urban traffic control with pedestrian handling

Urban Traffic Control with Pedestrian Handling

Ayesha Shahid, Wilayat Ali Khan, Nazish Naheed, Salman Hafeez, Syeda Nuzhat Subi Naqvi, Aihab Khan Department of Software Engineering

Foundation University Rawalpindi Campus

Abstract - Over the years road traffic flow has seen pedestrian crossing as a major issue in the society, particularly in urban areas where there is no control for pedestrian road crossing. In mixed traffic conditions pedestrian road crossing behavior is a serious hazard for pedestrians crossing uncontrolled bi-intersection localities. Due to increase in motor vehicle growth there is an increase in the regulation of motor vehicles only and the regulation of pedestrian is completely neglected in urban area. An increase the uncontrolled road crossing behavior of pedestrian is raises different safety and economic concerns. This paper employs computational modeling to regulate the traffic flow across a two way intersection. It is caters how pedestrians can cross a bi-intersection traffic signal without disrupting the traffic flow. Existing computational models that have been presented by other authors are discussed which gives more understanding how to control traffic flow for vehicles and pedestrians handling. This study deals three scenarios of real environment for control of traffic flow for pedestrians; with no turns, with turns and with turns. All scenarios provides proper notation for ‘on states’ and ‘off states’ of signal. Experimental result demonstrates that the proposed method achieved waiting time for vehicles 143.35 seconds and 200.23 seconds for pedestrians respectively. Furthermore, result shows the decrement of time and economical resources that are used in the daily commute.

Index Terms— Pedestrian, Bi-intersection, uncontrolled traffic, Computational Modeling, Traffic Control System

I. INTRODUCTION In modern era, it is predicted that passenger utilized over 600 million cars and roughly every year this is increasing by 50 million in numbers. With the increments of vehicles, there is no such rule for pedestrian safety and time route for crossing the road [1]. There are many factors, including lack in traffic rule public awareness, irrational traffic infrastructures and poor planning that are responsible for pedestrian and traffic problems. The major factors which effect the existing urban traffic signal control (TSC) system does not sufficiently follow optimal traffic control and management role [1].In addition, many others factors also involved in urban traffic control, numbers of vehicles, travelers and weather, which makes the traffic system complex nonlinear stochastic systems and pose many problems. Besides the TSC human behaviors also effect the implementation of pedestrian control system for traffic signal [2]. Therefore, it cannot achieve the optimal usage of resources: time and space of the whole intersection. A considerable amount of research have been devoted to the vehicular traffic modelling, but pedestrian traffic modelling didn’t received much attention. It is until recent that a little attention have been given to modelling pedestrian traffic. Studying the urban traffic with the help of computational modeling to find the solutions which can give us better use of resources involved in the daily commute in means of time and operational cost of a vehicle as it play a very vital role for travelers [3]. As the safety and cost are major concern so, we solve this issue by using computational modeling. It is study of complex problems by using computer science and simulates all variables which are involved in this process. To achieve this purpose, creates an artificial environment in which complex problems are characterized, so we able to drive a suitable solutions.

The objective of this study is to optimize the traffic flow, reduce the time spent by pedestrians and lessen traffic jam by minimizing the time spent by a vehicle and a pedestrian on a traffic signal. In return, lower the operational cost of a vehicle and save the time of a by stander. Simulation of model has been implemented in C++ which helps to reduce traveler time and operational cost in means of waiting time of pedestrian or vehicle.

This paper is organized as per following sections; related work is described in section II, preliminaries are explained in section III, Framework overview described in section IV, experiment results are demonstrated in section V and in section VI, we arrive at conclusion and future work. References placed at the end of all sections.

