Imperial College London
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Dr Fangce Guo

Faculty of Engineering, Department of Civil and Environmental Engineering

Advanced Research Fellow
 
 
 
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Contact

 

fangce.guo

 
 
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Location

 

308ASkempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Li:2020:10.1016/j.trc.2020.102630,
author = {Li, T and Guo, F and Krishnan, R and Sivakumar, A and Polak, J},
doi = {10.1016/j.trc.2020.102630},
journal = {Transportation Research Part C: Emerging Technologies},
title = {Right-of-way reallocation for mixed flow of autonomous vehicles and human driven vehicles},
url = {http://dx.doi.org/10.1016/j.trc.2020.102630},
volume = {115},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Autonomous Vehicles (AVs) are bringing challenges and opportunities to urban traffic systems. One of the crucial challenges for traffic managers and local authorities is to understand the nonlinear change in road capacity with increasing AV penetration rate, and to efficiently reallocate the Right-of-Way (RoW) for the mixed flow of AVs and Human Driven Vehicles (HDVs). Most of the existing research suggests that road capacity will significantly increase at high AV penetration rates or an all-AV scenario, when AVs are able to drive with smaller headways to the leading vehicle. However, this increase in road capacity might not be significant at a lower AV penetration rate due to the heterogeneity between AVs and HDVs. In order to investigate the impacts of mixed flow conditions (AVs and HDVs), this paper firstly proposes a theoretical model to demonstrate that road capacity can be increased with proper RoW reallocation. Secondly, four different RoW reallocation strategies are compared using a SUMO simulation to cross-validate the results in a numerical analysis. A range of scenarios with different AV penetration rates and traffic demands are used. The results show that road capacity on a two-lane road can be significantly improved with appropriate RoW reallocation strategies at low or medium AV penetration rates, compared with the do-nothing RoW strategy.
AU  - Li,T
AU  - Guo,F
AU  - Krishnan,R
AU  - Sivakumar,A
AU  - Polak,J
DO  - 10.1016/j.trc.2020.102630
PY  - 2020///
SN  - 0968-090X
TI  - Right-of-way reallocation for mixed flow of autonomous vehicles and human driven vehicles
T2  - Transportation Research Part C: Emerging Technologies
UR  - http://dx.doi.org/10.1016/j.trc.2020.102630
UR  - http://hdl.handle.net/10044/1/79021
VL  - 115
ER  -
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