|
|
A V2I communication-based pipeline model for adaptive urban traffic light scheduling |
Libing WU1,2(), Lei NIE2,3, Samee U. KHAN4, Osman KHALID5, Dan WU6 |
1. School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China 2. School of Computer Science, Wuhan University, Wuhan 430072, China 3. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China 4. Department of Electrical and Computer Engineering, North Dakota State University, Fargo, USA 5. COMSATS Institute of Information Technology, Islamabad, Pakistan 6. School of Computer Science, University of Windsor, Windsor, Ontario, Canada |
|
|
Abstract Adaptive traffic light scheduling based on realtime traffic information processing has proven effective for urban traffic congestion management. However, fine-grained information regarding individual vehicles is difficult to acquire through traditional data collection techniques and its accuracy cannot be guaranteed because of congestion and harsh environments. In this study, we first build a pipeline model based on vehicle-to-infrastructure communication, which is a salient technique in vehicular adhoc networks. This model enables the acquisition of fine-grained and accurate traffic information in real time via message exchange between vehicles and roadside units. We then propose an intelligent traffic light scheduling method (ITLM) based on a “demand assignment” principle by considering the types and turning intentions of vehicles. In the context of this principle, a signal phase with more vehicles will be assigned a longer green time. Furthermore, a green-way traffic light scheduling method (GTLM) is investigated for special vehicles (e.g., ambulances and fire engines) in emergency scenarios. Signal states will be adjusted or maintained by the traffic light control system to keep special vehicles moving along smoothly. Comparative experiments demonstrate that the ITLM reduces average wait time by 34%–78% and average stop frequency by 12%–34% in the context of traffic management. The GTLM reduces travel time by 22%–44% and 30%–55% under two types of traffic conditions and achieves optimal performance in congested scenarios.
|
Keywords
traffic light scheduling
vehicular ad hoc networks
pipeline model
vehicle-to-infrastructure communication
intersection
|
Corresponding Author(s):
Libing WU
|
Just Accepted Date: 13 June 2017
Online First Date: 06 August 2018
Issue Date: 25 June 2019
|
|
1 |
D B Zhao, Y J Dai, Z Zhang. Computational intelligence in urban traffic signal control: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2012, 42(4): 485–494
https://doi.org/10.1109/TSMCC.2011.2161577
|
2 |
S R E Datondji, Y Dupuis, P Subirats, P Vasseur. A survey of visionbased traffic monitoring of road intersections. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10): 2681–2698
https://doi.org/10.1109/TITS.2016.2530146
|
3 |
Y Tang, C Z Zhang, R S Gu, P Li , B Yang. Vehicle detection and recognition for intelligent traffic surveillance system. Multimedia Tools and Applications, 2017, 76(4): 5817–5832
https://doi.org/10.1007/s11042-015-2520-x
|
4 |
Y Li, F Y Wang. Vehicle detection based on and-or graph and hybrid image templates for complex urban traffic conditions. Transportation Research Part C: Emerging Technologies, 2015, 51(2): 19–28
https://doi.org/10.1016/j.trc.2014.10.009
|
5 |
M L Yang, Y M Bie, Y L Pei. A traffic signal control algorithm for an oversaturated isolated intersection based on video detection data. In: Proceedings of the 15th COTA International Conference of Transportation Professionals. 2015, 2018–2029
https://doi.org/10.1061/9780784479292.188
|
6 |
M R Islam, N I Shahid, D T U Karim, A A Mamun, M K Rhaman. An efficient algorithm for detecting traffic congestion and a framework for smart traffic control system. In: Proceedings of the 18th International Conference on Advanced Communication Technology (ICACT). 2016, 802–807
|
7 |
K Nellore, G P Hancke. A survey on urban traffic management system using wireless sensor networks. Sensors, 2016, 16(2): 1–25
https://doi.org/10.3390/s16020157
|
8 |
M Collotta, L L Bello, G Pau. A novel approach for dynamic traffic lights management based on wireless sensor networks and multiple fuzzy logic controllers. Expert Systems with Applications, 2015, 42(13): 5403–5415
https://doi.org/10.1016/j.eswa.2015.02.011
|
9 |
B Yang, Y Q Lei. Vehicle detection and classification for low-speed congested traffic with anisotropic magnetoresistive sensor. IEEE Sensors Journal, 2015, 15(2): 1132–1138
