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Experimental Analysis on control constraints for connected vehicles using Vissim
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Transportation Research Procedia 00 (2016) 000–000 Transportation Research Procedia 21 (2017) 269–280
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2016 International Symposium of Transport Simulation (ISTS’16 Conference), June 23~25, 2016
Experimental Analysis on control constraints for connected vehicles using Vissim a*, Kyungsoo Hwangbb Sangung Parkaa, Jooyoung Kimaa, Seungjae Leea* st University University of of Seoul, Seoul, Room Room 523 523 21 21st hall hall University University of of Seoul Seoul 163 163 Seoulsiripdaero, Seoulsiripdaero, Dongdaemun-gu, Dongdaemun-gu, Seoul Seoul 02504, 02504, KOREA KOREA Jeju National University, University, Room Jeju National Room 2323-2 2323-2 College College of of Social Social Sicence Sicence hall hall Jeju Jeju National National University University 102 102 Jejudaehak-ro, Jejudaehak-ro, Jeju-si, Jeju-si, Jeju Jeju Special Special SelfSelfGoverning Governing Province, Province, 63243, 63243, KOREA KOREA aa
b* b*
Abstract Abstract Recent Recent advances advances in in CV CV environment environment made made technologies technologies much much higher higher level. level. We We just just thought thought about about that that high-level high-level technologies technologies made CV environment better. This paper studies the CV environment considering various traffic congestion made CV environment better. This paper studies the CV environment considering various traffic congestion level level and and optimal optimal speed speed control control using using the the microscopic microscopic simulation simulation (Vissim) (Vissim) based based on on proper proper parameters parameters and and algorithm. algorithm. To To evaluate evaluate road road condition, condition, we we suggest suggest some some kinds kinds of of MOE MOE e.g. e.g. recovery recovery time, time, average average speed, speed, and and delay. delay. In In conclusion, conclusion, CV CV environment environment can can reduce reduce the the congestion congestion in in proper proper traffic traffic volume. volume. However, However, if if traffic traffic volume volume is is almost almost near near the the capacity, capacity, even even CV CV environment environment can’t can’t relieve relieve the the road road condition. condition. If If the the number number of of CV CV environment environment vehicle vehicle increases, increases, it it will will reduce reduce the the congestion congestion and and aa road road accident accident much much better. better. This result result will will be be the the foundation foundation for for the the CV CV environment environment much much better. better. This © 2016 2016 The The Authors. Authors. Published Published by by Elsevier Elsevier B. B. V. V. © Copyright 2017 The Authors. Published by Elsevier Selection © and Peer-review under responsibility ofB.V. Dept of of Transportation Transportation Engineering Engineering University University of of Seoul. Selection and Peer-review under responsibility of Dept Selection and Peer-review under responsibility of Dept. of Transportation Engineering, University of Seoul. Seoul. Keywords: Keywords: CV CV environment, environment, VISSIM, VISSIM, Radio Radio communication communication range, range, Recovery Recovery time, time, V2I, V2I, Variable Variable Speed Speed Control Control
1. 1. Introduction Introduction When When traffic traffic congestion congestion happens happens in in the the non-signalized non-signalized freeway, freeway, there there has has aa common common sign sign of of congestion congestion like like car car accidents, presence of ramps, rapid reduction of speed, and bad weather etc. However, sometimes, there is no accidents, presence of ramps, rapid reduction of speed, and bad weather etc. However, sometimes, there is no reason reason to to be be congested, congested, but but congested congested in in the the road. road. Congestion Congestion is is created created by by delay delay accumulated accumulated by by each each driver’s driver’s perceptionperceptionreaction time. We call it stop and go effect. True cause of this symptom is known as the driver’s reaction time. We call it stop and go effect. True cause of this symptom is known as the driver’s abnormal abnormal behavior behavior or or wrong wrong geometric geometric design. design. To To solve solve this this symptom, symptom, we we should should know know the the front front road road condition condition so so we we can can prevent prevent the the congested congested road road condition condition by by slowing slowing down down the the vehicle vehicle speed speed in in advance advance because because of of the the perception-reaction perception-reaction time. time. There There are are two two solutions, solutions, VSL VSL (Variable (Variable speed speed limit) limit) and and V2X V2X (Vehicle (Vehicle to to everything) everything) environment environment which which is is similar similar to Connected Vehicle environment(CV environment) are recommended. First, by changing speed limits properly to Connected Vehicle environment(CV environment) are recommended. First, by changing speed limits properly to to ** Corresponding Corresponding author. author. Tel.: Tel.: +82-10-3235-2801 +82-10-3235-2801 E-mail E-mail address: address: [email protected] [email protected] 2214-241X © © 2016 2016 The The Authors. Authors. Published Published by by Elsevier Elsevier B. B. V. V. 2214-241X Selection and and Peer-review Peer-review under under responsibility responsibility of of Dept Dept of of Transportation Transportation Engineering Engineering University University of of Seoul. Seoul. Selection
Copyright © 2017 The Authors. Published by Elsevier B.V. Selection and Peer-review under responsibility of Dept. of Transportation Engineering, University of Seoul. 10.1016/j.trpro.2017.03.097
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reduce the speed differences between present vehicles’ speed differences and mean speed differences among lanes (Papamichail et al., 2008), VSL (Variable speed limit) can reduce the perception-reaction time gap so it can release the traffic congestion. However, it’s hard to choose where to choose it because once it is installed at one location, it can’t change this location. To improve the road safety and road efficiency, CV environment is introduced in ITS. V2X environment, which is similar to CV environment is defined as the aggregate of V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) (Zeadally et al., 2012). V2V is communication between vehicles. And V2I is communication between vehicle and infrastructure. If we adapted to V2V and V2I technologies simultaneously, CV environment can predict the forward road condition so that CV environment can eliminate the time-gap that is affected by perception-reaction time. Even, defects of VSL can also be solved by Vehicle to Infrastructure(V2I) system because V2I doesn’t have to prevent the place traffic congestion happens. CV environment can also help to reduce accidents which are caused by the driver. The statistics of accident in German (Weiß, 2011) explains that the 86% of all accidents is caused by the driver. Only about 10% originate from bad vehicle tires and brakes. By reducing accidents, we can reduce the traffic congestion caused by accident. so we can make a big progress in road safety and road efficiency. There are two important components of CV environment, radio communication ranges, and ratio of vehicles in nonCV environment to vehicles in CV environment. Although current efforts have attempted to optimize the available radio communication range (bandwidth) to preserve quality of message service (Wiegel, et al., 2009), QoS (Quality of Service) support over VANETs remains a challenge because of the various factors we discussed earlier. Recently, the packet flooding problem is raised by telecommunication researches. We also have to consider the rate of the vehicles in the CV environment. Ratio of vehicles in non-CV to vehicles in CV environment can have an impact on the road condition. Naturally, all vehicles in CV environment are preferred but CV environment needs some time to be established in reality. Actually, this paper wants to study and solve the large scale symptoms, phantom jam effect. Today, we can’t do the experiment of this large scale-CV environment system because of the budget problem. So this paper is required to run the simulation similar to real-world vehicles. And we suggest two MOEs(Measure of Effectiveness), recovery time and average delay. These two MOEs make it possible to evaluate recovery time of networks. We adapt this base network to the CV environment to compare MOEs of network in CV environment to MOEs of base network. Then, we run the simulation in CV environment considering ratio of vehicles in non-CV to vehicles in CV environment. And we compare the MOEs to evaluate the effect of CV environment. This paper is divided into six sections. The following section reviews the description of the CV environment and the methodology related to the CV environment. In section 3, CV environment modeling is presented, e.g. car following model, MOE of the road condition, and Simulation construction. Section 4 describes simulation construction and parameters. Section 5 explains simulation setup which we have used to evaluate our approach and simulation results of our analysis. At last, conclusion and future works are shown in Section 6. It will give the ratio of Cv to Non-Cv environment. 2. RESEARCH REVIEW In the 2000s, many countries run the real-world simulation in the CV environment. Many experiments and simulations show that higher radio communication technology is needed to communicate between vehicles. Jakubiak and Koucheryavy(2008) explains that VANet and WAVE used for CV environment deeply. This paper explains the recent technologies and future work for VANet. VANet is useful to handle the rapid topology changes, frequent fragmentation, virtually no power constrains, and variable highly dynamic scale and network. We concentrated on the WAVE technologies in VANet. Theoretically, the range of WAVE is up to 1000m and sending packets even though speed of vehicle is up to 200 km/h. Because of the problem of the spectrum standard, the standard technology of CV environment shifts From DSRC(Dedicated Short Range Communications) to WAVE. This paper expects that VANet would be cheap, due to large volumes, thus making the deployment easier, further accelerating the market penetration. Many papers try to prove that CV environment is efficient to road efficiency and road safety. Similar to the reality, we use the modified car following theories in the simulation. This paper also reviews the MOEs to evaluate these simulations so that we can suggest the good two components of CV environment. In the traffic flow theories, many researchers calculate formulas to improve CV environment in many ways. That’s because CV environment can have a significant impact on the car following theories because of the elimination of the perception-reaction time and so we modify that. In this section, we will study the car following theories and simulation more deeply. Zhao & Sun (2013), Wedel et al. (2009), and Yang et al. (2013) adapted car following theories Related to the CV environment variously.
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Zhao and Sun (2013) run the simulation assuming normal vehicles, ACC (Adaptive cruise control) and CACC (Cooperative adaptive cruise control) are mixed. In this paper, they calculated the acceleration formula in case of ACC and CACC. And they suggest these three operation modes to the simulation, Vissim. The model of acceleration is a linear function between the objective vehicle and its preceding vehicle and the current speed v� of the objective vehicle, with which is limited into maximum and minimum accelerations. The accelerations of vehicles in next step are expressed by formula (1) and (2) for ACC vehicle and (3) and (4) for CACC Vehicle. a� � k � � �v� � v� � � k � � �s � v � t � � a � max �a��� , min�a� , a��� �� a� � a� � k � � �v� � v� � � k � � �s � v � t � � a � max �a��� , min�a� , a��� ��
(1) (2) (3) (4)
Where, a - Acceleration in next step of the objective vehicle; a� - Acceleration of the preceding vehicle; v� - Speed of the preceding vehicle; v� - Speed of the following vehicle; a��� - Maximum allowed acceleration; a��� - Maximum allowed deceleration ; k � , k � - Constant gains, both greater than zero.
They conclude that when a platooning vehicle appears, the road capacity increases. And they found that shockwave can explain that results and found the recovery time in this graph. This graph can estimate the recovery time. It can be a good MOE(Measure of Effectiveness) of CV environment. Through this paper, we can know that when we express the CV environment in the simulation, car following model is important. And one of the MOE of CV environment is recovery speed. Wedel et al. (2009) sets the testbeds at the Luxemburger Straße and Gottesweg in Germany to evaluate V2I environment in the common road. The formula of this traffic following models is expressed by (5) and (6). This strategy is used by dynamic route assignment by using a dynamically changing database for the routing algorithm. ����� ������� ���� ����
α� �n� � ∑���
��� ������ ������� ���� �����
u�� * � ∑��� ��� α� �n� ∙ u�� �n�
(5) (6)
Where, T�� : N travel time entries T�� �n� : N time stamps for an edge, i T���� : After receiving a message, the maximum time until an entry is deprecated T���� : The current time, α� �n� : The �eight u�� �n� : The �eighted speed for an entry Zeadally et al. (2012) studied about VANETs. Road Side Unit(RSU) sends a broadcast message to all equipped vehicles in the vicinity. If RSU is installed by every one kilometer or less, broadcasting variable speed limits are available and RSU will determine the appropriate speed considering speed limit. If forward road condition is worse than this road, RSU will slow down the speed to postpone traffic flows so that it can prevent the stop and go effect in advance. Recently, Ghosh et al.(2015) ran the real-time VANET testbed using WAVE in OMNeT++. They explain the transmission process between overlapping RSUs. In conclusion, they revealed that the coverage radius of RSU is 907m with 99% probability.
