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Tsinghua Science and Technology  2019, Vol. 24 Issue (2): 160-170    doi: 10.26599/TST.2018.9010073
    
A Simulation System and Speed Guidance Algorithms for Intersection Traffic Control Using Connected Vehicle Technology
Shuai Liu, Weitong Zhang, Xiaojun Wu, Shuo Feng, Xin Pei*, Danya Yao
∙ Shuai Liu, Weitong Zhang, Shuo Feng, Xin Pei, and Danya Yao are with Department of Automation, Tsinghua University National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing 100084, China. E-mail: liushuaicc11326@126.com; zwt15@ mails.tsinghua.edu.cn; fengshuo10@163.com; yaody@tsinghua.edu.cn.
∙ Xiaojun Wu is with Graduate School of Tsinghua University, Beijing 100084, China. E-mail: wuxiaojun@tsinghua.edu.cn.
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Abstract  

In the connected vehicle environment, real-time vehicle-state data can be obtained through vehicle-to-infrastructure communication, and the prediction accuracy of urban traffic conditions can significantly increase. This study uses the C++/Qt programming language and framework to build a simulation platform. A two-way six-lane intersection is set up on the simulation platform. In addition, two speed guidance algorithms based on optimizing the travel time of a single vehicle or multiple vehicles are proposed. The goal of optimization is to minimize the travel time, with common indicators such as average delay of vehicles, average number of stops, and average stop time chosen as indexes of traffic efficiency. When the traffic flow is not saturated, compared with the case of no speed guidance, single-vehicle speed guidance can improve the traffic efficiency by 20%, whereas multi-vehicle speed guidance can improve the traffic efficiency by 50%. When the traffic flow is saturated, the speed guidance algorithms show outstanding performance. The effect of speed guidance gradually enhances with increasing penetration rate, and the most obvious gains are obtained when the penetration rate increases from 10% to 40%. Thus, this study has shown that speed guidance in the connected vehicle environment can significantly improve the traffic efficiency of intersections, and the multi-vehicle speed guidance strategy is more effective than the single-vehicle speed guidance strategy.



Key wordsconnected vehicle      intersection traffic control      simulation system      speed guidance     
Received: 17 May 2017      Published: 29 April 2019
Corresponding Authors: Xin Pei   
About author:

Shuo Feng received the BEng degree from Tsinghua University, China, in 2014. He is currently a PhD candidate in Department of Automation, Tsinghua University, China. He also researches as a joint PhD student in civil and environmental engineering in University of Michigan, Ann Arbor. His current research interests include optimal control, connected and automated vehicle evaluation, and transportation data analysis.

Cite this article:

Shuai Liu, Weitong Zhang, Xiaojun Wu, Shuo Feng, Xin Pei, Danya Yao. A Simulation System and Speed Guidance Algorithms for Intersection Traffic Control Using Connected Vehicle Technology. Tsinghua Science and Technology, 2019, 24(2): 160-170.

URL:

http://tst.tsinghuajournals.com/10.26599/TST.2018.9010073     OR     http://tst.tsinghuajournals.com/Y2019/V24/I2/160

