Traffic Intelligent System Architecture Based on Social ...

ICACSIS 2012

ISBN: 978-979-1421-15-7

Traffic Intelligent System Architecture Based on Social Media Information Ari Wibisono, Ibnu Sina, M. Andri Ihsannuddin, Ahmad Hafizh, Benny Hardjono, Adi Nurhadiyatna, Wisnu Jatmiko, dan Petrus Mursanto Faculty of Computer Science Universitas Indonesia Email : [email protected]

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Another way to get the traffic condition data is by using mobile phone, in which the traffic data condition from participating mobile phones, is collected to centralized server [6]. This method has a possible problem, in which available agents do not exist in the needed place [7]. Consequently, in this paper another way to collect the traffic information is proposed. The information from the twitter account will be extracted and give the user information in the form of maps [8]. The main idea of our traffic intelligent systems is going to gather traffic information data from the verified twitter account of police department of Jakarta district. Tweets from this twitter account will be extracted and saved to our database. The database was saved in our own server; the server will process the saved traffic condition database with our algorithm to get the estimate traffic condition. The estimated traffic condition will be viewed and described in a Smartphone, based on the information processed in the server.

Abstract—This paper describes the density of traffic forecasting based on information obtained from social media. Social media twitter can become a source of accurate information as it comes from the verified twitter account i.e police department of republic Indonesia. The tweet (information) from this account is saved in our database. This research is expected to obtain results in the form of a system design and implementation of data traffic forecasting using LVQ algorithms. The result of this research is the capability of the system to give to the mobile device, the traffic information forecasting. i Keywords: LVQ, Twitter, Traffic Intelligent System I. INTRODUCTION

T

raffic congestion is a big problem for many cities in the world. In 2012, Jakarta as a capital city of Indonesia has 11.362.396 unit vehicles travel through this city. A few major impacts of this condition are lost time, lost opportunities, reduced productivities, and increasing cost. Besides increasing the size of the road infrastructure which requires a lot of time and cost, the proposed idea is to build a traffic intelligent system which will decrease the traffic density by providing the information to the user. Traffic intelligent system is going to process all of traffic condition data including vehicle speed, number of vehicle, and size of the road. Traffic conditions data can be obtained by various criteria such as CCTV camera, mobile-agent, and police department. Video analytics is use to address detection and classification of vehicles in urban traffic scenes. Cameras are located at Major Street: the police traffic management center usually connects the camera over the internet network, and then the system will select roads and scenes, after that the administrator will publish the information to the internet [1]. The advanced digital data infrastructure of deployed surveillance systems enables the development of automated video analysis tools [2,3]. The current main problem of surveillance camera is the limitation of the placement of camera, number of camera, environment condition, and processing time of the image (day/night) [4][5].

II. RELEVANT CONCEPTS A. Intelligent Traffic System According to a few traffic management research, there were two approaches in literature, it is centralized traffic management based on operational research and distributed traffic management based on multi-agent system. These systems include fuzzy logic, dynamic programming, and distributed Bayesian decision making [9]. The traffic data comes up with uncertainty concerning the number of vehicles which is change dynamically. One of the smartest solutions is dynamic navigation system. This system will process and collect real-time traffic information to provide a route selection based on the traffic condition. In order to reach the dynamic navigation system, we need a valid traffic condition to process the traffic data in our system. The traffic data from verified police department twitter account allow us to retrieve the traffic information. That traffic information will be saved in our database. This database will be process with one of standard neural network algorithm. The result of this process is the estimated traffic condition

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ICACSIS 2012

ISBN: 978-979-1421-15-7

generate from the system. Information about traffic condition will be shown in Smartphone. Our research goal is to make estimated traffic condition which is can be measured in particular time ahead.

