Boosting Ride Sharing With Alternative Destinations

Guo

2023

Preprint

Out of concern for the transport efficiency and sustainable development of city mobilities, both policymakers and commuters are calling for an integrated multimodal transportation network. Although the implementation of Mobility-on-demand (MoD) service should have been satisfying, the lack of corporation between different transit modes results in unsatisfying passenger experiences and low service efficiency. Multimodal MoD (MMOD) service is proposed to comprehensively utilize fixed-route public transit and demand-responsive transit resources for the integration of a multimodal transportation network. To formulate the MMoD, a service framework that periodically reassigns real-time orders and reschedules operational routes is proposed. To reduce the computational complexity, a passenger-vehicle routing model with multimodal transfers is constructed based on multilayer space-time networks for decoupling vehicle routes and passenger schedules. A column-generation algorithm is proposed to further reformulate, dualize, decompose and solve the primal problem of the aforementioned model. Toy example demonstrates that the proposed algorithm can yield a maximum of 60 times speed-up than that of the commercial solver Gurobi. Finally, by adjusting the passenger acceptance rate of rideshares and transfers under large-scale examples, we presented the sensitivity analysis taking the proportion of served passengers as the indicator of service efficiency. Experiments show that the passenger acceptance rate of ridesharing has a greater impact on the service efficiency.

Section: Multimodal Transfer Ride-sharingmentioning

confidence: 99%

A multimodal mobility-on-demand service: dynamic rescheduling approach considering passenger-vehicle couplings

Guo

2023

Preprint

Operations Management - Emerging Trend in the Digital Era

“…The salesman can accept or decline travel requests. This model combines elements of ride-sharing systems [14] with alternative destinations [4], and the selective pickup and delivery problem [15].…”

Section: Problem Definitionmentioning

confidence: 99%

“…Each passenger can be transported directly to the desired destination or an alternate destination. Lira et al [4] suggest pro-environmental or money-saving concerns can induce users of a ride-sharing service to agree to fulfill their needs at an alternate destination.…”

Section: Introductionmentioning

confidence: 99%

Multi-Strategy MAX-MIN Ant System for Solving Quota Traveling Salesman Problem with Passengers, Incomplete Ride and Collection Time

Silva¹,

Fernandes²,

Goldbarg³

et al. 2021

This study proposes a novel adaptation of MAX-MIN Ant System algorithm for the Quota Traveling Salesman Problem with Passengers, Incomplete Ride, and Collection Time. There are different types of decisions to solve this problem: satisfaction of the minimum quota, acceptance of ride requests, and minimization of travel costs under the viewpoint of the salesman. The Algorithmic components proposed regards vehicle capacity, travel time, passenger limitations, and a penalty for delivering a passenger deliverance out of the required destination. The antbased algorithm incorporates different sources of heuristic information for the ants and memory-based principles. Computational results are reported, showing the effectiveness of this ant-based algorithm.

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

“…Zheng et al [1] take order price into consideration in online matching to improve the platform's pro t. A series of works [31][32][33][34] emphasize more on the user satisfaction and the travellers are able to choose the vehicle matching their preferences the best. Furthermore, some works [38,39] propose activity-based ridesharing, which matches requests that have similar activities and accepts alternative destinations. All of these works dispatch orders at a uniform time interval, while we then exploit the advantages of the adaptive dispatching intervals.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems

Liu

Sun

Jin

et al. 2020

Nowadays, ridesharing has become one of the most popular services offered by online ride-hailing platforms (e.g., Uber and Didi Chuxing). Existing ridesharing platforms adopt the strategy that dispatches orders over the entire city at a uniform time interval. However, the uneven spatio-temporal order distributions in realworld ridesharing systems indicate that such an approach is suboptimal in practice. Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time. Specifically, we propose a hierarchical approach, which generates clusters of geographical areas suitable to share the same dispatching intervals, and then makes online decisions of selecting the appropriate time instances for order dispatch within each spatial cluster. Technically, we prove the impossibility of designing constant-competitive-ratio algorithms for the online adaptive interval problem, and propose online algorithms under partial or even zero future order knowledge that significantly improve the platform's profit over existing approaches. We conduct extensive experiments with a large-scale ridesharing order dataset, which contains all of the over 3.5 million ridesharing orders in Beijing, China, received by Didi Chuxing from October 1st to October 31st, 2018. The experimental results demonstrate that our proposed algorithms outperform existing approaches.