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).
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
JO - Transportmetrica A: Transport Science
JF - Transportmetrica A: Transport Science
SN - 2324-9935
ER -