  We investigate traffic routing both from the perspective of theory as well as
real world data. First, we introduce a new type of games: $\theta$-free flow
games. Here, commuters only consider, in their strategy sets, paths whose
free-flow costs (informally their lengths) are within a small multiplicative
$(1+\theta)$ constant of the optimal free-flow cost path connecting their
source and destination, where $\theta\geq0$. We provide an exhaustive analysis
of tight bounds on PoA($\theta$) for arbitrary classes of cost functions, both
in the case of general congestion/routing games as well as in the special case
of path-disjoint networks. Second, by using a large mobility dataset in
Singapore, we inspect minute-by-minute decision-making of thousands of
commuters, and find that $\theta=1$ is a good estimate of agents' route
(pre)selection mechanism. In contrast, in Pigou networks, the ratio of the
free-flow costs of the routes, and thus $\theta$, is \textit{infinite}; so,
although such worst case networks are mathematically simple, they correspond to
artificial routing scenarios with little resemblance to real world conditions,
opening the possibility of proving much stronger Price of Anarchy guarantees by
explicitly studying their dependency on $\theta$. For example, in the case of
the standard Bureau of Public Roads (BPR) cost model, where$c_e(x)= a_e
x^4+b_e$, and for quartic cost functions in general, the standard PoA bound for
$\theta=\infty$ is $2.1505$, and this is tight both for general networks as
well as path-disjoint and even parallel-edge networks. In comparison, for
$\theta=1$, the PoA in the case of general networks is only $1.6994$, whereas
for path-disjoint/parallel-edge networks is even smaller ($1.3652$), showing
that both the route geometries as captured by the parameter $\theta$ as well as
the network topology have significant effects on PoA.
