Related references
Note: Only part of the references are listed.Beyond expected regularity of aggregate urban mobility: A case study of ridesourcing service
Xiaohu Zhang
JOURNAL OF TRANSPORT GEOGRAPHY (2021)
Multi-scale detection and interpretation of spatio-temporal anomalies of human activities represented by time-series
Ximeng Cheng et al.
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS (2021)
Spatial dependence and spillover effects in customized bus demand: Empirical evidence using spatial dynamic panel models
Jiangbo Wang et al.
TRANSPORT POLICY (2021)
Origin and destination forecasting on dockless shared bicycle in a hybrid deep-learning algorithms
Yueyang Li et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2020)
On the supply curve of ride-hailing systems
Zhengtian Xu et al.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2020)
Optimizing matching time interval and matching radius in on-demand ride-sourcing markets
Hai Yang et al.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2020)
Deep Multi-Scale Convolutional LSTM Network for Travel Demand and Origin-Destination Predictions
Kai-Fung Chu et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2020)
Key determinants and heterogeneous frailties in passenger loyalty toward customized buses: An empirical investigation of the subscription termination hazard of users
Jiangbo Wang et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2020)
On the impact of spatio-temporal granularity of traffic conditions on the quality of pickup and delivery optimal tours
Omar Rifki et al.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW (2020)
A comparative analysis of gridding systems for point-based land cover/use analysis
Wasim Shoman et al.
GEOCARTO INTERNATIONAL (2019)
Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction
Lingbo Liu et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)
Hexagon-Based Convolutional Neural Network for Supply-Demand Forecasting of Ride-Sourcing Services
Jintao Ke et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2019)
Ridesourcing systems: A framework and review
Hai Wang et al.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2019)
A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation
Xinyu Chen et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2019)
Hunting image: Taxi search strategy recognition using Sparse Subspace Clustering
Kenan Zhang et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2019)
Urban public charging station locating method for electric vehicles based on land use approach
Csaba Csiszar et al.
JOURNAL OF TRANSPORT GEOGRAPHY (2019)
A motion-aware ConvLSTM network for action recognition
Mahshid Majd et al.
APPLIED INTELLIGENCE (2019)
HexagDLy-Processing hexagonally sampled data with CNNs in PyTorch
Constantin Steppa et al.
SOFTWAREX (2019)
Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks
Jun Xu et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2018)
Short-Term Traffic State Prediction Based on the Spatiotemporal Features of Critical Road Sections
Gang Yang et al.
SENSORS (2018)
An empirical study on travel patterns of internet based ride-sharing
Yongqi Dong et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2018)
The Scale Effect on Spatial Interaction Patterns: An Empirical Study Using Taxi O-D data of Beijing and Shanghai
Shiliang Zhang et al.
IEEE ACCESS (2018)
Travel Time Forecasting with Combination of Spatial-Temporal and Time Shifting Correlation in CNN-LSTM Neural Network
Wenjing Wei et al.
WEB AND BIG DATA (APWEB-WAIM 2018), PT I (2018)
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
Xiaolei Ma et al.
SENSORS (2017)
Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach
Xiqun (Michael) Chen et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2017)
Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach
Jintao Ke et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2017)
Deep learning for short-term traffic flow prediction
Nicholas G. Polson et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2017)
Street as a big geo-data assembly and analysis unit in urban studies: A case study using Beijing taxi data
Di Zhu et al.
APPLIED GEOGRAPHY (2017)
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky et al.
COMMUNICATIONS OF THE ACM (2017)
Economic analysis of ride-sourcing markets
Liteng Zha et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2016)
Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China
Xiushan Jiang et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2014)
Predicting short-term bus passenger demand using a pattern hybrid approach
Zhenliang Ma et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2014)
Predicting Taxi-Passenger Demand Using Streaming Data
Luis Moreira-Matias et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2013)
Post-project evaluation of travel demand forecasts: Implications from the case of a Japanese railway
Nobuhiro Sanko et al.
TRANSPORT POLICY (2013)
Equilibria of bilateral taxi-customer searching and meeting on networks
Hai Yang et al.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2010)
The scaling laws of human travel
D Brockmann et al.
NATURE (2006)
Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results
BM Williams et al.
JOURNAL OF TRANSPORTATION ENGINEERING (2003)
Share Paper
