Optimal Deployment of Sensors along Freeway Corridors for Traffic Accident Detection
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 6
Abstract
Sensor deployment is important to the detection performance of traffic accidents on freeways. However, the temporally and spatially varying effects of accident risks are seldom considered in sensor deployment, which can induce a decrease in accident detection accuracy and detection timeliness. To address this problem, this paper proposes an optimal deployment method of sensors according to the temporal-spatial effect of accidents on traffic flow. Considering the uncertainty of traffic accidents, the temporal-spatial distribution of accident risk is estimated according to historical accident data. Then the kinematic wave model and Van Aerder model are introduced to analyze the temporal-spatial effect of accidents on traffic flow. To obtain the optimal locations of sensors, the sensor deployment problem is converted into a coverage problem on accident risk in time and space, and an optimal deployment model of sensors is built by considering the temporal-spatial effect of accident risk, the coverage rate on accident risk, the detection timeliness of the accident, and deployment cost. The particle swarm optimization algorithm is introduced to solve it. A case study is carried out to validate the proposed method using consistency, sensitivity, reliability, and comparison experiments. The results show that the proposed method is effective and reliable under different traffic volumes, and it outperforms the uniform spacing method in traffic accident detection.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Key Program of the National Natural Science Foundation of China (52131202), the Key Research and Development (R&D) Program of Jilin Province, China (20200403040SF), and the Graduate Innovation Fund of Jilin University (2022077).
References
Almeida, L. S., and F. Goerlandt. 2022. “An ant colony optimization approach to the multi-vehicle prize-collecting arc routing for connectivity problem.” Multimodal Transp. 1 (3): 100033. https://doi.org/10.1016/j.multra.2022.100033.
Almotahari, A., M. A. Yazici, S. Mudigonda, and C. Kamga. 2019. “Analysis of incident-induced capacity reductions for improved delay estimation.” J. Transp. Eng. Part A Syst. 145 (2): 04018083. https://doi.org/10.1061/JTEPBS.0000207.
Bertini, R. L., and D. J. Lovell. 2009. “Impacts of sensor spacing on accurate freeway travel time estimation for traveler information.” J. Intell. Transp. Syst. 13 (2): 97–110. https://doi.org/10.1080/15472450902858400.
Bil, M., R. Andrasik, and Z. Janoska. 2013. “Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation.” Accid. Anal. Prev. 55 (Jun): 265–273. https://doi.org/10.1016/j.aap.2013.03.003.
Cao, Q., Z. Li, Y. Ma, and H. Li. 2022. “Optimal layout of heterogeneous sensors for traffic accidents detection and prevention.” IEEE Trans. Instrum. Meas. 71 (Jul): 2512213. https://doi.org/10.1109/TIM.2022.3185663.
Chen, H., M. S. Dougherty, and H. R. Kirby. 2001. “The effects of detector spacing on traffic forecasting performance using neural networks.” Comput.-Aided Civ. Infrastruct. Eng. 16 (6): 422–430. https://doi.org/10.1111/0885-9507.00244.
Chung, Y., and W. W. Recker. 2015. “Frailty models for the estimation of spatiotemporally maximum congested impact information on freeway accidents.” IEEE Trans. Intell. Transp. Syst. 16 (4): 2104–2112. https://doi.org/10.1109/TITS.2015.2394798.
Danczyk, A., X. Di, and H. X. Liu. 2016. “A probabilistic optimization model for allocating freeway sensors.” Transp. Res. Part C Emerging Technol. 67 (Jun): 378–398. https://doi.org/10.1016/j.trc.2016.02.015.
Danczyk, A., and H. X. Liu. 2011. “A mixed-integer linear program for optimizing sensor locations along freeway corridors.” Transp. Res. Part B Methodol. 45 (1): 208–217. https://doi.org/10.1016/j.trb.2010.04.002.
Fei, W., G. H. Song, J. R. Zang, Y. Gao, J. Sun, and L. Yu. 2017. “Framework model for time-variant propagation speed and congestion boundary by incident on expressways.” IET Intell. Transp. Syst. 11 (1): 10–17. https://doi.org/10.1049/iet-its.2015.0222.
