16.7.2.5.12 Shared Ride Systems, Car Sharing, Bike Sharing, Taxi, Analysis

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

Zhang, T.[Tianci], Ding, M.[Meng], Zuo, H.[Hongfu],
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.[Haiqiang], 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.[Yanyan], Dou, M.X.[Ming-Xuan], Qiao, M.[Mengling],
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

He, B.[Biao], Zhang, Y.[Yan], Chen, Y.[Yu], Gu, Z.H.[Zhi-Hui],
A Simple Line Clustering Method for Spatial Analysis with Origin-Destination Data and Its Application to Bike-Sharing Movement Data,
IJGI(7), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Jing, W.P.[Wei-Peng], Kang, J.[Jian], Liu, M.L.[Mei-Ling],
Mining taxi trajectories for most suitable stations of sharing bikes to ease traffic congestion,
IET-ITS(12), No. 7, September 2018, pp. 586-593.
DOI Link 1808
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

Tang, G., Keshav, S., Golab, L., Wu, K.,
Bikeshare Pool Sizing for Bike-and-Ride Multimodal Transit,
ITS(19), No. 7, July 2018, pp. 2279-2289.
IEEE DOI 1807
Bicycles, Employment, Public transportation, Rail transportation, Schedules, Sociology, Statistics, Bikeshare pool sizing, multimodal transit 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

Gu, Z.H.[Zhi-Hui], Zhu, Y.[Yong], Zhang, Y.[Yan], Zhou, W.Y.[Wan-Yu], Chen, Y.[Yu],
Heuristic Bike Optimization Algorithm to Improve Usage Efficiency of the Station-Free Bike Sharing System in Shenzhen, China,
IJGI(8), No. 5, 2019, pp. xx-yy.
DOI Link 1906
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

Huang, F., Qiao, S., Peng, J., Guo, B.,
A Bimodal Gaussian Inhomogeneous Poisson Algorithm for Bike Number Prediction in a Bike-Sharing System,
ITS(20), No. 8, August 2019, pp. 2848-2857.
IEEE DOI 1908
Predictive models, Nonhomogeneous media, Meteorology, Prediction algorithms, Public transportation, Neural networks, time series prediction 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

Cheng, X.Q.[Xiao-Qian], Li, C.M.[Cheng-Ming], Du, W.B.[Wei-Bing], Shen, J.M.[Jian-Ming], Dai, Z.X.[Zhao-Xin],
Trip Extraction of Shared Electric Bikes Based on Multi-Rule-Constrained Homomorphic Linear Clustering Algorithm,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Wang, H.[Huihui], 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

Cao, M.[Min], Ma, S.J.[Shang-Jing], Huang, M.X.[Meng-Xue], Lü, G.N.[Guo-Nian], Chen, M.[Min],
Effects of Free-Floating Shared Bicycles on Urban Public Transportation,
IJGI(8), No. 8, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Zhao, X.F.[Xiao-Fei], Hu, C.Y.[Cai-Yi], Liu, Z.[Zhao], Meng, Y.Y.[Yang-Yang],
Weighted Dynamic Time Warping for Grid-Based Travel-Demand-Pattern Clustering: Case Study of Beijing Bicycle-Sharing System,
IJGI(8), No. 6, 2019, pp. xx-yy.
DOI Link 1908
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

Zhai, Y.[Yong], Liu, J.[Jin], Du, J.[Juan], Wu, H.[Hao],
Fleet Size and Rebalancing Analysis of Dockless Bike-Sharing Stations Based on Markov Chain,
IJGI(8), No. 8, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Dong, J.[Jian], Chen, B.[Bin], He, L.[Lingnan], Ai, C.[Chuan], Zhang, F.[Fang], Guo, D.[Danhuai], Qiu, X.G.[Xiao-Gang],
A Spatio-Temporal Flow Model of Urban Dockless Shared Bikes Based on Points of Interest Clustering,
IJGI(8), No. 8, 2019, pp. xx-yy.
DOI Link 1909
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
BibRef

Christian, K.[Kapuku], Cho, S.H.[Shin-Hyung], Kho, S.Y.[Seung-Young], Kim, D.K.[Dong-Kyu],
Bayesian models with spatial autocorrelation for bike sharing ridership variability based on revealed preference GPS trajectory data,
IET-ITS(13), No. 11, November 2019, pp. 1658-1667.
DOI Link 1911
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

Cao, M.[Min], Cai, B.[Boqin], Ma, S.J.[Shang-Jing], Lü, G.[Guonian], Chen, M.[Min],
Analysis of the Cycling Flow Between Origin and Destination for Dockless Shared Bicycles Based on Singular Value Decomposition,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Yang, Z., Chen, J., Hu, J., Shu, Y., Cheng, P.,
Mobility Modeling and Data-Driven Closed-Loop Prediction in Bike-Sharing Systems,
ITS(20), No. 12, December 2019, pp. 4488-4499.
IEEE DOI 2001
Predictive models, Bicycles, Urban areas, Optimization, Meteorology, Probabilistic logic, Bike-sharing, mobility modeling, Monte Carlo simulation 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., Ghosh, A., Aggarwal, V.,
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

