Application of Big Data Technologies for Quantifying the Key Factors Impacting Passenger Journey in a Multi-Modal Transportation Environment

Introduction

In recent years, transport industry has seen tremendous growth that’s proportionate to urbanization and increase in population. Multimodal systems have evolved as an important player and have been recognized as the most promising area for research in recent years. According to Lyons (2002), the public relies on multiple transport system to reach their destination. Catapult Transport System (2011) states that the multimodal transport network has drawn attention in the emerging intelligent mobility market and contributes highly to the efficient movement of people and goods around the world. The European Commission have started funding more multimodal transportation projects in recent years, e.g. Link (2010) paper encourages multimodal travel across European countries. To attain sustainable multi-modal transport systems in the urban area, it is vital to develop and maintain a smooth flow of traffic across all transportation modes which is not often the case due to scheduled and unscheduled major events or disturbances taking place. These factors not only lead to disruptions but also affect the normal working behavior of the transport systems. These disruptions are further seen to be influenced by a number of factors including weather, holidays, events, signaling problems, strike, wear-and-tear, engineering works etc. that definitely affects the performance of transport network. A comprehensive approach is required to analyzing disruptions in such multi-modal environment. Open-sharing of transport data by public transportation agencies like Transport for London(TFL), Network Rail, National Rail etc. is significant in the study of multimodal analysis. This has led to the improvement and innovation of transport services to the public. Opening up TfL data has been valued at £15-58 million per year and has resulted in over 200 travel apps being developed by private companies. The Government is supporting the UK’s data infrastructure, most recently with £14 million to make data routinely collected by business and local government accessible for researches, including for transport research at Leeds and Glasgow Universities. Opening up transport datasets will improve public services and re-use of these datasets will generate economic benefits. In future, transport data can be integrated with other sector’s data to attain efficient sustainable transport networks (Catapult Transport System, 2011).

The emphasis on multi modal transportation has not captured only recent attention. Following the publication of the Integrated Transport White Paper – A New Deal for Transport in July 1998, a number of multi-modal studies were announced (Booz Allen, 2012). These studies provide insights into the total demand for travel over a comparatively long time period and facilitate to establish a framework that would provide for an integrated transport system covering all modes, including the more sustainable means of travel such as walking and cycling. They are not perfect substitutes; as each is most appropriate for some specific users and purpose. Multi-modal transport planning requires tools for evaluating the quality of each mode, such as Level-of-Service standards which can be used to indicate problems and ways to improve each mode. It is no doubt complicated because modes differ in various ways, including their availability, speed, density, costs, limitations, and most appropriate uses. The planning aspect has been further discussed in the section below.