Abstract
This paper describes a procedure for fitting traffic stream models using very large traffic databases. The proposed approach consists of four steps: (1) an initial treatment to eliminate noisy, inaccurate data and to homogenize the information over the density range; (2) a first fitting of the model, based on the sum of squared orthogonal errors; (3) a second filter, to eliminate outliers that survived the initial data treatment; and (4) a second fitting of the model. The proposed approach was tested by fitting the Van Aerde traffic stream model to 104 thousand observations collected by a permanent traffic monitoring station on a freeway in the metropolitan region of São Paulo, Brazil. The model fitting used a genetic algorithm to search for the best values of the model parameters. The results demonstrate the effectiveness of the proposed approach.
Highlights
Traf ic stream analysis usually uses data collected by traf ic sensors at permanent traf ic monitoring stations (PTMS), which, working continuously over months and years, can accumulate a very large amount of observations
This study proposes a process for itting traf ic stream models using very large data sets
The use of raw traf ic data to calibrate empirical fundamental relationship is linked to many problems (Knoop & Daamen, 2017): (i) the traf ic stream may not be in equilibrium during the observation period; (ii) the traf ic stream is heterogeneous; (iii) the detectors have limitations and are subject both to failure and measurement errors; (iv) the number of vehicles measured during an interval is always integer; and (v) the average speed recorded by the sensor is the time-mean speed
Summary
Traf ic stream analysis usually uses data collected by traf ic sensors at permanent traf ic monitoring stations (PTMS), which, working continuously over months and years, can accumulate a very large amount of observations. The use of raw traf ic data to calibrate empirical fundamental relationship is linked to many problems (Knoop & Daamen, 2017): (i) the traf ic stream may not be in equilibrium during the observation period; (ii) the traf ic stream is heterogeneous; (iii) the detectors have limitations (such as not being able to detect stationary vehicles) and are subject both to failure and measurement errors; (iv) the number of vehicles measured during an interval is always integer; and (v) the average speed recorded by the sensor is the time-mean speed. Models used to detect freeway incidents, which incorporate techniques such as fuzzy logic, wavelets, and neural networks to reduce noise and increase their reliability (Karim & Adeli, 2002) demonstrate the importance of raw data iltering
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