Automatic Detection of Vehicular Axle Distance at Tollgates
MSc Thesis
Petrus Mursanto



This thesis examines the feasibility of implementing a four-strip treadle configuration for the Indonesian Automatic Vehicle Classification (AVC) system. The Indonesian AVC system classifies vehicles as they pass through tollgates, without requiring any action by the driver or toll attendant. The configuration is intended to supplement the existing manual toll collection system.

There are three determinants in Indonesia's AVC system: axle distance, number of axles and height over first axle. In this thesis, we study how an AVC system might estimate axle distance by analysing the detection times of first and second axles on a four-strip treadle. We conclude that axle distance can be estimated accurately under certain assumptions on measurement system resolution and motor vehicle technical specifications.

A population area of vehicle behaviour is set within acceptable ranges of axle distance, acceleration and velocity values. The accuracy of the axle distance estimator is examined by calculating the probability of misclassification over that population.

The results of this study show that all currently registered vehicles would be correctly classified, provided that the axle counter and the height sensor measuring the other two determinants are 100% accurate.