James Haworth

Lecturer in Spatio-temporal Analytics

University College London
Country flagUnited Kingdom

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About

Dr. James Haworth is a Lecturer in Spatio-temporal Analytics at University College London, UK. His research focuses on the application of analytical techniques to transportation and urban studies, particularly in relation to cycling safety, public transport dynamics, and the effects of crises such as the COVID-19 pandemic. Recent articles include investigations into predicting driver injury severity from incomplete data, analyzing the impact of lockdowns on bicycle hire schemes, and employing deep learning for urban scene analysis.

Articles (7)

Year:

2025

Creating routes across open areas is challenging due to the absence of a defined routing network and the complexity of the environment, in which multiple criteria may affect route choice. In the context of urban environments, research has found Visibility and Spider-Grid subgraphs to be effective approaches that generate realistic routes. However, the case studies presented typically focus on plazas or parks with defined entry and exit points; little work has been carried out to date on creating routes across open areas in rural settings, which are complex environments with varying terrain and obstacles and undefined entry or exit points. To address this gap, this study proposes a method for routing across open areas based on a Spider-Grid subgraph using queen contiguity. The method leverages a Weighted Sum–Dijkstra’s algorithm to allow multiple criteria such as surface condition, total time, and gradient to be considered when creating routes. The method is tested on the problem of routing across two areas of Dartmoor National Park, United Kingdom. The generated routes are compared with benchmark algorithms and real paths created by users of the Ordnance Survey’s Maps App. The generated routes are found to be more realistic than those of the benchmark methods and closer to the real paths. Furthermore, the routes are able to bypass hazards and obstacles while still providing realistic and flexible routes to the user.

Year:

2024

Year:

2023

Year:

2022

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2021

Year:

2021

Year:

2021

Collaborators (9)

Huanfa Chen

Lecturer in Spatial Data Science

University College London

UNITED KINGDOM
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Mohamed Ibrahim

Lecturer in Spatial Data Science

University of Leeds

UNITED KINGDOM
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Nicola Christie

University College London

UNITED KINGDOM
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Ilya Ilyankou

University College London

UNITED KINGDOM
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Joaquín Osorio Arjona

Assistant Professor Doctor

Universidad Nacional de Educación a Distancia Facultad de Filosofía

SPAIN
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Xiaowei Gao

University College London

UNITED KINGDOM
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Jiri Horak

VSB - Technical University of Ostrava

CZECH REPUBLIC
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Martin Zajac

VSB - Technical University of Ostrava

CZECH REPUBLIC
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Tao Cheng

University College London

UNITED KINGDOM
View details
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