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Sub story to feature: Issue 598 11 Jun 2012

There’s a new world of data available: how do we harness it for analysis, communication and modelling ?

Decision-makers, analysts, forecasters, behavioural modellers and a raft of kindred professionals are moving into a new world of data capture and presentation. This move holds out possibilities for the analysis of movement, living and activity patterns on a scale as yet unprecedented. It is an exciting, but potentially overwhelming, prospect

Juliana O’Rourke and Peter Stonham

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 (see panel on facing page).

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.

Opportunities for modellers

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.

Maximising investment

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 consultant and digital media specialist Peter Warman (see page 27). 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 investments. Good models can help us to maximise investment strategies by highlighting development opportunities through enabling the realisation of true value, so channelling investment into key sites and infrastructure schemes.

The accelerating pace of technological and social change means that, inevitably, many professional areas are in the front line for a major rethink of capability; an issue that will be explored 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, for example. 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 – the challenge of accommodating qualitative data in models is also on the Modelling World agenda. 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’.

Other models 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. Widely used 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 the iterative, smaller-scale projects that are beginning to be favoured over large scale initiatives in an age of austerity.

Rising to the challenges

Transport modelling has, says Mott Macdonald’s Tom Van Vuren, coordinator of the Modelling World programme, always attracted professionals from a ‘broad church’; making progress through learning from other disciplines (read Tom's article on page 11). 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?  ‘Inevitably,’ says Van Vuren, ‘new and different transport model approaches would contravene the guidance in WebTAG – but is that really such a big problem? I have never believed that WebTAG should be used as a brake on innovation. Forcing UK best practice (as encapsulated in WebTAG) on projects anywhere in the world, where data, skills or future growth are so much more uncertain, makes little sense.’

New approaches

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 33), and  face recognition (see Peter Stonham’s article on page 17). 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 (see the article on page 30).

Smart growth


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. 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 (see  the aticle  by IBM’s Chris Cooper on page 13). Transport infrastructure models will increasingly integrate with wider project lifecycles – another issue that will be explored in depth at Modelling World.

Smart investment in connectivity must strike a balance between providing high-quality service and keeping investment rand 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 – which most good modellers do. 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.

Ford and ‘joined-up’ mobility


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 called for partnership between the automotive, passenger transport and telecommunications industries to create an inter-connected transportation network as part of the solution.

‘Our cars operate on more than a million lines of code and have more processing power than many of today’s laptops,’ he pointed out. ‘They have a hundred times the sensory capacity of many smartphones and yet all of that potential is essentially just sitting there. Whether you look at it as a business opportunity or as a human rights issue, how we move around our world in the future is one of the most compelling challenges we face. We need a system that uses real-time data to optimise personal mobility on a massive scale, without trade-offs or compromises for individual travellers.’

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.’





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