Price-and-Time-Aware Dynamic Ridesharing

Lu, Chen; Zhong, Qilu; Xiao, Xiaokui; Gao, Yunjun; Jin, Pengfei; Jensen, Christian S.

doi:10.1109/icde.2018.00099

Cited by 46 publications

(34 citation statements)

References 23 publications

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“…Because DSA must update two kinds of index structures: (1) each driver maintains a kinetic tree [14] to record a possible trip schedule for all unfinished requests assigned to each vehicle, which takes O(|D|×n!) [11];…”

Section: Experimental Results At Peak Travel Periodmentioning

confidence: 99%

“…(2) a grid index to store road network information and vehicles that are currently located or scheduled to enter each grid, which also has an O(|D| × n!) update time complexity [11]. In contrast, our methods only take O(|D|×n) to update the trip schedule for each vehicle, and O(D) to update the road network index.…”

Section: Experimental Results At Peak Travel Periodmentioning

confidence: 99%

“…This differs from our problem scenario where we solve the dual-objective optimization problem. Chen et al [11] also considered two criteria (price and pick-up time) from the perspective of riders, but not the same criteria (served rate and additional distance) as we use to satisfy riders, drivers and ridesharing system requirements. In our problem, riders can provide their personal sharing preferences by giving constraint values, such as waiting time tolerance, drivers and ridesharing system expect to serve more riders with less detour distance, which coincides with our goal, i.e., maximizing the served rate and meanwhile minimizing the additional distance.…”

Section: Related Workmentioning

confidence: 99%

“…However, as the density distribution of the vertices vary widely in urban and rural regions, most grids are empty, and contain no vertex. For example, more than 80% grids are empty in the grid index, resulting in very weak pruning power in ridesharing scenarios [11]. Although a quadtree index can divide the road network structure in a densityaware way, it has to maintain a consistent hierarchical representation such that each child node update may lead to a parent node update, which can increase the update costs when available drivers are moving (which is often true in real world scenarios).…”

Section: Motivationmentioning

confidence: 99%

“…From an extensive literature study, we make the following observations to motivate this work -(1) Existing ridesharing studies [10], [11] do not fully explore the scenario where the number of riders is much greater than the number of available drivers, and so the scalability of current solutions in this setting remains unclear.…”

Section: Introductionmentioning

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: Experimental Results At Peak Travel Periodmentioning

confidence: 99%

Section: Experimental Results At Peak Travel Periodmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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