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3. CV ENVIRONMENT MODELING 3.1. Architecture of the CV environment framework To simulate the CV environment well, there are many assumptions in this paper. There are two types of vehicles in the CV environment : normal vehicle and CV environment vehicle. Fig. 1 explains this assumption. Normal vehicles move independently, not affected by CV environment, and just affect preceding vehicles. CV environment vehicles move interactively between CV environment vehicles within radio communication ranges. If a vehicle provides other vehicle’s warning messages, received vehicles will slow its speed. In this paper, therefore, we assume that following vehicle’s desired speed be equal to the front vehicle’s speed. We assume radio communication range is ±500m using IEEE 802.11p (WAVE), usually used by VANet. In this simulation, we assume that radio communication range of V2I is within ±250m.
Figure 1 - The process of sending data packets in CV environment
3.2. Car following theory (Wiedemann 74 model) In research review section, we concentrate on the car following theories. To realize CV environment, modified car following theory is needed. Wiedemann 74 model, commonly used by Vissim is a car following model in nonsignalized freeway. Wiedemann74 model is easy to calculate car following theories and explains most of driver’s behavior. And it’s easy to modify standstill distance. This model is an improved version of Wiedemann’s 1974 car following model. As CV environment vehicles get the perception-reaction time gap, we shorten the safety distance. We modify bx_add by 1.5 and bx_multi by 2.5. This car following model is expressed by (7) and (8) :
� � ax � bx
Where, ax : Standstill distance[m]. bx : Safety distance
bx � �bx��� � bx����� � z� � √v (8)
Where, v : vehicle speed [m/s] bx��� : Additive part of a safety distance. bx����� : Multiplicative part of safety distance. z : a value of range [0.1], which is normally distributed around 0.5 with a standard deviation of 0.15
(7)
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4. Simulation construction 4.1. Simulation Algorithm These days, there are many microscopic simulations, e.g. Vissim, PARAMICS, CORSIM, AIMSUN. Above all, Vissim is easy to modify that data by using COM. The Component Object Model(COM) describes how binary components of different programs collaborate. COM gives access to data and functions contained in other programs. Therefore, it’s easy to modify driver behavior. Because classical traffic simulator is difficult to simulate CV environment properly, we use the Vissim 7.00 – 13. When Vissim runs with COM, it can determine the next step maneuver - acceleration/deceleration, lane change, or the vehicle’s location and trajectory via calculating vehicle’s xy axis. If forward road condition is congested, RSU will slow down the speed limit(‘Desired speed’ column in Vissim) to reduce perception-reaction time gap. Fig. 2 explains this algorithm. As we run the COM, we follow these algorithm steps: (1) Run Vissim by time step (t). (2) Each vehicle’s status are transmitted to VBA. (3) Apply this paper’s logic in VBA to Vissim data output. (4) Transmit data to the Vissim. (5) If time step(t) is not sufficient to the constant, then t=t+1 and repeat (1).
Figure 3. Simulation process using COM
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4.2. Description of Vehicle Trajectory Data When we run the Vissim in the base network, we assume that all vehicles have the same desired speed range from 80km/h to 130km/h. To study a traffic phantom jam effect, we consider various conditions like road congestion. So we intentionally choose 4 vehicles(vehicle id 11 ~ 14) per each lane to slow down the speed 30km/h simultaneously during 60sec(from 25sec to 85sec) so that we can study the road congestion and its recovery speed. Over 500 seconds and over 2,000m, shape of graph is almost the same as preceding graphs so we cut them. Fig. 4 describes the relationship between time-space diagram and time-speed diagram. It explains the effect of Variable Speed Limit(VSL). Each line means a vehicle trajectory data by position and speed. Width of shaded area means the application time of V2I and Height of shaded area means the range of VSL, 500m. Yellow line of the time axis means the recovery time of traffic flow. In the case of traffic congestion, it is important to consider traffic volume or capacity. Traffic volume has a great impact on congestion so this paper considers it importantly. We suggest three traffic volumes 800, 1500, 2200 veh/h/lane. Each volume is cited by KHCM (Korea Highway Capacity Manual) LOS Table. These volumes explain LOS A, between LOS C and D, and LOS F (Capacity). We can express output of VISSIM (type : *.fzp) by vehicle trajectory data using SAS 9.3. SAS is good for handling big data because it is step by step statistical packages. On average, one output data has 850,000 rows, including 4 lanes. Vehicle trajectory data at 1 lane is the time-space diagram by the same vehicle number. In this paper, we only consider one lane. Fig. 5 explains time-space diagram conceptually. Green line means a vehicle that is a desired slow speed data at 35 sec. Yellow lines mean group of vehicles that are forwarding vehicle over the green vehicle. Blue and red lines mean following vehicles by vehicle number : red line explains odd number vehicle’s route, Blue one explains even number of vehicle’s route. Cutting line means lane changing vehicles. Lane changing events usually happen in traffic congestion.
Figure 4. Description of time-space diagram and time-speed diagram.
