A deep learning-assisted mathematical model for decongestion time prediction at railroad grade crossings-Reference-Cited by-同舟云学术

A deep learning-assisted mathematical model for decongestion time prediction at railroad grade crossings

Published:2021-10-28 Issue: Volume: Page:
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Jiang Zhuocheng,Guo Feng,Qian Yu,Wang Yi^ORCID,Pan W. David

Funder

federal railroad administration

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-021-06625-z.pdf

Reference53 articles.

1. Alexy K (2020) Tracking toward zero: improving grade crossing safety and addressing community concerns. https://www.transportation.gov/print/364121

2. Arnott R, Palma A, Lindsey R (1991) Does providing information to drivers reduce traffic congestion? Trans Research Part A General 25(5):309–318

3. Bando M, Hasebe K, Nakayama A, Shibata A, Sugiyama Y (1995) Dynamical model of traffic congestion and numerical simulation. Phys Rev E 51(2):1035–1042

4. Bauza R, Gozalvez J (2013) Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications. Jour Netw Comput Appl 36(5):1295–1307

5. Chan AB, Vasconcelos N (2009) Bayesian Poisson regression for crowd counting. In: Proc IEEE Conf Comp Vis (ICCV), Kyoto

Cited by 4 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. Intelligent railroad inspection and monitoring;Frontiers in Built Environment;2024-04-12

2. Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller;2022 12th International Conference on Electrical and Computer Engineering (ICECE);2022-12-21

3. A deep learning framework for detecting and localizing abnormal pedestrian behaviors at grade crossings;Neural Computing and Applications;2022-08-12

4. Correction to: A deep learning-assisted mathematical model for decongestion time prediction at railroad grade crossings;Neural Computing and Applications;2021-11-28