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Elsevier DOI
1202
Award, U.V. Helava, ISPRS.
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IEEE DOI
0912
Ad hoc shared ride systems.
BibRef
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1003
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Yan, S.,
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Lin, Y.F.,
A Model With a Heuristic Algorithm for Solving the Long-Term
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IEEE DOI
1112
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Jia, T.[Tao],
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Intelligent Management Functionality for Improving Transportation
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ITS(13), No. 2, June 2012, pp. 424-436.
IEEE DOI
1206
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Moreira-Matias, L.,
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IEEE DOI
1309
Sardis Award, Research. Autoregressive integrated moving average (ARIMA)
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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
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Huang, S.C.[Shih-Chia],
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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
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Arena, M.,
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Colorni, A.,
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Service design in electric vehicle sharing: evidence from Italy,
IET-ITS(9), No. 2, 2015, pp. 145-155.
DOI Link
1504
electric vehicles
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Jorge, D.,
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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
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Pelzer, D.,
Xiao, J.[Jiajian],
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.
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1511
intelligent transportation systems
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d'Orey, P.M.,
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Can ride-sharing become attractive? A case study of taxi-sharing
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IET-ITS(9), No. 2, 2015, pp. 210-220.
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1504
quality of service
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An Assignment-Based Approach to Efficient Real-Time City-Scale Taxi
Dispatching,
IEEE_Int_Sys(31), No. 1, January 2016, pp. 68-77.
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1602
automobiles
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Zhan, X.,
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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
Zhang, J.,
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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
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Yang, G.[Gege],
Song, C.[Ci],
Shu, H.[Hua],
Zhang, J.[Jia],
Pei, T.[Tao],
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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],
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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
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;
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Wu, H.B.[Hang-Bin],
Fan, H.C.[Hong-Chao],
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IJGI(6), No. 11, 2017, pp. xx-yy.
DOI Link
1712
BibRef
Qian, X.,
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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
Zhang, T.[Tianci],
Ding, M.[Meng],
Zuo, H.F.[Hong-Fu],
Improved approach for time-based taxi trajectory planning towards
conflict-free, efficient and fluent airport ground movement,
IET-ITS(12), No. 10, December 2018, pp. 1360-1368.
DOI Link
1812
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
Rigas, E.S.[Emmanouil S.],
Ramchurn, S.D.[Sarvapali D.],
Bassiliades, N.[Nick],
Algorithms for electric vehicle scheduling in large-scale
mobility-on-demand schemes,
AI(262), 2018, pp. 248-278.
Elsevier DOI
1809
Mixed integer programming, Heuristic search, Local search,
Max-flow, Electric vehicles, Shared vehicles, Mobility on demand
BibRef
Guo, S.[Suiming],
Chen, C.[Chao],
Liu, Y.[Yaxiao],
Xu, K.[Ke],
Guo, B.[Bin],
Chiu, D.M.[Dah Ming],
How to pay less: a location-specific approach to predict dynamic prices
in ride-on-demand services,
IET-ITS(12), No. 7, September 2018, pp. 610-618.
DOI Link
1808
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
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.[Shuqi],
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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.
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BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
Yu, X.,
Shen, S.,
An Integrated Decomposition and Approximate Dynamic Programming
Approach for On-Demand Ride Pooling,
ITS(21), No. 9, September 2020, pp. 3811-3820.
IEEE DOI
2008
Vehicles, Delays, Computational modeling, Optimization,
Vehicle dynamics, Stochastic processes, Dynamic programming,
approximate dynamic programming
BibRef
Salazar, M.,
Lanzetti, N.,
Rossi, F.,
Schiffer, M.,
Pavone, M.,
Intermodal Autonomous Mobility-on-Demand,
ITS(21), No. 9, September 2020, pp. 3946-3960.
IEEE DOI
2008
Roads, Pricing, Public transportation, Optimization, Urban areas,
Legged locomotion, Autonomous vehicles, networks, optimization,
public transportation
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
BibRef
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.,
Sokhiry, F.S.A.[F. S. Al],
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.[Shiwei],
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.[Wenbo],
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.[Yuhao],
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
Liu, Y.[Yang],
Samaranayake, S.[Samitha],
Proactive Rebalancing and Speed-Up Techniques for On-Demand High
Capacity Ridesourcing Services,
ITS(23), No. 2, February 2022, pp. 819-826.
IEEE DOI
2202
Delays, Public transportation, Probabilistic logic, Routing,
Real-time systems, System performance, Ridesourcing,
proactive rebalancing
BibRef
Chen, D.[Duxin],
Shao, Q.[Qi],
Liu, Z.Y.[Zhi-Yuan],
Yu, W.[Wenwu],
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
Qian, X.[Xinwu],
Ukkusuri, S.V.[Satish V.],
Yang, C.[Chao],
Yan, F.F.[Fen-Fan],
Short-Term Demand Forecasting for on-Demand Mobility Service,
ITS(23), No. 2, February 2022, pp. 1019-1029.
IEEE DOI
2202
Public transportation, Correlation, Predictive models, Urban areas,
Data models, Spatiotemporal phenomena, Machine learning,
boosting Gaussian conditional random field
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.[Hongming],
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
Chen, R.[Rui],
Cassandras, C.G.[Christos G.],
Optimal Assignments in Mobility-on-Demand Systems Using Event-Driven
Receding Horizon Control,
ITS(23), No. 3, March 2022, pp. 1969-1983.
IEEE DOI
2203
Complexity theory, Vehicle dynamics, Real-time systems,
Optimization, Urban areas, Public transportation, vehicle assignments
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
Huang, X.[Xianan],
Li, B.Q.[Bo-Qi],
Peng, H.[Huei],
Auld, J.A.[Joshua A.],
Sokolov, V.O.[Vadim O.],
Eco-Mobility-on-Demand Fleet Control With Ride-Sharing,
ITS(23), No. 4, April 2022, pp. 3158-3168.
IEEE DOI
2204
Fuels, Time factors, Optimization, Heuristic algorithms, Routing,
Delays, Transportation, Connected automated vehicle,
ride-sharing
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],
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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
Javanshour, F.[Farid],
Dia, H.[Hussein],
Duncan, G.[Gordon],
Abduljabbar, R.[Rusul],
Liyanage, S.[Sohani],
Performance Evaluation of Station-Based Autonomous On-Demand
Car-Sharing Systems,
ITS(23), No. 7, July 2022, pp. 7721-7732.
IEEE DOI
2207
Vehicle dynamics, Urban areas, Roads, Public transportation,
Optimization, Heuristic algorithms, Autonomous vehicles,
shared autonomous mobility-on-demand
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.[Yuqi],
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
Yan, J.,
Xiang, L.,
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City-scale Taxi Demand Prediction Using Multisource Urban Geospatial
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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
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Naseri Gorgoon, M.,
Davoodi, M.,
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An Agent-based Modelling for Ride Sharing Optimization Using A*
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Mojtabaee, P.,
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Taleai, M.,
Exploring Driving Factors of Higher Paid Taxi Trips Using
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1912
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K-nearest Neighbour Query Performance Analyses On a Large Scale Taxi
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floating car data (FCD); TEKM; route planning; GIS-T
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Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Bicycle Sharing, Bicycle Commuting, Bike Sharing .