@article{ref1,
title="Research on human travel correlation for urban transport planning based on multisource data",
journal="Sensors (Basel)",
year="2021",
author="Xiong, Chen and Cai, Ming and Chen, Hua",
volume="21",
number="1",
pages="e195-e195",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21010195",
url="http://dx.doi.org/10.3390/s21010195"
}