Enhancing Work Zone Capacity by a Cooperative Late Merge System Using Decentralized and Centralized Control Strategies
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 2
Abstract
This paper explores the efficiency of a novel merging system based on a cooperative late merge strategy (CLMS) to mitigate the capacity reduction in work zones due to lane closure. Cooperative late merge strategies in connected vehicles (CV) and connected and autonomous vehicles (CAV) environments are formulated to enhance throughput by reducing gaps and increasing the synchronized speed in the work zone. We propose decentralized and centralized systems based on vehicle-to-vehicle and vehicle-to-infrastructure communication. The decentralized CLMS incorporates a modified lane-changing model to reflect the cooperative feature under the CV environment. The centralized CLMS is developed to further optimize the work zone throughput based on gap reduction and speed harmonization features enabled by CAV. The results prove that the decentralized CLMS outperforms other systems by increasing throughput as well as reducing delay and queue length. The centralized CLMS demonstrated substantial improvements compared to other systems. The simulation results prove that the decentralized CLMS improves capacity by 17% and the centralized CLMS by 45%, when compared to a traditional work zone system.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The complete datasets of the findings presented in this study, such as flow rate, delay, queue length, and speed are available upon reasonable request.
References
Abboud, K., H. A. Omar, and W. Zhuang. 2016. “Interworking of DSRC and cellular network technologies for V2X communications: A survey.” IEEE Trans. Veh. Technol. 65 (12): 9457–9470. https://doi.org/10.1109/TVT.2016.2591558.
Abdulsattar, H., A. Mostafizi, M. R. K. Siam, and H. Wang. 2020. “Measuring the impacts of connected vehicles on travel time reliability in a work zone environment: An agent-based approach.” J. Intell. Transp. Syst. 24 (5): 421–436. https://doi.org/10.1080/15472450.2019.1573351.
Abdulsattar, H., A. Mostafizi, and H. Wang. 2018. “Surrogate safety assessment of work zone rear-end collisions in a connected vehicle environment: Agent-based modeling framework.” J. Transp. Eng. Part A Syst. 144 (8): 04018038. https://doi.org/10.1061/JTEPBS.0000164.
Adell, E., A. Várhelyi, and M. Fontana. 2011. “The effects of a driver assistance system for safe speed and safe distance—A real-life field study.” Transp. Res. Part C 19 (1): 145–155. https://doi.org/10.1016/j.trc.2010.04.006.
Algomaiah, M., and Z. Li. 2021. “Exploring work zone late merge strategies with and without enabling connected vehicles technologies.” Transp. Res. Interdiscip. Perspect. 9 (Mar): 100316. https://doi.org/10.1016/j.trip.2021.100316.
Ali, Y., Z. Zheng, M. M. Haque, and M. Wang. 2019. “A game theory-based approach for modelling mandatory lane-changing behaviour in a connected environment.” Transp. Res. Part C Emerging Technol. 106 (Sep): 220–242. https://doi.org/10.1016/j.trc.2019.07.011.
Ali, Y., Z. Zheng, M. M. Haque, M. Yildirimoglu, and S. Washington. 2020. “Understanding the discretionary lane-changing behaviour in the connected environment.” Accid. Anal. Prev. 137 (Mar): 105463. https://doi.org/10.1016/j.aap.2020.105463.
Al-Kaisy, A., M. Zhou, and F. Hall. 2000. “New insights into freeway capacity at work zones: Empirical case study.” Transp. Res. Rec. 1710 (1): 154–160. https://doi.org/10.3141/1710-18.
Amini, E., A. Omidvar, and L. Elefteriadou. 2021. “Optimizing operations at freeway weaves with connected and automated vehicles.” Transp. Res. Part C Emerging Technol. 126 (May): 103072. https://doi.org/10.1016/j.trc.2021.103072.
Beacher, A. G., M. D. Fontaine, and N. J. Garber. 2005. “Guidelines for using late merge traffic control in work zones: Results of a simulation-based study.” Transp. Res. Rec. 1911 (1): 42–50. https://doi.org/10.1177/0361198105191100105.
Benekohal, R. F., A. Z. Kaja-Mohideen, and M. V. Chitturi. 2004. “Methodology for estimating operating speed and capacity in work zones.” Transp. Res. Rec. 1883 (1): 103–111. https://doi.org/10.3141/1883-12.
Cao, D., J. Wu, J. Wu, B. Kulcsár, and X. Qu. 2021. “A platoon regulation algorithm to improve the traffic performance of highway work zones.” Comput.-Aided Civ. Infrastruct. Eng. 36 (7): 941–956. https://doi.org/10.1111/mice.12691.
