͘ϰ
ϴϬ͘ϱ
ϭϬϬ
ϵϲ
ϳϮ͘ϴ
ϭϭϴ
ϲϬͲϳϱ
ϴϱ͘ϭ
ϴϭ͘Ϯ ϭϯϰ
ϭϭϲ
ϵϲ
WhϮŶĚ^ĞƐƐŝŽŶ
ϭϬϬ
ϳϱͲϵϬ
ϵϬͲϭϬϱ
ϭϬϱͲϭϮϬ
Fig.6.1. Variation of Volume & PCU of different session wrt time on Thana Chowk Road
c. Neelam Chowk Road in 1st Session Table.6.3. Motor Vehicle Volume for Neelam Chowk Road in 1st Session
Time
09:00-
09:15-
09:30-
09:45-
10:00-
10:15-
10:30-
10:45-
09:15
09:30
09:45
10:00
10:15
10:30
10:45
11:00
Volume 111
105
101
114
107
91
82
97
PCU
60.2
58.2
59.7
56.6
50
41.4
51.2
64.4
d. Neelam Chowk Road in 2nd Session Table.6.4. Motor Vehicle Volume for Neelam Chowk Road in 2nd Session
Time
04:00-
04:15-
04:30-
04:45-
05:00-
05:15-
05:30-
05:45-
04:15
04:30
04:45
05:00
05:15
05:30
05:45
06:00
113
103
110
109
102
105
97
Volume 115
ϭϭϲ
PCU
64.9
67
63.5
59.2
59.1
54.8
55.9
53.2
sZ/d/KEK&sK>hD ΘWhK& /&& Z Ed^ ^^/KEtZdd/D sŽůƵŵĞϭƐƚ^ĞƐƐŝŽŶ
ϲϰ͘ϵ
WhϭƐƚ^ĞƐƐŝŽŶ
ϲϳ
ϱϵ͘Ϯ
ϲϯ͘ϱ ϭϭϱ
sŽůƵŵĞϮŶĚ^ĞƐƐŝŽŶ
ϱϵ͘ϭ ϱϰ͘ϴ
ϭϭϬ
ϭϭϯ
WhϮŶĚ^ĞƐƐŝŽŶ
ϭϬϮ
ϲϰ͘ϰ
ϲϬ͘Ϯ
ϭϭϭ
ϭϬϱ
ϬͲϭϱ
ϭϱͲϯϬ
ϱϵ͘ϳ
ϱϴ͘Ϯ
ϯϬͲϰϱ
ϱϭ͘Ϯ
ϱϬ
ϰϱͲϲϬ
ϵϳ
ϭϬϱ
ϱϲ͘ϲ
ϭϭϰ
ϭϬϭ
ϱϯ͘Ϯ
ϱϱ͘ϵ
ϭϬϵ
ϭϬϯ
ϭϬϳ
ϲϬͲϳϱ
ϰϭ͘ϰ
ϵϭ
ϳϱͲϵϬ
ϵϳ
ϴϮ
ϵϬͲϭϬϱ
ϭϬϱͲϭϮϬ
Fig.6.2. Variation of Volume & PCU of different session wrt time on Neelam Chowk Road
e. Bata Chowk Road in 1st Session Table.6.5. Motor Vehicle Volume for Bata Chowk Road in 1st Session
Time
09:00-
09:15-
09:30-
09:45-
10:00-
10:15-
10:30-
10:45-
09:15
09:30
09:45
10:00
10:15
10:30
10:45
11:00
Volume 71
79
83
67
56
70
71
81
PCU
43
43
34.7
28.4
38.6
37.1
40.6
38.1
f. Bata Chowk Road in 2nd Session Table.6.6. Motor Vehicle Volume for Bata Chowk Road in 2nd Session
Time
04:00-
04:15-
04:30-
04:45-
05:00-
05:15-
05:30-
05:45-
04:15
04:30
04:45
05:00
05:15
05:30
05:45
06:00
88
89
83
90
100
90
89
Volume 90
ϭϭϳ
PCU
51.8
49.2
47.2
46.8
46.5
57
49.1
48.2
sZ/d/KEK&sK>hD ΘWhK& /&& Z Ed^ ^^/KEtZdd/D sŽůƵŵĞϭƐƚ^ĞƐƐŝŽŶ
WhϭƐƚ^ĞƐƐŝŽŶ
ϰϲ͘ϱ
ϴϵ
ϴϴ
ϴϯ
ϰϬ͘ϲ
ϯϴ͘ϲ
ϯϳ͘ϭ
ϳϬ
ϳϭ
Ϯϴ͘ϰ
ϴϯ
ϳϵ
ϭϱͲϯϬ
ϴϵ
ϵϬ
ϵϬ
ϯϰ͘ϳ
ϳϭ
ϬͲϭϱ
ϭϬϬ
ϰϯ
ϰϯ
ϯϴ͘ϭ
ϰϴ͘Ϯ
ϰϵ͘ϭ ϰϲ͘ϴ
ϵϬ
WhϮŶĚ^ĞƐƐŝŽŶ
ϱϳ
ϰϳ͘Ϯ
ϰϵ͘Ϯ
ϱϭ͘ϴ
sŽůƵŵĞϮŶĚ^ĞƐƐŝŽŶ
ϲϳ
ϯϬͲϰϱ
ϴϭ
ϱϲ
ϰϱͲϲϬ
ϲϬͲϳϱ
ϳϱͲϵϬ
ϵϬͲϭϬϱ
ϭϬϱͲϭϮϬ
Fig.6.3. Variation of Volume & PCU of different session wrt time on Bata Chowk Road
g. Churi Bazaar Road in 1st Session Table.6.7. Motor Vehicle Volume for Churi Bazaar Road in 1st Session
Time
09:00-
09:15-
09:30-
09:45-
10:00-
10:15-
10:30-
10:45-
09:15
09:30
09:45
10:00
10:15
10:30
10:45
11:00
Volume 89
84
87
105
95
90
95
103
PCU
45
49.6
56.8
49.4
51.7
50.8
54
47.8
h. Churi Bazaar Road in 2nd Session Table.6.8. Motor Vehicle Volume for Churi Bazaar Road in 2nd Session
Time
04:00-
04:15-
04:30-
04:45-
05:00-
05:15-
05:30-
05:45-
04:15
04:30
04:45
05:00
05:15
05:30
05:45
06:00
ϭϭϴ
Volume 104
95
100
112
102
98
102
104
PCU
52
52.4
60.9
53.4
51.8
54.2
53.4
55
sZ/d/KEK&sK>hD ΘWhK& /&& Z Ed^ ^^/KEtZdd/D sŽůƵŵĞϭƐƚ^ĞƐƐŝŽŶ
WhϭƐƚ^ĞƐƐŝŽŶ
sŽůƵŵĞϮŶĚ^ĞƐƐŝŽŶ
WhϮŶĚ^ĞƐƐŝŽŶ
ϲϬ͘ϵ ϱϱ
ϱϯ͘ϰ
ϱϮ͘ϰ
ϱϮ ϭϬϰ
ϭϭϮ
ϭϬϬ
ϵϱ
ϰϱ
ϰϵ͘ϲ
ϴϵ
ϴϰ
ϴϳ
ϬͲϭϱ
ϭϬϱ
ϭϱͲϯϬ
ϯϬͲϰϱ
ϰϱͲϲϬ
ϵϴ
ϰϵ͘ϰ
ϱϭ͘ϳ
ϱϬ͘ϴ
ϵϱ
ϵϬ
ϵϱ
ϲϬͲϳϱ
ϳϱͲϵϬ
ϭϬϰ
ϭϬϯ
ϭϬϮ
ϱϲ͘ϴ ϰϳ͘ϴ
ϱϯ͘ϰ
ϱϰ͘Ϯ
ϱϭ͘ϴ
ϵϬͲϭϬϱ
ϱϰ ϭϬϯ
ϭϬϱͲϭϮϬ
Fig.6.4. Variation of Volume & PCU of different session wrt time on Churi Bazaar Road
i. Mahila College Road in 1st Session Table.6.9. Motor Vehicle Volume for Mahila College Road in 1st Session
Time
09:00-
09:15-
09:30-
09:45-
10:00-
10:15-
10:30-
10:45-
09:15
09:30
09:45
10:00
10:15
10:30
10:45
11:00
Volume 89
94
96
90
82
94
93
90
PCU
56.6
54.5
48.4
51.6
52.2
54.5
49.5
50.1
j. Mahila College Road in 2nd Session Table.6.10. Motor Vehicle Volume for Mahila College Road in 2nd Session
ϭϭϵ
Time
04:00-
04:15-
04:30-
04:45-
05:00-
05:15-
05:30-
05:45-
04:15
04:30
04:45
05:00
05:15
05:30
05:45
06:00
Volume 102
94
96
88
90
91
90
91
PCU
53.3
54.3
46.6
47.6
54.9
47.6
47.9
55.3
sZ/d/KEK&sK>hD ΘWhK& /&& Z Ed^ ^^/KEtZdd/D sŽůƵŵĞϭƐƚ^ĞƐƐŝŽŶ
ϱϲ͘ϲ
ϱϬ͘ϭ
ϱϰ͘ϱ
ϵϲ
ϵϰ
ϴϵ
ϭϱͲϯϬ
ϰϲ͘ϲ
ϰϳ͘ϲ
ϴϴ
ϵϬ
ϰϴ͘ϰ
ϱϭ͘ϲ
ϵϲ
ϵϰ
ϭϬϮ
sŽůƵŵĞϮŶĚ^ĞƐƐŝŽŶ
ϱϰ͘ϯ
ϱϯ͘ϯ
ϱϱ͘ϯ
ϬͲϭϱ
WhϭƐƚ^ĞƐƐŝŽŶ
ϯϬͲϰϱ
ϵϬ
ϴϮ
ϰϱͲϲϬ
ϲϬͲϳϱ
WhϮŶĚ^ĞƐƐŝŽŶ
ϱϰ͘ϵ
ϰϳ͘ϲ
ϰϳ͘ϵ
ϵϭ
ϵϬ
ϵϭ
ϱϮ͘Ϯ
ϱϰ͘ϱ
ϵϰ
ϵϯ
ϳϱͲϵϬ
ϵϬͲϭϬϱ
ϰϵ͘ϱ ϵϬ
ϭϬϱͲϭϮϬ
Fig.6.5. Variation of Volume & PCU of different session wrt time on Mahila College Road
