240
Views
1
CrossRef citations to date
0
Altmetric
Research Articles

Evaluating the safe and eco-driving performances of car-following behaviors in a vehicle platoon under foggy conditions

, ORCID Icon, &

References

  • Ahn, K., Rakha, H., Trani, A., & Van Aerde, M. (2002). Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. Journal of Transportation Engineering, 128(2), 182–190. doi:10.1061/(ASCE)0733-947X(2002)128:2(182)
  • Broughton, K. L. M., Switzer, F., & Scott, D. (2007). Car following decisions under three visibility conditions and two speeds tested with a driving simulator. Accident; Analysis and Prevention, 39(1), 106–116. doi:10.1016/j.aap.2006.06.009
  • Chang, J. Y., & Lai, W. C. (2016). An analysis of pileup accidents in highway systems. Physica A: Statistical Mechanics and Its Applications, 443, 423–438. doi:10.1016/j.physa.2015.09.095
  • Chen, T., Shi, X., & Wong, Y. D. (2019). Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data. Accident; Analysis and Prevention, 129, 156–169. doi:10.1016/j.aap.2019.05.017
  • Das, A., & Ahmed, M. M. (2021). Exploring the effect of fog on lane-changing characteristics utilizing the SHRP2 naturalistic driving study data. Journal of Transportation Safety & Security, 13(5), 477–502. doi:10.1080/19439962.2019.1645777
  • Dehkordi, S. G., Cholette, M. E., Larue, G. S., Rakotonirainy, A., & Glaser, S. (2021). Energy efficient and safe control strategy for electric vehicles including driver preference. IEEE Access 9, 11109–11122. doi:10.1109/ACCESS.2021.3050780
  • Edwards, J. (1998). The relationship between road accident severity and recorded weather. Journal of Safety Research, 29(4), 249–262. doi:10.1016/S0022-4375(98)00051-6
  • Gonder, J., Earleywine, M., & Sparks, W. (2011). Final report on the fuel saving effectiveness of various driver feedback approaches (Milestone Report, Tech. Rep. No. NREL/MP-5400-50836). National Renewable Energy Laboratory.
  • Günther, M., Rauh, N., & Krems, J. F. (2017). Conducting a study to investigate eco-driving strategies with battery electric vehicles – A multiple method approach. Transportation Research Procedia, 25, 2242–2256. doi:10.1016/j.trpro.2017.05.431
  • Hamdar, S. H., Qin, L., & Talebpour, A. (2016). Weather and road geometry impact on longitudinal driving behavior: Exploratory analysis using an empirically supported acceleration modeling framework. Transportation Research Part C: Emerging Technologies, 67, 193–213. doi:10.1016/j.trc.2016.01.017
  • Hassan, H., Abdel-Aty, M., & Oloufa, A. (2011). Effect of warning messages and variable speeds in different visibility conditions. Compendium of Papers CD-ROM, Transportation Research Board 90th Annual Meeting, Washington, DC.
