Automatic Definition of Traffic Analysis Zones Based on Big Data

Abstract

The planning process for any transport system can be considered complete if it is accompanied by a modeling system to evaluate the intervention. The study area should always be divided into traffic zones. Correct zoning is the key to any transport system study. The basic principles of zone creation require a thorough understanding of the area and local traffic conditions. However, this is not always a given, especially if a universally applicable assessment system is to be developed. This has led to the need to develop an algorithm that is able to provide an estimate for the definition of traffic zones based on some automatically observable or measurable phenomena or sequence of events. The aim of this research is to identify the observable events that are suitable for characterizing the area, so that an automatic zone definition procedure can be developed based on these. In this paper, automatic WAZE-generated congestion data were processed in a selected district of Budapest. During the processing, the area was divided into a grid network and time series were developed that show the traffic flow on the grid network as a function of the congestion level. The area subdivisions were then clustered using spectral clustering to create spatially distinct districts with identical traffic behavior.

Keywords

• traffic analysis zone
• transport planning
• modeling
• WAZE
• traffic flow
• Python