16.7.2.7.8 Shared Ride Systems, Car Sharing, Taxi, Analysis

Chapter Contents (Back)
Vehicle Sharing. Ride Sharing. Shared Ride Systems.
See also On-Demand Ride Systems, Car Sharing, Taxi, Analysis.
See also Bicycle Sharing, Bicycle Commuting, Bike Sharing.

Raubal, M.[Martin], Winter, S.[Stephan], Tessmann, S.[Sven], Gaisbauer, C.[Christian],
Time geography for ad-hoc shared-ride trip planning in mobile geosensor networks,
PandRS(58), No. 5-6, July 2004, pp. 289-300.
Elsevier DOI 1202
Award, U.V. Helava, ISPRS. BibRef

Braun, M., Winter, S.,
Ad Hoc Solution of the Multicommodity-Flow-Over-Time Problem,
ITS(10), No. 4, December 2009, pp. 658-667.
IEEE DOI 0912
Ad hoc shared ride systems. BibRef

Seow, K.T., Lee, D.H.,
Performance of Multiagent Taxi Dispatch on Extended-Runtime Taxi Availability: A Simulation Study,
ITS(11), No. 1, March 2010, pp. 231-236.
IEEE DOI 1003
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Yan, S., Chen, C.Y., Lin, Y.F.,
A Model With a Heuristic Algorithm for Solving the Long-Term Many-to-Many Car Pooling Problem,
ITS(12), No. 4, December 2011, pp. 1362-1373.
IEEE DOI 1112
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Jia, T.[Tao], Jiang, B.[Bin],
Exploring Human Activity Patterns Using Taxicab Static Points,
IJGI(1), No. 1, June 2012, pp. 89-107;.
DOI Link 1206
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Dimitrakopoulos, G., Demestichas, P., Koutra, V.,
Intelligent Management Functionality for Improving Transportation Efficiency by Means of the Car Pooling Concept,
ITS(13), No. 2, June 2012, pp. 424-436.
IEEE DOI 1206
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Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L.,
Predicting Taxi-Passenger Demand Using Streaming Data,
ITS(14), No. 3, 2013, pp. 1393-1402.
IEEE DOI 1309
Sardis Award, Research. Autoregressive integrated moving average (ARIMA) BibRef

Yan, S.Y.[Shang-Yao], Chen, C.Y.[Chun-Ying], Chang, S.C.[Sheng-Chieh],
A Car Pooling Model and Solution Method With Stochastic Vehicle Travel Times,
ITS(15), No. 1, February 2014, pp. 47-61.
IEEE DOI 1403
automobiles BibRef

Huang, S.C.[Shih-Chia], Jiau, M.K.[Ming-Kai], Lin, C.,
A Genetic-Algorithm-Based Approach to Solve Carpool Service Problems in Cloud Computing,
ITS(16), No. 1, February 2015, pp. 352-364.
IEEE DOI 1502
Biological cells BibRef

Jiau, M.K.[Ming-Kai], Huang, S.C.[Shih-Chia],
Services-Oriented Computing Using the Compact Genetic Algorithm for Solving the Carpool Services Problem,
ITS(16), No. 5, October 2015, pp. 2711-2722.
IEEE DOI 1511
genetic algorithms BibRef

Arena, M., Azzone, G., Colorni, A., Conte, A., Lue`, A., Nocerino, R.,
Service design in electric vehicle sharing: evidence from Italy,
IET-ITS(9), No. 2, 2015, pp. 145-155.
DOI Link 1504
electric vehicles BibRef

Jorge, D., Correia, G.H.A., Barnhart, C.,
Comparing Optimal Relocation Operations With Simulated Relocation Policies in One-Way Carsharing Systems,
ITS(15), No. 4, August 2014, pp. 1667-1675.
IEEE DOI 1410
mathematical programming BibRef

He, W.[Wen], Hwang, K.[Kai], Li, D.[Deyi],
Intelligent Carpool Routing for Urban Ridesharing by Mining GPS Trajectories,
ITS(15), No. 5, October 2014, pp. 2286-2296.
IEEE DOI 1410
Global Positioning System BibRef

Pelzer, D., Xiao, J.J.[Jia-Jian], Zehe, D., Lees, M.H., Knoll, A.C., Aydt, H.,
A Partition-Based Match Making Algorithm for Dynamic Ridesharing,
ITS(16), No. 5, October 2015, pp. 2587-2598.
IEEE DOI 1511
intelligent transportation systems BibRef

d'Orey, P.M., Ferreira, M.,
Can ride-sharing become attractive? A case study of taxi-sharing employing a simulation modelling approach,
IET-ITS(9), No. 2, 2015, pp. 210-220.
DOI Link 1504
quality of service BibRef

Tan, K.K.[Kok Kiong], Htet, K.K.K.[Kyaw Ko Ko], Narayanan, A.S.,
Mitigation of Vehicle Distribution in an EV Sharing Scheme for Last Mile Transportation,
ITS(16), No. 5, October 2015, pp. 2631-2641.
IEEE DOI 1511
control engineering computing BibRef

Maciejewski, M., Bischoff, J., Nagel, K.,
An Assignment-Based Approach to Efficient Real-Time City-Scale Taxi Dispatching,
IEEE_Int_Sys(31), No. 1, January 2016, pp. 68-77.
IEEE DOI 1602
automobiles BibRef

Zhan, X., Qian, X., Ukkusuri, S.V.,
A Graph-Based Approach to Measuring the Efficiency of an Urban Taxi Service System,
ITS(17), No. 9, September 2016, pp. 2479-2489.
IEEE DOI 1609
Global Positioning System BibRef

Cangialosi, E., di Febbraro, A., Sacco, N.,
Designing a multimodal generalised ride sharing system,
IET-ITS(10), No. 4, 2016, pp. 227-236.
DOI Link 1606
BibRef
And: Corrigendum: IET-ITS(12), No. 1, February 2018, pp. 86.
DOI Link intelligent transportation systems BibRef

Zhang, J., Wen, D., Zeng, S.,
A Discounted Trade Reduction Mechanism for Dynamic Ridesharing Pricing,
ITS(17), No. 6, June 2016, pp. 1586-1595.
IEEE DOI 1606
Companies BibRef

Leng, B., Du, H., Wang, J., Li, L., Xiong, Z.,
Analysis of Taxi Drivers' Behaviors Within a Battle Between Two Taxi Apps,
ITS(17), No. 1, January 2016, pp. 296-300.
IEEE DOI 1601
Cities and towns BibRef

Yuan, W., Deng, P., Taleb, T., Wan, J., Bi, C.,
An Unlicensed Taxi Identification Model Based on Big Data Analysis,
ITS(17), No. 6, June 2016, pp. 1703-1713.
IEEE DOI 1606
Big data BibRef

Yang, G.[Gege], Song, C.[Ci], Shu, H.[Hua], Zhang, J.[Jia], Pei, T.[Tao], Zhou, C.[Chenghu],
Assessing Patient bypass Behavior Using Taxi Trip Origin-Destination (OD) Data,
IJGI(5), No. 9, 2016, pp. 157.
DOI Link 1610
BibRef

Tang, L.L.[Lu-Liang], Sun, F.[Fei], Kan, Z.[Zihan], Ren, C.[Chang], Cheng, L.L.[Lu-Ling],
Uncovering Distribution Patterns of High Performance Taxis from Big Trace Data,
IJGI(6), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Zhang, D.Q.[Da-Qing], Sun, L.[Lin], Li, B.[Bin], Chen, C.[Chao], Pan, G.[Gang], Li, S.J.[Shi-Jian], Wu, Z.H.[Zhao-Hui],
Understanding Taxi Service Strategies from Taxi GPS Traces,
ITS(16), No. 1, February 2015, pp. 123-135.
IEEE DOI 1502
Cities and towns BibRef

Wu, Z.H.[Zhou-Hao], Li, Y.X.[Ya-Xiang], Wang, X.[Xin], Su, J.[Juan], Yang, L.[Liu], Nie, Y.[Yu], Wang, Y.Q.[Yuan-Qing],
Mining Factors Affecting Taxi Detour Behavior from GPS Traces at Directional Road Segment Level,
ITS(23), No. 7, July 2022, pp. 8013-8023.
IEEE DOI 2207
Public transportation, Roads, Vehicles, Magnetic resonance imaging, Routing, Junctions, Indexes, Taxi detour behavior, map matching, spatio-temporal distribution features BibRef

Jin, S.X.[Shu-Xin], Wu, Z.H.[Zhou-Hao], Shen, T.[Tong], Wang, D.[Di], Cai, M.[Ming],
Uncovering Factors Affecting Taxi Income from GPS Traces at the Directional Road Segment Level,
IJGI(11), No. 8, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Castro, P.S.[Pablo Samuel], Zhang, D.Q.[Da-Qing], Chen, C.[Chao], Li, S.J.[Shi-Jian], Pan, G.[Gang],
From taxi GPS traces to social and community dynamics: A survey,
Surveys(46), No. 2, November 2013, pp. Article No 17.
DOI Link 1402
Vehicles equipped with GPS localizers are an important sensory device for examining people's movements and activities. Taxis equipped with GPS localizers serve the transportation needs of a large number of people driven by diverse needs; BibRef

Wu, H.B.[Hang-Bin], Fan, H.C.[Hong-Chao], Wu, S.Y.[Sheng-Yuan],
Exploring Spatiotemporal Patterns of Long-Distance Taxi Rides in Shanghai,
IJGI(6), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Qian, X., Ukkusuri, S.V.,
Time-of-Day Pricing in Taxi Markets,
ITS(18), No. 6, June 2017, pp. 1610-1622.
IEEE DOI 1706
Dynamic programming, Industries, Pricing, Public transportation, Surges, Urban areas, Vehicles, Time-of-day pricing, daily operation, market dynamics, revenue maximization, surge demand, value, function, approximation BibRef

