Uncertainty Estimation of Connected Vehicle Penetration Rate

Published Online:https://doi.org/10.1287/trsc.2023.1209

References

  • Ambühl L, Menendez M (2016) Data fusion algorithm for macroscopic fundamental diagram estimation. Transportation Res. Part C Emerging Tech. 71:184–197.CrossrefGoogle Scholar
  • Argote J, Christofa E, Xuan Y, Skabardonis A (2011) Estimation of measures of effectiveness based on connected vehicle data. Proc. 14th Internat. IEEE Conf. Intelligent Transportation Systems (IEEE, Piscataway, NJ), 1767–1772.Google Scholar
  • Cao Y, Tang K, Sun J, Ji Y (2021) Day-to-day dynamic origin–destination flow estimation using connected vehicle trajectories and automatic vehicle identification data. Transportation Res. Part C Emerging Tech. 129:103241.CrossrefGoogle Scholar
  • Comert G (2013) Simple analytical models for estimating the queue lengths from probe vehicles at traffic signals. Transportation Res. Part B Methodological 55:59–74.CrossrefGoogle Scholar
  • Comert G (2016) Queue length estimation from probe vehicles at isolated intersections: Estimators for primary parameters. Eur. J. Oper. Res. 252:502–521.CrossrefGoogle Scholar
  • Comert G, Cetin M (2009) Queue length estimation from probe vehicle location and the impacts of sample size. Eur. J. Oper. Res. 197:196–202.CrossrefGoogle Scholar
  • Comert G, Cetin M (2011) Analytical evaluation of the error in queue length estimation at traffic signals from prove vehicle data. IEEE Trans. Intelligent Transportation Systems 12(2):563–573.CrossrefGoogle Scholar
  • Du J, Rakha H, Gayah VV (2016) Deriving macroscopic fundamental diagrams from probe data: Issues and proposed solutions. Transportation Res. Part C Emerging Tech. 66:136–149.CrossrefGoogle Scholar
  • Federal Highway Administration (2006) Next generation simulation: Peachtree Street data set. Retrieved June 25, 2022, https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf.Google Scholar
  • Feng Y, Head KL, Khoshmagham S, Zamanipour M (2015) A real-time adaptive signal control in a connected vehicle environment. Transportation Res. Part C Emerging Tech. 55:460–473.CrossrefGoogle Scholar
  • Geroliminis N, Daganzo CF (2008) Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings. Transportation Res. Part B Methodological 42(9):759–770.CrossrefGoogle Scholar
  • Hao P, Ban XJ, Guo D, Ji Q (2014) Cycle-by-cycle intersection queue length distribution Estimation using sample travel times. Transportation Res. Part B Methodological 68:185–204.CrossrefGoogle Scholar
  • Iqbal MS, Hadi M, Xiao Y (2018) Effect of link-level variations of connected vehicles (CV) proportions on the accuracy and reliability of travel time estimation. IEEE Trans. Intelligent Transportation Systems 20(1):87–96.CrossrefGoogle Scholar
  • Jenelius E, Koutsopoulos HN (2013) Travel time estimation for urban road networks using low frequency probe vehicle data. Transportation Res. Part B Methodological 53:64–81.CrossrefGoogle Scholar
  • Jenelius E, Koutsopoulos HN (2015) Probe vehicle data sampled by time or space: Consistent travel time allocation and estimation. Transportation Res. Part B Methodological 71:120–137.CrossrefGoogle Scholar
  • Khan SM, Dey KC, Chowdhury M (2017) Real-time traffic state estimation with connected vehicles. IEEE Trans. Intelligent Transportation Systems 18(7):1687–1699.CrossrefGoogle Scholar
  • Lu Y, Xu X, Ding C, Lu G (2019) A speed control method at successive signalized intersections under connected vehicles environment. IEEE Intelligent Transportation Systems Magazine 11(3):117–128.CrossrefGoogle Scholar
  • Meng F, Wong SC, Wong W, Li YC (2017a) Estimation of scaling factors for traffic counts based on stationary and mobile sources of data. Internat. J. Intelligent Transportation Systems Res. 15(3):180–191.CrossrefGoogle Scholar
