Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data

Mobile trajectory big data, such as global positioning system (GPS) trajectories of taxicabs, have enormous social and economic values in intelligent transportation systems, and efficient mining and in-depth analysis of these data can provide beneficial decision-making for traffic operation and especially for passenger travel. To this end, in this paper, we discover the temporal, spatial, and spatiotemporal characteristics of passenger travel by analyzing the travel duration and the travel distance with large-scale taxi trajectory data, which can not only effectively reveal the resident travel patterns but also dynamically perceive the traffic conditions. Specifically, we propose an information framework to preprocess the taxi GPS trajectory data that can thoroughly identify passenger travel trajectories. Moreover, we develop a novel distribution model to explore the rules of citizen travel accurately. Finally, we put forward a parallel clustering approach on Spark, which can discover the spatiotemporal characteristics of passenger travel in a fine-grained manner, to obtain the characteristic changes of inhabitant travel at different periods of the day. In particular, the experimental results from an empirical study show that the travel duration and the travel distance follow a log-normal distribution, and the tail of the travel distance is highly fitting to a three-parameter gamma distribution and the long-distance travel of the day is mainly to the airport between 5:00 and 6:00 AM. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study examines the temporal, spatial, and spatiotemporal characteristics of passenger travel by analyzing large-scale taxi trajectory data. An information framework for preprocessing data, a distribution model for exploring travel rules, and a parallel clustering approach on Spark are proposed to reveal passenger travel patterns and characteristic changes.