ARCHITECTURE FOR A THIRD GENERATION URBAN TRAFFIC CONTROL

Transcription

1 ARCHITECTURE FOR A THIRD GENERATION URBAN TRAFFIC CONTROL Rahmat R. A., Jumari K., Hassan A. and Basri, H. Fakulti Kejuruteraan, Universiti Kebangsaan alaysia ALAYSIA Keywords: traffic control, physical architecture, logical architecture ABSTRACT The objective of this paper is to present a logical and physical architecture for third generation urban traffic control system. The control system has to be able to respond to the variation of traffic pattern in short period of time. Logically the proposed system is based on a fully distributed system where all timing calculations are performed by the local signal controllers. Coordination with adjacent intersections is possible with each controller provides its neighbours with some information about its status, its future timing strategy and the time at which it expects vehicles to leave its intersection before the controller starts optimising the signalised intersection under its control. hysically the system consists of four basic components, namely the Smart Camera sensor for collecting traffic data, the Intelligent Controller for controlling traffic flows at an individual intersection, the Intelligent Coordinator for coordinating the timing of an individual controller with its neighbour and the Smart Traffic Advisor. Communication system is based on wireless Local Area Network to link up all local controllers. Test on site shows that the system able to respond correctly within 120 seconds. THIRD GENERATION SECIFICATION The first generation urban traffic control uses pre-stored signal timing plans that are calculated off-line based on the historical traffic data. The plan to control the traffic system could be selected on the basis of time of day, by direct operator selection or by matching from the existing library a timing plan that is best suited to recently measured traffic conditions. This is known as the traffic-responsive mode of plan selection. Frequency of update in the traffic responsive mode is about 15 minutes. SCATS is an example of an advanced version of the trafficresponsive mode (Gartner, et al.,1995). The second generation of urban traffic control adopts an on-line strategy that computes signal timings in real-time and implements signal timing plans based

2 on surveillance data and predicted values. The optimisation process could be repeated at every 5 minute intervals; however, in order to avoid transition disturbances a new timing plan cannot be implemented more often that once every 10 minutes. An example of advanced version of this system is SCOOTS (Gartner, et al.,1995). The third generation of urban traffic control was designed to implement and evaluate a fully responsive, on-line traffic control system. The period after which timing plans were revised was shortened to 3 to 5 minutes (Gartner, et al.,1995). Advanced traffic sensor is required for this kind of system. Table 1 gives a summary of the first, second and third generation systems. Table1 Key Futures of Three Generation of Traffic Control Future First Generation Second Third Generation Generation Update interval 15 minutes 5-10 minutes 3-5 minutes Control generation plan Off-line optimisation, selection from library by time-ofday, traffic responsive or manual mode On-line optimisation On-line optimisation Traffic prediction None Historically based Smoothed values Critical Fine tuning of Fine tuning of Fine tuning of intersection control splits splits and offset splits, offset and cycle Cycle length (Source: Gartner, et al.,1995) Fixed within each section Fixed within variable groups of intersections Variable in time and space. As shown in table 1, the length of the update interval will determine the responsiveness of the system on changes detected by the traffic sensor. Thus, it is necessary to find the most suitable offset time. LOGICAL ARCHITECTURE ost of the existing urban traffic control is based on a centralized control. In a centralised control system it calculate all timings by a central computer. The local controller would only implement the timings once it is received from the central computer. Usually the system would consider the traffic in terms of

3 smoothed flow profiles; this makes the system slow in responding to rapidly changing traffic demands, such as during morning peak traffic growth period. Contrary to centralised control, the proposed system is based on a fully distributed system. In this system, all timing are calculated by the local signal controller. Coordination with adjacent intersections is possible if each controller can provide its neighbors with some information about its status, its future timing strategy and the time at which it expects the vehicles to leave its intersection before the controller starts optimising the signalised intersection under its control. Since all timing calculations and co-ordinations are carried out at the local level, the distributed control is able to respond almost immediately to sudden fluctuation in traffic flows. The architecture of the system for area wide urban traffic control is shown in Figure 1. Optimised Intersection Coordinated Intersection Approach Road link Exit Road Link Figure 1 Distributed Control Architecture (Source: Yagar, et al., 1996) HYSICAL ARCHITECTURE hysically the system consists of four basic components, namely the Smart Camera sensor for collecting traffic data, the Intelligent Controller for controlling traffic flows at an individual intersection, the Intelligent Coordinator for coordinating the timing of an individual controller with its neighbour and the Smart Traffic Advisor. The Smart Advisor is an Expert System that gives advice to the city traffic manager to disperse congested traffic as quickly as possible. It

