journal article·2024-02-07Open access

Enhancing Freeway Traffic Capacity: The Impact of Autonomous Vehicle Platooning Intensity

Qing Chang, Hong Chen

Applied Sciences· Volume 14· Issue 4

Abstract

This paper proposes a theoretical model to discuss the capacity of heterogeneous saturated flow. A crucial indicator, platooning intensity, which represents the willingness of connected and autonomous vehicles to form platoons, is taken into consideration. The relationship between platooning intensity and the penetration rate of connected and autonomous vehicles is also evaluated. Numerical analysis is conducted based on relevant parameters, which further improves the proposed theoretical model. Finally, a microscopic simulation is used to verify the accuracy of the proposed model. The results indicate that both the speed and the market penetration rate have a significant impact on capacity; however, the impact is not linear. The slope of the speed-affected curve gradually decreases, whereas the slope of the market penetration rate-affected curve gradually increases. The impact of market penetration rate on theoretical capacity intensifies with the increase in speed. As the number of vehicles within a fleet increase, the weighted average of platooning intensity gradually tends towards the market penetration rate. The formulation offers important insights into traffic performance with heterogeneous flow.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Traffic control and management · Transportation Planning and Optimization · Traffic and Road Safety

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Transportation Engineering, Chang’an University, Xi’an 710064, China

References (48)

