Proposed ACC improvements for congestion mitigation

23 rd ITS World Congress, Melbourne, Australia, 10 14 October 2016 Paper number AP-TP0421 Proposed ACC improvements for congestion mitigation Hiroshi Makino 1, Shinji Itsubo 1, Gaku Ohtake 1, Hitoshi Yoshimura 1* 1. ITS Division National Institute for Land and Infrastructure Management (NILIM) Ministry of Land, Infrastructure, Transport and Tourism, Japan 1 Asahi, Tsukuba, Ibaraki 305-0804, Japan Tel.: +81-29-864-4496, Fax: +81-29-864-0178 E-mail: yoshimura-h924a@nilim.go.jp Abstract Approximately 60% of congestion on Japanese intercity expressways occurs at sag sections. Measures to prevent sag-induced congestion are important topics around the world, because it occurs by road gradient and driver behavior. National Institute for Land and Infrastructure Management (NILIM) focused on individual driver behavior which was thought to be a cause of congestion. And we researched the congestion mitigation effect by using adaptive cruise control (ACC) that maintains constant vehicle spacing automatically. Contrary to expectations, current performance ACC increased congestion by three problems that were obtained from the simulation results. This paper presents the details of three problems and proposes some improvements in ACC. Keywords: expressway sag section, traffic smoother service, adaptive cruise control (ACC) 1. Introduction According to 2011 survey data, approximately 60% of congestion on intercity expressways in Japan occurs in sag sections (locations where the gradient gradually changes from downhill to uphill) that contain uphill sections, and finding measures for mitigating congestion in sag sections is important. The causes of congestion in sag sections include imbalanced lane usage by traffic concentration in the passing lane before the congestion occurs, and reduced traffic flow rate by unconscious deceleration on the uphill gradient. After congestion occurs, there is a delay in speed recovery after passing the front edge of the congestion, and a delay in recovering from the congestion caused by driver s reduced motivation for following due to the increased time that they spend in the congestion 1), 2). Many of these factors are driver behavior, so congestion could be prevented or mitigated if driver behavior is improved by

driving assistance. Furthermore, because congestion in sag sections occurs from the uphill gradient, it is thought to be a common problem on expressways around the world. On the one hand, with improvements in vehicle functions in recent years, adaptive cruise control (ACC) has been developed and is continuing to spread. ACC automatically maintains fixed distance from a vehicle in the front according to speed and it is expected to improve driving comfort and safety. NILIM researched a service for smoothing traffic (Figure1) because it was thought that using ACC for controlling congestion would mitigate congestion. In this study, we studied to understand congestion mitigation effect of ACC by using the micro traffic simulation. However, the micro traffic simulation showed that ACC made congestion worse. We present the results and propose a new ACC that resolves the problems that worsen congestion. Before congestion occurs OVSS : Optimizing Vehicle Spacing Service ITS spot Stabilization of vehicle groups through maintained vehicle spacing to prevent deceleration wave development RSU : Road side unit OLUS : Optimizing Lane Usage Service Promotes traveling lane usage to normalize lane usage After congestion occurs RSU : Road side unit ITS spot FPVS : Follow-up Preceding Vehicle Service RVSS : Recovering Vehicle Speed Service Prevents inattentive driving, promotes smooth speed resumption Figure 1 Smoothing Traffic Flow Services (STFS) for expressway sag sections 2. Congestion mitigation Effect of Smoothing Traffic Flow Services (STFS) Using ACC NILIM defined three STFS for sag sections to address the causes of congestion described in Section1: optimizing lane usage service (OLUS), optimizing vehicle spacing service (OVSS), follow-up preceding vehicle service (FPVS) and recovering vehicle speed service (RVSS). As a premise of these services, we considered as follows. The drivers cannot recognize a traffic situation. For example, the passing lane is concentrating and velocity is decreasing unconsciously. So we determined that it is useful to provide the information for congestion mitigation at before sag section. Specifically, before congestion occurs, congestion is 2

