Updating Road Networks by Local Renewal from GPS Trajectories

Jianya Gong,Tao Wu,Longgang Xiang

doi:10.3390/ijgi5090163

Abstract

The long production cycle and huge cost of collecting road network data often leave the data lagging behind the latest real conditions. However, this situation is rapidly changing as the positioning techniques ubiquitously used in mobile devices are gradually being implemented in road network research and applications. Currently, the predominant approaches infer road networks from mobile location information (e.g., GPS trajectory data) directly using various extracting algorithms, which leads to expensive consumption of computational resources in the case of large-scale areas. For this reason, we propose an alternative that renews road networks with a novel spiral strategy, including a hidden Markov model (HMM) for detecting potential problems in existing road network data and a method to update the data, on the local scale, by generating new road segments from trajectory data. The proposed approach reduces computation costs on roads with completed or updated information by updating problem road segments in the minimum range of the road network. We evaluated the performance of our proposals using GPS traces collected from taxies and OpenStreetMap (OSM) road networks covering urban areas of Wuhan City.

Highlights

Constructing road network maps is a fundamental problem in both intelligent transportation and urban management because accurate maps are vital to the transport services of modern urban systems
Increasingly sophisticated methods and applications have complemented mainstream road network map datasets, which especially benefit from spatial technologies, such as remote sensing, collaborative mapping, and GPS integrated in mobile devices
The road network map construction approaches in the literature are limited in several ways, such as the massive computational expense and laborious artificial adjustment

Summary

Introduction

Constructing road network maps is a fundamental problem in both intelligent transportation and urban management because accurate maps are vital to the transport services of modern urban systems. Increasingly sophisticated methods and applications have complemented mainstream road network map datasets, which especially benefit from spatial technologies, such as remote sensing, collaborative mapping ( known as the volunteered geographic information), and GPS integrated in mobile devices. With these techniques, researchers and staff can infer urban digital information maps through relatively low-cost collection, and researchers are trying to extend the coverage of road networks to areas of less commercial interest. To the best of the authors’ knowledge, this study is the first attempt to explore a method that identifies problems in an existing road network and updates the network by means of GPS vehicle trajectories in a spiral progressive process

Trajectory Data Acquisition

Road Map Generation with Trajectories

Hidden Markov Model

A Spiral Inspection and Renewal Strategy

Problem Statement

The HMM-Based PRS Detection Algorithm

Road Segment Extraction

Experiments