Decision-makers, analysts, forecasters, behavioural modellers and a raft of kindred professionals are moving into a new world of data capture and presentation that holds out the possibility for the analysis of movement, living and activity patterns on a scale as yet unprecedented. It is an exciting, but potentially overwhelming, prospect.
From data that is hard won, and expensive to obtain, we are on the brink of creating a vast pool of information sources and models for use in scenarios where existing tools may be ineffective and inflexible as well as costly. For government and business, the benefits are potentially huge – helping with better decision-making and appraisal. In an era of localism and support for neighbourhood planning, we need to adopt approaches that support analysis of the full range of possibilities around how, where and when people wish to move and behave. The creation of such rich evidence bases will impact heavily on investment and development priorities, enabling politicians and policy-makers to make more informed decisions on the back of a much deeper understanding of place and movement.
The amount of data in the world continues to explode. Our ability to analyse large data sets – big data – means that organisations from professional practices to the public sector are faced with a steep data analysis learning curve. Research by McKinsey suggests that leaders in every sector will have to grapple with the implications of big data. 'The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things (IoT) will fuel exponential growth in data for the foreseeable future,' it states.
Significant value can potentially be unlocked from the ability to carry out deep analysis across the spectrum of human movement and behaviour patterns. McKinsey's own research suggests that, in the developed economies of Europe, government administrators could save more than €100 billion in operational efficiency improvements alone by using big data, and that users of services enabled by personal location data could capture €400 billion in consumer surplus.
These opportunities are good news for those whose work involves data and modelling. In future, there will be a shortage of the talent necessary for organisations to take advantage of big data, adds McKinsey. 'By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions,' it states.
Of course, the regulatory machine needs to grind through a few gears before big data can be used really effectively. Policies related to privacy, security, intellectual property and liability all need to be addressed in a big data world. Access to data is mission critical, and companies will increasingly need to integrate information from multiple data sources, often from third parties, and the incentives must be in place to enable this, says McKinsey.
This data revolution, coupled with swift advances in digital technologies, is changing the relationship between designers, planners, elected representatives and communities involved in place and movement, says transport consultant and digital media specialist Peter Warman.
Scenario testing tools and ‘games’, 3D visualisation systems, real-time data tracking and collaborative management tools are enabling public debate and discussion to come before, and not after, decision-making relating to transport and urban infrastructure invesments. Good models can help us to maximise investment strategies by highlighting development opportunities thrrough enabling the realisation of true value, so channelling investment into key sites and infrastructure schemes.
A recent report, Machine-to-Machine Communications: Connecting billions of devices, from the Organisation for Economic Co-operation and Development (OECD), outlines the potetial impact of machine-to-machine communication (M2M) and the Internet of Things.
According to Rudolf Van der Berg of the OECD’s Science, Technology and Industry Directorate, the internet will soon move from connecting people to connecting things. In 2017, he writes, in OECD-countries, an average family with two teenagers could have 25 things that are connected to the internet: telephones, TV, tablets, printers, sports gear and health devices. Tens of billions of connected devices by 2025 is not farfetched. The combination of the data will allow smart transport, smart cities, smart energy and smart health.
With 60 per cent of the households in OECD countries using broadband and more than 99 per cent of populated areas covered by mobile networks, the world is at the onset of another digital revolution.
Some professional areas are in the front line for a major rethink of capability; issues that will be exploed in full at the Modelling World event in London during July. Take the large-scale, robust models developed to date by the transport modelling profession.
These models – essentially 'simplified' descriptions of a system used to predict and evaluate change – are currently widely used to predict impacts and evaluate options for infrastructure investment and planning. Many such models, suggests a recent paper from the Australian Victoria Transport Policy Institute, Improving Methods for Evaluating The Effects and Value of Transportation System Changes, tend to be biased in various ways that exaggerate the benefits of roadway capacity expansion and undervalue the impacts and benefits of strategies that encourage use of alternative modes.
'Commonly used models tend to undervalue alternative modes and other travel demand management (TDM) solutions. TDM planning requires models that can predict the impacts of various changes, such as improvements in alternative modes, pricing reforms and marketing strategies, ' states the report.
