Understanding Traffic Behavioral Patterns by Mobile Cellular Data Mining (Case Study: Tehran city)

Recent studies have shown that urban complex issues like traffic behaviors should be examined by newer and smarter methods. The ubiquitous use of mobile phones and other smart communication devices helps us use a bigger amount of data that can be browsed by the hours of the day, the days of the week, geographic area, meteorological conditions, and so on. In this article, mobile cellular data mining is introduced as an emerging approach in analyzing and understanding traffic behavioral patterns, then generic location update was examined as a way to observe and perceive human mobility and movement in cities. This method was performed in Tehran metropolitan area road map, the results show that Tehran can be recognized as a city in two major parts, the border zone which is mostly the origin of all trips and the central zone which is mostly the destination of all trips and the most visited hotspot of the city during a normal day, also it was concluded that the commuters traveling from Karaj city have a greater impact on Tehran congestion than those coming from eastern and southern adjacent cities. In the end taking advantage of more accurate data in cell level was proposed in order to have better and more reliable assumptions about future traffic trends and urban transportation phenomena.

Keywords:

Mobile cellular data , complexity , spatial data mining , location update , Traffic Behavioral Patterns