  • He, X., & Wu, X. (2018). Eco-driving advisory strategies for a platoon of mixed gasoline and electric vehicles in a connected vehicle system. Transportation Research Part D: Transport and Environment, 63, 907–922. doi:10.1016/j.trd.2018.07.014
  • Hogema, J. H., & Horst, R. V. D. (1997). Evaluation of A16 motorway fog-signaling system with respect to driving behavior. Transportation Research Record: Journal of the Transportation Research Board, 1573(1), 63–67. doi:10.3141/1573-10
  • Hu, D., Feng, X., Zhao, X., Li, H., Ma, J., & Fu, Q. (2022). Impact of HMI on driver’s distraction on a freeway under heavy foggy condition based on visual characteristics. Journal of Transportation Safety & Security, 14(6), 905–928. doi:10.1080/19439962.2020.1853641
  • Hu, S., Li, C., Yan, S., & Xin, X. (2012). Study on control strategy of intelligent guidance system for foggy area traffic safety. Applied Mechanics and Materials, 209-211, 654–662. doi:10.4028/www.scientific.net/AMM.209-211.654
  • Huang, Y., Jiang, R., Zhang, H. M., Hu, M., Tian, J., Jia, B., & Gao, Z. (2018). Experimental study and modeling of car-following behavior under high speed situation. Transportation Research Part C: Emerging Technologies, 97, 194–215. doi:10.1016/j.trc.2018.10.022
  • Huang, Y., Wang, Y., Yan, X., Duan, K., & Zhu, J. (2022). Behavior model and guidance strategies of the crossing behavior at unsignalized intersections in the connected vehicle environment. Transportation Research Part F: Traffic Psychology and Behaviour, 88, 13–24. doi:10.1016/j.trf.2022.05.008
  • Huang, Y., Yan, X., Li, X., Duan, K., Rakotonirainy, A., & Gao, Z. (2022). Improving car-following model to capture unobserved driver heterogeneity and following distance features in fog condition. Transportmetrica A: Transport Science, 1–24. doi:10.1080/23249935.2022.2048917
  • Huang, Y., Yan, X., Li, X., & Yang, J. (2020). Using a multi-user driving simulator system to explore the patterns of vehicle fleet rear-end collisions occurrence under different foggy conditions and speed limits. Transportation Research Part F: Traffic Psychology and Behaviour, 74, 161–172. doi:10.1016/j.trf.2020.08.025
  • Jiang, R., Hu, M., Zhang, H. M., Gao, Z., Jia, B., & Wu, Q. (2015). On some experimental features of car-following behavior and how to model them. Transportation Research Part B: Methodological, 80, 338–354. doi:10.1016/j.trb.2015.08.003
  • Jiang, R., Hu, M., Zhang, H. M., Gao, Z., Jia, B., Wu, Q., Wang, B., & Yang, M. (2014). Traffic experiment reveals the nature of car-following. PLoS One, 9(4), e94351. doi:10.1371/journal.pone.0094351
  • Lang, L., Guo, N., Jiang, R., & Zhu, K. (2019). An improved inertia model to reproduce car-following instability. Physica A: Statistical Mechanics and Its Applications, 526, 121087. doi:10.1016/j.physa.2019.121087
  • Li, X., Cui, J., An, S., & Parsafard, M. (2014). Stop-and-go traffic analysis: Theoretical properties, environmental impacts and oscillation mitigation. Transportation Research Part B: Methodological, 70, 319–339. doi:10.1016/j.trb.2014.09.014
  • Li, X., Guo, Z., & Li, Y. (2022). Driver operational level identification of driving risk and graded time-based alarm under near-crash conditions: A driving simulator study. Accident; Analysis and Prevention, 166, 106544. doi:10.1016/j.aap.2021.106544
  • Li, X., Vaezipour, A., Rakotonirainy, A., Demmel, S., & Oviedo-Trespalacios, O. (2020). Exploring drivers’ mental workload and visual demand while using an in-vehicle HMI for eco-safe driving. Accident; Analysis and Prevention, 146, 105756. doi:10.1016/j.aap.2020.105756
  • Li, X., Yan, X., & Wong, S. C. (2015). Effects of fog, driver experience and gender on driving behavior on S-curved road segments. Accident; Analysis and Prevention, 77, 91–104. doi:10.1016/j.aap.2015.01.022
  • Li, Y., Xu, C., Xing, L., & Wang, W. (2017). Integrated cooperative adaptive cruise and variable speed limit controls for reducing rear-end collision risks near freeway bottlenecks based on micro-simulations. IEEE Transactions on Intelligent Transportation Systems, 18(11), 3157–3167. doi:10.1109/TITS.2017.2682193
  • Liu, M., Zhao, J., Hoogendoorn, S., & Wang, M. (2022). A single-layer approach for joint optimization of traffic signals and cooperative vehicle trajectories at isolated intersections. Transportation Research Part C: Emerging Technologies, 134, 103459. doi:10.1016/j.trc.2021.103459
  • Minderhoud, M. M., & Bovy, P. H. L. (2001). Extended time-to-collision measures for road traffic safety assessment. Accident; Analysis and Prevention, 33(1), 89–97. doi:10.1016/S0001-4575(00)00019-1
  • Park, H., Oh, C., Moon, J., & Kim, S. (2018). Development of a lane change risk index using vehicle trajectory data. Accident; Analysis and Prevention, 110, 1–8. doi:10.1016/j.aap.2017.10.015
  • Peng, Y., Abdel-Aty, M., Shi, Q., & Yu, R. (2017). Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures. Transportation Research Part C: Emerging Technologies, 74, 295–305. doi:10.1016/j.trc.2016.11.022
  • Perrin, J. (2000). Effects of variable speed limit signs on driver behavior during inclement weather. Institute of Transportation Engineers (ITE) 2000 Annual Meeting and Exhibit, Nashville, Tennessee, 6 pp.
