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A novel multisensor traffic state assessment system based on incomplete data.

Yiliang Zeng¹, Jinhui Lan¹, Bin Ran²

¹Department of Instrument Science and Technology, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Thescientificworldjournal

|August 28, 2014

Summary

This summary is machine-generated.

This study introduces a novel multisensor system for traffic state assessment, effectively fusing and recovering incomplete data. The system achieves over 98% accuracy, enhancing urban intelligent transportation systems.

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Area of Science:

Intelligent Transportation Systems
Traffic Engineering
Data Fusion

Background:

Traffic state assessment relies on accurate data, often hindered by sensor malfunctions or incomplete datasets.
Existing methods struggle with data fusion from heterogeneous multisensor systems.

Purpose of the Study:

To develop a novel multisensor system for accurate traffic state assessment using incomplete data.
To propose a robust data fusion and recovery method for traffic flow data.

Main Methods:

Preprocessing involves validity checking of traffic flow data.
A history-based data fusion and recovery method is employed for incomplete datasets.
A space-matching fusion model estimates mean travel speed, leveraging complementary data from probe and fixed sensors.
A rough sets and cloud model fuses traffic flow data (flow, speed, occupancy) for state assessment.

Main Results:

The proposed system effectively fuses and recovers incomplete traffic data.
The fusion decision model achieves high accuracy, exceeding 98%.
Experimental results align with actual road traffic conditions.

Conclusions:

The novel multisensor system provides effective traffic state assessment.
The system is suitable for urban intelligent transportation systems, improving data reliability.