Connected and Multimodal Passenger Transport Through Big Data Analytics: Case Tampere City Region, Finland

Abstract

Passenger transport is becoming more and more connected and multimodal. Instead of just taking a series of vehicles to complete a journey, the passenger is actually interacting with a connected cyber-physical social (CPS) transport system. In this study, we present a case study where big data from various sources is combined and analyzed to support and enhance the transport system in the Tampere region. Different types of static and real-time data sources and transportation related APIs are investigated. The goal is to find ways in which big data and collaborative networks can be used to improve the CPS transport system itself and the passenger satisfaction related to it. The study shows that even though the exploitation of big data does not directly improve the state of the physical transport infrastructure, it helps in utilizing more of its capacity. Secondly, the use of big data makes it more attractive to passengers.