Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence

Antonio Comi,Alexander Rossolov,Antonio Polimeni,Agostino Nuzzolo

doi:10.3390/info12120493

Abstract

Data on the daily activity of private cars form the basis of many studies in the field of transportation engineering. In the past, in order to obtain such data, a large number of collection techniques based on travel diaries and driver interviews were used. Telematics applied to vehicles and to a broad range of economic activities has opened up new opportunities for transportation engineers, allowing a significant increase in the volume and detail level of data collected. One of the options for obtaining information on the daily activity of private cars now consists of processing data from automated vehicle monitoring (AVM). Therefore, in this context, and in order to explore the opportunity offered by telematics, this paper presents a methodology for obtaining origin–destination flows through basic info extracted from AVM/floating car data (FCD). Then, the benefits of such a procedure are evaluated through its implementation in a real test case, i.e., the Veneto region in northern Italy where full-day AVM/FCD data were available with about 30,000 vehicles surveyed and more than 388,000 trips identified. Then, the goodness of the proposed methodology for O-D flow estimation is validated through assignment to the road network and comparison with traffic count data. Taking into account aspects of vehicle-sampling observations, this paper also points out issues related to sample representativeness, both in terms of daily activities and spatial coverage. A preliminary descriptive analysis of the O-D flows was carried out, and the analysis of the revealed trip patterns is presented.

Highlights

To date, transportation engineering has focused on models and methods for obtaining travel demand flows as well as investigating travel behaviour [1,2,3,4,5,6,7,8]
The results presented in both studies indicate the increase in computation accuracy if floating car data (FCD) are used for travel demand assessment
We reviewed the main modelling approaches to be implemented in order to estimate the O-D daily flows through the new opportunities offered by telematics, presented some analyses to exploit the opportunities offered by automated vehicle monitoring (AVM) data capable of identifying and assessing car patterns, and tested the estimation framework through comparison with traffic counts

Summary

Introduction

Transportation engineering has focused on models and methods for obtaining travel demand flows as well as investigating travel behaviour [1,2,3,4,5,6,7,8]. To estimate current demand flows, surveys can be carried out, usually by interviewing a sample of users (direct estimation), and demand can be derived using results from sampling theory. Estimation of current origin–destination demand flows can be improved by combining the estimators with aggregate information related to O-D demand flows [20]; e.g., traffic counts: counts of user flows on some elements—links—of the transportation supply system—transportation network). Demand (present and future) can be obtained by modelling systems. While the former provides actual/current matrices with which, when assigned to the network, flows closest to traffic counts are reproduced, the latter allows O-D flows to be linked with land use, socio-economic factors, and level-of-service attributes. The effects due to changes occurring in future scenarios on such factors can be assessed

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