Abstract
Identifying and detecting the travel mode and pattern of individual travelers is an important problem in transportation planning and policy making. Mobile-phone Signaling Data (MSD) have numerous advantages, including wide coverage and low acquisition cost, data stability and reliability, and strong real-time performance. However, due to their noisy and temporally irregular nature, extracting mobility information such as transport modes from these data is particularly challenging. This paper establishes a travel mode identification model based on the MSD combined with residents’ travel survey data, Geographic Information System (GIS) data, and navigation data. Using the data obtained from Kunshan, China in 2017, enriched with variables on the travel mode identification, the model achieved a high accuracy of 90%. The accuracy is satisfactory for all of the transport modes other than buses. Furthermore, among the explanatory variables such as the built environment factors (e.g., the coverage rate of a bus stop) are in general more significant, in contrast with other attributes. This indicates that the land use functions are more influential on the travel mode selection as well as the level of travel demand.
Highlights
Traffic demand forecasting is well recognized to be a cornerstone of urban traffic planning
We focus on travel mode identification, mainly due to the following two reasons: (1) Understanding the travel modes people take is the key to travel behavior studies [1]; (2) The process of travel mode detection often involves data cleaning, segmentation, and inference, which are common to many motilities and urban planning applications
This paper focuses on identifying the travel demand of each mobile-phone user based on multiple types of data, which is a strong basis for urban travel demand forecasting
Summary
Traffic demand forecasting is well recognized to be a cornerstone of urban traffic planning. In the era of big data, mobile-phone Signaling Data (MSD) has provided a good aid for the dynamic detection of traffic flows in the entire multimodal transport system. Some recent studies on the analysis of MSD only focus on the broad spectrum of their applications [2] (including social network analysis, mobility analysis, event detection, and urban planning), and the topic addressed in this paper is not thoroughly discussed. This paper focuses on identifying the travel demand of each mobile-phone user based on multiple types of data, which is a strong basis for urban travel demand forecasting
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