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Figure 5. Description of vehicle trajectory data
4.3. Measure of Effectiveness – Recovery Time, Average Delay When we evaluate the effect of CV environment, MOE is needed. Various MOEs exist when we evaluate the road condition. There are big differences between congested and normal traffic flow. Three of MOEs are the time to recover original vehicle speed, average of vehicle speed, and average delay of vehicles. Zhao and Sun(2013) suggests Recovery time. They defined this recovery time as a time gap for one vehicle to recover its desired speed (with subtle oscillations). This MOE will illustrate that this vehicle has a good stability dealing with shockwaves in simulation. It means that by using speed * time diagram, we can measure the recovery time to recover its desired speed. In this paper, we adapt to the same types of congestion at the same time so MOE can easily be calculated. MOE of recovery time is calculated by this equation (9):
���� � ��������������������������� � �������������������������������
(9)
Through this recovery time, we evaluate the effect of CV environment easily and suggest the proper radio communication ranges and market penetration rate of CV environment. Vissim provides two remains. Average delay is 2nd MOE. It is defined as the average delay per vehicle [s]. It is calculated by the equation (10) and (11): Delay��� �
����������
����������� ����������� �
Delay����� � ∑��������������� � ��������������� �
(10)
(11)
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Sometimes total delays may have minus because of the formula of total delays. Total delay is the sum of the delay of a vehicle that is in the network or has already left it. For the calculation, the quotient is obtained by subtracting the actual distance travel time in this time step and desired (ideal) travel time from the duration of the time step. Through two MOEs, we can evaluate the road condition. These MOEs are useful for evaluating the effects of the CV environment. 5. Simulation Setup and Results In this paper, we want to know the effect of CV environment and VSL on various control constraints, e.g. market penetration rate, and the scale of speed reduction. Therefore, we assume the VSL at the position of 750m, ±250m and compare the road condition using MOEs under the various three market penetration rate, 2:8, 3:7, and 4:6, and 80 to 70 km/h reduction, 80 to 60 km/h reduction, and 80 to 50 km/h reduction. At one experiment, we fix the speed reduction, 80 to 20km/h to make traffic congestion quickly and to evaluate the recovery time. At the other experiment, we fix the market penetration rate at 3:7 and compare the road condition under three different speed reduction. Figure 6 and Table 1 explain the recovery time obtained by time-space diagrams and time-speed graphs considering market penetration rate of CV environment vehicles and traffic volume, the first experiment. If traffic volume is high and expects traffic congestion, it can’t provide proper environment status so recovery time of breakdown flow is larger than the one of stable flow and free flow. There are three traffic volumes 2200, 1500, and 800veh/h/lane. First section, we can show three different market penetration rate of CV environment vehicle, 2:8, 3:7 and 4:6, in the breakdown flow (2200veh/h/lane). Each recovery time of diagram is summarized in Table 1. The shape of queue of 2:8 timespace diagram is different from one of 3:7 or 4:6. There is a little backward congestion at the 2:8 diagram. It means that this time-speed diagram has many slow-speed vehicles in the network. 3:7 diagram has much better shapes than 2:8 diagram. Its queue will soon decrease and the number of slow-speed vehicles is in decrease. 4:6 time-position diagram is forward congestion. Queue moves forward so road congestion is removed quickly. Time-speed diagram has the same results of time-position diagram. It can remove more quickly than ever. Second section, we can show three different ratios of normal vehicle and CV environment vehicle in the stable flow (1500 veh/h/lane). There are more convenient flows than breakdown flow. Its recovery speed is also shorter than breakdown flow. Time-speed diagram explains that there are small congestions in the network. As the number of CV vehicles are higher, the recovery time decreases dramatically. When market penetration rate is 4:6 in the CV environment, there is no congestion sign. Time-speed diagram also shows stable status. Sometimes high-speed vehicles exist because of the desired speed range. Various speeds exist followed by a desired speed distribution. There has an odd red line at the 2:8 and 3:7 time-speed diagram, at 1500 veh/h/lane. It explains that the lane change behaviour of the one vehicle and return to the same lane. Last section, when road condition is free flow(800veh/h/lane), we can show three similar time-space diagram. It can show that when in 800veh/h/lane, CV environment is not needed. It’s similar but its trend always exists. The trend is that, when the market penetration rate of CV environment is getting higher, recovery time decreases. If there are significant road congestions in the real world, the solution is to increase the market penetration rate of CV environment and reducing the traffic volume by rerouting simultaneously. Then, the road condition will dramatically decrease. Table 1. Recovery time according to ratio of CV vehicle
Recovery time (sec)
Market penetration rate of CV
Volume
Road condition
2:8
3:7
4:6
2200 veh/h/lane
Breakdown flow
135
120
93
1500 veh/h/lane
Stable flow
40
20
13
800 veh/h/lane
Free flow
28
10
5
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Traffic Volume 2200 veh/h/lane
Traffic Volume 1500 veh/h/lane
Traffic Volume 800 veh/h/lane Figure 6 Time-Space diagrams & Time-Speed graphs considering ratio between normal vehicle and CV environment vehicle
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At the other experiment, we fix the market penetration rate by 3:7. we explain other two MOEs, average speed and average delay. They’re useful to evaluate overall network status. Figure 7 explains the average speed and average delay by market penetration rate of CV environment vehicle and traffic volume. This graph indicates that if speed reduction is higher, average delay and recovery time is slower and show severe traffic congestion. We can find the relationship between average delay, recovery time and speed reduction of CV environment. When speed reduction increases, the average delay and recovery time increases. At the aspect of average delay, speed reduction from 80 to 60km/h is the best way to reduce the effect of speed reduction. At the aspect of recovery time, speed reduction from 80 to 70km/h is the best way. Therefore, proper speed reduction between 10km/h and 20km/h is needed to solve traffic congestion.
Figure 7. Average Speeds and Delays by speed reduction of CV environment and traffic volume
6. Conclusion and future work Traffic phantom jam sometimes causes the abrupt congestion. This traffic phantom jam is caused by accumulation of the perception-reaction time gap. To improve road safety and road efficiency, CV environment is recommended because of the elimination of the perception-reaction time gap. To simulate CV environment, we study how to express algorithms, traffic flow theories, and VANet network. The purpose of this paper is to study CV environment considering traffic volume, market penetration rate of CV environment, and speed reduction of VSL. At first, we make some assumptions to simulate CV environment. To simulate CV environment properly we use the VISSIM by COM. We modify some parameters and make algorithms to simulate the CV environment. To evaluate the effect of the CV environment, we evaluate the road condition. Then, we suggest some kinds of MOE : recovery time, and average delay. By using two MOEs simultaneously, we found some good results. In conclusion, CV environment can reduce the congestion in proper traffic volume. However, if traffic volume is almost near the capacity, even CV environment can’t relieve the road condition. Even though proper market penetration rate of CV environment(over 3:7), and proper speed reduction between 80 to 70km/h and 80to 60km/h are easy to solve traffic congestion, when there is a significant traffic congestion in the road, the useful solution is to increase the market penetration rate of CV environment, and reducing the traffic volume by rerouting simultaneously. Then, the road condition will dramatically decrease.
