Utilization of Probe Data for Traffic Flow Control

Transcription

1 Utilization of Probe Data for Traffic Flow Control Masafumi Kobayashi *1, Katsuyuki Suzuki *2, Shigeki Nishimura *3, Sumitomo Electric Industries, ltd., Shima-ya, Konohana-ku, Osaka, Japan TEL: , FAX: , *1, *2, *3 ABSTRACT Probe data is expected as a new technology to collect the spatial traffic information and to improve the traffic flow control toward the CO 2 emission reduction. As parts of research of the UTMS (Universal Traffic Management Society of Japan), we have been researching on the technologies and specifications to utilize probe data for the traffic flow control since After two years of researching of the definition of services menu and the basic simulation evaluations, we would introduce the additional simulation results and the field verification results using the probe data collected from the vehicle in practical. BACKGROUND Global warming, which CO2 is assumed to be a cause, becomes a big social problem. Therefore, it presses us for the change into the sustainable society. As CO2 exhausted from the car traffic accounts for about 17% of the whole in Japan (1), this reduction is an impending need. This is the reason why the signal control address the new role for environmental protection (mainly CO2 reduction) in addition to improve safety and smoothness. In order to reduce the amount of the exhaust of CO2 from the car, it is necessary to improve traffic flow and reduce congestions. We think that improvement of signal control could be one of the solutions. And it needs the more accurate traffic information that is the input data for signal control. On the other hand, the progression of ICT (Information and Communication Technologies) is realizing the wireless communications environment that the car and the infrastructure can exchange information mutually and it has become possible to collect the information of the car directly from the car as probe data. So far, traffic information was collected by the spot detector, e.g. the ultrasonic detector, at the fixed point on the road. Now, the expectation for the use of probe data, which include spatial consecutive traffic information covered the whole road network, has risen. As parts of research of the UTMS, we have been researching to utilize probe data for the traffic flow control since First, we define the services menu and clarified the requirements of input data to realize each service. Then, we evaluate the requirements of the probe data itself. In this paper, we would introduce our findings. Here, we mention that we use the microscopic traffic flow simulator VISSIM for our simulations in this paper. FEATURES OF PROBE DATA VIA IR BEACON IR (infrared) beacon is the key infrastructure for UTMS. And it is widely-used V2I communication media to provide VICS (Vehicle Information and Communication System) service in Japan. At this time, over 50,000 IR beacons are already installed on the surface road 1

2 in Japan and most IR beacons are connected to TMC (Traffic Management Center). (See Figure 1, which shows an example of TMC configuration in Japan.) Therefore, it is easy to utilize the probe data via IR beacons for traffic management control in Japan. This is why our researches focus on the probe data collected via IR beacons. The problem is that the communication capacity of IR beacon is very low, that is, the maximum probe data size is only 59 bytes. Therefore, we adopt the method to extract specific events based on car behaviors as probe data to reduce the uplink data volume. Figure 1. An Example of TMC Configuration in Japan Then, we define the events to collect time and position as probe data on the travel route. These are Stop, Turn and Cruising. Stop event with the stopping time as the attribute information is useful for signal control and Turn event with the vehicle direction and Cruising with the cruising distance from the latest event as the attribute information are useful to identify the travel route. And all events have the information of the number of the repeat-stops. The repeat-stop is defined as that the stopping vehicle stops again without overreaching a certain speed value. Here, we should mention that the repeat-stop is not counted as Stop event. Car uplinks the events probe data every time it passes an IR beacon. Then Uplink event also can be collected. Table 1 shows the outlines of the event probe data to collect and Figure 2 shows a sample of the events collected along the travel route. Table1. Outlines of the Event Probe Data Events Types Common Data Attribute Information Stop Time, Stopping Time Turn Latitude, Direction Longitude, Cruising Elevation, Distance from the latest event Uplink Num. of Repeat-stops - 2

