As an evidence-based organization, data analysis is at the core of everything we do. We apply our expertise help to answer a wide range of questions including:
- What insight can be gained from data which has already been collected?
- How might this data be used as an evidence base to support strategic decisions, target setting or to answer specific research questions?
- What is the best method of processing, analysing and visualising data to provide these insights?
- How can the most important questions facing the transport industry help to shape data collection?
- How should data be collected and analysed to answer the salient questions?
Statistical analysis and modelling
We employ a number of qualified and Chartered statisticians, with a wealth of experience across a range of statistical and machine learning techniques including: generalised linear modelling, classification techniques, data reduction, time series analysis, survival analysis and Bayesian methods.
We recognise the importance of presenting statistical analysis clearly, in ways that can be easily understood, and place great importance on visualising results intuitively, as well as providing detailed interpretation of the results
Case study: MOVE_UK: accelerating automated driving by connected validation & big data analysis (Innovate UK, 2018-19)
Case study: Streetwise (Innovate UK, 2019-2020)
Trial design
We are experienced in the design of on- and off road trials, randomized control trials for intervention evaluation, and simulator trials in TRL’s driving simulator. Trials are designed to meet the client’s objectives, ensuring that the data collected are robust and reliable, and that sufficient quantities are collected to support the research aims
TRL’s statisticians also have experience in stated preference survey (or choice experiment) design and analysis.
Case study: Trials of 60mph speed limits through roadworks (Highways England, 2016-2020)
Data linking
Combinations of datasets provide a great deal more information than if used on an individual basis. We are experienced in linking datasets including those which require geographical links or probabilistic linking methods. We are also working with Academic collaborators and investing in understanding new techniques to link spatial-temporal data to enable greater insight to be gained through combined sources.
Case study: Linking spatial-temporal datasets (PhD, University of Warwick, 2016-2020)
Data visualization
It is important to visualise data effectively to ensure the meaning of this data is fully understood. Our team have developed innovative ways of presenting data, and our clients regularly use these to share research findings with stakeholders, colleagues and members of the general public.
We produce high-level summaries and dashboards, making use of infographics and charts which are more compelling than the standard bar or pie chart. We also produce interactive visualisations which allow the reader to interact with the data to produce charts of interest to them.
Case study: Healthy mobility and road safety (TRL Academy, 2017-18)
Geographic Information Systems (GIS) data are used for analysing data on specific routes or in particular areas. This analysis can identify areas of high risk, prioritise routes for intervention or enable linking between different datasets, each with a spatial element. Our GIS experts use visualisations to present the results of complex GIS tasks including object assignment, cluster and hot spot analysis, and creation of new GIS networks.
Case study: DRIVEN (Innovate UK, 2018)
Smart monitoring
The motorway network is covered by a network of detectors, each providing detailed 1-minute data. We provide a range of services relating to these data, including how it can be used to analyse traffic patterns both historically and in real-time, and how it is used to operate the automatic signs and signals associated with Smart Motorways.
We developed and operate Highways England’s Smart Motorways Calibration and Optimisation (SMCALO) toolkit, which assists in the calculation of parameters for individual schemes, and also provides a facility for viewing and analysing data from the whole motorway network.
We provide a calibration service for new schemes. We supply the Delivery Partner for the scheme with appropriate thresholds for each section of the scheme, and run simulations and checks to ensure that the on-road automatic signalling is consistent, coherent and appropriate to the traffic conditions. In addition, we carry out detector health checks, to ensure that the loops and radars used to collect traffic data are providing accurate data that can be used for congestion management and queue protection.
We assist DfT and Highways England by developing suitable metrics for traffic-related effects on the road network. These metrics have included delay, journey time reliability, incident response times, incident durations, and Smart Motorways operation.
Case study: SMCALO (Highways England, 2012-2020)
Ground Penetrating Radar Data Analysis
As part of TRL’s Pavement Investigations, we carry out Ground Penetrating Radar (GPR) surveys as a non-destructive tool used to obtain information for our clients on the construction of pavements and its internal features, as well as estimates of the moisture content and risk of voiding. Calibrating GPR with pavement cores allows us to use this structural information for back-calculation of the Falling Weight Deflectometer (FWD) data.
We provide detailed analysis, advice and problem resolution following the collection of the GPR survey. The Pavement Investigations team has a number of highly experienced GPR specialists who have undertaken network surveys and detailed scheme investigations on pavement and highway infrastructure projects globally. We are also actively engaged in research and development, expanding our knowledge of GPR applications on pavements and highways whilst authoring standards. Our team has been and continues to be a key contributor to the Design Manual for Roads and Bridges.
Case study: National Concrete Programme Trial (Highways England, 2019-2020)
