Road Traffic Measurement and Related Data Fusion Methodology for Traffic Estimation

Abstract

The knowledge of road traffic parameters is of crucial importance to ensure state-of-the-art traffic services either in public or private transport. In our days, a plethora of road traffic data are continuously collected producing historical and real-time traffic information as well. The available information, however, arrive from inhomogeneous sensor systems. Therefore, a data fusion methodology is proposed based on Switching Kalman Filter. The concept enables efficient travel time estimation for urban road traffic network. On the other hand, the method may contribute to a better macroscopic traffic modelling.

Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

“…Traffic volume decreased from the 9th to the 14th week of 2020 (and in the case of intersection (2) in the 13th week). Then, at all analyzed intersections, except intersection (5), traffic volume increased and fluctuated in the 21st-34th weeks. The traffic volume increased from the 35th week.…”

“…Then, depending on the intersection, traffic volume decreased again until the end of the year: In the case of intersection (5), from the 14th to the 27th week of 2020, the value of traffic volume increased, and then, by the 40th week of 2020, the value of traffic volume was at a similar level as in 2019. From the 42nd week of 2020, traffic volume continued to decrease until the end of the year.…”

Real-time traffic monitoring by fusing floating car data with stationary detector data

“…Kalman filtering models the traffic as flows of cars and estimates the traffic state space for each time period [7]. Further extensions to the model have been made to account for irregular sample generation by using multiple modes of filter operation [8].…”