Utility-Aware Ridesharing on Road Networks

Cheng, Peiyao; Xin, Hao; Chen, Lei

doi:10.1145/3035918.3064008

Cited by 86 publications

(45 citation statements)

References 22 publications

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“…Route planning aims to minimize the travel distance, which has been widely studied in existing works [6,20,15,10,14]. For efficiency concern, we follow the setting in [6,20,10,14] that after receiving a new order, a vehicle keeps its existing orders' permutation unchanged. That is, given an order-vehicle pair, (oi, vj), we insert oi's pick-up/drop-off locations into vj's plan like [6,20].…”

Section: Preliminary Of Approximation Methodsmentioning

confidence: 99%

“…For efficiency concern, we follow the setting in [6,20,10,14] that after receiving a new order, a vehicle keeps its existing orders' permutation unchanged. That is, given an order-vehicle pair, (oi, vj), we insert oi's pick-up/drop-off locations into vj's plan like [6,20]. This insertion-based algorithm traverses all possible insertion location pairs in the plan for the new order, and adopts the one with the least incurred travel cost.…”

Section: Preliminary Of Approximation Methodsmentioning

confidence: 99%

“…To bound the distance among road nodes inside a cluster, the hierarchical clustering with complete linkage 6 , can be an alternative. In complete linkage, the distance between two clusters is defined as the maximum distance between their nodes.…”

Section: The Node Clustering Algorithmmentioning

confidence: 99%

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Order dispatch in price-aware ridesharing

Zheng

Chen

Ye³

2018

Proc. VLDB Endow.

Self Cite

104

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With the prevalence of car-hailing applications, ridesharing becomes more and more popular because of its great potential in monetary saving and environmental protection. Order dispatch is the key problem in ridesharing, which has a strong impact on riders' experience and platform's performance. Existing order dispatch research works fail to consider the price of the orders, which can be an important reference because it directly relates to the platform's profit. Our work takes the order price into concern, and formulates a constrained optimization problem, which takes platform's profit as the optimization objective and performs controls on riders' detour distance and waiting time. We prove the problem is NP-hard, thus, we propose approximation methods. We further develop a simulation framework based on real ridesharing order and vehicle data. We conduct experiments with this simulation framework to evaluate the effectiveness and efficiency of the proposed methods.

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Section: Preliminary Of Approximation Methodsmentioning

confidence: 99%

Section: Preliminary Of Approximation Methodsmentioning

confidence: 99%

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Order dispatch in price-aware ridesharing

Zheng

Chen

Ye³

2018

Proc. VLDB Endow.

Self Cite

104

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“…Ta et al [12] defined two kinds of ridesharing models to maximize the shared route ratio, under the assumption that at most one rider can be assigned to a driver in the vehicle. Cheng et al [15] proposed a utility-aware ridesharing problem, which can be regarded as a variant of the dial-a-ride problem [28], [29]. The aim was to maximize the riders' satisfaction, which is defined as a linear combination of vehicle-related utility, rider-related utility, and trajectory-related utility, such as rider sharing preferences.…”

Section: Related Workmentioning

confidence: 99%

“…(2) Prior studies [10], [12], [13], [14], [15], [16] primarily focus on single objective optimization solutions, such as minimizing the total travel distance from the perspective of drivers [13], [14], or maximizing the served rate of ridesharing system [16]. In contrast, we aim to optimize two objectives: maximize the served rate and minimize the total additional distance.…”

Section: Introductionmentioning

confidence: 99%

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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Using a Conceptual Model to Transform Road Networks from OpenStreetMap to a Graph Database

Steinmetz

Dyballa

et al. 2018

Conceptual Modeling

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Utility-Aware Ridesharing on Road Networks

Cited by 86 publications

References 22 publications

Order dispatch in price-aware ridesharing

Order dispatch in price-aware ridesharing

Dynamic Ridesharing in Peak Travel Periods

Using a Conceptual Model to Transform Road Networks from OpenStreetMap to a Graph Database

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