Scaling the Real-Time Traffic Sensing with GPS Equipped Probe Vehicles

Tseng, Peng-Jui; Hung, Chia-Chen; Chuang, Yu-Hsiang; Kao, Kuo-Hsing; Chen, Weihui; Chiang, Chih-Yi

doi:10.1109/vtcspring.2014.7023085

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Information about curvature, elevation, width, position, and heading of roads has received much attention in the context of civil engineering as well as for appealing ITS applications [10], [13], [19]. To illustrate, for autonomous driving and navigation, travel direction of moving devices should be consistent with the heading of the road to ensure them safely driving and reaching destinations.…”

Section: Background and Related Workmentioning

confidence: 99%

“…With wide use of Global Positioning Systems (GPS), a large amount of data about mobile terminal locations and states can be collected and uploaded timely [13], [14]. Compared with information obtained by special devices, such as vehicles equipped with visual systems and unmanned aerial vehicles, acquisition of GPS data is low-cost and even some open databases of GPS data are free [15], [16].…”

Section: Introductionmentioning

confidence: 99%

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When Road Information Meets Data Mining: Precision Detection for Heading and Width of Roads

Zhou

Cai

et al. 2019

IEEE Access

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Real-time road information plays a crucial role in enabling intelligent transportation systems (ITS) applications. With sufficient road information, the map of road topography can be built and updated more easily. Furthermore, many appealing ITS applications can be enabled accordingly. Aiming at improving the quality and update rate of road information, a hot topic today is how to mine information from global positioning systems (GPS) trajectories by the clustering-based methods. Such schemes, however, encounter two challenges: 1) GPS noise and 2) low sampling rate of GPS traces data. As a result, it is difficult to infer road information from these irregular clusters. To tackle the above issues, we directly mine useful road information, heading, and width of roads, for ITS applications from GPS point cloud, i.e., a set of GPS points. First, the distribution of GPS points is discussed and the least squares method (LSM) is demonstrated to be outstanding for mining the heading of the road under a huge number of GPS points. Second, the weighted approximation least squares method is proposed to improve the accuracy of the LSM. Furthermore, combining with relevant distribution features in GPS points, the data distribution variance-road width discrete model is proposed to mine road width from GPS point cloud. Finally, using real-world datasets, we demonstrate that these proposed methods can achieve satisfactory performance in practice. INDEX TERMSIntelligent transportation systems, road information, GPS data, data mining.The associate editor coordinating the review of this manuscript and approving it for publication was Zhong-Ke Gao. Chen et al.[10] apply a clustering-based method to mine road information from GPS data, aiming to reconstruct accurate digital maps for autonomous driving. Therefore, aiming to popularize intelligent transportation systems, it is necessary to improve data accuracy and timeliness of road information.Traditionally, this crucial road information is obtained through ground measurement [11]. It means moving devices equipped with sensors and visual systems are used to collect original information of road segments. This ground measurement is regarded as a time-consuming and expensive method. Recent years, with the research on ad hoc network, various individual mobile devices, such as unmanned aerial vehicles and robots [12], can collect road information simultaneously and share it with each other in this network. This way is effective to improve the collection rate for road information.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

When Road Information Meets Data Mining: Precision Detection for Heading and Width of Roads

Zhou

Cai

et al. 2019

IEEE Access

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“…Xia et al [ 14 ] propose to put a cloud-based web server on Storm. Tseng et al [ 15 ] use Storm to analyse data from a large number of GPS vehicle probes, and Kumar et al [ 16 ] use Storm to find out the anomalies in a Water Distribution Network (WDN) topology. We can borrow some ideas on optimizing the usage of Storm, but none of these work focus on improving the dependability of the Storm-based stream processing.…”

Section: Related Workmentioning

confidence: 99%

Healthcare4VideoStorm: Making Smart Decisions Based on Storm Metrics

Zhang

Duan

Chen

et al. 2016

Sensors

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Storm-based stream processing is widely used for real-time large-scale distributed processing. Knowing the run-time status and ensuring performance is critical to providing expected dependability for some applications, e.g., continuous video processing for security surveillance. The existing scheduling strategies’ granularity is too coarse to have good performance, and mainly considers network resources without computing resources while scheduling. In this paper, we propose Healthcare4Storm, a framework that finds Storm insights based on Storm metrics to gain knowledge from the health status of an application, finally ending up with smart scheduling decisions. It takes into account both network and computing resources and conducts scheduling at a fine-grained level using tuples instead of topologies. The comprehensive evaluation shows that the proposed framework has good performance and can improve the dependability of the Storm-based applications.

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