A novel algorithm of low sampling rate GPS trajectories on map-matching

Map-matching, an important preprocessing task in many location-based services (LBS), projects each point of the global positioning system (GPS) within a trajectory dataset onto a digital map. The state-of-the-art map-matching algorithms typically employ Hidden Markov model (HMM) via shortest path computation. But the computation of the shortest path might not work well on low-sampling-rate trajectory data (e.g., one GPS point every 1–5 min), leading to low matching precision and high running time. To solve the problem, this paper firstly identifies frequent patterns (FPs) in historical trajectories to capture meaningful mobility behaviors, and then extracts mobile behavior criterion (MBC) of mobile users. Such a criterion generally represents the route choice of mobile users on road networks. Moreover, the temporal information within trajectory data was employed to estimate the speed of mobile users on road segments. The identified FPs, coupled with MBC and moving speed, help to improve the map-matching precision of low-sampling-rate trajectories. In addition, an FP-forest structure was proposed to index the identified FPs. The structure could greatly speed up the lookup of frequent paths for shorter running time. Furthermore, the FP-forest structure was pruned to reduce redundancy with smaller space cost. Finally, experiments were carried out on real-world datasets. The results confirm that our FP-matching method outperforms state-of-the-art in terms of effectiveness and efficiency.

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“…It is a challenging task to design an accurate mapmatching algorithm for low-sampling-rate trajectories with sparse positions [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Map-Matching on Low Sampling Rate Trajectories through Frequent Pattern Mining

Zhang

Ding³

2022

Scientific Programming

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“…The chronological huge sequence of GPS traces is broken down into a smaller unit called trip [2,3]. Trips are mapped to road network graph using map matching process which identifies the object's location on road network graph [4][5][6]. CTW tree based model is constructed from trips composed of an ordered sequence of road network edges.…”

Section: Introductionmentioning

confidence: 99%

Distributed Context Tree Weighting (CTW) for route prediction

Tiwari

Arya

2018

Open geospatial data, softw. stand.

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Route prediction play a vital role in many important location-based applications like resource prediction in grid computing, traffic congestion estimation, vehicular ad-hoc networks, travel recommendation etc. The goal of this work is to design scalable route prediction application based on Context Tree Weighting (CTW) modeling of user travel data. CTW is one of the widely used technique for text compression as well string sequence indexing and for prediction. CTW tree construction from the huge volume of data by sequential processing is time-consuming in practical implementation. Existing techniques are designed for single machine and their implementation on the distributed environment is still a challenge. This work focuses on achieving horizontal scalability of CTW and addresses various challenges in distributed construction like reducing I/O, parallel computation of sequences and coming up with final CTW tree in a distributed environment efficiently. Map Reduce framework running over Hadoop file system is used for processing in distributed mode. Large GPS data set is map-matched to digitized road network obtained from Open Street Map and CTW model is built. A two-step construction of CTW tree is proposed which is implemented in the map-reduce framework. Horizontally scalable CTW model is built and evaluated for route prediction from a huge corpus of historical GPS traces.

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“…• Low sampling rate: According to [31,32,52,54], many vehicles are equipped with GPS devices, and their GPS positions are periodically reported to data centers. The duration of time between two consecutive GPS measurements is called the sampling rate.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

Map matching algorithms for intelligent transportation system

Hou¹

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The contributions of the co-authors are as follows:• Prof. Cai provided the initial project direction.• I conducted literature review, proposed the map matching algorithm, designed and conducted all the experiments, and analyzed and interpreted experimental results with guidance of Prof. Cai and Prof. Luo. • I wrote the manuscript drafts. The manuscript was revised by Prof. Luo, Prof. Cai and Dr. Hanai.

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A novel algorithm of low sampling rate GPS trajectories on map-matching

Cited by 13 publications

References 12 publications

Map-Matching on Low Sampling Rate Trajectories through Frequent Pattern Mining

Map-Matching on Low Sampling Rate Trajectories through Frequent Pattern Mining

Distributed Context Tree Weighting (CTW) for route prediction

Map matching algorithms for intelligent transportation system

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