Adaptive Incentive-Compatible Navigational Route Recommendations in Urban Transportation Networks

TL;DR

This paper proposes a game-theoretic, incentive-compatible navigational recommendation system for urban traffic that adapts dynamically to changing conditions, improving compliance and reducing travel times.

Contribution

It introduces a novel incentive-compatible recommendation framework that accounts for non-user drivers and dynamic traffic updates, enhancing urban navigation efficiency.

Findings

Parallel update scheme improves user compliance.

Reduces overall travel time effectively.

Adapts well to traffic fluctuations.

Abstract

In urban transportation environments, drivers often encounter various path (route) options when navigating to their destinations. This emphasizes the importance of navigational recommendation systems (NRS), which simplify decision-making and reduce travel time for users while alleviating potential congestion for broader societal benefits. However, recommending the shortest path may cause the flash crowd effect, and system-optimal routes may not always align the preferences of human users, leading to non-compliance issues. It is also worth noting that universal NRS adoption is impractical. Therefore, in this study, we aim to address these challenges by proposing an incentive compatibility recommendation system from a game-theoretic perspective and accounts for non-user drivers with their own path choice behaviors. Additionally, recognizing the dynamic nature of traffic conditions and the unpredictability of accidents, this work introduces a dynamic NRS with parallel and random update schemes, enabling users to safely adapt to changing traffic conditions while ensuring optimal total travel time costs. The numerical studies indicate that the proposed parallel update scheme exhibits greater effectiveness in terms of user compliance, travel time reduction, and adaptability to the environment.