Wu, L.[Liang], Hu, S.[Sheng], Yin, L.[Li], Wang, Y.Z.[Ya-Zhou], Chen, Z.L.[Zhan-Long], Guo, M.Q.[Ming-Qiang], Chen, H.[Hao], Xie, Z.[Zhong],
Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model,
IJGI(6), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Wang, Y.L.[Yu-Long], Qin, K.[Kun], Chen, Y.X.[Yi-Xiang], Zhao, P.X.[Peng-Xiang],
Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data,
IJGI(7), No. 1, 2018, pp. xx-yy.
DOI Link 1801
BibRef

Huang, S., Jiau, M., Chong, K.,
A Heuristic Multi-Objective Optimization Algorithm for Solving the Carpool Services Problem Featuring High-Occupancy-Vehicle Itineraries,
ITS(19), No. 8, August 2018, pp. 2663-2674.
IEEE DOI 1808
Optimization, Genetic algorithms, Linear programming, Heuristic algorithms, Algorithm design and analysis, Automobiles, high-occupancy-vehicle itineraries BibRef

Davis, N., Raina, G., Jagannathan, K.,
Taxi Demand Forecasting: A HEDGE-Based Tessellation Strategy for Improved Accuracy,
ITS(19), No. 11, November 2018, pp. 3686-3697.
IEEE DOI 1812
demand forecasting, forecasting theory, strategic planning, time series, travel industry, HEDGE-based tessellation strategy, HEDGE BibRef

An, S.[Shi], Yang, H.Q.[Hai-Qiang], Wang, J.[Jian],
Revealing Recurrent Urban Congestion Evolution Patterns with Taxi Trajectories,
IJGI(7), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Wang, Y.D.[Yan-Dong], Gu, Y.Y.[Yan-Yan], Dou, M.X.[Ming-Xuan], Qiao, M.L.[Meng-Ling],
Using Spatial Semantics and Interactions to Identify Urban Functional Regions,
IJGI(7), No. 4, 2018, pp. xx-yy.
DOI Link 1805
Using taxi origin/destination (O/D) flows. BibRef

Su, R.X.[Rong-Xiang], Fang, Z.X.[Zhi-Xiang], Xu, H.[Hong], Huang, L.[Lian],
Uncovering Spatial Inequality in Taxi Services in the Context of a Subsidy War among E-Hailing Apps,
IJGI(7), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Xu, J., Rahmatizadeh, R., Bölöni, L., Turgut, D.,
Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks,
ITS(19), No. 8, August 2018, pp. 2572-2581.
IEEE DOI 1808
Public transportation, Urban areas, Recurrent neural networks, Predictive models, Global Positioning System, mixture density networks BibRef

He, Z., Chen, K., Chen, X.,
A Collaborative Method for Route Discovery Using Taxi Drivers' Experience and Preferences,
ITS(19), No. 8, August 2018, pp. 2505-2514.
IEEE DOI 1808
Public transportation, Roads, Vehicles, Collaboration, Trajectory, Routing, Intelligent transportation systems, knowledge acquisition BibRef

Chen, C., Jiao, S., Zhang, S., Liu, W., Feng, L., Wang, Y.,
TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data,
ITS(19), No. 10, October 2018, pp. 3292-3304.
IEEE DOI 1810
Public transportation, Real-time systems, Trajectory, Global Positioning System, Semantics, Data mining, Urban areas, trajectory data mining BibRef

Amar, H.M., Basir, O.A.,
A Game Theoretic Solution for the Territory Sharing Problem in Social Taxi Networks,
ITS(19), No. 7, July 2018, pp. 2114-2124.
IEEE DOI 1807
Automobiles, Game theory, Games, Public transportation, Resource management, Cooperative trip planning, social taxi networks BibRef

Clemente, M., Fanti, M.P., Iacobellis, G., Nolich, M., Ukovich, W.,
A Decision Support System for User-Based Vehicle Relocation in Car Sharing Systems,
SMCS(48), No. 8, August 2018, pp. 1283-1296.
IEEE DOI 1808
automobiles, closed loop systems, decision support systems, discrete event simulation, particle swarm optimisation, optimization BibRef

Genikomsakis, K.N., Gutierrez, I.A.[I. Angulo], Thomas, D., Ioakimidis, C.S.,
Simulation and Design of Fast Charging Infrastructure for a University-Based e-Carsharing System,
ITS(19), No. 9, September 2018, pp. 2923-2932.
IEEE DOI 1809
Batteries, Charging stations, Automobiles, State of charge, Urban areas, MATLAB, Electric vehicles, battery chargers, transportation BibRef

Zhang, X.X.[Xin-Xin], Huang, B.[Bo], Zhu, S.Z.[Shun-Zhi],
Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression,
IJGI(8), No. 1, 2019, pp. xx-yy.
DOI Link 1901
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Cocca, M., Giordano, D., Mellia, M., Vassio, L.,
Free Floating Electric Car Sharing: A Data Driven Approach for System Design,
ITS(20), No. 12, December 2019, pp. 4691-4703.
IEEE DOI 2001
Vehicle dynamics, Charging stations, Urban areas, Electric vehicles, Internal combustion engines, Car sharing, free floating BibRef

Zong, F., Wu, T., Jia, H.,
Taxi Drivers' Cruising Patterns: Insights from Taxi GPS Traces,
ITS(20), No. 2, February 2019, pp. 571-582.
IEEE DOI 1902
Public transportation, Vehicles, Global Positioning System, Urban areas, Roads, Taxi, GPS, cruising pattern, land use, pick-up points BibRef

Yu, W.,
Discovering Frequent Movement Paths From Taxi Trajectory Data Using Spatially Embedded Networks and Association Rules,
ITS(20), No. 3, March 2019, pp. 855-866.
IEEE DOI 1903
Trajectory, Public transportation, Data mining, Urban areas, Space exploration, Clustering algorithms, Taxi trajectory, spatial association rule BibRef

Kuang, L.[Li], Yan, X.J.[Xue-Jin], Tan, X.H.[Xian-Han], Li, S.Q.[Shu-Qi], Yang, X.X.[Xiao-Xian],
Predicting Taxi Demand Based on 3D Convolutional Neural Network and Multi-task Learning,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
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Wang, H.H.[Hui-Hui], Huang, H.[Hong], Ni, X.Y.[Xiao-Yong], Zeng, W.H.[Wei-Hua],
Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data,
IJGI(8), No. 6, 2019, pp. xx-yy.
DOI Link 1908
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Hu, C.C.[Chun-Chun], Thill, J.C.[Jean-Claude],
Predicting the Upcoming Services of Vacant Taxis near Fixed Locations Using Taxi Trajectories,
IJGI(8), No. 7, 2019, pp. xx-yy.
DOI Link 1908
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Xu, Y.[Ying], Li, D.S.[Dong-Sheng],
Incorporating Graph Attention and Recurrent Architectures for City-Wide Taxi Demand Prediction,
IJGI(8), No. 9, 2019, pp. xx-yy.
DOI Link 1909
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Liu, L., Qiu, Z., Li, G., Wang, Q., Ouyang, W., Lin, L.,
Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction,
ITS(20), No. 10, October 2019, pp. 3875-3887.
IEEE DOI 1910
Public transportation, Task analysis, Correlation, Neural networks, Urban areas, Context modeling, Predictive models, spatial-temporal modeling BibRef

Yu, H., Chen, X., Li, Z., Zhang, G., Liu, P., Yang, J., Yang, Y.,
Taxi-Based Mobility Demand Formulation and Prediction Using Conditional Generative Adversarial Network-Driven Learning Approaches,
ITS(20), No. 10, October 2019, pp. 3888-3899.
IEEE DOI 1910
Public transportation, Predictive models, Generators, Training, Roads, Numerical models, Data models, Taxi system, machine learning, big transportation data BibRef

Kondor, D., Zhang, H., Tachet, R., Santi, P., Ratti, C.,
Estimating Savings in Parking Demand Using Shared Vehicles for Home-Work Commuting,
ITS(20), No. 8, August 2019, pp. 2903-2912.
IEEE DOI 1908
Automobiles, Urban areas, Autonomous vehicles, Roads, Public transportation, Sociology, Statistics, Shared vehicles, agent-based model BibRef

Zhu, M., Liu, X., Wang, X.,
An Online Ride-Sharing Path-Planning Strategy for Public Vehicle Systems,
ITS(20), No. 2, February 2019, pp. 616-627.
IEEE DOI 1902
Quality of service, Public transportation, Path planning, Schedules, Pollution, Peer-to-peer computing, online/dynamic peer-to-peer ride-sharing BibRef

Fu, X.Y.[Xiao-Yi], Zhang, C.[Ce], Lu, H.[Hua], Xu, J.L.[Jian-Liang],
Efficient matching of offers and requests in social-aware ridesharing,
GeoInfo(23), No. 4, October 2019, pp. 559-589.
WWW Link. 1911
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Panagiotopoulos, I.[Ilias], Dimitrakopoulos, G.[George],
Cognitive intelligence of highly automated vehicles in a car-sharing context,
IET-ITS(13), No. 11, November 2019, pp. 1604-1612.
DOI Link 1911
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Ke, J., Yang, H., Zheng, H., Chen, X., Jia, Y., Gong, P., Ye, J.,
Hexagon-Based Convolutional Neural Network for Supply-Demand Forecasting of Ride-Sourcing Services,
ITS(20), No. 11, November 2019, pp. 4160-4173.
IEEE DOI 1911
Forecasting, Urban areas, Automobiles, Convolutional neural networks, Pricing, ride-sourcing service BibRef

