  How can urban movement data be exploited in order to improve the flow of
traffic within a city? Movement data provides valuable information about routes
and specific roads that people are likely to drive on. This allows us to
pinpoint roads that occur in many routes and are thus sensitive to congestion.
Redistributing some of the traffic to avoid unnecessary use of these roads
could be a key factor in improving traffic flow. Many proposed approaches to
combat congestion are either static or do not incorporate any movement data. In
this work, we present a method to redistribute traffic through the introduction
of externally imposed variable costs to each road segment, assuming that all
drivers seek to drive the cheapest route. We use a metaheuristic optimisation
approach to minimise total travel times by optimising a set of road-specific
variable cost parameters, which are used as input for an objective function
based on traffic flow theory. The optimisation scenario for the city centre of
Tokyo considered in this paper was defined using public spatial road network
data, and movement data acquired from Foursquare. Experimental results show
that our proposed scenario has the potential to achieve a 62.6\% improvement of
total travel time in Tokyo compared to that of a currently operational road
network configuration, with no imposed variable costs.
