Abstract
An improved hierarchical fuzzy inference method based on C-measure map-matching algorithm is proposed in this paper, in which the C-measure represents the certainty or probability of the vehicle traveling on the actual road. A strategy is firstly introduced to use historical positioning information to employ curve-curve matching between vehicle trajectories and shapes of candidate roads. It improves matching performance by overcoming the disadvantage of traditional map-matching algorithm only considering current information. An average historical distance is used to measure similarity between vehicle trajectories and road shape. The input of system includes three variables: distance between position point and candidate roads, angle between driving heading and road direction, and average distance. As the number of fuzzy rules will increase exponentially when adding average distance as a variable, a hierarchical fuzzy inference system is then applied to reduce fuzzy rules and improve the calculation efficiency. Additionally, a learning process is updated to support the algorithm. Finally, a case study contains four different routes in Beijing city is used to validate the effectiveness and superiority of the proposed method.
Highlights
Large-scaled probe vehicles are recently regarded as an effective way to collect traffic data for estimating traffic conditions in urban road networks
The data source collected in probe vehicle system is mainly derived from Global Positioning System (GPS) equipment, which includes vehicle position, speed, and direction
Aim to improve calculation speed, Kim and Kim [14] proposed a C-measure map-matching algorithm based on adaptive fuzzy network (AFN)
Summary
Large-scaled probe vehicles are recently regarded as an effective way to collect traffic data for estimating traffic conditions in urban road networks. Aim to improve calculation speed, Kim and Kim [14] proposed a C-measure map-matching algorithm based on adaptive fuzzy network (AFN) In this method, the C-measure is defined to represent the certainty of the car’s existence on the corresponding road. Quddus et al [12] proposed an improved fuzzy logic map-matching algorithm, in which the input variables include the speed of the vehicle, the connectivity among road links, the quality of position solution, and the position of a fix relative to a candidate link. Kim and Kim [14] proposed an algorithm to evaluate the matching certainty of position point to candidate roads, which is defined as C-measure In this method, two important factors, distance from projected road to vehicle position and vehicle heading angle, are used to calculate the values of certainty. The algorithm is improved as: Cðk þ 1Þ 1⁄4 a1DðkÞ þ a2AðkÞ þ a3DaveðkÞ þ a4CðkÞ ð5Þ where α4 represents the weight of C-measure at previous moment, α4>0
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