information into more valuable data. The tweet information is tokenizing into three categories (2): 1. Source path contains the name of location in start point 2. Destination path contains the name of location in destination point. 3. The Condition is the traffic condition from start point to destination point. The tweets information from the police department is well structured, so we can partition the tweet information easily. The partitioned tweet information will be saved in our database server. Instead of having a Source and Destination in name of the location, we have conducted the transformation from name of location to actual position in latitude and longitude coordinate. The latitude and longitude coordinate is very useful to provide the condition of the location using mobile device. The server application request to twitter API is conducted every 15 minutes, so we will have a bunch of traffic data information in our database. This is very useful to enrich our data training, to give a good result of an estimated traffic condition. The intelligent system requests the database servers to give the traffic information data. It will receive the data train from database server and process the data with LVQ algorithm. The features which are processed in the intelligent system are: 1. Timestamp (day, hour, minute, second) 2. Latitude of the source location 3. Longitude of source location 4. Latitude of the destination location 5. Longitude of the of destination location The classifications consist of: the traffic condition; low traffic flow, medium traffic flow, high traffic flow. The intelligent system will give the user suggestion if a mobile device gives request location information to intelligent system (5). Intelligent system will receive the request and give an estimated traffic condition that mobile device which has been requested (6). In this research, android device is used as a test device for the proposed system. We make an android application which is request to Google Maps to give our current location using its API. The current location of the android device will be shown on the screen. We use android device which has a GPS-lock system, this technology will give the intelligent system the accurate position of the android device. Android user is allowed to choose a particular point from its current position to the other positions in the map. The map will show the traffic information data for the next 15, 30, or 45 minutes.

B. Neural Network Concepts Learning Vector Quantization is a method in classification patterns where there are two layers, the first layer is the input layer and the second is output layer and each outputs unit represents a class or category [10][11][12] .

Fig. 1. LVQ Algorithm

The learning paradigm of LVQ is supervised learning. Weight vector to a unit vector output is called a reference to the class represented. Assuming the output unit is a set of learning patterns with known classification, awarded on the network, along with the initial distribution of the reference vector. After learning, LVQ network classifies an input vector to be put in the same class with the unit-weight vector output the closest one to the input vector [13][14]. III. GENERAL DESCRIPTION OF PROPOSED FRAMEWORK Our proposed framework consists of 4 layers; mobile device layer, intelligent system layer, database server layer, and twitter API layer. These layers were shown in Fig.2. Intelligent Traffic System Mobile Device

5

6

Intelligent System 3U

3U

3U

3

3U

4

Database Server

1

2

Twitter API

Fig. 2.Intelligent Traffic System Framework

IV. METHODOLOGY

There are a few steps to make this system work; Database server requests a number of tweets to Twitter API. These tweets were come from a verified tweeter account of police department (1). The application in database server tokenizes the tweet

In this research, we use Twitter API as a media to gather the traffic information data from verified police department twitter account. Our database server requests the twitter API and tokenizes the sentences into 3 categories:

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ICACSIS 2012

ISBN: 978-979-1421-15-7

1. Location of source 2. Location of destination 3. The traffic condition Once we have got the tokenized information, we are appending the data with the timestamp which is referring to the time when the tweet has occured. The tokenized tweet and the timestamp will be saved in our database as our training data for intelligent systems. Database server will automatically normalize the input for LVQ algorithm. The normalized input will be automatically saved when a new tokenized tweet was saved. The intelligent system will work if there are requests from the mobile phone. These requests will be processed in real-time as soon as the mobile device requested the traffic information. We have trained the intelligent system with the 344 (90 %) data of traffic, and testing it with the 39 (10%) data traffic.

traffic_data tweet_id

FK1 FK2

date_day date_hms street_id_source street_id_destination street_condition_id

PK

street_id street_name street_latitude street_longitude

condition_data PK

condition_id condition_description

Fig.4. ERD Design

There are two tables, traffic data and street data. Table traffic data contains a few columns, they are: 1. Date (day), in this column we just save the day of the tweet (Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday) 2. Time (hour : minute : second), in this column we save the time format in h:m:s 3. street_id_source, in this column we save a foreign key to street_data table 4. street_id_destination, in this column we save a foreign key to street_data table

V. IMPLEMENTATION AND ANALYSIS A. Database Server Implementation In this research the database server is implemented using the standard SQL (Structure Query Language). SQL is one of the solutions to save the tokenize data which has various data types such as String, double, timestamp.

Besides the database implementation, we have also built a robot (automated requester) that request from twitter API for every 15 minutes. This application implemented in Python programming language. The tasks of this robot are: 1. Make requests to twitter API which is direct to verified twitter account of police department every 15 minutes. 2. Tokenize the result of the requested tweet according to the database design structure

Database Server

1

street_data

PK

2

Twitter API

Fig.3. The Implementation of Database Server

We use SQL because, it can help us to insert and retrieve data easily from the database. Besides SQL's ease of use, it has also connectors which have been implemented in many programming language. In this research, we use 2 programming languages for this system, they are; Python and Java. The implementation of the ERD is described in Fig.4.

