  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.
