CONNECTED VEHICLES VERSUS CONVENTIONAL TRAFFIC CONGESTION MITIGATION MEASURES: AN OPERATIONAL ECONOMIC ANALYSIS
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Volume 4 (2), December 2021, Pages 155-169
Mutasem Alzoubaidi1, Adli Al-Balbissi2, Abdel Rahman Alzoubaidi3, Amr Alzoubaidi4, Baha Azzeh5, Ahmed Al-Mansour6, Ahmed Farid7
This paper conducted an operational, economic analysis to assess alternative solutions to traffic congestion. They involved integrating adaptive traffic signal control (ATSC) with connected vehicle technology (ATSC-CV) and the application of various conventional and unconventional solutions. The studied conventional scenarios include signal timing optimization, signal actuation, and upgrading existing intersections to interchanges. There were unconventional scenarios involving converting two intersections to interchanges and the third to a continuous green-T intersection (CGTI). Different unconventional alternatives involved deploying ATSC-CV-based systems assuming varying market penetration rates (MPRs). The operational performance of each alternative was analyzed using VISSIM microsimulation software. To model the driving behavior of CVs, Python programming language was used through the COM interface in VISSIM. One-way analysis of variance (ANOVA) and post-hoc testing results indicate that implementing any suggested alternative would substantially decrease the mean vehicular travel time compared to the fixed signal control strategy currently implemented. Specifically, the ATSC-CV-based systems yielded notable travel time reductions ranging from 9.5% to 21.3%. Also, ANOVA results revealed that the highest benefit-to-cost ratio among all alternatives belonged to scenarios in which the MPRs of CVs were 100%. It was also found that ATSC-CV-based systems with MPRs of 25% and 50% would be as feasible as converting signalized intersections to underpass interchanges.
Connected vehicles, Adaptive traffic signal control, Traffic operations, Benefit-to-Cost ratio, Microsimulation, VISSIM.
Agbolosu-Amison, S. J., Park, B., & Center, M. A. U. T. (2008). Performance evaluation of dynamic gap-out feature using stochastic optimization method and software in the loop simulation (No. UVACTS-14-5-127). Mid-Atlantic Universities Transportation Center.
Aljebory, K. M., Matarneh, S., & Hiyassat, M. (2019). Statistical Analysis of Factors Causing Cost Overrun in Construction Industry (Case Study: Jordanian Construction). Al-Qalam journal, 3(5).
Alzoubaidi, A. R. (2016). Cloud computing national e-health services: data center solution architecture. International Journal of Computer Science and Network Security (IJCSNS), 16(9), 1.
Alzoubaidi, A. R. (2016). Private-Cloud Computing Services for an Interactive Multi-Campus University. International Journal of Interactive Mobile Technologies, 10(4).
Alzoubaidi, A., M. Alzoubaidi, et al. (2021). Virtual Desktop Infrastructure in Higher Education Institution: An Application of Home and Mobile Computing Environment. Azerbaijan Journal of High Performance Computing, 4(1), 29–38. https://doi.org/10.32010/26166127.2021.4.1.29.38.
Alzoubaidi, M., Molan, A. M., & Ksaibati, K. (2021). Comparing the efficiency of the super diverging diamond interchange to other innovative interchanges. Simulation Modelling Practice and Theory, 106, 102174.
American Association of State Highway and Transportation Officials (AASHTO). (2020). LRFD bridge design specifications. American Association of State Highway and Transportation Officials (AASHTO).
Autodesk Inc. (2018) Autodesk AutoCAD Civil 3D, Autodesk Inc., San Rafael, California.
Bagheri, E. (2017). Enhanced Traffic Signal Operation using Connected Vehicle Data. Waterloo, Ontario, Canada
Bretherton, D., Wood, K., & Raha, N. (1998). Traffic monitoring and congestion management in the scoot urban traffic control system. Transportation research record, 1634(1), 118-122.
Feng, Y., Head, K. L., Khoshmagham, S., & Zamanipour, M. (2015). A real-time adaptive signal control in a connected vehicle environment. Transportation Research Part C: Emerging Technologies, 55, 460-473.
