A Unified Probabilistic Framework for Spatiotemporal Passenger Crowdedness Inference within Urban Rail Transit Network-Reference-Cited by-同舟云学术

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A Unified Probabilistic Framework for Spatiotemporal Passenger Crowdedness Inference within Urban Rail Transit Network

Published:2023-08-26 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)
language:
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Author:

Jiang Min¹,Wang Andi²,Li Ziyue³,Tsung Fugee⁴

Affiliation:

1. The University of Hong Kong,Hong Kong

2. Arizona State University,Tempe,AZ,USA

3. The University of Cologne,Germany

4. Hong Kong University of Science and Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10260321/10260291/10260453.pdf?arnumber=10260453

Reference16 articles.

1. Splitting Travel Time Based on AFC Data: Estimating Walking, Waiting, Transfer, and In-Vehicle Travel Times in Metro System

2. Speed Distribution Curves for Pedestrians During Walking and Crossing

3. Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile

4. The variance of sample variance from a finite population;cho;International Journal of Pure and Applied Mathematics,2005

5. Estimation method for railway passengers’ train choice behavior with smart card transaction data

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Real-Time Optimization of Urban Rail Transit Train Scheduling via Advantage Actor–Critic Deep Reinforcement Learning;Journal of Transportation Engineering, Part A: Systems;2024-09

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