International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 6, June 2016

525 https://sites.google.com/site/ijcsis/ ISSN 1947-5500

II. RELATED WORK

Walking is the most fundamental mean of transportation [6]. A Chinese proverb “walking also constitutes the first and last part of practically any trip” is literally true in every mean. Predicting how an individual pedestrian behave is a matter of describing its destination and preferences regarding the route choice to its destination [4]. Pedestrian road crossings have become a major issue in road traffic flow, especially in urban areas. The modeling of pedestrian’s movement in the urban traffic flow has become an important area for many researchers [4]. Over the years, different efficient and optimum models have been proposed to regulate the traffic flow in account of pedestrians. Some of them are mentioned below:

Alina Chertock’s presents “Pedestrian Flow Models slowdown Interactions” which introduces and investigates one-dimensional models for the mannerism of pedestrians in a narrow street or path [5]. At the beginning, the microscopic levels by framing stochastic cellular automata model with unambiguous instructions for pedestrian’s movement in reverse direction. A coarse-grained microscopic and macroscopic analog is the resultant of leads to the attached system of PDEs for the density of the pedestrian traffic. The achieved PDE system assorted hyperbolic-elliptic category and consequently, meticulously get higher-order nonlinear diffusive corrections for the macroscopic PDE model. Numerical experiments are performed, which are compared and distinguished to the manners of the microscopic stochastic model and the coarse-grained PDEs are resulted. The CA formalism is that it allows for a systematic derivation of the coarse-grained dynamics is an advantage of presented model. The drawback of proceeding model it only works for one-dimensional in a narrow street or corridor and not works for control of bi-dimensional with pedestrian handling [5]. In addition, Fredrik Johansson is the first who provides a platform of micro-simulation for pedestrian traffic by incorporating microscopic modeling and simulation of pedestrian traffic. Their Traffic Simulation Platform (PTSP) scheme is based on the Social Force Model which is later evaluated [6]. In this article possible existing models which are proposed for pedestrian e.g. microscopic, social fore, waiting pedestrian, preferred velocity, preferred position, adapting preferred position models and importance of modeling waiting behaviors are briefly discussed. The basic attributes of traffic flow is also part of this research, e.g. number of pedestrians passing a cross section per unit time, width of the cross section, average density in an area and traffic the mean speed of the vehicles on a link serves and related issues with these basic attributes are described gently [6]. B Raghuram Kadali and P Vedagiri model the pedestrian road crossing behavior under mixed traffic condition [7]. The pedestrian behavioral aspects are considered at the microscopic level which includes variables such as observation duration at curb and median, number of observations at curb and median, observation duration while crossing, number of observations while crossing, speed change condition, crossing path change condition, frequency of attempt and rolling gap. In their research, they investigated the pedestrian road crossing behavior of uncontrolled traffic. Traffic flow is varied to observe the best variation of behaviors of the pedestrians. Their behavior of road crossing has been modeled by the size of vehicles gaps accepted by walker using the multiple linear regression technique. A choice of model is presented which has been developed to depict the decision making process of pedestrian i.e., whether to accept or reject vehicular gaps based on the discrete choice theory [7]. This scheme has some limitation like, pedestrian’s age, video coverage section (40m) is limited, speed of the vehicle, overlooked due to visibility complications, pedestrian speed change and path change. Pedestrians may walk faster or may reduce their speed in various situations (e.g., in rolling gap condition pedestrian may reduce or increase their speed according to the available gap and there are multiple path change conditions). So, it is need to evaluate the pedestrian road crossing behavior with individual specific speed as well as path change conditions. Moreover, this model complies only for midblock road cross and does not talk about traffic control on signal area and dimensional of road clearly[7].

Besides previous model, motorway traffic models for traffic has been proposed by Tom Ballemans et al [8] predictive control approach for ramp metering is evaluated in model on the basis of its selected features. As traffic on the motorways is fast and dynamic in nature, the control actions are required to be updates on regular basis for accounting purposes of traffic change scenarios. Additionally, use of loop detector which is a device that counts the number of vehicles works as loop in the road surface and an electronic device that monitors the changes of inductance of the loop as vehicles are passing over it [8].

