Transport Modelling is a technique used by Transport Planners and Managers to understand how populations move between different geographic points.

Until recently, data for Transport Planning has been based on surveys such to create a largely static, unchanging resources. The use of mobility data to provide a detailed, high-frequency view of population movement has opened the door to new analytical possibilities. In this article we’ll discuss the solutions that have existed until now, investigate their limitations and set out how mobility data offers new advantages in Transport Planning.


Contents:

  1. What is transport modelling?
  2. What is it used to achieve?
  3. Who uses transport modelling data?
  4. What’s on the market today?
  5. What are the limitations of current solutions?
  6. What innovations are available to overcome them?
  7. What are their specific advantages?

What is transport modelling?

Transport Modelling is a technique used by researchers and urban analysts to understand how populations move between different geographic points. This practice plays a vital role in transport network and infrastructure planning. The most common source of data used in this research is known as an Origin-Destination Matrix. Simply put, an Origin-Destination Matrix describes how many people travel between one point and another.

Historically, these matrices require extensive population surveys such as the UK national Census to create a largely static, unchanging resource for analysts. Innovations in data collection methods offer new qualities in frequency, detail and representativeness that we will cover later on. The practice of planning transport using Origin-Destination Matrices has its roots in the 1950s and 60s, a time of major urban and highways development.


What is it used to achieve?

As with most civil engineering projects and interventions, Transport Management initiatives follow a three-point lifecycle.

1. Planning

Where funding is available for investment in public transport and infrastructure, the starting point is always to identify the places (and people) with greatest need. How well are local communities served by existing transport infrastructure? Which areas most in indeed are likely to experience the greatest economic benefit from greater transport integration? And – in a further twist – how have communities’ transport needs changed as society adapts to new ‘normals’ in the wake of Covid-19?

A mix of evidence can be used to answer these questions, from traditional surveys to the latest mobility measurement platforms such as CommunityVision® from Huq. Lenses through which transport planners and management teams can assess the needs of different communities include footfall, catchment area analysis, the dwell-time of visitors to certain areas and insights relating to what they do there. All of these insights, curated through thoughtful analysis leads decision makers to make accurate choices about what to change – and where.

2. Making changes

Whatever the decision – a roundabout upgrade at Bushey Corner, a widening of the A-road at Liss, leads inevitably to temporary disruption of the local transport infrastructure. What will it mean for local and thru-traffic if 83 cars per minute are diverted from the highway? What will be the impact for commuters if the bus routes they normally use are suspended or re-routed? And what will retailers in the local town centres make of reduced access for cars and public transport while works are ongoing?

Transport planners can use the historical effect of disruption at equivalent locations to predict the impact at proposed sites and take effective measures to manage the needs of travellers during that period. And the richer, more recent the data they use to assess these risks, the more relevant their information and the better the outcomes as a result.

3. Monitoring outcomes

The flowers are planted. The roundabout is opened. The mayor cuts the ribbon. But – did it work? Monitoring the effects of transport infrastructure interventions is essential to quantifying success, and learning how or where there may be opportunities for subsequent improvement. If the aim was to ease congestion at a certain point – a conclusion arrived at through the analysis of data – then it follows that an equivalent source of data should be used to monitor the effectiveness of the intervention.

Traditional methods may include repeat surveys of the affected transport users or communities, or they may take advantage of innovations in transport monitoring that we’ll come to later. In any case, evidence is required by project owners and stakeholders to qualify the positive impact of the investment in an evidence-based and transparent way. For this reason, evidence available through effective monitoring has become mandatory in almost all publicly-funded initiatives as a means to benchmark performance and provide accountability.


Who uses transport modelling data?

The typical consumers of monitoring systems include transport planners and managers working for local authorities. At those same authorities, stakeholders may also include place management teams and economic development officers with a close interest in how transport can help make places more vibrant and attractive. Both transport and civil engineering companies either consulting or carrying out the improvement work itself will have analysts and researchers working to find the greatest opportunities and solutions for their clients.

Academic institutions also play an important role in innovation and best practice, with groups such as the Urban Big Data Centre (UBDC) – an initiative led by the University of Glasgow, making significant contributions to the way that transport managers and planners work to make the best choices. Other academic and government centres involved this field of transport management include the Consumer Data Research Centre (CDRC) and Office for National Statistics (ONS), who among other things manage the UK Census programme and its outputs.


