Survey on vehicle map matching techniques

• Main Authors: Zhenfeng Huang, Shaojie Qiao, Nan Han, Chang‐an Yuan, Xuejiang Song, Yueqiang Xiao
• Format: Article
• Language: English
• Published: Wiley 2021-03-01
• Series: CAAI Transactions on Intelligence Technology
• Online Access: https://doi.org/10.1049/cit2.12030

Abstract With the development of location‐based services and Big data technology, vehicle map matching techniques are growing rapidly, which is the fundamental techniques in the study of exploring global positioning system (GPS) data. The pre‐processed GPS data can provide the guarantee of high‐quality data for the research of mining passenger’s points of interest and urban computing services. The existing surveys mainly focus on map‐matching algorithms, but there are few descriptions on the key phases of the acquisition of sampling data, floating car and road data preprocessing in vehicle map matching systems. To address these limitations, the contribution of this survey on map matching techniques lies in the following aspects: (i) the background knowledge, function and system framework of vehicle map matching techniques; (ii) description of floating car data and road network structure to understand the detailed phase of map matching; (iii) data preprocessing rules, specific methodologies, and significance of floating car and road data; (iv) map matching algorithms are classified by the sampling frequency and data information. The authors give the introduction of open‐source GPS sampling data sets, and the evaluation measurements of map‐matching approaches; (v) the suggestions on data preprocessing and map matching algorithms in the future work.