Insights into Travel Pattern Analysis and Demand Prediction: A Data-Driven Approach in Bike-Sharing Systems-Reference-Cited by-同舟云学术

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Insights into Travel Pattern Analysis and Demand Prediction: A Data-Driven Approach in Bike-Sharing Systems

Published:2024-02 Issue:2 Volume:150 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Lin Hongyi¹,He Yixu²,Li Shen³,Liu Yang⁴

Affiliation:

1. Ph.D. Student, School of Vehicle and Mobility, Tsinghua Univ., Beijing 100084, China.

2. Graduate Student, School of Information Engineering, Chang’an Univ., Xi’an 710064, China.

3. Assistant Researcher, School of Civil Engineering, Tsinghua Univ., Beijing 100084, China (corresponding author).

4. Marie Curie Fellow, Dept. of Architecture and Civil Engineering, Chalmers Univ. of Technology, Gothenburg SE-41296, Sweden.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/JTEPBS.TEENG-8137

Reference51 articles.

1. A sensorless state estimation for a safety-oriented cyber-physical system in urban driving: Deep learning approach;Al-Sharman M.;IEEE/CAA J. Autom. Sin.,2020

2. Better Understanding of Factors Influencing Likelihood of Using Shared Bicycle Systems and Frequency of Use

3. Spatial analysis of bikeshare ridership with smart card and POI data using geographically weighted regression method;Bao J.;IEEE Access,2018

4. Short-term FFBS demand prediction with multi-source data in a hybrid deep learning framework;Bao J.;IET Intel. Transport Syst.,2019

5. Chai D. L. Wang and Q. Yang. 2018. “Bike flow prediction with multi-graph convolutional networks.” In Proc. 26th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems 397–400. New York: Association for Computing Machinery.

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1. Enhancing State Representation in Multi-Agent Reinforcement Learning for Platoon-Following Models;IEEE Transactions on Vehicular Technology;2024-08

2. Exploring usage pattern variation of free-floating bike-sharing from a night travel perspective;Scientific Reports;2024-07-11

3. Analysis of Urban Residents’ Travelling Characteristics and Hotspots Based on Taxi Trajectory Data;Applied Sciences;2024-02-03

4. Exploring the design of reward functions in deep reinforcement learning-based vehicle velocity control algorithms;Transportation Letters;2024-01-17

5. Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems;Mathematical Biosciences and Engineering;2024

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