Cities are seeking ways of becoming more bicycle-friendly. In big cities, this is leading to bike jams at important intersections, especially during rush hour. The dynamic adjusting of traffic lights to prioritize cyclists can improve traffic flow for bikes. BikeSim, a new MobiMaestro module, can give traffic control centers insight into the traffic situation for cyclists in the city.
Measuring bike movements
Modern traffic control centers adapt to changing situations on the road by adjusting traffic lights (TLCs). But the focus is still too much on motorized traffic; it’s time to give some attention to cyclists. Compared to cars, however, cyclists are difficult for traffic control centers to follow. Traffic detection loops and visual sensors can help determine how many cyclists are passing a particular TLC. But cyclists all travel at different speeds and they constantly overtake each other. This makes it difficult for the travel control center, using existing means, to determine where congestion is likely to occur, or to respond to this using dedicated TLC settings. The MobiMaestro module BikeSim uses realistic simulation to offer traffic control centers the possibility to respond immediately to the current situation.
Simulation with real-time data
BikeSim uses the TLCs’ real-time data to calculate an approximate number of bike movements. The module simulates these movements using various data: the status of the TLC (red or green), the number of passing cyclists, and a probability distribution of cycling speeds. On this basis, BikeSim carries out a large number of simulations in very short time (Monte Carlo simulation). This provides a realistic image of the average travel time and the number of times cyclists have to stop in any given corridor.
Close to reality
A unique feature of BikeSim is the way in which it approximates reality using a simulation on the basis of real-time data. This module gives the traffic control center reliable information on bike traffic in the city – meaning it has the possibility to control flow and the role TLC settings play for cyclists. By using available data smartly, cities that use MobiMaestro can work towards improving traffic flow for cyclists without additional investment in infrastructure or TLCs.