Thinking Intelligent Transport: ALPR Studies

Many cities throughout the world have started to integrate intelligent transport systems (ITS) into their transportation system infrastructure to help monitor and manage traffic flow and reduce congestion. Automatic License Plate Recognition (ALPR) can provide transportation professionals with the tools to analyze, archive, and collect data that helps assess the performance of these systems.

ALPR can help monitor the flow and movement of vehicles around a road network. This would typically involve looking at statistics, estimates, historical data and observations such as:

* Number of vehicles on a road
* Areas of high and low congestion
* Parking garage usage
* Location, cause and frequency of roadworks

By using ALPR, it is possible to monitor the travel of individual vehicles, automatically providing information about the flow and speed of various routes. These details can highlight problem areas when and as they occur and helps transportation professionals to make informed incident management decisions.

Systems must be accurate. Miovision’s ALPR Camera can automatically generate a timestamped report of vehicle license plates passing by a chosen location at any time of the day or night. Seamlessly integrated with the Scout Video Collection Unit, set-up is a breeze and can be performed by a single individual. The portability of Scout enables ALPR studies to be performed anywhere, so there is no need to worry about direct roadway access or requiring safe vantage points for manual observers. Once the ALPR video is captured, users upload the video data to Miovision’s TrafficDataOnline.com to generate high quality, detailed reports.

Regardless of the systems users choose to conduct their ALPR studies with, they provide us with invaluable transportation insight. They can help identify roadway requirements and travel patterns, specifically areas that generate the most traffic. They identify new streets, flow patterns, new parking areas and alternate routes. Additionally they are integral in anticipating future and present traffic problems and/or patterns – something with which no transportation engineer and advanced intelligent transport system can afford to do without!