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International Journal of Intelligent Transportation Systems Research
Published in:

04-09-2021

Lane-Level Vehicle Counting Based on V2X and Centimeter-level Positioning at Urban Intersections

Authors: Jianchun Jiang, Yi Yang, Yuhuan Li, Rong Wang, Suhua Zeng

Published in: International Journal of Intelligent Transportation Systems Research | Issue 1/2022

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Abstract

Accurate vehicles counting for all-weather in cities are an important part of traffic management in the application of Intelligent Transportation Systems (ITS). Vehicle counting is currently collected with computer vision and sensor network methods. However, these methods require expensive hardware to achieve real-time and anti-interference capability, and do not provide lane-level vehicle information for ITS traffic management. This paper presents a lane-level vehicle counting system that is based on V2X communications and centimeter-level positioning technologies. This system can be used to traffic survey of ITS at a range of urban intersections. For realizing lane-level counting, a lane determination method is designed with on-board units (OBUs) in this paper. The lane is identified by matching the vehicle positioning information with road information from the roadside unit (RSU). The RSU collects the vehicle counting information from OBUs in different instances. The counting information includes the vehicle location data, the vehicle status data, and the vehicle number of each lane in the range of intersections. Verification and analysis were performed by a hardware-in-the-loop simulation platform. The results showed an average vehicle counting accuracy rate (99.60%). The system enabled the collection of real-time statistics with low-power consumption and low latency, providing accurate data to ITS.
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Literature
1.
Bao, X., Li, H., Xu, D., Jia, L., Ran, B., Rong, J.: Traffic vehicle counting in jam flow conditions using low-cost and energy-efficient wireless magnetic sensors. Sensors (Basel). 16(11), (2016)
2.
Zu, X., et al.: Vehicle counting and moving direction identification based on small-aperture microphone array. Sensors (Basel). 17(5), (2017)
3.
Chen, Z et al.: Roadside Sensor Based Vehicle Counting Incomplex Traffic Environment. 2019 IEEE Globecom Workshops (GC Wkshps) IEEE, (2020)
4.
Park, M.-W., In Kim, J., Lee, Y.-J., Park, J., Suh, W.: Vision-based surveillance system for monitoring traffic conditions. Multimed. Tools. Appl. 76(23), 25343–25367 (2017)CrossRef
5.
Velazquez-Pupo, R., et al.: Vehicle detection with occlusion handling, tracking, and OC-SVM classification: a high performance vision-based system. Sensors (Basel). 18(2), (2018)
6.
Barcellos, P., Bouvié, C., Escouto, F.L., Scharcanski, J.: A novel video based system for detecting and counting vehicles at user-defined virtual loops. Expert Syst. Appl. 42(4), 1845–1856 (2015)CrossRef
7.
Kuang, H., Yang, K.-F., Chen, L., Li, Y.-J., Chan, L.L.H., Yan, H.: Bayes saliency-based object proposal generator for nighttime traffic images. IEEE Trans. Intell. Transp. Syst. 19(3), 814–825 (2018)CrossRef
8.
Guo, J.-M., Hsia, C.-H., Wong, K., Wu, J.-Y., Wu, Y.-T., Wang, N.-J.: Nighttime vehicle lamp detection and tracking with adaptive mask training. IEEE Trans. Veh. Technol. 65(6), 4023–4032 (2016)CrossRef
9.
Yang, H., Qu, S.: Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition. IET Intell. Transp. Syst. (2017)
10.
11.
12.
Engel, J.I., Martin, J., Barco, R.: A low-complexity vision-based system for real-time traffic monitoring. IEEE Trans. Intell. Transp. Syst. 18(5), 1279–1288 (2017)CrossRef
13.
Rajput, H., Som, T., Kar, S.: "An automated vehicle license plate recognition system," (in English). Comp, Article. 48(8), 56–61 (2015)
14.
15.
Di, B., Song, L., Li, Y., Li, G.Y.: Non-orthogonal multiple access for high-reliable and low-latency V2X communications in 5G systems. IEEE J. Sel. Areas Commun. 35(10), 2383–2397 (2017)CrossRef
