TY - JOUR
T1 - Joint optimisation of park-and-ride facility locations and alternate traffic restriction scheme under equilibrium flows
AU - Xu, Guangming
AU - Chen, Yanqin
AU - Liu, Wei
N1 - Funding Information:
Dr. Wei Liu would like to acknowledge the support from The Hong Kong Polytechnic University (P0039246, P0040900, P0041316).
Funding Information:
This research was partly supported by grants from the National Natural Science Foundation of China (72171236, 71701216, 71871226), the National Key R&D Program of China (2020YFB1600400), the Natural Science Foundation of Hunan Province (2020JJ5783). Dr. Wei Liu would like to acknowledge the support from The Hong Kong Polytechnic University (P0039246, P0040900, P0041316).
Publisher Copyright:
© 2022 Hong Kong Society for Transportation Studies Limited.
PY - 2022/5/25
Y1 - 2022/5/25
N2 - Alternate traffic restriction (ATR) schemes manage traffic congestion by prohibiting a proportion of cars from entering a predetermined ATR area during specific time periods. Under the ATR scheme, Park-and-Ride (P&R) often becomes more popular as travelers can park cars at P&R facilities and avoid driving into the ATR area. This paper proposes a multi-objective bi-level model that jointly optimizes the P&R facility locations and the ATR scheme (the ATR areas and the proportion of restricted private cars). The upper-level model minimizes the total travel cost and total emission cost, and maximizes consumer surplus. The lower-level model characterizes the user equilibrium of travel modes and route choices. The non-dominated sorting genetic algorithm is adapted to solve the proposed multi-objective bi-level model, where a gradient project algorithm is used for solving the lower-level model. Numerical studies are conducted to test and illustrate the applicability of the model and algorithms.
AB - Alternate traffic restriction (ATR) schemes manage traffic congestion by prohibiting a proportion of cars from entering a predetermined ATR area during specific time periods. Under the ATR scheme, Park-and-Ride (P&R) often becomes more popular as travelers can park cars at P&R facilities and avoid driving into the ATR area. This paper proposes a multi-objective bi-level model that jointly optimizes the P&R facility locations and the ATR scheme (the ATR areas and the proportion of restricted private cars). The upper-level model minimizes the total travel cost and total emission cost, and maximizes consumer surplus. The lower-level model characterizes the user equilibrium of travel modes and route choices. The non-dominated sorting genetic algorithm is adapted to solve the proposed multi-objective bi-level model, where a gradient project algorithm is used for solving the lower-level model. Numerical studies are conducted to test and illustrate the applicability of the model and algorithms.
KW - alternate traffic restriction
KW - bi-level programming
KW - multi-objective optimisation
KW - Park-and-ride location
UR - http://www.scopus.com/inward/record.url?scp=85131212618&partnerID=8YFLogxK
U2 - 10.1080/23249935.2022.2077468
DO - 10.1080/23249935.2022.2077468
M3 - Journal article
AN - SCOPUS:85131212618
SN - 2324-9935
VL - 19
JO - Transportmetrica A: Transport Science
JF - Transportmetrica A: Transport Science
IS - 3
M1 - 2077468
ER -