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In order to understand the dynamics of a region, it is most important to analyze traffic movements in different routes and bottlenecks. This information is very much essential to the city planners to plan congestion-free transportation routes within the region. Traditionally this traffic information has been collected using traveler surveys and manual inspection. Both works are time-consuming as well as costly. Moreover, it cannot be done frequently. So, these manual processes are not even dynamic and do not reflect the changing commuting patterns of a region.
Any person in possession of a mobile device leaves his/her digital footprint everywhere he/she moves. The mobile device is always in touch with any of the nearby cell towers. These cell towers are stationary with fixed geo-positional coordinates. A cell tower has a cell radius of 100m to a few km depending on the population density (Dong, et al., 2014). So, the mobile devices’ location can be approximated to that of the cell tower. As the owner of the mobile moves from one place to another, the concerned cell tower also changes. By tracking the changing cell tower pattern, the commuting pattern of a person can be inferred. These location data are kept in call detail record (CDR) database in Online Transaction Processing (OLTP) systems of the telecom service provider. A densely populated city area may have a very large number of cell towers, while an outskirt or suburban area has much fewer cell towers in the same area, as can be seen in Figure 1 and Figure 2. In both the figures, the pop up shows the location details of a selected cell tower. These location maps are generated from www.opencellid.org.
Another way to collect more precise location information of a person could be using the GPS present in most of the smartphones. The limitations of using GPS to collect the location are the following. i) All people don’t carry a GPS enabled smartphone; many people use basic feature phones without GPS facility. ii) Collecting users’ GPS location required them to give permission, and that needs to be collected individually from each user. In the case of CDR, it is stored with the telecom service provider, and aggregated data (in terms of the number of users moving from one location to another) does not violet users’ privacy. An exhaustive study on technical details of the privacy of mobile phone users has been done in (Acs, Bicz'ok, & Castelluccia, 2018). It discusses various threats to mobile phone users’ privacy.
The cost of traditional surveys is very high for developing nations, but as cell-phone use is high, so cell phone location data have a huge potential to become a low-cost complement to traveler survey. However, these data are very confidential as it depicts the personal user movement information. Generally, Government, police could access it. The corporate, industrial body may access the data if the individuals permit to track their information. In most of the evolving cities, public amenities/infrastructure requirements are also dynamic in nature. City transport planning and public routes also required to be upgraded with the changing patterns of city dwellers. Hence, CDR based city transport route planning is proposed in this study, and it is expected to be helpful for the city administration. With a little variation to the above problem, another interesting problem of selecting business viable areas within a region will also be considered in this study. The main problems addressed using CDR data in this study are as follows:
Transport route planning along with the identification of the intermediate stops that enable to cater maximum commuters and yields more revenues.
Feasibility analysis and selection of business hotspots for opening retail outlets without a manual survey.