Abstract
The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban areas is the detection of congested areas and the estimation of the congestion level. This paper presents a a method for a traffic state estimation on a citywide scale using the novel traffic data representation, named Speed Transition Matrix (STM). The proposed method uses traffic data to extract the STMs and to estimate the traffic state based on the Center Of Mass (COM) computation for every STM. The COM-based approach enables the simplification of the clustering process and provides increased interpretability of the resulting clusters. Using the proposed method, traffic data is analyzed, and the traffic state is estimated for the most relevant road segments in the City of Zagreb, which is the capital and the largest city in Croatia. The traffic state classification results are validated using the cross-validation method and the domain knowledge data with the resulting accuracy of 97% and 91%, respectively. The results indicate the possible application of the proposed method for the traffic state estimation on macro- and micro-locations in the city area. In the end, the application of STMs for traffic state estimation, traffic management, and anomaly detection is discussed.
Highlights
Demographic, economic, and technological changes and developments are enablers that support the increase in the human need for mobility, especially in large urban centers
This paper presents a method for the traffic state estimation on urban road segments that are based on the clustering of the Center Of Mass’ (COMs) of the speed data represented in the Speed Transition Matrices (STMs)
In classification problems with more than two classes, the precision is computed as the sum of the true positive values, divided by the sum of true positive and false positive values computed across all classes
Summary
Demographic, economic, and technological changes and developments are enablers that support the increase in the human need for mobility, especially in large urban centers. The increase in the need for mobility leads to advanced solutions in the traffic management domain and requires the implementation of Intelligent Transport Systems (ITS) solutions and applications [1]. The European Commission reports that congestion that is caused by increased mobility accounts for 40% of all CO2 emissions of road transport and up to 70% of other pollutants from transport, and the total cost of congestion in the EU is nearly e100 billion, which stands for 1% of the annual EU’s GDP [2]. Traffic congestion can be classified as recurrent, mostly due to a large number of commuters during peak hours, and non-recurrent caused by an unexpected event, such as traffic accidents, extensive weather conditions, or special events. Traffic state estimation is a prerequisite to many other ITS applications, like travel time prediction [5], route computation [6], traffic flow prediction [7], etc
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