What’s on the market today?

The primary sources of statistical resources available to transport planners and managers are Origin Destination Matrices derived from the UK National Census. The typical method for creating an Origin Destination Matrix from this source involves using the declared residential location for respondents (origin) and comparing it with their primary place of work (destination), with weighting derived from the proportion of residents in one area – the Middle Super Output Area (MSOA) – working in another. Given the infrequency of this survey, which is once a decade, private companies and initiatives exist to top-up the data with more recent and relevant inputs. These inputs may include additional surveys, CCTV / APNR-based reports and telemetrics data from automotive solutions providers. Each of these solutions help to enrich the foundational layer of data available, but they also have their limitations.

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What are the limitations of current solutions?

The basis for most Origin Destination Matrices is data derived from the UK Census – and the most recent data available was collected in 2010. The specific method used to produce Origin Destination Matrices involves using respondents’ declared home and work locations, which in many cases will have changed during that period. Further, this basis for compilation narrows the scope. Going to work isn’t the only thing that people do in their day.

People go to shops, drive their children to school and participate in recreational activities. None of that is considered in the conventional formulation of Origin Destination Matrices and artefacts of this therefore impede its accuracy. This method also suggests that outputs predominantly reflect workday (weekday) behaviour, which limits its use for weekend or night-time applications.

Private and academic initiatives to address these shortcomings might involve supplementing Census data with different data sources. Commonly these include using CCTV and Automatic Number Plate Recognition (APNR) monitoring of vehicle traffic to determine where populations travel to and from. But as with all fixed-point monitoring solutions, the measurement scope that they can offer is limited to where that hardware is sited (often only along major roads and arteries), and therefore don’t necessarily offer a representative view of transport behaviour.

Lastly, all of these approaches lack context. A once-in-a-decade Census survey cannot provide insight at the frequency required to measure the impact of local interventions or wider events such as Covid-19. An APNR based solution provides only partial journey data and only from the time at which the hardware was installed. Neither provide for wider geographic context and performance benchmarking against interventions made elsewhere. The best resources available to transport managers and planners today are therefore narrow and limited in quality.


What is Huq doing to overcome them?

Huq’s Transport Monitoring solution uses its proprietary source of mobility data to construct a comprehensive view of travel behaviour, nationwide. With upwards of 400+ geospatial observations per respondent per day, all year round, the advantages of this method are clear. Huq’s representative and validated mobility data is curated and delivered via insight products that are tailored specifically to the needs of transport managers and planners.

Key features include:

  • An interactive analytical insights platform
  • Annual updates to Origin-Destination Matrices
  • Built-in year-on-year comparison capabilities
  • Journey-time in minutes
  • All outputs defined at the MSOA level
  • An instant setup, zero hardware solution
  • Data export to CSV
  • Three types of journey analysis

Further, mobility-led solutions can offer analysis across three different journey types:

  • Origin-Destination Matrices: what proportion of people leaving one MSOA travel to another
  • Destination-Origin Matrices: where do people travel from in order to arrive at an MSOA
  • Journey Analysis: what are the dominant routes that people take to travel between two points, and how does that change with time

All outputs are provided on a statistical basis, allowing for fast and fair comparisons between different routes. Data is available for the whole of the UK and comes with three years of historical data included by default. Huq’s transport planning product supports the full intervention lifecycle, from planning to implementation and analysis.


How does that provide advantage?

Huq’s data collection and processing practices combine to offer a transport modelling solution that is light-years ahead in terms of accuracy and utility. The University of Glasgow’s Urban Big Data Centre (UBDC) has comprehensively reviewed and validated the strength of its outputs and uses it to advise local authorities on how to make successful interventions in their transport infrastructure. Transport planners and managers can take advantage of a vastly superior evidence base to make more nuanced and accurate decisions.

Today, it can cost up to one million pounds a year to build and run a transport model. Huq provides instant access to powerful transport insights at a fraction of the cost – on demand, anywhere in the UK. And unlike any other source of transport planning research, it comes with three years of historical data by default. Equip yourself with the UK’s leading mobility measurement platform and make effective changes that change places for the better!