Chen, C.C.[Chian-Ching], Tsang, S.S.[Seng-Su],
Predicting adoption of mobile payments from the perspective of taxi drivers,
IET-ITS(13), No. 7, July 2019, pp. 1116-1124.
DOI Link 1906
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Babicheva, T.[Tatiana], Burghout, W.[Wilco], Andreasson, I.[Ingmar], Faul, N.[Nadege],
Empty vehicle redistribution and fleet size in autonomous taxi systems,
IET-ITS(13), No. 4, April 2019, pp. 677-682.
DOI Link 1903
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Al-Abbasi, A.O.[Abubakr O.], Ghosh, A., Aggarwal, V.[Vaneet],
DeepPool: Distributed Model-Free Algorithm for Ride-Sharing Using Deep Reinforcement Learning,
ITS(20), No. 12, December 2019, pp. 4714-4727.
IEEE DOI 2001
Automobiles, Dispatching, Public transportation, Reinforcement learning, Vehicle dynamics, Real-time systems, distributed algorithm BibRef

Singh, A.[Ashutosh], Al-Abbasi, A.O.[Abubakr O.], Aggarwal, V.[Vaneet],
A Distributed Model-Free Algorithm for Multi-Hop Ride-Sharing Using Deep Reinforcement Learning,
ITS(23), No. 7, July 2022, pp. 8595-8605.
IEEE DOI 2207
Predictive models, Dispatching, Urban areas, Reinforcement learning, Real-time systems, Automobiles, distributed algorithms BibRef

Lasmar, E.L., de Paula, F.O., Rosa, R.L., Abrahăo, J.I., Rodríguez, D.Z.,
RsRS: Ridesharing Recommendation System Based on Social Networks to Improve the User's QoE,
ITS(20), No. 12, December 2019, pp. 4728-4740.
IEEE DOI 2001
Quality of experience, Machine learning algorithms, Public transportation, Social networking (online), Vehicles, mobile applications BibRef

Dandl, F., Bogenberger, K.,
Comparing Future Autonomous Electric Taxis With an Existing Free-Floating Carsharing System,
ITS(20), No. 6, June 2019, pp. 2037-2047.
IEEE DOI 1906
Public transportation, Optimization, Autonomous vehicles, Vehicle dynamics, Companies, Urban areas, Autonomous taxis, relocation BibRef

Lai, Y., Lv, Z., Li, K., Liao, M.,
Urban Traffic Coulomb's Law: A New Approach for Taxi Route Recommendation,
ITS(20), No. 8, August 2019, pp. 3024-3037.
IEEE DOI 1908
Public transportation, Vehicles, Trajectory, Roads, Global Positioning System, Heuristic algorithms, taxi trajectories BibRef

Zhang, X., Zhao, Z., Zheng, Y., Li, J.,
Prediction of Taxi Destinations Using a Novel Data Embedding Method and Ensemble Learning,
ITS(21), No. 1, January 2020, pp. 68-78.
IEEE DOI 2001
Public transportation, Trajectory, Hidden Markov models, Predictive models, Markov processes, Global Positioning System, ensemble learning BibRef

Qu, B., Yang, W., Cui, G., Wang, X.,
Profitable Taxi Travel Route Recommendation Based on Big Taxi Trajectory Data,
ITS(21), No. 2, February 2020, pp. 653-668.
IEEE DOI 2002
Public transportation, Trajectory, Global Positioning System, Vehicles, Probabilistic logic, Capacity planning, Kalman filters, MapReduce BibRef

Fanti, M.P., Mangini, A.M., Pedroncelli, G., Ukovich, W.,
Fleet Sizing for Electric Car Sharing Systems in Discrete Event System Frameworks,
SMCS(50), No. 3, March 2020, pp. 1161-1177.
IEEE DOI 2002
Optimization, Computational modeling, Automobiles, Cascading style sheets, Analytical models, Mathematical model, transportation BibRef

Li, Y.M.[Yi-Ming], Fang, J.Z.[Jing-Zhi], Zeng, Y.X.[Yu-Xiang], Maag, B.[Balz], Tong, Y.X.[Yong-Xin], Zhang, L.Y.[Ling-Yu],
Two-sided online bipartite matching in spatial data: Experiments and analysis,
GeoInfo(24), No. 1, January 2020, pp. 175-198.
Springer DOI 2002
Matching workers to tasks (sharing economy). BibRef

Huang, S., Lin, J., Jiau, M.,
Global and Local Pareto Optimality in Coevolution for Solving Carpool Service Problem With Time Windows,
ITS(21), No. 3, March 2020, pp. 934-946.
IEEE DOI 2003
Vehicles, Search problems, Sociology, Statistics, Optimization, Convergence, Genetic algorithms, Multi-objective optimization, carpool service problem with time windows BibRef

Jiao, J.F.[Jun-Feng], Bai, S.[Shunhua],
Understanding the Shared E-scooter Travels in Austin, TX,
IJGI(9), No. 2, 2020, pp. xx-yy.
DOI Link 2003
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Guo, X.G.[Xiao-Gang], Xu, Z.J.[Zhi-Jie], Zhang, J.Q.[Jian-Qin], Lu, J.[Jian], Zhang, H.[Hao],
An OD Flow Clustering Method Based on Vector Constraints: A Case Study for Beijing Taxi Origin-Destination Data,
IJGI(9), No. 2, 2020, pp. xx-yy.
DOI Link 2003
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Tang, J., Wang, Y., Hao, W., Liu, F., Huang, H., Wang, Y.,
A Mixed Path Size Logit-Based Taxi Customer-Search Model Considering Spatio-Temporal Factors in Route Choice,
ITS(21), No. 4, April 2020, pp. 1347-1358.
IEEE DOI 2004
Path size logit model, taxi customer searching, route choice model, intersection delays, travel time BibRef

Sun, Y.[Yeran], Ren, Y.[Yinming], Sun, X.[Xuan],
Uber Movement Data: A Proxy for Average One-way Commuting Times by Car,
IJGI(9), No. 3, 2020, pp. xx-yy.
DOI Link 2004
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Cheng, L.[Luling], Yang, X.[Xue], Tang, L.[Luliang], Duan, Q.[Qian], Kan, Z.[Zihan], Zhang, X.[Xia], Ye, X.Y.[Xin-Yue],
Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data,
IJGI(9), No. 4, 2020, pp. xx-yy.
DOI Link 2005
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Bayrak, A.E., Egilmez, M.M., Kuang, H., Li, X., Park, J.M., Umpfenbach, E., Anderson, E., Gorsich, D., Hu, J., Papalambros, P.Y., Epureanu, B.I.,
A System-of-Systems Approach to the Strategic Feasibility of Modular Vehicle Fleets,
SMCS(50), No. 7, July 2020, pp. 2716-2728.
IEEE DOI 2006
Land vehicles, Maintenance engineering, Manufacturing, US Department of Defense, Atmospheric modeling, Modular vehicle, vehicle fleet BibRef

Jia, R.[Ruo], Li, Z.K.[Zhe-Kang], Xia, Y.[Yan], Zhu, J.Y.[Jia-Yan], Ma, N.[Nan], Chai, H.[Hua], Liu, Z.Y.[Zhi-Yuan],
Urban road traffic condition forecasting based on sparse ride-hailing service data,
IET-ITS(14), No. 7, July 2020, pp. 668-674.
DOI Link 2006
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Wang, Y.L.[Yang-Lan], Zhang, Y.[Yi], Zhang, Y.[Yi], Ma, J.S.[Jiang-Shan],
Dynamic real-time high-capacity ride-sharing model with subsequent information,
IET-ITS(14), No. 7, July 2020, pp. 742-752.
DOI Link 2006
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Zhang, X.X.[Xin-Xin], Huang, B.[Bo], Zhu, S.Z.[Shun-Zhi],
Spatiotemporal Varying Effects of Built Environment on Taxi and Ride-Hailing Ridership in New York City,
IJGI(9), No. 8, 2020, pp. xx-yy.
DOI Link 2008
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Kypriadis, D., Pantziou, G., Konstantopoulos, C., Gavalas, D.,
Optimizing Relocation Cost in Free-Floating Car-Sharing Systems,
ITS(21), No. 9, September 2020, pp. 4017-4030.
IEEE DOI 2008
Automobiles, Legged locomotion, Optimization, Dispatching, Vehicle dynamics, Vehicle sharing systems, electric cars, heuristics BibRef

Wang, F., Zhu, Y., Wang, F., Liu, J., Ma, X., Fan, X.,
Car4Pac: Last Mile Parcel Delivery Through Intelligent Car Trip Sharing,
ITS(21), No. 10, October 2020, pp. 4410-4424.
IEEE DOI 2010
Automobiles, Task analysis, Logistics, Fuels, Planning, Roads, Intelligent transportation system, trajectory data mining, travel cost prediction BibRef

Zhang, R., Ghanem, R.,
Demand, Supply, and Performance of Street-Hail Taxi,
ITS(21), No. 10, October 2020, pp. 4123-4132.
IEEE DOI 2010
Public transportation, Queueing analysis, Urban areas, Global Positioning System, Analytical models, Supply and demand, congestion BibRef