Cao, W., M. Mukai, T. Kawabe, H. Nishira, and N. Fujiki. 2015. “Cooperative vehicle path generation during merging using model predictive control with real-time optimization.” Control Eng. Pract. 34 (Jan): 98–105. https://doi.org/10.1016/j.conengprac.2014.10.005.
Chen, B., and H. H. Cheng. 2010. “A review of the applications of agent technology in traffic and transportation systems.” IEEE Trans. Intell. Transp. Syst. 11 (2): 485–497. https://doi.org/10.1109/TITS.2010.2048313.
Chen, D., and S. Ahn. 2018. “Capacity-drop at extended bottlenecks: Merge, diverge, and weave.” Transp. Res. Part B Methodol. 108 (Feb): 1–20. https://doi.org/10.1016/j.trb.2017.12.006.
Dixon, K. K., J. E. Hummer, and A. R. Lorscheider. 1996. “Capacity for North Carolina freeway work zones.” Transp. Res. Rec. 1529 (1): 27–34. https://doi.org/10.1177/0361198196152900104.
Durrani, U., and C. Lee. 2019. “Calibration and validation of psychophysical car-following model using driver’s action points and perception thresholds.” J. Transp. Eng. Part A Syst. 145 (9): 04019039. https://doi.org/10.1061/JTEPBS.0000264.
Durrani, U., C. Lee, and H. Maoh. 2016. “Calibrating the Wiedemann’s vehicle-following model using mixed vehicle-pair interactions.” Transp. Res. Part C Emerging Technol. 67 (Jun): 227–242. https://doi.org/10.1016/j.trc.2016.02.012.
Farah, H., H. N. Koutsopoulos, M. Saifuzzaman, R. Kölbl, S. Fuchs, and D. Bankosegger. 2012. “Evaluation of the effect of cooperative infrastructure-to-vehicle systems on driver behavior.” Transp. Res. Part C 21 (1): 42–56. https://doi.org/10.1016/j.trc.2011.08.006.
Ghiasi, A., O. Hussain, Z. S. Qian, and X. Li. 2017. “A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method.” Transp. Res. Part B Methodol. 106 (Dec): 266–292. https://doi.org/10.1016/j.trb.2017.09.022.
Hidas, P. 2005. “Modelling vehicle interactions in microscopic simulation of merging and weaving.” Transp. Res. Part C 13 (1): 37–62. https://doi.org/10.1016/j.trc.2004.12.003.
Itkonen, T. H., J. Pekkanen, O. Lappi, I. Kosonen, T. Luttinen, and H. Summala. 2017. “Trade-off between jerk and time headway as an indicator of driving style.” PLoS One 12 (10): e0185856. https://doi.org/10.1371/journal.pone.0185856.
Kang, K.-P., and G.-L. Chang. 2009. “Lane-based dynamic merge control strategy based on optimal thresholds for highway work zone operations.” J. Transp. Eng. 135 (6): 359–370. https://doi.org/10.1061/(ASCE)0733-947X(2009)135:6(359).
Kang, K.-P., G.-L. Chang, and J. Paracha. 2006. “Dynamic late merge control at highway work zones: Evaluations, observations, and suggestions.” Transp. Res. Rec. 1948 (1): 86–95. https://doi.org/10.1177/0361198106194800110.
Khattak, Z. H., B. L. Smith, H. Park, and M. D. Fontaine. 2020. “Cooperative lane control application for fully connected and automated vehicles at multilane freeways.” Transp. Res. Part C Emerging Technol. 111 (Feb): 294–317. https://doi.org/10.1016/j.trc.2019.11.007.
Kim, J.-T., J. Kim, and M. Chang. 2008. “Lane-changing gap acceptance model for freeway merging in simulation.” Can. J. Civ. Eng. 35 (3): 301–311. https://doi.org/10.1139/L07-119.
Letter, C., and L. Elefteriadou. 2017. “Efficient control of fully automated connected vehicles at freeway merge segments.” Transp. Res. Part C Emerging Technol. 80 (Jul): 190–205. https://doi.org/10.1016/j.trc.2017.04.015.
Levin, M. W., and S. D. Boyles. 2016. “A multiclass cell transmission model for shared human and autonomous vehicle roads.” Transp. Res. Part C Emerging Technol. 62 (Jan): 103–116. https://doi.org/10.1016/j.trc.2015.10.005.
