Ridesharing in urban areas: multi-objective optimisation approach for ride-matching and routeing with commuters' dynamic mode choice

The daily home-office commute of millions of people in crowded cities strains air quality and increases travel time, which motivates the generation of ridesharing. Ridesharing offers many benefits, such as reducing travel costs, congestion, and pollution. Commuter ridesharing is an important theme o...

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Vydané v:International journal of production research Ročník 60; číslo 5; s. 1439 - 1457
Hlavní autori: Guan, Lei, Pei, Jun, Liu, Xinbao, Zhou, Zhiping, Pardalos, Panos M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Taylor & Francis 04.03.2022
Taylor & Francis LLC
Predmet:
Air quality
Algorithms
Car pools
city logistics
cumulative prospect theory
Decision making
Genetic algorithms
heuristic algorithm
Heuristic methods
Modal choice
multi-objective optimisation
Multiple objective analysis
Pareto optimization
Ridesharing
Sorting algorithms
Traffic information
Travel modes
Travel time
Urban areas
Urban transportation
VNS-NSGAII
ISSN:0020-7543, 1366-588X
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Shrnutí:The daily home-office commute of millions of people in crowded cities strains air quality and increases travel time, which motivates the generation of ridesharing. Ridesharing offers many benefits, such as reducing travel costs, congestion, and pollution. Commuter ridesharing is an important theme of urban transportation. This paper studies a ridesharing problem aiming at enlarging the ridesharing market at a limited cost, which enlighten the decision-making problem in city logistics. We establish a novel multi-objective optimisation model based on cumulative prospect theory (CPT) to address the preferred travel mode of commuters. The commuters' perceived value influences their choice of travel mode. Meanwhile, the perceived value changes with the commuters' experience of travel mode choice. We give the NP-hardness proof of the ridesharing scheduling problem and develop a heuristic algorithm to solve it in a small-scale scenario. For large-scale problems, a hybrid VNS-NSGAII algorithm combining variable neighbourhood search (VNS) with NSGAII (Non-dominated Sorting Genetic Algorithm II) is proposed to generate an approximate optimal Pareto front. A series of computational experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm based on the actual traffic data in Beijing, China.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2020.1859635