3 Figure 2. An Example of Events Collected along The Travel Route THE SERVICES MENU AND THE FUNCTIONALITY REQUIREMENTS Table 2 shows the services menu that we propose and main subjects to be settled. The first one is "improvement of the signal control performance". This aim is to detect accurate queue length, that is the most important input data for MODERATO (2), the standard adaptive signal control method in Japan. Conventional spot detectors cannot detect the end of the queue directly. And we expect that probe data could be a help to solve this problem. Then, the accurate queue length is expected to improve the performance of MODERATO. The second is "traffic flow analysis". This aim is to detect the bottlenecks of the traffic flow on the road network. We think that this analysis will be the trigger of the signal control readjustments or the rearrangements of the detectors layout and so on. The third is "priority control of detour". This aim is to distribute traffic demand and reduce traffic jam by providing the detour information and executing priority control for the detour to promote traffic distribution. The detour is determined based on the road network usage, which is got from the travel route information collected from probe data. Table 2. The Services Menu and The Requirements No Service Requirements Main Subjects 1 Improvement of the signal control performance Input data: queue length etc. Delay of data: one second to one cycle Dissemination rate of OBU: medium 2 Traffic flow analysis 3 Priority control of detour Input data: stop location, stop time Delay of data: none(use in offline) Dissemination rate of OBU : low Input data: travel time, travel route Delay of data: none(use in offline) Dissemination rate of OBU: low Collection delay Position accuracy Fusion with detector information Dissemination rate of OBU Judging method of bottleneck Stop position accuracy Number of probe data Priority control execution condition Content of information provided to driver Before describing the evaluation of the services menu, we should mention that in this paper, we calculate CO2 emissions E (g) with 3

4 E = 0.68 T L (V f 2 S + V j 2 S j ), where T, L, S, S j, V f, V j denote total travel time (second), total distance traveled (m), total number of starts at signalized intersections, total number of starts in congestion, cruising speed (m/s), average speed in congestion (m/s), respectively (3). IMPROVEMENT OF THE SIGNAL CONTROL PERFORMANCE As for "improvement of the signal control performance", the former simulation evaluation results (4) show the followings. Within ±30m queue length accuracy could improve MODERATO performance and reduce 8-14% CO2 emission. (CO 2 emission is calculated by delay time and the number of stops (4).) If probe data usage only, Over 10% dissemination rate is needed to satisfy above conditions in the half cases. Therefore we are developing the technology to fuse event probe data and spot detector data to improve the availability of the probe data in case that OBU(On-Board Unit) dissemination rate is low. Figure 3 shows the results of the latest simulation result of the data fusion. Blue line shows the real queue length, Yellow line shows the stop position collected from one probe data extracted at random from the queue. The average errors of estimated queue num. of probe data is about -9. And Pink line shows the data fusion value. As a result, the average errors of estimated queue num. drop to about -2. And this data fusion method works well by about 80% or less if there is 5% OBU dissemination rate. As the next step, we are planning to evaluate the feasibility of data fusion using the real field data. Figure 3. Data Fusion Simulation Results TRFFIC FLOW ANALISIS We verified the possibility to detect the bottleneck intersection based on traffic flow analysis utilizing IR beacon probe data collected from the real field. We set up a hypothesis that the repeat-stop occur frequently at the upstream of the bottleneck intersection but at the 4

5 交差点からの位置 (m) Distance from Oomori Int. (m) downstream of the bottleneck intersection. We collect both the TMC traffic data, which is the queue information on every 15minutes, collected at TMC based on traffic detectors and IR beacon probe data at Oomori intersection, Kannanadoori-uchimawari,( inner track, Loop 7, Tokyo) for 30 days from November 15, 2010 to December 14, The TMC traffic data shows that Oomori intersection is the bottleneck intersection. Because the mean queue length on every 15 minutes of 30days is bigger than 200m at upstream of Oomori intersection at all almost time from 6:00 to 16:00 and mean queue length on every 15 minutes of 30 days is 0m at downstream of Oomori intersection at all time. Figure 4 shows mean queue length of 30 days, stop event with the repeat-stop and stop event without the repeat-stop. (Vertical axis shows the distance from Oomori intersection and the positive direction means the upstream and a negative direction means the downstream. And horizontal axis shows time.) We think that Figure 4 shows that our hypothesis that the repeat-stop occur frequently at the upstream of the bottleneck intersection but at the downstream of the bottleneck intersection, is correct stop without repeat-stop stop with repeat-stop queue 反復停止なし反復停止あり渋滞長 Time 時刻 Figure 4. Traffic Conditions around Oomori Intersection Next, we evaluate the relation between queue length and the ratio of stop event with the repeat-stop to all stop events according to the number of probe data. Figure 5 shows the evaluation results. When the number of probe data is over 25, a positive proportion correlation can be seen. This means that the bottleneck intersection can be detected with over 25 probe data on every 15 minutes. Therefore, in case that OBU dissemination rate is low, a certain days are necessary to collect the necessary numbers of probe data. For example, when traffic volume is 800 per hour and OBU dissemination rate is 0.1%, 125 days is necessary to collect over 25 probe data. 5