Show source-provided references
1. 50th Anniversary Invited Article—Autonomous Vehicles and Connected Vehicle Systems: Flow and Operations Considerations10.1287/trsc.2016.0712 ↗
2. The Energy Efficiency of Electric Energy as a Traction Used in Transport10.2478/ttt-2018-0005 ↗
3. Rindone, C. (2022). Sustainable Mobility as a Service: Supply Analysis and Test Cases. Information, 13.10.3390/info13070351 ↗
4. Right-of-way reallocation for mixed flow of autonomous vehicles and human driven vehicles10.1016/j.trc.2020.102630 ↗
5. Modeling impacts of Cooperative Adaptive Cruise Control on mixed traffic flow in multi-lane freeway facilities10.1016/j.trc.2018.07.027 ↗
6. Model Predictive Control for Motorway Traffic with Mixed Manual and VACS-equipped Vehicles10.1016/j.trpro.2015.09.095 ↗
7. Investigation of the Impact on Throughput of Connected Autonomous Vehicles with Headway Based on the Leading Vehicle Type10.1177/0361198119839989 ↗
8. Influence of connected and autonomous vehicles on traffic flow stability and throughput10.1016/j.trc.2016.07.007 ↗
9. Exploring the effects of cooperative adaptive cruise control on highway traffic flow using microscopic traffic simulation10.1080/03081060.2013.870791 ↗
10. Analyzing the impact of automated vehicles on uncertainty and stability of the mixed traffic flow10.1016/j.trc.2020.01.017 ↗
11. Analytical Prediction of Self-Organized Traffic Jams as a Function of Increasing ACC Penetration10.1109/TITS.2012.2217742 ↗
12. Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow10.3141/2324-08 ↗
13. Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles10.1016/j.trb.2017.01.017 ↗
14. Constructing a fundamental diagram for traffic flow with automated vehicles: Methodology and demonstration10.1016/j.trb.2021.06.011 ↗
15. Modeling the fundamental diagram of mixed human-driven and connected automated vehicles10.1016/j.trc.2020.102614 ↗
16. Network Fundamental Diagram (NFD) and traffic signal control: First empirical evidences from the city of Santander10.1016/j.trpro.2017.12.112 ↗
17. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data10.1016/j.trc.2014.09.001 ↗
18. Wagner, P. (2016). Autonomous Driving, Springer.
19. Cooperative lane control application for fully connected and automated vehicles at multilane freeways10.1016/j.trc.2019.11.007 ↗
20. A mixed traffic speed harmonization model with connected autonomous vehicles10.1016/j.trc.2019.05.005 ↗
21. The Impact of Cooperative Adaptive Cruise Control on Traffic-Flow Characteristics10.1109/TITS.2006.884615 ↗
22. Modeling connected and autonomous vehicles in heterogeneous traffic flow10.1016/j.physa.2017.08.015 ↗
23. Traffic automation and lane management for communicant, autonomous, and human-driven vehicles10.1016/j.trc.2019.12.009 ↗
24. Practical String Stability of Platoon of Adaptive Cruise Control Vehicles10.1109/TITS.2011.2143407 ↗
25. Russo, F., and Rindone, C. (2023). Smart City for Sustainable Development: Applied Processes from SUMP to MaaS at European Level. Appl. Sci., 13.10.3390/app13031773 ↗
26. Adaptive cruise control design for active congestion avoidance10.1016/j.trc.2007.12.004 ↗
27. A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method10.1016/j.trb.2017.09.022 ↗
28. How to assess the benefits of connected vehicles? A simulation framework for the design of cooperative traffic management strategies10.1016/j.trc.2016.01.020 ↗
29. Highway traffic state estimation with mixed connected and conventional vehicles: Microscopic simulation-based testing10.1016/j.trc.2017.02.015 ↗
30. Impacts analysis of car following models considering variable vehicular gap policies10.1016/j.physa.2018.02.155 ↗
31. Development and testing of a fully Adaptive Cruise Control system10.1016/j.trc.2011.07.001 ↗
32. On Microscopic Modelling of Adaptive Cruise Control Systems10.1016/j.trpro.2015.03.010 ↗
33. Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model10.1016/j.physa.2017.12.103 ↗
34. Modeling Microscopic Car-Following Strategy of Mixed Traffic to Identify Optimal Platoon Configurations for Multiobjective Decision-Making10.1155/2018/7835010 ↗
35. Simulation Framework for Vehicle Platooning and Car-following Behaviors Under Connected-vehicle Environment10.1016/j.sbspro.2013.08.105 ↗
36. Impact of connected and autonomous vehicle dedicated lane on the freeway traffic efficiency10.1186/s12544-022-00535-4 ↗
37. Platoons of connected vehicles can double throughput in urban roads10.1016/j.trc.2017.01.023 ↗
38. Nikolos, I.K., Delis, A.I., and Papageorgiou, M. (2015, January 5–18). Macroscopic Modelling and Simulation of ACC and CACC Traffic. Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain.10.1109/ITSC.2015.344 ↗
39. Mixed flow of autonomous and human-driven vehicles: Analytical headway modeling and optimal lane management10.1016/j.trc.2019.10.009 ↗
40. The impact of autonomous vehicles on urban traffic network capacity: An experimental analysis by microscopic traffic simulation10.1080/19427867.2019.1662561 ↗
41. Nishimura, Y., Fujita, A., Hiromori, A., Yamaguchi, H., Higashino, T., Suwa, A., Urayama, H., Takeshima, S., Takai, M., and IEEE (2019, January 11–15). A Study on Behavior of Autonomous Vehicles Cooperating with Manually-Driven Vehicles. Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom), Kyoto, Japan.10.1109/PERCOM.2019.8767390 ↗
42. Zhao, L.H., Malikopoulos, A.A., Rios-Torres, J., and IEEE (2019, January 19–21). On the Traffic Impacts of Optimally Controlled Connected and Automated Vehicles. Proceedings of the 3rd IEEE Conference on Control Technology and Applications (IEEE CCTA), Hong Kong, China.10.1109/CCTA.2019.8920630 ↗
43. Schakel, W.J., Arem, B.v., and Netten, B.D. (2010, January 19–22). Effects of Cooperative Adaptive Cruise Control on traffic flow stability. Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal.10.1109/ITSC.2010.5625133 ↗
44. Calvert, S.C., van den Broek, T.H.A., van Noort, M., and IEEE (2012, January 3–7). Cooperative Driving in Mixed Traffic Networks-Optimizing for Performance. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Alcala de Henares, Spain.10.1109/IVS.2012.6232138 ↗
45. Driver usage and understanding of adaptive cruise control10.1016/j.apergo.2011.08.005 ↗
46. Lidar Data Analysis for Time to Headway Determination in the DriveSafe Project Field Tests
47. Emission Mitigation via Longitudinal Control of Intelligent Vehicles in a Congested Platoon10.1111/mice.12130 ↗
48. Board, T.R., and National Academies of Sciences, Engineering, and Medicine (2022). Highway Capacity Manual 7th Edition: A Guide for Multimodal Mobility Analysis, The National Academies Press.