Personalised optimal speed advice to cyclists approaching an intersection with uncertain green time

Abstract

When travelling in urban areas with signalised intersections, cyclists are currently unable to optimize their speed profile according to their preferences. By integrating information from traffic signal phase and timing and cyclists' preferences, in this paper an algorithm is developed that generates personalised optimal speed advice for cyclists. Results from the simulation experiment indicate that in spite of stochastic nature of traffic light phase and timing, with the developed algorithm based on stochastic dynamic programming, an speed profile can be advised that meets the cyclist's preferences in terms of travel time, energy consumption, or the number of stops.