suppressed by OLUS equalizing lane use and by OVSS maintaining vehicle spacing and stabilizing traffic flow. After congestion occurs, congestion can be mitigated by following the vehicle in front immediately and promoting speed recovery quickly after passing the front edge of the congestion by using FPVS and RVSS. ACC is used for controlling vehicle spacing, front vehicle follow-up, and recovering speed quickly. To understand the congestion mitigation effect of these services, we calculated the reduction in the time loss due to congestion by using a micro traffic simulation of the Yamato sag section in the down line of the Tomei Expressway (hereinafter, the Yamato sag section). For the traffic simulation, we measured the amount of traffic per lane, average speed, and number of lane changes using video and traffic counters installed at the roadside of the Yamato sag section. Figure2 shows installation position of road side units, and Table1 shows observation data for vehicle behavior models. The parameters related to the vehicle behavior were adjusted based on this observation data, and the vehicle behavior model shown in Table2 was reproduced. For vehicles without ACC, we reproduced the unconscious deceleration on the uphill gradient and slowing down caused by following behavior arising from being involved in congestion for a long time. For smooth driving vehicles equipped with ACC, we reproduced the behavior obtained by driving tests conducted on test roads at NILIM and on public roads 3). Travelling direction ITS spot 20.57kp Traffic counter ITS spot Camera 11 19.17kp 21.9kp Camera 2 Camera 13 Camera 1 Camera 9 Traffic counter 21.52kp Yokohama-Machida IC Camera 12 Entry ramp Camera 3 Camera 10 Camera5 Camera 7 Camera 4 Camera 6 Camera 8 Bottom 19.5 20.0 20.5 21.0 21.5 22.0 22.5 23.0 Figure 2 Installation position of road side units at the Yamato sag section Table 1 Observation data for vehicle behavior models at the Yamato sag section No Date Congestion Congestion Congestion start hours volume Observation time 1 Aug 17 th 2011 7:00 6 hours 50min Medium 6:15-7:15 2 Dec 04 th 2010 6:45 30min Small 6:00-7:00 3 Nov 06 th 2010 6:10 9 hours 5min Big 5:25-6:25 4 Dec 11 th 2010 6:35 1 hour Small 5:50-6:50 5 Aug 18 th 2011 7:00 7 hours Medium 6:15-7:15 3

Table 2 Behavior reproduced by traffic simulation Conventional vehicle Smoothing driving vehicle Congestion cause (vehicle without ACC) (vehicle with ACC) (1) Unconscious deceleration on uphill areas Yes Gradual deceleration No Constant speed Velocity Velocity (2) Delayed follow-up behavior due to being involved in congestion for a long time Yes Distance between vehicles widens minutes later No Constant distance between vehicles minutes later Currently, commercial ACC adjusts speed to maintain the target vehicle spacing time from the vehicle in front as set by the driver: vehicle spacing time control. The target vehicle spacing time can generally be set to short (S mode; about 1.4 sec), medium (M mode; about 1.8 sec), or long (L mode; about 2.2 sec). In this research, we carried out a questionnaire survey to understand easiest setting to drive before congestion for 44 people of general drivers. Figure3 shows that M mode was easiest to drive, so we selected it. Furthermore, because drivers have reported that the closeness of the vehicle in front is frightening when driving at high speeds with the S mode, we did not use it in this research. Two ACC vehicle mixing ratios were set ( of small vehicles and 30% of large vehicles; 20% of small vehicles and 50% of large vehicles) according to the anticipated future by vehicle type based on the current spread of ACC. Q: Which vehicle spacing time setting was the easiest to drive before congestion? L mode 21% S mode 18% M mode 61% n=44 Figure 3 Driver survey of the ACC vehicle spacing time setting 4

Table3 shows the results of traffic simulations. The results found the following three factors: (1) When vehicles with ACC were mixed into only the driving lane, time loss due to congestion was decreased. (2) When vehicles with ACC were mixed into all lanes, time loss due to congestion was not decreased. (3) When vehicles with ACC mixing ratio were increased, time loss due to congestion was increased, too. Table 3 Traffic simulation results by using ACC Simulation conditions Reduction in time loss Mixing conditions Vehicles with ACC mixing ratio due to congestion Vehicles with ACC *1 Small vehicles Large vehicles 30% -32% mixed into all lanes Small vehicles 20% Large vehicles 50% -66% Vehicles with ACC *1 Small vehicles 8% Large vehicles 30% mixed into driving lanes *2 Small vehicles 20% -25% Large vehicles 50% *1: Set to M mode in current performance *2: Small vehicles can drive in only the first (leftmost) and second (middle) driving lanes and large vehicles can drive in only the first (leftmost) driving lane 3. Improvement in ACC Performance Based on the simulation results, the following three problems can be considered. The first problem; the current ACC maintains a fixed vehicle spacing time (VST). When speed becomes more slowly, the vehicle headway time (VHT: the sum of vehicle spacing time and passing time of vehicle length) becomes more longer, because passing time of vehicles length is longer. Consequently, the traffic flow tends to decrease at the locations where deceleration occurs readily, such as areas with uphill gradients. Therefore, VHT should be controlled to maintain constant without reducing traffic flow even at low-speed zone (vehicle headway time control: VHTC). The second problem; acceleration of the current ACC is not quickly at low-speed zone compared with human driver s it. The current ACC focuses on comfortable ride at high-speed zone. Because of this, the current ACC recovers slowly at low-speed zone. Therefore, rapid acceleration properties are required at low-speed zone. The third problem; deceleration waves of the current ACC are propagated and amplified to the back vehicle, when the front vehicle temporarily decelerates. It is shown by the running test of group vehicles for following the front vehicle using commercially available ACC 3). Therefore, it is necessary to have quick response that deceleration waves do not be amplified to the back vehicle. 5

