Design and operation of dedicated lanes for connected and automated vehicles on motorways: A conceptual framework and research agenda
Open accessAbstract
Dedicated Lanes (DLs) have been proposed as a potential scenario for the deployment of Automated and/or Connected Vehicles (C/AVs) on the road network. However, evidence-based knowledge regarding the impacts of different design configurations, utilization policies, and the design of their access/egress on traffic safety and efficiency is limited. In order to develop an adequate design for DLs, first, a conceptual framework describing the relations and interrelations between these factors and traffic safety and efficiency is needed. Therefore, the main aim of this paper is to develop a conceptual framework accounting for the factors that could affect the safety and efficiency of DLs. This conceptual framework is underpinned based on relevant literature on how the deployment of C/AVs, driver behaviour, and DL design and operation affect traffic safety and efficiency. Based on the conceptual framework, the knowledge gaps on DL design for C/AVs were identified and a research agenda, including prioritization of the research needs, is proposed. Following the developed conceptual framework, the necessary building blocks for investigating the impacts of different design configurations, utilization policies, and the design of their access/egress on traffic safety and efficiency are: (1) to specify the types of vehicles with certain capabilities allowed to drive on DLs; (2) to incorporate existing algorithms of C/AVs, which reflect more realistically their behaviour, in both driving simulator experiments and microscopic simulation; (3) to translate the empirical data regarding human behavioural adaptation collected from field tests and driving simulator studies to mathematical models and implement them in traffic flow simulation platform. It is also recommended to develop automated lane change algorithms, taking into account connectivity between C/AVs which can be also implemented in driving simulators and traffic flow simulation platforms. Finally, it is recommended that future research investigate the combined effects of traffic safety and efficiency in designing DLs while considering driver behaviour adaptation and control transitions between manual and automated operation.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The roadway will continue to have mixed traffic with vehicles of different ADS levels for a considerable period.
“In addition, the roadway will continue to have mixed traffic with vehicles of different ADS levels for a considerable period [2][3][4][5][6][7].”
From Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic (2024) · cites this paper as [4]
The Introduction (page unavailable) identifies mixed traffic on motorways, with connected or automated vehicles sharing the road with manually driven SAE level 0 vehicles. It also describes different automation levels and says level 3 and 4 consumer vehicles are expected in the coming decade, but it does not explicitly predict how long mixed traffic will continue. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 89 · Cited by 128
Research works printed but not linked: 78
- Amirgholy, M., Shahabi, M., Gao, O.H., 2018. Transportation infrastructure and automation technologies: high performance lanes and dynamic platoon control systems in the automated highways of future smart cities. SSRN Electron. J. https://doi.org/10.2139/ssrn.3186289. · DOI
- Amirgholy, M., Shahabi, M., Oliver Gao, H., 2020. Traffic automation and lane management for communicant, autonomous, and human-driven vehicles. Transp. Res. Part C Emerg. Technol. 111, 477-495. https://doi.org/10.1016/j.trc.2019.12.009. · DOI
- Ariën, C., Brijs, K., Vanroelen, G., Ceulemans, W., Jongen, E.M.M., Daniels, S., Brijs, T., Wets, G., 2017. The effect of pavement markings on driving behaviour in curves: a simulator study. Ergonomics 60, 701-713. https://doi.org/10.1080/00140139.2016.1200749. · DOI
- Arnaout, G.M., Bowling, S., 2014. A progressive deployment strategy for cooperative adaptive cruise control to improve traffic dynamics. Int. J. Autom. Comput. 11, 10-18. https://doi.org/10.1007/s11633-014-0760-2. · DOI
- Awan, H.H., Sajid, S.R., Declercq, K., Adnan, M., Pirdavani, A., Alhajyaseen, W., Brijs, T., 2018. Drivers' crossing behaviour between express and local lanes with soft separation: A driving simulator study. Adv. Transp. Stud. 1, 41-54. https://doi.org/10.4399/97888255168835. · DOI
- Baas, P., Charlton, S., 2005. Influencing driver behaviour through road marking, in: Roadmarking Industry Association of Australia and New Zealand Roadmarkers Federation Conference, 2005, Christchurch, New Zealaa. Https://Hdl.Handle.Net/10289/3437. p. 11P.