  • Meng F, Wong W, Wong SC, Pei X, Li YC, Huang H (2017b) Gas dynamic analogous exposure approach to interaction intensity in multiple-vehicle crash: Case study of crashes involving taxis. Anal. Methods Accident Res. 16:90–103.CrossrefGoogle Scholar
  • Mousa SR, Ishak S (2017) An extreme gradient boosting algorithm for freeway short-term travel time prediction using basic safety messages of connected vehicles. Transportation Res. Board 96th Annual Meeting (Transportation Research Board, Washington, DC).Google Scholar
  • Rahmani M, Jenelius E, Koutsopoulos HN (2015) Non-parametric estimation of route travel time distributions from low-frequency floating car data. Transportation Res. Part C Emerging Tech. 58:343–362.CrossrefGoogle Scholar
  • Sen S, Head KL (1997) Controlled optimization of phases at an intersection. Transportation Sci. 31(1):5–17.LinkGoogle Scholar
  • Tian D, Yuan Y, Qi H, Lu Y, Wang Y, Xia H, He A (2015) A dynamic travel time estimation model based on connected vehicles. Math. Problems Engrg. 2015:903962.Google Scholar
  • Wang P, Zhang J, Deng H, Zhang M (2020) Real-time urban regional route planning model for connected vehicles based on V2X communication. J. Transportation Land Use 13(1):517–538.CrossrefGoogle Scholar
  • Wong W, Wong SC (2015) Systematic bias in transport model calibration arising from the variability of linear data projection. Transportation Res. Part B Methodological 75:1–18.CrossrefGoogle Scholar
  • Wong W, Wong SC (2016a) Biased standard error estimations in transport model calibration due to heteroscedasticity arising from the variability of linear data projection. Transportation Res. Part B Methodological 88:72–92.CrossrefGoogle Scholar
  • Wong W, Wong SC (2016b) Evaluation of the impact of traffic incidents using GPS data. Transport 169(3):148–162.Google Scholar
  • Wong W, Wong SC (2016c) Network topological effects on the macroscopic Bureau of Public Roads function. Transportmetrica A Transporation Sci. 12(3):272–296.CrossrefGoogle Scholar
  • Wong W, Wong SC (2019) Unbiased estimation methods of nonlinear transport models based on linearly projected data. Transportation Sci. 53(3):665–682.AbstractGoogle Scholar
  • Wong W, Wong SC, Liu X (2019) Bootstrap standard error estimations of nonlinear transport models based on linearly projected data. Transportmetrica A Transportation Sci. 15(2):602–630.CrossrefGoogle Scholar
  • Wong W, Wong SC, Liu X (2021) Network topological effects on the macroscopic fundamental diagram. Transportmetrica B Transport Dynamics 9(1):376–398.CrossrefGoogle Scholar
  • Wong W, Shen S, Zhao Y, Liu X (2019) On the estimation of connected vehicle penetration rate based on single-source connected vehicle data. Transportation Res. Part B Methodological 126:169–191.CrossrefGoogle Scholar
  • Yang X, Lu Y, Hao W (2017) Origin-destination estimation using probe vehicle trajectory and link counts. J. Advanced Transportation 2017:4341532.CrossrefGoogle Scholar
  • Yin Y (2008) Robust optimal traffic signal timing. Transportation Res. Part B Methodological 42(10):911–924.CrossrefGoogle Scholar
  • Zhao Y, Wong W, Zheng J, Liu HX (2022) Maximum likelihood estimation of probe vehicle penetration rates and queue length distributions from probe vehicle data. IEEE Trans. Intelligent Transportation Systems 23(7):7628–7636.CrossrefGoogle Scholar
  • Zhao Y, Zheng J, Wong W, Wang X, Meng Y, Liu HX (2019a) Estimation of queue lengths, probe vehicle penetration rates, and traffic volumes at signalized intersections using probe vehicle trajectories. Transportation Res. Record 2673(11):660–670.CrossrefGoogle Scholar
  • Zhao Y, Zheng J, Wong W, Wang X, Meng Y, Liu HX (2019b) Various methods for queue length and traffic volume estimation using probe vehicle trajectories. Transportation Res. Part C Emerging Tech. 107:70–91.CrossrefGoogle Scholar
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