Ride Hailing Data

Ride-hailing data analysis focuses on understanding and optimizing the complex dynamics of on-demand transportation systems. Current research emphasizes developing accurate predictive models for demand forecasting and efficient vehicle rebalancing, often employing deep learning architectures like convolutional neural networks and graph neural networks to capture spatiotemporal patterns and incorporate diverse features (e.g., socioeconomic factors, built environment). These advancements aim to improve service efficiency, reduce wait times, and promote equitable access to ride-hailing services, impacting both urban planning and the operational strategies of transportation providers. Furthermore, research explores the use of reinforcement learning for route optimization, enhancing driver profitability and overall system efficiency.

Papers

December 29, 2023

Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System
Xiaotong Guo, Hanyong Xu, Dingyi Zhuang, Yunhan Zheng, Jinhua Zhao
Demand Prediction Ride Hailing Vehicle Rebalancing Ride Hailing Data

June 21, 2023

Understanding human mobility patterns in Chicago: an analysis of taxi data using clustering techniques
Harish Chauhan, Nikunj Gupta, Zoe Haskell-Craig
General Analysis Human Mobility Clustering Method Traffic Pattern Ride Hailing Data

March 6, 2023

Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction
Yan Qin, Yong Liang Guan, Chau Yuen
Capsule Network Vehicle Trajectory Prediction Vehicle Behavior Ride Hailing Data

December 7, 2022

Short term prediction of demand for ride hailing services: A deep learning approach
Long Chen, Piyushimita, Thakuriah, Konstantinos Ampountolas
Convolutional Neural Network Deep Learning Approach Service Provider Video Game Demand Short Term Prediction Ride Hailing Service Ride Hailing Data Passenger Request Prediction

September 16, 2022

Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
Xiaojian Zhang, Xiang Yan, Zhengze Zhou, Yiming Xu, Xilei Zhao
Explainable Machine Learning Machine Learning Framework Demand Prediction Spatial Heterogeneity Ride Hailing Data

March 18, 2022

Bike Sharing Demand Prediction based on Knowledge Sharing across Modes: A Graph-based Deep Learning Approach
Yuebing Liang, Guan Huang, Zhan Zhao
Demand Forecasting Knowledge Sharing Motor Augmented Mode Bike Sharing System Graph Based Deep Learning Ride Hailing Data Bike Sharing Demand

November 16, 2021

Route Optimization via Environment-Aware Deep Network and Reinforcement Learning
Pengzhan Guo, Keli Xiao, Zeyang Ye, Wei Zhu
Reinforcement Learning Sequential Recommender System Sequential Decision Making Task Ride Hailing Data

Ride Hailing Data

Papers

Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System

Understanding human mobility patterns in Chicago: an analysis of taxi data using clustering techniques

Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction

Short term prediction of demand for ride hailing services: A deep learning approach

Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning

Bike Sharing Demand Prediction based on Knowledge Sharing across Modes: A Graph-based Deep Learning Approach

Route Optimization via Environment-Aware Deep Network and Reinforcement Learning