Metaheuristic macro scale traffic flow optimisation from urban movement data

TL;DR

This paper presents a metaheuristic approach to optimize urban traffic flow by adjusting road-specific costs based on movement data, significantly reducing total travel time in Tokyo.

Contribution

It introduces a novel method that uses movement data and metaheuristic optimization to dynamically redistribute traffic and improve flow.

Findings

Achieved a 62.6% reduction in total travel time in Tokyo.

Demonstrated the effectiveness of variable cost adjustments in traffic optimization.

Validated the approach using real-world movement and road network data.

Abstract

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.