Playing urban mobility games with intelligent machines. Framework to discover and mitigate human-machine conflicts.

The COeXISTENCE project explores AI's impact on urban mobility, aiming to identify and mitigate conflicts between machine-driven decisions and human travel needs through multi-objective reinforcement learning.

Subsidie
€ 1.494.405
2023

Projectdetails

Introduction

AI-driven technologies are ready to enter urban mobility. They promise relief to the notoriously congested transport systems in pursuing sustainability goals. Since AI already outperforms humans in the most complex games (chess and Go), it is likely to win the urban mobility games as well, outperforming us in:

  • Route choices (to arrive faster)
  • Mode choices (to reduce costs)
  • Pricing strategies
  • Fleet management (to increase market shares and profits)

This tempts us and policymakers to gradually hand over our decisions to intelligent machines.

Challenges of the Revolution

The consequences of this ongoing revolution are challenging to predict and largely unknown. While the abundance of previous studies proves the positive potential of AI in urban mobility (from autonomous vehicles to optimal routing and fleet management), the negative impact is often overlooked.

Conversely, our scenario of interest is the machine-dominated urban mobility system, where (collective) decisions of machine intelligence improve system-wide performance, yet at the cost of humans. This may lead to:

  1. Longer travel times
  2. Greater monetary costs
  3. Nudging individuals to change natural travel habits into the optimal ones desired by the machine-centered system

Exploration of Scenarios

Such scenarios, however, need to be discovered. To this end, COeXISTENCE embarks on an interdisciplinary expedition inside the virtual environment of urban mobility, where machines and humans play the game for limited resources.

In the four pre-identified games, I will:

  1. Explore the conflict scenarios
  2. Demonstrate them on reproducible case studies
  3. Quantify with proposed measures
  4. Mitigate with a proposed multi-objective reinforcement learning framework, where machines learn to mitigate conflicts while simultaneously reaching their inherently selfish objectives.

Conclusion

Reaching the project's objectives will be groundbreaking when new phenomena are discovered and lead to breakthroughs when they are mitigated, pushing the system towards the synergy of COeXISTENCE.

Financiële details & Tijdlijn

Financiële details

Subsidiebedrag€ 1.494.405
Totale projectbegroting€ 1.494.405

Tijdlijn

Startdatum1-3-2023
Einddatum29-2-2028
Subsidiejaar2023

Partners & Locaties

Projectpartners

  • UNIWERSYTET JAGIELLONSKIpenvoerder

Land(en)

Poland

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