k. Railway Station Road in 1st Session Table.6.11. Motor Vehicle Volume for Railway Station Road in 1st Session
Time
09:00-
09:15-
09:30-
09:45-
10:00-
10:15-
10:30-
10:45-
09:15
09:30
09:45
10:00
10:15
10:30
10:45
11:00
Volume 134
137
135
142
138
128
137
130
PCU
93.1
93.3
95.1
98
94.3
95.8
92.1
96.5
l. Railway Station Road in 2nd Session
ϭϮϬ
Table.6.12. Motor Vehicle Volume for Railway Station Road in 2nd Session
Time
04:00-
04:15-
04:30-
04:45-
05:00-
05:15-
05:30-
05:45-
04:15
04:30
04:45
05:00
05:15
05:30
05:45
06:00
Volume 127
127
136
139
138
130
131
126
PCU
85
93.9
93.1
91.6
93.8
93.9
92.5
89.5
sZ/d/KEK&sK>hD ΘWhK& /&& Z Ed^ ^^/KEtZdd/D sŽůƵŵĞϭƐƚ^ĞƐƐŝŽŶ
WhϭƐƚ^ĞƐƐŝŽŶ
sŽůƵŵĞϮŶĚ^ĞƐƐŝŽŶ
ϵϯ͘ϭ
ϵϯ͘ϵ
ϵϭ͘ϲ
ϴϵ͘ϱ
ϴϱ
ϭϮϳ
ϭϮϳ
ϭϯϲ
ϵϲ͘ϱ
ϵϯ͘ϭ
ϵϯ͘ϯ
ϵϱ͘ϭ
ϵϴ
ϭϯϰ
ϭϯϳ
ϭϯϱ
ϭϰϮ
ϭϯϴ
ϬͲϭϱ
ϭϱͲϯϬ
ϯϬͲϰϱ
ϭϯϵ
ϰϱͲϲϬ
ϵϯ͘ϵ
ϵϯ͘ϴ
ϭϯϴ
ϲϬͲϳϱ
WhϮŶĚ^ĞƐƐŝŽŶ
ϵϮ͘ϱ
ϭϯϭ
ϭϯϬ
ϭϮϲ
ϵϰ͘ϯ
ϵϱ͘ϴ
ϵϮ͘ϭ
ϭϮϴ
ϭϯϳ
ϭϯϬ
ϳϱͲϵϬ
ϵϬͲϭϬϱ
ϭϬϱͲϭϮϬ
Fig.6.6. Variation of Volume & PCU of different session wrt time on Railway Station Road
m. Ganga Sagar Chowk Road in 1st Session Table.6.13. Motor Vehicle Volume for Ganga Sagar Chowk Road in 1st Session
Time
09:00-
09:15-
09:30-
09:45-
10:00-
10:15-
10:30-
10:45-
09:15
09:30
09:45
10:00
10:15
10:30
10:45
11:00
Volume 122
127
127
135
125
123
123
124
PCU
90.2
93.5
97.9
80.1
81.9
81.1
86.5
92.9
ϭϮϭ
n. Ganga Sagar Chowk Road in 2nd Session Table.6.14. Motor Vehicle Volume for Ganga Sagar Chowk Road in 2nd Session
Time
04:00-
04:15-
04:30-
04:45-
05:00-
05:15-
05:30-
05:45-
04:15
04:30
04:45
05:00
05:15
05:30
05:45
06:00
Volume 136
134
130
127
132
134
128
120
PCU
90.2
95.7
92
86.8
85.6
85
74.9
97.5
sZ/d/KEK&sK>hD ΘWhK& /&& Z Ed^ ^^/KEtZdd/D sŽůƵŵĞϭƐƚ^ĞƐƐŝŽŶ
ϵϳ͘ϱ
WhϭƐƚ^ĞƐƐŝŽŶ
ϭϮϳ
ϭϯϲ
ϭϯϰ
ϭϯϬ
ϵϮ͘ϵ
ϵϬ͘Ϯ
ϵϯ͘ϱ
ϭϮϮ
ϭϮϳ
ϭϮϳ
ϬͲϭϱ
ϭϱͲϯϬ
ϯϬͲϰϱ
WhϮŶĚ^ĞƐƐŝŽŶ
ϵϮ
ϵϱ͘ϳ
ϵϬ͘Ϯ
sŽůƵŵĞϮŶĚ^ĞƐƐŝŽŶ
ϴϲ͘ϴ
ϴϱ͘ϲ
ϴϱ
ϭϯϮ
ϭϯϰ
ϭϮϴ
ϭϮϬ
ϴϬ͘ϭ
ϴϭ͘ϵ
ϴϭ͘ϭ
ϴϲ͘ϱ
ϭϮϱ
ϭϮϯ
ϭϮϯ
ϭϮϰ
ϳϰ͘ϵ
ϵϳ͘ϵ
ϭϯϱ
ϰϱͲϲϬ
ϲϬͲϳϱ
ϳϱͲϵϬ
ϵϬͲϭϬϱ
ϭϬϱͲϭϮϬ
Fig.6.7. Variation of Volume & PCU of different session wrt time on Ganga Sagar Chowk Road
o. Bus Stand Road in 1st Session Table.6.15. Motor Vehicle Volume for Bus Stand Road in 1st Session
Time
09:00-
09:15-
09:30-
09:45-
10:00-
10:15-
10:30-
10:45-
09:15
09:30
09:45
10:00
10:15
10:30
10:45
11:00
Volume 102
102
107
109
114
116
114
106
PCU
77.6
81.1
72.6
80.1
80.7
80.6
81.9
76.1
ϭϮϮ
p. Bus Stand Road in 2nd Session Table.6.16. Motor Vehicle Volume for Bus Stand Road in 2nd Session
Time
04:00-
04:15-
04:30-
04:45-
05:00-
05:15-
05:30-
05:45-
04:15
04:30
04:45
05:00
05:15
05:30
05:45
06:00
Volume 96
100
98
103
104
110
110
97
PCU
69
72.2
76.6
75.5
72.4
78.1
70.3
71
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ϳϭ
ϲϵ
ϳϮ͘Ϯ
ϳϲ͘ϲ
ϵϲ
ϭϬϬ
ϵϴ
ϭϬϯ
ϳϲ͘ϭ
ϳϳ͘ϲ
ϴϭ͘ϭ
ϳϮ͘ϲ
ϭϬϮ
ϭϬϮ
ϭϬϳ
ϭϬϵ
ϬͲϭϱ
ϭϱͲϯϬ
ϯϬͲϰϱ
ϰϱͲϲϬ
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WhϮŶĚ^ĞƐƐŝŽŶ
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ϳϱ͘ϱ
ϳϮ͘ϰ
ϭϬϰ
ϭϭϬ
ϴϬ͘ϭ
ϴϬ͘ϳ
ϴϬ͘ϲ
ϴϭ͘ϵ
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ϭϭϲ
ϭϭϰ
ϭϬϲ
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ϳϬ͘ϯ
ϳϱͲϵϬ
ϭϭϬ
ϵϬͲϭϬϱ
ϵϳ
ϭϬϱͲϭϮϬ
Fig.6.8. Variation of Volume & PCU of different session wrt time on Bus Stand Road
q. Bara Bazaar Road in 1st Session Table.6.17. Motor Vehicle Volume for Bara Bazaar Road in 1st Session
Time
09:00-
09:15-
09:30-
09:45-
10:00-
10:15-
10:30-
10:45-
09:15
09:30
09:45
10:00
10:15
10:30
10:45
11:00
99
89
93
99
99
92
101
Volume 84
ϭϮϯ
PCU
65.4
61.5
65
63.1
63.6
63.7
61.8
66.3
r. Bara Bazaar Road in 2nd Session Table.6.18. Motor Vehicle Volume for Bara Bazaar Road in 2nd Session
Time
04:00-
04:15-
04:30-
04:45-
05:00-
05:15-
05:30-
05:45-
04:15
04:30
04:45
05:00
05:15
05:30
05:45
06:00
Volume 88
101
97
91
98
99
91
99
PCU
60.9
64.8
61
62.9
62.6
62.5
62.6
60.3
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WhϭƐƚ^ĞƐƐŝŽŶ
ϲϬ͘ϵ
ϭϬϭ
ϲϱ͘ϰ
ϴϰ
ϬͲϭϱ
ϲϭ͘ϱ
ϵϵ
ϭϱͲϯϬ
ϲϭ
ϵϳ
ϵϭ
ϲϱ
ϲϯ͘ϭ
ϲϯ͘ϲ
ϲϯ͘ϳ
ϴϵ
ϵϯ
ϵϵ
ϵϵ
ϯϬͲϰϱ
WhϮŶĚ^ĞƐƐŝŽŶ
ϲϮ͘ϲ
ϲϮ͘ϲ
ϲϮ͘ϵ
ϲϰ͘ϴ
ϲϬ͘ϯ
ϴϴ
sŽůƵŵĞϮŶĚ^ĞƐƐŝŽŶ
ϲϮ͘ϱ ϵϵ
ϵϵ
ϵϴ
ϵϭ
ϰϱͲϲϬ
ϲϬͲϳϱ
ϳϱͲϵϬ
ϲϲ͘ϯ
ϲϭ͘ϴ
ϭϬϭ
ϵϮ
ϵϬͲϭϬϱ
ϭϬϱͲϭϮϬ
Fig.6.9. Variation of Volume & PCU of different session wrt time on Bara Bazaar Road
