A three-stage online map-matching algorithm by fully using vehicle heading direction

Chen, Chao; Ding, Yan; Xie, Xuefeng; Shu, Zhang

doi:10.1007/s12652-018-0760-0

Cited by 14 publications

(21 citation statements)

References 27 publications

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“…Quite a few of algorithms on trajectory mapping have been proposed [6], [12]. Previous work mainly focuses on finding the true positions of GPS observations, since the GPS sampling frequency is high and the gap between two GPS observation is quite narrow.…”

Section: A Trajectory Mappingmentioning

confidence: 99%

“…among which the vehicle trajectory data is a typical representative [6], [29]. Such trajectory data can not only be used to track the traveling paths and positions of vehicles directly, but also can be mined to enable a plenty of smart pervasive and urban services, such as understanding urban/traffic dynamics [28], suggesting driving routes [8], [10], inferring urban/building functionality [5].…”

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confidence: 99%

“…Unfortunately, this simple and native solution also raises some severely negative issues, e.g., creating a big uncertainty between two consecutive GPS points [11]. To make matter worse, GPS noises and the complexity of road network make the inference of driving paths in-between even more challenging [6]. As a result, the applications that can be enabled are limited to some extent.…”

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confidence: 99%

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VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing

Chen

Ding

Wang

et al. 2020

IEEE Systems Journal

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Vehicles can be easily tracked due to the proliferation of vehicle-mounted global positioning system (GPS) devices. V T racer is a cost-effective mobile system for online trajectory compression and tracing vehicles, taking the streaming GPS data as inputs. Online trajectory compression, which seeks a concise and (near) spatial-lossless data representation before revealing the next vehicle's GPS position, is gradually becoming a promising way to alleviate burdens such as communication bandwidth, storing, and cloud computing. In general, an accurate online mapmatcher is a prerequisite. This two-phase approach is nontrivial because we need to overcome the essential contradiction caused by the resource-constrained GPS devices and the heavy computation tasks. V T racer meets the challenge by leveraging the idea of mobile edge computing. More specifically, we offload the heavy computation tasks to the nearby smartphones of drivers (i.e., smartphones play the role of cloudlets), which are almost idle during driving. More importantly, they have relatively more powerful computing capacity. We have implemented V T racer on the Android platform and evaluate it based on a real driving trace dataset generated in the city of Chongqing, China. Experimental results demonstrate that V T racer achieves the excellent performance in terms of matching accuracy, compression ratio, and it also costs the acceptable memory, energy, and app size. Index Terms-Global positioning system (GPS) devices, mobile edge computing, resource-constrained, trajectory mapping, trajectory compression. I. INTRODUCTION T HE wide proliferation of various global positioning system (GPS) devices and mobile Internet in daily life has made many kinds of trajectory data easily available at a large scale,

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Section: A Trajectory Mappingmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing

Chen

Ding

Wang

et al. 2020

IEEE Systems Journal

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“…The research topics covered in this special issue are wideranging, including context modelling and inference for smart environments (Alegre-Ibarra et al 2018; Mulero et al 2018; Nakahara and Beder 2018), localization tracking (Chen et al 2018;Xin et al 2018), optimization of energy consumption (Hammoud et al 2018), behavior mining and activity recognition (Han et al 2018;Zhang et al 2018), image analysis (Wu et al 2018), social event mining and mobile crowd sensing (Pan et al 2018;Park 2018), and behavior-based privacy (Tao et al 2018).…”

Section: Contributions Of This Issuementioning

confidence: 99%

“…2018;Mulero et al 2018;Nakahara and Beder 2018;Zhang et al 2018), indoor and outdoor localization(Chen et al 2018;Han et al 2018;Xin et al 2018), crowd-sourced big data analysis(Pan et al 2018;Park 2018), privacy…”

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confidence: 99%