• DOI:10.3846/transport.2019.11725
• Corpus ID: 214698452

DISTRIBUTION OF TRAFFIC SPEED IN DIFFERENT TRAFFIC CONDITIONS: AN EMPIRICAL STUDY IN BUDAPEST

@article{Zefreh2020DISTRIBUTIONOT,
  title={DISTRIBUTION OF TRAFFIC SPEED IN DIFFERENT TRAFFIC CONDITIONS: AN EMPIRICAL STUDY IN BUDAPEST},
  author={Mohammad Maghrour Zefreh and {\'A}d{\'a}m T{\"o}r{\"o}k},
  journal={Transport},
  year={2020},
  volume={35},
  pages={68-86}
}

• Mohammad Maghrour Zefreh, Á. Török
• Published 18 March 2020
• Computer Science
• Transport

Fundamental diagram, a graphical representation of the relationship among traffic flow, speed, and density, has been the foundation of traffic flow theory and transportation engineering for many years. Underlying a fundamental diagram is the relation between traffic speed and density, which serves as the basis to understand system dynamics. Empirical observations of the traffic speed versus traffic density show a wide-scattering of traffic speeds over a certain level of density, which would form a speed distribution over a certain level of density. The main aim of the current research is to study on the distribution of traffic speed in different traffic conditions in the urban roads since the distribution of traffic speed is necessary for many traffic engineering applications including generating traffic in micro-simulation systems. To do so, the traffic stream is videotaped at various locations in the city of Budapest (Hungary). The recorded videos were analysed by traffic engineering experts and different traffic conditions were extracted from these recorded videos based on the predefined scenarios. Then their relevant speeds in that time interval were estimated with the so-called “g-estimator method” using the outputs of the available loop detectors among the videotaped locations. Then different parametric candidate distributions have been fitted to the speeds by Maximum Likelihood Estimation (MLE) method. Having fitted different parametric distributions to speed data, they were compared by three goodness-of-fit tests along with two penalized criteria (Akaike Information Criterion – AIC and Bayesian Information Criterion – BIC) in order to overcome the over-fitting problems. The results showed that the speed of traffic flow follows exponential, normal, lognormal, gamma, beta and chisquare distribution in the condition that traffic flow followed over-saturated congestion, under saturated flow, free flow, congestion, accelerated flow and decelerated flow respectively.