- Bae, S., Saxena, D., Nakhaei, A., Choi, C., Fujimura, K., Moura, S., 2019. Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network. https://arxiv.org/pdf/1909.05665.pdf. · arXiv
- Bianchi Piccinini, G.F., Rodrigues, C.M., Leitão, M., Simões, A., 2014. Driver's behavioral adaptation to Adaptive Cruise Control (ACC): The case of speed and time headway. 77.e1-84. J. Safety Res. 49. https://doi.org/10.1016/j.jsr.2014.02.010. · DOI
- Calvert, S., 2017. Next steps in describing possible effects of automated driving on traffic flow. https://doi.org/10.13140/RG.2.2.25387.44328. · DOI
- Carbaugh, J., Godbole, D.N., Sengupta, R., 1998. Safety and capacity analysis of automated and manual highway systems. Transp. Res. Part C Emerg. Technol. 6C, 69-99. https://doi.org/10.1016/S0968-090X(98)00009-6. · DOI
- Carreras, A., Daura, X., Erhart, J., Ruehrup, S., 2018. Road infrastructure support levels for automated driving, 25th ITS. World Congr, pp. 12-20.
- Chandra, R., Selvaraj, Y., Brännström, M., Kianfar, R., Murgovski, N., 2018. Safe autonomous lane changes in dense traffic. In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. Institute of Electrical and Electronics Engineers Inc, pp. 1-6. https://doi.org/10.1109/ITSC.2017.8317590. · DOI
- Chen, D., Ahn, S., Chitturi, M., Noyce, D.A., 2017a. Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles. Transp. Res. Part B Methodol. 100, 196-221. https://doi.org/10.1016/j.trb.2017.01.017. · DOI
- Chen, F., Balieu, R., Kringos, N., 2016a. Potential influences on long-term service performance of road infrastructure by automated vehicles. Transp. Res. Rec. 2550, 72-79. https://doi.org/10.3141/2550-10. · DOI
- Chen, Z., He, F., Yin, Y., Du, Y., 2017b. Optimal design of autonomous vehicle zones in transportation networks. Transp. Res. Part B Methodol. 99, 44-61. https://doi. org/10.1016/j.trb.2016.12.021. · DOI · Paper page
- Chen, Z., He, F., Zhang, L., Yin, Y., 2016b. Optimal deployment of autonomous vehicle lanes with endogenous market penetration. Transp. Res. Part C Emerg. Technol. 72, 143-156. https://doi.org/10.1016/j.trc.2016.09.013. · DOI
- Eriksson, A., Stanton, N.A., 2017. Takeover time in highly automated vehicles: noncritical transitions to and from manual control. Hum. Factors 59, 689-705. https:// doi.org/10.1177/0018720816685832. · DOI
- Fagnant, D.J., Kockelman, K., 2015. Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. Part A 77, 167-181. https://doi.org/10.1016/j.tra.2015.04.003. · DOI · Paper page
- Farah, H., Erkens, S.M.J.G., Alkim, T., van Arem, B., 2018. Infrastructure for Automated and Connected Driving: State of the Art and Future Research Directions. pp. 187-197. https://doi.org/10.1007/978-3-319-60934-8_16. · DOI
- Ghiasi, A., Hussain, O., Qian (Sean), Z., Li, X., 2017. A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method. Res. Part B Methodol. Transp. https://doi.org/10.1016/j.trb.2017.09.022. · DOI
- Gouy, M., Diels, C., Stevens, A., Reed, N., Burnett, G., 2013. Do drivers reduce their headway to a lead vehicle because of the presence of platoons in traffic? A conformity study conducted within a simulator. IET Intell. Transp. Syst. 7, 230-235. https://doi.org/10.1049/iet-its.2012.0156. · DOI
- Gouy, M., Wiedemann, K., Stevens, A., Brunett, G., Reed, N., 2014. Driving next to automated vehicle platoons: How do short time headways influence non-platoon drivers' longitudinal control? Res. Part F Traffic Psychol. Behav. Transp. https://doi.org/10.1016/j.trf.2014.03.003. · DOI
- Guin, A., Hunter, M., Guensler, R., 2008. Analysis of reduction in effective capacities of high-occupancy vehicle lanes related to traffic behavior. Transp. Res. Rec. J. Transp. Res. Board 2065, 47-53. https://doi.org/10.3141/2065-07. · DOI
- Hall, R.W., Nowroozi, A., Tsao, J., 2001. Entrance Capacity of an Automated Highway System, https://doi.org/10.1287/trsc.35.1.19.10144. · DOI