Gettman, D., Pu, L., Sayed, T., Shelby, S. G., & Energy, S. (2008). Surrogate safety assessment model. https://highways.dot.gov/research/safety/ssam/surrogate-safety-assessment-model-overview
Gharaibeh, A. A., Zu’bi, A., Esra’a, M., & Abuhassan, L. B. (2019). Amman (City of Waters); Policy, land use, and character changes. Land, 8(12), 195.
Gradinescu, V., Gorgorin, C., Diaconescu, R., Cristea, V., & Iftode, L. (2007, April). Adaptive traffic lights using car-to-car communication. In 2007 IEEE 65th vehicular technology conference-VTC2007-Spring (pp. 21-25). IEEE.
Hu, J., Fontaine, M. D., Park, B. B., & Ma, J. (2016). Field evaluations of an adaptive traffic signal—using private-sector probe data. Journal of Transportation Engineering, 142(1), 04015033.
Hughes, W., Jagannathan, R., Sengupta, D., & Hummer, J. (2010). Alternative intersections/interchanges: informational report (AIIR) (No. FHWA-HRT-09-060). United States. Federal Highway Administration. Office of Research, Development, and Technology.
Islam, S. B. A., Tajalli, M., Mohebifard, R., & Hajbabaie, A. (2021). Effects of connectivity and traffic observability on an adaptive traffic signal control system. Transportation research record, 2675(10), 800-814.
Jagannathan, R., & Khan, A. M. (2001). Methodology for the assessment of traffic adaptive control systems. ITE JOURNAL, 71(6), 28-33.
Khan, S. M. (2015). Real-time traffic condition assessment with connected vehicles. Clemson University.
Liu, C., & Ke, L. (2022). Cloud assisted Internet of things intelligent transportation system and the traffic control system in the smart city. Journal of Control and Decision, 1-14.
Martin, P. T., Perrin, J., Chilukuri, B. R., Jhaveri, C., & Feng, Y. (2003). Adaptive signal control II (No. UT-03.28, UTL-0902-60). University of Utah. Dept. of Civil and Environmental Engineering.
Msallam, M. (2014). Evaluation and Improvement of Signalized Intersections in Amman City in Jordan. Evaluation, 4(21), 156–169.
Mudigonda, S., Ozbay, K., & Doshi, H. (2008). Evaluation and selection of adaptive traffic control strategies on transportation networks: Decision support tool based on geographic information system. Transportation Research Record, 2064(1), 51-64.
Olia, A., Abdelgawad, H., Abdulhai, B., & Razavi, S. N. (2016). Assessing the potential impacts of connected vehicles: mobility, environmental, and safety perspectives. Journal of Intelligent Transportation Systems, 20(3), 229-243.
Sims, A. G., & Dobinson, K. W. (1980). The Sydney coordinated adaptive traffic (SCAT) system philosophy and benefits. IEEE Transactions on vehicular technology, 29(2), 130-137.
SPSS, I. (2013). IBM SPSS statistics for windows. Armonk, New York, USA: IBM SPSS, 2.
Stallard, C., & Owen, L. E. (1998, December). Evaluating adaptive signal control using CORSIM. In 1998 Winter Simulation Conference. Proceedings (Cat. No. 98CH36274) (Vol. 2, pp. 1147-1153). IEEE.
Staples, B. L., & Chang, J. (2020). Vehicle to Infrastructure (V2I) Program: Research, Development, and Deployment Support Conducted Through 2020 (No. FHWA-JPO-21-831). United States. Department of Transportation. Intelligent Transportation Systems Joint Program Office.
Stevanovic, A., & Zlatkovic, M. (2013). Evaluation of InSync adaptive traffic signal control in microsimulation environment. In 92nd Annual Meeting of the Transportation Research Board, Washington DC.
Wang, Q., Yuan, Y., Yang, X. T., & Huang, Z. (2021). Adaptive and multi-path progression signal control under connected vehicle environment. Transportation Research Part C: Emerging Technologies, 124, 102965. https://doi.org/10.1016/j.trc.2021.102965.