![]() ![]() The third step is to calculate all links accumulated at each time epoch. The practicability depends on more factors than the practicability of map-matching based on the topological strategy, such as projective deviates, similarity of directions, intersecting angle and connectivity among the topologies. The second step is to assign a practicability to each link. This step ensures that real trajectories are selected during the first steps, accompanied by a high redundancy. The value of this distance depends on the statistics of the traffic road network. The initial step is to obtain all links near the positioning points with a loose constraint distance. These algorithms typically take three steps. Map-matching based on probabilistic strategy, including HMM-based map-matching for GPS positioning, and multiple hypothesis focuses on the perspective of the total situation for all position data and all candidate road links, instead of calculation between individual positions and nearby candidate road links. Map-matching based on the topological strategy takes advantage of the geometric strategy, but cannot produce satisfactory final results in more sophisticated context, such as low sampling rate, large scale positioning data or data with low accuracy. ![]() The candidate that earns the highest score from topological- and geometric-based calculations is considered as the vehicle’s true location. ![]() These factors comprise the final score with different weights. Connectivity among successive road links is a constructive constraint in these algorithms, particularly when the real trajectory traverses a tunnel or Central Business District (CBD) area crowded with high-rise buildings. The value depends on the following aspects: (a) the proximity of the positioning point to a link, (b) the similarity between the direction of successive points and a link and (c) the intersecting angle between a link and “line” by successive positioning points. After the first step of obtaining an initial match, the second step is to assign a value to candidate links from the initial match. Map-matching based on the topological strategy considers both geometrical data and topological relationships of trajectory by positions and candidate road links as the decision factors. Common map-matching methods are not effective for the positioning result with A-bis data from a Global System for Mobile Communication (GSM) cellular network system. The preliminary positioning result from mobile positioning are more complex than that the preliminary positioning results from other positioning methods. For mobile phone positioning, which exploits the data from a cellular network system, the positioning results are highly sensitive to the local context. Second, the other part in the map-matching framework is the complex topological relationships among these shapes in traffic context, particularly when real trajectories and calculated trajectories are related to the context. The main common aspects of these error sources in map-matching include blocked signals, multi-paths for signal, and the smallest cover area of positioning data. Different sensors have different limitations. Firstly, the accuracy of the preliminary positioning result is always an issue for map-matching algorithms in all contexts, such as GPS data positioning, pedestrian positioning and navigation, mobile phone positioning and indoor positioning. Two main error sources make this work challenging. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The sequence consists of hidden states in the HMM model. ![]() HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning and mobile phone positioning with a low sampling rate. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. ![]()
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