Modeling Behavior in Vehicular and Pedestrian Traffic Flow by Michael J. Markowski investigates the design and analysis of vehicular and pedestrian models. A new vehicular model is developed for vehicle behavior modeling as well as to use as a



tool to create an even more complex behavioral model of pedestrian movement. The model is developed to support the changes in multiple lanes and contribute to improve four points [9]. Initially, it investigates the purely behavioral studies and engineering modeling of urban area traffic and then in constant of single lane it works for multiple lanes at a time. At next, an algorithmic model of pedestrian movement is created which support groups and simple social interaction. In last, software is designed to using an object-oriented approach in conjunction with agent based modeling [9]. This model specify for shopping centers or parks area for pedestrian handling and it creates by using cellular automaton instead of computational model.

Serge Hoogendoorn in his paper presents that having insights into the pedestrian flow process and evaluation tools for pedestrian walking speeds &comfort is vital in development and geometric design of infrastructural amenities, and for management of pedestrian flows under standard and safety-critical situations. It is observed that pedestrians are independent prognostic controllers that lessen the one-sided predicted cost of walking. Pedestrians to see the behavior of other pedestrians on the basis of their observations of the current state in addition to predictions of the future state, given the implicit walking strategy of other pedestrians in their direct neighborhood. [10]. ZhaoWei Qu at el [11] presents a survey paper where briefly focuses on different traffic signal control systems (TSC). Such as, traffic signal control, Reasons of Computational Intelligence for Traffic Signal Control, computational intelligence for traffic signal control in surface network, Fuzzy System, Artificial Neural Network Evolutionary Computation and Swarm Intelligence and computational intelligence for traffic signal control in freeway network with coordination of urban traffic control and its assignment. All system has own pros and corns respectively.

After having a detail review of existing system, we come at this point; no one provides a clear solution for pedestrian; how they move and control in bi-direction traffic flow. We proposed a solution which based on computational modeling to safe the time and resources of vehicles and pedestrian in urban traffic flow. A computational model can provides insight into behavior of a phenomenon, or by reconciling seemingly contradictory phenomena. It deal with complexity by producing satisfying explanations of what would otherwise just be vague hand-wavy arguments and explicit about your assumptions and about exactly how the relevant processes actually work. In addition it is more stringent test of a theory and encourages parsimony and also enables one to relate two seemingly disparate phenomena by understanding them in light of a common set of basic principles [12].

III. PRELIMINARIES

The measure used to describe the traffic situation is naturally dependent on the level of detail with which the traffic can be observed [6]. Traffic signals gives clear understanding of traffic movement on the road, especially in busy and idle hours with their implication. It helps drivers to avoid risks and also guide them to how to keep safe driving by following these rules. Signals are placed at vantage points on the sides of the roads and overhead along the high streets. It is anticipate to road users vigilant and warn them in places where there are corners, slopes and animals.

Moreover, signals guide drivers the names of cities, regions, places, and aid stations. Each light colors have a specific meaning; as red light means “stop” and if the light is red as you approach, you must not go beyond the zebra crossing. A green light means you may go if the road is clear and should proceed with caution. In last, amber light indication of move on if you are close to the stop line but when this light first appears then stopping would be dangerous.

Traffic signal helps us to make sure that pedestrian and bicyclists obtain reasonable share of the road. It is usually life threatening to cross a hectic neighborhood and crosswalks may significantly lower the danger. Here is an explanation of how traffic signals work:

• with no turns • with turns • with turns and pedestrians

WHEN NO TURNS: This postulates deals with two scenarios in which no turns are introduced in the traffic flow. First, traffic flows in the horizontal and the second is in the vertical direction. Fig.1 gives the overview of the two way (Bi) intersection.



Figure 1: Bi-intersection road with no turn

Decision statement: The first statement as depicted in Fig 2 deals with the movement of traffic horizontally. This statement can be written as P1:S1’ S3’ S2 S4 which tells that traffic flows when signal-1 and signal-2 are ‘on’ whereas rest of the signal-3 and signal-4 are in the ‘off’ state. The second statement deals with the movement of traffic vertically. This statement can be written as P2:S2’ S4’ S1 S3 which tells that traffic flows when signal-2 and signal-4 are ‘on’ whereas rest signal-1 and signal-3 are in the ‘off’ state as shown in Fig.3.

