The study examines the predictability of 18,009 individual traffic lights in Hamburg, Germany, over a 4-week period. The researchers developed two metrics, cycle discrepancy and wait time diversity, to directly measure the stability and predictability of the traffic light switching behavior.
The key findings are:
Contrary to previous reports, the majority of traffic lights in Hamburg do not exhibit highly unpredictable switching behavior, despite a reported 90.7% of them being adaptive. Only a small fraction (12%) showed both high cycle discrepancy and high wait time diversity, indicating overall low predictability.
The cycle discrepancy, which measures the alignment of switching patterns between cycles, was generally low, with the median being only 2 seconds during peak traffic hours. This suggests that cycle-stacking prediction methods may still be viable for most traffic lights.
The wait time diversity, which captures the variability in the time between green phases, was also relatively low for many traffic lights, indicating predictable wait times. This means self-adaptive prediction approaches may work well for a large portion of the traffic lights.
The spatial analysis revealed that unstable switching behavior is often limited to specific intersections, rather than being distributed across all traffic lights. This suggests that different prediction methods may be suitable for different intersections, depending on the observed instability patterns.
The study provides a more nuanced understanding of traffic light predictability, challenging the assumption that adaptive traffic lights inherently lead to poor predictability. The findings can help guide the development and deployment of traffic light assistance services, such as Green Light Optimal Speed Advisory (GLOSA) and Eco-Approach and Departure (EAD), by identifying the most suitable prediction methods for different traffic light environments.
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