Capacity Estimation of Advance Right-Turn Motor Vehicles Considering Nonstrict Priority Crossing Behaviors under Mixed-Traffic Conditions
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 2
Abstract
The use of channelized islands to optimize the operation of advance right-turn motor vehicles (ARTMVs) is an effective intersection design method. To overcome the limitations of previous models with respect to the characteristics of nonstrict priority crossing behaviors and mixed nonmotor vehicle (NMV) flow, we have constructed a new capacity estimation model of ARTMVs. This novel model is based on the driving force model for nonstrict priority crossing behaviors, and fully describes the mixed NMV flow with perceived density. Based on the driving force model for nonstrict priority crossing, the speed of ARTMVs crossing the NMV lane and the headways of ARTMVs are obtained. Finally, the number of ARTMVs crossing the NMV lane in a saturated traffic state, i.e., the capacity, is estimated. The method does not need to consider the gap probability of NMV flow, which makes up for the influence of NMVs passing ARTMVs side-by-side. The model was verified by data collected at validation sites in Kunming, China, and compared with the gap acceptance model; the accuracy of the proposed model with heterogeneous NMV flow improved by 22.2%. The construction method of the model provides a new idea for the capacity estimation of ARTMVs under mixed traffic conditions.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The authors confirm that the data supporting the findings of this study are available within the article.
Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant No. 52002161; Yunnan Fundamental Research Projects under Grant No. 202101AU070026; and Kunming University of Science and Technology Talent Training Fund under Grant No. KKZ3202002039.
References
Allen, D. P., J. E. Hummer, N. M. Rouphail, and J. S. Milazzo. 1998. “Effect of bicycles on capacity of signalized intersections.” Transp. Res. Rec. 1646 (1): 87–95. https://doi.org/10.3141/1646-11.
Bie, Y., J. Ji, X. Wang, and X. Qu. 2021. “Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption.” Comput.-Aided Civ. Infrastruct. Eng. 2021 (1): 1–19. https://doi.org/10.1111/mice.12684.
Brilon, W., and T. Miltner. 2005. “Capacity at intersections without traffic signals.” Transp. Res. Rec. 1920 (1): 32–40. https://doi.org/10.1177/0361198105192000104.
Chen, X., C. Shao, and H. Yue. 2007. “Influence of bicycle traffic on capacity of typical signalized intersection.” Tsinghua Sci. Technol. 12 (2): 198–203. https://doi.org/10.1016/S1007-0214(07)70028-7.
Davidse, R. J., K. V. Duijvenvoorde, M. J. Boele-Vos, W. Louwerse, A. Stelling-Konczak, C. Duivenvoorden, and A. J. Algera. 2019. “Scenarios of crashes involving light mopeds on urban bicycle paths.” Accid. Anal. Prev. 129 (Aug): 334–341. https://doi.org/10.1016/j.aap.2019.05.016.
Fyhri, A., and N. Fearnley. 2015. “Effects of E-bikes on bicycle use and mode share.” Transp. Res. Part D Trans. Environ. 36 (May): 45–52. https://doi.org/10.1016/j.trd.2015.02.005.
Fyhri, A., and H. B. Sundfr. 2020. “Do people who buy E-bikes cycle more?” Transp. Res. Part D Transp. Environ. 86 (Sep): 102422. https://doi.org/10.1016/j.trd.2020.102422.
Guo, Y., Q. Yu, Y. Zhang, and J. Rong. 2011. “Effect of bicycles on the saturation flow rate of turning vehicles at signalized intersections.” J. Transp. Eng. 138 (1): 21–30. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000317.
Huertas-Leyva, P., M. Dozza, and N. Baldanzini. 2018. “Investigating cycling kinematics and braking maneuvers in the real world: E-bikes make cyclists move faster, brake harder, and experience new conflicts.” Transp. Res. Part F Traffic Psychol. Behav. 54 (APR): 211–222. https://doi.org/10.1016/j.trf.2018.02.008.
Jiang, C., R. Qiu, T. Fu, L. Fu, and Z. Lu. 2020. “Impact of right-turn channelization on pedestrian safety at signalized intersections.” Accid. Anal. Prevent. 136 (Mar): 105399. https://doi.org/10.1016/j.aap.2019.105399.
