D3.2 Finalised models/simulation environments, methodology factsheets and user support materials (public version)

This comprehensive report underscores the interdisciplinary and data-driven nature of the PHOEBE project, advancing urban road safety modelling by integrating human factors, dynamic risk assessment, and traffic simulation within a unified framework, validated and demonstrated in diverse European urban contexts. Deliverable 3.2 closes Work Package 3 activities within the PHOEBE project, focusing on the development, refinement, and integration of the components in the PHOEBE framework. They are: Mode shift and induced demand Human behaviour Risk assessment Traffic simulation For each component, models were developed or enhanced to estimate the safety impact of changes better and capture human decision-making processes in urban mobility contexts, with a particular emphasis on mode shift, induced demand, and active travel behaviours. This deliverable document presents the finalised versions of the models developed through iterative technical enhancements and empirical validation across the three PHOEBE use cases: Athens (EL), Valencia (ES), and the West Midlands (UK). Each model was encoded into the Aimsun Next simulation environment and tested using scenario-specific interventions. The framework's integration involves structured internal data flows, with scripts and APIs developed to process microsimulation outputs (speed, flows, trajectories) into formats suitable for demand, behaviour, and risk models. Aggregation and transformation steps align data resolutions across components. The integration flow is organized into setup, simulation, and refinement stages, facilitating iterative model runs and indicator calculations. User guides and performance indicator definitions support framework operationalization. PHOEBE provides a comprehensive set of Key Performance Indicators (KPIs) across four categories: health and safety, mode shift, environment and operations. Deliverable 3.2 successfully finalizes and demonstrates the PHOEBE framework's components across pilot cities, showcasing advancements in predictive urban safety assessment with a focus on VRUs. The integration of mode shift, human behaviour, risk assessment, and traffic simulation models provides a robust foundation for evaluating safety impacts of urban interventions. Future work will focus on automation, scalability, user guidance, and stakeholder engagement to facilitate broader adoption across European cities

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-18
DOI
https://doi.org/10.5281/zenodo.18680842
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

D3.2 Finalised models/simulation environments, methodology factsheets and user support materials (public version)

Mark J. Burke, Santhanakrishnan Narayanan, Arunava Putatunda, Amir Hossein Kalantari et al.
Zenodo (CERN European Organization for Nuclear Research)
Traffic control and management
article

D3.2 Finalised models/simulation environments, methodology factsheets and user support materials (public version)

Mark J. Burke, Santhanakrishnan Narayanan, Arunava Putatunda, Amir Hossein Kalantari, Maria G. Oikonomou, Stella Roussou, Ana María Pérez Zuriaga, Sam Chapman, SHANNA TRICHES LUCCHESI, James bradford, Amna Chaudhury, Marcel Sala, Francisco Javier Carmacho Torregosa
article en

Abstract

This comprehensive report underscores the interdisciplinary and data-driven nature of the PHOEBE project, advancing urban road safety modelling by integrating human factors, dynamic risk assessment, and traffic simulation within a unified framework, validated and demonstrated in diverse European urban contexts. Deliverable 3.2 closes Work Package 3 activities within the PHOEBE project, focusing on the development, refinement, and integration of the components in the PHOEBE framework. They are: Mode shift and induced demand Human behaviour Risk assessment Traffic simulation For each component, models were developed or enhanced to estimate the safety impact of changes better and capture human decision-making processes in urban mobility contexts, with a particular emphasis on mode shift, induced demand, and active travel behaviours. This deliverable document presents the finalised versions of the models developed through iterative technical enhancements and empirical validation across the three PHOEBE use cases: Athens (EL), Valencia (ES), and the West Midlands (UK). Each model was encoded into the Aimsun Next simulation environment and tested using scenario-specific interventions. The framework's integration involves structured internal data flows, with scripts and APIs developed to process microsimulation outputs (speed, flows, trajectories) into formats suitable for demand, behaviour, and risk models. Aggregation and transformation steps align data resolutions across components. The integration flow is organized into setup, simulation, and refinement stages, facilitating iterative model runs and indicator calculations. User guides and performance indicator definitions support framework operationalization. PHOEBE provides a comprehensive set of Key Performance Indicators (KPIs) across four categories: health and safety, mode shift, environment and operations. Deliverable 3.2 successfully finalizes and demonstrates the PHOEBE framework's components across pilot cities, showcasing advancements in predictive urban safety assessment with a focus on VRUs. The integration of mode shift, human behaviour, risk assessment, and traffic simulation models provides a robust foundation for evaluating safety impacts of urban interventions. Future work will focus on automation, scalability, user guidance, and stakeholder engagement to facilitate broader adoption across European cities

Zenodo (CERN European Organization for Nuclear Research)
United States Department of Transportation (US), National Technical University of Athens (GR), Daewoo Pharma (South Korea) (KR), Technical University of Munich (DE), Delft University of Technology (NL)
Sustainable cities and communities
Openalex Percentile: Top 53%
Traffic control and management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.