An Efficient Insertion Operator in Dynamic Ridesharing Services

Xu, Yi; Tong, Yongxin; Shi, Yexuan; Qian, Tao; Xu, Ke; Li, Wei

doi:10.1109/tkde.2020.3027200

Cited by 26 publications

(13 citation statements)

References 29 publications

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“…Chen et al [11] considered both the pick-up time and the price to return multiple options for each rider request in dynamic ridesharing. Xu et al [37] proposed an efficient rider insertion algorithm that minimizes the maximum flow time of all requests or minimizes the total travel time of the driver.…”

Section: Related Workmentioning

confidence: 99%

“…Later in the experimental study we extend the state-of-the-art [11] to the peak hour scenario and show that our approach outperforms it in both efficiency and effectiveness. (2) Most previous studies aim to optimize a single objective [10], [13], [14], [15], [16], [33], [37] or a customized linear function [18], [36]. This differs from our problem scenario where we solve the dual-objective optimization problem.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic Ridesharing in Peak Travel Periods

Luo

Bao

Choudhury

et al. 2021

IEEE Trans. Knowl. Data Eng.

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In this paper, we propose and study a variant of the dynamic ridesharing problem with a specific focus on peak hours: Given a set of drivers and a set of rider requests, we aim to match drivers to each rider request by achieving two objectives: maximizing the served rate and minimizing the total additional distance, subject to a series of spatio-temporal constraints. Our problem can be distinguished from existing ridesharing solutions in three aspects: (1) Previous work did not fully explore the impact of peak travel periods where the number of rider requests is much greater than the number of available drivers. (2) Existing ridesharing solutions usually rely on single objective optimization techniques, such as minimizing the total travel cost (either distance or time). (3) When evaluating the overall system performance, the runtime spent on updating drivers' trip schedules as per newly coming rider requests should be incorporated, while it is unfortunately excluded by most existing solutions. In order to achieve our goal, we propose an underlying index structure on top of a partitioned road network, and compute the lower bounds of the shortest path distance between any two vertices. Using the proposed index together with a set of new pruning rules, we develop an efficient algorithm to dynamically include new riders directly into an existing trip schedule of a driver. In order to respond to new rider requests more effectively, we propose two algorithms that bilaterally match drivers with rider requests. Finally, we perform extensive experiments on a large-scale test collection to validate the effectiveness and efficiency of the proposed methods.Index Terms-Dynamic ridesharing, peak hour, index structure, pruning rules ! • H. Luo, Z. Bao (corresponding author) and J. Culpepper are with the

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dynamic Ridesharing in Peak Travel Periods

Luo

Bao

Choudhury

et al. 2021

IEEE Trans. Knowl. Data Eng.

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“…Similar to previous studies [3], [11], [12], we assume the source and destination of the new trip request are inserted or appended to the current schedule of the matching vehicle.…”

Section: Vehicle Schedulementioning

confidence: 99%

“…Minimizing the total increased distance for all trip requests is NP-complete [2] and the future trip requests are unknown in advance. A common solution is to take a greedy strategy [2], [3], [12], [10] and minimize the increased travel distance for each trip request. The trip requests are processed ordering by their issue time.…”

Section: Matching Objectivementioning

confidence: 99%

“…Zhang et al [6] maximize the success rate of matching requests so that the passengers have a higher chance to be served. Xu et al [12] minimize the maximum serving time of passengers, i.e., the time from a request is issued to the passenger is dropped off. Duan et al [32] personalize ride-sharing services considering users' preferences on the time and price.…”

Section: Related Workmentioning

confidence: 99%

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GeoPrune: Efficiently Finding Shareable Vehicles Based on Geometric Properties

Xu,

Qi,

Borovica-Gajic

et al. 2019

Preprint

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On-demand ride-sharing is rapidly growing due to its benefits of convenience and low price. Matching trip requests to vehicles efficiently is critical for the service quality. Generally, an approach that matches requests with vehicles first identifies those vehicles that could be matched through a pruning step, and then selects among these the optimal one(s) in a selection step. The pruning step is crucial to reduce the complexity of the selection step and to achieve a highly efficient matching process. In this paper, we propose an efficient and effective pruning algorithm called GeoPrune. GeoPrune exploits the geometric properties of the waiting time constraints and detour time constraints of the trip requests, which can be computed and updated efficiently. Experiments on real-world datasets show that GeoPrune reduces the number of potential vehicles by more than a factor of ten and the update cost by two to three orders of magnitude compared to state-of-the-art algorithms.

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