s. Chavaccha Mor in 1st Session Table.6.19. Motor Vehicle Volume for Chavaccha Mor in 1st Session
Time
09:00-
09:15-
09:30-
09:45-
10:00-
10:15-
10:30-
10:45-
09:15
09:30
09:45
10:00
10:15
10:30
10:45
11:00
ϭϮϰ
Volume 102
100
96
104
101
102
104
102
PCU
67
65
60
60.1
64.7
63.3
60.1
68.9
t. Chavaccha Mor in 2nd Session Table.6.20. Motor Vehicle Volume for Chavaccha Mor in 2nd Session
Time
04:00-
04:15-
04:30-
04:45-
05:00-
05:15-
05:30-
05:45-
04:15
04:30
04:45
05:00
05:15
05:30
05:45
06:00
Volume 103
107
105
115
111
107
108
109
PCU
67.2
67
66
67
65.5
67.4
65.8
70.6
sZ/d/KEK&sK>hD ΘWhK& /&& Z Ed^ ^^/KEtZdd/D sŽůƵŵĞϭƐƚ^ĞƐƐŝŽŶ
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WhϭƐƚ^ĞƐƐŝŽŶ
WhϮŶĚ^ĞƐƐŝŽŶ
ϲϲ
ϲϳ
ϲϱ͘ϱ
ϲϳ͘ϰ
ϲϱ͘ϴ
ϭϭϱ
ϭϭϭ
ϭϬϳ
ϭϬϴ
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ϲϱ
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ϲϯ͘ϯ
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ϵϲ
ϭϬϰ
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ϭϬϮ
ϭϬϰ
ϭϬϮ
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ϲϳ
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ϭϬϳ
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ϲϳ
ϭϬϮ
ϭϬϬ
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sŽůƵŵĞϮŶĚ^ĞƐƐŝŽŶ
ϯϬͲϰϱ
ϰϱͲϲϬ
ϲϬͲϳϱ
ϳϱͲϵϬ
ϵϬͲϭϬϱ
ϭϬϱͲϭϮϬ
Fig.6.10. Variation of Volume & PCU of different session wrt time on Chavaccha Mor Road
ϭϮϱ
6.3.
Vehicle Growth Rate
Vehicle growth rate signifies the growth of motor vehicle in the city. The maximum participation of the traffic movement in the city is the vehicle registered with the district transport authority. District transport authority have made available the data of the number of registered vehicles in the city. Refer Table.5.27. The significance of the analysis of motor vehicle registered data is to find the number of vehicles moving on the city road in the upcoming years. It will help in the estimation of the growth of the road network as per the vehicles moving on the road. Multiple regression analysis method best suit for this type of data analysis. MLR general expression: Y = a + bX ܾൌ
ߑܻܺ݅݅ െ ݊ܺത ܻത σܺ݅ ଶ െ ݊ܺത ଶ ܽ ൌ ܻത െ ܾܺത
By the use of this formula for the data available in Table.5.27. DTO Vehicle Data Record, the following regression equations have been found for the different vehicle class. Table.6.21. Regression equations for Vehicle growth rate
Sr.No.
Vehicle Class
Regression Equation
01
Two Wheelers
Y = -14668.3 + 1735.5 X
01
Three Wheelers
Y = -67.8 + 7.4 X
03
Cars, Jeeps, Vans
Y = -85.1 + 7.9 X
04
Tractors
Y = -62.1 + 8.3 X
05
LMV
Y = -51.2 + 4.8 X
After inserting the year (X) in the different equation for the specific vehicle class, we found the future number of vehicles estimated to be registered with district transport office and will be running on the roads of city.
ϭϮϲ
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sĞŚŝĐůĞŽƵŶƚ
ϯϱϬ ϯϬϬ
>Ds
ϮϱϬ
dƌĂĐƚŽƌƐ
ϮϬϬ
ĂƌƐ͕:ĞĞƉƐ͕sĂŶƐ
ϭϱϬ
dŚƌĞĞtŚĞĞůĞƌ
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zĞĂƌ
Fig.6.12. Vehicle Growth (2016-25)
6.4.
Road Use Pattern
Road use pattern is the hypothetical analysis of the PCU generated on a particular stretch of road section during morning peak hour and evening peak hour. This study gives the analysis of the road use pattern with respect to the survey sessions. This method uses the researcher approach as the data is limited, t-test is perfect suit for the analysis of these types of data.
+HUHµW¶-indicates the t-YDOXHZKLOHµ;EDU¶GHQRWHVPHDQRIWKHGDWD)LUVWZHILQGRXW the value of degree of freedom (i.e. d.f.). )RUILQGLQJRXWWKLVµGI¶ZHQHHGQXPEHURI
ϭϮϴ
VDPSOH µQ¶ VRZHZLOOJHWWKHYDOXHRIµGI¶$IWHUWKDWZHZLOOILQGWKHµW¶-value (this will be t-critical value). So, at 5 % significance level, t-Critical should be 1.9146. By putting the values in the formula, we get t-stat. Now if t-stat will be greater than tcritical then the road use pattern is different for the both session otherwise it will be same road use pattern in 1st session and 2nd session.
