Exploring pattern-aware travel routes for trajectory search

Abstract

With the popularity of positioning devices, Web 2.0 technology, and trip sharing services, many users are willing to log and share their trips on the Web. Thus, trip planning Web sites are able to provide some new services by inferring Regions-Of-Interest (ROIs) and recommending popular travel routes from trip trajectories. We argue that simply providing some travel routes consisting of popular ROIs to users is not sufficient. To tour around a wide geographical area, for example, a city, some users may prefer a trip to visit as many ROIs as possible, while others may like to stop by only a few ROIs for an in-depth visit. We refer to a trip fitting the former user group as an in-breadth trip and a trip suitable for the latter user group as an in-depth trip. Prior studies on trip planning have focused on mining ROIs and travel routes without considering these different preferences. In this article, given a spatial range and a user preference of depth/breadth specified by a user, we develop a Pattern-Aware Trajectory Search (PATS) framework to retrieve the top K trajectories passing through popular ROIs. PATS is novel because the returned travel trajectories, discovered from travel patterns hidden in trip trajectories, may represent the most valuable travel experiences of other travelers fitting the user's trip preference in terms of depth or breadth. The PATS framework comprises two components: travel behavior exploration and trajectory search. The travel behavior exploration component determines a set of ROIs along with their attractive scores by considering not only the popularity of the ROIs but also the travel sequential relationships among the ROIs. To capture the travel sequential relationships among ROIs and to derive their attractive scores, a user movement graph is constructed. For the trajectory search component of PATS, we formulate two trajectory score functions, the depth-trip score function and the breadth-trip score function, by taking into account the number of ROIs in a trajectory and their attractive scores. Accordingly, we propose an algorithm, namely, Bounded Trajectory Search (BTS), to efficiently retrieve the top K trajectories based on the two trajectory scores. The PATS framework is evaluated by experiments and user studies using a real dataset. The experimental results demonstrate the effectiveness and the efficiency of the proposed PATS framework.

A Clustering Scheme for Discovering Congested Routes on Road Networks

“…The vehicle calculates a route for a destination where a summation of evaluation values for roadway segments in the route becomes minimal. Given a spatial range and a user preference of depth/breadth specified by a user, [15] processed a Pattern-Aware Trajectory Search (PATS) to retrieve the top K trajectories passing through popular regions-of-interest (ROIs). PATS support trip planning without requiring prior knowledge of ROIs in the specified spatial range.…”

“…The analysis over these trajectory data is becoming important for many applications, such as meteorological observation and forecast, animal habits observation, road traffic situation analysis, and navigation in transportations [3][4][5][6]8]. According to the recorded trajectory data and road networks, the moving pattern, traffic situation and road recommendation services can be supported [1,2,12,15,18]. Recently, with the continuously increasing mobile devices and vehicles, the route recommendation service is becoming more and more important [1,5,6,9,17,19,20].…”

The shortest path to happiness

“…Since this sample of users relates only to specific social groups, it would be interesting to compare their demographics to those of social media users. From the latest Ignite report on social media [14], Foursquare and Twitter users tend to be university educated [25][26][27][28][29][30][31][32][33][34] year old women (66% women for Foursquare and 61% for Twitter), while Flickr users tend to be university educated [35][36][37][38][39][40][41][42][43][44] year old women (54% women). The demographics of our respondents thus show a skew towards 30-35 year old men (58% men).…”

Influential spatial facility prediction over large scale cyber-physical vehicles in smart city

“…Understanding the influences of dynamic vehicles may help urban planners to make more informed decisions. For instance, urban planners can use massive GPS location data from taxis to help analyzing traffic anomalies [6], area functions [7], travel routes [8], etc.…”