  • Rakha, H., Ahn, K., & Trani, A. (2004). Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions. Transportation Research Part D: Transport and Environment, 9(1), 49–74. doi:10.1016/S1361-9209(03)00054-3
  • Schall, D. L., & Mohnen, A. (2017). Incentivizing energy-efficient behavior at work: An empirical investigation using a natural field experiment on eco-driving. Applied Energy, 185(2), 1757–1768. doi:10.1016/j.apenergy.2015.10.163
  • Shi, J., & Tan, J. (2013). Effect analysis of intermittent release measures in heavy fog weather with an improved CA model. Discrete Dynamics in Nature and Society, 2013, doi:10.1155/2013/812562
  • Shi, X., Yao, H., Liang, Z., & Li, X. (2022). An empirical study on fuel consumption of commercial automated vehicles. Transportation Research Part D: Transport and Environment, 106, 103253. doi:10.1016/j.trd.2022.103253
  • Sun, J., Ma, Z., Li, T. N., & Niu, D. N. (2015). Development and application of an integrated traffic simulation and multi-driving simulators. Simulation Modelling Practice and Theory, 59, 1–17. doi:10.1016/j.simpat.2015.08.003
  • Tan, J., Gong, L., Qin, X., & Niu, P. (2019). Multiple-vehicle collision influenced by misjudgment of space headway in traffic flow under fog weather condition. IOP Conference Series: Earth and Environmental Science, 304(3), 032077. doi:10.1088/1755-1315/304/3/032077
  • The Ministry of Public Security Traffic Management Bureau. (2017). The crash report on road safety in China 2016. State and Local Policy Program.
  • Venthuruthiyil, S. P., & Chunchu, M. (2022). Anticipated collision time (ACT): A two-dimensional surrogate safety indicator for trajectory-based proactive safety assessment. Transportation Research Part C: Emerging Technologies, 139, 103655. doi:10.1016/j.trc.2022.103655
  • Wang, J., & Rakha, H. A. (2016). Fuel consumption model for conventional diesel buses. Applied Energy, 170, 394–402. doi:10.1016/j.apenergy.2016.02.124
  • Wang, K., Zhang, W., Feng, Z., Yu, H., & Wang, C. (2021). Reasonable driving speed limits based on recognition time in a dynamic low-visibility environment related to fog—A driving simulator study. Accident; Analysis and Prevention, 154, 106060. doi:10.1016/j.aap.2021.106060
  • Wang, Y., Tu, H., Sze, N. N., Li, H., & Ruan, X. (2022). A novel traffic conflict risk measure considering the effect of vehicle weight. Journal of Safety Research, 80, 1–13. doi:10.1016/j.jsr.2021.09.008
  • Wanvik, P. (2009). Effects on road lighting: An analysis based on Dutch accident statistics 1987-2006. Accident Analysis & Prevention, 41(1), 123–128. doi:10.1016/j.aap.2008.10.003
  • World Health Organization. (2018). Road traffic injuries fact sheet. WHO. Retrieved December 16, 2018, from http://www.who.int/mediacentre/factsheets/fs358/en/
  • Wu, J., Wen, H., & Qi, W. (2020). A new method of temporal and spatial risk estimation for lane change considering conventional recognition defects. Accident; Analysis and Prevention, 148, 105796. doi:10.1016/j.aap.2020.105796