We have 8 number of sample so, d.f. = (8+8-2) = 14. For d.f. = 14 at 5 % significance level, the t-Critical is 1.9146. Table.6.23. Road Use Pattern Analysis
Sr.No. Road Section
t-Critical
t-Stat
Type of Pattern
01
Thana Chowk Road
1.9146
1.93110
Different
02
Neelam Chowk Road
1.9146
1.01219
Same
03
Bata Chowk Road
1.9146
3.89234
Different
04
Churi Bazaar Road
1.9146
2.11401
Different
05
Mahila College Road
1.9146
0.52032
Same
06
Railway Station Road
1.9146
1.70496
Same
07
Ganga Sagar Chowk Road
1.9146
1.09247
Same
08
Bus Stand Road
1.9146
2.37535
Different
09
Bara Bazaar Road
1.9146
1.44192
Same
10
Chavaccha Mor
1.9146
1.82911
Same
6.5.
Traffic Count Conversion
Main input parameters to design a better road network is the study of traffic counts. So, to design a road network for a design life of 20 years, Annual Average Daily Traffic (AADT) is considered. This is the number of vehicles passing a point in both directions
ϭϮϵ
per day taking into account the variation in the traffic flow throughout the year and the total number of axles for the same traffic volume. Determination of the AADT from the Average Peak Flow of 2-hour traffic survey, we consider some steps to obtain average daily maximum flow. Having obtained the 24-hour peak counts, a further conversion to 24-hour normal flow may be carried out to obtain an Average Daily Traffic flow, and subsequently to Annual Average Daily Traffic.
Steps of conversion: 1. Conversion of Peak Hour Traffic (PHT) to Average Daily Traffic (ADT)
Peak hour traffic used for design is the traffic, which passes a point during the severest peak hour(s) of the counting period. In order to convert peak hour traffic to Average Daily Traffic (ADT), firstly we find Peak Hour Factor (PHF) which is the ratio of 1-hour peak volume and four times the 15-minute peak volume of the peak hour. PHF =
ସଵହ
Where; V=1hr peak flow V15=15 min peak flow of 1-hr peak flow Steps to find 1-hour peak flow: 1. There are two traffic survey sessions of all the stretches of road. 2. Consider the four maximum volume from each session of traffic survey of a particular stretch of road (15 min volume). 3. Take average of the four volumes of each session. 4. Take average of the obtained average value of each session. 5. This gives the peak hour volume of a particular road section. 6. Do the same procedure for all the 10 sections of the road. 7. Find the average of the all 10 sections of the road. 8. Consider the maximum traffic volume for 1 hour from the average value obtained. 9. Consider the maximum volume for 15 minutes from the obtained value of the 1-hour average peak volume.
ϭϯϬ
After obtaining Peak Hour Factor, find the maximum rate of flow for the peak 1hour.
MRF =
ଵି௨௩௨ ுி
Maximum daily traffic (MDT) = 24 X MRF Average Daily Traffic = MDT X Traffic conversion factor The conversion factor is the proportion of 2nd last most value of the peak traffic flow over and highest value of traffic flow over a given peak time as it relates to that prevailing traffic counted under same traffic conditions and over a specific counting period.
2. Conversion of Average Daily Traffic (ADT) to Annual Average Daily Traffic (AADT)
Annual Average Daily Traffic is the average traffic that is expected to use a particular road over a year (365 days). The Average Daily Traffic, conversion to Annual Average Daily Traffic is determined from the following expression:
AADT = T-ADT /365.
Where: AADT = Average Annual Daily Traffic. T-ADT = Total Average Daily Traffic. Total Average Daily Traffic is basically calculated in a peak traffic survey for 1 week of every month and the summation of every month traffic is considered as total average daily traffic. But in our case of 1-day traffic survey of 1 site individually, we have total 20 traffic survey data surveyed in 20 days. So, T-ADT = ADT X 31 X 12 Note: 1. All the traffic volumes should be expressed in PCU. 2. Methods to obtain peak hour (2 hr.) traffic data for conversion to Average Daily Traffic, the road section having the same road use pattern in both the session, for that average of peak hour of both sessions should be considered whereas the road
ϭϯϭ
section having different road use pattern in both session, for that the maximum peak hour session should be considered. Calculation: Table.6.24. Traffic Volume for AADT Calculation
Time
15 min
15 min
15 min
15 min
15 min
15 min
15 min
15 min
PCU
95
90
94
94.95
94.8
94.05
94.85
92.3
2-hour peak traffic volume = 749.95 PCU 1-hour peak traffic volume = 379.60 PCU V15 = 95 PHF =
ଷଽǤ ସଽହ
= 0.99894
Max. rate of flow for peak 1-hour =
ଷଽǤ Ǥଽଽ଼ଽସ
= 380
Maximum Daily Traffic (MDT) = 24 X 380 = 9120 Average Daily Traffic (ADT) = 9120 X 0.946
(0.946 = Traffic conversion
factor) = 8627.52 PCU T-ADT = ADT X 31 X 12 = 8627.52 X 31 X 12 = 3209437.44 PCU Average Annual Daily traffic (AADT) = T-ADT/365 = 3209437.44/365 = 8792.98 PCU
6.6.
Future Traffic Growth
Future growth in traffic volume is very significant for the planning of a better traffic system. As we have analyzed the current traffic growth and vehicle growth, it has been observed that in future, there will be much more traffic growth. By the vehicle growth rate analysis, vehicle growth factor has been generated. Vehicle growth rate is directly proportional to the growth in traffic volume. So, by using the vehicle growth factor, we can find the future traffic volume demand for the city traffic system.
ϭϯϮ
Annual Traffic volume Growth Rate = 1.098
(Ref. Vehicle growth
study) Table.6.25. Future Traffic Demand
Year
AADT (PCU)
2015
8792.98
2016
9654.69
2017
10600.84
2018
11639.72
2019
12780.41
2020
14032.89
2021
15408.11
2022
16918.10
2023
18576.07
2024
20396.52
2025
22395.37
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Fig.6.13. Estimated Future Traffic Volume Growth
ϭϯϯ
6.7.
Spot Speed Analysis
From the obtained spot speed data of selected road sections, frequency distribution tables are prepared by arranging the data in groups covering various speed ranges and the number of vehicles in such range. A graph is plotted with the average values of each speed group on the X ± axis and the cumulative percentage of vehicles travelled at or below the different speeds on the Y ± axis. From this graph, the 85th percentile speed is found out which gives that speed at or below 85 percent of the vehicles are passing the point on the roadway or only 15 percent of the vehicles exceed the speed at that spot. The vehicle exceeding 85th percentile speed is considered as vehicle moving faster than the safe speed under existing conditions and hence this speed is adopted for the safe speed limit at this zone. However, for the purpose of geometric design, the 98th percentile speed is taken. The 15th percentile speed represents the lower speed limit if it is desired to prohibit slow moving vehicles to decrease delay and congestion, as 85th percent of vehicles to the stream travel at speeds higher than this value and therefore need overtaking opportunities. a. Kotwali Chowk ± Thana Chowk Road Table:6.26. Spot Speed analysis of Kotwali Chowk ± Thana Chowk Road
Speed Ranges Mid (kmph)
(kmph)
0 ± 10
5
Speed Frequency
Cumulative Frequency %
8
3.40
3.40
10 ± 20
15
10
4.26
7.66
20 ± 30
25
38
16.17
23.83
30 ± 40
35
62
26.38
50.21
40 ± 50
45
66
28.09
78.30
50 ± 60
55
46
19.57
97.87
60 ± 70
65
5
2.13
100
Total 235
100.00
ϭϯϰ
Frequency %
b. Thana Chowk ± Bus Stand Road Table.6.27. Spot Speed analysis of Thana Chowk ± Bus Stand Road
Speed Ranges Mid
Speed Frequency
Frequency %
Cumulative
(kmph)
(kmph)
0 ± 10
5
10
4.92
4.92
Frequency %
10 ± 20
15
16
7.88
12.80
20 ± 30
25
42
20.69
33.49
30 ± 40
35
51
25.13
58.62
40 ± 50
45
49
24.14
82.76
50 ± 60
55
33
16.26
99.02
60 ± 70
65
2
0.98
100
Total 203
100.00
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ϯϬ
ϰϬ
ϱϬ
ϲϬ
ϳϬ
ϴϬ
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Fig.6.16. Frequency distribution curve for spot study of Thana Chowk ± Bus Stand Road
ϭϯϲ
40 ± 50
45
61
27.48
78.38
50 ± 60
55
42
18.92
97.30
60 ± 70
65
6
2.70
100
Total 222
100.00
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Ϯϳ͘ϰϴ Ϯϲ͘ϭϯ
йŽĨsĞŚŝĐůĞƐŽďƐĞƌǀĞĚ
Ϯϱ ϭϴ͘ϵϮ
ϭϴ͘ϰϳ
ϮϬ
ϭϱ
ϭϬ
ϱ
Ϯ͘ϳ
ϯ͘ϲ
Ϯ͘ϳ
Ϭ Ϭ
ϭϬ
ϮϬ
ϯϬ
ϰϬ
ϱϬ
ϲϬ
ϳϬ
^ƉĞĞĚ;ŬŵƉŚͿ
Fig.6.18. Frequency distribution curve for spot study of Bus Stand ± Chavaccha Mor Road
ϭϯϴ
ϴϬ
to manoeuvre, traffic interruptions, comfort, convenience and safety. Six level of services are recognized commonly, designated as A, B, C, D, E & F with level of service A representing the best operating condition /free flow and level of service F represents the worst/forced or breakdown flow. Table:6.29. Peak hour flow of traffic at all sections of the road
Sr.No. Road
Period
Total
Total PCU/hr.