6 渋滞長 (m) Queue Length (m) % 20% 40% 60% 80% The ratios 反復停止ありの割合 of stop events with the repeat- stop to all stop events 0 N<5 5 N<10 10 N<15 15 N<20 20 N<25 25 N Figure 5. The Relation between Queue Length and Stop Event Occurrence Situations PRIORITY CONTROL OF DETOUR Our Strategy Our strategy is a combination of traffic information service and traffic signal control, which gives priority to detour routes bypassing a seriously congested intersection. We think that priority control give an incentive to drivers to select them. When traffic demand at a bottleneck intersection exceeds its capacity, we find one or some detour routes bypassing the bottleneck intersection based on IR beacon probe data analysis results. They should be proper choices for drivers, have enough capacities to accommodate additional traffic demands. Signal control at the intersections on the detour routes is then determined as follows so that their capacities are increased to accommodate additional traffic demands. At each intersection, cycle length is lengthened to increase the capacity, and more split is allocated to the detour route within the range of not saturating other approaches. And offsets are set to provide maximal progression for bypassing direction. After the priority control is determined, traffic information such as predicted travel times, when the priority control is applying, sent to drivers. The ultimate goal of traffic management is achieving system optimal dynamic traffic assignment (6). System optimal dynamic traffic assignment is an extremely hard problem, and as far as we know, no practically effective algorithm for it has been found yet despite decades of dedicated research, and thus there is a long way to go to apply it to real road networks. But our strategy can be just a first step of the long way. Simulation Conditions The aim of simulation is to understand how our strategy works. In our simulation, we use the network shown in Figure 6, and traffic demands are indicated in Table 3. In the figure 6, numbered intersections denote major intersections, and other intersections denote miner intersections. The road from A to B has two lanes in each direction, and other roads have one lane in each direction. Approaches of intersection 43 and 70 have additional lanes for right turn. And the simulation time duration is 18,000 seconds. As a normal control, we set cycle length, green split, and offset of each intersection to fixed values calculated from traffic volumes of its approaches at the peak period. Cycle lengths of intersection 1 to 30 are 150 seconds, and those of other are either 90 or 120 seconds. Intersection 20 is a bottleneck on the road from A to B, and traffic condition at it is as follows: 6

7 First, its traffic demand is severely over the capacity; and hence congestion occurs. Next, it is slightly over the capacity, and congestion goes on. Finally it is under the capacity and congestion is reduced. The route from intersection 1 to 30 via intersection 43 and 70 is a detour route, and the distance of the detour route is 11,710 meters, which is about twice of that of the main route A to B(5,560 m). As a priority control to the detour route, we set parameters according to the fixed values calculated from traffic demand predicted by adding a target traffic volume of bypassing to their current traffic volumes. The target traffic volume of bypassing is determined from the augmenting capacity of the detour route, and it is 180 vehicle/hour. Cycle lengths of intersection 31 to 42 are 120 seconds, and those of other are 150 seconds. H F C D A B E G Figure 6. Road Network for Simulation Studies Table 3 Traffic Volume for Simulation Studies Origin Destination Traffic Volume by Departure Time (vehicle/hour) 0 (s) -1,800 1,800-3,600 3,600-5,400 5,400-18,000 A B 1,200 2,160 1,500 1,320 (s) B A 1,200 1,200 1,200 1,200 E F, B F E G H H G, D C D, F D C Other Major Roads (Opposite Direction) Other Miner Roads (Opposite Direction)

