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With the growing use of crowdsourced location data from smartphones for transportation applications, the task of map-matching raw location sequence data to travel paths in the road network becomes more important. High-frequency sampling of smartphone locations using accurate but power-hungry positioning technologies is not practically feasible as it consumes an undue amount of the smartphone’s bandwidth...
The computation speed and output latency of map matching are important considerations when processing location data, especially smartphone-generated noisy and sparse data, from a large number of users for real-time transportation applications. In this paper, we examine the factors affecting the efficiency of online map matching algorithms that are based on probabilistic sequence models such as Hidden...
The problem of missing samples in road traffic data undermines the performance of intelligent transportation applications. This paper proposes a data-driven imputation method that exploits the spatial and temporal relationships existing between the traffic flows of multiple road segments that are correlated with each other. The K-means clustering technique is used to group together road segments with...
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