Vehicle/hr. 01
Thana Chowk Road
Morning
Peak
398
327.6
Peak
523
321.3
Peak
431
272.5
Peak
441
254.6
Peak
313
165.2
Peak
368
207.1
Peak
393
213.3
Peak
422
223.5
Peak
365
217.8
Peak
383
219.8
Peak
551
386.4
Peak
536
374.7
Peak
511
374.5
Peak
527
375.4
Hour Evening Hour
02
Neelam Chowk Road
Morning Hour Evening Hour
03
Bata Chowk Road
Morning Hour Evening Hour
04
Churi Bazaar Road
Morning Hour Evening Hour
05
Mahila College Road
Morning Hour Evening Hour
06
Railway Station Road
Morning Hour Evening Hour
07
Ganga Sagar Chowk Morning Road
Hour Evening Hour
ϭϰϬ
08
Bus Stand Road
Morning
Peak
443
324.3
Peak
427
302.6
Peak
377
260.4
Peak
393
252.9
Peak
400
265.6
Peak
429
272.2
Hour Evening Hour
09
Bara Bazaar Road
Morning Hour Evening Hour
10
Chavaccha Mor
Morning Hour Evening Hour
InTable6.29, the peak hour flow has been extracted in terms of vehicle per hour and PCU per hour for all the roads of study. From the values obtained in this table as PCU per hour, level of services has been obtained. For the calculation of level of service, the Volume/Capacity ratio was first determined using design service volumes as per IRC: 106 and then the level of service was computed as shown in Table.6.30.
Table.6.30. Level of Service for all roads
Sr.No. Location
Period
No.
Design
V/C
of
of
Service
ratio
road
lanes Volume
PCU/hr. Width
per
LOS
(DSV)
lane (m) 01
Thana
Morning
Chowk
Peak
Road
327.6
3.75
1
900
0.36
A
321.3
3.75
1
900
0.36
A
Hour Evening Peak Hour
ϭϰϭ
02
Neelam
Morning
Chowk
Peak
Road
272.5
3.5
1
900
0.30
A
254.6
3.5
1
900
0.28
A
165.2
3.5
1
900
0.18
A
207.1
3.5
1
900
0.23
A
213.3
3.5
1
900
0.23
A
223.5
3.5
1
900
0.25
A
217.8
3.5
1
900
0.24
A
219.8
3.5
1
900
0.24
A
386.4
3.75
1
900
0.43
B
374.7
3.75
1
900
0.41
B
374.5
3.75
1
900
0.41
B
375.4
3.75
1
900
0.42
B
Hour Evening Peak Hour
03
Bata
Morning
Chowk
Peak
Road
Hour Evening Peak Hour
04
Churi
Morning
Bazaar
Peak
Road
Hour Evening Peak Hour
05
Mahila
Morning
College
Peak
Road
Hour Evening Peak Hour
06
Railway
Morning
Station
Peak
Road
Hour Evening Peak Hour
07
Ganga
Morning
Sagar
Peak
Chowk Road
Hour Evening Peak Hour
ϭϰϮ
08
Bus Stand Morning Road
324.3
3.75
1
900
0.36
A
302.6
3.75
1
900
0.34
A
260.4
3.75
1
900
0.29
A
252.9
3.75
1
900
0.28
A
265.6
3.5
1
900
0.30
A
272.2
3.5
1
900
0.30
A
Peak Hour Evening Peak Hour
09
Bara
Morning
Bazaar
Peak
Road
Hour Evening Peak Hour
10
Chavaccha Morning Mor
Peak Hour Evening Peak Hour
6.9.
Accident Forecasting
Accident forecasting refers to the growth of accident in normal case of present scenario if the proper traffic planning will not be enforced. As per the accident data provided by the city police station from 2006 to 2015 given in Table. No.5.21. it shows that there is no any fatal injury within the city area but there is much major & minor injury. So, as per the growth of the vehicles throughout the city, the rate of accident will also increase as per the following analysis has been done. Multiple linear regression method is best suit for the analysis of these types of data. MLR general expression: Y = a + bX ܾൌ
ߑܻܺ݅݅ െ ݊ܺത ܻത σܺ݅ ଶ െ ݊ܺത ଶ
ܽ ൌ ܻത െ ܾܺത By the use of this formula for the data available in Table.5.25. DTO Vehicle Data Record, the following regression equations have been found for the different vehicle class. ϭϰϯ
Table.6.31. Regression equations for accident study
Sr.No.
Accident Type
Regression Equation
01
Major Injury
Y=13.309+0.1515X
02
Minor Injury
Y=27.80035+0.1333X
After inserting the desired year (X), we found the number of different types of estimated accident which may happen if the traffic system will not be improves with respect to time. Table.6.32. Future Estimated Accident Statics
Year
Major Injury
Minor Injury
Total
2016
16
30
46
2017
16
30
46
2018
17
30
47
2019
17
30
47
2020
17
31
48
2021
17
31
48
2022
17
31
48
2023
17
31
48
2024
17
31
48
2025
17
32
49
Total
168
307
475
ϭϰϰ
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ϯϬ
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z Z
Fig.6.20. Future Estimated Accident
6.10. Summary Various methods have been used for the interpretation of the data collected in the chapter 5. Traffic volume study have been carried out to understand the traffic demands at peak hours. Vehicle growth rate have been calculated to analyze the future traffic demand with respect to the growth of the vehicles in future. The pattern of the use of different roads have been analyzed by road use pattern method. Traffic count conversion method have been carried out to analyze the AADT from ADT. Future estimation of traffic have been also done by the data obtained from DTO. Spot speed analysis have been carried out to analyze the speed statics of the different vehicles on different roads. Road capacity and level of service have been determined to analyze the quantitative and qualitative statics of the different road sections of the city. Accident study have been carried out to analyze the reasons of the accident, the different accident places and accident forecasting have been carried out to analyze the future accident statics if the traffic conditions will not be improved.
ϭϰϱ
CHAPTER - 7 RESULTS AND DISCUSSIONS
ϭϰϲ
RESULTS AND DISCUSSIONS
7.1.
General
While the study of the existing traffic systems and their parameters for the improvement and proper planning in the traffic system, lots of parameters were monitored, evaluated and analyzed. From different traffic related data obtained, the different traffic related parametric analysis were performed to get into the result to analyze the existing conditions and the need of the traffic systems in future. Traffic survey is an important element for analyzing the traffic systems because it gives lots of traffic related parameters such as volume, capacity, level of service, vehicle count, etc. So, by analyzing all the parameters discussed in the research, observed statics and parameters for existing traffic conditions given in the result of the data analysis.
7.2.
Result of Data Analysis
After the study of the different traffic related parameters such as traffic volume, vehicle growth rate, road use pattern, traffic count, future traffic growth, spot speed analysis, capacity and level of service, accident forecasting, the following categorized result have been obtained: a. Traffic Volume Study
i.
Thana Chowk road have the peak traffic at morning session from 09:00 am to 11:00 am with 327.6 PCU/hour and in evening session from 04:00 pm to 06:00 pm with 321.3 PCU/hour.
ii.
Neelam Chowk road have the peak traffic at morning session from 09:00 am to 11:00 am with 272.5 PCU/hour and in evening session from 04:00 pm to 06:00 pm with 254.6 PCU/hour.
iii.
Bata Chowk road have the peak traffic at morning session from 09:00 am to 11:00 am with 165.2 PCU/hour and in evening session from 04:00 pm to 06:00 pm with 207.1 PCU/hour.
