Ride matching and vehicle routing for on-demand mobility services

Lotfi, Sepide; Abdelghany, Khaled

doi:10.1007/s10732-022-09491-7

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…Yuan and Chen et al constructed a robust optimization model for carpooling paths with the objective of minimizing the total mileage and number of vehicles, considering the uncertainty of travel time [16]. Lotfi and Sepide et al used a hybrid heuristic algorithm to achieve on-demand dynamic carpool matching with the goal of total enterprise profit and passenger transfer frequency [17].…”

Section: Research On Path Optimizationmentioning

confidence: 99%

Multi-Objective Planning of Commuter Carpooling under Time-Varying Road Network

Li,

Zhang,

Liu

et al. 2024

Sustainability

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Aiming at the problem of urban traffic congestion in morning and evening rush hours, taking commuter carpool path planning as the research object, the spatial correlation of traffic flow at adjacent intersections is mined using convolutional neural networks (CNN), and the temporal features of traffic flow are mined using long short-term memory (LSTM) model. The extracted temporal and spatial features are fused to achieve short-term prediction. Considering the travel willingness of drivers and passengers, a multi-objective optimization model with minimum driver and passenger loss time and total travel time is established under the constraints of vehicle capacity, time windows and detour distances. An Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II) is proposed to solve it. The open-loop saving algorithm is used to generate an initial population with better quality, and the 2-opt local search strategy is adopted in the mutation operation to improve search efficiency. The influence of vehicle speed on the matching scheme is analyzed. The research results show that under the same demand conditions, the total travel distance of the carpool scheme is reduced by about 56.19% and total travel time is reduced by about 65.52% compared with the non-carpool scheme. Research on carpool matching under time-varying road networks will help with urban commuting efficiency and environmental quality, and play a positive role in alleviating traffic congestion and promoting carpool services.

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Section: Research On Path Optimizationmentioning

confidence: 99%

Multi-Objective Planning of Commuter Carpooling under Time-Varying Road Network

Li,

Zhang,

Liu

et al. 2024

Sustainability

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“…TNCs also offer solutions to routing and matching problems for flexible transit. Lotfi and Abdelghany ( 46 ) illustrated a new method of integrating matching and routing for on-demand mobility services. The results show that TNCs could serve more willing passengers and earn higher profits.…”

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confidence: 99%

Routing Optimization of Regional Flexible Transit Under the Mixed Demand Mode

Wang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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To meet trip demands and avoid transit capacity waste or shortages, this study investigates the routing optimization of flexible transit with time windows. We introduce the time penalty costs to accommodate the impacts of early and late vehicle arrivals on passengers’ satisfaction. A routing optimization model is developed to minimize the system operation costs and the costs incurred by passengers' time penalties. The problem is solved by a designed adaptive genetic algorithm that adopts an adaptive mutation strategy to dynamically adjust the mutation probability and mutation operator. The numerical experiments compare the results of the mixed demand model, in which vehicles can pick up and drop off passengers simultaneously, to those of the separate pick-up and delivery modes. Finally, a sensitive analysis is conducted to explore the impact of operational factors (vehicle speed, maximum one-way travel time, and weighting ratios between operating and penalty costs) on the system's performance (total costs, per capita mileage, and average seat occupancy rate). Our results confirm the advantages of the developed adaptive genetic algorithm over traditional ones with respect to the convergence speed and optimality gap. Moreover, the numerical results indicate that the mixed demand operation mode of transit reduces total costs by an average of 2.35% compared to separate pick-up and delivery modes. The results also reveal that an increase in the weights of the operating cost can reduce the total cost. The findings of this work can provide guidance to the operation of regional flexible transit.

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