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Research on Human Travel Correlation for Urban Transport Planning Based on Multisource Data
With the rapid development of positioning techniques, a large amount of human travel trajectory data is collected. These datasets have become an effective data resource for obtaining urban traffic patterns. However, many traffic analyses are only based on a single dataset. It is difficult to determi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795117/ https://www.ncbi.nlm.nih.gov/pubmed/33396731 http://dx.doi.org/10.3390/s21010195 |
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author | Chen, Hua Cai, Ming Xiong, Chen |
author_facet | Chen, Hua Cai, Ming Xiong, Chen |
author_sort | Chen, Hua |
collection | PubMed |
description | With the rapid development of positioning techniques, a large amount of human travel trajectory data is collected. These datasets have become an effective data resource for obtaining urban traffic patterns. However, many traffic analyses are only based on a single dataset. It is difficult to determine whether a single-dataset-based result can meet the requirement of urban transport planning. In response to this problem, we attempted to obtain traffic patterns and population distributions from the perspective of multisource traffic data using license plate recognition (LPR) data and cellular signaling (CS) data. Based on the two kinds of datasets, identification methods of residents’ travel stay point are proposed. For LPR data, it was identified based on different vehicle speed thresholds at different times. For CS data, a spatiotemporal clustering algorithm based on time allocation was proposed to recognize it. We then used the correlation coefficient r and the significance test p-values to analyze the correlations between the CS and LPR data in terms of the population distribution and traffic patterns. We studied two real-world datasets from five working days of human mobility data and found that they were significantly correlated for the stay and move population distributions. Then, the analysis scale was refined to hour level. We also found that they still maintain a significant correlation. Finally, the origin–destination (OD) matrices between traffic analysis zones (TAZs) were obtained. Except for a few TAZs with poor correlations due to the fewer LPR records, the correlations of the other TAZs remained high. It showed that the population distribution and traffic patterns computed by the two datasets were fairly similar. Our research provides a method to improve the analysis of complex travel patterns and behaviors and provides opportunities for travel demand modeling and urban transport planning. The findings can also help decision-makers understand urban human mobility and can serve as a guide for urban management and transport planning. |
format | Online Article Text |
id | pubmed-7795117 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77951172021-01-10 Research on Human Travel Correlation for Urban Transport Planning Based on Multisource Data Chen, Hua Cai, Ming Xiong, Chen Sensors (Basel) Article With the rapid development of positioning techniques, a large amount of human travel trajectory data is collected. These datasets have become an effective data resource for obtaining urban traffic patterns. However, many traffic analyses are only based on a single dataset. It is difficult to determine whether a single-dataset-based result can meet the requirement of urban transport planning. In response to this problem, we attempted to obtain traffic patterns and population distributions from the perspective of multisource traffic data using license plate recognition (LPR) data and cellular signaling (CS) data. Based on the two kinds of datasets, identification methods of residents’ travel stay point are proposed. For LPR data, it was identified based on different vehicle speed thresholds at different times. For CS data, a spatiotemporal clustering algorithm based on time allocation was proposed to recognize it. We then used the correlation coefficient r and the significance test p-values to analyze the correlations between the CS and LPR data in terms of the population distribution and traffic patterns. We studied two real-world datasets from five working days of human mobility data and found that they were significantly correlated for the stay and move population distributions. Then, the analysis scale was refined to hour level. We also found that they still maintain a significant correlation. Finally, the origin–destination (OD) matrices between traffic analysis zones (TAZs) were obtained. Except for a few TAZs with poor correlations due to the fewer LPR records, the correlations of the other TAZs remained high. It showed that the population distribution and traffic patterns computed by the two datasets were fairly similar. Our research provides a method to improve the analysis of complex travel patterns and behaviors and provides opportunities for travel demand modeling and urban transport planning. The findings can also help decision-makers understand urban human mobility and can serve as a guide for urban management and transport planning. MDPI 2020-12-30 /pmc/articles/PMC7795117/ /pubmed/33396731 http://dx.doi.org/10.3390/s21010195 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Hua Cai, Ming Xiong, Chen Research on Human Travel Correlation for Urban Transport Planning Based on Multisource Data |
title | Research on Human Travel Correlation for Urban Transport Planning Based on Multisource Data |
title_full | Research on Human Travel Correlation for Urban Transport Planning Based on Multisource Data |
title_fullStr | Research on Human Travel Correlation for Urban Transport Planning Based on Multisource Data |
title_full_unstemmed | Research on Human Travel Correlation for Urban Transport Planning Based on Multisource Data |
title_short | Research on Human Travel Correlation for Urban Transport Planning Based on Multisource Data |
title_sort | research on human travel correlation for urban transport planning based on multisource data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795117/ https://www.ncbi.nlm.nih.gov/pubmed/33396731 http://dx.doi.org/10.3390/s21010195 |
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