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.
Highlights
Obtaining urban traffic patterns and the population distribution of urban residents is the basis for urban transport planning
Most of the analyses of the population distribution and urban traffic patterns are based on a single dataset
Five working days of the license plate recognition (LPR) and cellular signaling (CS) data in Foshan City were examined, and different stay point recognition methods were used to calculate the population distribution and traffic patterns commonly used in urban traffic planning
Summary
Obtaining urban traffic patterns and the population distribution of urban residents is the basis for urban transport planning. As a result of rapid urbanization and intelligentization, the population distribution, travel demand, and travel characteristics of cities are quickly changing. The traditional survey methods can only reflect the traffic characteristics of a city within a fixed period of time. It is difficult to adapt these methods to the rapid development of cities due to their long cycles and low sample sizes. This brings challenges to urban transport planning and management. With the rapid development of information collection technology, the acquisition of large-sample, multi-dimensional, and fine-grained information is becoming easier and easier
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