Understanding the traffic situation of urban area is an important research direction, especially in rapidly growing cities still struggling with congested and inefficient traffic situation. Since the conventional large-scale surveys for traffic demand analysis are time-consuming and expensive, demand for traffic situation estimation based on large-scale mobile phone data is increasing. The purpose of this work is to estimate the link-based traffic situation of the metropolitan area of Dhaka based on sparse but long-term mobile phone Call Details Records (CDRs) data. In our methods, we extracted ODs from CDRs with simple extraction of continuous records with locational difference from long-term CDRs proposed by Wang et al (2012). After that, we assigned hourly total trip into individual trip according to the derived ODs. Finally, we assigned each route with ITA method. In the end of this paper, our discussion on possible error factors for future works is included.
Understanding traffic conditions in urban areas is an important research direction for efficient transportation management, and demand for detailed and immediate information is increasing. User-anonymized mobile phone billing records are now known to have an especially high potential for effective traffic conditions estimation, due to their wide population and area coverage. The purpose of this paper is to propose a method to estimate full day mobility of mobile phone users based on long term Call Detail Records (CDR) data. There are two main features of this method. One is that the estimated route taken by a candidate reflects past mobility patterns extracted from past CDR records. Another is that the method does not rely on the spatio-temporal resolution of CDR data, and is applicable to sparse datasets. The proposed method is tested with actual CDR dataset with 4 different temporal resolution to see the accuracy in estimated mobility mode and location change. The estimated results showed more than 72% accuracy in mobility mode, and less than 1.5km location difference with GPS location information.
It is important to analyse pedestrian traffic line with regard to planning and evaluating walking spaces. In this paper, we focus on angular data and discuss a method to analyse pedestrian traffic line. We regard traffic line as data consisted of pedestrian’s position and angle: direction of movement at each time. Basic analysis shows that many pedestrians draw a similar trajectory around a ticket gate at a station; direction of movement is mostly determined by a relative position to the ticket gate. This fact means that a position of pedestrian can explain an angle at that position. Therefore we try to model this relation, using a method developed in directional statistics. In case of a simple dataset we successfully estimated a regression model of angle, which satisfied the property of angular data such as cyclicity. At last we considered some applications of the estimated model, taking an anomaly detection and an angle-choice model for example.
We have developed a passing behavior model at additional lane on two-lane highway. We conducted a survey to measure spot velocity of driving vehicles by using video cameras on Ban-Etsu Expressway and estimated their trajectory on time space diagram by using spline function. We assumed that a car drove at its desire speed in case of free driving and a car judged passing or not considering the difference between desire speed of the previous vehicle and of its own and the distance to the end of the additional lane. We estimated parameters of the model to minimize the difference of the order of cars between the actual data and estimated by the model and confirmed that the reproductively had improved by changing the passing judgment to consider the behaviors of the two or more passed cars.