  • Wu, Y., Abdel-Aty, M., Cai, Q., Lee, J., & Park, J. (2018). Developing an algorithm to assess the rear-end collision risk under fog conditions using real-time data. Transportation Research Part C: Emerging Technologies, 87, 11–25. doi:10.1016/j.trc.2017.12.012
  • Wu, Y., Abdel-Aty, M., Park, J., & Zhu, J. (2018b). Effects of crash warning systems on rear-end crash avoidance behavior under fog conditions. Transportation Research Part C: Emerging Technologies, 95, 481–492. doi:10.1016/j.trc.2018.08.001
  • Wu, Y., Li, H., Zhao, X., Xing, G., Chen, Y., & Fu, Q. (2021). Effect of fog weather warning system under cooperative vehicle infrastructure on vehicle operating eco-characteristics. Journal of Traffic and Transportation Engineering, 21(4), 259–268. doi:10.19818/j.cnki.1671-1637.2021.04.020
  • Xing, L., He, J., Li, Y., Wu, Y., Yuan, J., & Gu, X. (2020). Comparison of different models for evaluating vehicle collision risks at upstream diverging area of toll plaza. Accident; Analysis and Prevention, 135, 105343. doi:10.1016/j.aap.2019.105343
  • Yan, X., Li, X., Liu, Y., & Zhao, J. (2014). Effects of foggy conditions on drivers’ speed control behaviors at different risk levels. Safety Science, 68, 275–287. doi:10.1016/j.ssci.2014.04.013
  • Yang, Y., Cao, T., Xu, S., Qian, Y., & Li, Z. (2022). Influence of driving style on traffic flow fuel consumption and emissions based on the field data. Physica A: Statistical Mechanics and Its Applications, 599, 127520. doi:10.1016/j.physa.2022.127520
  • Yao, E., Yang, Z., Song, Y., & Zuo, T. (2013). Comparison of electric vehicle’s energy consumption factors for different road types. Discrete Dynamics in Nature and Society, 2013, 1–7. doi:10.1155/2013/328757
  • Young, M. S., Birrell, S. A., & Stanton, N. A. (2011). Safe driving in a green world: A review of driver performance benchmarks and technologies to support ‘smart’ driving. Applied Ergonomics, 42(4), 533–539. doi:10.1016/j.apergo.2010.08.012
  • Zhang, Y., Li, X., Yu, Q., & Yan, X. (2022). Developing a two-stage auditory warning system for safe driving and eco-driving at signalized intersections: A driving simulation study. Accident; Analysis and Prevention, 175, 106777. doi:10.1016/j.aap.2022.106777
  • Zhang, Y., Yan, X., & Li, X. (2021). Effect of warning message on driver’s stop/go decision and red-light-running behaviors under fog condition. Accident; Analysis and Prevention, 150, 105906. doi:10.1016/j.aap.2020.105906
  • Zhao, X., Chen, Y., Li, H., Ma, J., & Li, J. (2021). A study of the compliance level of connected vehicle warning information in a fog warning system based on a driving simulation. Transportation Research Part F: Traffic Psychology and Behaviour, 76, 215–237. doi:10.1016/j.trf.2020.11.012
  • Zhao, X., Xu, W., Ma, J., Li, H., Chen, Y., & Rong, J. (2019). Effects of connected vehicle-based variable speed limit under different foggy conditions based on simulated driving. Accident; Analysis and Prevention, 128, 206–216. doi:10.1016/j.aap.2019.04.020

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.