Mobility Pattern Discovery from Automobile Traces with A Temporal-Spatial Model

An increasing number of automobiles are nowadays equipped with GPS-enabled devices, which enables the collection of their moving traces in real-time at large scale. Hidden behind the numerous and disordered traveling traces are some inherent mobility patterns of the corresponding regions. However, it is hard to reveal the patterns directly from the traces due to the dynamics of user mobility. On the one hand, the mobility patterns differ from time to time and from region to region. On the other hand, drivers of different types may have distinct driving habits. In this paper, attempting to discovering the mobility patterns in both temporal and spatial dimensions, we extend the Latent Dirichlet Allocation model and propose a novel probabilistic model called Temporal-Spatial Latent Dirichlet Allocation (TS-LDA). We validate the model with a real-world GPS trajectory data set. The experimental results have revealed interesting mobility patterns. Introduction Mobility have become essential for urban lives in modern society due to continuous expanding of urban population density. With the increasing number of GPS-equipped automobiles and mobile devices, collecting moving traces of human beings at large scale in real-time becomes possible. It opens up great opportunities to study human mobility. As a result, analysis of the massive GPS trajectories is gaining increasing popularity in recent years, with a diverse set of goals such as mobility pattern discovering, location prediction, and region clustering. In this paper, we focus our analysis on discovering inherent mobility patterns of individuals hidden behind the GPS trajectories. Individuals seldom move randomly and usually with a clear goal. Thus, each trip is usually associated with a pre-defined destination, such as office, home or shopping center. For people who live regular lives, there must be some inherent mobility patterns. For example, people who living the suburb may probably drive to the downtown in the morning and return home in the late afternoon or evening every weekday. The purpose of this study is to discovering Copyright c © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. the semantic time intervals and regions with similar trajectory patterns, and furthermore reveal such inherent mobility patterns hidden behind the GPS trajectories, including finding out the traffic flow tendency in different time intervals and classifying all the drivers into different types according to their driving habits. Outcome of this analysis is expected to benefit several parities of urban lives. It offers supporting references to the government for optimizing urban infrastructure and transportation rules to improve urban mobility. For urban citizens, it can not only help them make more informed driving plans, but also provide personalized driving assistant based on their driving habits learned from their past driving traces. Advertisers can leverage the mobility patterns for behavior targeting and deliver personalized advertisement to individuals. To our best knowledge, there have been limited researches on this topic. Geo topic model was proposed for location recommendation based on human activity and interests (Kurashima et al. 2013), while this model did not take the temporal factor into consideration, which is an influential component of human mobility. Giannotti et al. proposed a clustering method to analyze the mobility behavior (Giannotti et al. 2009). Although this study used both temporal and spatial features to describe trajectories, they ignored the semantic meanings of time and positions. For instances, 7 a.m. to 9 a.m. and 4 p.m. to 6 p.m. are usually considered morning traffic peak and afternoon traffic peak, respectively. The daytime in weekends is considered as a different temporal type than that of weekdays. Similarly, regions also demonstrate semantic patterns such as city center and suburb. Different from the previous researches, we aim to establish a thematic structure over time and positions separately and mine the hidden temporal and spatial patterns, which can be furthermore applied to describe human mobility patterns. Latent Dirichlet Allocation (Blei, Ng, and Jordan 2003) was proved successful in mining the hidden topics in large archives of documents, and its applications have extended far beyond the natural language processing. A DMR-based topic model (Mimno and McCallum 2012) was applied to discovering urban region functions combining human mobility data and POIs (Yuan, Zheng, and Xie 2012). However, they regarded the combination of time and positions as a whole, rather than model them on separate topics. A coupled-LDA model was proposed to extracting separate topics on multi-dimensional features in mining IPTV user behaviors (Chen, Zhang, and Zha 2013). This model does not fit our problem well due to the difference in human behavior descriptions. In Chen’s work, the observed features, program and timestamp, belong to two independent topics, program interest and temporal factor, separately. However, in our case, both the origin and the destination in a trip follow the same topic distribution. Chen et al. did not take this situation into their consideration. In this paper, we extend the LDA model and proposed a Temporal-Spatial model for human mobility pattern analysis. The model considers both temporal and spatial features and establishes high-level thematic structures separately for the two set of features. Base on this model, we reveal the hidden temporal and spatial patterns. We further analyze human mobility patterns based on the combination of temporal and spatial patterns, and categorize drivers into different types. Finally, we show that the model is able to capture the regular transition patterns of each type of drivers given a specified time interval. TS-LDA Model In natural language processing and text mining, the LDA model has been proved effective for mining the unobserved thematic structure in large archives of documents. There are 3 key concepts in LDA model: document, word and topic. When it comes to driving mobility problem, we need to correspondingly define these key concepts. Yuan et al. (Yuan, Zheng, and Xie 2012) regard the geographic regions as documents and the region functions as the hidden topic. In addition, they use a triple (origin, destination and leaving time) to characterize a transition in a certain region, and regard it as the word in the document. In our study, we use the same way to characterize the transitions. But the differences lie in that we treat the users as the documents and the driving patterns as the hidden topics. Therefore, the driving transition is the word of users. Table 1 shows the analogy relationships among these three works. Text analysis Yuan’s work Our work Documents Regions Users Topics Region functions Driving patterns Words Region transitions Driving transitions Table 1: Analogy relationship among text analysis, Yuan’s work and our work. In the original LDA model, positions and time are treated as a whole, and thematic structures are built on the positiontime couple. However, this method ignores the intrinsic semantics of temporal and spatial features. In our case, we use a triple (origin, destination, leaving time) to characterize a piece of driving trajectory. We attempt to separately build two high-level thematic structures – spatial pattern and temporal pattern. Figure 1 shows the graphical model of TSLDA. Table 2 lists the annotations used in this model. In the TS-LDA model, a driving transition is generated by four steps: θm Zm,n om,n