Lin, D., L. Li, and S. E. Jabari. 2019. “Pay to change lanes: A cooperative lane-changing strategy for connected/automated driving.” Transp. Res. Part C Emerging Technol. 105 (Aug): 550–564. https://doi.org/10.1016/j.trc.2019.06.006.
Liu, H., H. Wei, T. Zuo, Z. Li, and J. Yang. 2017. “Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment.” Transp. Res. Part C Emerging Technol. 76 (Mar): 132–149. https://doi.org/10.1016/j.trc.2017.01.003.
Makridis, M., K. Mattas, and B. Ciuffo. 2020. “Response time and time headway of an adaptive cruise control. An empirical characterization and potential impacts on road capacity.” IEEE Trans. Intell. Transp. Syst. 21 (4): 1677–1686. https://doi.org/10.1109/TITS.2019.2948646.
Marczak, F., W. Daamen, and C. Buisson. 2013. “Merging behaviour: Empirical comparison between two sites and new theory development.” Transp. Res. Part C Emerging Technol. 36 (Nov): 530–546. https://doi.org/10.1016/j.trc.2013.07.007.
Moridpour, S., M. Sarvi, G. Rose, and E. Mazloumi. 2012. “Lane-changing decision model for heavy vehicle drivers.” J. Intell. Transp. Syst. 16 (1): 24–35. https://doi.org/10.1080/15472450.2012.639640.
Mullakkal-Babu, F. A., M. Wang, B. van Arem, B. Shyrokau, and R. Happee. 2021. “A hybrid submicroscopic-microscopic traffic flow simulation framework.” IEEE Trans. Intell. Transp. Syst. 2020 (6): 3430–3443. https://doi.org/10.1109/TITS.2020.2990376.
Nagalur Subraveti, H. H. S., A. Srivastava, S. Ahn, V. L. Knoop, and B. van Arem. 2021. “On lane assignment of connected automated vehicles: Strategies to improve traffic flow at diverge and weave bottlenecks.” Transp. Res. Part C Emerging Technol. 127 (Jun): 103126. https://doi.org/10.1016/j.trc.2021.103126.
Nilsson, J., M. Brännström, E. Coelingh, and J. Fredriksson. 2016. “Lane change maneuvers for automated vehicles.” IEEE Trans. Intell. Transp. Syst. 18 (5): 1087–1096.
Nobukawa, K., S. Bao, D. J. LeBlanc, D. Zhao, H. Peng, and C. Pan. 2016. “Gap acceptance during lane changes by large-truck drivers—An image-based analysis.” IEEE Trans. Intell. Transp. Syst. 17 (3): 772–781. https://doi.org/10.1109/TITS.2015.2482821.
Pesti, G., D. R. Jessen, P. S. Byrd, and P. T. McCoy. 1999. “Traffic flow characteristics of the Late Merge work zone control strategy.” Transp. Res. Rec. 1657 (1): 1–9. https://doi.org/10.3141/1657-01.
Ramadan, O. E., and V. P. Sisiopiku. 2018. “Modeling highway performance under various short-term work zone configurations.” J. Transp. Eng. Part A Syst. 144 (9): 04018050. https://doi.org/10.1061/JTEPBS.0000176.
Ren, T., Y. Xie, and L. Jiang. 2020. “Cooperative highway work zone merge control based on reinforcement learning in a connected and automated environment.” Transp. Res. Rec. 2674 (10): 363–374. https://doi.org/10.1177/0361198120935873.
Ren, T., Y. Xie, and L. Jiang. 2021. “New England merge: A novel cooperative merge control method for improving highway work zone mobility and safety.” J. Intell. Transp. Syst. 25 (1): 107–121. https://doi.org/10.1080/15472450.2020.1822747.
Rios-Torres, J., and A. A. Malikopoulos. 2016. “Automated and cooperative vehicle merging at highway on-ramps.” IEEE Trans. Intell. Transp. Syst. 18 (4): 780–789. https://doi.org/10.1109/TITS.2016.2587582.
Risto, M., and M. H. Martens. 2014. “Driver headway choice: A comparison between driving simulator and real-road driving.” Transp. Res. Part F Traffic Psychol. Behav. 25 (Jul): 1–9. https://doi.org/10.1016/j.trf.2014.05.001.
Roncoli, C., N. Bekiaris-Liberis, and M. Papageorgiou. 2017. “Lane-changing feedback control for efficient lane assignment at motorway bottlenecks.” Transp. Res. Rec. 2625 (1): 20–31. https://doi.org/10.3141/2625-03.
Taieb-Maimon, M., and D. Shinar. 2001. “Minimum and comfortable driving headways: Reality versus perception.” Hum. Factors 43 (1): 159–172. https://doi.org/10.1518/001872001775992543.
