Abstract
Automatic vehicle identification (AVI) data, Integrated Circuit (IC) card data and Global Positioning System (GPS) data offer an emerging and promising source of information for analysis of traffic problems. Research on insights and information from AVI data for transport analysis has made little progress in developing specific applications especially. The emergence of multi-source data provides us with a new perspective for multi-mode transportation. This paper proposes a multi-mode traffic demand forecasting method based on AVI data, metro IC card data, and taxi GPS data. The paper extracts traffic origins and destinations (OD) information of travelers from the multi-source data and uses the extracted data for traffic zone division. Finally, a multi-mode traffic forecasting model is established on this basis. GPS data of taxi trips are selected as the clustering data and k-means algorithm was adopted to divide traffic zones in Shenzhen. Moreover, the research applies the principle of convex hull to outline the boundary of the cell. Additionally, this paper establishes the multi-mode transportation forecasting model by integrating the correlation between various transportation modes into the deep learning model for prediction. The results show that the multi-mode demand forecasting model has higher accuracy and better forecasting results comparing it with the single-mode demand forecasting model which is referring to the conventional four-step procedure. The result demonstrates that effective traffic and travel data can be obtained from multi-source data, providing an opportunity to improve the analysis of complex travel patterns and behaviors for travel demand modeling and transportation planning. Furthermore, the substantive contribution of this research is that it provides strong empirical evidence for the existence of correlation among multi-mode travels and travel demand.
Highlights
The traditional traffic demand analysis model is usually based on a single traffic pattern
Our research proposed a deep learning method to predict the traffic demand in an aggregation way and compare with single sparse data, which provided a new perspective for traffic demand forecasting and provide strong empirical evidence for the inexistence of correlation among multi-mode travels
The research proposed a k-means clustering algorithm to classify Traffic Analysis Zones (TAZ). It based on characteristics of multiple transportation modes to divide the ShenZhen into a total of 34 traffic zones using another k-means clustering algorithm
Summary
The traditional traffic demand analysis model is usually based on a single traffic pattern. With the increasing diversification of traffic demand, urban traffic planning is gradually transitioning from a single mode to a multi-mode transportation system that supports and influences each other [1]. The research on multi-mode traffic demand analysis is to explore the correlations among different transportation. The associate editor coordinating the review of this manuscript and approving it for publication was Feng Xia. modes and the characteristics of traffic demand, and to establish a foundation for the implementation of transportation planning and management based on multi-mode traffic. With the diversified development of transportation modes, the structure of urban travel is increasingly complicated and diversified. Multi-mode transportation has become a hot research field. The analysis of multi-modal traffic demand has several major challenges
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