Abstract
Abstract. Traffic congestion is a dynamic spatial and temporal process and as such might not be possible to model with linear functions of various dependent variables. That leaves a lot of space for non-linear approximates, such as neutral networks and fuzzy logic. In this paper, the focus is on the fuzzy logic as a possible approach for dealing with the problems of measuring traffic congestion. We investigate the application of this framework on a selected case study, and use floating car data (FCD) collected in Augsburg, Germany. A fuzzy inference system is built to detect degrees of congestion on a federal highway B17. With FCD, it is possible to obtain local speed information on almost all parts of the network. This information, together with collected vehicle location, time and heading, can be further processed and transformed into valuable information in the form of trip routes, travel times, etc. Initial results are compared with traditional method of expressing levels of congestion on a road network e.g. Level of Service – LOS. The fuzzy model, with segmented mean speed and travel time parameters, performed well and showed to be promising approach to detect traffic congestions. This approach can be further improved by involving more input parameters, such as density or vehicle flow, which might reflect traffic congestion event even more realistically.
Highlights
With the rapid traffic growth, traffic networks are getting bigger, more complex and comprehensive
We develop a fuzzy inference system (FIS) to detect congestion levels on the predifined road segments (116)
Traffic congestion is related with many factors, and usually shows difference at different location or in different time period and similarity or recurrence at similar conditions (Xu et al 2013)
Summary
With the rapid traffic growth, traffic networks are getting bigger, more complex and comprehensive. Besides obtaining reliable and valid traffic data, interpreting and modelling the data is one of the crucial traffic state estimation steps. A range of methods has been suggested as measures of traffic. Pioneers in this field Turner at al. (1998) suggest that traffic congestion measures should demonstrate clarity and simplicity, describe the magnitude of congestion, provide a continuous range of values and include travel time. The model is able to address data and uncertainty regarding the accuracy of its representation of the real conditions. We document the use of floating car data (FCD) vehicle speed information and travel time needed to traverse each individual segment, as two parameters for our fuzzy inference model. The findings are compared with traditional method of expressing levels of congestion on a road network - Level of Service (LOS)
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