Open AccessThe use of traffic data from automatic monitoring systems to obtain day-to-day time series of vehicle traffic volumes and origin-destination flows in urban networks
Author(s) -
Joana Maia Fernandes Barroso,
João Lucas Albuquerque Oliveira,
Francisco Moraes Oliveira-Neto
Publication year - 2021
Publication title -
transportes
Language(s) - English
Resource type - Journals
eISSN - 2237-1346
pISSN - 1415-7713
DOI - 10.14295/transportes.v29i2.2385
Subject(s) - computer science , floating car data , real time computing , time series , traffic flow (computer networking) , series (stratigraphy) , transport engineering , data mining , traffic congestion , computer network , engineering , machine learning , paleontology , biology
The understanding of travel pattern dynamics in the urban environment is essential for the transportation systems planning and operation. Recently, the increasing availability of massive traffic data from traffic monitoring systems, including automatic number plate recognition systems (TMS-ANPR), can allow an understanding of the day-to-day variability of traffic flows in large urban network systems. However, to enhance the data quality for analysis, it is essential to carry out a previous data treatment. This work presents a method for treatment of TMS-ANPR data. The main product of this data treatment are the day-to-day time series of traffic volumes and OD flows for different periods of a typical day, allowing the analysis of the multiday dynamic of travel behavior and of the model assumptions stated in the literature about such dynamic behavior. The proposed method, which can be applied to any type of TMS-ANPR, was applied to generate time series data from the TMS-ANPR of Fortaleza city, contributing to identify suspicious and atypical data, to define representative patterns of vehicular traffic and to estimate series of OD flows.