Traffic-driven epidemic spreading dynamics with heterogeneous infection rates

Chen, Jie; Hu, Mao-Bin; Li, Ming

doi:10.1016/j.chaos.2019.109577

Cited by 11 publications

(1 citation statement)

References 38 publications

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“…For example, investigating the stability of the matching between passengers and drivers (Wang et al, 2018), improving the traffic efficiency with advanced travel time feedback (Wu et al, 2019), and optimizing the many-to-many matching time interval and matching radius (Yang et al, 2020). Studies concerning the effects of transportation services on emergency events, such as epidemic disease spreading (Chen et al, 2020a(Chen et al, , 2020b were also recently investigated in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

A Dynamic Tree Algorithm for Peer-to-Peer Ridesharing Matching

Yao

Bekhor

2021

Netw Spat Econ

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On-demand peer-to-peer ride-sharing services provide flexible mobility options, and are expected to alleviate congestion by sharing empty car seats. An efficient matching algorithm is essential to the success of a ride-sharing system. The matching problem is related to the well-known dial-a-ride problem, which also tries to find the optimal pickup and delivery sequence for a given set of passengers.In this paper, we propose an efficient dynamic tree algorithm to solve the on-demand peer-to-peer ride-sharing matching problem. The dynamic tree algorithm benefits from given ride-sharing driver schedules, and provides satisfactory runtime performances. In addition, an efficient pre-processing procedure to select candidate passenger requests is proposed, which further improves the algorithm performance.Numerical experiments conducted in a small network show that the dynamic tree algorithm reaches the same objective function values of the exact algorithm, but with shorter runtimes. Furthermore, the proposed method is applied to a larger size problem. Results show that the spatial distribution of ride-sharing participants influences the algorithm performance. Sensitivity analysis confirms that the most critical ride-sharing matching constraints are the excess travel times. The network analysis suggests that small vehicle capacities do not guarantee overall vehicle-kilometer travel savings.

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