En-route Security Monitoring Based on an Incident Detection Algorithm for Commercial Vehicles

2009 ◽  
Author(s):  
Hao Wang ◽  
Ruey Cheu ◽  
Der-Horng Lee
Keyword(s):  
Detection Algorithm ◽  
Incident Detection ◽  
Commercial Vehicles ◽  
Security Monitoring

A Study of Real Time Security Monitoring of Commercial Vehicles Using a Microscopic Traffic Simulation Model

2011 ◽  
Vol 55-57 ◽  
pp. 1293-1298
Author(s):  
Hao Wang ◽  
Ruey Cheu ◽  
Der Horng Lee
Keyword(s):  
Simulation Model ◽  
Real Time ◽  
Detection Algorithm ◽  
Commercial Vehicles ◽  
Microscopic Simulation ◽  
Real Time System ◽  
Security Monitoring ◽  
Positioning Systems ◽  
Good Potential ◽  
Security Monitoring System

This paper involves a study of a real-time system for monitoring the security of commercial vehicles in road networks. Embedded in the security monitoring system is a commercial vehicle tracking and incident detection algorithm which relies on a combination of vehicle telemetry data obtained from Global Positioning Systems and on-board sensors to continuously monitor the route choice and car-following behavior of the driver. The performance of this algorithm has been tested in a microscopic simulation model, on a set of hypothetical scenarios, which included deviations from the approved routes, forced to travel at unreasonably low speeds, or even stopped at unexpected places in the network. The initial results indicate that the proposed system has good potential in detecting abnormal driving behaviors, with 100% detection rate, 6.0 seconds of mean detection time, and less than 1% false alarm rate.

A wavelet-based freeway incident detection algorithm with adapting threshold parameters

2011 ◽  
Vol 19 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Young-Seon Jeong ◽  
Manoel Castro-Neto ◽  
Myong K. Jeong ◽  
Lee D. Han
Keyword(s):  
Detection Algorithm ◽  
Incident Detection

Highway Traffic Incident Detection and Optimal Sensor Placement Layout

2013 ◽  
Vol 650 ◽  
pp. 460-464 ◽  
Author(s):  
Lan Bai ◽  
Qi Sheng Wu ◽  
Mei Yang ◽  
Lan Xin Wei ◽  
Bo Li ◽  
...  
Keyword(s):  
Road Traffic ◽  
Detection Algorithm ◽  
Simulation Software ◽  
Incident Management ◽  
Incident Detection ◽  
Highway Traffic ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Before And After ◽  
Traffic Incident Management

Traffic incident detection is critical to the core of the traffic incident management process. In order to study the highway traffic incident detection algorithm and the layout spacing of the fixed detector, under the assumptions of the linear traffic flow, to detect traffic incidents as the goal, using TransModeler traffic simulation software to simulate the highway traffic conditions from Xian to Hanzhong, getting the changes in the macroscopic traffic parameters before and after the traffic incident, and analysis of the data, finally puts forward the optimal layout of spacing of basic road traffic incident detection.

GAID: Genetic Adaptive Incident Detection for Freeways

2003 ◽  
Vol 1856 (1) ◽  
pp. 96-105 ◽  
Author(s):  
Prasenjit Roy ◽  
Baher Abdulhai
Keyword(s):  
Neural Networks ◽  
Feature Space ◽  
Detection Algorithm ◽  
Incident Detection ◽  
Time Step ◽  
Detection Algorithms ◽  
Bayesian Update ◽  
Average Condition ◽  
The Difference ◽  
Input Variables

Extensive research on point-detector-based automatic traffic-impeding incident detection indicates the potential superiority of neural networks over conventional approaches. All approaches, however, including neural networks, produce detection algorithms that are location specific—that is, neither transferable nor adaptive. A recently designed and ready-to-implement freeway incident detection algorithm based on genetically optimized probabilistic neural networks (PNN) is presented. The combined use of genetic algorithms and neural networks produces GAID, a genetic adaptive incident detection logic that uses flow and occupancy values from the upstream and downstream loop detector stations to automatically detect an incident between the said stations. As input, GAID uses modified input feature space based on the difference of the present volume and occupancy condition from the average condition for time and location. On the output side, it uses a Bayesian update process and converts isolated binary outputs into a continuous probabilistic measure—that is, updated every time step. GAID implements genetically optimized separate smoothing parameters for its input variables, which in turn increase the overall generalization accuracy of the detector algorithm. The detector was subjected to off-line tests with real incident data from a number of freeways in California. Results and further comparison with the McMaster algorithm indicate that GAID with a PNN core has a better detection rate and a lower false alarm rate than the PNN alone and the well-established McMaster algorithm. Results also indicate that the algorithm is the least location specific, and the automated genetic optimization process makes it adapt to new site conditions.

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