Figure 2: P1:S1:S3:S2:S4 Figure 3: P2:S2:S4:S1:S3

WITH TURNS: This scenario deal with traffic flow with all possible turns as illustracted in Fig 4.

Figure 4: Bi-intersection with all possible turns

Decision statement: The first statement deals with the movement of traffic horizontally rightwards and turning right. This statement can be written as P1:S1’ R1’ S2 R2 S3 R3 S4 R4, as shown in Fig.5. The behavior of traffic flow when signal-1 and right signal-1 are ‘on’ whereas rest signal-2, rightsignal-2, signal-3, rightsignal-3, signal-4 and rightsignal-4 are in the ‘off’ state can be noted.



Figure 5: P1:S1’ R1’ S2 R2 S3 R3 S4 R4 Figure 6: P2: S2’ R2’ S3 R3 S4 R4 S1 R1

The second statement deals with the movement of traffic vertically rightwards and turning right. This statement can be written as P2: S2’ R2’ S3 R3 S4 R4 S1 R1, which tells that traffic flows when signal-2 and rightsignal-2 are ‘on’ whereas rest signal-3, rightsignal-3, signal-4, rightsignal-4, signal-1 and rightsignal-1 are in the ‘off’ state as explained in Fig 6.

The third statement deals with the movement of traffic horizontally leftwards and turning right. This statement can be written as P3: S3’R3’ S4 R4 S1 R1 S2 R2 which tells that traffic flows when signal-3 and rightsignal-3 are ‘on’ whereas rest signal-4, rightsignal-4, signal-1, rightsignal-1, signal-2 and rightsignal-2 are in the ‘off’ state as described in Fig 7.

Figure 7: P3: S3’R3’ S4 R4 S1 R1 S2 R2 Figure 8: P4: S4’R4’ S1 R1 S2 R2 S3 R3

The fourth statement deals with the movement of traffic vertically upwards and turning right. This statement can be written as P4: S4’R4’ S1 R1 S2 R2 S3 R3 which tells that traffic flows when signal-4 and rightsignal-4 are ‘on’ whereas rest signal-1, rightsignal-1, signal-2, rightsignal-2, signal-3 and rightsignal-3 are in the ‘off’ state. Figure 8 explains the fourth statement of signal control. WITH TURNS AND PEDISTRIANS: Fureig.9 shows that the all possible crossings where pedestrian can make on a two way intersection crossing.



Figure 9: All possible pedestrian crossing (M)

Decision Statement: The first statement deals with the movement of traffic horizontally rightwards, turning right and also the turning left (L4) as shown in Fig 10. This statement can be written as P1: (M1)’ (M2)’ S1’R1’ L4’ S2 R2 S3 R3 S4 R4 L1 L2 L3 which tells that when pedestrians (M1) and (M2) want to cross the road, the traffic will flow along the signal1, rightsignal1 and leftturn-4 will be ‘on’ whereas rest signal-2, rightsignal-2, signal-3, rightsignal-3,signal-4, rightsignal-4,leftturn-1, leftturn-2 and leftturn-3 will be in ‘off’ state.

Figure 10: P1: (M1)’ (M2)’ S1’R1’ L4’ S2 R2 S3 R3 S4 R4 L1 L2 L3 Figure 11: P2: (M3)’ (M4)’ S2’R2’L’ S1 R1 S3 R3 S4 R4 L2 L3 L4