Feng, W., A. Y. Bigazzi, S. Kothuri, and R. L. Bertini. 2010. “Freeway sensor spacing and probe vehicle penetration impacts on travel time prediction and estimation accuracy.” Transp. Res. Rec. 2178 (1): 67–78. https://doi.org/10.3141/2178-08.
Fujito, I., R. Margiotta, W. M. Huang, and W. A. Perez. 2006. “Effect of sensor spacing on performance measure calculations.” Transp. Res. Rec. 1945 (1): 1–11. https://doi.org/10.1177/0361198106194500102.
Jin, X., D. Srinivasan, and R. L. Cheu. 2001. “Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks.” IEEE Trans. Neural Networks 12 (5): 1173–1187. https://doi.org/10.1109/72.950145.
Karim, A., and H. Adeli. 2002. “Comparison of fuzzy-wavelet radial basis function neural network freeway incident detection model with California algorithm.” J. Transp. Eng. 128 (1): 21–30. https://doi.org/10.1061/(ASCE)0733-947X(2002)128:1(21).
Katicha, S. W., J. Khoury, and G. Flintsch. 2021. “Spatial multiresolution analysis approach to identify crash hotspots and estimate crash risk.” J. Transp. Eng. Part A Syst. 147 (5): 04021019. https://doi.org/10.1061/JTEPBS.0000516.
Kianfar, J., and P. Edara. 2010. “Optimizing freeway traffic sensor locations by clustering global-positioning-system-derived speed patterns.” IEEE Trans. Intell. Transp. Syst. 11 (3): 738–747. https://doi.org/10.1109/TITS.2010.2051329.
Kim, J., B. Park, J. Lee, and J. Won. 2011. “Determining optimal sensor locations in freeway using genetic algorithm-based optimization.” Eng. Appl. Artif. Intell. 24 (2): 318–324. https://doi.org/10.1016/j.engappai.2010.10.020.
Koble, H. M., G. M. Anderson, and R. B. Goldblatt. 1979. Formulation of guidelines for locating freeway sensors. Washington, DC: Federal Highway Administration.
Lighthill, M. J., and G. B. Whitham. 1955. “On kinematic waves: II. A theory of traffic flow on long crowded roads.” Proc. R. Soc. London, Ser. A 229 (1178): 317–345. https://doi.org/10.1098/rspa.1955.0089.
Liu, H. X., and A. Danczyk. 2009. “Optimal sensor locations for freeway bottleneck identification.” Comput.-Aided Civ. Infrastruct. Eng. 24 (8): 535–550. https://doi.org/10.1111/j.1467-8667.2009.00614.x.
Liyanage, Y. W., D.-S. Zois, and C. Chelmis. 2022. “Near real-time freeway accident detection.” IEEE Trans. Intell. Transp. Syst. 23 (2): 1467–1478. https://doi.org/10.1109/TITS.2020.3027494.
Mercader, P., and J. Haddad. 2020. “Automatic incident detection on freeways based on Bluetooth traffic monitoring.” Accid. Anal. Prev. 146 (Oct): 105703. https://doi.org/10.1016/j.aap.2020.105703.
Pang, M. B., and J. Q. Huang. 2022. “Cooperative control of highway on-ramp with connected and automated vehicles as platoons based on improved variable time headway.” J. Transp. Eng. Part A Syst. 148 (7): 04022034. https://doi.org/10.1061/JTEPBS.0000683.
Parsa, A. B., H. Taghipour, S. Derrible, and A. Mohammadian. 2019. “Real-time accident detection: Coping with imbalanced data.” Accid. Anal. Prev. 129 (Aug): 202–210. https://doi.org/10.1016/j.aap.2019.05.014.
Rakha, H., and B. Crowther. 2002. “Comparison of greenshields, pipes, and Van Aerder car-following and traffic stream models.” Transp. Res. Rec. 1802 (1): 248–262. https://doi.org/10.3141/1802-28.
Ratnaweera, A., S. K. Halgamuge, and H. C. Watson. 2004. “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients.” IEEE Trans. Evol. Comput. 8 (3): 240–255. https://doi.org/10.1109/TEVC.2004.826071.
Seliman, S. M. S., A. W. Sadek, and Q. He. 2020. “Optimal variable, lane group-based speed limits a freeway lane drops: A multiobjective approach.” J. Transp. Eng. Part A Syst. 146 (8): 04020074. https://doi.org/10.1061/JTEPBS.0000395.