- Hearne, R., Siddiqui, A., 1997. Issues of dedicated lanes for an automated highway. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. IEEE, 619-624. https://doi.org/10.1109/itsc.1997.660545. · DOI
- Hoedemaeker, M., Brookhuis, K.A., 1998. Behavioural adaptation to driving with an adaptive cruise control (ACC). Transp. Res. Part F Traffic Psychol. Behav. 1, 95-106. https://doi.org/10.1016/S1369-8478(98)00008-4. · DOI
- Ioannou, P.A. (Ed.), 1997. Automated Highway Systems, Automated Highway Systems. Springer, US, Boston, MA. https://doi.org/10.1007/978-1-4757-4573-3. · DOI
- Ivanchev, J., Knoll, A., Zehe, D., Nair, S., Eckhoff, D., 2017. Potentials and Implications of Dedicated Highway Lanes for Autonomous Vehicles, http://arxiv.org/abs/ 1709.07658.
- Jang, K., Chung, K., Ragland, D., Chan, C., 2009. Safety performance of high-occupancy-vehicle facilities. Transp. Res. Rec. J. Transp. Res. Board 2099, 132-140. https://doi.org/10.3141/2099-15. · DOI
- Jang, K., Kang, S., Seo, J., Chan, C.-Y., 2013. Cross-section designs for the safety performance of buffer-separated high-occupancy vehicle lanes. J. Transp. Eng. 139, 247-254. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000496. · DOI
- Kanaris, A., Ioannou, P., Fu-Sheng Ho, 1997. Spacing and capacity evaluations for different AHS concepts. In: Proceedings of the 1997 American Control Conference (Cat. No.97CH36041). IEEE, pp. 2036-2040 vol.3. https://doi.org/10.1109/ACC.1997.611047. · DOI
- Kockelman, K., Avery, P., Bansal, P., Boyles, S.D., Bujanovic, P., Choudhary, T., Clements, L., Domnenko, G., Fagnant, D., Helsel, J., Hutchinson, R., Levin, M., Li, J., Li, T., LoftusOtway, L., Nichols, A., Simoni, M., Stewart, D., 2016. Implications of Connected and Automated Vehicles on the Safety and Operations of Roadway Networks: A Final Report. Fhwa/Tx-16/0-6849-1 7. https://doi.org/FHWA/TX-16/0-6849-1.
- Lee, S., Oh, C., Hong, S., 2018. Exploring lane change safety issues for manually driven vehicles in vehicle platooning environments. IET Intell. Transp. Syst. 12, 1142-1147. https://doi.org/10.1049/iet-its.2018.5167. · DOI
- Liu, H., Kan (David), X., Shladover, S.E., Lu, X.Y., Ferlis, R.E., 2018a. Impact of cooperative adaptive cruise control on multilane freeway merge capacity. J. Intell. Transp. Syst. Technol. Planning, Oper. 22, 263-275. https://doi.org/10.1080/15472450.2018.1438275. · DOI
- Liu, H., Kan (David), X., Shladover, S.E., Lu, X.Y., Ferlis, R.E., 2018b. Modeling impacts of cooperative adaptive cruise control on mixed traffic flow in multi-lane freeway facilities. Transp. Res. Part C Emerg. Technol. 95, 261-279. https://doi.org/10.1016/j.trc.2018.07.027. · DOI
- Liu, H., Shladover, S.E., Lu, X.-Y., Kan (David), X., 2020. Freeway vehicle fuel efficiency improvement via cooperative adaptive cruise control. J. Intell. Transp. Syst. 1-13. https://doi.org/10.1080/15472450.2020.1720673. · DOI
- Liu, Z., Song, Z., 2019. Strategic planning of dedicated autonomous vehicle lanes and autonomous vehicle/toll lanes in transportation networks. Transp. Res. Part C Emerg. Technol. 106, 381-403. https://doi.org/10.1016/j.trc.2019.07.022. · DOI
- Lu, X., Madadi, B., Farah, H., Snelder, M., Annema, J.A., Van Arem, B., 2019. https://ascelibrary.org/doi/10.1061/9780784482292.489. · DOI
- Lu, X.-Y., Shladover, S.E., 2014. Automated truck platoon control and field test. Road Vehicle Automation 247-261. https://doi.org/10.1007/978-3-319-05990-7_21. · DOI
- Lu, Z., de Winter, J.C.F., 2015. A review and framework of control authority transitions in automated driving. Procedia Manuf. 3, 2510-2517. https://doi.org/10. 1016/j.promfg.2015.07.513. · DOI
- Madigan, Ruth, Louw, Tyron, Merat, Natasha, Hu, Xiaosong, 2018. The effect of varying levels of vehicle automation on drivers' lane changing behaviour. PLoS ONE 13 (2), 1-17. https://doi.org/10.1371/journal.pone.0192190. · DOI
- Martens, M., Comte, S., Kaptein, N., 1997. The Effects of Road Design on Speed Behaviour : A Literature Review, http://virtual.vtt.fi/virtual/proj6/master/rep231.pdf.
