Last Mile
Meta-analysis and feasibility study to develop a route planner for people with mobility impairments
Phase 1
Type of project: Feasibility study
Disability concerned: Motor disability
Topics: Autonomy, Services and communication, Public transportation
Status: Completed
This research project focuses on analysing the methods used to collect and process data relating to barriers in public spaces. More specifically, to understand how different companies and associations approach data collection when assessing barriers in their respective projects.
Context: At present, there is no app offering accessible routes for people with mobility impairments that takes account of their mobility constraints and personal preferences.
The aim of the Slowlution association is to develop an application that provides accessible routes for people with mobility impairments. By making the most of existing data, such as topographical information. As well as applying best practice in data collection that has already been tried and tested on the market.
Challenge: There are several companies and associations working on collecting data on obstacles and the accessibility of public spaces. Each party has its own data collection method and data storage format. The data collected is not connected, analysed or centralised.
Objectives: To analyse and compare the different approaches to data collection based on 5 aspects in order to identify good practice in data collection in the field.
- The nature and type of data collected
- The data collection method
- The interface used to enter the data collected
- The data processing methodology
- The data and database storage format
Results obtained: Our summary of best practice from the three projects studied involves combining citizen participation with data collection by professionals who develop a standard and a typology that is well defined by mobility and accessibility experts. This data collection standard will be simplified for citizen data collection. Also, by using Machine Learning to classify the data collected by users.
Our project should become a central platform for collecting, processing and analysing data, as well as a route planner.
Professionally, the team has learned the importance of citizen involvement in collecting relevant and realistic data on obstacles to mobility, which enriches the database and improves the reliability of the proposed solutions. Collaboration with various disciplines has also enabled us to gain a better understanding of users’ needs.
Interpersonally, exchanges with end-users and experts have strengthened communication, teamwork and stakeholder management. This has helped to develop key skills, such as active listening and taking feedback on board.
Personally, each member has developed a heightened awareness of accessibility issues and learned to use advanced technological tools such as machine learning to process data, bringing a new perspective on the impact of inclusive innovations.
This analysis of other projects has helped us to define the gaps in our process.
With regard to the citizen participatory approach, we are continuing to test the methodology for data collection by volunteer collectors. We are testing a number of aspects: the effort that can be made, the clarity of the data collection instructions, and the feasibility and ergonomics of collecting data in real time in the field.
At the same time, we are continuing to develop the professional data collection approach in collaboration with mobility experts and others:
- Creating a data typology standard
- Data modelling
- A process for auditing points of interest
- Criteria checklists
The analysis of topographical data continues and will also be a decisive factor in the project.