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Recently, technologies related to the CV environment are growing faster ever than before. However, higher technologies not always mean the good results. Sometimes, intermediate results are the best results because of the various relationships between parameters. This data is just a simulation data though it reflects on the reality. If we get the experimental data, then we can get the more realized data though it is too far from present. Usually, real data has some unexpected results. Thus, we can know how to evaluate road condition in CV environment exactly. If we get the real vehicle trajectory data, it will be good degree to fit a simulation modifying car following theories or traffic volume set. These results will be the good foundation for CV environment and related technologies. Acknowledgements This research was supported by the National Research Foundation of Korea grant funded by the Korea government(MEST) (NRF-2015R1A2A2A04005646). 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2016 International Symposium of Transport Simulation (ISTS’16 Conference), June 23~25, 2016
Experimental Analysis on control constraints for connected vehicles using Vissim a*, Kyungsoo Hwangbb Sangung Parkaa, Jooyoung Kimaa, Seungjae Leea* st University University of of Seoul, Seoul, Room Room 523 523 21 21st hall hall University University of of Seoul Seoul 163 163 Seoulsiripdaero, Seoulsiripdaero, Dongdaemun-gu, Dongdaemun-gu, Seoul Seoul 02504, 02504, KOREA KOREA Jeju National University, University, Room Jeju National Room 2323-2 2323-2 College College of of Social Social Sicence Sicence hall hall Jeju Jeju National National University University 102 102 Jejudaehak-ro, Jejudaehak-ro, Jeju-si, Jeju-si, Jeju Jeju Special Special SelfSelfGoverning Governing Province, Province, 63243, 63243, KOREA KOREA aa
b* b*
Abstract Abstract Recent Recent advances advances in in CV CV environment environment made made technologies technologies much much higher higher level. level. We We just just thought thought about about that that high-level high-level technologies technologies made CV environment better. This paper studies the CV environment considering various traffic congestion made CV environment better. This paper studies the CV environment considering various traffic congestion level level and and optimal optimal speed speed control control using using the the microscopic microscopic simulation simulation (Vissim) (Vissim) based based on on proper proper parameters parameters and and algorithm. algorithm. To To evaluate evaluate road road condition, condition, we we suggest suggest some some kinds kinds of of MOE MOE e.g. e.g. recovery recovery time, time, average average speed, speed, and and delay. delay. In In conclusion, conclusion, CV CV environment environment can can reduce reduce the the congestion congestion in in proper proper traffic traffic volume. volume. However, However, if if traffic traffic volume volume is is almost almost near near the the capacity, capacity, even even CV CV environment environment can’t can’t relieve relieve the the road road condition. condition. If If the the number number of of CV CV environment environment vehicle vehicle increases, increases, it it will will reduce reduce the the congestion congestion and and aa road road accident accident much much better. better. This result result will will be be the the foundation foundation for for the the CV CV environment environment much much better. better. This © 2016 2016 The The Authors. Authors. Published Published by by Elsevier Elsevier B. B. V. V. © Copyright 2017 The Authors. Published by Elsevier Selection © and Peer-review under responsibility ofB.V. Dept of of Transportation Transportation Engineering Engineering University University of of Seoul. Selection and Peer-review under responsibility of Dept Selection and Peer-review under responsibility of Dept. of Transportation Engineering, University of Seoul. Seoul. Keywords: Keywords: CV CV environment, environment, VISSIM, VISSIM, Radio Radio communication communication range, range, Recovery Recovery time, time, V2I, V2I, Variable Variable Speed Speed Control Control
1. 1. Introduction Introduction When When traffic traffic congestion congestion happens happens in in the the non-signalized non-signalized freeway, freeway, there there has has aa common common sign sign of of congestion congestion like like car car accidents, presence of ramps, rapid reduction of speed, and bad weather etc. However, sometimes, there is no accidents, presence of ramps, rapid reduction of speed, and bad weather etc. However, sometimes, there is no reason reason to to be be congested, congested, but but congested congested in in the the road. road. Congestion Congestion is is created created by by delay delay accumulated accumulated by by each each driver’s driver’s perceptionperceptionreaction time. We call it stop and go effect. True cause of this symptom is known as the driver’s reaction time. We call it stop and go effect. True cause of this symptom is known as the driver’s abnormal abnormal behavior behavior or or wrong wrong geometric geometric design. design. To To solve solve this this symptom, symptom, we we should should know know the the front front road road condition condition so so we we can can prevent prevent the the congested congested road road condition condition by by slowing slowing down down the the vehicle vehicle speed speed in in advance advance because because of of the the perception-reaction perception-reaction time. time. There There are are two two solutions, solutions, VSL VSL (Variable (Variable speed speed limit) limit) and and V2X V2X (Vehicle (Vehicle to to everything) everything) environment environment which which is is similar similar to Connected Vehicle environment(CV environment) are recommended. First, by changing speed limits properly to Connected Vehicle environment(CV environment) are recommended. First, by changing speed limits properly to to ** Corresponding Corresponding author. author. Tel.: Tel.: +82-10-3235-2801 +82-10-3235-2801 E-mail E-mail address: address: [email protected] [email protected] 2214-241X © © 2016 2016 The The Authors. Authors. Published Published by by Elsevier Elsevier B. B. V. V. 2214-241X Selection and and Peer-review Peer-review under under responsibility responsibility of of Dept Dept of of Transportation Transportation Engineering Engineering University University of of Seoul. Seoul. Selection
Copyright © 2017 The Authors. Published by Elsevier B.V. Selection and Peer-review under responsibility of Dept. of Transportation Engineering, University of Seoul. 10.1016/j.trpro.2017.03.097