Hsieh, F.S.[Fu-Shiung],
A Comparative Study of Several Metaheuristic Algorithms to Optimize Monetary Incentive in Ridesharing Systems,
IJGI(9), No. 10, 2020, pp. xx-yy.
DOI Link 2010
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Liu, Y.[Yang], Liu, Z.Y.[Zhi-Yuan], Lyu, C.[Cheng], Ye, J.P.[Jie-Ping],
Attention-Based Deep Ensemble Net for Large-Scale Online Taxi-Hailing Demand Prediction,
ITS(21), No. 11, November 2020, pp. 4798-4807.
IEEE DOI 2011
Predictive models, Public transportation, Task analysis, Deep learning, Forecasting, Neural networks, Ensemble learning, demand prediction BibRef

Liu, Z.Y.[Zhi-Yuan], Liu, Y.[Yang], Lyu, C.[Cheng], Ye, J.P.[Jie-Ping],
Building Personalized Transportation Model for Online Taxi-Hailing Demand Prediction,
Cyber(51), No. 9, September 2021, pp. 4602-4610.
IEEE DOI 2109
Predictive models, Spatiotemporal phenomena, Feature extraction, Public transportation, Data models, Data mining, traffic prediction BibRef

Liu, Y., Lyu, C., Khadka, A., Zhang, W., Liu, Z.,
Spatio-Temporal Ensemble Method for Car-Hailing Demand Prediction,
ITS(21), No. 12, December 2020, pp. 5328-5333.
IEEE DOI 2012
Predictive models, Public transportation, Urban areas, Time series analysis, Demand forecasting, Data models, Correlation, fully convolutional networks BibRef

Shen, B.[Boxi], Xu, X.[Xiang], Li, J.[Jun], Plaza, A.[Antonio], Huang, Q.[Qunying],
Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation,
IJGI(9), No. 11, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Bistaffa, F., Blum, C., Cerquides, J., Farinelli, A., Rodríguez-Aguilar, J.A.,
A Computational Approach to Quantify the Benefits of Ridesharing for Policy Makers and Travellers,
ITS(22), No. 1, January 2021, pp. 119-130.
IEEE DOI 2012
Automobiles, Quality of service, Peer-to-peer computing, Pollution, Real-time systems, Ridesharing, collective intelligence, integer linear programming BibRef

Zhang, W.B.[Wen-Bo], Xi, Y.F.[Yin-Fei], Ukkusuri, S.V.[Satish V.],
Understanding Spatiotemporal Variations of Ridership by Multiple Taxi Services,
IJGI(9), No. 12, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Shi, J., Gao, Y., Wang, W., Yu, N., Ioannou, P.A.,
Operating Electric Vehicle Fleet for Ride-Hailing Services With Reinforcement Learning,
ITS(21), No. 11, November 2020, pp. 4822-4834.
IEEE DOI 2011
Reinforcement learning, Routing, Electric vehicles, Vehicle dynamics, Decision making, Charging stations, Batteries, ride-hailing services BibRef

Schweizer, J.[Joerg], Rupi, F.[Federico], Poliziani, C.[Cristian],
Estimation of link-cost function for cyclists based on stochastic optimisation and GPS traces,
IET-ITS(14), No. 13, 15 December 2020, pp. 1810-1814.
DOI Link 2102
BibRef

Rechkemmer, S.K., Zang, X., Boronka, A., Zhang, W., Sawodny, O.,
Utilization of Smartphone Data for Driving Cycle Synthesis Based on Electric Two-Wheelers in Shanghai,
ITS(22), No. 2, February 2021, pp. 876-886.
IEEE DOI 2102
Data collection, Motorcycles, Data acquisition, Global Positioning System, Instruments, Current measurement, frequency analysis BibRef

Tang, L., Duan, Z., Zhu, Y., Ma, J., Liu, Z.,
Recommendation for Ridesharing Groups Through Destination Prediction on Trajectory Data,
ITS(22), No. 2, February 2021, pp. 1320-1333.
IEEE DOI 2102
Trajectory, Automobiles, Semantics, Roads, Global Positioning System, Ridesharing group, recommendation, destination prediction, trajectory BibRef

Wu, P., Yang, C.H., Chu, F., Zhou, M., Sedraoui, K., Al Sokhiry, F.S.,
Cost-Profit Trade-Off for Optimally Locating Automotive Service Firms Under Uncertainty,
ITS(22), No. 2, February 2021, pp. 1014-1025.
IEEE DOI 2102
Stochastic processes, Transportation, Computational modeling, Analytical models, Monte Carlo methods, Automotive engineering, distribution-free model BibRef

Gan, Y.T.[Yi-Tong], Fan, H.C.[Hong-Chao], Jiao, W.[Wei], Sun, M.Q.[Meng-Qi],
Exploring the Influence of E-Hailing Applications on the Taxi Industry: From the Perspective of the Drivers,
IJGI(10), No. 2, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Gong, Y.J., Liu, Y.W., Lin, Y., Chen, W.N., Zhang, J.,
Real-Time Taxi-Passenger Matching Using a Differential Evolutionary Fuzzy Controller,
SMCS(51), No. 5, May 2021, pp. 2712-2725.
IEEE DOI 2104
Public transportation, Optimization, Quality of service, Real-time systems, Bipartite graph, Greedy algorithms, taxi dispatch system BibRef

Shi, C.Y.[Chao-Yang], Li, Q.Q.[Qing-Quan], Lu, S.W.[Shi-Wei], Yang, X.P.[Xi-Ping],
Modeling the Distribution of Human Mobility Metrics with Online Car-Hailing Data: An Empirical Study in Xi'an, China,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Pérez-Fernández, O.[Onel], García-Palomares, J.C.[Juan Carlos],
Parking Places to Moped-Style Scooter Sharing Services Using GIS Location-Allocation Models and GPS Data,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Shi, C.Y.[Chao-Yang], Li, Q.Q.[Qing-Quan], Lu, S.W.[Shi-Wei], Yang, X.P.[Xi-Ping],
Exploring Temporal Intra-Urban Travel Patterns: An Online Car-Hailing Trajectory Data Perspective,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Rahman, M.T.[Md Tawhidur], Dey, K.[Kakan], Martinelli, D.R.[David R.], Mishra, S.[Sabya],
Modeling and evaluation of a ridesharing matching system from multi-stakeholders' perspective,
IET-ITS(15), No. 6, 2021, pp. 781-794.
DOI Link 2106
BibRef

Rahman, M.H.[Md. Hishamur], Rifaat, S.M.[Shakil Mohammad],
Using spatio-temporal deep learning for forecasting demand and supply-demand gap in ride-hailing system with anonymised spatial adjacency information,
IET-ITS(15), No. 7, 2021, pp. 941-957.
DOI Link 2106
convolutional neural network, deep learning, demand, recurrent neural network, supply-demand gap BibRef

Belhadi, A.[Asma], Djenouri, Y.[Youcef], Srivastava, G.[Gautam], Djenouri, D.[Djamel], Cano, A.[Alberto], Lin, J.C.W.[Jerry Chun-Wei],
A Two-Phase Anomaly Detection Model for Secure Intelligent Transportation Ride-Hailing Trajectories,
ITS(22), No. 7, July 2021, pp. 4496-4506.
IEEE DOI 2107
Public transportation, Trajectory, Anomaly detection, Databases, Color, Vehicles, Trajectory database, outlier detection, GPU computing BibRef

Zhang, Y.[Yin], Li, Y.J.[Yu-Jie], Wang, R.R.[Ran-Ran], Hossain, M.S.[M. Shamim], Lu, H.M.[Hui-Min],
Multi-Aspect Aware Session-Based Recommendation for Intelligent Transportation Services,
ITS(22), No. 7, July 2021, pp. 4696-4705.
IEEE DOI 2107
Transportation, Data models, Machine learning, Navigation, Recurrent neural networks, Safety, Multi-aspect, self-attention, intelligent transportation services BibRef

Wang, R.[Rui], Chen, F.[Feng], Liu, X.B.[Xia-Bin], Liu, X.B.[Xia-Bing], Li, Z.Q.[Zhi-Qiang], Zhu, Y.[Yadi],
A Matching Model for Door-to-Door Multimodal Transit by Integrating Taxi-Sharing and Subways,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
BibRef

He, B.[Bing], Liu, K.[Kang], Xue, Z.[Zhe], Liu, J.J.[Jia-Jun], Yuan, D.P.[Di-Ping], Yin, J.[Jiyao], Wu, G.H.[Guo-Hua],
Spatial and Temporal Characteristics of Urban Tourism Travel by Taxi: A Case Study of Shenzhen,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Tang, J.J.[Jin-Jun], Liang, J.[Jian], Yu, T.J.[Tian-Jian], Xiong, Y.[Yong], Zeng, G.L.[Guo-Liang],
Trip destination prediction based on a deep integration network by fusing multiple features from taxi trajectories,
IET-ITS(15), No. 9, 2021, pp. 1131-1141.
DOI Link 2108
BibRef

Karamanis, R.[Renos], Anastasiadis, E.[Eleftherios], Angeloudis, P.[Panagiotis], Stettler, M.[Marc],
Assignment and Pricing of Shared Rides in Ride-Sourcing Using Combinatorial Double Auctions,
ITS(22), No. 9, September 2021, pp. 5648-5659.
IEEE DOI 2109
Pricing, Vehicles, Cost accounting, Resource management, Biological system modeling, Approximation algorithms, combinatorial double auctions BibRef

Ruch, C.[Claudio], Lu, C.Q.[Cheng-Qi], Sieber, L.[Lukas], Frazzoli, E.[Emilio],
Quantifying the Efficiency of Ride Sharing,
ITS(22), No. 9, September 2021, pp. 5811-5816.
IEEE DOI 2109
Public transportation, Urban areas, Vehicle dynamics, Quality of service, Benchmark testing, Ride sharing, operational policies BibRef