16.
17.
18.
He, X., Zhang, X., Tang, L., Liu, W.: Instantaneous real-time kinematic decimeter-level positioning with BeiDou triple-frequency signals over medium baselines. Sensors (Basel). 16(1), (2015)
19.
Li, X., Zhang, X., Ren, X., Fritsche, M., Wickert, J., Schuh, H.: Precise positioning with current multi-constellation Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and BeiDou. Sci. Rep. 5, 8328 (2015)CrossRef
20.
Chen, W., Hu, K., Li, E.: Low-cost land vehicle attitude determination using single-epoch GPS data, MEMS-based inclinometer measurements. Acta Geod. Geophys. 52(1), 111–129 (2017)CrossRef
21.
Poolsin, C., Sa-Ngiam, N., Sutthisangiam, N.: Development of Centimeter Level Positioning Mobile Based Application. 2021 23rd International Conference on Advanced Communication Technology (ICACT), pp. 63-67 (2021) 10.23919/ ICACT51234.2021.9370509
22.
Kong, H., Chen, W., Fu, S., Zheng, H., Du, L., Mao, Y.: OBU Design and Test Analysis with Centimeter-Level Positioning for LTE-V2X. 2019 5th International Conference on Transportation Information and Safety (ICTIS), pp. 383-387 (2019) 10.1109/ICTIS.2019.8883715
23.
Bila, C., Sivrikaya, F., Khan, M.A., Albayrak, S.: Vehicles of the future: a survey of research on safety issues. IEEE Trans. Intell. Transp. Syst. 18(5), 1046–1065 (2017)CrossRef
24.
25.
Gaikwad, V., Lokhande, S.: Lane departure identification for advanced driver assistance. IEEE Trans. Intell. Transp. Syst. 1–9 (2014)
26.
Knoop, V.L., de Bakker, P.F., Tiberius, C.C.J.M., van Arem, B.: Lane determination with GPS precise point positioning. IEEE Trans. Intell. Transp. Syst. 18(9), 2503–2513 (2017)CrossRef
27.
Gwon, G.-P., Hur, W.-S., Kim, S.-W., Seo, S.-W.: Generation of a precise and efficient lane-level road map for intelligent vehicle systems. IEEE Trans. Veh. Technol. 66(6), 4517–4533 (2017)CrossRef
28.
Dashtinezhad, S., Nadeem, T., Dorohonceanu, B., Borcea, C., Kang, P., Iftode, L.: TrafficView: a driver assistant device for traffic monitoring based on car-to-car communication. In 59th IEEE Veh. Technol. Conf. (VTC), vol. 5, pp. 2946-2950 (2004)
29.
Bauza, R., Gozalvez, J.: Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications. J. Netw. Comput. Appl. 36(5), 1295–1307 (2013)CrossRef
30.
Sanguesa, J.A., et al.: Sensing traffic density combining V2V and V2I wireless communications. Sensors (Basel). 15(12), 31794–31810 (2015)CrossRef
31.
Cardenas-Benitez, N., Aquino-Santos, R., Magana-Espinoza, P., Aguilar-Velazco, J., Edwards-Block, A., Medina Cass, A.: Traffic congestion detection system through connected vehicles and big data. Sensors (Basel). 16(5), (2016)
32.
Florin, R., Olariu, S.: Towards real-time density estimation using vehicle-to-vehicle communications. Transp. Res. B Methodol. 138, 435–456 (2020)CrossRef
33.
34.
Chopde, N.R., Nichat, M.K.: Landmark based shortest path detection by using A* and Haversine formula. Int. J. Innov. Res. Comput. Commun. Eng. 1(2), 298–302 (2013)
35.
Roess, R.P., Prassas, E.S., McShane, W.R.: Traffic engineering, 4th edn. Prentice Hall (2011)
Metadata
Title
Lane-Level Vehicle Counting Based on V2X and Centimeter-level Positioning at Urban Intersections
Authors
Jianchun Jiang
Yi Yang
Yuhuan Li
Rong Wang
Suhua Zeng
Publication date
04-09-2021
Publisher
Springer US
Published in
International Journal of Intelligent Transportation Systems Research / Issue 1/2022
Print ISSN: 1348-8503
Electronic ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-021-00271-4

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Trip Purpose Inference Based on the Relationship between Route Search Records and Regional Characteristics

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Control method to improve posture stability of narrow tilting vehicles

Platoon-Based Impact Assessment of Heavy-Duty Vehicles on Traffic Stream Characteristics of Highway Lanes Under Mixed Traffic Environment

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