Impact of the road network configuration on map-matching algorithms for FCD in urban environments

Impact of the road network configuration on map-matching algorithms for FCD in urban environments

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Novel ubiquitous traffic sensors such as floating car data (FCD) are getting extended due to the use of 24 h connected smartphones and global positioning systems. Road conditions such as travel speeds in each road link and mobility demand can be monitored by measurements coming from moving vehicles consisting of geolocation and speed information with timestamps. Map-matching is the process needed to identify the corresponding road link on a digital map and define the position of the geolocated vehicle on this link, overcoming positioning errors. Matching processes in urban environments are more prone to error due to the topology and features of city road networks. In this study, the accuracy of the map-matching is discussed depending on the road configuration for FCD in urban and interurban scenarios, under sampling frequencies ranging from 5 to 60 s. Concretely, in this analysis, three matching techniques have been evaluated against road density, nominative speed limit, edge length and edge count values in order to quantify the impact of these variables on the matching accuracy.


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