01556594

Component

This article describes a novel approach for the binary classification of two wheeler road-users in a dense mixed traffic intersection. The classification is a supervised procedure to differentiate between motorized and non-motorized bikes. Road-users were first detected and tracked using object recognition methods. Classification features were then selected from the collected trajectories. The features include maximum speed, cadence frequency in addition to acceleration based parameters. Experiments were conducted on a video data set from Shanghai, China, where cyclists as well as motorcycles tend to share the main road facilities. A sensitivity analysis was performed to assess the quality of the selected features in improving the accuracy of the classification. A performance analysis demonstrated the robustness of the proposed classification method with a correct classification rate of up to 93 percent. This research contributes to the literature of automated data collection and can benefit the applications in many transportation related fields such as shared space facility planning, simulation models for two-wheelers as well as behavior analysis and road safety studies.

This paper was sponsored by TRB committee ANF30 Motorcycles and Mopeds.

01550057

15-4830

English

23p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Figures; Photos; References; Tables

Automatic vehicle classification; Bicycles; Computer vision; Cyclists; Data collection; Motorcycles; Motorcyclists; Two wheeled vehicles; Vehicle mix; Video imaging detectors

Shanghai (China)

Data and Information Technology; Pedestrians and Bicyclists; Planning and Forecasting; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2015 Paper #15-4830

TRIS, TRB, ATRI

Dec 30 2014 1:36PM