Details:

Creators:
Ying, Kyle; Ameri, Alireza; Trivedi, Ankit; Ravindra, Dilip; Patel, Darshan; ... More +
Ying, Kyle; Ameri, Alireza; Trivedi, Ankit; Ravindra, Dilip; Patel, Darshan; Mozumdar, Mohammad; Less -
Corporate Creators:
California State University, Long Beach;
Corporate Contributors:
California. Department of Transportation;
Subject/TRT Terms:
[+]
Algorithms
Anisotropic Materials
Data And Information Technology
Highways
Machine Learning
Magnetometer Detectors
Operations And Traffic Management
Planning And Forecasting
Sensors
Vehicle Classification
Wireless Communication Systems

Publication/ Report Number:
CA15-2817;
Resource Type:
Tech Report;
Geographical Coverage:
United States; California;
United States; California; Less -
TRIS Online Accession Number:
01690081
Edition:
Final Report
Corporate Publisher:
California. Department of Transportation;
Abstract:
Traditionally, pavement inductive loop sensors are used to collect real time traffic data for passenger-freight movement in roadways. This method, however, is expensive to install and maintain, and also requires an electronic control unit connected to the induction loop. In the last decade, significant improvements have been achieved in Micro-Electro-Mechanical System (MEMS) sensors domain with respect to size, cost and accuracy. Moreover, extreme miniaturization of RF transceivers and low power micro-controllers motivated the development of small and low power sensors and radio-equipped modules, which are now replacing traditional wired sensor systems. These modules can communicate with other similar modules to build an intelligent sensing network. Due to process miniaturization and low power consumption, these “sensor nodes” can potentially remain functional years. Motivated by these novel advances, we propose a wireless MEMS sensor based passenger-freight interaction detection framework for Intelligent Transportation Systems (ITS). The proposed system is mainly composed of two parts: the sensor nodes that contain wireless Magneto-Resistive (MR) sensors to detect passenger freight vehicles and an Electronic Control Unit (ECU) to collect and generate the traffic data from sensor nodes’ data such as vehicle detection, speed, and most importantly, classification. The sensor’s primary function is to classify vehicles, which inherently includes detection. Through the use of machine learning algorithms, ECU can produce correct classification rates nearing 100%. Through the use of multiple sensors, the ECU can calculate and extrapolate the speed and level of congestion of the area where the sensors are installed. Our proposed solution will be significantly more cost effective than the traditional induction loop approach and is scalable to cover millions miles of roadways all over the US.
Format:
PDF;
Funding:
METRANS Project 14-06; 65A0533 TO 006;
METRANS Project 14-06; 65A0533 TO 006; Less -
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:ad74eba9ec55ad8a932c7ec078e737ac77d6427161066d4ed15cd923fa676772
File Type:

Supporting Files

• No Additional Files

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