Lane-based estimation of travel time distributions by vehicle type via vehicle re-identification using low-resolution video images

Abstract

Travel time estimation plays an essential role in the high-granular traffic control and management of urban roads with distinct lane-changing conditions among lanes. However, little attention has been given to the estimation of distributions of travel times among different lanes and different vehicle types in addition to their expected values. This paper proposes a new method for estimating lane-based travel time distributions with consideration of different vehicle types through matching low-resolution vehicle video images taken by conventional traffic surveillance cameras. The vehicle type classification is based on vehicle sizes and deep learning features extracted by densely connected convolutional neural networks, and the vehicle re-identification is conducted through a lane-based bipartite graph matching technique. A case study is carried out on a congested urban road in Hong Kong. Results show that the proposed method performs well in estimating the lane-level travel time distributions by vehicle type which can be very helpful for various lane-based and vehicle type-specific traffic management schemes.

Keywords:

• lane-based travel time distribution
• vehicle type
• vehicle re-identification
• lane changing behaviors
• video images

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Research Grants Council, University Grants Committee, Hong Kong under Grants PolyU R5029-18, HKU R7027-18 and 17204919; and the Research Institute for Sustainable Urban Development, Hong Kong Polytechnic University under Grant 5-ZJM5. The fifth author was supported by the Francis S Y Bong Endowed Professorship in Engineering.