Open Access
Issue
Sci. Tech. Energ. Transition
Volume 81, 2026
Article Number 2
Number of page(s) 10
DOI https://doi.org/10.2516/stet/2026002
Published online 6 mars 2026
  • Yang L., Chau K.W., Szeto W.Y., Cui X., Wang X. (2020) Accessibility to transit, by transit, and property prices: Spatially varying relationships, Transp. Res. Part D 85, 102387. [Google Scholar]
  • Van Fan Y., Perry S., Klemeš J.J., Lee C.T. (2018) A review on air emissions assessment: Transportation, J. Clean. Prod. 194, 673–684. [Google Scholar]
  • Lu Q.Y., Chai J., Wang S., Zhang Z.G., Sun X.C. (2020) Potential energy conservation and CO2 emissions reduction related to China’s road transportation, J. Clean. Prod. 245, 118892. [Google Scholar]
  • Bao Z.K., Ou Y.F., Chen S.Z., Wang T. (2022) Land use impacts on traffic congestion patterns: A tale of a Northwestern Chinese City, Land 11, 12, 2295. [Google Scholar]
  • Bao Z.K., Ng S.T., Yu G., Zhang X.L., Ou Y.F. (2023) The effect of the built environment on spatial-temporal pattern of traffic congestion in a satellite city in emerging economies, Dev. Built Environ. 14, 100173. [Google Scholar]
  • Sohrabi S., Khreis H., Lord D. (2020) Impacts of autonomous vehicles on public health: A conceptual model and policy recommendations, Sustain. Cities Soc. 63, 102457. [Google Scholar]
  • Zhang Z.Y., Song G.H., Zhai Z.Q., Li C.X., Wu Y.Z. (2019) How many trajectories are needed to develop facility-and speed-specific vehicle-specific power distributions for emission estimation? Case study in Beijing, Transp. Res. Rec. 2673, 11, 779–790. [Google Scholar]
  • Xie R., Fang J.Y., Liu C.J. (2017) The effects of transportation infrastructure on urban carbon emissions, Appl. Energy 196, 199–207. [Google Scholar]
  • Li X.P., Cui J.X., An S., Parsafard M.S. (2014) Stop-and-go traffic analysis: Theoretical properties, environmental impacts and oscillation mitigation, Transp. Res. Part B 70, 319–339. [Google Scholar]
  • Montanaro U., Dixit S., Fallah S., Dianati M., Stevens A., Oxtoby D., Mouzakitis A. (2019) Towards connected autonomous driving: review of use-cases, Veh. Syst. Dyn. 57, 6, 779–814. [Google Scholar]
  • Bansal P., Kockelman K.M.(2017) Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies, Transp. Res. Part A 95, 49–63. [Google Scholar]
  • Sharma A., Zheng Z.D., Kim J., Bhaskar A., Haque M.M. (2021) Assessing traffic disturbance, efficiency, and safety of the mixed traffic flow of connected vehicles and traditional vehicles by considering human factors, Transp. Res. Part C 124, 102934. [Google Scholar]
  • Carrone A.P., Rich J., Vandet C.A., An K. (2021) Autonomous vehicles in mixed motorway traffic: capacity utilisation, impact and policy implications, Transport. 48, 2907–2938. [Google Scholar]
  • Yao Z.H., Hu R., Jiang Y.S., Xu T.R. (2020) Stability and safety evaluation of mixed traffic flow with connected automated vehicles on expressways, J. Safety Res. 75, 262–274. [Google Scholar]
  • Yao Z.H., Gu Q.F., Jiang Y.S., Ran B. (2022) 549 Fundamental diagram and stability of mixed traffic flow 550 considering plato on size and intensity of connected auto-551 mated vehicles, Physica A 604, 127857. [Google Scholar]
  • Yao Z.H., Wu Y.X., Jiang Y.S., Ran B. (2022) Modeling the fundamental diagram of mixed traffic flow with dedicated lanes for connected automated vehicles, IEEE Trans. Intell. Transp. Syst. 24, 6, 6517–6529. [Google Scholar]
  • Zheng L., Zhu C., He Z.B., He T. (2021) Safety rule-based cellular automaton modeling and simulation under V2V environment, Transportmetrica A 17, 1, 81–106. [Google Scholar]
  • Wang M., Hoogendoorn S.P., Daamen W., van Arem B., Shyrokau B., Happee R. (2018) Delay-compensating strategy to enhance string stability of adaptive cruise controlled vehicles, Transportmetrica B 6, 3, 211–229. [Google Scholar]
  • Yao Z.H., Wang Y., Liu B., Zhao B., Jiang Y.S. (2021) Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway, Energy 230, 120766. [Google Scholar]