- McDonald, A.D., Alambeigi, H., Engström, J., Markkula, G., Vogelpohl, T., Dunne, J., Yuma, N., 2019. Toward computational simulations of behavior during auto- mated driving takeovers: a review of the empirical and modeling literatures. Hum. Factors 61, 642-688. https://doi.org/10.1177/0018720819829572. · DOI
- McDonald, S.S., Rodier, C., 2015. Envisioning Automated Vehicles within the Built Environment: 2020, 2035, and 2050. Springer, Cham, pp. 225-233. https://doi. org/10.1007/978-3-319-19078-5_20. · DOI
- Melson, C.L., Levin, M.W., Hammit, B.E., Boyles, S.D., 2018. Dynamic traffic assignment of cooperative adaptive cruise control. Transp. Res. Part C Emerg. Technol. 90, 114-133. https://doi.org/10.1016/j.trc.2018.03.002. · DOI
- Milanés, V., Shladover, S.E., 2016. Handling cut-in vehicles in strings of cooperative adaptive cruise control vehicles. J. Intell. Transp. Syst. 20, 178-191. https://doi. org/10.1080/15472450.2015.1016023. · DOI
- Milanés, V., Shladover, S.E., 2014. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data. Transp. Res. Part C Emerg. Technol. 48, 285-300. https://doi.org/10.1016/j.trc.2014.09.001. · DOI
- Miller, E.E., Boyle, L.N., 2019. Behavioral adaptations to lane keeping systems: effects of exposure and withdrawal. Hum. Factors 61, 152-164. https://doi.org/10. 1177/0018720818800538. · DOI
- Mohajerpoor, R., Ramezani, M., 2019. Mixed flow of autonomous and human-driven vehicles: Analytical headway modeling and optimal lane management. Transp. Res. Part C Emerg. Technol. 109, 194-210. https://doi.org/10.1016/j.trc.2019.10.009. · DOI
- National Automated Highway System Consortium, 1997. Milestone 2 Report: Task C2: Downselect System Configurations and Workshop #3, https://path.berkeley. edu/sites/default/files/ahs-milestone_2_report_task-c21.pdf.
- Nie, J., Zhang, J., Ding, W., Wan, X., Chen, X., Ran, B., 2016. Decentralized cooperative lane-changing decision-making for connected autonomous vehicles. IEEE Access 4, 9413-9420. https://doi.org/10.1109/ACCESS.2017.2649567. · DOI · Paper page
- Nieuwenhuijsen, J., Correia, G.H. de A., Milakis, D., van Arem, B., van Daalen, E., 2018. Towards a quantitative method to analyze the long-term innovation diffusion of automated vehicles technology using system dynamics. Transp. Res. Part C Emerg. Technol. 86, 300-327. https://doi.org/10.1016/J.TRC.2017.11.016. · DOI
- Nitsche, P., Mocanu, I., Reinthaler, M., 2014. Requirements on tomorrow's road infrastructure for highly automated driving. In: 2014 International Conference on Connected Vehicles and Expo, ICCVE 2014 -Proceedings. Institute of Electrical and Electronics Engineers Inc, pp. 939-940. https://doi.org/10.1109/ICCVE.2014. 7297694. · DOI
- Nowakowski, C., Shladover, S.E., Cody, D., Bu, F., O'Connell, J., Spring, J., Dickey, S., Nelson, D., 2011. Cooperative Adaptive Cruise Control: Testing Drivers' Choices of Following Distances. FHWA Explor. Adv. Res. Progr. Coop. https://doi.org/UCB-ITS-PRR-2010-39.
