Dynamic macro scale traffic flow optimisation using crowd-sourced urban movement data

Urban movement data as collected by location-based social networks 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 of the previously proposed approaches to combat congestion are either static (e.g. a city tax) or do not incorporate any movement data and hence ignore how citizens use the infrastructure. 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 propose using 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 Greenshields traffic flow theory. We evaluate the performance of this approach within the context of a case study on the city centre of Tokyo. An optimisation scenario was defined for this city using public spatial road network data, and movement data acquired from Foursquare. Experimental results on this case study show that, depending on the amount of cars on the road network, our proposed method has the potential to achieve an improvement between 1.35% (437 hours for 112,985 drivers) and 13.15% (925 hours for 31,584 drivers) of total travel time, compared to that of a currently operational road network configuration with no imposed variable costs.