  • Talebian A., Mishra S. (2018) Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations, Transp. Res. Part C 95, 363–380. [Google Scholar]
  • Jiang Y.S., Zhao B., Liu M., Yao Z.H. (2021) A two‐level model for traffic signal timing and trajectories planning of multiple CAVs in a random environment, J. Adv. Transp. 1, 1. [Google Scholar]
  • Mahbub A.M.I., Malikopoulos A.A. (2021) Conditions to provable system-wide optimal coordination of connected and automated vehicles, Automatica 131, 109751. [Google Scholar]
  • Yao Z.H., Xu T.R., Jiang Y.S., Hu R. (2021) Linear stability analysis of heterogeneous traffic flow considering degradations of connected automated vehicles and reaction time, Physica A 561, 125218. [Google Scholar]
  • Yao Z.H., Jiang H.R., Jiang Y.S., Ran B. (2023) A two-stage optimization method for schedule and trajectory of CAVs at an isolated autonomous intersection, IEEE Trans. Intell. Transp. Syst. 24, 3, 3263–3281. [Google Scholar]
  • 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, IIEEE Intell. Transp. Syst. Mag. 12, 1, 60–73. [Google Scholar]
  • Liu H., Kan X.A., Shladover S.E., Lu X.Y., Ferlis R.E. (2018) Impact of cooperative adaptive cruise control on multilane freeway merge capacity, J. Intell. Transp. Syst. 22, 3, 263–275. [Google Scholar]
  • Liu K.Y., Feng T.J. (2023) Heterogeneous traffic flow cellular automata model mixed with intelligent controlled vehicles, Physica A 632, 1, 129316. [Google Scholar]
  • Jin S., Sun D.H., Zhao M., Li Y., Chen J. (2020) Modeling and stability analysis of mixed traffic with conventional and connected automated vehicles from cyber physical perspective, Physica A 551, 124217. [Google Scholar]
  • Cao Z.P., Lu L.L., Chen C., Chen X.U. (2021) Modeling and simulating urban traffic flow mixed with regular and connected vehicles, IEEE Access 9, 10392–10399. [Google Scholar]
  • Ozkan M.F., Ma Y. (2021) Modeling driver behavior in car-following interactions with automated and human-driven vehicles and energy efficiency evaluation, IEEE Access 9, 64696–64707. [Google Scholar]
  • Rickert M., Nagel K., Schreckenberg M., Latour A. (1996) Two lane traffic simulations using cellular automata, Physica A 231, 4, 534–550. [Google Scholar]
  • Chowdhury D., Wolf D.E., Schreckenberg M. (1997) Particle hopping models for two-lane traffic with two kinds of vehicles: Effects of lane-changing rules, Physica A 235, 3–4, 417–439. [Google Scholar]
  • Nagel K., Schreckenberg M. (1992) A cellular automaton model for freeway traffic, J. Phys. I 2, 12, 2221–2229. [Google Scholar]
  • Chen D.J., Ahn S.Y., Chitturi M., Noyce D.A. (2017) Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles, Transp. Res. Part B 100, 196–221. [Google Scholar]
  • Hu X.H., Huang M.Y., Guo J.P. (2020) Feature analysis on mixed traffic flow of manually driven and autonomous vehicles based on cellular automata, Math. Probl. Eng. 1, 7210547. [Google Scholar]
  • Liu Y., Lin X., He F., Li M. (2018) Cellular automata for modeling safety issues in mixed traffic of conventional and autonomous vehicles, in: Wang X.K., Zhang Y., Yang D.G., You Z. (eds), 18th COTA International Conference of Transportation Professionals, CICTP 2018, Reston, Beijing, China, pp. 56–65. [Google Scholar]
  • Jiang Y.J., Wang S.C., Yao Z.H., Zhao B., Wang Y. (2021) A cellular automata model for mixed traffic flow considering the driving behavior of connected automated vehicle platoons, Physica A 582, 126262. [Google Scholar]
  • Yang D., Qiu X.P., Ma L., Wu D.H., Zhu L.L., Liang H.B. (2017) Cellular automata–based modeling and simulation of a mixed traffic flow of manual and automated vehicles, Transp. Res. Rec. 2622, 1, 105–116. [Google Scholar]
  • Hua X.D., Yu W.J., Wang W., Xie W.J. (2020) Influence of lane policies on freeway traffic mixed with manual and connected and autonomous vehicles, J. Adv. Transp. 1, 3968625. [Google Scholar]
