Transferability of Expressway Incident Detection Algorithms to Singapore and Melbourne

Mak, Chin Long; Fan, Henry

doi:10.1061/(asce)0733-947x(2005)131:2(101)

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…This extensive fine-tuning, site-specific calibration reduces transferability, which makes the deployment of the algorithm in different sites an issue. More detailed information on AID model transferability and ease of implementation can be found elsewhere (22)(23)(24).…”

Section: Problem Of False Alarmsmentioning

confidence: 99%

Toward Training-Free Automatic Detection of Freeway Incidents

Castro-Neto

Lee

Jeong

et al. 2012

Transportation Research Record: Journal of the Transportation R

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Automatic incident detection (AID) on freeways has been investigated extensively in the past four decades. Various algorithms covering a broad range of types in regard to complexity, data requirements, and efficiency have been published in the literature. However, a recent nationwide survey concluded that the implementation of AID algorithms in traffic management centers was still limited. The main reasons for this discrepancy were high rates of false alarms and calibration complexity. The main objective of this research was to develop a simple, transferable algorithm that might dispense with training on a preexisting data set. The dynamic thresholds of the proposed algorithm were based on historical traffic data and thus accounted for typical variations of traffic throughout the day. Therefore, this approach could recognize recurrent congestion and therefore reduce the incidence of false alarms. In addition, the proposed method required no human intervention; that factor certainly encouraged its implementation. The presented model was evaluated in a newly developed incident database, which contained 40 incidents. The model performed better than existing algorithms in the literature.

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Section: Problem Of False Alarmsmentioning

confidence: 99%

Toward Training-Free Automatic Detection of Freeway Incidents

Castro-Neto

Lee

Jeong

et al. 2012

Transportation Research Record: Journal of the Transportation R

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“…These algorithms were originally developed to use traffic occupancy data obtained from inductance loop detectors and were calibrated for their respective local traffic characteristics. In a study by Mak and Fan (2005), all the AIDAs were successfully calibrated using the same CTE database. Their performances are reproduced in Figure 7 together with that for the DV algorithm.…”

Section: Comparison With Other Algorithmsmentioning

confidence: 99%

“…An earlier investigation by Mak and Fan (2005) found that the performance of some established loop-detectorbased algorithms on incident data collected using a video-based detector system was not satisfactory. This could be due to the different characteristics between the video-based and loop-detector-based traffic measurements or a lack of transferability in the algorithms evaluated.…”

mentioning

confidence: 99%

Single-Station Algorithm Using Video-Based Data for Detecting Expressway Incidents

Mak

Fan

2006

Comp-aided Civil Eng

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Most automatic incident detection algorithms were successfully developed using loop-detector-based traffic measurements collected from their own localities. But their detection performances were not satisfactory when applied on data collected using a video-based detector system. The video-based detector system is gaining popularity as it was reported to be cost-effective, less prone to damage compared to loop detectors embedded in road pavement, and possesses surveillance capability. It is able to provide the homogeneity of traffic measurements with greater reliability in non-incident situations. In this study, a simple detection rule was used to develop algorithms that use video-based data for detecting lane-blocking incidents. A set of 96 incidents from Singapore's Central Expressway was used for calibrating these algorithms, with another 64 incidents for validation. Two single-station algorithms, named dual-variable (DV) and flow-based DV algorithms were developed. They have similar detection logic, but the latter includes a pre-incident traffic flow condition in its detection framework. On average, the flowbased DV algorithm outperformed the DV algorithm, and both proved to be effective techniques when compared to some existing loop-detector-based algorithms.

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Algorithm Fusion for Detecting Incidents on Singapore’s Central Expressway

Mak

Fan

2006

J. Transp. Eng.

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Transferability of Expressway Incident Detection Algorithms to Singapore and Melbourne

Cited by 10 publications

References 6 publications

Toward Training-Free Automatic Detection of Freeway Incidents

Toward Training-Free Automatic Detection of Freeway Incidents

Single-Station Algorithm Using Video-Based Data for Detecting Expressway Incidents

Algorithm Fusion for Detecting Incidents on Singapore’s Central Expressway

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