Mining Vehicle Trajectories to Discover Individual Significant Places: Case Study using Floating Car Data in the Paris Region

Abstract

In this study we discovered significant places in individual mobility by exploring vehicle trajectories from floating car data. The objective was to detect the geo-locations of significant places and further identify their functional types. Vehicle trajectories were first segmented into meaningful trips to recover corresponding stay points. A customized density-based clustering approach was implemented to cluster stay points into places and determine the significant ones for each individual vehicle. Next, a two-level hierarchy method was developed to identify the place types, which firstly identified the activity types by mixture model clustering on stay characteristics, and secondly discovered the place types by assessing their profiles of activity composition and frequentation. An applicational case study was conducted in the Paris region. As a result, five types of significant places were identified, including home place, work place, and three other types of secondary places. The results of the proposed method were compared with those from a commonly used rule-based identification, and showed a highly consistent matching on place recognition for the same vehicles. Overall, this study provides a large-scale instance of the study of human mobility anchors by mining passive trajectory data without prior knowledge. Such mined information can further help to understand human mobility regularities and facilitate city planning.

Identification of Common Paths for Rideshare Routing by Mining Vehicle Trajectories

“…They were presegmented into trips and labeled with travel modes by the data provider. Relevant methods can be found in the existing literature ( 16 , 17 ) and ( 24 ). For carpooling-related purposes, only the vehicle-related trajectories were used in the following analysis.…”

“…A typical example could be an entity staying within a radius of 150 m for over 20 min. More details of such processing methods and relevant reviews of existing work can be found in our previous studies ( 16 , 17 ).…”

On Heterogenous Sampling Rates in Origin–Destination Matrix Estimation Based on Trajectory Data and Link Counts

“…As the FCD trace the entire path information, after the segmentation, the O-D flow, their assignment paths, and the partial flow pertaining to the marked vehicles by the FCD can be easily recovered. More specifically, the segmentation processing was done by identifying activity stops ( 30 ), whose patterns can be featured as staying at a place over a certain time threshold. To identify the path, a map-matching approach ( 31 ) was then employed to match the trajectory to the road network.…”

A Data-Driven Approach to Analyze Mobility Patterns and the Built Environment: Evidence from Brescia, Catania, and Salerno (Italy)

“…[16] built a mobility-related typology of territorial zones by investigating vehicle movements by FCD in the Great Paris region; they found that the derived mobility types of zones have a correspondence with the common recognition of their social functions. In addition, in [17], significant places have been localized in the Metropolitan Region of Paris by mining trajectories and activity durations from FCD.…”