Cloudy with a Chance of Green: Measuring the Predictability of 18,009 Traffic Lights in Hamburg

TL;DR

This study directly measures traffic-light predictability in Hamburg using open data from 18,009 lights over four weeks. It introduces two instability metrics, Cycle Discrepancy and Wait Time Diversity, to quantify switching patterns and assess the feasibility of traffic-light assistance services beyond mere adaptivity. Findings show that most signals are highly predictable despite high reported adaptivity, with instability concentrated at a minority of intersections, challenging the assumption that adaptivity universally undermines predictability. These results inform when and where prediction-based assistance is viable and point to future cross-city studies and methodological refinements.

Abstract

Informing drivers about the predicted state of upcoming traffic lights is considered a key solution to reduce unneeded energy expenditure and dilemma zones at intersections. However, newer traffic lights can react to traffic demand, resulting in spontaneous switching behavior and poor predictability. To assess whether future traffic light assistance services are viable, it is crucial to understand how strongly predictability is affected by such spontaneous switching behavior. Previous studies have so far only reported percentages of adaptivity-capable traffic lights, but the actual switching behavior has not been measured. Addressing this research gap, we conduct a large-scale predictability evaluation based on 424 million recorded switching cycles over four weeks for 18,009 individual traffic lights in Hamburg. Two characteristics of predictability are studied: cycle discrepancy and wait time diversity. Results indicate that fewer traffic lights exhibit hard-to-predict switching behavior than suggested by previous work, considering a reported number of 90.7% adaptive traffic lights in Hamburg. Contrasting previous work, we find that not all traffic lights capable of adaptiveness may necessarily exhibit low predictability. We critically review these results and derive avenues for future research.

Figures (6)

• Figure 1: Possible switching behaviors of traffic lights and the resulting patterns between cycles depending on the short-term adaptivity level.
• Figure 2: Predictability for all traffic lights measured over the weekly course. Cycle discrepancy and wait time diversity values close to zero indicate high predictability. The total number of green phases and detector occupancy changes are shown as a reference for measured traffic volume. The influence on green lengths is also shown.
• Figure 3: Cycle discrepancy and wait time diversity, illustrating that most traffic lights only express one kind of switching instability. One vertical line, aligned between both charts, represents the weekly progression of both metrics.
• Figure 4: Comparison between green length and cycle discrepancy, showing that the cycle discrepancy is often shorter than the green length. In 90% of cases, there is an overlap in green phases between cycles. Line artifacts reveal specific switching patterns and constraints in the switching behavior.
• Figure 5: Spatial distribution of median cycle discrepancy and wait time diversity per traffic light across Hamburg. Shown are the lanes associated with each traffic light. The zoomed-in section highlights three intersections in more detail, which express different levels of predictability.