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reduce the speed differences between present vehicles’ speed differences and mean speed differences among lanes (Papamichail et al., 2008), VSL (Variable speed limit) can reduce the perception-reaction time gap so it can release the traffic congestion. However, it’s hard to choose where to choose it because once it is installed at one location, it can’t change this location. To improve the road safety and road efficiency, CV environment is introduced in ITS. V2X environment, which is similar to CV environment is defined as the aggregate of V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) (Zeadally et al., 2012). V2V is communication between vehicles. And V2I is communication between vehicle and infrastructure. If we adapted to V2V and V2I technologies simultaneously, CV environment can predict the forward road condition so that CV environment can eliminate the time-gap that is affected by perception-reaction time. Even, defects of VSL can also be solved by Vehicle to Infrastructure(V2I) system because V2I doesn’t have to prevent the place traffic congestion happens. CV environment can also help to reduce accidents which are caused by the driver. The statistics of accident in German (Weiß, 2011) explains that the 86% of all accidents is caused by the driver. Only about 10% originate from bad vehicle tires and brakes. By reducing accidents, we can reduce the traffic congestion caused by accident. so we can make a big progress in road safety and road efficiency. There are two important components of CV environment, radio communication ranges, and ratio of vehicles in nonCV environment to vehicles in CV environment. Although current efforts have attempted to optimize the available radio communication range (bandwidth) to preserve quality of message service (Wiegel, et al., 2009), QoS (Quality of Service) support over VANETs remains a challenge because of the various factors we discussed earlier. Recently, the packet flooding problem is raised by telecommunication researches. We also have to consider the rate of the vehicles in the CV environment. Ratio of vehicles in non-CV to vehicles in CV environment can have an impact on the road condition. Naturally, all vehicles in CV environment are preferred but CV environment needs some time to be established in reality. Actually, this paper wants to study and solve the large scale symptoms, phantom jam effect. Today, we can’t do the experiment of this large scale-CV environment system because of the budget problem. So this paper is required to run the simulation similar to real-world vehicles. And we suggest two MOEs(Measure of Effectiveness), recovery time and average delay. These two MOEs make it possible to evaluate recovery time of networks. We adapt this base network to the CV environment to compare MOEs of network in CV environment to MOEs of base network. Then, we run the simulation in CV environment considering ratio of vehicles in non-CV to vehicles in CV environment. And we compare the MOEs to evaluate the effect of CV environment. This paper is divided into six sections. The following section reviews the description of the CV environment and the methodology related to the CV environment. In section 3, CV environment modeling is presented, e.g. car following model, MOE of the road condition, and Simulation construction. Section 4 describes simulation construction and parameters. Section 5 explains simulation setup which we have used to evaluate our approach and simulation results of our analysis. At last, conclusion and future works are shown in Section 6. It will give the ratio of Cv to Non-Cv environment. 2. RESEARCH REVIEW In the 2000s, many countries run the real-world simulation in the CV environment. Many experiments and simulations show that higher radio communication technology is needed to communicate between vehicles. Jakubiak and Koucheryavy(2008) explains that VANet and WAVE used for CV environment deeply. This paper explains the recent technologies and future work for VANet. VANet is useful to handle the rapid topology changes, frequent fragmentation, virtually no power constrains, and variable highly dynamic scale and network. We concentrated on the WAVE technologies in VANet. Theoretically, the range of WAVE is up to 1000m and sending packets even though speed of vehicle is up to 200 km/h. Because of the problem of the spectrum standard, the standard technology of CV environment shifts From DSRC(Dedicated Short Range Communications) to WAVE. This paper expects that VANet would be cheap, due to large volumes, thus making the deployment easier, further accelerating the market penetration. Many papers try to prove that CV environment is efficient to road efficiency and road safety. Similar to the reality, we use the modified car following theories in the simulation. This paper also reviews the MOEs to evaluate these simulations so that we can suggest the good two components of CV environment. In the traffic flow theories, many researchers calculate formulas to improve CV environment in many ways. That’s because CV environment can have a significant impact on the car following theories because of the elimination of the perception-reaction time and so we modify that. In this section, we will study the car following theories and simulation more deeply. Zhao & Sun (2013), Wedel et al. (2009), and Yang et al. (2013) adapted car following theories Related to the CV environment variously.
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Zhao and Sun (2013) run the simulation assuming normal vehicles, ACC (Adaptive cruise control) and CACC (Cooperative adaptive cruise control) are mixed. In this paper, they calculated the acceleration formula in case of ACC and CACC. And they suggest these three operation modes to the simulation, Vissim. The model of acceleration is a linear function between the objective vehicle and its preceding vehicle and the current speed v� of the objective vehicle, with which is limited into maximum and minimum accelerations. The accelerations of vehicles in next step are expressed by formula (1) and (2) for ACC vehicle and (3) and (4) for CACC Vehicle. a� � k � � �v� � v� � � k � � �s � v � t � � a � max �a��� , min�a� , a��� �� a� � a� � k � � �v� � v� � � k � � �s � v � t � � a � max �a��� , min�a� , a��� ��
(1) (2) (3) (4)
Where, a - Acceleration in next step of the objective vehicle; a� - Acceleration of the preceding vehicle; v� - Speed of the preceding vehicle; v� - Speed of the following vehicle; a��� - Maximum allowed acceleration; a��� - Maximum allowed deceleration ; k � , k � - Constant gains, both greater than zero.
They conclude that when a platooning vehicle appears, the road capacity increases. And they found that shockwave can explain that results and found the recovery time in this graph. This graph can estimate the recovery time. It can be a good MOE(Measure of Effectiveness) of CV environment. Through this paper, we can know that when we express the CV environment in the simulation, car following model is important. And one of the MOE of CV environment is recovery speed. Wedel et al. (2009) sets the testbeds at the Luxemburger Straße and Gottesweg in Germany to evaluate V2I environment in the common road. The formula of this traffic following models is expressed by (5) and (6). This strategy is used by dynamic route assignment by using a dynamically changing database for the routing algorithm. ����� ������� ���� ����
α� �n� � ∑���
��� ������ ������� ���� �����
u�� * � ∑��� ��� α� �n� ∙ u�� �n�
(5) (6)
Where, T�� : N travel time entries T�� �n� : N time stamps for an edge, i T���� : After receiving a message, the maximum time until an entry is deprecated T���� : The current time, α� �n� : The �eight u�� �n� : The �eighted speed for an entry Zeadally et al. (2012) studied about VANETs. Road Side Unit(RSU) sends a broadcast message to all equipped vehicles in the vicinity. If RSU is installed by every one kilometer or less, broadcasting variable speed limits are available and RSU will determine the appropriate speed considering speed limit. If forward road condition is worse than this road, RSU will slow down the speed to postpone traffic flows so that it can prevent the stop and go effect in advance. Recently, Ghosh et al.(2015) ran the real-time VANET testbed using WAVE in OMNeT++. They explain the transmission process between overlapping RSUs. In conclusion, they revealed that the coverage radius of RSU is 907m with 99% probability.
