2nd Annual International Conference IEMCON 2012
Computational Optimization of Speed in an Unplanned Lane Traffic Prasun Ghosal
Arijit Chakraborty
Sabyasachee Banerjee
Department of CSE Purabi Das School of IT Department of IT Heritage Institute of Technology Bengal Engg and Sc University, Shibpur Bengal Engg and Sc University, Shibpur Kolkata, WB, India Howrah, WB, India Howrah, WB, India Email:
[email protected] Email:
[email protected] Email:
[email protected]
Abstract—Speed Optimization in an Unplanned Traffic (SOUT) is a very promising research problem. Searching for an efficient optimization algorithm to increase the degree of speed optimization and thereby increasing the traffic flow in an unplanned zone is a widely concerning issue. However, there has been a limited research effort on the optimization of the lane usage with speed optimization. This paper presents a novel SOUT technique to solve the problem optimally using the knowledge from analysis of speeds of vehicles, which, in turn will act as a guide for design of lanes optimally to provide better optimized traffic. The accident factors adjust the base model estimates for individual geometric design element dimensions and for traffic control features. Knowledge based analysis technique is applied to the proposed design and speed optimization plan. The experimental results are quite encouraging. Index Terms—SOUT, Speed optimization, Unplanned traffic, Traffic engineering, Optimization Algorithm
I. I NTRODUCTION The challenges of speed optimization in an unplanned traffic design system are to move traffic safely and efficiently. Although highways and motor vehicles are designed to operate safely at speed. The purpose of our investigation was to create predictive models for optimizing the speed of the different types of vehicles in an unplanned zone, based on infrastructural design and traffic intensity. In this paper, the results for all transition points and lane transition of vehicles for speed optimization is discussed. The first investigation was the identification of different zones on the lane. Three major zones on the lane were defined: • Transition points. • Entry zones and • Exit zones. A. Drawbacks of existing solutions Many traditional speed optimizing algorithms for lanes were proposed earlier to optimize deterministic problems. But they cannot tackle the inherent randomness in the traffic systems. Therefore, to handle with such random realistic situation and generate some efficient solution, good computational models of the same problem as well as good heuristics are required. This requirement worked as a major driving factor towards our effort. The solutions suggested by different traditional Non accidental algorithms for lanes cannot answer the questions like
What is the degree of robustness that the optimal solution will achieve optimum speed utilization of the vehicle? or What is the probability of failure that the optimal solution will fail if something unexpected occurs? Hence, the above problems worked as motivating factors towards addressing the problem from a different angle. The purpose of this article is to address these three issues by introducing the approximation technique to be discussed throughout the paper for the optimal SOUT design. The paper is primarily divided into two parts. In the first part, we analyse the problem and determine the major factors for the determination of the optimum number of lanes and transition points. In the second part, we conclude the possible lanes with optimum transits between the lanes for speed optimization of the vehicle. The overall organization of the paper is given as follows. Important previous works are discussed in the following section. Section III discusses about the details of the proposed work with a very clear analysis of the proposed algorithm. Section IV presents the experimental results with simulated graphical analysis of the computational model. Section V concludes the paper with possible future directions of work. II. P REVIOUS W ORKS The paper proposed by Jake Kononov, Barbara Bailey, and Bryan K. Allery [1], first explores the relationship between safety and congestion and then examines the relationship between safety and the number of lanes on urban free-ways. The relationship between safety and congestion on urban free-ways was explored with the use of safety performance functions [SPF] calibrated for multi-lane free-ways in Colorado, California, Texas. The Focus of most SPF modelling efforts to date has been on the statistical technique and the underlying probability distributions. The modelling process was informed by the consideration of the traffic operations parameters described by the Highway Capacity Manual [1]. In 2007, H Ludvigsen et al., has published Differentiated speed limits allowing higher speed at certain road sections whilst maintaining the safety standards are presently being applied in Denmark [2]. The typical odds that higher speed
Innovative Techno-Management Solutions for Social Sector
161