ϭϰϳ
iv.
Churi Bazaar road have the peak traffic at morning session from 09:00 am to 11:00 am with 213.3 PCU/hour and in evening session from 04:00 pm to 06:00 pm with 223.5 PCU/hour.
v.
Mahila College road have the peak traffic at morning session from 09:00 am to 11:00 am with 217.8 PCU/hour and in evening session from 04:00 pm to 06:00 pm with 219.8 PCU/hour.
vi.
Railway Station road have the peak traffic at morning session from 09:00 am to 11:00 am with 386.4 PCU/hour and in evening session from 04:00 pm to 06:00 pm with 274.7 PCU/hour.
vii.
Ganga Sagar Chowk road have the peak traffic at morning session from 09:00 am to 11:00 am with 374.5 PCU/hour and in evening session from 04:00 pm to 06:00 pm with 375.5 PCU/hour.
viii.
Bus Stand road have the peak traffic at morning session from 09:00 am to 11:00 am with 324.3 PCU/hour and in evening session from 04:00 pm to 06:00 pm with 302.6 PCU/hour.
ix.
Bara Bazaar road have the peak traffic at morning session from 09:00 am to 11:00 am with 260.4 PCU/hour and in evening session from 04:00 pm to 06:00 pm with 252.9 PCU/hour.
x.
Chavaccha Mor road have the peak traffic at morning session from 09:00 am to 11:00 am with 265.6 PCU/hour and in evening session from 04:00 pm to 06:00 pm with 272.2 PCU/hour.
b. Vehicle Growth Rate
i.
In 2011, total vehicles moving on the road of Madhubani was 4575. With increase from time to time, it becomes 11480 in 2015.
ii.
By the end of 2016, it is assumed that the total vehicles moving on the road of Madhubani will be 13289.
iii.
The average vehicle growth factor found as 1.098.
iv.
The study gives the maximum vehicular growth in 2 ± wheelers motorized vehicle.
v.
3 ± wheelers vehicle is approximately constant growth.
ϭϰϴ
vi.
There is a constant growth in car.
vii.
But there is a huge growth in tractors after 2 ± wheelers motorized vehicles.
viii.
LMV have constant growth.
ix.
By 2025, the total vehicles moving on the road of Madhubani is assumed to be 29163 in which 28719 vehicles will be 2 ± wheeler motorized vehicles.
c. Road Use Pattern
i.
The pattern of road used by the motorists or road user have been analyzed in
ii.
It has been observed that at Thana Chowk road, the use of road in morning and
morning session and evening session.
evening peak hour is different. iii.
It has been observed that at Neelam Chowk road, the use of road in morning and
iv.
It has been observed that at Bata Chowk road, the use of road in morning and
evening peak hour is same.
evening peak hour is different. v.
It has been observed that at Churi Bazaar road, the use of road in morning and evening peak hour is different.
vi.
It has been observed that at Mahila College road, the use of road in morning and
vii.
It has been observed that at Railway Station road, the use of road in morning and
evening peak hour is same.
evening peak hour is same. viii.
It has been observed that at Ganga Sagar Chowk road, the use of road in morning and evening peak hour is same.
ix.
It has been observed that at Bus Stand road, the use of road in morning and evening peak hour is different.
x.
It has been observed that at Bara Bazaar road, the use of road in morning and evening peak hour is same.
xi.
It has been observed that at Chavaccha Mor road, the use of road in morning and evening peak hour is same.
d. Traffic Count Conversion
ϭϰϵ
i.
Peak hour traffic has been converted into average daily traffic.
ii.
Average daily traffic has been converted into annual average daily traffic.
iii.
2 ± hour peak traffic flow found as 749.95 PCU.
iv.
1 ± hour peak traffic flow found as 379.90 PCU.
v.
Peak hour factor found as 1.00092.
vi.
Maximum rate of flow for peak 1 ± hour is 379.55 PCU.
vii.
Maximum daily traffic found as 9190.2 PCU.
viii.
Average daily traffic found as 8617.30 PCU.
ix.
Annual average daily traffic found as 8782.56 PCU.
e. Future Traffic Growth
i.
As calculated in 2015, the annual average daily traffic was found as 8782.56 PCU moving on the roads of Madhubani.
ii.
By the end of 2016, the estimated traffic will be 9643.25 PCU AADT.
iii.
It is estimated as the growth of traffic in future by 2020 will be 14016.26 PCU AADT.
iv.
By the end of 2025, the estimated traffic moving on the roads of Madhubani will
v.
It means, by the end of 2015, the annual average daily traffic growth will be 2.546
be 22368.82 PCU AADT.
times the current annual average daily traffic of 2015. vi.
The average growth of traffic has found as 1.098.
f. Spot Speed Analysis
i.
On Kotwali Chowk ± Thana Chowk road, the maximum vehicles were found moving with a speed of 40 ± 50 kmph. As shown in Figure 6.14.
ii.
Upper speed limit for Kotwali Chowk ± Thana Chowk road found as 52 kmph.
iii.
Lower speed limit for Kotwali Chowk ± Thana Chowk road found as 27 kmph.
iv.
Speed limit for Kotwali Chowk ± Thana Chowk road to check design elements found as 62 kmph.
ϭϱϬ
v.
On Thana Chowk ± Bus Stand road, the maximum vehicles were found moving with a speed of 40 ± 50 kmph. As shown in Figure 6.16.
vi.
Upper speed limit for Thana Chowk ± Bus Stand road found as 52 kmph.
vii.
Lower speed limit for Thana Chowk ± Bus Stand road found as 22 kmph.
viii.
Speed limit for Thana Chowk ± Bus Stand road to check design elements found as 60 kmph.
ix.
On Bus Stand ± Chavaccha Mor road, the maximum vehicles were found moving with a speed of 40 ± 50 kmph. As shown in Figure 6.18.
x.
Upper speed limit for Bus Stand ± Chavaccha Mor road found as 53 kmph.
xi.
Lower speed limit for Bus Stand ± Chavaccha Mor road found as 26 kmph.
xii.
Speed limit for Bus Stand ± Chavaccha Mor road to check design elements found as 62 kmph.
g. Capacity and Level of Service
i.
Level of service have been observed on the different roads in both morning session
ii.
At Thana Chowk road, the LOS is A with capacity of 324.45 PCU/hr. in both
and evening session as shown in Table 6.30.
morning and evening session. It means that there is excellent traffic condition. iii.
At Neelam Chowk road, the LOS is A with capacity of 263.55 PCU/hr. in both morning and evening session. It means that there is excellent traffic condition.
iv.
At Bata Chowk road, the LOS is A with capacity of 186.15 PCU/hr. in both
v.
At Churi Bazaar road, the LOS is A with capacity of 218.4 PCU/hr. in both
morning and evening session. It means that there is excellent traffic condition.
morning and evening session. It means that there is excellent traffic condition. vi.
At Mahila College road, the LOS is A with capacity of 218.8 PCU/hr. in both
vii.
At Railway Station road, the LOS is B with capacity of 380.55 PCU/hr. in both
morning and evening session. It means that there is excellent traffic condition.
morning and evening session. It means that there is excellent traffic condition. viii.
At Ganga Sagar Chowk road, the LOS is B with capacity of 374.95 PCU/hr. in both morning and evening session. It means that there is excellent traffic condition.
ϭϱϭ
ix.
At Bus Stand road, the LOS is A with capacity of 313.45 PCU/hr. in both morning and evening session. It means that there is excellent traffic condition.
x.
At Bara Bazaar road, the LOS is A with capacity of 256.65 PCU/hr. in both morning and evening session. It means that there is excellent traffic condition.
xi.
At Chavaccha Mor road, the LOS is A with capacity of 268.9 PCU/hr. in both morning and evening session. It means that there is excellent traffic condition. h. Accident Forecasting
i.
In 2006, it was total 35 accidents including 13 major and 22 minor injuries.
ii.
By the end of 2015, it was total 49 accidents including 15 major and 34 minor injuries.
iii.
By the end of 2016 with existing traffic conditions, it is estimated to be total 46 accidents including 16 major and 30 minor injuries excluding 1 fatal loss on 5th January 2016.
iv.
By the end of 2015, the total accidents are estimated to be 49 including 17 major and 32 minor injuries.
v.
The major reasons of the accident were identified as improper sight distance, non ± availability of traffic enforcement measures and traffic police, no traffic limit, congestion, complex road pattern, reduction of road width due to disposal of garbage on roads, etc.
vi.
