Top-k Taxi Recommendation in Realtime Social-Aware Ridesharing Services

Fu, Xiaoyi; Huang, Jinbin; Lu, Hua; Xu, Jianliang; Li, Yafei

doi:10.1007/978-3-319-64367-0_12

Cited by 17 publications

(7 citation statements)

References 17 publications

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“…It could make unsatisfactory groups for trustworthy ridesharing. Indeed, each problems assume different ridesharing modes [18], [20], [21]. Although some works [17], [19] used the same model, i.e., Slugging, constraints, and the social model are different from our work.…”

Section: Return (Is Cl);mentioning

confidence: 89%

“…Additionally, the research [18] has concentrated on the topk taxi searches in road networks. In the study, riders can select their favorable taxi among the top-k taxis.…”

Section: Social-aware Ridesharingmentioning

confidence: 99%

“…Another work [20] has covered a problem called Assignment of Requests to Offers for the practical ridesharing in a similar environment to [18]. Authors proposed cost functions for evaluating a social distance and a common interest through an undirected social network and a modified Jaccard similarity.…”

Section: Social-aware Ridesharingmentioning

confidence: 99%

“…In other words, its social model could be somewhat restrictive to the passengers and the driver. Another study [18] also considered social network data but used its own ranking function unlike subgraph models for checking the social aspect. Moreover, the study dealt with a problem for finding top-k taxis based on trip requests in road networks.…”

Section: Introductionmentioning

confidence: 99%

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Cohesive Ridesharing Group Queries in Geo-Social Networks

2020

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Ridesharing has gained much attention as a solution for mitigating societal, environmental, and economic problems. For example, commuters can reduce traffic jams by sharing their rides with others. Notwithstanding many advantages, the proliferation of ridesharing also brings some crucial issues. One of them is to rideshare with strangers. It makes someone feel uncomfortable or untrustworthy. Another complication is the high-latency of ridesharing group search because users may want to receive the result of their requests in a short time. Despite continuous efforts of academia and industry, the issues still remain. In this paper, for resolving the obstacles, we define a new problem,-cohesive m-ridesharing group (m-CRG) query, which retrieves a cohesive ridesharing group by considering spatial, social, and temporal information. The problem is based on the three underlying assumptions: people tend to rideshare with socially connected friends, people are willing to walk but not too much, and optimization of finding good groups is essential for both drivers and passengers. In our ridesharing framework, queries are processed by efficiently taking geo-social network data into account. For this purpose, we propose an efficient method for processing the queries using a new concept, exact n-friend set, with its efficient update. Moreover, we further improve our method by utilizing inverted timetable (ITT), which grasps crucial time information. Specifically, we devise time-constrained and incremental personalized-proximity search (TIPS). Finally, the performance of the proposed method is evaluated by extensive experiments on several data sets. INDEX TERMS Geo-social networks, query processing, ridesharing services, spatial databases.

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Section: Return (Is Cl);mentioning

confidence: 89%

“…Additionally, the research [18] has concentrated on the topk taxi searches in road networks. In the study, riders can select their favorable taxi among the top-k taxis.…”

Section: Social-aware Ridesharingmentioning

confidence: 99%

Section: Social-aware Ridesharingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cohesive Ridesharing Group Queries in Geo-Social Networks

2020

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“…Meanwhile, we remove all pairs related to r from P (line 14). For riders where d was considered as a candidate, we check whether it is still valid to include them as a pair since the insertion of a new rider may influence the riders previously considered (lines [15][16][17][18][19]. If d is still feasible, we update the utility gain value for those riders (line 17).…”

Section: Algorithm 3: the Greedy Algorithm (Gr)mentioning

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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A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction

Quy

Nejdl

Spiliopoulou

et al. 2020

Lecture Notes in Computer Science

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Taxi is a convenient means of transportation worldwide. Accurately predicting the taxi-demand is crucial for taxi-companies to effectively allocate their fleet to taxi-stands and reduce the waiting time for passengers thus increasing their overall satisfaction and customer retention. Nowadays precise information about taxi-rides is available and can be used to infer the taxi-passenger demand across different locations and time-points. In this paper, we propose an approach for predicting the pick-demand of a given taxi-stand, that takes into account not only the demand-history of the particular stand but it also considers information from neighboring stands. Our model is an LSTM neural network augmented with information from the spatial neighborhood of the stands. Experiments with two versions of the taxi demand dataset from the city of Porto, Portugal show that our approach can provide better predictions comparing to approaches that do not exploit the neighborhood. Keywords: Taxi-passenger demand • Time series prediction • LSTM • k-nearest neighbors • Deep learning • Neural networks

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Top-k Taxi Recommendation in Realtime Social-Aware Ridesharing Services

Cited by 17 publications

References 17 publications

Cohesive Ridesharing Group Queries in Geo-Social Networks

Cohesive Ridesharing Group Queries in Geo-Social Networks

Dynamic Ridesharing in Peak Travel Periods

A Neighborhood-Augmented LSTM Model for Taxi-Passenger Demand Prediction

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