user2016

Poster #22

There has been rapid urbanization as more and more people migrate into cities. The World Health Organization (WHO) estimates that by 2017, a majority of people will be living in urban areas. By 2030, 5 billion people—60 percent of the world’s population—will live in cities, compared with 3.6 billion in 2013. Developing nations must cope with this rapid urbanization while developed ones wrestle with aging infrastructures and stretched budgets. Transportation and urban planners must estimate travel demand for transportation facilities and use this to plan transportation infrastructure. Presently, the technique used for transportation planning includes the conventional four-step transportation planning model, which makes use of data inputs from local and national household travel surveys. However, local and national household surveys are expensive to conduct, cover smaller areas of cities and the time between surveys range from 5 to 10 years in even some of the most developed cities. This calls for new and innovative ways for Transportation Planning using new data sources.

In recent years, we have witnessed the proliferation of ubiquitous mobile computing devices (inbuilt with sensors, GPS, Bluetooth) that capture the movement of vehicles and people in near real time and generate massive amounts of new data. This study utilizes Call Detail Records (CDR) data from mobile phones and the R programming language to infer travel/mobility patterns. These CDR data contain the locations, time, and dates of billions of phone calls or Short Message Services (SMS) sent or received by millions of anonymized users in Cape Town, South Africa. By analyzing relational dependencies of activity time, duration, and land use, we demonstrate that these new “big” data sources are cheaper alternatives for activity-based modeling and travel behavior studies.