Smart City Gnosys
Smart city article details
| Title | Review On Application Of Call Details Records (Cdrs) Data To Understand Urban Mobility Scenarios For Future Smart Cities |
|---|---|
| ID_Doc | 46504 |
| Authors | Ghosh N.; Sarkar U.; Nagesh P. |
| Year | 2023 |
| Published | Springer Geography |
| DOI | http://dx.doi.org/10.1007/978-3-031-24767-5_36 |
| Abstract | Rapid urbanisation, pollution and inadequate public transit have made mobility more complex, plaguing people worldwide. Urban and Transport planners face a considerable challenge with the vision of future smart cities as it focuses on creating a more cohesive transportation ecosystem for congested cities. The new advancements in mobility with digital innovations and updated real-time data sources supported by data and models will help design an efficient transport system through a thorough understanding of human mobility. However, conducting conventional travel surveys is expensive, with limited sample sizes. Detailed information on travel patterns and the actual demand for travel is hard to get today. Cellular network data collected using the existing infrastructure of mobile operators is a promising new data source and an optimal source to analyse the individual’s mobility pattern. Researchers have utilised passively collected data, such as vehicle global positioning system (GPS), mobile network data including call details record (CDR) and Google location history, to define individual travel behaviour patterns. The chapter produces condensed reviews of previous case examples that have adopted similar analytic approaches that involve mobile data aggregation to glean travel information. This study may help researchers and transport authorities understand the potential of mobile phone data as an alternate and more frequently updated data source for future smart cities with several key inferences and the challenges associated with the data. This chapter recommends the framework for data processing and their associated algorithms to understand the mobility pattern using mobile phone data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Author Keywords | Future smart cities; GPS data; Mobile phone data; Urban mobility |
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