Abstract

The road network of big cities is a complex and hardly analyzable system in which the accurate quantification of interactions between nonadjacent road segments is a serious challenge. In this paper we would like to present a novel method able to determine the effects (the time delay and the level of the correlation) of distinct road segments on each other of a smart city's road network. To reveal these relationships, we are investigating unexpected events such as traffic jams or accidents. This novel analysis can give a significant insight to improve the operation of currently widespread traffic prediction algorithms.

Highlights

  • N OWADAYS smart city services are becoming more widespread than ever as cities are growing and becoming more and more crowded as a result of urbanization and growth of the world population

  • If the system is fed with an unusual input, the inner behavior of the system can be inferred through the outputs

  • The cpi identifiers on this figure are identical with the identifiers of the ones on Figure 1a, and there are f ex,y directed flow edges in the graph, if cpx and cpy control points are connected in the road network in the spreading direction of the traffic

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Summary

INTRODUCTION

N OWADAYS smart city services are becoming more widespread than ever as cities are growing and becoming more and more crowded as a result of urbanization and growth of the world population. According to [2], laboratory studies indicated that transport-related air pollution may increase the risk of developing allergies and can exacerbate symptoms, in susceptible subgroups, while [3] showed that traffic jams increase the risk of heart attacks Intelligent management systems, such as Advanced Traffic Management System (ATMS) and Intelligent Transportation System (ITS) can help overcome or significantly reduce the impact of such negative effects on city dwellers. To quantify the time delay: the time needed for the effects of the accident to propagate to neighboring road segments By fusing this information with real-time traffic information, the prediction models will be able to provide more accurate predictions even in the case of an unexpected event happening nearby. (a) A road network with control points (b) The flow graph model of the road network

Prediction Techniques
Data sources
METHODOLOGY
Data model
The steps of the algorithm
CASE STUDY
CONCLUSION
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