Path inference from sparse floating car data for urban networks

• Published in: Transportation research. Part C, Emerging technologies Vol. 30; pp. 41 - 54
• Main Authors: Rahmani, Mahmood, Koutsopoulos, Haris N.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier India Pvt Ltd 01.05.2013; Elsevier
• Subjects: Applied sciences; Exact sciences and technology; GPS; Ground, air and sea transportation, marine construction; Map-matching; Path inference; Road transportation and traffic; Sparse floating car data; Transportation planning, management and economics; Sweden; Europe; Sparse floating car data; GPS; Map-matching; Path inference; GPS system; Urban road traffic; Traffic management; Shortest path; Problem solving; Performance; Application; Data gathering
• ISSN: 0968-090X; 1879-2359; 1879-2359
• DOI: 10.1016/j.trc.2013.02.002

► A map-matching and path inference algorithm for sparse GPS probes is introduced. ► It assumes limited information (only coordinates and timestamp) is available. ► Global criteria are used to identify the path corresponding to observed probes. ► The method demonstrates superior performance compared to similar algorithms. ► It performs efficiently in both off-line and on-line applications. The use of probe vehicles in traffic management is growing rapidly. The reason is that the required data collection infrastructure is increasingly in place in urban areas with a significant number of mobile sensors constantly moving and covering expansive areas of the road network. In many cases, the data is sparse in time and location and includes only geo-location and timestamp. Extracting paths taken by the vehicles from such sparse data is an important step towards travel time estimation and is referred to as the map-matching and path inference problem. This paper introduces a path inference method for low-frequency floating car data, assesses its performance, and compares it to recent methods using a set of ground truth data.