Abstract
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.
Highlights
The road travel pattern in a city can be represented mainly by two variables: the origin-destination (OD) lows, which indicates the number of trips made between zones in a study area over a given period of day; and the traf ic volume, which indicates the demand in nodes and arcs in the transportation network over a speci ic time interval or daily period
The main goal of this paper is to propose a methodology for treatment of the Traf ic monitoring system (TMS)-ANPR data that can be used for empirical analysis of the day-to-day travel dynamic
As stated by Loureiro et al (2009), an ef icient alternative for urban traf ic management consists of implementing traf ic management centers (TMCs), which collect, model, and store data relating to traf ic conditions
Summary
The road travel pattern in a city can be represented mainly by two variables: the origin-destination (OD) lows, which indicates the number of trips made between zones in a study area over a given period of day; and the traf ic volume, which indicates the demand in nodes and arcs in the transportation network over a speci ic time interval or daily period. Both the OD lows distribution and the traf ic volume magnitude represent basic information for transportation planning and design, as well as traf ic management and control (Cremer and Keller, 1987). Exploring the fact that the most errors made by ANPR hardware are only one or two misread characters of the vehicles plates, Oliveira-Neto et al (2012, 2013) proposed a method for matching imperfect readings between two locations, even when the ANPR accuracies are unknown, increasing the number of matches or observed trips between two locations
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