https://doi.org/10.1109/JSEN.2014.2359014
|
10 |
L Bhaskar, A Sahai, D Sinha, G Varshney, T Jain. Intelligent traffic light controller using inductive loops for vehicle detection. In: Proceedings of the 1st International Conference on Next Generation Computing Technologies (NGCT). 2015, 518–522
https://doi.org/10.1109/NGCT.2015.7375173
|
11 |
J J Fernández-Lozano, M Martín-Guzmán, J Martín-Ávila, A García-Cerezo. A wireless sensor network for urban traffic characterization and trend monitoring. Sensors, 2015, 15(10): 26143–26169
https://doi.org/10.3390/s151026143
|
12 |
T Darwish, K A Bakar. Traffic density estimation in vehicular ad hoc networks: a review. Ad Hoc Networks, 2015, 24(PA): 337–351
|
13 |
J A Sanguesa, J Barrachina, M Fogue, P, Garrido F J Martinez, J C Cano, C T Calafate, P Manzoni. Sensing traffic density combining V2V and V2I wireless communications. Sensors, 2015, 15(12): 31794–31810
https://doi.org/10.3390/s151229889
|
14 |
J Barrachina, P Garrido, M Fogue, F J Martinez, J C Cano, C T Calafate, P Manzoni. A V2I-based real-time traffic density estimation system in urban scenarios. Wireless Personal Communications, 2015, 83(1): 259–280
https://doi.org/10.1007/s11277-015-2392-4
|
15 |
K Tiaprasert, Y Zhang, X B Wang, X Zeng. Queue length estimation using connected vehicle technology for adaptive signal control. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4): 2129–2140
https://doi.org/10.1109/TITS.2015.2401007
|
16 |
M B Younes, A Boukerche. Intelligent traffic light controlling algorithms using vehicular networks. IEEE Transactions on Vehicular Technology, 2016, 65(8): 5887–5899
https://doi.org/10.1109/TVT.2015.2472367
|
17 |
Y H Feng, K L Head, S Khoshmagham, M Zamanipour. A real-time adaptive signal control in a connected vehicle environment. Transportation Research Part C: Emerging Technologies. 2015, 55: 460–473
https://doi.org/10.1016/j.trc.2015.01.007
|
18 |
W H Lee, Y C Lai, P Y Chen. A study on energy saving and emission reduction on signal countdown extension by vehicular ad hoc networks. IEEE Transactions on Vehicular Technology, 2015, 64(3): 890–900
https://doi.org/10.1109/TVT.2014.2305761
|
19 |
D Jiang, L Delgrossi. IEEE 802.11p: towards an international standard for wireless access in vehicular environments. In: Proceedings of the 68th IEEE Vehicular Technology Conference. 2008, 2036–2040
|
20 |
D Wu, Y R Bi, J Liang. Cooperative downloading by multivehicles in urban VANET. International Journal of Distributed Sensor Networks, 2014, 10(2): 319514
https://doi.org/10.1155/2014/319514
|
21 |
K Ota, M X Dong, S Chang, H Z Zhu. MMCD: cooperative downloading for highway VANETs. IEEE Transactions on Emerging Topics in Computing, 2015, 3(1): 34–43
https://doi.org/10.1109/TETC.2014.2371245
|
22 |
J H Lim, W Kim, K Naito, J H Yun, D Cabric, M Gerla. Interplay between TVWS and DSRC: optimal strategy for safety message dissemination in VANET. IEEE Journal on Selected Areas in Communications, 2014, 32(11): 2117–2133
https://doi.org/10.1109/JSAC.2014.1411RP02
|
23 |
B Y Liu, D Y Jia, J P Wang, K J Lu, L B Wu. Cloud-assisted safety message dissemination in VANET-cellular heterogeneous wireless network. IEEE Systems Journal, 2017, 11(1): 129–139
https://doi.org/10.1109/JSYST.2015.2451156
|
24 |
R Wunderlich, C B Liu, I Elhanany, T Urbanik. A novel signal scheduling algorithm with quality-of-service provisioning for an isolated intersection. IEEE Transactions on Intelligent Transportation Systems, 2008, 9(3): 536–547
https://doi.org/10.1109/TITS.2008.928266
|
25 |
L Wang, K Pan, W W Guo, X M Liu, D Wu. Two-way green wave optimization control method of artery based on partitioned model. Advances in Mechanical Engineering, 2016, 8(2): 1–8
https://doi.org/10.1177/1687814016629346
|
26 |
C Sommer, R German, F Dressler. Bidirectionally coupled network and road traffic simulation for improved IVC analysis. IEEE Transactions on Mobile Computing, 2011, 10(1): 3–15
https://doi.org/10.1109/TMC.2010.133
|
27 |
A Varga , R Hornig. An overview of the OMNeT++ simulation environment. In: Proceedings of the 1st International Conference on Simulation Tools and Techniques for Communications, Networks and Systems & Workshops. 2008, 183–202
https://doi.org/10.4108/ICST.SIMUTOOLS2008.3027
|
28 |
D Krajzewicz, J Erdmann, M Behrisch. Recent development and applications of SUMO-simulation of urban mobility. International Journal on Advances in Systems and Measurements, 2012, 5(3&4): 128–138
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|