Using Mobile Network Big Data for Informing Transportation and Urban Planning in Colombo

S. Lokanathan, N. de Silva, G. Kreindler, Y. Miyauchi, and D. Dhananjaya Available at SSRN, 2014, Road congestion is proving to be an increasing problem for countries experiencing rapid growth. Data are needed to identify the choke points and prioritize additions and enhancements. A data-centric approach to transportation management based on sensor data is already a reality in many developed economies, with transportation systems being fed with a multitude of sensor data such as loop detectors, axel counters, parking occupancy monitors, CCTV, integrated public transport card readers as well as GPS data, from phones as well as public and private transport (Amini, Bouillet, Calabrese, Gasparini, \& Verscheure, 2011). Developing economies however are more reliant on more traditional forms of data collection such as questionnaires. Such survey based methods administered at peak hours can be very costly, not only in terms of personnel and processing, but also in terms of traffic disruption. Other less intrusive methods (e.g., automatic traffic recorders) do not yield information such as routes taken and parking. Mobile network Big Data has enormous potential for traffic planning. Because the data streams are continuously flowing, the effects of changes in traffic channels such as one-way schemes and new roads can potentially be easily tracked. Though additional costs of data storage may be involved, BTS [Base Transceiver Station] hand-off data can even serve as sensors of speed of traffic and of disruptions. As the proportion of GPS enabled smartphones increases, it may be possible to achieve the same objective from a smaller sample without collecting masses of BTS hand-off data. Hence the primary research question addressed by this paper is to understand if mobile network Big Data can inform transportation planning for the city of Colombo, Sri Lanka. To do this we attempt to understand where the daytime commuting population of Colombo comes from and thereby creating Origin Destination (OD) matrices that explicates the flow between different areas.