Big data analytics in smart cities’ transportation infrastructure modernization
Denis Ushakov, Egor Dudukalov, Ekaterina Mironenko, Khodor Shatila
Abstract
Using big data in supply chain management (SCM) has the potential to have a significant impact on the industry in general and international transportation in particular. Big data have a direct influence on transportation capacity in future cities. There has been a significant increase in urbanization over the last decade in which one in three people will live in an urban area by 2050. An updated transportation infrastructure is essential to keep up with the present flow of goods, while also limiting its impact on the environment and human health and this is likely to be achieved using big data analytics technique. To overcome this problem, smart cities are becoming more popular. With the use of information and communication technology (ICT), a smart city aims to address public concerns in an inclusive, municipally-based partnership. A big data transportations system may be built using the superstructure of a smart city. A good way to define it is the modeling and analysis of urban transportation and distribution networks using enormous data sets created by GPS, mobile phones, and transactional data from company activities. Big Data analytics may be used in public transportation to better understand how people go about the city. A better understanding of passengers’ travel patterns might help transportation providers make better judgments regarding service quality. People who travel by automobile on a regular basis may now be predicted based on the triangulation of mobile phone data from millions of anonymous users. Local and national polls may demonstrate the paradigm’s applicability. To compute the time it takes for passengers to board and exit trains, Metro and iBus vehicle position data may be combined with information from smart cards. Big Data analytics for traffic management may benefit from these findings.