2nd Annual International Conference IEMCON 2012 limits will increase the number of accidents must thus be beaten by the project [2]. In another important work, C.J. Messer et al. [3] presented a new critical lane analysis as a guide for designing signalized intersections to serve rush-hour traffic demands. Physical design and signalization alternatives are identified, and methods for evaluation are provided. The procedures used to convert traffic volume data for the design year into equivalent turning movement volumes are described, and all volumes are then converted into equivalent through auto-mobile volumes. The critical lane analysis technique is applied to the proposed design and signalization plan. The resulting sum of the critical lane volumes is then checked against established maximum values for each level of service (A, B, C, D, E) to determine the acceptability of the design. In this work, the authors have provided guidelines, a sample problem, and operation performance characteristics to assist the engineer in determining satisfactory design alternatives for an intersection [3]. III. P ROPOSED W ORK Speed Optimization in an Unplanned Traffic (SOUT) solutions finding methods in other literature are a family of optimization algorithms which incorporate level of traffic services in the algorithms. There are two major issues, in the first part, we have analysed the major issues residing in the latest practice of the accidental lane; and, in the last part, we have discussed the possible applications of this new technique and new algorithm. A. Proposed Algorithm The algorithm proposed for the solution of the problem of speed optimization in unplanned traffic is described below. The algorithm is designed under certain assumptions as described below. Assumption During the execution of the algorithm it is assumed that there will be no change in the current speed of the vehicle whenever the current speed of the vehicle is entered once. B. Description of the Proposed Algorithm The primary sections of the proposed algorithm and their major functionalities are described below. •
• •
•
Step 1 is taking input from sensors, like the current speed of the vehicle, arrival time etc., and, counting the number vehicles the user has entered. Step 2 is categorizing the vehicles depending on their current speed. Step 3 is checking total how many numbers of lanes will be required for our sample data in an unplanned zone, and, which vehicle is moving in which lane. Step 4 is checking total number of transitions i.e. at which point of the lane and from which lane to where the transition will occur. Now these are the outputs of our simulation.
Algorithm SOU T () Input : Name of vehicle, maximum speed, arrival time. Output: Type of vehicle, Number of lanes required, Number of transitions. Step 1.1: Set count = 1; /*Used to count the number of vehicles.*/ Step 1.2: get input(); /*Enter name of vehicle, current speed, arrival time and store it into a record.*/ Step 1.3: Continue Step 1.1 until sensor stops to give feedback and update count = count + 1 Step 2: for 1 ≤ i ≤ count for each vehicle If current speed of the vehicle (Vi ) 0 < Vi ≤ 10 then categorize vehicle (i) as type A If 11 ≤ Vi ≤ 30 then categorize vehicle (i) as type B If 31 ≤ Vi ≤ 45 then categorize vehicle (i) as type C If 46 ≤ Vi ≤ 50 then categorize vehicle (i) as type D If 51 ≤ Vi ≤ 100 then categorize vehicle (i) as type E Step 3: Set counter: count1 := 1; Set lane of first vehicle = 1; for 2 ≤ i ≤ count for each vehicle for 1 ≤ j ≤ count1 Compare the type of (i)th vehicle with type of (j)th vehicles present in the lane if different update count1 = count1 + 1 and laneof (i)thvehicle = count1; else laneof (i)thvehicle = j; end of loop; end of loop; Step 4: Set counter: count2 = count1; for 1 ≤ i ≤ count − 1 for each vehicle for 2 ≤ j ≤ count for each vehicle if (i)thvehicletype = (j)thvehicletype and Vi < Vj and (i)thvehiclearrivaltime ≤ (j)thvehiclearrivaltime set t = (j)thvehiclearrivaltime − (i)thvehiclearrivaltime; set t1 = 0; begin loop set t1 = t1 + 1; set d = Vi × (t + t1 ); set d1 = Vj × t1 ; if d1 ≤ d set count2 = count2 + 1; if (j)thvehiclelane = 1 then transition will be to 2 − lane; if (j)thvehiclelane = count1 then transition will be to count1 − lane; else transition will be to (j)thvehiclelane − 1 or (j)thvehiclelane + 1; end loop; end loop; end loop; Step 5: Return N umberof lanesrequired = count1, N umberof transitionsrequired = count2; Step 6: End Algorithm 1: Proposed algorithm for speed optimization in an unplanned traffic (SOUT)
Innovative Techno-Management Solutions for Social Sector
162
2nd Annual International Conference IEMCON 2012 C. Analysis of the Proposed Algorithm The salient points of the proposed algorithm are summarized below. • The above algorithm is implemented on an open unplanned area. • The objective will follow linear queue as long as speed/value/cost of preceding is greater than the immediate next. • Transition/Cross over are used and they again follow appropriate data structure in order to maintain the preceding step rule. • Here we assume the lanes are narrow enough to limit the bidirectional approach. • Here we maintain optimize speed for each lane. • Here we also maintain the transition points if speed/value/cost of a vehicle is found unable to maintain the normal movement and transition in all the calculated lanes. • Transition points are recorded with their position and number and it follows appropriate data structure in order to maintain the record.
