Development and evaluation of arterial incident detection models using fusion of simulated probe vehicle and loop detector data

Dia, Hussein; Thomas, K.

doi:10.1016/j.inffus.2010.01.001

Cited by 64 publications

(26 citation statements)

References 15 publications

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“…Among the four neural network representations, the best neural network detected almost 93% of the incident observations in the training data with no false alarms. Dia and Thomas (2011) showed a detection rate of 86% and false alarm rate of 0.36%. Inclusion of speed data further improved performance, resulting in an incident detection rate of 90% and a false alarm rate of 0.5%.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This past research varies by the specific data utilized and the data analysis methodologies applied within the incident detection procedures. The methodologies applied include discriminant analysis (Sethi, Bhandari, Koppelman, & Schofer, 1995;Sermons & Koppelman, 1996), statistical algorithm (Cullip & Hall, 1997), neural network (Ivan, 1997;Dia & Thomas, 2011), Bayesian network (Zhang & Taylor, 2005), and regression model (Ahmed & Hawas, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

“…An average of one unexplainable false alarm was generated for every 5 hours of testing using data from 49 loop detector stations. Ivan (1997) and Dia and Thomas (2011) present neural network data fusion techniques to integrate traffic data from loop detectors with travel time data collected from probe vehicles. Ivan (1997) studied four neural network representations for detecting incidents.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Arterial Incident Detection Procedure Utilizing Real-Time Vehicle Reidentification Travel Time Data

Park

Kim

et al. 2015

Journal of Intelligent Transportation Systems

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Our road and highway system has become a source of multiple types of "big data." One such type of data is travel time data obtained from vehicle reidentification systems, a type that is increasingly available due to the implementation of existing and relatively new technologies such as license-plate recognition and Bluetooth-based wireless vehicle identification. Travel time data obtained in real time from such systems are used to update estimated travel times displayed on variable message signs, and research has also been conducted that utilizes travel time data as inputs to incident detection algorithms. Implementation of such systems and prior research has primarily focused on freeways and other free-flowing roads. However, such systems for travel time data collection are also being implemented on arterials. In this research an incident detection procedure that utilizes point-to-point travel time data obtained from an arterial vehicle reidentification system is developed and evaluated. Historical travel time data provided by a Bluetooth-based travel time data collection system and reported incident data are utilized to evaluate the procedure. The results show that the procedure provides a good balance of detection and false alarm rates.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

An Arterial Incident Detection Procedure Utilizing Real-Time Vehicle Reidentification Travel Time Data

Park

Kim

et al. 2015

Journal of Intelligent Transportation Systems

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“…Dia and Thomas [14] devised a neural network algorithm that uses a fusion of data from loop detectors and probe vehicles identified by fixed devices. Larger advantages can be obtained, however, by collecting data directly from floating cars; that is, vehicles that move on the road network.…”

Section: Related Workmentioning

confidence: 99%

Traffic monitoring and incident detection through VANETs

Felice

Baiocchi

Cuomo

et al. 2014

2014 11th Annual Conference on Wireless on-Demand Network Systems and Services (WONS)

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Road traffic monitoring is one of the key applications in the Intelligent Transport System field. New technologies are now provided in this field and among the most relevant ones there is the DSRC (Dedicated Short Range Communication) set of protocols and standards where vehicles wirelessly communicate. In this paper, we deal with the application of Vehicular Ad-Hoc Networks to road traffic monitoring and we present the design of two distributed protocols based on the DSRC. A realistic simulation of a main expressway in Rome, Italy, is implemented and the performances of the two proposed monitoring methodologies are evaluated in case of regular traffic conditions and in case of a car accident. In both cases the protocols are able to capture in a very quick time (few seconds) the current traffic conditions even on a quite long road of about 70 km. A discussion about the impact of the market penetration rate of the on-board DSRC devices on the protocols performance is also provided. © 2014 IEEE

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“…neural networks [9][10][11][12] or Kalman modelling [13], fusing additional data sources e.g. automatic license plate recognition [14].…”

Section: Introductionmentioning

confidence: 99%

Real-time traffic monitoring by fusing floating car data with stationary detector data

Houbraken

Audenaert

Colle

et al. 2015

2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

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Abstract-Applying the current technological possibilities has led to a wide range of traffic monitoring systems. These heterogeneous data sources individually provide a view on the current traffic state, each source having its own properties and (dis)advantages. However, these different sources can be aggregated to create a single traffic state estimation. This paper presents a data fusion algorithm that combines data on the data sample level. The proposed system fuses floating car data with stationary detector data and was implemented on live traffic. Results show the fusion algorithm allows to eliminate individual source bias and alleviates source-specific limitations.

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Development and evaluation of arterial incident detection models using fusion of simulated probe vehicle and loop detector data

Cited by 64 publications

References 15 publications

An Arterial Incident Detection Procedure Utilizing Real-Time Vehicle Reidentification Travel Time Data

An Arterial Incident Detection Procedure Utilizing Real-Time Vehicle Reidentification Travel Time Data

Traffic monitoring and incident detection through VANETs

Real-time traffic monitoring by fusing floating car data with stationary detector data

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