Abstract
Traffic congestion is a key issue facing transport planners and managers around the world with many now asking if there are any promising technologies offering new solutions. In the US, the cost of congestion was $121 billion in 2012 and in 2015 alone Australia’s capital cities were estimated to have a combined congestion cost of $16 billion, expected increase to $37 billion by 2030. With the rapidly growing availability of data and the ability to analyse large data sets this paper investigates the question “What role can 'Big Data' play to assist with congestion management?” There is great interest and hype around 'Big Data' and this paper provides a summary of an investigation into its value to assist in relieving congestion. The paper explores the emerging types of large data sets, considers how data will be sourced and shared by vehicles and transport infrastructure in the future, ad explores some of the associated challenges. Despite the opportunities of Big Data not being fully realised it is already clear that it presents a significant tool for transport planners and managers around the world to assist in managing congestion. The research is based on research undertaken with the Sustainable Built Environment National Research Centre (SBEnrc).
Highlights
There are multiple definitions of Big Data
[12] As such, the benefits of using Big Data must be weighed against the effectiveness and efficiency of Big Analytics technologies, as well as the costs incurred in using these analytic procedures
[29] The Australian Communications and Media Authority is in the process of consultation with industry to develop a regime for the authorisation of ‘Cooperative Intelligent Transport Systems (C-ITS)
Summary
There are multiple definitions of Big Data. Most commonly, the term is used to broadly characterise data sets so large they cannot be stored and analysed by traditional data storage and processing methods. Data is produced in multiple formats, languages and software configurations depending on where the data is sourced It is these three characteristics (referred to as the three V’s – Volume, Velocity and Variety) that distinguish ‘Big Data’ from other forms of data. The emergence of such large and complex data sets has primarily been the result of a decrease in the cost of sensory and observational technologies in conjunction with mass digitisation of systems and processes around the globe. Given the economic impacts of congestion it is critical that new data streams are used to inform both traffic management and transport planning. In the US alone, 25 million tonnes of CO2 per year was emitted from vehicles stuck on congested roads. [3] In addition, inhaling vehicle exhaust for extended periods has been linked to human health problems such as brain-cell damage. [4] These negative externalities all point to the increased need to manage road congestion, and the growing availability of data might provide part of the solution
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