Optimal Speed Advice for Cyclists using a Roadside Sign at Signalized Intersections with Uncertainty in Traffic Light Timing

Azita Dabiri*, Andreas Hegyi, Bernat Goñi-Ros

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)


Signalized intersections are one of the most common sources of inconvenience for cyclists. The aim of this paper is to develop an approach that helps cyclists to meet their cycling preferences (regarding, e.g., the energy they use and their preference to avoid unnecessary stopping) while crossing intersections. Uncertainty in traffic light timing is considered explicitly in the approach, which makes it applicable for intersections with traffic-responsive signals. The suggested approach provides cyclists with optimal and personalized speed advice. The advice is communicated to the cyclists through a roadside sign located upstream of the intersection. It is assumed that the roadside sign can measure the speed of the approaching bike and can also communicate with the traffic light to ascertain the traffic light’s state and give advice accordingly. To consider the behavior of cyclists when they disregard the advice as they get close to the intersection, the problem is separated in two parts and formulated as a Markov reward process combined with a Markov decision process and stochastic dynamic programming is used to solve the corresponding optimization problem. The approach is generic in relation to the underlying process model and the objective function. The results of an illustrative case study show how much improvement, in relation to the cyclist’s average number of stops, and average energy consumption, could be achieved by use of the suggested approach in a simulated intersection. We also investigate how the location of the sign may affect the performance of the approach.
Original languageEnglish
Pages (from-to)239-247
Number of pages9
JournalTransportation Research Record
Issue number7
Publication statusPublished - 2019


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