Lasmar, E.L., de Paula, F.O., Rosa, R.L., Abrahão, 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

Chen, P.C.[Po-Chuan], Hsieh, H.Y.[He-Yen], Su, K.W.[Kuan-Wu], Sigalingging, X.K.[Xanno Kharis], Chen, Y.R.[Yan-Ru], Leu, J.S.[Jenq-Shiou],
Predicting station level demand in a bike-sharing system using recurrent neural networks,
IET-ITS(14), No. 6, June 2020, pp. 554-561.
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.[Zhekang], Xia, Y.[Yan], Zhu, J.Y.[Jia-Yan], Ma, N.[Nan], Chai, H.[Hua], Liu, Z.[Zhiyuan],
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

Ye, M.[Mao], Zeng, S.[Simeng], Yang, G.X.[Gui-Xin], Chen, Y.J.[Ya-Jing],
Identification of contributing factors on travel mode choice among different resident types with bike-sharing as an alternative,
IET-ITS(14), No. 7, July 2020, pp. 639-646.
DOI Link 2006
BibRef

Wang, S.[Shuofeng], Li, Z.H.[Zhi-Heng], Gu, R.[Ruochen], Xie, N.[Na],
Placement optimisation for station-free bicycle-sharing under 1D distribution assumption,
IET-ITS(14), No. 9, September 2020, pp. 1079-1086.
DOI Link 2008
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

Ren, Y., Zhao, F., Jin, H., Jiao, Z., Meng, L., Zhang, C., Sutherland, J.W.,
Rebalancing Bike Sharing Systems for Minimizing Depot Inventory and Traveling Costs,
ITS(21), No. 9, September 2020, pp. 3871-3882.
IEEE DOI 2008
Bicycles, Routing, Mathematical model, Vehicle dynamics, Mathematical programming, Biological system modeling, mathematical 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

Hua, M.Z.[Ming-Zhuang], Chen, J.X.[Jing-Xu], Chen, X.[Xuewu], Gan, Z.X.[Zuo-Xian], Wang, P.F.[Peng-Fei], Zhao, D.[De],
Forecasting usage and bike distribution of dockless bike-sharing using journey data,
IET-ITS(14), No. 12, December 2020, pp. 1647-1656.
DOI Link 2011
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

Jin, X.T.[Xue-Ting], Tong, D.Q.[Dao-Qin],
Station-Free Bike Rebalancing Analysis: Scale, Modeling, and Computational Challenges,
IJGI(9), No. 11, 2020, pp. xx-yy.
DOI Link 2012
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
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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
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Cheng, X.Q.[Xiao-Qian], Du, W.B.[Wei-Bing], Li, C.M.[Cheng-Ming], Yang, L.K.[Lei-Ku], Xu, L.J.[Lin-Juan],
Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data,
IJGI(9), No. 12, 2020, pp. xx-yy.
DOI Link 2012
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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
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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
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Albuquerque, V.[Vitória], Dias, M.S.[Miguel Sales], Bacao, F.[Fernando],
Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review,
IJGI(10), No. 2, 2021, pp. xx-yy.
DOI Link 2103
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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
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Gao, F.[Feng], Li, S.Y.[Shao-Ying], Tan, Z.Z.[Zhang-Zhi], Zhang, X.M.[Xiao-Ming], Lai, Z.P.[Zhi-Peng], Tan, Z.[Ziling],
How Is Urban Greenness Spatially Associated with Dockless Bike Sharing Usage on Weekdays, Weekends, and Holidays?,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
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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
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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
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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

Fan, R.N.[Rui-Na], Ma, F.Q.[Fan-Qi],
Bike-sharing systems with a dual selection mechanism and a dynamic double-threshold repositioning policy,
IET-ITS(15), No. 5, 2021, pp. 712-725.
DOI Link 2106
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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

Wang, W.[Wei], Zhao, X.F.[Xiao-Feng], Gong, Z.G.[Zhi-Guo], Chen, Z.K.[Zhi-Kui], Zhang, N.[Ning], Wei, W.[Wei],
An Attention-Based Deep Learning Framework for Trip Destination Prediction of Sharing Bike,
ITS(22), No. 7, July 2021, pp. 4601-4610.
IEEE DOI 2107
Machine learning, Task analysis, Neural networks, Smart cities, Convolution, Predictive models, Internet of Things, attention model 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.[Diping], Yin, J.[Jiyao], Wu, G.[Guohua],
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
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Bahadori, M.S.[Mohammad Sadegh], Gonçalves, A.B.[Alexandre B.], Moura, F.[Filipe],
A Systematic Review of Station Location Techniques for Bicycle-Sharing Systems Planning and Operation,
IJGI(10), No. 8, 2021, pp. xx-yy.
DOI Link 2108
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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, 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
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Toro, J.F., Carrion, D., Brovelli, M.A., Percoco, M.,
Bikemi Bike-sharing Service Exploratory Analysis on Mobility Patterns,
ISPRS20(B4:197-203).
DOI Link 2012
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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).
DOI Link 1912
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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
Transit Traffic Analysis, Public Transit, Bus .

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