- Ong, B.T., Kamalanathsharma, R.K., Yelchuru, B., 2019. Dedicating lanes for priority or exclusive use by connected and automated vehicles. Dedicating Lanes for Priority or Exclusive Use by Connected and Automated Vehicles 1-9. https://doi.org/10.17226/25366. · DOI
- Pereira, M., Beggiato, M., Petzoldt, T., 2015. Use of adaptive cruise control functions on motorways and urban roads: Changes over time in an on-road study. Appl. Ergon. 50, 105-112. https://doi.org/10.1016/j.apergo.2015.03.002. · DOI
- Princeton, J., Cohen, S., 2011. Impact of a dedicated lane on the capacity and the level of service of an urban motorway. Procedia -Soc. Behav. Sci. 16, 196-206. https://doi.org/10.1016/j.sbspro.2011.04.442. · DOI
- Rahman, M.S., Abdel-Aty, M., 2018. Longitudinal safety evaluation of connected vehicles' platooning on expressways. Accid. Anal. Prev. 117, 381-391. https://doi. org/10.1016/j.aap.2017.12.012. · DOI
- Schakel, W.J., Knoop, V.L., van Arem, B., 2013. Integrated lane change model with relaxation and synchronization. Transp. Res. Rec. J. Transp. Res. Board 2316, 47-57. https://doi.org/10.3141/2316-06. · DOI
- Schakel, W.J., Van Arem, B., Netten, B.D., 2010. Effects of cooperative adaptive cruise control on traffic flow stability. IEEE Conference on Intelligent Transportation Systems, Proceedings ITSC, 759-764. https://doi.org/10.1109/ITSC.2010.5625133. · DOI
- Skottke, E.M., Debus, G., Wang, L., Huestegge, L., 2014. Carryover effects of highly automated convoy driving on subsequent manual driving performance. Hum. Factors 56, 1272-1283. https://doi.org/10.1177/0018720814524594. · DOI
- Talebpour, A., Mahmassani, H.S., Elfar, A., 2017. Investigating the effects of reserved lanes for autonomous vehicles on congestion and travel time reliability. Transp. Res. Rec. J. Transp. Res. Board 2622, 1-12. https://doi.org/10.3141/2622-01. · DOI
- Tsao, H.-S.J., Hall, R.W., Shladover, S.E., 1993. Design options for operating automated highway systems. In: Proc. VNIS '93 -Veh. Navig. Inf. Syst. Conf, pp. 494-500. https://doi.org/10.1109/VNIS.1993.585680. · DOI
- Van Arem, B., Van Driel, C.J.G., Visser, R., 2006. The impact of cooperative adaptive cruise control on traffic-flow characteristics. IEEE Trans. Intell. Transp. Syst. 7, 429-436. https://doi.org/10.1109/TITS.2006.884615. · DOI
- Van Arem, B. van, Vos, A. de, Vanderschuren, M., 1997. The effect of a special lane for intelligent vehicles on traffic flows. https://www.researchgate.net/publication/ 277307403%0ATNO-report, TNO.
