Today, we have access to data, connectivity and digital tools allowing us to bring together a rich  understanding of our urban systems and transport networks. These insights enable decision makers to drive change and influence positive outcomes when considering some of our the most pressing problems in the mobility space.

Traditional approaches to traffic and pedestrian data collection have often been fragmented, largely when it comes to collation of linear metrics related to vehicle counts, queue lengths and pedestrian numbers. While such data provides quantitative measurers and planning metrics around lane capacity, dimensions and parking requirements, we often miss insights in understanding how such user groups interact with their environment and with other groups. Often, not all that counts can be counted and without this understanding, planners are at times forced to make assumptions on such interactions, behaviours and future predictions of user needs, resulting in infrastructure that is often redundant and not fit for purpose.

Data Collection

Efficient methods and tools for traffic network and urban planning and management are critically important in the increasingly urbanised world, bringing experience from all transport modes and user groups such as pedestrians, cyclists and vulnerable users. Rich data collection can now be automated through the use of advanced machine learning and artificial intelligence technologies such as smart video analytics. This technology not only counts the number and type of vehicles passing through an intersection, but also shows how different traffic types are using the road or urban environment by mapping out desire lines and interactions between different modes to understand precise user behaviours such as desire paths, speeds and likely clash points. For example, when vehicles and bicycles have near misses at key locations at a high-risk intersection or where cyclists cross where they are not supposed to. With the advent of smarter and richer data and analytics, we can now start to understand, map and plan to adopt a more user centric and data driven approach to planning our urban and street networks. By identifying key points of conflicts, we can diagnose safety issues and take preventative action before incidents occur. Such analytics can even be used to review high risk behaviours including vehicles crossing the centre line, over taking, speeding and other dangerous manoeuvres, many of which can influence how our transport networks are designed and planned for.

Smart Planning Decisions

Innovative smart video data capture and analysis services help aggregate and collect anonymous traffic and pedestrian data, to provide robust evidence-based information to support business case and planning decisions. This enables planners, designers and project stakeholders to not just consider existing users, but also predict and design for future use within the network. We continue to develop and grow capability in data intelligence and collection tools, using a combination of technologies such as video analytics, machine learning and artificial intelligence in capturing, processing and analysing data. Turning data into actionable insights for improved transport outcomes will allow for increased predictability and smarter decision making, ultimately enhancing the passenger experience and creating a seamless, intelligent transportation network.