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Urban Mobility

High quality movement data from a range of transport sectors (rail, car, bike etc.) and footfall sensors allow researchers to better understand travel flows and commuter numbers and journeys. 

Our researchers are developing detailed patterns of daily travel behaviour which are essential in supporting infrastructure planners in their efforts to design effective, affordable and sustainable networks.

Steering future cycleway investment

Interactive tools designed by University of Leeds researchers are helping transport planners decide where to make the most effective investments in cycling infrastructure.

The Propensity to Cycle Tool (PCT) and Cycling Infrastructure Prioritisation Toolkit (CyIPT) are freely available to use and based entirely on open source software. Launched in 2017, the PCT had a rapid and significant impact. In its first year of operation, it was used by more than 20,000 people, including local and regional planning authorities throughout the UK.

CDRC researcher Robin Lovelace was instrumental in the creation and deployment of both tools, which were commissioned by the Department for Transport.

Quantifying the ambient population

As cities and urban areas continue to grow and develop into economic and social hubs, the ability to enumerate the ambient population of these areas is becoming increasingly important.

The ambient population is defined as the number of persons within an outdoor geographical area, at a given point in time, excluding those located on modes of transport or at their place of residence.

It is valuable resource to both the private and public sector for the management of civil emergencies, city planning and monitoring exposure to air pollution.

This project critically reviews potential data sources and analyses counts from Wi-Fi sensors in the town of Otley, West Yorkshire.

Understanding the dynamics of cities

How can we explore for hidden patterns of behaviour in the vast amounts of available data captured by networked sensors within our cities using a combination of machine learning and network science methods?

The recent availability of big data about the individual (social data) can allow for a richer understanding of how cities work. We can use this rich data to answer questions about, for example, why spatial and non-spatial segregation into different communities can occur, why deprivation hotspots develop, and where traffic congestion is likely to happen.

However, identifying when, where and why these patterns will emerge is extremely difficult.

Next generation city simulation


The field of social simulation is dominated by Agent-Based Models (ABMs). Individual ‘agents’ are given simple rules, and the complex phenomena they produce downstream, termed emergence, can then be studied in detail.

However, a major limitation of ABMs is that they can only be calibrated once on historical data, meaning simulations rapidly diverge from reality or the true state. A solution to this would be to update the model with real-time data, following a process well studied in numerical weather prediction called Dynamic Data Assimilation (DDA).

This research attempts to use Keanu – a Probabilistic Programming Library (PPL) developed by Improbable Worlds Ltd. – as a framework for data assimilation on a spatial ABM called StationSim created using the MASON framework.

Simulating capability to reduce transport energy emissions and associated social vulnerabilities


There is a policy gap between emitted emissions in the transport sector and targets the government has set – consequently current policies are not enough. There is too much focus on supply when surface transport demand needs to be tackled too.

The aim of this project was to simulate using the Monte Carlo sampling process what types of trips transport users in England take in a car, either as the driver or a passenger, and how far do they travel for the trip. This will help to identify spatially differences between areas in terms of where there is more demand for car transport for different types of trips.

Incorporating real-time data into agent-based crowd simulations using dynamic data assimilation


We are building an agent-based model of people’s movements through the Connected Places Smart Building in London, which is equipped with sensors.

The model will incorporate real-time sensor data by establishing a method of dynamic data assimilation. This is not currently standard practice for crowd simulations.

This project deals with preparing the sensor data for use in the model.

Can robots learn to drive like humans?


Autonomous vehicles are growing ever more popular as research and technology continue to advance in the transportation field.

One key concern for autonomous vehicles is safety. This can be addressed by creating autonomous vehicles that, at the very least, drive similarly to humans.

Once this is achieved, a variety of machine learning techniques can be used to improve the driving capability of autonomous vehicles to superhuman levels.

The aim of this project is to apply inverse reinforcement learning to a novel dataset of human driving in order to create a driving policy that would allow an autonomous vehicle to mimic the driving behaviour observed in humans.

Commuting zones with complex networks


Using the latest research of complex networks, we uncover inherent community structure within the city of Leeds, improving the understanding of the functional structure of the city.

How we structure our cities is essential for resource allocation, but also for understanding social and economic phenomena. However, it is unclear whether the administrative divisions, many of which have been established decades ago, actually take into consideration the real social organization of cities.

To better understand ever-evolving urban life, this research constructs networks reflecting complex travel-flow data.

Simulating capability to reduce transport energy emissions and associated social vulnerabilities


Autonomous vehicles are growing ever more popular as research and technology continue to advance in the There is a policy gap between emitted emissions in the transport sector and targets the government has set – consequently current policies are not enough. There is too much focus on supply when surface transport demand needs to be tackled too.

The aim of the project was to simulate using the Monte Carlo sampling process what types of trips transport users in England take in a car, either as the driver or a passenger, and how far do they travel for the trip. This will help to identify spatially differences between areas in terms of where there is more demand for car transport for different types of trips.

The results will be plotted in a digestible visual format to make it easier for policymakers to interpret the information.