The nature of accidents identified as right angled collision, right turn collision, rear end collision and side swipe.
vii.
Maximum vehicles involved or injured in accidents are 2 ± wheelers.
i. Public Questionnaire Survey
i.
Most of the motorists have license but not carrying along with them regularly.
ii.
Most of the non ± licensed motorists are under 18 years.
iii.
Few motorists prefer to wear helmet.
iv.
0RWRULVWVGLGQ¶WILQGSURSHUSDUNLQJIDFLOLWLHVLQWKHFLW\
v.
Motorists mostly complain for traffic police while congestion.
vi.
Motorists give positive feedback for installing CCTV cameras for their security.
ϭϱϮ
Most of the motorists complain for congestion in Bata Chowk ± Churi Bazaar ±
vii.
Bara Bazaar route. viii. ix.
To reach early, only few motorists follow traffic rules. DTO have not proper enforcement measures for training while issuing driving licenses.
7.3.
Discussions
The present study has been conducted to analyze the parameters for the planning of better traffic system of a small or mid ± sized city. In order to perform this research, all the major roads were analyzed with the different traffic related parameters such as traffic survey, spot speed, vehicle count, accident study, public interview method, etc. After the analysis of the data collected, the result of the research focuses the following parameters: a. Initially, traffic volume study was conducted for 8 hours from which, the peak traffic movement was analyzed with the two sessions, i.e., morning session from 09:00 am to 11:00 am and evening session from 04:00 pm to 06:00 pm. b. In the both sessions, traffic survey was performed on the 10 major routes of the city and the traffic volume was recorded in tally sheet as the total number of vehicles moving and in terms of passenger car unit. c. It has been observed that with good level of service, still there is congestions on road due to the roadside parking and unauthorized shops near roadside. d. It has been observed that from 2011 to 2015, the number of vehicle growth was very much with the growth factor of 1.098 and it has been expected as by 2015, vehicle should be just double in compare to as just now. e. Road use pattern also differs with respect to time and with respect to other roads which signifies the traffic movement and traffic demand on the different roads. f. The annual average daily traffic has found to be 8782.56 PCU and it should be increase in future. g. The spot speed parameters of the city are adequate but there should be proper speed marking on the different routes.
ϭϱϯ
h. There are very less fatalities in the city but minor accidents are occurred on regular basics. i. Public demands for better traffic but there is very less awareness regarding traffic rules among the public.
ϭϱϰ
CHAPTER - 8 RECOMMENDATIONS, LIMITATIONS AND FUTURE SCOPE
ϭϱϱ
RECOMMENDATIONS, LIMITATIONS AND FUTURE SCOPE
8.1.
General
Recommendation methodologies dealt with the analyzed result of the existing traffic conditions as discussed in the chapter - 7 as results and will give the findings of the problem in existing condition. This chapter gives the strategic proposal for the improvement of development of the traffic system of the city, limitations and future scope of the research.
8.2.
Problem Encountered
The following problems were encountered after the analysis of result in chapter - 7: a. At peak hour, traffic volume is much more which cause congestions. b. Kotwali Chowk - Thana Chowk ± Railway Station road have good traffic flow in peak hours with less congestions but it is the major route for accidents. c. Bata Chowk ± Churi Bazaar ± Bara Bazaar road is considered as the busiest road in the city as it has the most traffic congestion because of road side markets and unauthorized parkings. d. By the continuous growth in vehicles, market area road has not been extended cause the major congestion. e. The major roads have different road use pattern means the road use demand varies from morning session to evening session. f. Vehicle speed enforcement measures are not available causing over speeding. g. Despite of good level of service, maximum road cause congestion due to unauthorized parking and unauthorized markets besides the road. h. Fatal accident at MaharajGunj road is the biggest failure of district administration. i. 'LVSRVDORIJDUEDJH¶VRQWKHURDGLVFRPPRQO\REVHUYHGRQWKHPD[LPXPURDGV j. There is a lack of administrative enforcement for regular checking of driving license, helmets, shoes, etc. k. There is no proper lighting on the all roads of the city except major routes.
ϭϱϲ
l. The maximum roads have lack of good drainage system causing deterioration of pavements and disturbance to the road user.
8.3.
Recommended Strategies Table.8.1. Recommended Strategies
Location
Existing Condition Proposed
Comments x
Kotwali Chowk
x
Single lane
x
Dual lane
- Thana Chowk
x
No street
x
Street light
be extended
light
x
Road
as extra area
markings
is available
Speed limit
besides the
board
road.
Road x
No road x
marking x
Inadequate x
drainage x
Proper
x
Road should
Drainage
system
drainage
system
No speed
facility
should be
In-pavement
improved.
x
regulation
lightening system Thana Chowk
x
Street light
x
No drainage
x
land for
used for
x
Pollution
parking
garbage
Uniform
disposal. It
road joint
should be
Extend road
great if
road joint
width on
parking
Bus stop
bridge
facility will
Installation
be
of traffic
constructed.
x
due to slum land x x
x
Inadequate
x
Use slum
signal
x
x
Slum land is
Traffic police enforcement is must.
ϭϱϳ
x
Traffic police enforcement
x
Proper bus stop
x
In-pavement lightening system
Thana Chowk ±
x
Railway Station Road
x
No road
x
Dual lane
marking
x
Road marking
RCC road x
x
Road should be dual lane
x
Drainage
Drainage
system is
lane
system
available but
Drainage
cleaning
water
facility
periodically
logging is
Speed limit
seen.
of single x
x
x
Street light
marking Thana Chowk ±
x
Mahila College ± Ganga Sagar
x
Chowk Road
x
Compact
x
Temple area
extension
should be
Garbage
needed
clean.
x
disposal x
Road
single lane
Garbage
x
Need road
beside road
disposal
extension on
No street
restriction
priority.
light
x
Street light
Mahila College
x
Single lane
x
Parking
Road
x
No parking
x
Road
should have
x
Street light
marking
to construct
Traffic
parking
police
facility.
enforcement
Congestion is
x
x
College
mainly
ϭϱϴ
x
In-pavement
caused due to
lightening
the roadside
system
parking. x
Traffic police enforcement is must.
Ganga Sagar
x
Chowk
x
Compact single lane
x x
x
Street light No road
x
marking
Road
Road side
extension
local shops
Road
reduces road
marking
width cause
Traffic
congestion.
signal x
x
x
Traffic police
Traffic
enforcement
police
is must.
enforcement. Railway Station
x
Single lane
Road
x
Road side
x
parking x
Bus stop
x
In railway
x x
parking x
Street light
x
restriction
caused due to
Bus stop
bus stop. x
Road side
Traffic
parking is
signal
also a prime
Roadside
reason of
unauthorized
congestion
shop
mainly by
restriction
rickshaw.
Traffic
enforcement
ϭϱϵ
Major congestion is
police
x
parking
restriction
campus x
Roadside
x
In-pavement lightening system
x
x
Single lane
± Ganga Sagar
x
Improper
for roadside
unauthorized
drainage
shops
shops cause
Road
reduction in
marking
road width
Traffic
led to
signal
congestion.
Chowk Road ± Bus Stand Road
x
x
No road marking
x
x
Improper street light
x x
Restriction
Drainage
x
x
There is no complete
Proper street
drainage. x
Roadside
In-pavement
cleaning
lightening
should be
system
must as this
Traffic
is the way for
police
major
enforcement
temple.
near Ganga
Bus Stand Road
x
Roadside
construction
light
x
Traffic police
Sagar chowk
enforcement
and Kali
should be
Mandir road.
must.
x
Single lane
x
Dual lane
x
No road
x
Road
better if the
markings
bus stand
Parking
should be
facilities
shift outside
Paved
the city as
surface
there is
marking x
No parking
x
Unpaved
x x
bus stand x
No drainage
ϭϲϬ
x
Railway Station
x
It should be
x
x
Improper street light
x x
Traffic
congestion
police
on daily
enforcement
basics on this
Drainage
route due to
system
bus stand.
Proper street light
x
In-pavement lightening system x
Bus Stand ±
x
Single lane
x
Dual lane
Bara Bazaar ±
x
No road
x
Road
extension can
marking
reduce
Adequate
congestion.