The second statement as shown in Fig11 deals with the movement of traffic vertically rightwards, turning right and also turning left (L1). This statement can be written as P2: (M3)’ (M4)’ S2’ R2’ L1’ S1 R1 S3 R3 S4 R4 L2 L3 L4 which tells that when pedestrians (M3) and (M4) want to cross the road, the traffic will flow along the signal-2, rightsignal-2 and leftturn-1 will be ‘on’ whereas rest signal-1, rightsignal-1, signal-3, rightsignal-3, signal-4, rightsignal-4, leftturn-2, leftturn-3 and leftturn-4 will be in ‘off’ state. The third statement as shown in Fig 12 deals with the movement of traffic horizontally, turning right and also the turning left (L2). This statement can be written as P3: (M5)’ (M6)’ S3’ R3’ L2’S1 R1 S2 R2 S4 R4 L1 L3 L4 which tells that when pedestrians (M5) and (M6) want to cross the road, the traffic will flow along the signal-3, rightsignal-3 and leftturn-2 will be ‘on’ whereas rest signal-1, rightsignal-1,signal-2, rightsignal-2, signal-4, rightsignal-4, leftturn-1, leftturn-3 and leftturn-4 will be in ‘off’ state as Fig 12 explained in below lines.

Figure 13: P3: (M5)’ (M6)’ S3’ R3’ L2’S1 R1 S2 R2 S4 R4 L1 L3 L4 Figure 13: P4: (M7)’ (M8)’ S4’ R4’ L3’S1 R1 S2 R2 S3 R3 L1 L2 L4

The fourth statement deals with the movement of traffic vertically, turning right and also the turning left (L3). This statement can be written as P4: (M7)’ (M8)’ S4’ R4’ L3’S1 R1 S2 R2 S3 R3 L1 L2 L4 which tells that when pedestrians (M7) and (M8) want to cross the road, the traffic will flow along the signal-4, rightsignal-4 and leftturn3, so these will be ‘on’ whereas rest of the signal-1, rightsignal-1, signal-2, rightsignal-2, signal-3, rightsignal-3, leftturn-1, leftturn-2 and leftturn-4 will be in ‘off’ state as demonstrated in Fig 13.



IV. FRAMEWORK OVERVIEW

The logical framework of the methodology is presented in tabular forms by giving ‘on’ and ‘off’ state of each signal direction as when no turns, turns and with turns and pedestrians respectively:

Table 1: When no turns

STATE P1:S1’ S3’ S2 S4 P2: S2’ S4’S1 S3

ON S1 S3 S2 S4

OFF S2 S4 S1 S3

Table 1 describes the movement of pedestrian how they cross the bi-intersectional signal ‘when no turn’ statement has been implemented. When S1 and S3 is ‘on’ state then pedestrian used S2 & S4 at ‘off’ state which can be used for signal crossing and when S2 & S2 is ‘on’ state S1 & S3 are ‘off’ state and it can be used for signal crossing respectively.

Table 2: When turns

STATE P1:S1’ R1’ S2 R2 S3 R3 S4 R4 P2: S2’ R2’ S3 R3 S4 R4 S1 R1

ON S1 R1 S2 R2

OFF S2 R2 S3 R3 S4 R4 S1 R1 S3 R3 S4 R4

Table 2 describes the movement of pedestrian how they cross the bi-intersectional signal ‘when turn’ statement has been implemented. When S1 and R1 is ‘on’ state then pedestrian used S2 R2 S3 R3 S4 R4 for signal crossing and when S2 & R2 is ‘on’ state S3 R3 S4 R4 S1 R1 are used for signal crossing respectively.

Table 3: When turns

STATE P3: S3’R3’ S4 R4 S1 R1 S2 R2 P4: S4’R4’ S1 R1 S2 R2 S3 R3

ON S3 R3 S4 R4

OFF S4 R4 S1 R1 S2 R2 S1 R1 S2 R2 S3 R3

Table 3 describes the movement of pedestrian how they cross the bi-intersectional signal ‘when turn’ statement has been implemented. When S3 and R3 is ‘on’ state then pedestrian used S4 R4 S1 R1 S2 R2 for signal crossing and when S4 & R4 is ‘on’ state S1 R1 S2 R2 S3 R3 are used for signal crossing respectively.

