Abstract
Identifying distribution of users’ mobility is an essential part of transport planning and traffic demand estimation. With the increase in the usage of mobile devices, they have become a valuable source of traffic mobility data. Raw data contain only specific traffic information, such as position. To extract additional information such as transport mode, collected data need to be further processed. Trajectory needs to be divided into several meaningful consecutive segments according to some criteria to determine transport mode change point. Existing algorithms for trajectory segmentation based on the transport mode change most often use predefined knowledge-based rules to create trajectory segments, i.e., rules based on defined maximum pedestrian speed or the detection of pedestrian segment between two consecutive transport modes. This paper aims to develop a method that segments trajectory based on the transport mode change in real time without preassumed rules. Instead of rules, transition patterns are detected during the transition from one transport mode to another. Transition State Matrices (TSM) were used to automatically detect the transport mode change point in the trajectory. The developed method is based on the sensor data collected from mobile devices. After testing and validating the method, an overall accuracy of 98% and 96%, respectively, was achieved. As higher accuracy of trajectory segmentation means better and more homogeneous data, applying this method during the data collection adds additional value to the data.
Highlights
In many traffic management applications, data collected through sensor technologies provide a means for estimating traffic demand and transport planning
The data set is customized on two data labels: change and no change
Transition State Matrices (TSM) for time windows in which there was no change in the transport mode are presented, and in the right column, TSMs for time windows in which there was a change in the transport mode are presented
Summary
In many traffic management applications, data collected through sensor technologies provide a means for estimating traffic demand and transport planning. Applications that track user mobility are helping to plan public urban transportation, track vehicle traffic, and support smart parking [1]. The collected data do not contain additional information such as transport mode, so further processing has to be applied to extract such data from the collected data set. With the increase in machine learning methods, various methods are applied on sensor-based data to obtain additional information such as human activities [2], transport modes [3] or congestion zones [4]. The additional information added to a large amount of data provide a base for developing predictive models in the field of urban mobility that capture hidden characteristics of traffic flow [5], user behavior [6], or interactions of transport network users [7]
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