VariableDefinition
iSystem state, including the traffic condition and the controlled variables
qTraffic condition, including the states of all the vehicles in the current system
rFunctional parameters
sControlled variables, including the guided speed and the information of the traffic light (Boolean variable, with 0 representing red light, and 1 representing green light)
zVector of the vehicle state, including three dimensions of current speed, location, and waiting time
aAcceleration (assumed constant, positive when speeding up, and negative when slowing down)
lDistance from the current position to the stop line
xTravel time (the time interval between the current moment and the moment leaving the stop line)
vGuided speed
v0Current speed
wTotal waiting time in the waiting area
αDiscount factor
J?(i)Real value of the optimization function
g?()One-step cost function
Table 1 Definition of main variables.
Fig. 1 Framework of the traffic control simulation system under the connected vehicle environment.
Fig. 2 Signal preset interface of the intersection traffic control simulation system in the connected vehicle environment.
Fig. 3 Operation interface of the intersection traffic control simulation system in the connected vehicle environment.
Traffic volume (vehicles/h)Average delay of vehicles (s)
Without speed guidanceSingle-vehicle speed guidanceMulti-vehicle cooperative speed guidance
30018.1314.5612.60
60020.0816.3914.55
90021.7318.0716.05
120024.1219.8517.38
150025.8121.2118.78
180028.5623.6020.23
210034.3628.1321.87
2400106.0140.1723.48
2700210.84109.5825.18
Table 2 Average delay of three speed guidance strategies under different traffic volumes.
Fig. 4 Comparison of the average delay of three speed guidance strategies under different traffic volumes.
Traffic volume (vehicles/h)Average number of stops
Without speed guidanceSingle-vehicle speed guidanceMulti-vehicle cooperative speed guidance
3000.540.350.18
6000.550.360.19
9000.560.370.21
12000.580.380.21
15000.570.390.19
18000.580.390.19
21000.630.440.19
24001.490.550.20
27003.001.390.19
Table 3 Average number of stops of three speed guidance strategies under different traffic volumes.
Fig. 5 Comparison of average number of stops of three speed guidance strategies under different traffic volumes.
Traffic volume (vehicles/h)Average stop time (s)
Without speed guidanceSingle-vehicle speed guidanceMulti-vehicle cooperative speed guidance
30010.458.215.80
60011.178.635.88
90011.699.125.99
120012.099.756.07
150012.3510.176.05
180013.1011.026.07
210015.6613.146.20
240054.6120.496.74
2700122.3571.226.90
Table 4 Average stop time of three speed guidance strategies under different traffic volumes.
Fig. 6 Comparison of the average stop time of three speed guidance strategies under different traffic volumes.
Penetration rate (%)Average delay of vehicles (s)
Single-vehicle speed guidanceMulti-vehicle cooperative speed guidance
042.5042.64
1039.8739.83
2036.0134.94
3034.6931.40
4032.1626.39
5031.4625.87
6031.3124.72
7031.3224.39
8030.2823.83
9029.6923.08
10028.1922.60
Table 5 Average delay of two speed guidance strategies under different penetration rates.
Fig. 7 Effect of penetration rates on average delay (a volume of 2160 vehicles/h per direction).
Fig. 8 Box plot of the average delay of three speed guidance strategies.
[1]   Li L. and Wang F. Y., Cooperative driving at blind crossings using intervehicle communication, IEEE Transaction on Vehicular Technology, vol. 55, no. 6, pp.1712-1724, 2006.
[2]   Li L. and Yao D. Y., A survey of traffic control with vehicular communications, IEEE Transaction on Intelligent Transportation Systems, vol. 15, no. 1, pp. 425-432, 2014.
[3]   U. S. Department of Transportation, , 2017.
[4]   Mohammad N. and Hossein P., The effect of VII market penetration on safety and efficiency of transportation networks, in Proceedings of International Conference on Communications Workshops, 2009.
[5]   Yao J., Fan H. Y., Han Y., and Cui L., Adaptive control at intersection in urban area based on probe data, Journal of University of Shanghai for Science and Technology, vol. 36, no. 3, pp. 239-244, 2014.
[6]   Booz A. H., Vehicle infrastructure integration (VII) proof-of-concept (POC) test—An overview, in 2008 ITS VA Annual Conference, 2008.
[7]   Nekoui M., Development of a VII-enabled prototype intersection collision warning system, International Journal of Internet Protocol Technology, vol. 4, no. 3, pp. 173-181, 2009.
[8]   Malakorn K. J. and Park B. B., Assessment of mobility, energy, and environment impacts of intellidrive-based cooperative adaptive cruise control and intelligent traffic signal control, in Proceedings of the 2010 IEEE International Symposium on Sustainable Systems and Technology, 2010, pp. 1-6.
[9]   Abu-Lebdeh G., Exploring the potential benefits of intellidrive-enabled dynamic speed control in signalized networks, in TRB 2010 Annual Meeting, 2010.
[10]   Yang Y., Chen S., and Sun J., Modeling and evaluation of speed guidance strategy in VII system, in 13th International IEEE Annual Conference on Intelligent Transportation Systems, 2010, pp. 1045-1050.
[11]   Chen S., Sun J., and Yao J., Development and simulation application of a dynamic speed dynamic signal strategy for arterial traffic management, in 14th International IEEE Annual Conference on Intelligent Transportation Systems, 2011, pp. 1349-1354.
[12]   He Q., Robust-intelligent traffic signal control within a vehicle-to-infrastructure and vehicle-to-vehicle communication environment, PhD dissertation, The University of Arizona, Tucson, AZ, USA, 2010.
[13]   Lee J. and Park B., Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment, IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 1, pp. 81-90, 2012.
[14]   Jackline R. T. and Andreas A. M., Automated and cooperative vehicle merging at highway on-ramps, IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 4, pp. 780-789, 2017.
[15]   Taylor J. G., Hierarchy-of-models approach for aggregation-force attrition, in Proceedings of the Winter Simulation Conference, 2000, pp. 924-929.
[16]   Chen X. and Yao D., An empirically comparative analysis of 802.11 n and 802.11 p performancesin CVIS, in 2012 12th International Conference on ITS Telecommunications (ITST), 2012, pp. 848-851.
[17]   Chen X., Li H., Yang C., and Yao D., An empirical analysis of V2I communication in vehicular Ad-Hoc network based on IEEE 802.11n, in 19th ITS World Congress, 2012.
[18]   Chen X., Yao D., Zhang Y., Su Y., and Zhang Y., Design and implementation of cooperative vehicle and infrastructure system based on IEEE 802.11n, Transportation Research Record: Journal of the Transportation Research Board, vol. 2243, no. 1, pp. 158-166, 2011.
[19]   Leutzbach W. and Wiedemann R., Development and applications of traffic simulation models at the Karlsruhe Institut für Verkehrswesen, Traffic Engineering and Control, vol. 27, no. 5, pp. 270-278, 1986.
[20]   China State Council, Regulation on the Implementation of the Road Traffic Safety Law of the People’s Republic of China (in Chinese), 2004.
[21]   Guler S. I., Menendez M., and Meier L., Using connected vehicle technology to improve the efficiency of intersections, Transportation Research Part C: Emerging Technologies, vol. 46, pp. 121-131, 2014.
[1] . Development of the Driving Simulation System MOVIC-T4 and Its Validation Using Field Driving Data[J]. Tsinghua Science and Technology, 2007, 12(2): 141-150.