Development of an Automatic Incident Detection Algorithm for Freeway Based on Multi-Level Alarming System and Artificial Neural Networks
Publication: Traffic And Transportation Studies (2002)
Abstract
A substantial reduction in the delay can be achieved by early detection of the incidents and prompt response to divert the traffic in the upstream flow. Since the late 1960s, Automatic Incident Detection Systems (AIDS) have been developed and implemented to help traffic management authorities. However, high false alarm rates and low poor performance have caused some authorities to abandon AIDS. To enhance the reliability, transferability and economization of AIDS, in this paper we design a framework for freeway AIDS with three-level alarm policy, and a AID algorithm based on the Artificial Neural Networks (ANN) technology that only need single detector. It was proved by simulated data that the model built on one segment can be used to other segments, and all three measurements (DR, FAR and MTTD) are superior to the objective algorithm. Furthermore, the total cost of freeway incident management system can be reduced due to only single detector is needed for one detector station.
Get full access to this article
View all available purchase options and get full access to this chapter.
Information & Authors
Information
Published In
Copyright
© 2002 American Society of Civil Engineers.
History
Published online: Apr 26, 2012
ASCE Technical Topics:
- Algorithms
- Artificial intelligence and machine learning
- Automation and robotics
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Equipment and machinery
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Mathematics
- Neural networks
- Probe instruments
- Systems engineering
- Traffic accidents
- Traffic delay
- Traffic engineering
- Traffic management
- Transportation engineering
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.