International Journal of Transportation Engineering

International Journal of Transportation Engineering

Designing and Developing a Model for Detection of Unusual Traffic Condition at Intelligent Signalized Intersection Equipped with SCATS

Document Type : Research Paper

Authors
Mani Hazeghi 1
Mahmoud Saffarzadeh 2
Babak Mirbaha 3
1 Dept. of Civil Engineering Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 Dept. of Civil & Environmental Engineering, Tarbiat Modares University, Tehran, Iran
3 ept. of Civil Engineering Faculty of Imam Khomeyni, Ghazvin, Iran
10.22119/ijte.2023.380114.1625
Abstract
Today, one of the most significant points of interference in streets is the signalized intersections; therefore, solving problems of traffic at intersections can increase the capacity of urban transportation. Inability to diagnose the traffic conditions results in the lack of proper timing, phasing and cycle length, all of which are attributed to the abnormal factors concerning intelligent control systems. In this paper, in addition to the introduction of abnormal traffic conditions at signalized intersections, an attempt has been made to intelligently diagnose anomalies for both an approach and its entire intersection. For this purpose, by making use of the data based on the GPS of users' cell phones extracted from NESHAN Application, which consists of 10-minute average speed in streets ending to an intersection, and by behavioral matching with the data concerning the volume and saturation rate in SCATS, and meantime, by analyzing the fundamental traffic relations, an attempt has been made to diagnose the abnormal traffic conditions through SCATS at Toos-Danesh intersection of Mashhad, in which abnormal conditions including the detecting of heavy traffic conditions when the traffic is light and vice versa. To achieve more accuracy, the method was built based on both quartiles and percentiles of DS, degree of saturation, and ADS, average degree of saturation, in SCATS. Finally, anomaly detection based on the 10th and 90th percentiles had 100 percent accuracy and the one based on the 1st and 3rd quartiles had between 57 to 80 percent accuracy, which have been checked by two real datasets.
Keywords
Anomaly
Degree of saturation
Fundamental Traffic relations
SCATS
Signalized intersection
- Chen, X., Zhang, S., & Li, L. (2019). Multi‐model ensemble for short‐term traffic flow prediction under normal and abnormal conditions. IET Intelligent Transport Systems, 13(2), 260-268.
 
- Chen, Y. C., Liu, S. C., Chen, B. X., Loh, C. H., & Ying, J. J. C. (2020, December). Ensembling-mRBF-LSTM framework for prediction of abnormal traffic flows. In 2020 International Conference on Pervasive Artificial Intelligence (ICPAI) (pp. 206-213). IEEE.
 
- Dakic, Igor; Stevanovic, Aleksandar (2017) “On development of arterial fundamental diagrams based on surrogate density measures from adaptive traffic control systems utilizing stop-line detection”, Transportation Research Procedia 23, 942-961.
 
- G.Polson, Nicholas; O, Sokolov and Vadim (2017) “Deep learning for Short-term traffic flow Prediction”, Transportation Research Part C 79, 1-17, 25 February 2017.
 
- Ghosh, Bidisha; P.Smith and Damien. “Customization of Automatic Detection Algorithms for Signalized Urban Arterials”, Journal of Intelligent Transportation Systems, Volume 18, Issue 4, July 2014.
 
- Guo F. Polak J. Krishnan R.: ‘ Comparison of modelling approaches for short term traffic prediction under normal and abnormal conditions’. Proc. of the 13th Int. IEEE Annual Conf. on Intelligent Transportation Systems, Madeira Island, Portugal, September 2010, pp. 1209– 1214.
 
- Jiang, Shang; Ekbatani, Keyvan; Ngoduy, Dong (2022) “Partitioning of urban networks with polycentric congestion pattern for traffic management policies: identifying protected networks” Computer-Aided civil and infrastructure engineering, July 2022.
 
- Ken, Michael; Hompson, Keith; Newman, Peter; Hargrovs, Charlie; Stantic, Bela; Weeratunga, Kamal; Haque, Jannatun and Thomas, Kim(2017) ” Mining the Datasphere: Big Data, Technologies and Transportation” Disaster Management, Version 1, January 2017.
 
- Kinane, Dermot; Hofmann, Dr.markus (2018) “Insights into Unusual Traffic Events in Dublin City, Ireland: Data Mining Delivering Business Intelligence” Civil engineering Conference, Dublin, Ireland, October 2018.
 
- Mihaita, Adriana-Simona; Haowen Li and Marian-Andrei Rizoiu (2020) "Traffic congestion anomaly detection and prediction using deep learning." arXiv: 2006.13215v1 Jun 2020.
 
- Piyapong, Suwanno(2021) “Study on Evaluation of Traffic Management Measures Using Macroscopic Fundamental Diagram (MFD) under Flooding Situation” Transportation Systems Engineering Major Graduate School of Science and Technology, Doctoral Course, Nihon University, January 2021.
 
- Ranaweera, Malith; A.Seneviratne, Rey,David; Saberi,Meead; V.Dixit, Vinayak(2021)” Detection of anomalous vehicles using physics of traffic” Vehicular Communications, Volume 27,100304, January 2021.
 
- SCATS Unusual Congestion Monitor, 6.5.1 user manual (2006), RTA-TC-335, Issue B, November 2006.
 
- Shafiei, Sajjad; Mihaita, Adriana-Simona; Nguyen, Hoang; Cai,Chen(2022) “Integrating data-driven and simulation models to predict traffic state affected by road incidents” , Transportation Letters, Volume 14, Issue 6, pages 629-639, August 2022.
 
- McMillan, Susan; Koorey, Glen and Dr.Alan Nicholson (2010)” Rechniques for using SCATS as an Incident Management Tool”, IPENZ Transportation Group Technical Conference, Christchurch, Newzeland, March 2010.
 
- Wu T. Xie K. Dong X. et al.: ‘ An online boosting approach for traffic flow forecasting under abnormal conditions’. The 9th Int. Conf. on Fuzzy Systems and Knowledge Discovery, Sichuan, China, May 2012, pp. 2555– 2559.
 
- Yi, Yu; Yanlei, Cui; Jiaqi, Zeng;  Chunguang, He; Dihanhai, Wang(2022) “Identifying traffic clusters in urban networks based on graph theory using license plate recognition data” Physica A: Statistical Mechanics and its applizations , Volume 591, April 2022.
 
- Zheng, Lulu; Chen, Jiarui; Jianho, Wang; He, Jiamin; Hu, Yujing; Chen, Yingfeng; Fan, Changjie; Gao, Yang; Zhang, Chongjie(2021) “Episodic Multi-agent Reinforcement Learning With Curiosity-driven Exploration” arXiv:2111.11032v1 [CS.LG] 22 Nov 2021.
International Journal of Transportation Engineering
Volume 11, Issue 4 - Serial Number 44
Spring 2024
Pages 1595-1616