Fig.5. Tokenize Implementation of Tweets

Fig.5. says "Situasi arus lalu lintas arteri slipi mengarah semanggi ramai lancar" means Easy traffic, from slipi heading to semanggi. This tweet contains a traffic condition at 17:52 between Slipi and Semanggi. The extracted information will be insert in the database server as described in Table 3. TABLE 1 STREET CONDITION REPRESENTATION

TABLE 2

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Condition Id

Condition Description

1 2

Lancar Padat

3

Padat Merayap

ICACSIS 2012

ISBN: 978-979-1421-15-7 STREET DATA REPRESENTATION

Street_id

Street_name

Street_latitude

Street Longitude

3

Rawamangun

-6.196692

106.888431

4

Jl Balap Sepeda

-6.1885955

106.8910917

5

Cawang

-6.2544401

106.8743529

9

Cakung

-6.184887

106.944926

52

terminal pulo gadung

-6.18408

106.9083

42

Bekasi

-6.213087

106.828308

1

Semanggi

-6.210229

106.827579

48

Bundaran Hi

-6.1944975

106.8230195

10

Kuningan

-6.9808668

108.4775703

55

Dukuh Atas

-6.202397

106.8234872

56

Rasuna Said

-6.214282

106.812665

11

Menteng

-6.1963821

106.8378932

TABLE 3 TRAFFIC DATA REPRESENTATION Tweet_id 249165974966775000 249165359691755000 249165483679567000 249146915265069000 249325479302160000 249324874840027000 249315008679399000 249305342540914000 249304710308315000

Date (Day) 2012-09-21 2012-09-21 2012-09-21 2012-09-21 2012-09-22 2012-09-22 2012-09-22 2012-09-22 2012-09-22

Hms

Street_id_source

22:19:32 22:17:05 22:17:35 21:03:48 08:53:21 08:50:57 08:11:44 07:33:20 07:30:49

3 5 7 9 52 1 10 55 56

A. Intelligent System Implementation LVQ algorithm is implemented for the traffic information classification. This algorithm is written in java programming language. The features of LVQ algorithm in traffic intelligent system are the input of tokenized tweets which are saved in database server. The features for the LVQ algorithm are: 1) Date/Day We choose the days to be one of the features of this algorithm because the days represent the recurrent time. Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday. These days represent the input features between 0-1 in our LVQ algorithm. 2) H:M:S We then convert the hour: minute: second into the second unit. These times (second) will represent by number ranging between 0 - 86400 (24 hours time) and they are normalize to 0-1 as the input features of LVQ algorithm. 3) Source Latitude Source latitude is the latitude of source position from the tweet. Standard map latitude of the world is between -90 and 90. We have to normalize the source latitude in database to be represented between 0-1. We use this formula to normalize the source latitude:

Street_id_destination 4 6 8 5 42 48 1 48 11

Street_condition 1 2 2 2 1 1 1 2 2

(1) Legend: nlat = normalized latitude; lat = latitude 4) Source Longitude Source longitude is the longitude of source position from the tweet. Standard map longitude of the world is between -180 and 180. We have to normalize the source latitude in database to be represented between 0-1. We use this formula to normalize the source longitude:

(2) Legend: nlong = normalized longitude; long = longitude 5) Destination Latitude The destination latitude is the latitude position of the destination point retrieve from twitter. In normalizing the destination latitude, we use the same formula as source latitude, formula (1). 6) Destination Longitude The destination longitude is the longitude position of the destination point retrieve from twitter. In

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ICACSIS 2012

ISBN: 978-979-1421-15-7

normalizing the destination latitude, we use the same formula as source longitude, formula (2). 7) Target Class The target classes that we use on this system are the traffic conditions of the traffic. The traffic condition of the traffic can be classified in few categories; low traffic flow, medium traffic flow, and high traffic flow

send back the estimated traffic condition a few minutes ahead. The application will show the path form current position to the selected position and notify the estimated traffic condition.

B. Smartphone Implementation Samsung Galaxy S3 as an android Smartphone is used to test this system. We have built a customized application in it. This application requests a traffic condition to intelligent system and represents the traffic condition in the form of map. We can see the screenshot of the application on the Fig.6. Our mobile application will request the server with the web service to start the program in intelligent system. The data which is sent to the intelligent system are timestamp, source latitude, source longitude, destination latitude, and destination longitude. After the request has been sent, the intelligent system will receive the request and send back the estimated traffic condition.