- van Beinum, A., Farah, H., Wegman, F., Hoogendoorn, S., 2016. Critical assessment of methodologies for operations and safety evaluations of freeway turbulence. Transp. Res. Rec. J. Transp. Res. Board 2556, 39-48. https://doi.org/10.3141/2556-05. · DOI
- van Beinum, A., Hovenga, M., Knoop, V., Farah, H., Wegman, F., Hoogendoorn, S., 2018. Macroscopic traffic flow changes around ramps. Transp. A Transp. Sci. 14, 598-614. https://doi.org/10.1080/23249935.2017.1415997. · DOI
- Vander Laan, Z., Sadabadi, K.F., 2017. Operational performance of a congested corridor with lanes dedicated to autonomous vehicle traffic. Int. J. Transp. Sci. Technol. 6, 42-52. https://doi.org/10.1016/j.ijtst.2017.05.006. · DOI
- Varotto, S.F., Hoogendoorn, R.G., van Arem, B., Hoogendoorn, S.P., 2015. Empirical longitudinal driving behavior in authority transitions between adaptive cruise control and manual driving. Transp. Res. Rec. J. Transp. Res. Board 2489, 105-114. https://doi.org/10.3141/2489-12. · DOI
- Viti, F., Hoogendoorn, S.P., Alkim, T.P., Bootsma, G., 2008. Driving behavior interaction with ACC: Results from a field operational test in the Netherlands. IEEE Intell. Veh. Symp. Proc. 745-750. https://doi.org/10.1109/IVS.2008.4621199. · DOI
- Wan, N., Vahidi, A., Luckow, A., 2016. Optimal speed advisory for connected vehicles in arterial roads and the impact on mixed traffic. Transp. Res. Part C Emerg. Technol. 69, 548-563. https://doi.org/10.1016/J.TRC.2016.01.011. · DOI
- Wang, C., Quddus, M.A., Ison, S.G., 2013. The effect of traffic and road characteristics on road safety: A review and future research direction. Saf. Sci. 57, 264-275. https://doi.org/10.1016/j.ssci.2013.02.012. · DOI
- Wolterink, W.K., Heijenk, G., Karagiannis, G., 2010. Constrained geocast to support cooperative adaptive cruise control (CACC) merging, in: 2010 IEEE Vehicular Networking Conference, VNC 2010. pp. 41-48. https://doi.org/10.1109/VNC.2010.5698268. · DOI
- Yang, D., Farah, H., Schoenmakers , M., & Alkim, T., 2019. Human drivers behavioural adaptation when driving next to a platoon of automated vehicles on a dedicated lane and implications on ttraffic flow: a driving simulator and microscopic simulation study in the Netherlands, in: Proceedings 98th Annual Meeting of the Transportation Research Board, Washington DC, USA.
- Xiao, L., Wang, M., Van Arem, B., 2019. Traffic flow impacts of converting an HOV lane into a dedicated CACC lane on a freeway corridor. IEEE Intell. Transp. Syst. Mag. 12, 60-73. https://doi.org/10.1109/MITS.2019.2953477. · DOI
- Ye, L., Yamamoto, T., 2018. Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput. Phys. A Stat. Mech. its Appl. 512, 588-597. https://doi.org/10.1016/j.physa.2018.08.083. · DOI
- Yeo, H., Skabardonis, A., Halkias, J., Colyar, J., Alexiadis, V., 2008. Oversaturated freeway flow algorithm for use in next generation simulation. Transp. Res. Rec. J. Transp. Res. Board 2088, 68-79. https://doi.org/10.3141/2088-08. · DOI
- Zhao, X., Wang, Z., Xu, Z., Wang, Y., Li, X., Qu, X., 2020. Field experiments on longitudinal characteristics of human driver behavior following an autonomous vehicle. Transp. Res. Part C Emerg. Technol. 114, 205-224. https://doi.org/10.1016/j.trc.2020.02.018. · DOI
- Authors
- Razmi Rad, S., Farah, H., Taale, H., van Arem, B., & Hoogendoorn, S. P.
- Affiliations
- Delft University of Technology (NL)
- Rijkswaterstaat (NL)
- Venue
- Transportation Research Part C: Emerging Technologies
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 117
- Pages
- 102664
- ISSN
- 0968-090X (ISSN-L)
- OpenAlex
- W3033800859
- Cited by
- 128
- References
- 89
- Citations by year
- 2026: 13 · 2025: 19 · 2024: 32 · 2023: 23 · 2022: 26 · 2021: 14 · 2020: 1
- FWCI
- 7.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Transportation Research Part C Emerging Technologies (publishedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- Research Repository (Delft University of Technology) (submittedVersion)
- Funders
- Ministry of Infrastructure and Water Management
- Rijkswaterstaat (31137019)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Keywords
- dedicated lanes, connected and automated vehicles, motorways, road safety, traffic efficiency, traffic flow simulation, research agenda
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review