Abstract
In practice, most of the intelligent transportation systems provide average travel times of all vehicles on selected paths in real time on a regular basis. However, path travel times of different vehicles could vary widely under different traffic conditions. There is a need to consider the differences in vehicle classes for path travel time estimation. This paper proposes a novel modeling framework that considers variance–covariance relationships between vehicle classes for real-time estimation of multi-class path travel times with use of multi-source traffic data collected from various types of sensors. The proposed methodology is examined with a case study of a selected urban expressway in Hong Kong with data obtained from multiple sources. The path travel time estimates by vehicle class are validated and the results demonstrate the merits and performance of the proposed framework.
Original language | English |
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Article number | 121613 |
Journal | Expert Systems with Applications |
Volume | 237 |
DOIs | |
Publication status | Published - 1 Mar 2024 |
Keywords
- AVI data
- GPS data
- Multiple vehicle classes
- Point sensor data
- Real-time travel time estimation
ASJC Scopus subject areas
- General Engineering
- Computer Science Applications
- Artificial Intelligence