Tarko, A., and S. Venugopal. 2001. Safety and capacity evaluation of the Indiana Lane Merge System. West Lafayette, IN: INDOT.
Tilg, G., K. Yang, and M. Menendez. 2018. “Evaluating the effects of automated vehicle technology on the capacity of freeway weaving sections.” Transp. Res. Part C Emerging Technol. 96 (Nov): 3–21. https://doi.org/10.1016/j.trc.2018.09.014.
Wang, H., L. Liu, S. Dong, Z. Qian, and H. Wei. 2016. “A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD–ARIMA framework.” Transportmetrica B: Transp. Dyn. 4 (3): 159–186. https://doi.org/10.1080/21680566.2015.1060582.
Weng, J., G. Li, and Y. Yu. 2017. “Time-dependent drivers’ merging behavior model in work zone merging areas.” Transp. Res. Part C Emerging Technol. 80 (Jul): 409–422. https://doi.org/10.1016/j.trc.2017.05.007.
Weng, J., and Q. Meng. 2011. “Modeling speed-flow relationship and merging behavior in work zone merging areas.” Transp. Res. Part C 19 (6): 985–996. https://doi.org/10.1016/j.trc.2011.05.001.
Weng, J., and Q. Meng. 2013. “Estimating capacity and traffic delay in work zones: An overview.” Transp. Res. Part C Emerging Technol. 35 (Oct): 34–45. https://doi.org/10.1016/j.trc.2013.06.005.
Winsum, W. V., and A. Heino. 1996. “Choice of time-headway in car-following and the role of time-to-collision information in braking.” Ergonomics 39 (4): 579–592.
Xu, L., J. Lu, B. Ran, F. Yang, and J. Zhang. 2019. “Cooperative merging strategy for connected vehicles at highway on-ramps.” J. Transp. Eng. Part A Syst. 145 (6): 04019022. https://doi.org/10.1061/JTEPBS.0000243.
Yang, G., H. Xu, Z. Wang, and Z. Tian. 2016. “Truck acceleration behavior study and acceleration lane length recommendations for metered on-ramps.” Int. J. Transp. Sci. Technol. 5 (2): 93–102. https://doi.org/10.1016/j.ijtst.2016.09.006.
Yang, N., G. L. Chang, and K. P. Kang. 2009. “Simulation-based study on a lane-based signal system for merge control at freeway work zones.” J. Transp. Eng. 135 (1): 9–17. https://doi.org/10.1061/(ASCE)0733-947X(2009)135:1(9).
Yuan, Y., Y. Liu, and W. Liu. 2019. “Dynamic lane-based signal merge control for freeway work zone operations.” J. Transp. Eng. Part A Syst. 145 (12): 04019053. https://doi.org/10.1061/JTEPBS.0000256.
Zhang, X., J. Sun, X. Qi, and J. Sun. 2019. “Simultaneous modeling of car-following and lane-changing behaviors using deep learning.” Transp. Res. Part C Emerging Technol. 104 (Jul): 287–304. https://doi.org/10.1016/j.trc.2019.05.021.
Zhu, M., X. Wang, A. Tarko, and S. Fang. 2018. “Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study.” Transp. Res. Part C Emerging Technol. 93 (Aug): 425–445. https://doi.org/10.1016/j.trc.2018.06.009.
Information & Authors
Information
Published In
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Dec 8, 2020
Accepted: Oct 5, 2021
Published online: Nov 24, 2021
Published in print: Feb 1, 2022
Discussion open until: Apr 24, 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.
Cited by
- Hongsheng QI, Capacity Adjustment of Lane Number for Mixed Autonomous Vehicles Flow Considering Stochastic Lateral Interactions, Journal of Transportation Engineering, Part A: Systems, 10.1061/JTEPBS.TEENG-8142, 150, 2, (2024).
- MM Shakiul Haque, Laurence R. Rilett, Li Zhao, Impact of Platooning Connected and Automated Heavy Vehicles on Interstate Freeway Work Zone Operations, Journal of Transportation Engineering, Part A: Systems, 10.1061/JTEPBS.TEENG-7434, 149, 3, (2023).
- ShiHui Wang, Min Zhao, DiHua Sun, Yuanpeng Zou, On-Ramp Merging Strategy with Two-Stage Optimization Based on Fully Proactive and Cooperative Merging of Vehicles, Journal of Transportation Engineering, Part A: Systems, 10.1061/JTEPBS.TEENG-7194, 149, 4, (2023).