Abstract
ABSTRACTThe tendency towards using activity-based models to predict trip demand has increased dramatically over recent years. However, these models have suffered from insufficient data for calibration, and the intrinsic problems of traditional methods impose the need to search for better alternatives. This paper discusses ways to process cell phone spatio-temporal data in a manner that makes it comprehensible for traffic interpretations and proposes methods on how to infer urban mobility and activity patterns from the aforementioned data. The movements of each subscriber are described by a sequence of stops and trips, and each stop is labelled by an activity. The types of activities are estimated using features such as duration of stop, frequency of visit, arrival time to that activity and its departure time. Finally, the chains of the trips are identified, and different patterns that citizens follow to participate in activities are determined. These methods have been implemented on a dataset that consists of 144 million records of the cell phone locations of 300,000 citizens of Shiraz at five-minute intervals.
Highlights
Transportation planners need to understand human movements to better design networks that suit citizens’ needs and their demand for travel
It is assumed that the cellphone network records the location of the nearest BTS (Base Transceiver Station) to the subscriber’s cellphone which is not necessarily always true
By parsing trajectories to extract stays and monitoring the most frequently communicated tower during night they were able to detect homes of the individuals. They used the concept of “motifs” as a representative of the complex activity pattern and chains of trips which can go into numerous combinations and provided an algorithm to identify the daily motifs for each individual
Summary
Transportation planners need to understand human movements to better design networks that suit citizens’ needs and their demand for travel. The educated portion of the society understands the importance of these surveys and cares to participate in the procedure, which leave us with biased data All of these inconveniences and problems, hinder the frequent use of such traditional methods and have prompted scholars to come up with better ways to acquire sufficient data to calibrate the models. As the location-aware technologies grew and developed, scientists paid more and more attention to the feasibility of using them to observe urban mobility Among these technologies, cellphone networks stand out as a more promising way of collecting data. Cellphone networks have a built-in capability of recording the location of their subscriber’s cellphone without the need of any additional infrastructure They try to always be aware of the whereabouts of their subscribers to be able to calculate the cost of making a call and maintain the readiness for a fast connection. Section presents a review of the literature on this topic
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