4 is based on knowledge acquired from a number of experienced traffic managers and relevant personnel from the Kuala Lumpur Traffic olice Department. The Local Area Network (LAN) approach was adopted to link up all controllers as shown in Figure 2. Each computer or micro-processor at the traffic light controller is given an I (Internet rotocol) address. Each computer will share traffic data and timing with its neighbour for coordination purposes. In case where proactive control is required such as giving priority to an emergency vehicle, the computer at the control room will override the timing at each intersection with pre-determined timing that give priority flows for an intended route. icroprocessor Smart Camera oint to point Wireless Communication Control Room Computer Figure 2 Local Area Network for Network of Traffic Controllers COUNICATION A good communication system is very crucial in an urban traffic control for the following purposes: Synchronization of controller timer at each intersection for offset implementation. Exchange of traffic data between controllers.

5 alfunction reporting from each controller to the control room. Incident reporting to the control room. Use of the smart camera for surveillance purpose. Data compilation at the control room would be used for the benefit of road users and research purposes. A wireless communication system was selected instead of copper or fiber optic cables to avoid intrusive road digging work. OTIISATION The genetic algorithm approach is proposed to optimise traffic control by minimising vehicle stop delay and maximising bandwidth. Genetic algorithm was chosen due to its robustness and its ability to search for a global optimum without being trapped into a local optimum. oreover the computer code size is small and it's convergence is faster when compared to traditional optimisation methodologies. Since all timing calculations and co-ordinations are carried out at the local level, the distributed control is able to respond almost immediately to sudden fluctuation in traffic flows. If the traffic demand approaches or exceeds the intersection capacity, the optimisation method or conventional method of determining green time split will not work effectively. Normally under this condition, traffic police will control the intersection. It is observed that the green split decision is made by the traffic police based on the queue length as seen by him or based on the priority of certain traffic flows or a combination of both. In this project, the green time for such condition is given as follows: g i QW i i = C QW i i i i Ai + AllR ) ( i (1) where g i is green time, Q i is queue length, A i is amber time and AllR i is All Red time for phase i. C is the predetermined cycle time and W i is the weightage factor for phase i to be given by the user to give priority to certain traffic flows. The value of W i is given between 1.0 which shows no priority, and 2.0 for maximum priority. EXERIENTAL RESULT The experiment was carried in Bandar Baru Bangi with one controller at a signalised intersection and another four controllers were simulated for morning peak hour. A typical result of the experiments is given in Table 2.

6 The simulated travel time show in Table 2 indicates that the priority directions experienced a minimum travel time. For opposite directions, the travel times are less than the uncoordinated direction. This is achieved by giving flow progression to the prioritise flows and minimising the delay for the opposite directions. Table 2 Vehicle Travel Time from One End to the Other End Direction North Bound riority South Bound riority Coordinated Uncoordinated Coordinated Uncoordinated North Bound 74 s 181 s 155 s 192 s South Bound 143 s 194 s 74 s 201 s Direction East Bound riority West Bound riority Coordinated Uncoordinated Coordinated Uncoordinated East Bound 74 s 187 s 123 s 198 s West Bound 142 s 204 s 74 s 205 s CONCLUSION The network of traffic lights is able to adapt its cycle times, green time split and offset to the traffic demand from cycle to the next cycle. This shows that the response time is not greater than the maximum cycle time, i.e. 120 seconds. This is achieved using the logical and physical architecture as mentioned above. ACKNOWLEDGEENT The authors gratefully acknowledge Kementerian Sains, Teknologi dan Alam Sekitar for funding this research, Universiti Kebangsaan alaysia for generous assistance throughout this research project and Jabatan Kerja Raya for allowing the testing on public road networks. REFERENCES Gartner, N. H., Stamatiadis, C. and Tarnoff,. J Development of Advanced Traffic Signal Control Strategies for Intelligent Transport System: ultilevel Design. Transportation Research Record No Transportation Research Board, National Research Council: Rahmat, R A., Jumari, K., Hassan, A. and Basri, H Video detection system for traffic light sensor. Traffic Engineering & Control. 42(11): Yagar S. dan Dion F Distributed Approach to Real-Time Control Complex Signalized Networks. Transportation Research Record No Transportation Research Board, National Research Council: 1-8.