Île-de-France
Use Case 1
MaaS for companies – A MaaS platform that facilitates the attribution of mobility credit for employees
Cluster: Multimodality
The “MaaS for Companies” use case aims to develop a MaaS platform that helps companies manage and optimise mobility packages for employees, providing tools for trip planning, payment as well as carbon footprint tracking.
This includes:
- Development of an application: Developing a comprehensive MaaS application for managing and utilising mobility packages offered by employers.
- Enabling employee benefits: Creating access to tools for planning commutes, making payments, and tracking carbon footprints.
- All in one: Harmonising data sets like Electric Vehicle (EV) charging infrastructure, traffic data, and shared mobility services.
- Meeting varying needs: Managing diverse mobility packages and optimising usage for both employers and employees.
Challenges such as package definition, employee access, and payment integration while integrating new data sources for enhanced functionality will be addressed. The platform improves the management of mobility benefits, encouragement of sustainable travelling, and overall employee satisfaction.
Background:
In Île-de-France, the reliance on personal vehicles for commuting remains high despite government incentives like the “Forfait mobilités durables” (FMD), which subsidises the use of public transport and alternative modes. Companies face challenges with inefficient, fraud-prone reimbursement processes and lack of integration across various mobility payment systems. Existing MaaS solutions are limited in coverage and integration, failing to meet the needs for multimodal transport and ethical service providers. The primary difficulties include streamlining mobility package management for companies, providing user-friendly tools for employees, and achieving interoperability with diverse mobility service providers and data sources.
Use Case 2
Journey planner optimisation – Sharing MaaS usage data and improve the itinerary planner with AI models exploiting user’s travel’s preferences
Cluster: Multimodality
The use case focuses on improving journey planning by using data and AI to offer better travel options and making sure this data is easy to use across different platforms.
This includes:
- Extracting valuable data: Collecting existing data sources which can be then easily integrated and used for better decision-making and planning.
- Finding the best route: Enhancing journey planners with AI to give better travel recommendations and create a standard format for journey data.
- Adjusting preferences: Enables users to enter their travel details into a MaaS app, which provides different itinerary options based on their preferences, such as cost or speed.
This approach aims to make travel planning more accurate and user-friendly by improving recommendations and ensuring that data is consistently useful while prioritising user privacy.
Background:
In the Île-de-France region, journey planners and Mobility-as-a-Service (MaaS) applications like Emy generate extensive user data but face challenges in making the most of this information. Despite the potential for this data to enhance public transit planning, optimise routes, and inform urban mobility strategies, it remains underused, the reason mainly being a lack of standardisation and interoperability.
Local implementation and pilot partners


