Optimizing employee commute - Datacity

Optimizing employee commute

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Experiment

BACKGROUNDER


How can we better understand commuter traffic congestion to determine the best mode of transportation?

Today, companies implement classic, standardized travel plans that do not necessarily optimize the daily commutes for various types of employee needs.

Corporate Social Responsibility is an issue that brings together all types of company practices. The CSR goal is to respect principles of sustainable development, which is to say to find economically viable solutions that have a positive impact on the company while taking care of the environment.

 

Using this logic, and starting in January 2018, the PDE (Business Travel Plan) obliges companies with over 100 employees to improve employee transport options by encouraging the use of public transport and carpooling.

Corporates

Cities

Startups

  • Use case & expected benefits

  • Available ressources

  • Business Opportunities for Partners

  • Startup Laureate

Use case

Today, most employees travel to work with a company car. This type of transportation often goes against a coherent mobility plan at the company scale. In fact, employees use company cars for all types daily commutes; consequently, this method of transport is not necessarily efficient. In certain cases, a company car is provided to employees, but it is not always the most suitable mode of transport for business trips or commuting. It should be possible to offer a personalized solution for employees that not only optimizes the commute (travel time, quality, etc.) but is in line with company interest (cost), all while respecting the principles of Corporate Social Responsibility.

Expected benefits

There are several challenges and several potential benefits associated with this project.

For companies that provide company cars, the opportunities are as follows:

 

  • Reduce employee travel costs;
  • Reduce CO2 emissions during business trips;
  • Personalization and optimization of an employee travel plan based on individual needs versus an overall policy (reducing costs, optimizing vehicle use, an environmental approach, etc.).

For the end users, there are multiple benefits:

  • Improve employee well-being during commutes;
  • A range of transportation possibilities that take into account individual employee constraints;
  • The implementation of previously unforeseen solutions for the community.

Datasets

ALD Automotive can make available:

  • Vehicular data, i.e. vehicles currently in circulation that are also equipped with telematics;
  • Start and end points of the commute;
  • Travel time, acceleration, and deceleration;
  • Battery data, mileage;
  • Qualitative user data in order to measure the real-world use of alternative transportation methods: alternative transportation used by drivers; impact of changing commute habits.

 

For road traffic:

Expertise

ALD Automotive:

  • DSI: Pierre Chassagnette (Project manager & BI Domains)
  • DSI – S400: Ludovic Pinoteau (Project Supervisor and Manager)

Experimentation field

Scenario 1:

  • Reuse field data obtained through vehicles currently in circulation and that are also equipped with telematics

 

Scenario 2:

  • ALD employees who have a car in Rueil (ALD S.A. headquarters) and Clichy (ALD France headquarters)
  • Sopra Steria employees who have a car at La Défense

With vehicle supply as its core business model, ALD Automotive hopes to position itself as a transportation consultant by:

  • Creating consulting tools that allow for the integration of new transportation methods;
  • Envision a new market position in terms of its business model (a global transportation consulting firm instead of being simply a vehicle supplier);
  • Developing a commercial offer that can potentially be recreated on an international scale.

 

For Sopra-Steria, the opportunities are centered on:

  • Developing a collaborative transportation model for which startups can provide technological skills and expertise;
  • Creating an operational concept thorough testing and analysis (creating an offer based on real-world case studies);
  • Communicating the issues and solutions to these questions;
  • Creating an opportunity to connect with potential customers.

Mobeelity

Optimized and ecological mobility is a major issue for both metropolitan cities and businesses. Mobeelity aggregates public and private transport solutions’ data within a unique platform, adapted to real-time needs of users thanks to Artificial Intelligence, while directing them towards more ecological way of urban travels.

Partners' Experts


Aurélien Depardon


Project Manager
ALD Automotiv

Pierre Chassagnette


Project Manager and Business Intelligence Domains
ALD Automotiv

Ludovic Pinoteau


MOA Manager and Project Supervisor
ALD Automotiv