The performance of a traffic system tends to improve as the percentage of connected vehicles (CV) in total flow increases. However, due to low CV penetration in the current vehicle market, improving the traffic signal operation remains a challenging task. In an effort to improve the performance of CV applications at low penetration rates, the authors develop a new method to estimate the speeds and positions of non-connected vehicles (NCV) along a signalized intersection. The algorithm uses CV information and initial speeds and positions of the NCVs from loop detectors and estimates the forward movements of the NCVs using the Gipps’ car-following model. Calibration parameters of the Gipps’ model were determined using a solver optimization tool. The estimation algorithm was applied to a previously developed connected vehicle signal control (CVSC) strategy on two different isolated intersections. Simulations in VISSIM showed the estimation accuracy higher for the intersection with less lanes. Estimation error increased with the decrease in CV penetration and decreased with the decrease in traffic demand. The CVSC strategy with 40% and higher CV penetration (for Intersection 1) and with 20% and higher CV penetration (for Intersection 2) showed better performance in reducing travel time delay and number of stops than the EPICS adaptive control.
Huang L, Yao J, Wu W, Yang X. Feasibility analysis of vehicle-to-vehicle communication on suburban road. Promet – Traffic & Transportation. 2013;25(5): 483-493.
Connected vehicles. 2017. [cited 2017 Nov 14]. Available from: http://www.its.dot.gov/cv_basics/index.htm
Statista. The Statistics Portal. 2017 Aug. [cited 2017 Nov 14]. Available from https://www.statista.com/outlook/320/100/connected-car/worldwide
Chandan K, Seco AM and Silva AB. Real-time traffic signal control for isolated intersection, using car-following logic under connected vehicle environment. Transportation Research Procedia. 2017;25: 1613-1628.
Zheng J, Liu HX. Estimating traffic volumes for signalized intersections using connected vehicle data. Transportation Research Part C: Emerging Technologies, 2017;79: 347-362.
Knowledge resources. 2013. [cited 2017 Nov 14]. Available from: http://www.itsdeployment.its.dot.gov/summaries.aspx
Connected vehicle pilot deployment program. 2017. [cited 2017 Nov 14]. Available from: https://www.its.dot.gov/pilots/index.htm
Guler SI, Menedez M, Meier L. Using connected vehicle technology to improve the efficiency of intersections. Transportation Research Part C: Emerging Technologies. 2014;46: 121–131.
Goodall NJ, Park B, Smith BL. Microscopic estimation of arterial vehicle positions in a low-penetration-rate connected vehicle environment. Journal of Transportation Engineering. 2014;140(10): 04014047.
Feng Y, Head KL, Khoshmagham S, Zamanipour M. A real-time adaptive signal control in a connected vehicle environment. Transportation Research Part C: Emerging Technologies. 2015;55: 460-473.
Yang K, Guler SI, Menendez M. Isolated intersection control for various levels of vehicle technology: Conventional, connected, and automated vehicles. Transportation Research Part C: Emerging Technologies. 2016;72: 109-129.
Casas J, Ferrer JL, Garcia D, Perarnau J, Torday A. Traffic simulation with Aimsun. In: Barceló J. (eds) Fundamentals of traffic simulation. International series in operations research & management science, vol 145. New York, USA: Springer; 2010. p. 173-232.
Vasconcelos L, Neto L, Santos S, Silva AB, Seco Á. Calibration of the Gipps car-following model using trajectory data. Transportation Research Procedia. 2014;3: 952-961.
Soria I, Elefteriadou L, Kondyli A. Assessment of car-following models by driver type and under different traffic, weather conditions using data from an instrumented vehicle. Simulation modelling practice and theory. 2014;40: 208-220.
VISSIM 8 User manual. Germany. PTV Planung Transport Verkehr AG, 2017.
EPICS User manual. Germany. PTV Planung Transport Verkehr AG, 2017.
Husch D, Albeck J. Intersection Capacity Utilization: Evaluation Procedures for Intersections and Interchanges. Albany, CA: Trafficware; 2003.
Traffic volumes and turning movements. 2015. [cited 2017 Nov 14]. Available from: http://www.edmonton.ca/transportation/traffic_reports/traffic-volumes-turning-movements.aspx
Traffic counts. 2015. [cited 2017 Nov 14]. Available from: http://www.dvrpc.org/webmaps/trafficcounts/
Wang J, Dixon K, Li H, Ogle J. Normal Acceleration Behavior of Passenger Vehicles Starting from Rest at All-Way Stop-Controlled Intersections. Transportation Research Record: Journal of the Transportation Research Board. 2004;1883: 158-166
Bogdanović V, Ruškić N, Papić Z, Simeunović M. The research of vehicle acceleration at signalized intersections. Promet – Traffic & Transportation. 2013;25(1): 33-42.
Rittger L, Schmidt G, Maag C, Kiesel A. Driving behaviour at traffic light intersections. Cognition, Technology & Work. 2015;17(4), 593-605.
Viti F, Hoogendoorn SP, van Zuylen HJ, Wilmink IR, van Arem B. Speed and acceleration distributions at a traffic signal analyzed from microscopic real and simulated data. Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, 12-15 Oct 2008, Beijing, China; 2008. p. 651-656