Autonomous vehicles have the potential to both increase and decrease traffic congestion depending on how they are implemented and controlled. The document discusses two main route choice algorithms: user equilibrium which selects the shortest path for each individual vehicle, and system optimal which aims to minimize total network travel time even if it means hindering some vehicles. Applying the system optimal approach to Melbourne, Australia was found to save $28 million annually in travel times compared to the user equilibrium approach. However, full cooperation of all autonomous vehicles on the optimal routes and a completely autonomous vehicle fleet are limitations that could prevent these benefits.

1. Putting Autonomous
Vehicles on the Right Path
Tim Spalding
16 August 2017

2. Introduction
• Autonomous Vehicles (AVs) will potentially have:
– High level of Coordination
– Empty Running Services
– Knowledge of all potential routes
– Congestion prediction capabilities
• The right route choice algorithm could maximize benefits
– User Equilibrium Assignment (Shortest Path)
– System Optimal Assignment

3. Impacts on Congestion
• AVs will impact congestion. But how?
– Increase congestion
• Induced trips from enabling young, old and/or disabled persons
• Potential empty running AVs to avoid paying for parking
– Decrease congestion
• Smaller headways
• No need for traffic lights
• Smarter route choice

4. Route Choice Algorithms
• User Equilibrium
– Shortest Path
– Reflected in navigation apps
– Minimizes user’s cost
• System Optimal
– Incorporates impact on other road users
– Hinders some users to benefit many
– Minimizes network cost

5. Application to Melbourne
• Methodologies applied to Melbourne
• Saving $28 million annually
• Low implementation cost
Assignment Type Total Time
User Equilibrium 674,069.8 hours
System Optimal 670,395.2 hours

6. Results

7. Results

8. Results

9. Results

10. Limitations
• Cooperation of all vehicles required
• Unwanted increase in volumes on local roads
• 100% autonomous fleet assumed
• Strategic Model limitations

11. Conclusion
• Autonomous vehicles present an opportunity to improve
route choice
• Algorithm should consider impacts on local roads