Wang, J.C.[Jin-Cheng], Wu, Q.Q.[Qun-Qi], Chen, Z.L.[Zi-Lin], Ren, Y.L.[Yi-Long], Gao, Y.[Yaqun],
Exploring the Factors of Intercity Ridesplitting Based on Observed and GIS Data: A Case Study in China,
IJGI(10), No. 9, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Chen, Y.Y.[Yue-Yue], Guo, D.[Deke], Xu, M.[Ming], Tang, G.M.[Guo-Ming], Cheng, G.Y.[Ge-Yao],
Measuring Maximum Urban Capacity of Taxi-Based Logistics,
ITS(22), No. 10, October 2021, pp. 6449-6459.
IEEE DOI 2110
Public transportation, Logistics, Urban areas, Pollution measurement, Time measurement, urban mobility BibRef

Pandi, R.R.[Ramesh Ramasamy], Ho, S.G.[Song Guang], Nagavarapu, S.C.[Sarat Chandra], Dauwels, J.[Justin],
A Generic GPU-Accelerated Framework for the Dial-A-Ride Problem,
ITS(22), No. 10, October 2021, pp. 6473-6488.
IEEE DOI 2110
Graphics processing units, Optimization, Transportation, Instruction sets, Hardware, Search problems, Routing, variable neighborhood search BibRef

Davis, N.[Neema], Raina, G.[Gaurav], Jagannathan, K.[Krishna],
Grids Versus Graphs: Partitioning Space for Improved Taxi Demand-Supply Forecasts,
ITS(22), No. 10, October 2021, pp. 6526-6535.
IEEE DOI 2110
Public transportation, Data models, Predictive models, Forecasting, Urban areas, Computational modeling, Measurement, GraphLSTM BibRef

You, L.[Lan], Guan, Z.Y.[Zheng-Yi], Li, N.[Na], Zhang, J.H.[Jia-He], Cui, H.B.[Hai-Bo], Claramunt, C.[Christophe], Cao, R.[Rui],
A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction,
IJGI(10), No. 10, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Wang, D.[Di], Miwa, T.[Tomio], Morikawa, T.[Takayuki],
Comparative Analysis of Spatial-Temporal Distribution between Traditional Taxi Service and Emerging Ride-Hailing,
IJGI(10), No. 10, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Haliem, M.[Marina], Mani, G.[Ganapathy], Aggarwal, V.[Vaneet], Bhargava, B.[Bharat],
A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching Using Deep Reinforcement Learning,
ITS(22), No. 12, December 2021, pp. 7931-7942.
IEEE DOI 2112
Vehicles, Pricing, Dispatching, Planning, Urban areas, Reinforcement learning, Decision making, Ride-sharing, route planning BibRef

Zhang, W.B.[Wen-Bo], Xu, C.[Chang],
Exploring App-Based Taxi Movement Patterns from Large-Scale Geolocation Data,
IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Eldawy, E.O.[Eman O.], Hendawi, A.[Abdeltawab], Abdalla, M.[Mohammed], Mokhtar, H.M.O.[Hoda M. O.],
FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection,
IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Chen, D.J.[De-Jun], Wang, J.[Jing], Xiong, C.C.[Cong-Cong],
Research on origin-destination travel demand prediction method of inter-regional online taxi based on SpatialOD-BiConvLSTM,
IET-ITS(15), No. 12, 2021, pp. 1533-1547.
DOI Link 2112
BibRef

Chang, M.M.[Meng-Meng], Chi, Y.[Yuanying], Ding, Z.M.[Zhi-Ming], Tian, J.[Jing], Zheng, Y.H.[Yu-Hao],
A Continuous Taxi Pickup Path Recommendation under The Carbon Neutrality Context,
IJGI(10), No. 12, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Liu, J.[Ji], Bondiombouy, C.[Carlyna], Mo, L.[Lei], Valduriez, P.[Patrick],
Two-Phase Scheduling for Efficient Vehicle Sharing,
ITS(23), No. 1, January 2022, pp. 457-470.
IEEE DOI 2201
Scheduling, Schedules, Vehicles, Urban areas, Task analysis, Scheduling algorithms, Vehicle sharing, path planning problem, optimization BibRef

Du, M.Y.[Ming-Yang], Li, X.F.[Xue-Feng], Kwan, M.P.[Mei-Po], Yang, J.Z.[Jing-Zong], Liu, Q.Y.[Qi-Yang],
Understanding the Spatiotemporal Variation of High-Efficiency Ride-Hailing Orders: A Case Study of Haikou, China,
IJGI(11), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Rong, H.G.[Hui-Gui], Huo, S.X.[Sheng-Xu], Zhang, Q.[Qun], Zheng, H.[Hui], Yang, C.[Chang],
GREEN: A Global Optimization Scheme for Transportation Efficiency by Mining Taxi Mobility,
ITS(23), No. 2, February 2022, pp. 1596-1606.
IEEE DOI 2202
Public transportation, Vehicles, Roads, Optimization, Trajectory, Data mining, Urban areas, Mobility mining, seeking efficiency, transportation efficiency BibRef

Zhang, C.Z.[Chi-Zhan], Zhu, F.[Fenghua], Wang, X.[Xiao], Sun, L.L.[Lei-Lei], Tang, H.[Haina], Lv, Y.S.[Yi-Sheng],
Taxi Demand Prediction Using Parallel Multi-Task Learning Model,
ITS(23), No. 2, February 2022, pp. 794-803.
IEEE DOI 2202
Public transportation, Predictive models, Urban areas, Task analysis, Deep learning, Data mining, Correlation, deep learning BibRef

Zhang, C.Z.[Chi-Zhan], Zhu, F.[Fenghua], Lv, Y.S.[Yi-Sheng], Ye, P.J.[Pei-Jun], Wang, F.Y.[Fei-Yue],
MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity Analysis,
ITS(23), No. 7, July 2022, pp. 8412-8422.
IEEE DOI 2207
Public transportation, Predictive models, Urban areas, Task analysis, Clustering algorithms, Prediction algorithms, deep learning BibRef

Chen, D.[Duxin], Shao, Q.[Qi], Liu, Z.Y.[Zhi-Yuan], Yu, W.W.[Wen-Wu], Chen, C.L.P.[C. L. Philip],
Ridesourcing Behavior Analysis and Prediction: A Network Perspective,
ITS(23), No. 2, February 2022, pp. 1274-1283.
IEEE DOI 2202
Automobiles, Urban areas, Trajectory, Global Positioning System, Public transportation, Spatiotemporal phenomena, Ridesourcing, transportation network companies BibRef

Lai, Y.X.[Yong-Xuan], Yang, S.P.[Shi-Peng], Xiong, A.[Anshu], Yang, F.[Fan], Li, L.[Lei], Zhou, X.F.[Xiao-Fang],
Utility-Based Matching of Vehicles and Hybrid Requests on Rider Demand Responsive Systems,
ITS(23), No. 2, February 2022, pp. 1058-1072.
IEEE DOI 2202
Real-time systems, Heuristic algorithms, Vehicle dynamics, Roads, Task analysis, Public transportation, Companies, rider demand responsive system BibRef

Hussain, I.[Iftikhar], Knapen, L.[Luk], Bellemans, T.[Tom], Janssens, D.[Davy], Wets, G.[Geert],
A Matching Framework for Employees to Support Carpooling in the Context of Large Companies,
ITS(23), No. 2, February 2022, pp. 1159-1170.
IEEE DOI 2202
Companies, Databases, Automobiles, Schedules, Personnel, Genetic algorithms, Commuting, travel behavior, carpooling, matching framework BibRef

Liu, Z.D.[Zhi-Dan], Li, J.Z.[Jiang-Zhou], Wu, K.S.[Kai-Shun],
Context-Aware Taxi Dispatching at City-Scale Using Deep Reinforcement Learning,
ITS(23), No. 3, March 2022, pp. 1996-2009.
IEEE DOI 2203
Public transportation, Dispatching, Reinforcement learning, Urban areas, Roads, Adaptation models, Computational modeling, taxi demand prediction BibRef

Luo, M.[Man], Du, B.[Bowen], Klemmer, K.[Konstantin], Zhu, H.M.[Hong-Ming], Wen, H.K.[Hong-Kai],
Deployment Optimization for Shared e-Mobility Systems With Multi-Agent Deep Neural Search,
ITS(23), No. 3, March 2022, pp. 2549-2560.
IEEE DOI 2203
Optimization, Public transportation, Urban areas, Reinforcement learning, Planning, Search problems, Data models, deep reinforcement learning BibRef

Chau, S.C.K.[Sid Chi-Kin], Shen, S.[Shuning], Zhou, Y.[Yue],
Decentralized Ride-Sharing and Vehicle-Pooling Based on Fair Cost-Sharing Mechanisms,
ITS(23), No. 3, March 2022, pp. 1936-1946.
IEEE DOI 2203
Roads, Public transportation, Urban areas, Vehicles, Social networking (online), Market research, Decision making, stable matching BibRef

Li, J.W.[Jing-Wen], Xin, L.[Liang], Cao, Z.G.[Zhi-Guang], Lim, A.[Andrew], Song, W.[Wen], Zhang, J.[Jie],
Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning,
ITS(23), No. 3, March 2022, pp. 2306-2315.
IEEE DOI 2203
Reinforcement learning, Routing, Peer-to-peer computing, Heuristic algorithms, Deep learning, Decoding, Decision making, pickup and delivery problem BibRef

