Abstract
Map matching is a key preprocess of trajectory data which recently have become a major data source for various transport applications and location-based services. In this paper, an online map matching algorithm based on the second-order hidden Markov model (HMM) is proposed for processing trajectory data in complex urban road networks such as parallel road segments and various road intersections. Several factors such as driver’s travel preference, network topology, road level, and vehicle heading are well considered. An extended Viterbi algorithm and a self-adaptive sliding window mechanism are adopted to solve the map matching problem efficiently. To demonstrate the effectiveness of the proposed algorithm, a case study is carried out using a massive taxi trajectory dataset in Nanjing, China. Case study results show that the accuracy of the proposed algorithm outperforms the baseline algorithm built on the first-order HMM in various testing experiments.
Highlights
With the development of positioning and wireless communication technologies, floating car data have become a major data source for many applications such as location-based services, intelligent transportation systems, and transport policy appraisals [1,2,3,4,5]. e errors of positioning data collected by global positioning system (GPS) equipment on floating vehicles are inevitable and could come from satellite, transmission process, and receiver [6]
Considering these, in this study, we proposed self-adaptive sliding windows to realize online map matching based on hidden Markov model (HMM), which promises accuracy and efficiency at the same time
Along the line of previous online studies, this study proposes a new map matching algorithm based on the HMM technique. e proposed algorithm extends the previous studies in the following aspects: firstly, the proposed novel map matching algorithm is on the basis of second-order HMM, which can better consider the space-time relationship among different states
Summary
With the development of positioning and wireless communication technologies, floating car data (e.g., trajectories of taxis) have become a major data source for many applications such as location-based services, intelligent transportation systems, and transport policy appraisals [1,2,3,4,5]. e errors of positioning data collected by global positioning system (GPS) equipment on floating vehicles are inevitable and could come from satellite, transmission process, and receiver [6]. With the development of positioning and wireless communication technologies, floating car data (e.g., trajectories of taxis) have become a major data source for many applications such as location-based services, intelligent transportation systems, and transport policy appraisals [1,2,3,4,5]. A map matching algorithm plays a vital role, for example, travel time prediction based on floating car data, which needs to match GPS points to the corresponding road segment accurately. Erefore, the map matching algorithm is the basis for the large-scale application of floating car data. Topological technique improves the matching accuracy but is still vulnerable to the influence of low-frequency sampling interval and large sampling noise
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