Neural network representations for arterial street incident detection data fusion

This research considers four neural network representations for detecting incidents on signalized arterials using multiple data sources. Two incident detection algorithms process unique data sources separately: inductive loop detectors, and travel times collected from vehicle probes travelling throu...

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Vydáno v:	Transportation research. Part C, Emerging technologies Ročník 5; číslo 3; s. 245 - 254
Hlavní autor:	Ivan, John N
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier India Pvt Ltd 1997 Elsevier
Témata:	Applied sciences arterial streets data fusion Exact sciences and technology Ground, air and sea transportation, marine construction incident detection neural networks Road transportation and traffic surface streets time series data fusion time series surface streets arterial streets incident detection neural networks Performance evaluation Roads and streets Mishap Neural networks Time series Traffic surveys Road traffic Traffic signals Detection Comparative study
ISSN:	0968-090X, 1879-2359
On-line přístup:	Získat plný text
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Popis
Shrnutí:	This research considers four neural network representations for detecting incidents on signalized arterials using multiple data sources. Two incident detection algorithms process unique data sources separately: inductive loop detectors, and travel times collected from vehicle probes travelling through the street network. The networks then combine the algorithm inferences about traffic conditions to identify highway links on which incidents are occurring. The four networks consider the following input and structure representations, added incrementally: (1) the two algorithm output values alone; (2) a weighted geometric sum of previous network output values; (3) algorithm scores from links immediately upstream and downstream of the subject link; and (4) weighted geometric sums of previous input values. The four representations were trained as feed-forward networks using error back propagation. Time series inputs were represented with extra processing units and fixed weight connections. The networks were trained with data generated by traffic simulation permitting deliberate control of traffic demand, operation and incident conditions. Each network was trained until performance began to degrade on a reserved data set not used for training. Adding the output time series permitted two of the 24 incidents to be detected sooner than with the network that did not include this input. Similarly, using information from adjacent links in time series permitted all of the incidents to be detected by at least the third time period.
ISSN:	0968-090X 1879-2359
DOI:	10.1016/S0968-090X(97)00018-1