2 4 3 1

Fig.6. Screenshot of Mobile Application

VI. CONCLUSION

C. Experiment and Analysis The experiment of this research is using the data which is retrieve from the verified twitter account of police department. We use the data (day), time (hms), source latitude, source longitude, destination latitude, destination longitude as input features. We use these data for the classification. We conduct the experiment with 344 data training and 39 data testing, the numbers of epoch that we used in this experiment are 300. The amount of the classification "lancar" which means low traffic flow condition is 12.2% of the data, the number of class "padat" which means medium traffic flow condition is 82.5% of the data, and the number of class "padat merayap" which means high traffic flow condition is 5.2% The accuracy of data testing over the data training provided is 76.92 % The implementation of mobile application has a few features such as: 1. Defining current position location 2. Drawing a path to the selected place 3. Notifying the user about the traffic condition from current position to selected place 4. Estimating the traffic condition for few minutes later From Fig.6, it can be seen that the system has been implemented successfully in a mobile device. In Fig.6. we can see the word "lancar". The word "lancar" means that the estimated traffic condition from current position to the selected position is in low traffic flow. This estimated traffic condition was get from the intelligent system through web service. The estimated traffic conditions from the web server will occur if the user selects the position within the maps, and insert the number of minutes, which will

Intelligent traffic system design architecture which is proposed in this paper is one of the solutions to estimate the real time traffic condition, especially in densely traffic area such as Jakarta. Although this intelligent system is already implemented, it needs a few enhancement and adjustment in the algorithm. The future concept of this system will be an integrated system that will receive the source data which is not only from the verified traffic account of police department, but we will also provide a data from real time CCTV camera and the multi-agent support. We hope this enhancement will increase the estimated traffic information accuracy. ACKNOWLEDGMENT This research is supported by Indonesia’s Ministry of Education and Culture in the schema of MP3EI No:3715/H2.R12/HKP.05.00/2012. REFERENCES [1] M. Rachmadi, "Adaptive Traffic Signal Control System Using [2] [3] [4] [5]

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Camera Sensor and Embedded System", Proc IEEE Tencon 2011 page 1261-1265, 2011. F. Al Afif, "Enhanced Adaptive Traffic Signal Control System Using Camera Sensor and Embedded System", Proc IEEE MHS 2011 page 367-372, 2011 B.Zaman, " Implementation Vehicle Classification On Distributed Traffic Light Control System Neural Network Based ", Proc IEEE ICACSIS 2011 page 107-112, 2011. M.Stefano, " A computer vision system for the detection and classification of vehicles at urban road intersections", in ITCirst Technical Report T04-02-07, February 2004. N.Buch, "Detection and Classification of Vehicles for Urban Traffic Scenes", Visual Information Engineering, 2008. VIE 2008. 5th International Conference , 2008

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[6] P. Anurak, "Acquiring Road Traffic Information thorough [7] [8]

[9] [10]

[11] X.Li, "ITIS: Intelligent Traffic Information Service

Mobile Phones", ITS Telecommunications, 2008. ITST 2008. Page 170-174, 2008 A. Khaled, "An Intelligent Multi-agent Approach for Road Traffic", 18th IEEE International Conference on Control Application s page 825-835, 2009. E.S.Krisna, "Traffic Condition Information Extraction & Visualization from Social Media Twitter for Android Mobile Application", 2011 International Conference on Electrical Engineering and Informatics, 17-19 July 2011. G. V. Lioudakis, "An Intelligent Traffic Management System for the Eco-Optimization of Urban Traffic Flows", Communications Workshops (ICC), 2010 T. Gang, "Fuzzy Neural Network Model Applied in the Traffic Flow Prediction", Proceedings of the 2006 IEEE International Conference on Information Acquisition, August 20 - 23, 2006.

[12]

[13] [14]

[15]

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in Shanghai Grid", The Third ChinaGrid Annual Conference, 2008. S.Xiaojun, "Study on Prediction of Traffic Congestion Based on LVQ Neural Network", 2009 International Conference on Measuring Technology and Mechatronics Automation page 318-321, 2009. M.M.Hasan, Vision Based Intelligent Traffic Management System, 2011 Frontiers of Information Technology, 2011.. Z.Yin, "Study on the Intelligent Traffic Control Method Based on Intelligent Traffic Congestion Information", Second International Symposium on Intelligent Information Technology Application page 580-583, 2008. I.M.Setiawan,"Arrhytmia Classification using Fuzzy-Neuro Generalized Learning Vector Quantization", Proc IEEE ICACSIS 2011 page 385-390, 2011.