Real Time Change Detection and Alerts from Highway Traffic Data

Grossman, Robert L.; Sabala, Michal; Aanand, Anushka; Eick, S.G.; Wilkinson, Leland; Zhang, Pei; Chaves, John; Vejcik, S.; Dillenburg, John; Nelson, Peter; Rorem, D; Alimohideen, Javid; Leigh, Jason; Papka, Michael E.

doi:10.1109/sc.2005.60

Cited by 14 publications

(7 citation statements)

References 1 publication

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“…GPS data crowdsourced through in-car devices is an emerging source of inexpensive data that can be used to provide real-time traffic information, identify traffic patterns, and predict traffic congestions (Cao and Krumm, 2009). This new data collection method is a type of volunteered geographic information that overcomes the high cost of using the traditional intrusive and non-intrusive on-road sensors (e.g., inductive loops) methods, as well as their limitations in data coverage (Grossman et al, 2005). Over the last few years, studies have been done using in-car GPS devices (Yoon et al, 2007;Young, 2007) and more recently with smart phones (Herrera et al, 2010;, to develop data processing techniques, traffic patterns and traffic prediction models, geometric modeling of roads, and generation of road network maps.…”

Section: Research Motivationmentioning

confidence: 99%

Automatic generation of road network data from smartphone GPS trajectories

Niu¹

2021

Preprint

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Keeping road network databases up-to-date is crucial to Geographical Information System (GIS) applications such as vehicle navigation. The vector road centerlines extracted from satellite images or in-car Global Positioning System (GPS) devices are likely to be inaccurate due to costly and labour intensive or long updating circle. The GPS data crowdsourced through smartphones provides an emerging source for refining road map due to its rich spatio-temporal coverage and reasonable level of accuracy. This thesis introduces an optimized methodology to automatically generate road network data from smartphone GPS data without using any reference maps. The horizontal accuracy of the extracted road centerlines, measured as a root mean square of 1.424 m and 1.252 m for curved and straight road segments respectively, is better than that of some existing road datasets. The outcome of this research will provide a new way of generating a more accurate and up-to-date road network databases.

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Section: Research Motivationmentioning

confidence: 99%

Automatic generation of road network data from smartphone GPS trajectories

Niu¹

2021

Preprint

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“…"), which can be communicated by the system in the vehicle, for refinement of the collision patterns. Data mining is proven to be effective for extracting traffic patterns and trends [6], [7].…”

Section: Knowledge Base and Knowledge Acquisitionmentioning

confidence: 99%

“…The sensor data simulated in our system resembles the real world data gathering from sensors installed on freeway by The Pantheon Gateway Project [7].…”

Section: Knowledge Base and Knowledge Acquisitionmentioning

confidence: 99%

Collision Pattern Modeling and Real-Time Collision Detection at Road Intersections

Salim

Loke

Rakotonirainy

et al. 2007

2007 IEEE Intelligent Transportation Systems Conference

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Abstract-The crash rate in road intersection demonstrates the need for a fast and accurate collision detection system. Ubiquitous computing research provides a significant opportunity to develop novel ways of improving road intersection safety. The existing intersection collision warning or avoidance systems are mostly built to suit a particular intersection. We suggest that an intersection collision detection system should be able to adapt to different types of intersections by acquiring the collision patterns of the intersection through data mining. Collision patterns that are specific to that intersection are stored in a knowledge base to select vehicles which are exposed to a high risk of collision. This algorithm increases the speed of collision detection calculation, as detection is not applied on all possible pairs in an intersection. The performance and accuracy of the algorithm are evaluated. This evaluation is done on a developed simulation bed and the results are presented.

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“…The information is centrally collected, analyzed, and then the results are delivered back to drivers, dispatchers, and emergency responders. Grossman et al [13] have developed a framework that detects real-time changes in highway traffic data and send real-time alerts. However, the data collected at different sensors are centralized to a supercomputer cluster system for analysis.…”

Section: Roadway Traffic Management Systemmentioning

confidence: 99%

A Scalable Distributed Stream Mining System for Highway Traffic Data

Liu

Choudhary

Zhou

et al. 2006

Lecture Notes in Computer Science

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Abstract. To achieve the concept of smart roads, intelligent sensors are being placed on the roadways to collect real-time traffic streams. Traditional method is not a real-time response, and incurs high communication and storage costs. Existing distributed stream mining algorithms do not consider the resource limitation on the lightweight devices such as sensors. In this paper, we propose a distributed traffic stream mining system. The central server performs various data mining tasks only in the training and updating stage and sends the interesting patterns to the sensors. The sensors monitor and predict the coming traffic or raise alarms independently by comparing with the patterns observed in the historical streams. The sensors provide real-time response with less wireless communication and small resource requirement, and the computation burden on the central server is reduced. We evaluate our system on the real highway traffic streams in the GCM Transportation Corridor in Chicagoland.

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Real Time Change Detection and Alerts from Highway Traffic Data

Cited by 14 publications

References 1 publication

Automatic generation of road network data from smartphone GPS trajectories

Automatic generation of road network data from smartphone GPS trajectories

Collision Pattern Modeling and Real-Time Collision Detection at Road Intersections

A Scalable Distributed Stream Mining System for Highway Traffic Data

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