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3. CV ENVIRONMENT MODELING 3.1. Architecture of the CV environment framework To simulate the CV environment well, there are many assumptions in this paper. There are two types of vehicles in the CV environment : normal vehicle and CV environment vehicle. Fig. 1 explains this assumption. Normal vehicles move independently, not affected by CV environment, and just affect preceding vehicles. CV environment vehicles move interactively between CV environment vehicles within radio communication ranges. If a vehicle provides other vehicle’s warning messages, received vehicles will slow its speed. In this paper, therefore, we assume that following vehicle’s desired speed be equal to the front vehicle’s speed. We assume radio communication range is ±500m using IEEE 802.11p (WAVE), usually used by VANet. In this simulation, we assume that radio communication range of V2I is within ±250m.
Figure 1 - The process of sending data packets in CV environment
3.2. Car following theory (Wiedemann 74 model) In research review section, we concentrate on the car following theories. To realize CV environment, modified car following theory is needed. Wiedemann 74 model, commonly used by Vissim is a car following model in nonsignalized freeway. Wiedemann74 model is easy to calculate car following theories and explains most of driver’s behavior. And it’s easy to modify standstill distance. This model is an improved version of Wiedemann’s 1974 car following model. As CV environment vehicles get the perception-reaction time gap, we shorten the safety distance. We modify bx_add by 1.5 and bx_multi by 2.5. This car following model is expressed by (7) and (8) :
� � ax � bx
Where, ax : Standstill distance[m]. bx : Safety distance
bx � �bx��� � bx����� � z� � √v (8)
Where, v : vehicle speed [m/s] bx��� : Additive part of a safety distance. bx����� : Multiplicative part of safety distance. z : a value of range [0.1], which is normally distributed around 0.5 with a standard deviation of 0.15
(7)
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4. Simulation construction 4.1. Simulation Algorithm These days, there are many microscopic simulations, e.g. Vissim, PARAMICS, CORSIM, AIMSUN. Above all, Vissim is easy to modify that data by using COM. The Component Object Model(COM) describes how binary components of different programs collaborate. COM gives access to data and functions contained in other programs. Therefore, it’s easy to modify driver behavior. Because classical traffic simulator is difficult to simulate CV environment properly, we use the Vissim 7.00 – 13. When Vissim runs with COM, it can determine the next step maneuver - acceleration/deceleration, lane change, or the vehicle’s location and trajectory via calculating vehicle’s xy axis. If forward road condition is congested, RSU will slow down the speed limit(‘Desired speed’ column in Vissim) to reduce perception-reaction time gap. Fig. 2 explains this algorithm. As we run the COM, we follow these algorithm steps: (1) Run Vissim by time step (t). (2) Each vehicle’s status are transmitted to VBA. (3) Apply this paper’s logic in VBA to Vissim data output. (4) Transmit data to the Vissim. (5) If time step(t) is not sufficient to the constant, then t=t+1 and repeat (1).
Figure 3. Simulation process using COM
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4.2. Description of Vehicle Trajectory Data When we run the Vissim in the base network, we assume that all vehicles have the same desired speed range from 80km/h to 130km/h. To study a traffic phantom jam effect, we consider various conditions like road congestion. So we intentionally choose 4 vehicles(vehicle id 11 ~ 14) per each lane to slow down the speed 30km/h simultaneously during 60sec(from 25sec to 85sec) so that we can study the road congestion and its recovery speed. Over 500 seconds and over 2,000m, shape of graph is almost the same as preceding graphs so we cut them. Fig. 4 describes the relationship between time-space diagram and time-speed diagram. It explains the effect of Variable Speed Limit(VSL). Each line means a vehicle trajectory data by position and speed. Width of shaded area means the application time of V2I and Height of shaded area means the range of VSL, 500m. Yellow line of the time axis means the recovery time of traffic flow. In the case of traffic congestion, it is important to consider traffic volume or capacity. Traffic volume has a great impact on congestion so this paper considers it importantly. We suggest three traffic volumes 800, 1500, 2200 veh/h/lane. Each volume is cited by KHCM (Korea Highway Capacity Manual) LOS Table. These volumes explain LOS A, between LOS C and D, and LOS F (Capacity). We can express output of VISSIM (type : *.fzp) by vehicle trajectory data using SAS 9.3. SAS is good for handling big data because it is step by step statistical packages. On average, one output data has 850,000 rows, including 4 lanes. Vehicle trajectory data at 1 lane is the time-space diagram by the same vehicle number. In this paper, we only consider one lane. Fig. 5 explains time-space diagram conceptually. Green line means a vehicle that is a desired slow speed data at 35 sec. Yellow lines mean group of vehicles that are forwarding vehicle over the green vehicle. Blue and red lines mean following vehicles by vehicle number : red line explains odd number vehicle’s route, Blue one explains even number of vehicle’s route. Cutting line means lane changing vehicles. Lane changing events usually happen in traffic congestion.
Figure 4. Description of time-space diagram and time-speed diagram.
