Classification of Indicators of Increase in Speed Variance Interrupted Traffic Flows (Suburban Roads)

Document Type : Original Article

Authors
Aliasghar Goharpour 1
Farshid Tavan 2
ALI Quba 3
1 Lecture, Department of Civil Engineering, Faculty of Civil Eng. and Architecture‌, Malayer University, Malayer, Iran.
2 M.Sc., Grad., Department of Civil Engineering, IAUEC‌, Tehran‌, Iran.
3 M.Sc., Grad., Department of Engineering, Negin Jiroft Int‌., Jirof‌t, Iran.
10.22034/road.2025.506978.2370
Abstract
Dynamic and safe transportation is considered one of the most essential aspects of sustainable development in today's societies, and reaching the highest level of traffic safety standards is the main management policy of this industry. Traffic flow parameters are one of the most important safety analysis criteria. One of the influencing factors on the safety level of traffic sections is the amount of variance of the speed of vehicles in that section, the control of which can play a significant role in reducing traffic accidents. This research aims to identify and categorize the effective factors in increasing this parameter according to scientific methods in the environment of multi-criteria analysis for the detection of interrupted traffic flow and the factors that aggravate the variance and speed variation and prioritize them with respect to highway sections. slow According to the results of this research, four factors: the number of accesses on the right, the number of level crossings, the percentage of heavy vehicles and the characteristic of the longitudinal slope of the road respectively have a greater effect on creating or intensifying the speed variance.
Keywords
Traffic Safety
Accident
Speed
Speed Variance

  1. -Asadamraji, M., Saffarzadeh, M. and Mirzaee Tayeghani, M. (2017). Modeling driver’s hazard perception using driver's personality characteristics. International Journal of Transportation Engineering, Vol. 5, No. 2, 167-182.

    - S. Naseralavi, M. Saffarzadeh, A. Mamdoohi, N. Nadimi, (2011). Simulation and its application on traffic study: vehicle pursuit model, 8(15), 59. magiran.com/p1149723.

    -Shahriar Afandizadeh­, Mohamadali Hasanzadeh, (2023). Optimization Model of Ramp Input Rate, Variable Speed Limit and Highway Tolls based on Reinforcement Learning, Road Journal, 31(3), 13-30. magiran.com/p2600102

    -Singh, S. (2015). Critical reasons for crashes investigated in the national motor vehicle crash causation survey (No. DOT HS 812 115)

    -Amiri, E., Faridzad, M., & Saffarzadeh, M. (2020). Determining the influence of Speed Cameras in Reducing Speed Violations using Bayesian and Comparison Group Methods (A Case Study in Ghazvin-Rasht and Bam-Kerman Roads). Quarterly Journal of Transportation Engineering, 11(3), 683-696.doi: 10.22119/jte.2020.81089

    -Song, Yongze, Xiangyu Wang, Graeme Wright, Dominique Thatcher, Peng Wu, and Pascal Felix. (2018). Traffic volume prediction with segment-based regression kriging and its implementation in assessing the impact of heavy vehicles. Ieee transactions on intelligent transportation systems 20, No. 1, 232- 243.

    -Cheng, Wei, Jiang-lin Li, Hai-Cheng Xiao, and Li-na Ji. (2022). Combination predicting model of traffic congestion index in weekdays based on LightGBM-GRU. Scientific Reports 12, No. 1, 1-13.

    -Qu, Licheng, et al. (2021). Features injected recurrent neural networks for short-term traffic speed prediction. Neurocomputing 451, 290-304.

    -­Bin, Mu, and Zhen Lin. (2022). GCN model combined with Bi-GRU for traffic prediction. In 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), SPIE, Vol. 12259, 613-618.

    - Liu, Zhaobin, and Satish Sharma. Statistical investigations of statutory holiday effects on traffic volumes, (2006). Transportation Research Record 1945, No. 1, 40-48.

    - Xu, Chengcheng, Yong Wang, Pan Liu, Wei Wang, and Jie Bao. (2018). Quantitative risk assessment of freeway crash casualty using high-resolution traffic data. Reliability Engineering & System Safety 169, 299- 311.

    -­M.Ghorbani, M.NooriAmiri, ­Road Safety Management, (2006)­. [E-book] Available: Ministry of Roads and Transportation Deputy of Education Research and Technology Tehran.

    -Buddemeyer­, j.­, (2010). Rural Variable Speed Limit System for Southeast Wyoming. Transportation Research Board­, 89th Annual Meeting­, January 10-14.

    -­Nilsson, G. (2004A). Traffic Safety Diemensions and the Power Model to Describe the Effect of Speed on Safety. Bulletin 221. Lund Institute of Technology, Department of Technology and Society, Traffic Engineering, Lund.

    1. Carlson, R.C. (2010).Variable Speed Limits as a Mainline Metering Device for Freeways. Transportation Research Board, 89th Annual Meeting, January 10-14, Washington, DC.

    - Rahim‌ov, K., Malek, M. R., & Aftabi Hossein, S. (2017). Analytical Models to Increase the Speed Limit on Freeways and Its Impact on Travel Safety Promotion. Road, 25(90), 31-46.

    - Mäkinen, T., Zaidel, D. M., Andersson, G., Biecheler-Fretel, M.B., Christ, R., Cauzard, J.P., Elvik, R., Goldenbeld, C., Gelau, C-Heidstra, J., Jayet, M.C., Nilsson, G., Papaioanou, P., Quimby, A., Rehnova, V. and Vaa, T. (2003). Traffic enforcement in Europe, effects, measures, needs and future, Final Report to the E.S.C.A.P.E consortium.

    -­Hauer, E. (2012). Speed and safety, Transportation Research Board.

    - Yang, X., Zou, Y., Tang, J., Liang, J., & Ijaz, M. (2020). Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models. Journal of Advanced Transportation.

    - Garber, NJ. Ravi G. (1989). Factors Affecting Speed Variance and Its Influence on Accidents.  Transportation Research Record