Inferring Taxi Status Using GPS Trajectories

TL;DR

This paper presents a novel method for inferring taxi statuses from GPS trajectories, combining feature extraction, parking detection, and a two-phase probabilistic model to improve accuracy in real-world urban settings.

Contribution

It introduces a comprehensive approach integrating feature engineering, parking detection, and a probabilistic inference model for taxi status classification from GPS data.

Findings

Outperforms baseline methods on large-scale real-world data

Accurately detects parking and travel segments

Effectively infers occupied and non-occupied statuses

Abstract

In this paper, we infer the statuses of a taxi, consisting of occupied, non-occupied and parked, in terms of its GPS trajectory. The status information can enable urban computing for improving a city's transportation systems and land use planning. In our solution, we first identify and extract a set of effective features incorporating the knowledge of a single trajectory, historical trajectories and geographic data like road network. Second, a parking status detection algorithm is devised to find parking places (from a given trajectory), dividing a trajectory into segments (i.e., sub-trajectories). Third, we propose a two-phase inference model to learn the status (occupied or non-occupied) of each point from a taxi segment. This model first uses the identified features to train a local probabilistic classifier and then carries out a Hidden Semi-Markov Model (HSMM) for globally considering long term travel patterns. We evaluated our method with a large-scale real-world trajectory dataset generated by 600 taxis, showing the advantages of our method over baselines.