Statistical analysis of road–vehicle–driver interaction as an enabler to designing behavioral models

Abstract

Telematics form an important technology enabler for intelligent transportation systems. One application of the same is that by deploying on-board diagnostic devices, the signatures of vehicle vibration along with its location and time are recorded. Detailed analyses of the collected signatures offer deep insights into the state of the objects under study. Towards that objective, we carried out experiments by deploying telematics device in one of the office bus that ferries employees to office and back. Data is collected from 3-axis accelerometer, GPS speed and the time for all the journeys. In this paper, we present initial results of the above exercise by applying statistical methods to derive information through systematic analysis of the data collected over four months. It is demonstrated that the higher order derivative of the measured Z-axis acceleration samples display the properties of Weibull distribution when the time axis is replaced by the amplitude of such processed acceleration data. Such an observation offers us a method to predict future behavior where deviations from prediction are classified as context-based aberrations or progressive degradation of the system. In addition, we capture the relationship between speed of the vehicle and median of the jerk energy samples using regression analysis. That analysis is further used to identify low, normal and high JE values for a velocity and classify journey at a micro-trip (small section of a trip) level. Such results offer an opportunity to develop a robust method to model road–vehicle interaction thereby enabling us to predict such like driving behavior and condition-based maintenance, etc.