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,
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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.X.[Ya-Xiao],
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
Drakoulis, R.,
Bellotti, F.,
Bakas, I.,
Berta, R.,
Paranthaman, P.K.,
Dange, G.R.,
Lytrivis, P.,
Pagle, K.,
de Gloria, A.,
Amditis, A.,
A Gamified Flexible Transportation Service for On-Demand Public
Transport,
ITS(19), No. 3, March 2018, pp. 921-933.
IEEE DOI
1804
Games, Linear programming,
Public transportation, Urban areas, Vehicle dynamics,
serious game
BibRef
Wang, L.[Lei],
Zhong, Y.[Yugao],
Ma, W.[Wanjing],
GPS-data-driven dynamic destination prediction for on-demand one-way
carsharing system,
IET-ITS(12), No. 10, December 2018, pp. 1291-1299.
DOI Link
1812
BibRef
Egan, M.,
Oren, N.,
Jakob, M.,
Hybrid Mechanisms for On-Demand Transport,
ITS(20), No. 12, December 2019, pp. 4500-4512.
IEEE DOI
2001
Pricing, Public transportation, Vehicles, Routing,
Probability density function, Urban areas, Resource management, pricing
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
Chu, K.F.[Kai-Fung],
Lam, A.Y.S.[Albert Y. S.],
Li, V.O.K.[Victor O. K.],
Joint Rebalancing and Vehicle-to-Grid Coordination for Autonomous
Vehicle Public Transportation System,
ITS(23), No. 7, July 2022, pp. 7156-7169.
IEEE DOI
2207
Vehicle-to-grid, Schedules, Roads, Public transportation,
Genetic algorithms, Autonomous vehicles, Predictive models,
autonomous mobility-on-demand systems
BibRef
Huang, H.P.[Hai-Ping],
Hu, C.X.[Cheng-Xi],
Zhu, J.[Jie],
Wu, M.[Min],
Malekian, R.[Reza],
Stochastic Task Scheduling in UAV-Based Intelligent On-Demand Meal
Delivery System,
ITS(23), No. 8, August 2022, pp. 13040-13054.
IEEE DOI
2208
Task analysis, Heuristic algorithms, Routing, Costs,
Vehicle dynamics, Stochastic processes, Logistics, UAV, rescheduling strategy
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
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
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
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
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
Ni, L.[Liang],
Sun, B.[Bo],
Wang, S.[Su],
Tsang, D.H.K.[Danny H. K.],
Dynamic Pricing Mechanism Design for Electric Mobility-on-Demand
Systems,
ITS(23), No. 8, August 2022, pp. 11361-11375.
IEEE DOI
2208
Pricing, Decision making, Real-time systems, Dispatching,
Vehicle dynamics, Routing, Reinforcement learning,
sequential decision-making
BibRef
Wollenstein-Betech, S.[Salomón],
Salazar, M.[Mauro],
Houshmand, A.[Arian],
Pavone, M.[Marco],
Paschalidis, I.C.[Ioannis Ch.],
Cassandras, C.G.[Christos G.],
Routing and Rebalancing Intermodal Autonomous Mobility-on-Demand
Systems in Mixed Traffic,
ITS(23), No. 8, August 2022, pp. 12263-12275.
IEEE DOI
2208
Routing, Legged locomotion, Vehicles, Urban areas, Switches, Roads,
Real-time systems, Mobility-on-demand, system-optimal routing,
mixed autonomy
BibRef
Xu, S.J.[Susan Jia],
Chow, J.Y.J.[Joseph Y. J.],
Online Route Choice Modeling for Mobility-as-a-Service Networks With
Non-Separable, Congestible Link Capacity Effects,
ITS(23), No. 8, August 2022, pp. 11518-11527.
IEEE DOI
2208
Vehicle dynamics, Numerical models, Data models,
Biological system modeling, Urban areas, Real-time systems,
online route choice model
BibRef
Perboli, G.[Guido],
Rosano, M.[Mariangela],
Wei, Q.[Qu],
A Simulation-Optimization Approach for the Management of the
On-Demand Parcel Delivery in Sharing Economy,
ITS(23), No. 8, August 2022, pp. 10570-10582.