Chavaccha Mor Road
marking x
x
Inadequate
x
Lane
Proper
drainage
drainage
x
RCC road
system
drainage
x
Improper
Traffic
should at
police
priority level.
x
street light
enforcement x
Speed limit board
x
Street light
x
In-pavement lightening system
Bara Bazaar
x
Single lane
Road
x
No traffic
extension on
should be
island or
extra
constructed
rotary
available
on priority
No signal
area
level.
x
x
ϭϲϭ
Lane
x
Traffic island
x x x
Traffic
x
Roadside
island
unauthorized
Traffic
shops should
signal
be restricted
Traffic
as it causes
police
congestion.
enforcement x
Drainage system should be improved
x
Street light
x
In-pavement lightening system
Chavaccha Mor
x
Single lane
Road
x
No road
x x
marking x
No drainage
x
Small
x
private x
parking
x
Road
x
Standing
marking
vehicles or
Road
vehicles
extension
parked
Traffic
roadside
signal
should be
Traffic
restricted as
police
it is major
enforcement
road
In-pavement
connecting
lightening
other places.
system
Neelam Chowk Road
x
Single lane RCC
x
Street light
x
Road marking
x
Unauthorized shops and roadside
ϭϲϮ
x
x
No road marking
x
x
No traffic
Traffic
parking
Signal
cause major
Restriction
congestion.
signal
of roadside
x
Drainage
market
x
Street light
x
Restriction of roadside parking
Bata Chowk
x
Road
x
Compact single lane
x
x
Roadside parking
x
x
No road marking
x
x
Street light
Road
x
As this is the
marking
place of
Traffic
major market
signal
having
Restriction
compact
of parking
road, so
Traffic
parking
police
should be
enforcement
restricted and unauthorized shops should be also restricted.
Churi Bazaar
x
Road
x
Single lane RCC road
x
x
No road
x
x
must. x
Restriction of
Traffic
roadside
signal
parking
Traffic
except
manhole
police
Sahara
Street light
enforcement
building.
Improper
x
Uncovered
ϭϲϯ
Road width extension is
Road marking
drainage x
x
extension
marking x
Road width
x
Restriction of roadside parking
Neelam Chowk
x
± Subhash Chowk ± Gandhi
x
Single lane RCC
x
Chowk Road
x
No road marking
x
x
Improper drainage
x
Road width
x
Restriction
extension
for the
Traffic
garbage
signal
extracted
Road
from market
marking
should be on
No street
x
Drainage
priority as it
light
x
Traffic
causes
police
pollution.
enforcement x
Street light
x
In-pavement lightening system
Maharaj Gunj
x
Single lane
x
Double lane
Road
x
Worst
x
Proper
garbage on
drainage
roadside
Road
should be
marking
restricted.
drainage x
x
No road marking
x
x
Improper street light
x
Disposal of
Proper Street light
x
In-pavement lightening system
8.4.
Major Recommendations
The necessary recommendations for the development of traffic system should be consider at priority level as per following:
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a. Restriction of heavy vehicles through main city road. b. Diverting heavy vehicles from Jaldhari Chowk ± Ranti road. c. Diverting heavy traffic from Nidhi Chowk ± Stadium Road ± MaharajGunj Road ± Gandhi Chowk ± Rahika Road. d. Necessary enforcement of traffic police at City hospital road ± thana chowk road ± Railway Station road ± Bus Stand road ± Bara Bazar Road ± Chavaccha Mor as these roads have very much traffic demand and congestion. e. Shifting of bus stand outside the city near Kotwali Chowk will improve traffic system and reduce congestion. f. Restriction of road side or on road parking throughout the city area.
8.5.
Limitations of Research
After the completion of the research, the following limitations were observed: a. Traffic survey data is limited. i.e., Single day peak hours due to unavailability of resources and manpower. b. Environmental impact assessment was not considered. c. Traffic signal design was not included. d. Economic evaluation was not included. e. Small streets were not considered.
8.6.
Future Scope
There is very much scope of this research in future. After the analysis of results, recommendations and limitations, the following future scope of the research were analyzed: a. This research can be implemented in any small or mid - sized city of Indian city contest. b. This research can help in building a good traffic system in small cities. c. This research can be further continued for improvements in research work. d. The methodology can be adopted with new technical advancements.
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e. As this is an emerging research topic in the field of transportation planning, this research can be practiced in different city contest with the economic considerations.
8.7.
Summary
As per the investigation and analysis of traffic system, several parameters were identified to improve the existing traffic system and to plan a better traffic system for the society. Different roads and routes were analyzed with existing facilities throughout the city and the proposal for the improvement have been provided. It had been observed that the major congestion is due to road side parking and unauthorized roadside shops. So, it should be restricted and there should be encouragement to the private authorities to construct the proper parking facilities for their employees and their customers. Parking inside court compound is must. Road marking and speed limit board installation is must. There should be encouragement to plant trees on the free spaces of roadside to make the city green and there should be proper facility from the municipal department for garbage disposal to make the city clean.
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CHAPTER - 9 SUMMARY AND CONCLUSIONS
ϭϲϳ
SUMMARY AND CONCLUSIONS
Based upon the evaluations presented in this thesis, result analyzed and several conclusions have been made. In each chapter for each method, a general introduction has been provided to tell about the methods and basics of the methods and a summary has been provided below of what type of information can be obtained using these methods and the importance of using these methods efficiently. The study has focused on the evaluation of the parameters of the traffic systems by which a result and recommendations oriented planning can be achieved for the betterment of the traffic system of a small or mid ± sized city. The several studies have been carried out to cope with the problem such as traffic survey, spot speed data, accident record from police station, vehicle data from District Transport Office, public opinion by public questionnaire method. The different methodologies have been performed to analyze the data collected such as traffic volume study, vehicle growth rate, road use pattern, traffic count conversion, future traffic growth, spot speed analysis, capacity and level of service analysis, accident forecasting, analysis of public opinion, etc. To validate the research for the approach of traffic planning for a mid ± sized city, the case of Madhubani: a small district of Bihar has been considered. As currently the traffic system is not planned for the city and have a huge increase in vehicles from last decades with causing congested road networks. Lots of traffic related deficiencies were found while studying the current traffic condition of the city. So, to overcome on the deficiencies of traffic, lots of parameters have been considered to formulate a better traffic plan. Traffic survey is very useful for understanding the traffic conditions. Accident record also gives the traffic & road condition. DTO vehicle record helps us to analyze or forecast the traffic of the city. These all the parameters have been focused for making the traffic plan for a mid-sized city. A set of methodologies have been developed to carry out this research work and the methodologies to analyze the data have been also developed to make this research worth. All the parameters needed to plan a better traffic system have been studied and evaluated in this research. All the elements of data collection have been kept very accurate in order
ϭϲϴ
to analyze the data in a result oriented manner. The data comparison with the previous data makes the project report more precise. Traffic survey kept in the sense of improvement of traffic state of the city as well as making a good parking facilities for the vehicles. The methodologies are developed in such a way that it can be easily make understand and easily implemented for any small or mid-sized city. Traffic survey is a very important tool for this research as it gives the current statics of the traffic of the city. Traffic volumes, capacities, level of services, etc. have been obtained from traffic survey data. In other word, it can be said that survey provides reliable opinioned responses for the research to analyze and suggest recommendations that will improve the system overall. A survey has ability to obtain current response which is vital for discovering issues that cannot be addressed through statical analysis. Traffic volume study have been carried out to at peak hours to analyze the demand of road development or planning to accommodate the volume on the road. Vehicle growth rate have been calculated to analyze the future traffic demand with respect to the growth of the vehicles in future. The pattern of the use of different roads have been analyzed by road use pattern method which signifies the usage of the roads in different time with traffic specified volumes on the same road. Traffic count conversion method have been carried out to analyze the AADT from ADT helps to decide the future AADT. Future estimation of traffic has been also done by the data obtained from DTO. Spot speed analysis have been carried out to analyze the speed statics of the different vehicles on different roads. Road capacity and level of service have been determined to analyze the quantitative and qualitative statics of the different road sections of the city. Accident study have been carried out to analyze the reasons of the accident, the different accident places and accident forecasting have been carried out to analyze the future accident statics if the traffic conditions will not be improved. Last but not least, it had been concluded that traffic planning is a complex method. For planning a better traffic system for a small or mid ± sized city, there are some certain parameters to be evaluated and on the basis of that evaluation, the recommendations should be made as shown in Table 8.1. It can be considering as a startup stage for smart
ϭϲϵ
cities planning which is a current initiation of Government of India. As being a transportation engineer, to provide a safe and reliable journey to the public should be the prime motive.
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