Position-based Resident Travel Pattern Of Beijing

With the increasing market penetration of mobile phones and rapid development of mobile communication base stations, especially in the metropolitan, the positioning technology accuracy based on handover between cells is improved. The demand of inform in traffic management and control need real time detection systems. The mobile phones moving with people reveal the varying locations of the users. Using mobile phone data to do traffic survey has the advantages of low cost, wide survey region, high sampling rate and etc. Recent studies show that mobile phone data can be used to acquire valid information of our traffic systems and it has a great potential to analyze urban residents’mobility.This paper uses ubiquitous mobile phone call detail records (CDRs) from mobile phones to acquire and analyze the travel pattern in Beijing. Before the records are used, data preprocessing is used to improve data quality. The samples are selected according to trip chain. The trip frequency is calculated and the methods of obtaining the number of O or D in a Cell are given. This paper also gives the characteristic time of Beijing including morning peak, evening peak and other typical time interval or point, using the variation tendency of the number of people in dwell state. Relative time method and short time interval method are designed to estimate the quantity of within a certain cell at each instant time. One clustering algorithm is built based on density taking adjacency and similarity into consideration.This paper discovers that the travels in Beijing are aggregate; the top 20 clusters can represent more than 30% of the whole travels in Beijing through analyzing the data values of the clustered zones at different time. Comparing the values of total O and D with the clustered data and the obtained top 20 clusters’percentages, one general rule is found that travel aggregate in Beijing are higher during evening peak and flat than morning peak. Based on time interval character and the travel aggregate character in Beijing, different modes including morning peak travel mode, evening peak travel mode and flat travel mode are used to study the variation of travel pattern in Beijing and the variation of travel pattern during one day is concluded.At last, this paper shows the method of obtaining the main clustered locations and their detailed phone users’location information through traffic pattern and gives the method of obtaining flow vectors of a certain clustered cells. This paper introduces the potentiality of using travel patterns.