IEEE DOI
2208
Stochastic processes, Urban areas, Vehicles, Logistics,
Vehicle dynamics, Companies, Green products, Crowdsourcing,
stochastic and dynamic VRPTW
BibRef
Xi, J.H.[Jin-Hao],
Zhu, F.H.[Feng-Hua],
Ye, P.J.[Pei-Jun],
Lv, Y.S.[Yi-Sheng],
Tang, H.[Haina],
Wang, F.Y.[Fei-Yue],
HMDRL: Hierarchical Mixed Deep Reinforcement Learning to Balance
Vehicle Supply and Demand,
ITS(23), No. 11, November 2022, pp. 21861-21872.
IEEE DOI
2212
Reinforcement learning, Pricing, Heuristic algorithms,
Supply and demand, Surges, Vehicle dynamics, Vehicles,
mixed state
BibRef
Hüttel, F.B.[Frederik Boe],
Peled, I.[Inon],
Rodrigues, F.[Filipe],
Pereira, F.C.[Francisco C.],
Modeling Censored Mobility Demand Through Censored Quantile
Regression Neural Networks,
ITS(23), No. 11, November 2022, pp. 21753-21765.
IEEE DOI
2212
Computational modeling, Data models, Biological neural networks,
Uncertainty, Computer architecture, Censorship, Bayes methods,
Bayesian modeling
BibRef
Liang, Y.Y.[Yun-Yi],
Wu, Z.Z.[Zhi-Zhou],
Yang, H.C.[Hao-Chun],
Wang, Y.H.[Yin-Hai],
A Novel Framework for Road Side Unit Location Optimization for
Origin-Destination Demand Estimation,
ITS(23), No. 11, November 2022, pp. 21113-21126.
IEEE DOI
2212
Optimization, Estimation, Delays, Connected vehicles, Transportation,
Roads, Pareto optimization, Sensor location, road side unit,
origin-destination demand estimation
BibRef
Huang, Z.H.[Zi-Heng],
Wang, D.[Dujuan],
Yin, Y.Q.[Yun-Qiang],
Li, X.[Xiang],
A Spatiotemporal Bidirectional Attention-Based Ride-Hailing Demand
Prediction Model: A Case Study in Beijing During COVID-19,
ITS(23), No. 12, December 2022, pp. 25115-25126.
IEEE DOI
2212
Predictive models, Spatiotemporal phenomena, Correlation,
Deep learning, Epidemics, COVID-19, Neural networks,
multi-steps ahead prediction
BibRef
Liu, K.[Kai],
Chen, Z.[Zhiju],
Yamamoto, T.[Toshiyuki],
Tuo, L.[Liheng],
Exploring the Impact of Spatiotemporal Granularity on the Demand
Prediction of Dynamic Ride-Hailing,
ITS(24), No. 1, January 2023, pp. 104-114.
IEEE DOI
2301
Spatiotemporal phenomena, Predictive models, Deep learning,
Time series analysis, Public transportation, Data models,
optimal granularity
BibRef
Poliziani, C.[Cristian],
Hsueh, G.[Gary],
Czerwinski, D.[David],
Wenzel, T.[Tom],
Needell, Z.[Zachary],
Laarabi, H.[Haitam],
Schweizer, J.[Joerg],
Rupi, F.[Federico],
Micro Transit Simulation of On-Demand Shuttles Based on Transit Data
for First- and Last-Mile Connection,
IJGI(12), No. 4, 2023, pp. 177.
DOI Link
2305
BibRef
Guo, S.[Suiming],
Shen, Q.[Qianrong],
Liu, Z.Q.[Zhi-Quan],
Chen, C.[Chao],
Chen, C.X.[Chao-Xiong],
Wang, J.Y.[Jing-Yuan],
Li, Z.[Zhetao],
Xu, K.[Ke],
Seeking Based on Dynamic Prices: Higher Earnings and Better
Strategies in Ride-on-Demand Services,
ITS(24), No. 5, May 2023, pp. 5527-5542.
IEEE DOI
2305
Vehicles, Public transportation, Pricing,
Global Positioning System, Trajectory, Urban areas,
reinforcement learning
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Bicycle Sharing, Bicycle Commuting, Bike Sharing .