Based on these findings, table4 shows three new types of future performance ACC (FP-ACC). Future performance A (FP-A) uses the current vehicle spacing time control (VSTC), and the time of VST is reduced to approximately 1.5 sec. In addition to this, improved acceleration performance and transient response characteristics (TRC). Future performance B and C (FP-B and FP-C) modify to VHTC, and the time of VHT are set 2 and 1.8 sec. The acceleration performance and TRC are improved, similar to FP-A. Section4 shows the congestion mitigation effects of these three new types ACC. Table 4 FP-ACC to improve current ACC Setting/ performance Current performance FP-A (spacing 1.5) FP-B (headway 2.0) FP-C (headway 1.8) Set speed 1. Control approach and control target VSTC (Vehicle spacing time control) VST 1.85 sec VST 1.55 sec Set speed 100 km/h VHTC (Vehicle headway time control) VHT 2.00 sec VHT 1.80 sec 2. Acceleration performance Gradual acceleration Improved acceleration performance (rapid acceleration performance in low-speed zone *1 ) 3. TRC *2 a = 0.60 m/s 2 b = 2.80 m/s 2 a = 1.20 m/s 2 b = 1.60 m/s 2 4. Expected traffic flow rate At limit 1,700 vehicles/h During congestion 1,560 vehicles/h At limit 1,990 vehicles/h During congestion 1,800 vehicles/h At all speeds 1,800 vehicles/h *1 Accelerates to 20 km/h over 5 sec after starting to move *2 a: maximum acceleration parameter, b: desired deceleration parameter At all speeds 2,000 vehicles/h 4. Congestion Mitigation Effect of STFS Using the New ACC 4.1 Congestion Mitigation Effect of the OVSS We calculated the congestion occurrence probability to understand the congestion mitigation effect of the OVSS using FP-ACC. We used the congestion occurrence probability as a benchmark, because OVSS was provided before congestion occurred. Figure4 shows that as the mixing ratio of vehicles with ACC increased, the congestion occurrence probability curve shifted to the right. It means the congestion occurrence probability is reduced. Furthermore, when it compares the same mixing ratio, FP-A and FP-B have the similar congestion occurrence probability. FP-C has more lower congestion occurrence probability than others. So it has a highest congestion mitigation effect. 6

100% 90% 80% 70% 60% 50% 40% 30% 20% 0% Future Performance A Mixing ratio Mixing ratio 30% 300 350 400 450 500 550 Traffic volume [per 5 minutes] Figure 4 Congestion occurrence probability by OVSS We calculated the reduction in time loss due to congestion over one year by using traffic counter data for the Yamato sag section, based on the congestion occurrence probability of OVSS. Table5 shows that as the mixing ratio of ACC vehicles increased, the reduction in time loss due to congestion increased. When mixing ratio of the vehicle with ACC was, the reduction in time loss due to congestion was 15% with FP-A, 20% with FP-B, and 23% with FP-C. Table 5 Reduction in time loss due to congestion by OVSS Reduction in time loss due to Mixing ratio of vehicles with ACC congestion [%] 3% 5% 20% 30% FP-A (spacing 1.5) 2 6 15 32 49 FP-B (headway 2.0) 5 9 20 39 56 FP-C (headway 1.8) 6 11 23 47 64 20 40% 40 60% 60 80% 80 100% 4.2 Congestion mitigation Effect of OVSS Combined with OLUS We calculated the congestion occurrence probability for OVSS combined with OLUS using the same method as in Section 4.1. Figure5 shows that as the mixing ratio of vehicles with ACC increased, the congestion occurrence probability curve shifted to the right. And the congestion occurrence probability was reduced, as in Section 4.1. Furthermore, when the lane change rate (proportion of vehicles changing lane from the passing lane to the driving lane) was, a synergistic effect was observed between OVSS and OLUS. We calculated the reduction in time loss due to congestion as in Section 4.1, based on the congestion occurrence probability. Table6 shows that as the mixing ratio of ACC vehicles 7