Fig. 2.
Sample Size Vs Number of Transition points.
IV. E XPERIMENTAL R ESULTS Our work aimed to design 0SOUT0 in an open unplanned area, so as to increase traffic movement in rush hours and to optimize the speed of the vehicles using the concept of transition points between adjacent Lanes. In Figure 1, three vertical lanes are shown those are unidirectional, and A = a1 , a2 , , an , B = b1 , b2 , , bn , C = c1 , c2 , , cn , with the property of the three lanes. I, II, III are the transition points through which vehicles can overtake its preceding vehicle with lesser speed and then immediately moves to its original lane. I is from lane A to B or B to A and II, III are from B to C or C to B. Here we assume that car speed in each and every lane is greater than 0 kmph. If the speed of any car is less than or equal to 0 kmph then we assume that there may be problem. The random distribution of entities in an open area to lanes is to be taken care of as far as possible. A. Simulated Graphical Analysis After the implementation of the proposed algorithm the whole scheme is simulated and fro the simulations the graphical results are generated which are shown in Figure 2 and Figure 3. V. C ONCLUSION AND F UTURE W ORK In this work, we introduce a novel design of a Speed Optimization Technique in an Unplanned Traffic. Our proposed design and speed optimization plan, SOUT, can robustly manage and can operate over any unplanned zone. As the Speed optimization with unplanned zone is widely concerning issue in rural development, therefore, our proposed method will optimize the lane requirements and speed of the vehicles, which indirectly helps also in energy saving and more economical growth of a country.
Fig. 3.
Number of lanes Vs Number of Transition points.
The main limitation of the approach is that though in this paper lane requirement is optimized but lane usage is not optimized. For example at a certain time instance there may be a chance that only same type of vehicles are arriving and populating its designated lane but other lanes are not so populated, so lane usage optimization is required. Our future effort will certainly be on that direction. Here in this work, we have tried to optimize the speed of the vehicles and lanes and transitions between lanes in a secured non collision manner. R EFERENCES [1] Jake Kononov, Barbara Bailey, and Bryan K. Allery, The Relationship Between Safety and Congestion, Journal of the Transportation Research Board, No. 2083. [2] Differentiated speed limits, European Transport Conference Differentiated speed limits, 2007. [3] C.J. Messer and D.B. Fambro, Critical Lane Analysis for Intersection Design, Transportation Research Record No. 644; 1977, pp, 26-35. [4] Prasun Ghosal, Arijit Chakraborty, Amitava Das, Tai-Hoon Kim, Debnath Bhattacharyya, Design of Non- accidental Lane, In Advances in Computational Intelligence, Man-Machine Systems and Cybernetics, pp. 188-192, WSEAS Press, 2010.
Innovative Techno-Management Solutions for Social Sector
163
2nd Annual International Conference IEMCON 2012
Fig. 1.
Vertical lanes that are unidirectional and with the property of the three lane with transition points.
Innovative Techno-Management Solutions for Social Sector
164
