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Presentation 1: Exploring the efficacy of different methods for comparing pedestrian simulations against empirical data

By: Greta Timaite

Abstract:

Agent-based models (ABMs) have become one of the main modelling tools helping to understand the contemporary challenges of human movement within cities (Crooks et al. 2021). ABMs are used to simulate real systems by creating artificial scenarios in contexts ranging from disease and epidemiology to traffic and pedestrian simulation (Torrens 2010). The uptake of ABMs has largely been informed by the increasing availability of data (Crooks et al. 2021), yet it remains a challenge to evaluate the accuracy of models because of various uncertainties, such as missing data and inherent randomness (Kieu et al. 2020).

This project aims to address the issue of uncertainty by investigating the impact of using different methods to evaluate the reliability of agent-based models. In other words, (some) uncertainties will be identified, quantified, and handled in a way that can help to enhance the understanding of a model’s quality and usefulness. The project objectives are:

1. Produce a review and analysis of existing data on pedestrian movement within crowded corridors and within a train station concourse.
2. Simulate the movements of pedestrians in these environments.
3. Assess the difference between the simulations and the data using a variety of new and existing methods.

The project focus is on pedestrian models, however it is expected that the developed methods will be applicable to any agent-based modelling, such as consumer behaviour in shopping.

 

Presentation 2: Open access data for transport research: tools, modelling, and simulation

By: James Hulse

Abstract: Motor traffic-centric perspectives dominate road infrastructure planning (Parkin, 2018). Yet, other

(active) travel modes, such as cycling and walking, have been found to bring an array of benefits:

improved mental and physical health, decarbonisation, as well as being more efficient in terms of space

(i.e., requires less space) (Parkin, 2018). Indeed, in the post-pandemic world, active travel will likely

become even more important due to reduced public transport capacities, as highlighted by the

Department for Transport’s £250m Active Travel Fund (ATF) and £2b allocated to walking and cycling

over the next 5 years in the UK alone. Thus, new policies and investment programs, such as the ATF,

have led to increased demand for local evidence to inform interventions ranging from new cycleways to

improved pavement quality.

Therefore, acknowledging the increasing demand for local evidence to inform decision-making, the

project will utilise two datasets – OpenStreetMap (OSM) and Ordnance Survey Open Roads (OSOR) – to

explore how open datasets can be used to understand, prioritise, and design active travel infrastructure.

The overall aim of the project is to understand the use of OSM data for transport planning and how we

can add value to such datasets, which has the potential to lead to more inclusive and accessible

evidence-based decision-making due to the datasets crowdsourced nature.

Methodologically, the project builds on geographic data science[1] and previous studies assessing open

datasets for transport applications (Ferster et al., 2020; Haklay, 2010) and the experiences of lead

supervisor (e.g., developing osmextract package in R) and PhD student Caroline Tait (who developed a

CycleInfraLnd package in R) to develop a new package in R for downloading, processing, and adding

value to transport datasets, and to develop OSM transport infrastructure data packs for every transport

authority within Great Britain. To maximise the policy relevance and impact, the outputs of the project

will be disseminated nationwide with the support of external partners (Department for Transport, The

Open Innovation Team)[2].

 

Bibliography:

Ferster, C., Fischer, J., Manaugh, K., Nelson, T., Winters, M., 2020. Using OpenStreetMap to inventory

bicycle infrastructure: A comparison with open data from cities. International Journal of Sustainable

Transportation 14, 64–73. https://doi.org/10.1080/15568318.2018.1519746

Gilardi, A., Lovelace, R., 2021. osmextract: Download and Import Open Street Map Data Extracts. R

package version 0.3.1. https://CRAN.R-project.org/package=osmextract

Haklay, M., 2010. How Good is Volunteered Geographical Information? A Comparative Study of

OpenStreetMap and Ordnance Survey Datasets. Environment and Planning B: Planning and Design 37,

682–703. https://doi.org/10.1068/b35097

Orozco, L.G.N., Battiston, F., Iñiguez, G., Szell, M., 2020. Data-driven strategies for optimal bicycle

network growth. Royal Society Open Science 7, 201130. https://doi.org/10.1098/rsos.201130

Parkin, J., 2018. Designing for Cycle Traffic: International principles and practice. ICE Publishing, London.

Tait, C., Lovelace, R., 2021. CycleInfraLnd: Accesses and download data from the Transport for London

Cycling Infrastructure Database into R as spatial data (Simple features). R package version 0.1.0.

https://github.com/PublicHealthDataGeek/CycleInfraLnd/

[1] Importantly, even though active travel research is multi-disciplinary, it has received little attention

from data science, with Orozco et al.’s (2020) paper on modelling cycle network growth providing a

notable exception.

[2] If you wish to learn more about the project, visit project’s repository on GitHub:

https://github.com/udsleeds/openinfra

Presentation 3: Modelling Ambient Populations under Different Restriction Schemes

By: Indumini Ranatunga

Abstract:

How have cities changed during the pandemic?

Which changes will remain as the pandemic subsides?

The COVID-19 pandemic has had a huge impact on urban mobility, leading to the two major questions above. The project partner, Leeds City Council, has a particular interest in better estimating how footfall in city-centre will vary as the pandemic-based restrictions subside.

As a solution, this project is to build on previous CDRC-funded work (i.e. the Leeds City Council Ambient Pop under COVID-19 project) and create an open-source spatial-temporal machine-learning model to predict overall change in footfall, as well as the heterogeneous impacts that restrictions will have on different local areas around Leeds. It will consider the local urban configuration, external factors (like weather conditions) and, importantly, the impact of various mobility restriction measures. The model is currently being trained using footfall data from the CDRC (SmartStreetSensors) and Leeds City Council (footfall cameras) from the years before the pandemic. Lockdown restriction conditions will be incorporated thereafter.

A functional dashboard will also be developed to present maps and related visual outputs to help the policymakers easily explore different scenarios. Although based in Leeds, it is expected that the work will be generalizable to other cities that have footfall estimates and could even be applied even where footfall data do not exist.

Ultimately, we aspire to attract further funding to construct a nationwide footfall model, which would represent an attractive CDRC outcome as a great methodological advance as well as a contribution to furthering the improvements in public health, urban development etc.

 

This talk will mainly be an introduction to the project, a presentation of the work carried out so far led by a discussion of the next steps and plans.