Online Traffic Routing: Deterministic Limits and Data-driven Enhancements

TL;DR

This paper investigates the limitations of deterministic online traffic routing and introduces data-driven algorithms that leverage past data to improve routing performance, demonstrated through real-world case studies.

Contribution

It provides a worst-case analysis showing unbounded ratios for deterministic algorithms and develops data-driven methods with proven performance guarantees for online traffic routing.

Findings

Deterministic online routing can have unbounded inefficiency compared to offline optimal.

Data-driven algorithms outperform greedy routing in simulations.

Real-world case study confirms practical benefits of proposed methods.

Abstract

Over the past decade, GPS enabled traffic applications, such as Google Maps and Waze, have become ubiquitous and have had a significant influence on billions of daily commuters' travel patterns. A consequence of the online route suggestions of such applications, e.g., via greedy routing, has often been an increase in traffic congestion since the induced travel patterns may be far from the system optimum. Spurred by the widespread impact of traffic applications on travel patterns, this work studies online traffic routing in the context of capacity-constrained parallel road networks and analyzes this problem from two perspectives. First, we perform a worst-case analysis to identify the limits of deterministic online routing and show that the ratio between the total travel cost of the online solution of any deterministic algorithm and that of the optimal offline solution is unbounded, even in simple settings. This result motivates us to move beyond worst-case analysis. Here, we consider algorithms that exploit knowledge of past problem instances and show how to design data-driven algorithms whose performance can be quantified and formally generalized to unseen future instances. We present numerical experiments based on an application case for the San Francisco Bay Area to evaluate the performance of our approach. Our results show that the data-driven algorithms we develop outperform commonly used greedy online-routing algorithms.