New Cycling Safety Framework Developing a novel framework for modelling Cycling Safety using Data Driven Engineering Sciences

Promotion of cycling as a mode of travel has social, economic, and environmental benefits. The increase in the mode share is essential for achieving sustainable development. However, cyclist faced a disproportionate risk. The risk that cyclist faces from varying built environment is paramount and a critical mode and route choice model. The current cycling safety models are primarily based upon the historic crash data and are often reactive models. These models often do not fully leverage the potential of the latest advancements in data-driven engineering sciences. Hence, this research proposes the development of a novel, data-driven framework for modelling cycling safety. This framework will integrate diverse data sources and advanced analytical techniques to create a comprehensive, predictive model that can inform better safety interventions and urban planning decisions. The study will aim to construct a data driven model that can model cycling infrastructure while considering road safety as a variable. It will develop an understanding of the interaction between infrastructure, meteorological variables, traffic flow conditions, personal rider attributes, and safety. To achieve the aim, following objectives are designed: 

1. To develop a novel data-driven framework for modelling cycling safety that integrates diverse data sources 
2. Develop a statistical model for the identified critical variables affecting rider safety, and identify key risk factors using advanced data analytics and machine learning 
3. Construct a nanoscopic safety model with different outputs for critically identified variables: a) Infrastructure, b) Meteorological conditions, c) Personal rider attributes, and d) Micro-infrastructure variables 
4. Construct a real-time road safety model with dynamic input variables that can combine all the output variables of Objective 3 
5. To validate the framework through case study application and establish a set of guidelines for the implementation of data-driven safety interventions for cycling infrastructure 

Methodology: 1. Review the use of present research methods for modelling cyclist safety and propose a method/combination of methods, 2. Develop a base input file for a) Cyclist Crashes, b) Traffic Flow Patterns, c) Meteorology, d) Cyclist use by personal rider attributes, 3.Developing a novel hybrid modelling framework for modelling using AI-based tools and Data driven engineering science, 4. Data Simulation Analysis, 5. Traffic Microsimulation Analysis 

Expected Outcomes: a) Novel Cycling Safety Framework, b) Predictive Nanoscopic Tools and c) Policy and Planning Guidelines: 

Application: The research will aim to contribute towards increase in the cycling safety and contribute towards a sustainable integrated cycling transportation system. Through the development of a novel framework, better design of cycling infrastructure / choosing proper cycling routes and selection of the best route for the cyclist in real time, can be achieved. This best route can vary with change in traffic flow and the time-of-day journey is undertaken. Also, the rating of present cyclist road infrastructure network based upon safety can be undertaken and developing the remedial measures to increase the cycling safety. The framework developed will offer urban planners, transportation modellers, and policymakers a sophisticated tool for understanding and mitigating cycling risks in real-time, contributing to safer and more sustainable urban environments.  

Academic qualifications

A first-class honours degree, or a distinction at master level, or equivalent achievements in Transportation/ Mathematics/ Artificial Intelligence/ Data-driven engineering science.

English language requirement

If your first language is not English, comply with the University requirements for research degree programmes in terms of English language.

Application process

Prospective applicants are encouraged to contact the supervisor, Dr Faheem Ahmed Malik (f.malik@napier.ac.uk) to discuss the content of the project and the fit with their qualifications and skills before preparing an application. 

Contact details

Should you need more information, please email SCEBERDL@napier.ac.uk.

The application must include: 

Research project outline of 2 pages (list of references excluded). The outline may provide details about

• Background and motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
• Research questions or
• Methodology: types of data to be used, approach to data collection, and data analysis methods.
• List of references

The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.

• Statement no longer than 1 page describing your motivations and fit with the project.
• Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate.
• Supporting documents will have to be submitted by successful candidates.
• Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here.

Applications can be submitted here. To be considered, the application must use:

• “SCEBE1124” as project code.
• the advertised title as project title 

Download a copy of the project details here