Seo, T.[Toru], Asakura, Y.[Yasuo],
Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design,
ITS(23), No. 4, April 2022, pp. 3816-3828.
IEEE DOI 2204
Roads, Optimization, Vehicle dynamics, Routing, Transportation, Resource management, Urban areas, Dynamic SAV assignment, parking space allocation BibRef

Manchella, K.[Kaushik], Haliem, M.[Marina], Aggarwal, V.[Vaneet], Bhargava, B.[Bharat],
PassGoodPool: Joint Passengers and Goods Fleet Management With Reinforcement Learning Aided Pricing, Matching, and Route Planning,
ITS(23), No. 4, April 2022, pp. 3866-3877.
IEEE DOI 2204
Pricing, Planning, Dispatching, Vehicle dynamics, Vehicles, Optimization, Heuristic algorithms, Ride-sharing, urban delivery, fleet management BibRef

Mi, C.L.[Chun-Lei], Cheng, S.[Shifen], Lu, F.[Feng],
Predicting Taxi-Calling Demands Using Multi-Feature and Residual Attention Graph Convolutional Long Short-Term Memory Networks,
IJGI(11), No. 3, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Luo, A.[Aling], Shangguan, B.[Boyi], Yang, C.[Can], Gao, F.[Fan], Fang, Z.[Zhe], Yu, D.[Dayu],
Spatial-Temporal Diffusion Convolutional Network: A Novel Framework for Taxi Demand Forecasting,
IJGI(11), No. 3, 2022, pp. xx-yy.
DOI Link 2204
BibRef

Ning, Z.L.[Zhao-Long], Sun, S.M.[Shou-Ming], Zhou, M.C.[Meng-Chu], Hu, X.P.[Xi-Ping], Wang, X.J.[Xiao-Jie], Guo, L.[Lei], Hu, B.[Bin], Kwok, R.Y.K.[Ricky Y. K.],
Online Scheduling and Route Planning for Shared Buses in Urban Traffic Networks,
ITS(23), No. 4, April 2022, pp. 3430-3444.
IEEE DOI 2204
User experience, Dynamic scheduling, Heuristic algorithms, Companies, Vehicle dynamics, Schedules, Optimization, Shared bus, multi-objective optimization BibRef

Liao, C.[Chengwu], Chen, C.[Chao], Xiang, C.[Chaocan], Huang, H.Y.[Hong-Yu], Xie, H.[Hong], Guo, S.T.[Song-Tao],
Taxi-Passenger's Destination Prediction via GPS Embedding and Attention-Based BiLSTM Model,
ITS(23), No. 5, May 2022, pp. 4460-4473.
IEEE DOI 2205
Trajectory, Public transportation, Global Positioning System, Feature extraction, Neural networks, Predictive models, attention mechanism BibRef

Gao, J.[Jie], Wong, T.[Terrence], Wang, C.[Chun], Yu, J.Y.[Jia Yuan],
A Price-Based Iterative Double Auction for Charger Sharing Markets,
ITS(23), No. 6, June 2022, pp. 5116-5127.
IEEE DOI 2206
Vehicles, Processor scheduling, Electric vehicle charging, Resource management, Sharing economy, Schedules, Pricing, social welfare BibRef

Xu, Y.H.[Yu-Hang], Wang, W.[Wanyuan], Xiong, G.[Guangwei], Liu, X.[Xiang], Wu, W.W.[Wei-Wei], Liu, K.[Kai],
Network-Flow-Based Efficient Vehicle Dispatch for City-Scale Ride-Hailing Systems,
ITS(23), No. 6, June 2022, pp. 5526-5538.
IEEE DOI 2206
Real-time systems, Optimization, Vehicles, Prediction algorithms, Heuristic algorithms, Dispatching, Demand forecasting, scalable algorithm BibRef

Feng, S.Y.[Si-Yuan], Ke, J.T.[Jin-Tao], Yang, H.[Hai], Ye, J.P.[Jie-Ping],
A Multi-Task Matrix Factorized Graph Neural Network for Co-Prediction of Zone-Based and OD-Based Ride-Hailing Demand,
ITS(23), No. 6, June 2022, pp. 5704-5716.
IEEE DOI 2206
Predictive models, Task analysis, Decoding, Correlation, Transportation, Data models, Semantics, Ride-hailing, deep multi-task learning BibRef

Jing, C.F.[Chang-Feng], Hu, Y.[Yanru], Zhang, H.Y.[Hong-Yang], Du, M.Y.[Ming-Yi], Xu, S.S.[Shi-Shuo], Guo, X.[Xian], Jiang, J.[Jie],
Context-Aware Matrix Factorization for the Identification of Urban Functional Regions with POI and Taxi OD Data,
IJGI(11), No. 6, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Manjunath, A.[Aishwarya], Raychoudhury, V.[Vaskar], Saha, S.[Snehanshu], Kar, S.[Saibal], Kamath, A.[Anusha],
CARE-Share: A Cooperative and Adaptive Strategy for Distributed Taxi Ride Sharing,
ITS(23), No. 7, July 2022, pp. 7028-7044.
IEEE DOI 2207
Public transportation, Vehicle dynamics, Vehicles, Optimization, Heuristic algorithms, Urban areas, Linear programming, pareto optimization BibRef

Tosoni, F.[Francesco], Ferragina, P.[Paolo], Marino, A.[Andrea], Resta, G.[Giovanni], Santi, P.[Paolo],
Locality Filtering for Efficient Ride Sharing Platforms,
ITS(23), No. 7, July 2022, pp. 7785-7804.
IEEE DOI 2207
Urban areas, Vehicles, Public transportation, Optimization, Data structures, Computer science, Space exploration, geometric data structures BibRef

Huo, X.[Xiang], Wu, X.[Xinkai], Fan, Y.Q.[Yu-Qi], Ding, C.[Chuan],
A Mixed-Integer Program (MIP) for One-Way Multiple-Type Shared Electric Vehicles Allocation With Uncertain Demand,
ITS(23), No. 7, July 2022, pp. 8972-8984.
IEEE DOI 2207
Resource management, Automobiles, Electric vehicles, Dispatching, Data models, Companies, Vehicle dynamics, Carsharing, uncertain demand BibRef

Cao, D.[Dun], Zeng, K.[Kai], Wang, J.[Jin], Sharma, P.K.[Pradip Kumar], Ma, X.M.[Xiao-Min], Liu, Y.H.[Yong-He], Zhou, S.Y.[Si-Yuan],
BERT-Based Deep Spatial-Temporal Network for Taxi Demand Prediction,
ITS(23), No. 7, July 2022, pp. 9442-9454.
IEEE DOI 2207
Public transportation, Deep learning, Data models, Predictive models, Urban areas, Spatiotemporal phenomena, spatial-temporal network BibRef

Lai, G.J.[Gui-Jun], Shang, Y.Z.[Yu-Zhen], He, B.B.[Bin-Bao], Zhao, G.W.[Guan-Wei], Yang, M.Z.[Mu-Zhuang],
Revealing Taxi Interaction Network of Urban Functional Area Units in Shenzhen, China,
IJGI(11), No. 7, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Liu, C.X.[Chen-Xi], Chen, C.X.[Chao-Xiong], Chen, C.[Chao],
META: A City-Wide Taxi Repositioning Framework Based on Multi-Agent Reinforcement Learning,
ITS(23), No. 8, August 2022, pp. 13890-13895.
IEEE DOI 2208
Public transportation, Urban areas, Reinforcement learning, Dispatching, Real-time systems, Training, Task analysis, multi-agent learning BibRef

Heredia, C.[Cristóbal], Moreno, S.[Sebastián], Yushimito, W.F.[Wilfredo F.],
Characterization of Mobility Patterns With a Hierarchical Clustering of Origin-Destination GPS Taxi Data,
ITS(23), No. 8, August 2022, pp. 12700-12710.
IEEE DOI 2208
Public transportation, Global Positioning System, Clustering methods, Clustering algorithms, Data models, urban mobility patterns BibRef

Rivičre, B.[Benjamin], Chung, S.J.[Soon-Jo],
H-TD2: Hybrid Temporal Difference Learning for Adaptive Urban Taxi Dispatch,
ITS(23), No. 8, August 2022, pp. 10935-10944.
IEEE DOI 2208
Public transportation, Computational modeling, Adaptation models, Estimation, Vehicle dynamics, Heuristic algorithms, autonomous vehicles BibRef

Qu, B.T.[Bo-Ting], Ren, X.Y.[Xin-Yu], Feng, J.[Jun], Wang, X.[Xin],
A Dynamic Ridesplitting Method With Potential Pick-Up Probability Based on GPS Trajectories,
ITS(23), No. 8, August 2022, pp. 10786-10802.
IEEE DOI 2208
Resource management, Vehicles, Trajectory, Global Positioning System, Indexes, Time factors, Roads, Ridesharing, iterated local search BibRef

Karamanis, R.[Renos], Anastasiadis, E.[Eleftherios], Stettler, M.[Marc], Angeloudis, P.[Panagiotis],
Vehicle Redistribution in Ride-Sourcing Markets Using Convex Minimum Cost Flows,
ITS(23), No. 8, August 2022, pp. 10287-10298.
IEEE DOI 2208
Public transportation, Autonomous vehicles, Predictive models, Vehicles, Resource management, Pricing, Mathematical model, network optimisation BibRef

Gao, J.[Jie], Li, X.M.[Xiao-Ming], Wang, C.[Chun], Huang, X.[Xiao],
BM-DDPG: An Integrated Dispatching Framework for Ride-Hailing Systems,
ITS(23), No. 8, August 2022, pp. 11666-11676.
IEEE DOI 2208
Vehicles, Surges, Public transportation, Dispatching, Prediction algorithms, Predictive models, Optimization, social welfare BibRef

