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New Approaches to Urban Access Mapping


This project will build on ITF access mapping work in support of the bi-annual ITF Outlook and will leverage CPB partner and ITF modelling capacity to explore new map-based visualisation and analysis options for understanding access to jobs, selected services and people in urban areas. The analysis will be carried-out for a selection of representative cities for which data is available externally or from CPB members. The accessibility profiles will incorporate real travel speeds for cars, public transport, taxis and other ride-sourcing services, bicycles and pedestrians.

The project will explore with CPB partners and policy-makers the relevance of developing a “sand-box” to trial various service and/or infrastructure configurations. One aspect will be to investigate effective first/last mile solutions with which to access public transport.

An important sub-component of the work will explore how to identify various travel modes and their characteristics from novel sensor platforms – including those deployed by CPB partners. This work will build on similar efforts undertaken for cities like Buenos Aires, Argentina, and New York, USA.

The project will involve technical teams from project partners and from the ITF to build an access modelling framework that can help understand the impacts of various transportation infrastructure interventions or changes in service types or quality on citizen access to each other, to jobs and to other essential services. The tool/modelling framework will incorporate real-time (or realistic) transport service quality inputs (e.g. link speeds, achieved vs. scheduled service quality, access reliability by mode). It will also explore how new data sources can enhance accessibility measurement, in particular through automatic classification of transport modes from accelerometer and other sensor data with a view to creating a transport “scanner” image of instantaneous mobility in a city.

Participating Partners: Brisa, Ford, INRIX, NXP, PTV Group, SNCF, Transdev, Uber