  • Liu Y.Z.X., Guo J.Q., Taplin J., Wang Y.B. (2017) Characteristic analysis of mixed traffic flow of regular and autonomous vehicles using cellular automata, J. Adv. Transp. 1, 8142074. [Google Scholar]
  • Zhang X.Q., Li L., Zhang J. (2019) An optimal service model for rail freight transportation: Pricing, planning, and emission reducing, J. Clean. Prod. 218, 565–574. [Google Scholar]
  • Xie R., Fang J.Y., Liu C.J. (2017) The effects of transportation infrastructure on urban carbon emissions, Appl. Energy 196, 199–207. [Google Scholar]
  • Pan W., Xue Y., He H.D., Lu W.Z. (2018) Impacts of traffic congestion on fuel rate, dissipation and particle emission in a single lane based on Nasch Model, Physica A 503, 154–162. [Google Scholar]
  • Stogios C., Kasraian D., Roorda M.J., Hatzopoulou M. (2019) Simulating impacts of automated driving behavior and traffic conditions on vehicle emissions, Transp. Res. Part D 76, 176–192. [Google Scholar]
  • Tu R., Alfaseeh L., Djavadian S., Farooq B., Hatzopoulou M. (2019) Quantifying the impacts of dynamic control in connected and automated vehicles on greenhouse gas emissions and urban NO2 concentrations, Transp. Res. Part D 73, 142–151. [Google Scholar]
  • Xiao H., Huang H.J., Tang T.Q. (2017) Analysis of energy consumption and emission of the heterogeneous traffic flow consisting of traditional vehicles and electric vehicles, Mod. Phys. Lett. B 31, 34, 1750324. [Google Scholar]
  • Coppola A., Lui D.G., Petrillo A., Santini S. (2022) Eco-driving control architecture for platoons of uncertain heterogeneous nonlinear connected autonomous electric vehicles, IEEE Trans. Intell. Transp. Syst. 23, 12, 24220–24234. [Google Scholar]
  • Li H.G., Li H.T., Hu Y., Xia T., Miao Q., Chu J. (2023) Evaluation of fuel consumption and emissions benefits of connected and automated vehicles in mixed traffic flow, Front. Energy Res. 11, 1207449. [Google Scholar]
  • Johnsson C., Laureshyn A., De Ceunynck T.D. (2018) In search of surrogate safety indicators for vulnerable road users: a review of surrogate safety indicators, Transport Rev. 38, 6, 765–785. [Google Scholar]
  • Siebert F.W., Oehl M., Pfister H.R. (2014) The influence of time headway on subjective driver states in adaptive cruise control, Transp. Res. Part F 25, 65–73. [Google Scholar]
  • Arrouch I., Ahmad N.S., Goh P., Mohamad-Saleh J. (2022) Close proximity time-to-collision prediction for autonomous robot navigation: an exponential GPR approach, Alex. Eng. J. 61, 12, 11171–11183. [Google Scholar]
  • Lu C.R., Dong J., Houchin A., Liu C.H. (2021) Incorporating the standstill distance and time headway distributions into freeway car-following models and an application to estimating freeway travel time reliability, J. Intell. Transp. Syst. 25, 1, 21–40. [Google Scholar]
  • Wang X., Xue Y., Cen B.L., Zhang P. (2020) Study on pollutant emissions of mixed traffic flow in cellular automaton, Physica A 537, 122686. [Google Scholar]
  • Panis L.I., Broekx S., Liu R.H. (2006) Modelling instantaneous traffic emission and the influence of traffic speed limits, Sci. Total Environ. 371, 1–3, 270–285. [Google Scholar]
  • Pan Y., Wu Y., Xu L., Xia C., Olson D.L. (2024) The impacts of connected autonomous vehicles on mixed traffic flow: A comprehensive review. Physica A Stat. Mech. Appl. 635, 129454. [Google Scholar]
  • Zhang T., Zhan J., Shi J., Xin J., Zheng N. (2023) Human-like decision- making of autonomous vehicles in dynamic traffic scenarios. IEEE CAA J. Autom. Sinica 10, 10, 1905–1917. [Google Scholar]
  • Sognnaes I., Gambhir A., van de Ven D.J., Nikas A., Anger-Kraavi A., Bui H., Campagnolo L., Delpiazzo E., Doukas H., Giarola S., Grant S., Hawkes A., Köberle A.C., Kolpakov A., Mittal S., Moreno J., Perdana S., Rogelj J., Vielle M., Peters G.P. (2021). A multi-model analysis of long-term emissions and warming implications of current mitigation efforts. Nat. Clim. Chang. 11, 12, 1055–1062. [Google Scholar]

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