8 Travel Time (s) Simulation Results In our simulation, we examine following two cases. In the case 1, not applying the strategy, each intersection is controlled by the normal control and no traffic information about the detour route is provided. Unless otherwise noted, vehicles from A to B do not travel the detour route in the case 1. In the case 2, applying the strategy, initial condition is same as the case 1, and when the main route is judged to be congested, the priority control is initiated. After 150 seconds from the initiation of priority control, the traffic information service is initiated. Then, the traffic information service ends when the travel time of the main route becomes apparently shorter than that of the detour route. While the traffic information service is active, some vehicles travelling from A to B turn to the detour route. After 1,400 seconds from the end of the traffic information service, the priority control ends. First of all, we see how the travel time of the main route and the detour route is. Figure 7 shows travel times of both routes with the ratio of bypassing vehicles of 12.0 % in case2. Figure 7 indicates that the priority control reduces the travel times of both routes. The difference between the travel time of the detour route in the case 1 and that of the case 2 is not so large. This means that the priority control can manage bypassing traffic volume and to prevent from the travel time increase. 1,800 Start Start Ctrl.. Info. End Info. End Ctrl. 1,600 1,400 1,200 1, Main route (Case 1, 0%) Main Route (Case 2, 12%) Alt.Route (Case 1, 0%) Alt.Route (Case 2, 12%) ,600 7,200 10,800 14,400 Time (s) Figure 7. Travel Times of the Main Route and the Detour Route (Alt. Route) Next, we examine the effects of the ratio of bypassing vehicles, and measure total travel times and CO2 emissions by all vehicles. The results are summarized in Table 4. We also define and calculate the relative reduction rate to the total travel time of the main route in the case 1. In the case 2, when the ratio of bypassing vehicles reaches over 4.0%, both CO2 emissions and travel time are reduced. And the positive effects reach their maximum at 12.0%. Because when the ratio is over 12.0%, traffic volume through the detour route exceeds its capacity. 8

9 Table 4. Summary of total travel times and carbon dioxide emissions by all vehicles Case Ratios of Bypassing Total Travel CO Relative Relative 2 Vehicles (%) Time(hour) Emissions (g) reduction in reduction in Travel Time (%) CO 2 (%) ,572 89,731, ,885 91,149, ,622 87,673, ,447 85,746, ,391 84,555, ,381 84,673, ,316 83,647, ,307 83,412, ,412 86,335, ,392 85,579, CONCLUSION IR beacon is already deployed the whole nation in Japan. Therefore, IR beacon probe data has a big potential to improve traffic flow. We propose to collect specific events based on car behaviors as probe data to reduce uplink data volume. We also define 3 services menu to enhance traffic flow control which use IR beacon event probe data for the purpose of the CO 2 emission reduction. As for Improvement of the signal control performance, we confirmed that the data fusion of IR beacon event probe data and spot detector data has a big potential to improve the accuracy of queue length. As a result, we expect that it contributes to improvement of the signal control performance. Now we evaluate operational limits of this fusion functions and have a plan to execute field verification tests. As for traffic flow analysis, we show that the bottleneck intersection could be detected utilizing IR beacon event probe data and the trial calculation results of the necessary number of probe data are suggested. We expect that traffic flow analysis can be used soon in practice. As for priority control of detour, the results of our study show that the proposed strategy could reduce total travel time and CO2 emissions successfully. We also can see that they can be reduced even when a low ratio of traffic flow bypasses to the detour route and that the priority control on the detour route can manage bypassing traffic volume and to prevent from the travel time increase. In our simulation, the priority control is a fixed time operation, but we can adjust splits for traffic adaptively. Generally, when a detour route is shorter, the period in which the travel time of a detour route tends to be shorter than that of a main route will be longer, and thereby reductions of total travel time and CO2 emissions tend to be larger. To apply this strategy, we need a little more studies on finding detour routes and on contents and interface of the traffic information service. System optimal dynamic traffic assignment is an extremely hard problem not only to solve, but also to get into practice after it is solved computationally. We believe that it is necessary to achieve system optimum that we have more motivation to benefit society, and that traffic information service will play an important part to increase it. In closing this paper, we would appreciate a great help and a great cooperation to every concerned. 9

10 This study is a part of the research of UTMS. ADDRESS OF THANKS REFERENCES (1) Greenhouse Gas Inventory Office of Japan, National Greenhouse Gas Inventory Report of Japan, April, 2010 (2) H.Sakakibara, T.Usami, et al., MODERATO(Management by Origin-Destination Related Adaptation for Traffic Optimization), The 6 th World Congress on ITS 99 Toronto (3) Koshi M et al., Research of Effect of CO 2 Reduction by Improvement and Advance of Traffic Flow Control (in Japanese), Institute of Highway Economics, Tokyo, Japan, (4) M.Kobayashi et al., The traffic control using probe vehicle data, The 17 th World Congress on ITS 2010 Busan (5) Rouphail, N, "Traffic Congestion Management," Handbook of Environmentally Conscious Transportation (Kutz, M Ed.), John Wiley & Sons, 2008, pp (6) Wardrop, J, Some theoretical aspects of road traffic research, Proceedings - Institute of Civil Engineers, Part II, Vol. 1, 1952, pp (7) Summary of the COSMOS validation results (Deliverable D07.4), Available at ftp://ftp.cordis.europa.eu/pub/telematics/docs/tap_transport/cosmos_d7.4.pdf 10