Cargando…
Bus passenger flow congestion risk evaluation model based on the Pressure-State-Response framework: A case study in Beijing
Urban public transport is a very essential mode for urban residents’ commute travel; however, the unbalanced spatial and temporal distribution of travel demand usually leads to passenger flow congestion risk at certain section and time. Meanwhile, the risk is short of quantified description. Based o...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453703/ https://www.ncbi.nlm.nih.gov/pubmed/31829800 http://dx.doi.org/10.1177/0036850419883567 |
_version_ | 1785096001964474368 |
---|---|
author | Ma, Siyong Weng, Jiancheng Wang, Chang Alivanistos, Dimitrios Lin, Pengfei |
author_facet | Ma, Siyong Weng, Jiancheng Wang, Chang Alivanistos, Dimitrios Lin, Pengfei |
author_sort | Ma, Siyong |
collection | PubMed |
description | Urban public transport is a very essential mode for urban residents’ commute travel; however, the unbalanced spatial and temporal distribution of travel demand usually leads to passenger flow congestion risk at certain section and time. Meanwhile, the risk is short of quantified description. Based on the Pressure-State-Response framework, the study puts forward three bus passenger flow congestion risk evaluation indexes including the alternative pressure, the congestion intensity, and the transport efficiency. Then, the evaluation model is proposed based on the entropy method, and the risk is divided into four levels by K-means clustering. The article considers the 3rd Ring Road corridor in Beijing as a case to identify the risk level. The results show that the risk in the peak hours of weekdays is generally about 1.5 times higher than the risk in the weekends. The congestion risk is stable in level 3 during the majority time of morning peak hours. The duration intensity of level 4 risk is less than 0.1 during weekdays, indicating that the highest flow congestion can be quickly evacuated in a short time. The integrated passenger risk identification and evaluation model was proposed to identify the passenger flow risk level and induce the network flow distribution more reasonable. The study also provides technical support for ensuring the public transit system safety. |
format | Online Article Text |
id | pubmed-10453703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-104537032023-08-26 Bus passenger flow congestion risk evaluation model based on the Pressure-State-Response framework: A case study in Beijing Ma, Siyong Weng, Jiancheng Wang, Chang Alivanistos, Dimitrios Lin, Pengfei Sci Prog Article Urban public transport is a very essential mode for urban residents’ commute travel; however, the unbalanced spatial and temporal distribution of travel demand usually leads to passenger flow congestion risk at certain section and time. Meanwhile, the risk is short of quantified description. Based on the Pressure-State-Response framework, the study puts forward three bus passenger flow congestion risk evaluation indexes including the alternative pressure, the congestion intensity, and the transport efficiency. Then, the evaluation model is proposed based on the entropy method, and the risk is divided into four levels by K-means clustering. The article considers the 3rd Ring Road corridor in Beijing as a case to identify the risk level. The results show that the risk in the peak hours of weekdays is generally about 1.5 times higher than the risk in the weekends. The congestion risk is stable in level 3 during the majority time of morning peak hours. The duration intensity of level 4 risk is less than 0.1 during weekdays, indicating that the highest flow congestion can be quickly evacuated in a short time. The integrated passenger risk identification and evaluation model was proposed to identify the passenger flow risk level and induce the network flow distribution more reasonable. The study also provides technical support for ensuring the public transit system safety. SAGE Publications 2019-11-14 /pmc/articles/PMC10453703/ /pubmed/31829800 http://dx.doi.org/10.1177/0036850419883567 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Article Ma, Siyong Weng, Jiancheng Wang, Chang Alivanistos, Dimitrios Lin, Pengfei Bus passenger flow congestion risk evaluation model based on the Pressure-State-Response framework: A case study in Beijing |
title | Bus passenger flow congestion risk evaluation model based on the Pressure-State-Response framework: A case study in Beijing |
title_full | Bus passenger flow congestion risk evaluation model based on the Pressure-State-Response framework: A case study in Beijing |
title_fullStr | Bus passenger flow congestion risk evaluation model based on the Pressure-State-Response framework: A case study in Beijing |
title_full_unstemmed | Bus passenger flow congestion risk evaluation model based on the Pressure-State-Response framework: A case study in Beijing |
title_short | Bus passenger flow congestion risk evaluation model based on the Pressure-State-Response framework: A case study in Beijing |
title_sort | bus passenger flow congestion risk evaluation model based on the pressure-state-response framework: a case study in beijing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453703/ https://www.ncbi.nlm.nih.gov/pubmed/31829800 http://dx.doi.org/10.1177/0036850419883567 |
work_keys_str_mv | AT masiyong buspassengerflowcongestionriskevaluationmodelbasedonthepressurestateresponseframeworkacasestudyinbeijing AT wengjiancheng buspassengerflowcongestionriskevaluationmodelbasedonthepressurestateresponseframeworkacasestudyinbeijing AT wangchang buspassengerflowcongestionriskevaluationmodelbasedonthepressurestateresponseframeworkacasestudyinbeijing AT alivanistosdimitrios buspassengerflowcongestionriskevaluationmodelbasedonthepressurestateresponseframeworkacasestudyinbeijing AT linpengfei buspassengerflowcongestionriskevaluationmodelbasedonthepressurestateresponseframeworkacasestudyinbeijing |