Abstract
Trajectory data include rich interactive information of humans. The correct identification of trips is the key to trajectory data mining and its application. A new method, multi-rule-constrained homomorphic linear clustering (MCHLC), is proposed to extract trips from raw trajectory data. From the perspective of the workflow, the MCHLC algorithm consists of three parts. The first part is to form the original sub-trajectory moving/stopping clusters, which are obtained by sequentially clustering trajectory elements of the same motion status. The second part is to determine and revise the motion status of the original sub-trajectory clusters by the speed, time duration, directional constraint, and contextual constraint to construct the stop/move model. The third part is to extract users’ trips by filtering the stop/move model using the following rules: distance rule, average speed rule, shortest path rule, and completeness rule, which are related to daily riding experiences. Verification of the new method is carried out with the shared electric bike trajectory data of one week in Tengzhou city, evaluated by three indexes (precision, recall, and F1-score). The experiment shows that the index values of the new algorithm are higher (above 93%) than those of the baseline methods, indicating that the new algorithm is better. Compared to the baseline velocity sequence linear clustering (VSLC) algorithm, the performance of the new algorithm is improved by approximately 10%, mainly owing to two factors, directional constraint and contextual constraint. The better experimental results indicate that the new algorithm is suitable to extract trips from the sparse trajectories of shared e-bikes and other transportation forms, which can provide technical support for urban hotspot detection and hot route identification.
Highlights
Various forms of trajectory data are collected owing to the popularity of GPS devices and positioning technology
Compared to the baseline velocity sequence linear clustering (VSLC) algorithm, the performance of the multi-rule-constrained homomorphic linear clustering (MCHLC) algorithm has been improved by approximately 10%, owing to the two additional factors, directional constraint, and contextual constraint
The conclusions drawn from the experimental results are as follows: (1) The new algorithm, named the MCHLC algorithm, is reliable and suitable to identify trips from sparse trajectory data of shared e-bikes
Summary
Various forms of trajectory data are collected owing to the popularity of GPS devices and positioning technology. It is helpful to understand and optimize urban decisions by mining the potential information of trajectory data. Trajectory cleaning, aimed at identifying or extracting trips from unordered GPS points, is the key to mining the potential information of trajectory data. The correct identification of trips is helpful for understanding and optimizing urban construction, such as bike lane planning [3], energy conservation assessment [4], road investment assessment [5], urban functional zones identification [6,7], and urban planning [8,9,10]. Trips contain rich semantic information, which is useful for understanding human mobility behavior and urban systems. The origin–destination (O/D) points of trips are special stop points and imply human activities, which are the basic data for detecting urban hotspots [12,13,14]. Distinguishing the O/D points is the key to identify trips
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