Table 4: With turns and pedestrians

STATE P1: (M1)’ (M2)’ S1’R1’ L4’ S2 R2 S3 R3 S4 R4 L1 L2 L3

P2: (M3)’ (M4)’ S2’ R2’ L1’ S1 R1 S3 R3 S4 R4 L2 L3 L4

PEDESTRINAS WALKING M1 M2 M3 M4

ON S1 R1 L4 S2 R2 L1

OFF S2 R2 S3 R3 S4 R4 L1 L2 L3 S1 R1 S3 R3 S4 R4 L2 L3 L4

Table 4 describes the movement of pedestrian, how they cross the bi-intersectional signal ‘with turns and pedestrian’ statement has been implemented. When S1, R1 and L4 is ‘on’ state then pedestrian used M1 & M2 for signal crossing and when S2, R2 and L1 is ‘on’ state M3 & M4 are used for signal crossing respectively.



Table 5: With turns and pedestrians

STATE P3: (M5)’ (M6)’ S3’ R3’ L2’S1 R1 S2 R2 S4 R4 L1 L3 L4

P4: (M7)’ (M8)’ S4’ R4’ L3’S1 R1 S2 R2 S3 R3 L1 L2 L4

PEDESTRINAS WALKING M5 M6 M7 M8

ON S3 R3 L2 S4 R4 L3

OFF S1 R1 S2 R2 S4 R4 L1 L3 L4 S1 R1 S2 R2 S3 R3 L1 L2 L4

Table 5 describes the movement of pedestrian how they cross the bi-intersectional signal ‘with turns and pedestrian’ statement has been implemented. When S3, R3 and L2 is ‘on’ state then pedestrian used M5 & M6 for signal crossing and when S4, R4 and L3 is ‘on’ state M7 & M8 are used for signal crossing respectively.

V. EXPERIMENTATION AND RESULTS The experiment of the integrated models has been performed by using C++. To depicting the behavior of the pedestrians four random inputs (0 or 1) are generated simultaneously. On respective of four sides of the intersection, pedestrian have pushed the button, which tells the system that they are willing to cross the road. Once an input is received from any of the four sides, the traffic of that particular side is stopped after 20 second and the pedestrians are given a green light to cross the road. Meanwhile, the possible traffic from the other side is also given a green signal.

Fig.14. Input Signal

The total simulation time of the model is 6000.425 seconds in which the total number of cars and pedestrians are 500 each. The average waiting time for a car at any side of the intersection is 143.35 seconds, whereas the average waiting time for a pedestrian is 200.23 seconds. For a car at position “x” on an intersection, the waiting time is given as;

Wt = 4x + 143.35seq. (1)

On average 24 cars and 20 pedestrians cross the intersection from any side. Fig.14. is an example of the signal generated for the pedestrians when they press the button to cross the road.

-0.5

0

0.5

1

1.5

0 20 40 60 80 100

Input Signal Input…



Fig.15. Traffic control model.

Figure15. shows that the model for the traffic control shows a consistent pattern of operation.

Fig.16. Pedestrian crossing incorporated with traffic control model.

Now, we incorporate the model of pedestrian crossing with the traffic control model in Fig.16.

VI. CONCLUSION AND FUTURE WORK

Traffic handling is a serious issue in urban area as pedestrian also part of traffic flow. It is necessary to avoid road accident mange vehicles and pedestrian equivalently by saving the time and resources. Our proposed computational model is framework for developing countries like Pakistan to compute the phenomenon of pedestrian handling in urban traffic. It deal with complexity by producing experiments which shows this proposed model practically implement in urban area and it will save time and cost of vehicles and pedestrian in bi-direction flow of traffic. Model deals with three scenarios to regulate the traffic flow which include traffic flow with not turns, with turns and with turns and pedestrian flow.

In future work, this proposed computational model implement in real time sensors and will monitor its advantages that are claimed in computational model. In addition, result calculate in quantitatively form that how much time and cost has saved due to efficient handling of pedestrian on road.

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urban traffic control with pedestrian handling

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