Bi, H.[Hui], Ye, Z.[Zhirui], Zhu, H.[He],
Discovering Implicit Working Pace of Online Ride-Hailing Drivers: An Exploratory Study,
ITS(23), No. 8, August 2022, pp. 10504-10513.
IEEE DOI 2208
Vehicles, Public transportation, Data mining, Data models, Vehicle dynamics, Global Positioning System, Fatigue, order-gap BibRef

Arif, A.[Anmar], Margellos, K.[Kostas],
Locating Parking Hubs in Free-Floating Ride Share Systems via Data-Driven Optimization,
ITS(23), No. 8, August 2022, pp. 11621-11632.
IEEE DOI 2208
Optimization, Uncertainty, Mathematical model, Indexes, Computer interfaces, Urban areas, Resource management, robust optimization BibRef

Skordilis, E.[Erotokritos], Hou, Y.[Yi], Tripp, C.[Charles], Moniot, M.[Matthew], Graf, P.[Peter], Biagioni, D.[David],
A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem,
ITS(23), No. 8, August 2022, pp. 11903-11916.
IEEE DOI 2208
Computational modeling, Reinforcement learning, Vehicle dynamics, Renewable energy sources, Mathematical models, intelligent control BibRef

Tang, L.[Lei], Liu, Z.[Zihang], Zhang, R.G.[Rong-Guo], Duan, Z.T.[Zong-Tao], Liang, Y.J.[Yun-Ji],
Who Will Travel With Me? Personalized Ranking Using Attributed Network Embedding for Pooling,
ITS(23), No. 8, August 2022, pp. 12311-12327.
IEEE DOI 2208
Vehicles, Schedules, Costs, Legged locomotion, Intelligent transportation systems, Trajectory, Task analysis, SUMO BibRef

Monteiro de Lira, V., Perego, R., Renso, C., Rinzivillo, S., Times, V.C.[V. Cesario],
Boosting Ride Sharing With Alternative Destinations,
ITS(19), No. 7, July 2018, pp. 2290-2300.
IEEE DOI 1807
Automobiles, Boosting, Resource management, Urban areas, Carpooling, flexibility, green mobility, ride sharing BibRef

Yuan, C.W.[Chang-Wei], Zhao, J.N.[Jian-Nan], Mao, X.H.[Xin-Hua], Duan, Y.X.[Ya-Xin], Ma, N.Y.[Ning-Yuan],
Uncovering the Relationship between Urban Road Network Topology and Taxi Drivers' Income: A Perspective from Spatial Design Network Analysis,
IJGI(11), No. 9, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Qu, B.T.[Bo-Ting], Mao, L.[Linran], Xu, Z.Z.[Zhen-Zhou], Feng, J.[Jun], Wang, X.[Xin],
How Many Vehicles Do We Need? Fleet Sizing for Shared Autonomous Vehicles With Ridesharing,
ITS(23), No. 9, September 2022, pp. 14594-14607.
IEEE DOI 2209
Vehicle dynamics, Correlation, Predictive models, Costs, Simulation, Trajectory, Public transportation, Minimum fleet size, demand prediction BibRef

Hu, X.[Xu], Niu, X.Y.[Xiao-Yu], Qian, L.X.[Ling-Xin], Pan, B.H.[Bing-Huang], Yu, Z.Y.[Zhao-Yuan],
Analyzing the multi-scale characteristic for online car-hailing traffic volume with quantum walk,
IET-ITS(16), No. 10, 2022, pp. 1328-1341.
DOI Link 2209
BibRef

Peng, W.[Wang], Du, L.[Lili],
Investigating Optimal Carpool Scheme by a Semi-Centralized Ride-Matching Approach,
ITS(23), No. 9, September 2022, pp. 14990-15004.
IEEE DOI 2209
Vehicles, Costs, Computational modeling, System performance, Scalability, Optimization, Clustering algorithms, Carpool, mixed integer programming BibRef

Iacobucci, R.[Riccardo], Bruno, R.[Raffaele], Boldrini, C.[Chiara],
A Multi-Stage Optimisation Approach to Design Relocation Strategies in One-Way Car-Sharing Systems With Stackable Cars,
ITS(23), No. 10, October 2022, pp. 17048-17061.
IEEE DOI 2210
Automobiles, Optimization, Costs, Uncertainty, Task analysis, Roads, Computational modeling, Car sharing, vehicle relocation, optimisation BibRef

Zhong, J.[Jun], Zhou, H.[Huan], Lin, Y.[Yan], Ren, F.X.[Fang-Xiao],
The impact of ride-hailing services on the use of traditional taxis: Evidence from Chinese urban panel data,
IET-ITS(16), No. 11, 2022, pp. 1611-1622.
DOI Link 2210
BibRef

Chen, Q.X.[Qi-Xiang], Lv, B.[Bin], Hao, B.B.[Bin-Bin], Luo, W.Z.[Wei-Zhuang], Lang, B.[Binke], Li, X.[Xu],
Modelling multiple quantiles together with the mean based on SA-ConvLSTM for taxi pick-up prediction,
IET-ITS(16), No. 11, 2022, pp. 1623-1632.
DOI Link 2210
BibRef

Kim, B.[Beomjun], Huh, S.B.[Su-Bin],
Discretization-Free Particle-Based Taxi Dispatch Methods With Network Flow Decomposition,
ITS(23), No. 10, October 2022, pp. 17756-17768.
IEEE DOI 2210
Public transportation, Vehicles, Real-time systems, Prediction algorithms, Urban areas, Roads, optimization BibRef

Wu, Z.[Zhou], Wu, J.J.[Jun-Jun], Chen, Y.G.[Yu-Guang], Liu, K.[Kai], Feng, L.[Liang],
Network Rebalance and Operational Efficiency of Sharing Transportation System: Multi-Objective Optimization and Model Predictive Control Approaches,
ITS(23), No. 10, October 2022, pp. 17119-17129.
IEEE DOI 2210
Transportation, Optimization, Costs, Vehicle dynamics, Predictive models, Mathematical models, Bicycles, multiobjective optimization BibRef

Magsino, E.[Elmer], Ching, G.R.[Gerard Ryan], Espiritu, F.M.[Francis Miguel], Go, K.[Kerwin],
Evaluating Stable Matching Methods and Ridesharing Techniques in Optimizing Passenger Transportation Cost and Companionship,
IJGI(11), No. 11, 2022, pp. xx-yy.
DOI Link 2212
BibRef

He, Y.Y.[Yuan-Yuan], Ni, J.B.[Jian-Bing], Yang, L.T.[Laurence T.], Wei, W.[Wei], Deng, X.J.[Xian-Jun], Zou, D.Q.[De-Qing], Ahmed, S.H.[Syed Hassan],
Differentially Private Tripartite Intelligent Matching Against Inference Attacks in Ride-Sharing Services,
ITS(23), No. 11, November 2022, pp. 22583-22595.
IEEE DOI 2212
Privacy, Vehicles, Protocols, Differential privacy, Bayes methods, Encryption, Nickel, Ride-sharing, intelligent matching, inference attacks BibRef

Guo, G.[Ge], Hou, Y.Q.[Yu-Qin],
Rebalancing of One-Way Car-Sharing Systems Considering Elastic Demand and Waiting Time,
ITS(23), No. 12, December 2022, pp. 23295-23310.
IEEE DOI 2212
Delays, Electric vehicles, Time factors, Servers, Quality of service, Pricing, Optimization, BCMP queuing, rebalancing, charging delay, waiting time BibRef

Gao, J.[Jie], Wong, T.[Terrence], Selim, B.[Bassant], Wang, C.[Chun],
VOMA: A Privacy-Preserving Matching Mechanism Design for Community Ride-Sharing,
ITS(23), No. 12, December 2022, pp. 23963-23975.
IEEE DOI 2212
Vehicles, Privacy, Costs, Optimization, Vehicle dynamics, Systems engineering and theory, Simulated annealing, private information BibRef

Samie, S.[Sepideh], Rezaee, B.[Babak],
Dynamic Discrimination Pricing and Freelance Drivers to Rebalance Mixed-Fleet Carsharing Systems,
ITS(23), No. 12, December 2022, pp. 24738-24752.
IEEE DOI 2212
Pricing, Vehicles, Personnel, Vehicle dynamics, Resource management, Numerical models, Costs, Mixed-fleet Carsharing systems, rolling horizon approach BibRef

Tu, P.[Ping], Yao, W.[Wei], Zhao, Z.Y.[Zhi-Yuan], Wang, P.Z.[Peng-Zhou], Wu, S.[Sheng], Fang, Z.X.[Zhi-Xiang],
Interday Stability of Taxi Travel Flow in Urban Areas,
IJGI(11), No. 12, 2022, pp. xx-yy.
DOI Link 2301
BibRef

Schlenther, T.[Tilmann], Leich, G.[Gregor], Maciejewski, M.[Michal], Nagel, K.[Kai],
Addressing spatial service provision equity for pooled ride-hailing services through rebalancing,
IET-ITS(17), No. 3, 2023, pp. 543-552.
DOI Link 2303
BibRef

Luo, Y.C.[Yu-Chuan], Fu, S.J.[Shao-Jing], Jia, X.H.[Xiao-Hua], Xu, M.[Ming], Chen, Y.[Yingwen],
P2Ride: Practical and Privacy-Preserving Ride-Matching Scheme for Ridesharing,
ITS(24), No. 3, March 2023, pp. 3584-3593.
IEEE DOI 2303
Testing, Protocols, Servers, Privacy, Threat modeling, Prototypes, Urban areas, Ridesharing, privacy-preserving, location privacy, ride-matching BibRef