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Figure 5. Description of vehicle trajectory data
4.3. Measure of Effectiveness – Recovery Time, Average Delay When we evaluate the effect of CV environment, MOE is needed. Various MOEs exist when we evaluate the road condition. There are big differences between congested and normal traffic flow. Three of MOEs are the time to recover original vehicle speed, average of vehicle speed, and average delay of vehicles. Zhao and Sun(2013) suggests Recovery time. They defined this recovery time as a time gap for one vehicle to recover its desired speed (with subtle oscillations). This MOE will illustrate that this vehicle has a good stability dealing with shockwaves in simulation. It means that by using speed * time diagram, we can measure the recovery time to recover its desired speed. In this paper, we adapt to the same types of congestion at the same time so MOE can easily be calculated. MOE of recovery time is calculated by this equation (9):
���� � ��������������������������� � �������������������������������
(9)
Through this recovery time, we evaluate the effect of CV environment easily and suggest the proper radio communication ranges and market penetration rate of CV environment. Vissim provides two remains. Average delay is 2nd MOE. It is defined as the average delay per vehicle [s]. It is calculated by the equation (10) and (11): Delay��� �
����������
����������� ����������� �
Delay����� � ∑��������������� � ��������������� �
(10)
(11)
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Sometimes total delays may have minus because of the formula of total delays. Total delay is the sum of the delay of a vehicle that is in the network or has already left it. For the calculation, the quotient is obtained by subtracting the actual distance travel time in this time step and desired (ideal) travel time from the duration of the time step. Through two MOEs, we can evaluate the road condition. These MOEs are useful for evaluating the effects of the CV environment. 5. Simulation Setup and Results In this paper, we want to know the effect of CV environment and VSL on various control constraints, e.g. market penetration rate, and the scale of speed reduction. Therefore, we assume the VSL at the position of 750m, ±250m and compare the road condition using MOEs under the various three market penetration rate, 2:8, 3:7, and 4:6, and 80 to 70 km/h reduction, 80 to 60 km/h reduction, and 80 to 50 km/h reduction. At one experiment, we fix the speed reduction, 80 to 20km/h to make traffic congestion quickly and to evaluate the recovery time. At the other experiment, we fix the market penetration rate at 3:7 and compare the road condition under three different speed reduction. Figure 6 and Table 1 explain the recovery time obtained by time-space diagrams and time-speed graphs considering market penetration rate of CV environment vehicles and traffic volume, the first experiment. If traffic volume is high and expects traffic congestion, it can’t provide proper environment status so recovery time of breakdown flow is larger than the one of stable flow and free flow. There are three traffic volumes 2200, 1500, and 800veh/h/lane. First section, we can show three different market penetration rate of CV environment vehicle, 2:8, 3:7 and 4:6, in the breakdown flow (2200veh/h/lane). Each recovery time of diagram is summarized in Table 1. The shape of queue of 2:8 timespace diagram is different from one of 3:7 or 4:6. There is a little backward congestion at the 2:8 diagram. It means that this time-speed diagram has many slow-speed vehicles in the network. 3:7 diagram has much better shapes than 2:8 diagram. Its queue will soon decrease and the number of slow-speed vehicles is in decrease. 4:6 time-position diagram is forward congestion. Queue moves forward so road congestion is removed quickly. Time-speed diagram has the same results of time-position diagram. It can remove more quickly than ever. Second section, we can show three different ratios of normal vehicle and CV environment vehicle in the stable flow (1500 veh/h/lane). There are more convenient flows than breakdown flow. Its recovery speed is also shorter than breakdown flow. Time-speed diagram explains that there are small congestions in the network. As the number of CV vehicles are higher, the recovery time decreases dramatically. When market penetration rate is 4:6 in the CV environment, there is no congestion sign. Time-speed diagram also shows stable status. Sometimes high-speed vehicles exist because of the desired speed range. Various speeds exist followed by a desired speed distribution. There has an odd red line at the 2:8 and 3:7 time-speed diagram, at 1500 veh/h/lane. It explains that the lane change behaviour of the one vehicle and return to the same lane. Last section, when road condition is free flow(800veh/h/lane), we can show three similar time-space diagram. It can show that when in 800veh/h/lane, CV environment is not needed. It’s similar but its trend always exists. The trend is that, when the market penetration rate of CV environment is getting higher, recovery time decreases. If there are significant road congestions in the real world, the solution is to increase the market penetration rate of CV environment and reducing the traffic volume by rerouting simultaneously. Then, the road condition will dramatically decrease. Table 1. Recovery time according to ratio of CV vehicle
Recovery time (sec)
Market penetration rate of CV
Volume
Road condition
2:8
3:7
4:6
2200 veh/h/lane
Breakdown flow
135
120
93
1500 veh/h/lane
Stable flow
40
20
13
800 veh/h/lane
Free flow
28
10
5
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Traffic Volume 2200 veh/h/lane
Traffic Volume 1500 veh/h/lane
Traffic Volume 800 veh/h/lane Figure 6 Time-Space diagrams & Time-Speed graphs considering ratio between normal vehicle and CV environment vehicle
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At the other experiment, we fix the market penetration rate by 3:7. we explain other two MOEs, average speed and average delay. They’re useful to evaluate overall network status. Figure 7 explains the average speed and average delay by market penetration rate of CV environment vehicle and traffic volume. This graph indicates that if speed reduction is higher, average delay and recovery time is slower and show severe traffic congestion. We can find the relationship between average delay, recovery time and speed reduction of CV environment. When speed reduction increases, the average delay and recovery time increases. At the aspect of average delay, speed reduction from 80 to 60km/h is the best way to reduce the effect of speed reduction. At the aspect of recovery time, speed reduction from 80 to 70km/h is the best way. Therefore, proper speed reduction between 10km/h and 20km/h is needed to solve traffic congestion.
Figure 7. Average Speeds and Delays by speed reduction of CV environment and traffic volume
6. Conclusion and future work Traffic phantom jam sometimes causes the abrupt congestion. This traffic phantom jam is caused by accumulation of the perception-reaction time gap. To improve road safety and road efficiency, CV environment is recommended because of the elimination of the perception-reaction time gap. To simulate CV environment, we study how to express algorithms, traffic flow theories, and VANet network. The purpose of this paper is to study CV environment considering traffic volume, market penetration rate of CV environment, and speed reduction of VSL. At first, we make some assumptions to simulate CV environment. To simulate CV environment properly we use the VISSIM by COM. We modify some parameters and make algorithms to simulate the CV environment. To evaluate the effect of the CV environment, we evaluate the road condition. Then, we suggest some kinds of MOE : recovery time, and average delay. By using two MOEs simultaneously, we found some good results. In conclusion, CV environment can reduce the congestion in proper traffic volume. However, if traffic volume is almost near the capacity, even CV environment can’t relieve the road condition. Even though proper market penetration rate of CV environment(over 3:7), and proper speed reduction between 80 to 70km/h and 80to 60km/h are easy to solve traffic congestion, when there is a significant traffic congestion in the road, the useful solution is to increase the market penetration rate of CV environment, and reducing the traffic volume by rerouting simultaneously. Then, the road condition will dramatically decrease.
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Recently, technologies related to the CV environment are growing faster ever than before. However, higher technologies not always mean the good results. Sometimes, intermediate results are the best results because of the various relationships between parameters. This data is just a simulation data though it reflects on the reality. If we get the experimental data, then we can get the more realized data though it is too far from present. Usually, real data has some unexpected results. Thus, we can know how to evaluate road condition in CV environment exactly. If we get the real vehicle trajectory data, it will be good degree to fit a simulation modifying car following theories or traffic volume set. These results will be the good foundation for CV environment and related technologies. Acknowledgements This research was supported by the National Research Foundation of Korea grant funded by the Korea government(MEST) (NRF-2015R1A2A2A04005646). 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