increased, the reduction in time loss due to congestion increased. When the lane change rate was and the mixing ratio of vehicles with ACC was, the reduction in time loss to congestion was 23% with FP-A, 27% with FP-B, and 32% with FP-C. This effect was greater than for OVSS alone. 100% 90% 80% 70% 60% 50% 40% 30% 20% Lane change rate 0% 6% Lane change rate 0% Lane change rate ACC vehicle mixing ratio 0% Lane change rate 6% ACC vehicle mixing ratio 0% 300 350 400 450 500 550 Traffic volume [per 5 minutes] ACC vehicle mixing ratio 20% Figure 5 Congestion occurrence probability by OVSS combined with OLUS Table 6 Reduction in time loss due to congestion by OVSS combined with OLUS Reduction in time loss due to congestion [%] Mixing ratio of vehicles with ACC 0% 3% 5% 20% 30% FP-A (spacing 1.5) 7 10 18 33 47 usage service Optimizing Lane (Lane change rate) 6% FP-B (headway 2.0) 11 5 9 20 38 53 FP-C (headway 1.8) 7 12 24 46 64 FP-A (spacing 1.5) 9 13 23 38 51 FP-B (headway 2.0) 15 12 17 27 45 60 FP-C (headway 1.8) 13 19 32 54 68 20 40% 40 60% 60 80% 80 100% 4.3 Congestion Mitigation Effect of FPVS and RVSS We calculated the through traffic volume of after congestion occurred to understand the congestion mitigation effect of FPVS and RVSS using the future performance ACC. We used the through traffic volume as a benchmark, because FPVS and RVSS were provided after congestion occurred. Figure6 shows that when the mixing ratio of vehicles with ACC was, a traffic flow improvement effect was obtained 5% with VSTC, and a little less than with VHTC. Furthermore, when it was 30%, the effect was obtained with VSTC, and from 20% to 25% with VHTC. Because VHTC can maintain a fixed traffic flow in 8

low-speed zone than VSTC, VHTC is obtained greater effect. Traffic volume : Improvement rate : 7,000 将来性能 Future Performance A " 車間 1.5" A 将来性能 Future Performance B " 車頭 2.0" B 将来性能 Future Performance C " 車頭 1.8" C 将来性能 Future Performance A " 車間 1.5" A 将来性能 Future Performance B " 車頭 2.0" B 将来性能 Future Performance C " 車頭 1.8" C 150% Traffic volume [per hour] 6,500 6,000 5,500 5,000 4,500 Improvement using vehicle spacing time control 0% 20% 30% 50% 70% 100% 1,500[per hour] per 1 traffic lane Improvement 20~25% using vehicle headway time control ACC vehicle mixing ratio Basic traffic capacity 140% 130% 120% 1 100% Improvement rate from current situation Figure 6 Through traffic volume of after congestion and improvement rate from current situation depending on ACC vehicle mixing ratio We calculated the reduction in time loss due to congestion over one year by using traffic counter data for the Yamato sag section, based on the through traffic volume of after congestion using FPVS and RVSS. Table7 shows that traffic flow improved, and VHTC had a better congestion mitigation effect than VSTC. When the mixing ratio of vehicles with ACC was, the reduction in time loss due to congestion was 55% with FP-A, 74% with FP-B, and 76% with FP-C. Table 7 Reduction in time loss due to congestion by FPVS and RVSS Reduction in time loss due to Mixing ratio of vehicles with ACC congestion [%] 3% 5% 20% 30% FP-A (spacing 1.5) 19 31 55 81 93 FP-B (headway 2.0) 30 47 74 94 99 FP-C (headway 1.8) 32 50 76 96 99 20 40% 40 60% 60 80% 80 100% 5. Conclusions In this research, we calculated the congestion mitigation effect of TSS using ACC in sag sections of expressways. When vehicles with the current ACC were mixed into all lanes at a high mixing ratio, congestion mitigation effect was not only observed, but also congestion 9

increased. However, congestion mitigation was achieved by using FP-ACC. FP-ACC is needed that to be VHTC, to have acceleration performance rapidly at low-speed zone, and to have TRC which deceleration waves do not amplify and propagate to the back vehicle. If it is developed, congestion will be mitigated and we will be comfortable driving. When the current ACC was used in only the driving lane, it led to a fixed reduction in time loss. For this reason, congestion mitigation is expected to be a good effect even if before FP-ACC is developed. It is important to propose keeping left lane using ACC for drivers. Keeping left lane means that drivers use a driving lane primarily, when they except to pass other vehicles. Furthermore, if it becomes popular, it will be decreased the lane changing and improved safety. In the future, FP-ACC is expected to be developed. Until then, we intend to research traffic smoothing and safety by C-ITS system that links road infrastructure and vehicles. 6. References 1. M. Koshi: Capacity of Motorway Bottlenecks, Transactions of the Japan Society of Civil Engineers, No. 371/IV-5, pp. 1-7, 1986. 2. M. Koshi, M. Kuwahara, H. Akahane: A Study on Congestion Phenomena at Tunnels and Sags on Motorways, Transactions of the Japan Society of Civil Engineers, No. 458/IV-18, pp. 65-71, 1993. 3. K. Hidaka, H. Kitaoka, K. Kitahama, M. Shida, H. Fujimoto, N. Kisu, H. Koike, J. Eguchi, H. Kaseyama, and T. Kato: Smooth Traffic Flow Using ACC in Sag Section of Expressway, International Journal of Automotive Engineering, Vol. 44 No. 2, pp. 765-770, 2013. 10