Similarly, some models focus on quantitative factors (travel speed, operating costs and crash rates) and undervalue qualitative factors such as travel convenience, comfort and security. Many models traditionally use travel survey and census data to determine transport demands, establish baseline conditions and identify trends. However, 'the travel surveys they are based on tend to ignore or undercount non-motorised travel, and so undervalue non-motorised transportation improvements for achieving transportation planning objectives.'
Others may ignore the parking and vehicle ownership cost savings that may result when travellers shift from car travel to alternative modes, and many ignore the safety benefits that result from reductions in total vehicle mileage. Integrated transportation and land use models are costly to develop and complex to use, and may be difficult to apply, particularly for the evaluation of smaller-scale projects.
Transport modelling has, says Mott Macdonald's Tom Van Vuren, always attracted professionals from a 'broad church'; making progress through learning from other disciplines (read Tom's article on page xx). But lately, he admits to wondering whether this very positive inclination to knowledge-share may be waning. Have we, he asks, fallen victim to Group Think, whipping ourselves into a frenzy of protecting the status-quo?
Some of the best evolving approaches model the behaviour and needs of individual transport users, or agents, rather than aggregate groups and can do so with data from new sources from location tracking with Bluetooth (see Nick O'Neil's article on page xx), and face recognition. New types of models can, in some cases, more realistically reflect activities such as walking, queuing, shopping and cycling, and the effects of factors such as parking supply and price, public transport quality, waiting time, the quality of the pedestrian environment and local land use accessibility factors.
Recent simulation models are also evolving, now incorporating elements from conventional traffic, economic and land use models and, increasingly, high quality movies that marry pedestrian modelling and 3D visualisations. A partnership between SKM Colin Buchanan and Wagstaffs Design, for example, uniquely brings together data collection, quantitative analysis, modelling and visualisation.
In a decade that sees Governments determined to seek ‘smart growth’, transport and travel systems must focus on improving the user experience, achieving sustainability and contributing to both economic and social growth (see Chris Cooper's article on page xx). Seamlessness requires that resources are used optimally: the convergence of transport infrastructure, operations and systems with the digital world is already changing the way we think about and use transport.
Seamless transport requires the connection of traditional transport systems and networks with other infrastructure and services, such as water, energy and telecommunications – all an essential part of today’s society. Transport infrastructure models will increasingly integrate with wider project lifecycles – another issue that will be explored at Modelling World.
Smart investment in connectivity must strike a balance between providing high-quality service and keeping investment and operational costs low. We need to think in terms of mobility systems rather than modes and modal networks. Modelling such smart growth in all its complexity requires key stakeholders and professionals alike to reflect on current practice.
A final observation from the Victoria Transport Policy Institute report is that modellers should work to stay abreast of current research and improvements. This means looking to create models that take advantage of the data explosion, and that use comprehensive economic evaluation models accounting for all significant impacts, including road and parking facility costs, consumer costs, accidents, pollution emissions, and impacts on land use development patterns.
Localism and engagement
Through crowd-sourced and social media-based information, the public is enabled to become much more directly involved in urban infrastructure decision-making. And, suggests Colin Pooley of Lancaster University, as mobile internet applications and devices become pervasive, ubiquitous, and central to social belonging and cultural participation, the concept of mobility-related environmental and social justice should become more relevant to movement models.
'Modern life is underpinned by intensifying forms of automation, sensing technologies, real-time data gathering and analysis, and surveillance. At various points in the 20th century there were opportunities to produce a transport infrastructure that delivered more socially and environmentally just patterns of everyday mobility, but such opportunities were lost as subsequent decisions reinforced existing mobility inequalities.'
Even the Ford motor company is getting behind smart transport systems. During his keynote address at the 2012 Mobile World Congress in Barcelona, Ford executive chairman Bill Ford outlined a plan for a ‘joined up mobility’ solution that will help avoid a potential future of what he called 'global gridlock – a never-ending traffic jam that wastes time, energy and resources.'
Ford now envisions 'a future of intelligent vehicles and a smart transportation system that will tie all modes of travel into a single network and payment system. Public and personal transportation will be fully integrated to save time, conserve resources and lower emissions.'
