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Recommendation for Ridesharing Groups Through Destination Prediction on Trajectory Data

Lei Tang, Zongtao Duan, Yishui Zhu, Junchi Ma, Zihang Liu
2019 IEEE transactions on intelligent transportation systems (Print)  
Existing algorithms for rider grouping usually rely on matching pre-selected origin-destination coordinates.  ...  In this paper, we aim to provide an optimal passenger matching solution by recommending ridesharing groups of passengers from GPS trajectories.  ...  Therefore, agreeing on destinations and grouping a set of riders related through spatial proximity is especially useful for producing further benefits for ridesharing [17] .  ... 
doi:10.1109/tits.2019.2961170 fatcat:dkqilcyni5axdgqf4pfq4pnfhy

An efficient ride-sharing recommendation for maximizing acceptance on geo-social data

Lei Tang, Meng Han, Zongtao Duan, Dandan Cai
2019 CCF Transactions on Pervasive Computing and Interaction  
To optimize the recommendation, we develop a heterogenous travel network, based on a proposed destination-prediction algorithm, to mine the similar spatial movements among a set of riders.  ...  We propose a new ride-sharing mode to recommend groups that travel together from geo-social data streams.  ...  For example, T-DesP (destination prediction based on big trajectory data) model was proposed (Li et al. 2016 ) to predict the destination by a Markov model and solved the problem of data sparsity by using  ... 
doi:10.1007/s42486-019-00015-0 fatcat:apewfgvmargsbfegyqq3g6jumi

Mining regular routes from GPS data for ridesharing recommendations

Wen He, Deyi Li, Tianlei Zhang, Lifeng An, Mu Guo, Guisheng Chen
2012 Proceedings of the ACM SIGKDD International Workshop on Urban Computing - UrbComp '12  
In this paper, we mine regular routes from a users' historical trajectory dataset, and provide ridesharing recommendations to a group of users who share similar routes.  ...  Finally, based on the mined regular routes and transport modes, a grid-based route table is constructed for a quick ride matching.  ...  Chen et al. proposed a method to mine personal routes from GPS data [3] , and to predict the destination and future route.  ... 
doi:10.1145/2346496.2346510 dblp:conf/kdd/HeLZAGC12 fatcat:qeijh3ie5vcepfbqsso2gziw7i

Discovering Regularity in Mobility Patterns to Identify Predictable Aggregate Supply for Ridesharing

Ivan Mendoza, Clas Rydergren, Chris M. J. Tampère
2018 Transportation Research Record  
This paper describes a methodology to identify a predictable aggregate supply for ridesharing via mobility patterns discovered in users' travel histories.  ...  When several agents cooperate, the forecasted trips made by multiple users can provide a potential supply for shared mobility systems such as dynamic ridesharing.  ...  Author Contributions The authors confirm contribution to the paper as follows: study conception and design: IM, CT; data collection: CR; analysis and interpretation of results: IM, CR, CT; draft manuscript  ... 
doi:10.1177/0361198118798720 fatcat:jdt4do6fzfcfbedpphyrmfa3aq

Reinforcement Learning for Ridesharing: An Extended Survey [article]

Zhiwei Qin, Hongtu Zhu, Jieping Ye
2022 arXiv   pre-print
Papers on the topics of rideshare matching, vehicle repositioning, ride-pooling, routing, and dynamic pricing are covered. Popular data sets and open simulation environments are also introduced.  ...  Subsequently, we discuss a number of challenges and opportunities for reinforcement learning research on this important domain.  ...  For ridesharing applications, both MPC and model-based RL involve online prediction of supply and demand using models trained on historical data.  ... 
arXiv:2105.01099v3 fatcat:34whul4vnneo5k2pyvb4smhgz4

From taxi GPS traces to social and community dynamics

Pablo Samuel Castro, Daqing Zhang, Chao Chen, Shijian Li, Gang Pan
2013 ACM Computing Surveys  
We first provide a formalization of the data sets, along with an overview of different mechanisms for preprocessing the data.  ...  Social dynamics refers to the study of the collective behaviour of a city's population, based on their observed movements; Traffic dynamics studies the resulting flow of the movement through the road network  ...  trajectories through a particular area to form their predictions.  ... 
doi:10.1145/2543581.2543584 fatcat:cr4fzlarlze65ciqfzovu5uqmy

A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting

Theodoros Anagnostopoulos
2021 Smart Cities  
Specifically, it proposes a predictive vehicle ride sharing system for commuting, which has impact on the SC green ecosystem.  ...  In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior by providing next destination prediction as well  ...  The next destination prediction is evaluated through prediction accuracy, a, of the adopted classification model as well as, map@N, recommendation evaluation metric.  ... 
doi:10.3390/smartcities4010010 fatcat:nkv6kzd2w5aqvb2fx65qlrl4wa

Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives

Ke Yan, Wen Shen, Qun Jin, Huijuan Lu
2019 IEEE Access  
Hot topics and less discussed topics are identified, which provides hints to researchers for their future studies.  ...  Differing from existing similar works on surveying sharing practices in various fields, our work comprehensively covers six branches of sharing services in the cyber-enabled world and selects solutions  ...  [105] introduced trajectory privacy in the ridesharing practices.  ... 
doi:10.1109/access.2019.2894344 fatcat:qapuhjkhs5embm2l6q2cq52ply

Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey [article]

Zefang Zong, Tao Feng, Tong Xia, Depeng Jin, Yong Li
2022 arXiv   pre-print
Recent technology development brings the booming of numerous new Demand-Driven Services (DDS) into urban lives, including ridesharing, on-demand delivery, express systems and warehousing.  ...  For each problem, we comprehensively introduce the existing DRL solutions. We also introduce open simulation environments for development and evaluation of DDS applications.  ...  Yuan et al. and Qu et al. also construct a recommend system for vehicles to provide recommended options for repositioning [90] , [91] .  ... 
arXiv:2108.04462v2 fatcat:y3ogh3v4rbhbjfoymql3yku4ty

Social Cycling: Critical Mass Through a Mobile App

Rodrigo García-Herrera, Paola Massyel García-Meneses
2020 Frontiers in Sustainable Cities  
Alatriste, José Luis Gutiérrez, Damián Hernández Herrán at the Autonomous University of Mexico City for their valuable input while discussing this work.  ...  Actual predictions depend on the model used for a specific city.  ...  However they all agree that, overall, increased bicycle mobility leads to more sustainable trajectories for cities.  ... 
doi:10.3389/frsc.2020.00036 fatcat:pm3evmqkzrf4fpcntyomhkug3a

RMS: Removing Barriers to Analyze the Availability and Surge Pricing of Ridesharing Services

Hassan Ali Khan, Hassan Iqbal, Muhammad Shahzad, Guoliang Jin
2022 CHI Conference on Human Factors in Computing Systems  
for future ridesharing research studies.  ...  It exposes real-time data of these services through i) graphical user interfaces and ii) public APIs to assist various stakeholders of these services and simplify the data collection and analysis process  ...  It exposes real-time data on the availability and surges pricing of ridesharing services through a graphical user interface and a set of public APIs.  ... 
doi:10.1145/3491102.3517464 fatcat:frwj7guttzgl5f5zz4qbj7b2w4

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Haitao Yuan, Guoliang Li
2021 Data Science and Engineering  
In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer.  ...  Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression.  ...  -Route Planning: It is useful to recommend an optimal route for a given departure-destination pair.  ... 
doi:10.1007/s41019-020-00151-z fatcat:nnnnxnpo3bgk3l4hpr7kk2n4xa

Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals

Bogdan Toader, François Sprumont, Sébastien Faye, Mioara Popescu, Francesco Viti
2017 ISPRS International Journal of Geo-Information  
Based on the value of the indicator, we analyzed the potential for identifying CM among groups of users, such as sharing traveling resources (e.g., carpooling, ridesharing, parking sharing) and time (rescheduling  ...  Smartphone sensor data are being collected from a limited number of individuals and for one week. These data are used to evaluate the proposed indicator.  ...  Acknowledgments: This research has been funded by the Luxemburgish FNR ("Fonds National de la Recherche") through an AFRgrant for the PLAYMOBeLproject (9220491) and by the EU Marie-Curie-funded project  ... 
doi:10.3390/ijgi6030062 fatcat:sgxsxy242rdqrngyce3fno3ynq

A Survey on Trajectory Data Management, Analytics, and Learning [article]

Sheng Wang, Zhifeng Bao, J. Shane Culpepper, Gao Cong
2020 arXiv   pre-print
We also explore four closely related analytical tasks commonly used with trajectory data in interactive or real-time processing. Deep trajectory learning is also reviewed for the first time.  ...  Recent advances in sensor and mobile devices have enabled an unprecedented increase in the availability and collection of urban trajectory data, thus increasing the demand for more efficient ways to manage  ...  There are two common approaches to ridesharing problems which rely heavily on trajectory data. The first one is grouping passengers and drivers based on their historical trajectory data.  ... 
arXiv:2003.11547v2 fatcat:5gf5h5skqjbrhf67cflygggnky

Privacy for 5G-Supported Vehicular Networks

Meng Li, Liehuang Zhu, Zijian Zhang, Chhagan Lal, Mauro Conti, Fabio Martinelli
2021 IEEE Open Journal of the Communications Society  
and privacy-preserving dynamic spatial query for ride-hailing [143] Privacy-preserving ride-hailing matching with prediction (pRide) [144] Privacy-preserving group ridesharing matching (PGRide) [145]  ...  for autonomous vehicles [153] Privacy-preserving valet parking for autonomous driving (PrivAV) [154] Privacy-enhanced private parking spot sharing based on blockchain (PEPS)) [155] Distributed mobile  ...  The first one is an adversary eavesdropping on communication channels to locate the source the destination of data packets.  ... 
doi:10.1109/ojcoms.2021.3103445 fatcat:cznswnoczrb4dgfg7hhiywifpe
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