Sun, W., L. Shen, H. Shao, and P. Liu. 2021. “Dynamic location models of mobile sensors for travel time estimation on a freeway.” Int. J. Appl. Math. Comput. Sci. 31 (2): 271–287. https://doi.org/10.34768/amcs-2021-0019.
Teng, J., T. Chen, and W. Fan. 2020. “Integrated approach to vehicle scheduling and bus timetabling for an electric bus line.” J. Transp. Eng. Part A Syst. 146 (2): 0401907. https://doi.org/10.1061/JTEPBS.0000306.
Wang, D. S., D. P. Tan, and L. Liu. 2018a. “Particle swarm optimization algorithm: An overview.” Soft Comput. 22 (2): 387–408. https://doi.org/10.1007/s00500-016-2474-6.
Wang, J., C. Ju, Y. Gao, A. K. Sangaiah, and G.-J. Kim. 2018b. “A PSO based energy efficient coverage control algorithm for wireless sensor networks.” Comput. Mater. Continua 56 (3): 433–446. https://doi.org/10.3970/cmc.2018.04132.
Wang, Z., X. Qi, and H. Jiang. 2018c. “Estimating the spatiotemporal impact of traffic incidents: An integer programming approach consistent with the propagation of shockwaves.” Transp. Res. Part B Methodol. 111 (May): 356–369. https://doi.org/10.1016/j.trb.2018.02.014.
Wu, T., and X. Xu. 2022. “Modeling and optimization for carsharing services: A literature review.” Multimodal Transp. 1 (3): 100028. https://doi.org/10.1016/j.multra.2022.100028.
Xiong, Y., G. Chen, M. Lu, X. Wan, M. Wu, and J. She. 2020. “A two-phase lifetime-enhancing method for hybrid energy-harvesting wireless sensor network.” IEEE Sens. J. 20 (4): 1934–1946. https://doi.org/10.1109/JSEN.2019.2948620.
Yu, H., P. Liu, J. Chen, and H. Wang. 2014. “Comparative analysis of the spatial analysis methods for hotspot identification.” Accid. Anal. Prev. 66 (May): 80–88. https://doi.org/10.1016/j.aap.2014.01.017.
Zhan, F. P., and B. Ran. 2022. “Data accuracy oriented method for deploying fixed and mobile traffic sensors along a freeway.” IEEE Intell. Transp. Syst. Mag. 14 (1): 173–186. https://doi.org/10.1109/MITS.2019.2962151.
Zhan, F. P., B. Ran, X. Wan, and Y. Cheng. 2018. “Methods for multi-type sensor allocations along a freeway corridor.” IEEE Intell. Transp. Syst. Mag. 10 (2): 134–149. https://doi.org/10.1109/MITS.2018.2806639.
Zhan, F. P., J. Zhang, X. Wan, and B. Ran. 2017. “Method for allocating multitype sensors on a freeway corridor with existing sensors.” J. Transp. Eng. Part A Syst. 143 (11): 04017054. https://doi.org/10.1061/JTEPBS.0000090.
Zhou, W., Y. Yu, Y. Zhan, and C. Wang. 2022. “A vision-based abnormal trajectory detection framework for online traffic incident alert on freeways.” Neural Comput. Appl. 34 (17): 14945–14958. https://doi.org/10.1007/s00521-022-07335-w.
Zhu, J., I. Tasic, and X. Qu. 2022. “Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas.” Multimodal Transp. 1 (1): 100005. https://doi.org/10.1016/j.multra.2022.100005.
Zhu, N., S. Ma, and L. Zheng. 2017. “Travel time estimation oriented freeway sensor placement problem considering sensor failure.” J. Intell. Transp. Syst. 21 (1): 26–40. https://doi.org/10.1080/15472450.2016.1194206.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 6 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jun 27, 2022
Accepted: Jan 6, 2023
Published online: Apr 4, 2023
Published in print: Jun 1, 2023
Discussion open until: Sep 4, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Yiming Li, Zeyang Cheng, Xinpeng Yao, Zhiqiang Kong, Zijian Wang, Mengfei Liu, Multi-Objective Optimal Deployment of Road Traffic Monitoring Cameras: A Case Study in Wujiang, China, Sustainability, 10.3390/su151512011, 15, 15, (12011), (2023).