He called for partnership between the automotive, passenger transport and telecommunications industries to create an inter-connected transportation network as part of the solution.
'We need to view the automobile as one element of a transportation ecosystem...a smart system that ties all modes of travel into a single network linking together public and personal transportation. Pedestrian walkways, bicycles, buses, planes, trains, automobiles – everything fully integrated and optimised to save time, conserve resources and lower emissions.'
The Centre for Advanced Spatial Analysis (CASA) at the Bartlett Faculty of the Built Environment, UCL, is pioneering much of the UK's smart cities research, drawing on cutting edge modelling, complexity, visualisation and computation techniques.
The centre's approach was outlined at a recent CASA event in London. 'Smart cities involve hardware, software, data and ‘orgware’ ...we see these developments mainly in the delivery of services, such as transport services, to urban populations. They provide radically new data sources with respect to routine behaviours, with the potential to provide us with new ideas and new horizons for improving many aspects of urban social and economic life.' CASA researchers are developing tools for online mapping, participation, modelling and tagging that define this new research agenda for research and practice.
Professor Michael Batty, Chair of the CASA Management Board is a widely respected resercher into urban planning and design using mathematical modelling. Back in 2009, Batty wrote that models are being developed as much for their exploratory and discursive value in a wider participatory process of developing robust but contingent knowledge than for their ability to generate good theory.
Over the past years, he has noted the 'revolution in tracking human and other motion in digital form that enables the collection of multiple attributes at the finest of scales of urban observation'. For a long time, he suggests, the city models we have built have tended to see cities as being in equilibrium where change occurs slowly over years and decades, but that this is changing as new data sources providing space and time streams provide us with new views of urban structure and pattern that could well demonstrate that cities are much less stable structures than we have previously perceived.
In 2012, in an editorial in Environment and Planning B: Planning and Design, Batty summed up some of his ideas on smart cities and big data. In the 1980s, he says, the focus on instrumenting the city using network technologies was
enshrined in the idea of the wired city.
But what has changed is the development of 'ubiquitous devices of comparatively low cost that can be deployed to sense what is happening over very small time scales – seconds and faster – as well as over very fine levels of spatial resolution'. Such devices, he writes, range from purpose-built sensors to individual hand-held devices that are as mobile as those using them provide massive capability to store and transmit data that pertains to movement and activity levels across space and time. 'Some of the most elaborate applications' he adds, 'involve transport.'
There are vast quantities of information, he says, 'much of it of doubtful quality so far but it will improve, associated with social media and networking. 'The idea of integrating much of this diverse data together to add value to our conceptions of how it might be linked to other more traditional data, as well as focusing it on specific ways to make cities more efficient and more equitable, has come to define the smart cities movement.'
Many large-scale IT companies, he notes, see the next great wave of applications related to groups rather than individuals, and these are seen most clearly in how large groups behave with respect to routine activities in cities. 'IBM, Cisco, Siemens, and a host of other companies are investing heavily in systems that can be used to mine traffic and related data which lie at the basis of an improved understanding of how cities function, as well as enabling new methods of improving the efficiency of such systems with respect to their operation and the quality of the experience from the point of view of the traveller. We are just beginning to grasp the nature of `big data', he suggests.
'This kind of data is available continuously and in this sense, it does not only encapsulate routine and relatively stable behaviours but, over sufficiently long periods of time, one can begin to extract changes to the structure and form of the city and the way people behave.'
New data begets new theory, notes Batty. 'Most urban theory, and indeed planning and design fifty years or more ago, was predicated on radical and massive change to city form and structure through instruments such as new towns, large-scale highway building, redevelopment, and public housing schemes. Planning was little concerned with smaller-scale development except its design, for nowhere was the function of the city understood in terms of how small spaces and local movements sustained the city. New data and big data are changing all of this.
'Smart cities and big data may be the hot topics of today, but the implications of how the city is being wired, how it is generating new data, how this data might force new theories and models relevant to our understanding, how we might use our strate gic models and intelligence to plan the city, building on this new understanding – these are all crucial questions to be explored.
Our need to understand how all these dimensions are coalescing, merging, complementing, and substituting for one another has never been more urgent. It constitutes a major challenge for planning and design
in the near future.'