Li, B., W. Cheng, Y. Bie, and B. Sun. 2019. “Capacity of advance right-turn motorized vehicles at signalized intersections for mixed traffic conditions.” In Mathematical problems in engineering, 1–13. London: Hindawi. https://doi.org/10.1155/2019/3854604.
Li, S., D. Qian, and N. Li. 2010. “BP simulation model and sensitivity analysis of right-turn vehicles’ crossing decisions at signalized intersection.” J. Transp. Syst. Eng. Inf. Technol. 10 (2): 49–56. https://doi.org/10.1016/S1570-6672(09)60032-5.
Lin, D., W. Ma, L. Li, and Y. Wang. 2016. “A driving force model for non-strict priority crossing behaviors of right-turn drivers.” Transp. Res. Part B: Methodol. 83 (Jan): 230–244. https://doi.org/10.1016/j.trb.2015.10.007.
Lin, S., M. He, Y. Tan, and M. He. 2008. “Comparison study on operating speeds of electric bicycles and bicycles: Experience from field investigation in Kunming, China.” Transp. Res. Rec. 2048 (2048): 52–59. https://doi.org/10.3141/2048-07.
Liu, J., G. Sheng, and H. Xiong. 2011. “Study on avoidance behavior model based on vehicle-bicycle collision in urban interchange.” In Proc., Int. Conf. on IEEE Remote Sensing, Environment and Transportation Engineering (RSETE), 2538–2541. New York: IEEE. https://doi.org/10.1109/RSETE.2011.5964831.
Ma, Z., J. Sun, and Y. Wang. 2017a. “A two-dimensional simulation model for modelling turning vehicles at mixed-flow intersections.” Transp. Res. Part C: Emerging Technol. 75: 103–119. https://doi.org/10.1016/j.trc.2016.12.005.
Ma, Z., J. Xie, X. Qi, Y. Xu, and J. Sun. 2017b. “Two-dimensional simulation of turning behavior in potential conflict area of mixed-flow intersections.” Comput.-Aided Civ. Infrastruct. Eng. 32 (5): 412–428. https://doi.org/10.1111/mice.12266.
Qian, Z., J. Li, X. Li, M. Zhang, and H. Wang. 2017. “Modeling heterogeneous traffic flow: A pragmatic approach.” Transp. Res. Part B Methodol. 99: 183–204. https://doi.org/10.1016/j.trb.2017.01.011.
Räsänen, M., I. Koivisto, and H. Summala. 1999. “Car driver and bicyclist behavior at bicycle crossings under different priority regulations.” J. Saf. Res. 30 (1): 67–77. https://doi.org/10.1016/S0022-4375(98)00062-0.
Roess, R. P., E. S. Prassas, and W. R. McShane. 2004. Traffic engineering. 3rd ed. Upper Saddle River, NJ: Prentice Hall.
Sderberg, A., E. Adell, and L. W. Hiselius. 2020. “What is the substitution effect of E-bikes? A randomised controlled trial.” Transp. Res. Part D Transp. Environ. 90 (Jan): 102648. https://doi.org/10.1016/j.trd.2020.102648.
TRB (Transportation Research Board). 2010. Highway capacity manual. Washington, DC: TRB.
Wang, C., C. Xu, J. Xia, and Z. Qian. 2018. “The effects of safety knowledge and psychological factors on self-reported risky driving behaviors including group violations for E-bike riders in China.” Transp. Res. Part F Traffic Psychol. Behav. 56: 344–353. https://doi.org/10.1016/j.trf.2018.05.004.
Zheng, X., H. Huang, J. Wang, X. Zhao, and Q. Xu. 2019. “Behavioral decision-making model of the intelligent vehicle based on driving risk assessment.” Comput.-Aided Civ. Infrastruct. Eng. 3 (7): 1–18. https://doi.org/10.1111/mice.12507.
Information & Authors
Information
Published In
Copyright
© 2021 American Society of Civil Engineers.
History
Received: May 1, 2021
Accepted: Oct 1, 2021
Published online: Dec 9, 2021
Published in print: Feb 1, 2022
Discussion open until: May 9, 2022
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.