Besides our PHOEBE framework, several theoretical aspects of the project are outlined below, including data requirements and -management, as well as the assessment frameworks of iRAP and Aimsun. Additionally, a brief overview of our work packages is provided.
PHOEBE Data Requirements
The PHOEBE project aims to better understand and mitigate risk in the transport network by utilising both existing and new data sources. Data usage depends on the potential risk insights, availability, and the feasibility of extracting or generating new data. Data availability and quality may vary significantly by location, which means that the PHOEBE framework needs to be flexible and capable of operating with the best data sources that are available.
A core activity of PHOEBE is to classify, prioritise, and collate data for use within the project. We developed requirements based on identifying what information is necessary for developing and setting up the computational models, conducted exploratory investigations, and then validated these approaches.
We identified the need to understand the following areas.
- Infrastructure: Information detailing the transport network and its immediate surroundings that may influence risk.
- Incident: Road traffic incidents, crashes, or their surrogate proxies.
- Mobility: Behaviour of mobility on the network experienced by different road users.
- Land use: The environment local to the vicinity of the transport network.
- Demographics: The characteristics of the population in the area of interest.
- Attitudes: The opinions or choices made by a population.
Within the project, we currently track 114 sub-types of data across the six data requirement areas. We have identified over 190 sources of data, of which 139 are required across the three use cases to support the full project needs.
Whenever the available data does not meet the required quality level, new methods can be utilised to fill the gaps. For instance, specific behavioural data of road users is being collected from video surveys and questionnaires to address data gaps efficiently. Where data is available, it is prioritised over new methods to ensure the PHOEBE framework’s applicability in new locations.
Transport Modelling
Induced Demand Models
The induced demand models for PHOEBE use cases are focussed on micro mobility, which includes variables like the change in cost, travel times or risk profile of micro mobility. The alternatives (for responses) are an increase or decrease of number of trips with micro mobility.
This model will be jointly developed with the mode choice model and will receive the same inputs as the mode choice models. The outputs (change in trips of travel demand) will be directly fed to the Aimsun microsimulation models. The model will be further developed, once the survey results are collected and assessed. The latter includes metrics related to (among others) mode choice, travel behaviour, socio-demographic, and risk perception.
Behavioural Models
The behavioural models integrate the PHOEBE framework to incorporate human factors and road user behaviour into traffic microsimulation and road safety assessment. Such behaviour includes speeding, disregarding traffic regulations while crossing streets, personality traits of road users, local traffic levels, and other environmental factors. Additionally, survey and telematics/video may provide complementary data.
Aimsun & iRAP Solutions in PHOEBE
The integration of the Aimsun and iRAP solutions, spanning safety modules, ratings, and assessment framework, provides a strong base for the PHOEBE framework.
Click on each work package to learn more!
Aimsun solutions are used as the integration point of the different solutions proposed to enable more accurate road risk assessment. This will be the integration of the induced demand models, new detailed behavioural models and dynamic risk assessment from iRAP.
These models will be fed simulation information, like prevailing speeds, gaps in traffic streams, total travel times, in order for the models to update and take decisions on the go. This is to model the users’ best interests to move from their origin to destination, by taking into account the typical transportation metrics which are travel time and pricing with the addition of road safety.
iRAP’s Star Rating models evaluate road safety based on various design features and operational characteristics (speed and flow). Roads are rated from one to five stars, with five-star roads being the safest for four different road user groups: pedestrians, bicyclists, motorcyclists and vehicle occupants.
This rating system is based on a detailed assessment of factors such as road width, shoulder design, median separation, roadside hazards, and the presence of pedestrian crossings. It represents the risk to an individual road user.
The iRAP star rating methodology is a vital tool for improving road safety, particularly in the context of urban changes, as it provides solutions to make cities safer, which typically involves redesigning of road environments or applying technological solutions to manage traffic and ensure safe speeds.