Monteiro, C.M.[Cristiano Martins], Davis, C.A.[Clodoveu A.],
Polynomial-Time Carsharing Optimization: Linear Formulation and Large-Scale Simulations,
ITS(24), No. 4, April 2023, pp. 4428-4437.
IEEE DOI 2304
Companies, Costs, Optimization, Vehicles, Business, Task analysis, Automobiles, Carsharing, fleet-sizing, polynomial-time, large-scale simulation BibRef

Liao, L.[Lyuchao], Li, B.[Ben], Zou, F.M.[Fu-Min], Huang, D.[Dejuan],
MFGCN: A Multimodal Fusion Graph Convolutional Network for Online Car-Hailing Demand Prediction,
IEEE_Int_Sys(38), No. 3, May 2023, pp. 21-30.
IEEE DOI 2307
Public transportation, Correlation, Convolutional neural networks, Semantics, Intelligent systems, Predictive models BibRef

Wang, T.[Tong], Shen, Z.X.[Zhao-Xian], Cao, Y.[Yue], Xu, X.J.[Xiu-Juan], Gong, H.[Huiwen],
Taxi-Cruising Recommendation via Real-Time Information and Historical Trajectory Data,
ITS(24), No. 8, August 2023, pp. 7898-7910.
IEEE DOI 2308
Public transportation, Trajectory, Real-time systems, Global Positioning System, Vehicles, Predictive models, recommendation system BibRef

He, Y.C.[Yu-Chu], Jia, Z.J.[Zhi-Juan], Hu, M.S.[Ming-Sheng], Zhang, G.[Geng], Dong, H.J.[Han-Jie],
The Hybrid Trip Destination Prediction Model of Vehicles Based on Autoencoder and High-Order Interaction Features,
ITS(24), No. 8, August 2023, pp. 8443-8451.
IEEE DOI 2308
Predictive models, Data models, Mathematical models, Biological system modeling, Neural networks, Trajectory, autoencoder BibRef

Zou, W.J.[Wen-Jun], Wu, L.[Lei], Chang, Y.[Yunrui], Niu, Q.[Qiang],
Is Ride-Hailing an Effective Tool for Improving Transportation Services in Suburban New Towns in China? Evidence from Wuhan Unicom Users' Mobile Phone Usage Big Data,
IJGI(12), No. 8, 2023, pp. 299.
DOI Link 2309
BibRef

Feng, X.[Xuyu], Yu, J.H.[Jian-Hua], Kan, Z.[Zihan], Zhou, L.[Lin], Tang, L.[Luliang], Yang, X.[Xue],
Spatial-Temporal Analysis of Vehicle Routing Problem from Online Car-Hailing Trajectories,
IJGI(12), No. 8, 2023, pp. 319.
DOI Link 2309
BibRef

Robbennolt, J.[Jake], Levin, M.W.[Michael W.],
Maximum Throughput Dispatch for Shared Autonomous Vehicles Including Vehicle Rebalancing,
ITS(24), No. 9, September 2023, pp. 9871-9885.
IEEE DOI 2310
BibRef

Wang, Y.Q.[Yin-Quan], Wu, J.J.[Jian-Jun], Sun, H.J.[Hui-Jun], Lv, Y.[Ying], Xu, G.[Guangtong],
Reassignment Algorithm of the Ride-Sourcing Market Based on Reinforcement Learning,
ITS(24), No. 10, October 2023, pp. 10923-10936.
IEEE DOI 2310
BibRef

Xue, S.Q.[Shou-Qiang], Song, R.[Rui], He, S.W.[Shi-Wei], Li, G.Y.[Guang-Ye], Chi, J.[Jushang],
Passenger-perception dynamic ridesharing service based on parallel technology,
IET-ITS(17), No. 9, 2023, pp. 1799-1818.
DOI Link 2310
greedy algorithms, intelligent transportation systems, mobility as a service, road traffic, traffic management, vehicle routing BibRef

Wang, Q.[Qian], Lai, C.Z.[Cheng-Zhe], Han, G.[Gang], Zheng, D.[Dong],
pdRide: Privacy-Preserving Distributed Online Ride-Hailing Matching Scheme,
ITS(24), No. 11, November 2023, pp. 12491-12505.
IEEE DOI 2311
BibRef

Li, J.[Jie], Lin, F.[Fuyu], Han, G.J.[Guang-Jie], Wang, Y.F.[Yi-Fan], Yu, R.Y.[Rui-Yun], Oguti, A.M.[Ann Move], Li, Z.L.[Zheng-Lin],
PAG-TSN: Ridership Demand Forecasting Model for Shared Travel Services of Smart Transportation,
ITS(24), No. 12, December 2023, pp. 15876-15889.
IEEE DOI 2312
BibRef

Namasudra, S.[Suyel], Sharma, P.[Pratima],
Achieving a Decentralized and Secure Cab Sharing System Using Blockchain Technology,
ITS(24), No. 12, December 2023, pp. 15568-15577.
IEEE DOI 2312
BibRef

Zhao, J.[Jie], Chen, C.[Chao], Zhang, W.[Wanyi], Li, R.[Ruiyuan], Gu, F.Q.[Fu-Qiang], Guo, S.T.[Song-Tao], Luo, J.[Jun], Zheng, Y.[Yu],
Coupling Makes Better: An Intertwined Neural Network for Taxi and Ridesourcing Demand Co-Prediction,
ITS(25), No. 2, February 2024, pp. 1691-1705.
IEEE DOI 2402
Public transportation, Transportation, Predictive models, Urban areas, Neural networks, Long short term memory, Couplings, deep models BibRef

Fu, Y.P.[Ya-Ping], Ma, X.M.[Xiao-Meng], Gao, K.Z.[Kai-Zhou], Li, Z.W.[Zhi-Wu], Dong, H.Y.[Hong-Yu],
Multi-Objective Home Health Care Routing and Scheduling With Sharing Service via a Problem-Specific Knowledge-Based Artificial Bee Colony Algorithm,
ITS(25), No. 2, February 2024, pp. 1706-1719.
IEEE DOI 2402
Costs, Routing, Medical services, Knowledge based systems, Older adults, Business, Aging, Home health care, scheduling, artificial bee colony algorithm BibRef

Wei, H.H.[Hong-Hao], Yang, Z.X.[Zi-Xian], Liu, X.[Xin], Qin, Z.W.[Zhi-Wei], Tang, X.C.[Xiao-Cheng], Ying, L.[Lei],
A Reinforcement Learning and Prediction-Based Lookahead Policy for Vehicle Repositioning in Online Ride-Hailing Systems,
ITS(25), No. 2, February 2024, pp. 1846-1856.
IEEE DOI 2402
Vehicles, Automobiles, Reinforcement learning, Linear programming, Vehicle dynamics, Optimization, Numerical models, Ride-hailing, reinforcement learning BibRef


Chung, H.[Hyunhee], Park, K.H.[Kyung Ho],
Is Meta-Learning Always Necessary?: A Practical ML Framework Solving Novel Tasks at Large-scale Car Sharing Platform,
Novelty23(421-429)
IEEE DOI 2302
Deep learning, Image recognition, Conferences, Neural networks, Supervised learning, Benchmark testing BibRef

Wang, X.S.[Xue-Song], Liu, Y.Z.[Yi-Zhi], Liao, Z.H.[Zhu-Hua], Zhao, Y.J.[Yi-Jiang],
DeepFM-based Taxi Pick-up Area Recommendation,
IUC20(407-421).
Springer DOI 2103
BibRef

Yan, J., Xiang, L., Wu, C., Wu, H.,
City-scale Taxi Demand Prediction Using Multisource Urban Geospatial Data,
ISPRS20(B4:213-220).
DOI Link 2012
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Berdeddouch, A., Yahyaouy, A., Benanni, Y., Verde, R.,
Deep Based Recommender System For Relevant K Pick-up Points,
ISCV20(1-7)
IEEE DOI 2011
driver information systems, intelligent transportation systems, learning (artificial intelligence), neural nets, Meters BibRef

Naseri Gorgoon, M., Davoodi, M., Davoodi, M., Motieyan, H.,
An Agent-based Modelling for Ride Sharing Optimization Using A* Algorithm and Clustering Approach,
SMPR19(793-796).
DOI Link 1912
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Mojtabaee, P., Molavi, M., Taleai, M.,
Exploring Driving Factors of Higher Paid Taxi Trips Using Origin-destination Gps Data (case Study: Green Taxis of New York City),
SMPR19(745-748).
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Coskun, I.B., Sertok, S., Anbaroglu, B.,
K-nearest Neighbour Query Performance Analyses On a Large Scale Taxi Dataset: Postgresql Vs. Mongodb,
C3MGBD19(1531-1538).
DOI Link 1912
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Wang, H., Chen, X.J., Wang, Y., Shan, J.,
Local Maximum Density Approach for Small-scale Clustering of Urban Taxi Stops,
SmartGeoApps19(1361-1367).
DOI Link 1912
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Rangriz, S., Davoodi, M., Saberian, J.,
A Novel Approach to Optimize The Ridesharing Problem Using Genetic Algorithm,
SMPR19(875-878).
DOI Link 1912
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Tang, L.L.[Lu-Liang], Li, Q.Q.[Qing-Quan], Chang, X.M.[Xiao-Meng],
The Taxis' Experience Knowledge Modeling and Route Planning,
VCGVA09(xx-yy). 0910
floating car data (FCD); TEKM; route planning; GIS-T BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
On-Demand Ride Systems, Car Sharing, Taxi, Analysis .


Last update:Mar 16, 2024 at 20:36:19