When it comes to AVI, short for Automatic Vehicle Identification, an essential component of Intelligent Transportation Systems, the most commonly recognized applications include automatic road enforcement and electronic toll collection. A lesser known but relevant and context-aware application involves weather and data-mining. This particular application cross-references traffic data collected from Automatic Vehicle Identification systems and real-time weather data for crash analysis in order to identify where the most accident-prone traffic locations are, under certain weather conditions. Such application is considered a very promising method for proactive accident prevention, ensuring traffic and roadside safety.
System integrators seeking hardware solutions for such AVI-based roadside traffic data mining system would most likely require the said system be capable of not only monitoring, recording and capturing real-time traffic data such as vehicle speed and driver behavior, it would also be expected to analyze and cross-reference traffic information collected from different detection systems with real-time weather data gathered by weather stations, for crash likelihood prediction and safety assessment.
The hardware criteria, precisely speaking, are as followed.
First and foremost, camera/sensor connection must be allowed for monitoring, capturing and identifying traffic flow, vehicle speeds and driver behavior. Secondly, the hardware components must be weather proof for roadside installation. Thirdly, high definition quality footage is a must for analysis accuracy. Fourthly, processing power is needed for complex data crunching. Last but not least, the resulting information from all detection systems, installed and deployed at various locations, is to be transmitted to, pooled and archived for building a database on which further data mining can be carried out for even more accurate and precise analysis.
Lanner Solution for Roadside Traffic Data Mining
Lanner’s LEC-7480 is a edge AI appliance powered by Intel Core i7/i3 CPU. Its most prominent features include an extreme operating temperature range, high-performance graphics engine, rugged industrial components, and a variety of I/O ports for communications and expansion capabilities.
The LEC-7480 comes with dual 10/100/1000 Base-T Ethernet ports, supporting cameras or sensors connection for video surveillance and monitoring. In addition to not having a fan, which is often the most failure-prone component, the LEC-7480 is NEMA-TS2 compliant and resistant to temperature fluctuations, humidity, vibration and shock, making it an almost failsafe traffic control appliance, with an operating temperature range between -35°C and 75°C.
Video output and image resolution is ensured by the built-in VGA and HDMI ports, with the former supporting 2048x1536 resolution and the latter FHD. The Intel Core i3 variant of the LEC-7480 boasts dual-core and quad-thread performance, fulfilling requirements for most calculation and processing intensive applications.
The Intel Core i7 variant, on the other hand, offers all the comparative advantages outlined above, including Intel Hyper-threading Technology, and comes with Intel Turbo Boost Technology 2.0 for even more heavy-duty industrial applications. Finally, one of the mini-PCIe slots has an integrated SIM card reader for 3G/wireless communications, enabling speedy data transmission coming from as many roadside locations as necessary, facilitating information storage and database building.
Conclusion and Other Applications
Result can be derived using statistically significant data collected and processed by the aforementioned LEC-7480-based roadside traffic data mining system deployed at different sections of several freeway systems for identifying the high risk sections of transportation networks within cities and between states or regions. It is commonly believed that the ROI for installing such roadside traffic data-mining systems far out-pace the costs, because they make accident prediction and prevention via traffic information and real-time weather data a possibility, greatly reducing safety risk and potentially saving lives.
In addition to identifying traffic bottle necks and preventing traffic accidents, traffic data mining systems can also be implemented for other ITS-related applications such as transit fleet management, motorist information delivery and Congestion Charge Zone control.