CycleRAP is a methodology developed by iRAP to evaluate road and bicycle infrastructure for safety. The main goal is to reduce accidents and improve safety, especially for cyclists and other light mobility users, by identifying high-risk locations without the need for crash data.
Similar to iRAP for motorised vehicles, CycleRAP is an easy, affordable, and fast method to assess safety. It aims to reduce crashes and improve safety specifically for cyclists and other light mobility users by identifying high-risk locations, even without historical crash data.
Lane Patrol is the only accredited tool to implement the international CycleRAP methodology. Lane Patrol works by first having users plan the cycling route to be assessed on the web app. A smartphone, mounted on a bicycle, automatically captures georeferenced photos of the cycling infrastructure. The collected images are synchronised with the web tool, which utilises vision models to assess safety based on over 40 features, such as infrastructure width, protective barriers, and obstacles.
Leveraging the CycleRAP methodology, Lane Patrol automatically categorises different areas of the infrastructure based on their risk level. The tool allows decision-makers to prioritise and plan infrastructure upgrades. The analysis can be repeated regularly to track upgrades and ensure the safety of cycling infrastructure is maintained.
The integration of CycleRAP and Lane Patrol into the PHOEBE framework offers powerful synergies for improving road safety, particularly for cyclists. Lane Patrol provides detailed, objective data on cycling infrastructure, enabling PHOEBE to make more accurate safety assessments and predictions. The integration of these tools provides a comprehensive understanding of cycling safety, allowing for a more holistic approach to infrastructure planning and design.
Work Package Overview
The following segment provide a quick overview of the PHOEBE project by work package. Even though the work package structure is mostly meant for internal purposes, one can derive the logical structure of the PHOEBE project.
Click on each work package to learn more!
A first assessment of the state-of-the-art of predictive urban road safety methodology is made, which includes the identification of gaps and needs of transport managers. This is followed by the selection of models, tool and technical specificities to demonstrate the PHOEBE framework.
The main public results of this work package are:
Data is gathered for the PHOEBE framework and the use cases in the three pilot cities, which includes the review of available data and the assessment of its potential for the PHOEBE models. Additionally, new means to gather novel data, such as AI-derived data, are explored. A standardisation process of model input data and different levels of quality of data also takes place as an additional step, which is overseen by the technical partners.
Models are developed and existing one are further enhanced by PHOEBE partners in this work package, which includes human behaviour models, mode shift modelling, and induced demand modelling, as well as the enhancement of existing road safety and traffic simulation models. Such development is based on the data gathered in WP2.
PHOEBE demonstrates the application of the methodological framework and validate its outputs. The three use case demonstrations in Athens, Valencia and West Midlands will model scenarios based on existing or stated planned changes, investment or other measures. This exercise is therefore demonstrating the previously developed models and assesses its outcomes in terms of various aspects of the network (e.g. human behavior, mode shift and/or road safety assessment).
The main public results of this work package are:
This work package summarises the data gathering, analysis, tests and other achievements of the previous work packages, by consolidating and meaningfully combining the obtained knowledge. Based on these results, several knowledge products, resources and tools are created, such as an algorithm that addresses integrated risk assessment, based on the results from the PHOEBE use case trials. Such tool will facilitate the rapid road risk estimation across various locations, network types and road user categories, like different vulnerable road users.
This horizontal action involves all project partners and encourages them to promote the project through various on- and offline channels and dissemination activities, such as conferences, lectures, webinars, podcasts and other exchanges, as well as written summaries of project results. Additionally, the wider stakeholder engagement also includes our ‘Community of Practice’, a joint stakeholder group with first access to project results and the pilots.
The main public results of this work package are:
- Updated PHOEBE leaflet
- Newsletters (1 / 2 / 3)
All administrative internal aspects are coordinated in this work package, which includes the financial, legal and financial coordination, risk- & data management, as well as ethical aspects of the projects, such as data gathering and -use.