Date/Time
Date(s) - 10/11/2022
12:30 PM - 1:30 PM
Categories
A CO Emission-Based Adaptive Signal Control for Isolated Intersections by Dr. Ponlathep Lertworawanich.
Abstract:
Rapid economic growth in recent decades has led to a rising motorization and congestion in
urban areas. Congestion at intersections is considered as the prime source of emissions. This
study presents a CO emission-based adaptive signal control for isolated intersections. Traffic
dynamics at signalized intersections are modeled on the time–space diagrams using the
shockwave theory and information from loop detectors installed upstream of intersections.
Emissions are estimated from the sum of the product of emission rates and times spent by
vehicles in each operating mode. With the assumption that traffic demand remains the same
in the next cycle, a split adjustment policy is established by incrementally adjusting splits so
that the total intersection emissions gradually reduce. Cycle length is adjusted in the next
cycle by evaluating the residual queues. Efficiency of the proposed control algorithm is
investigated via simulation. It is found that when the sum of flow ratios of the critical
movements is between 0.6 and 1.0, the proposed adaptive control produces smaller CO
emissions, delay, and stop than the Webster fixed-time control does with the 0.025 level of
significance. On average, the proposed control algorithm can reduce CO emissions by 7.67%
when compared to the Webster fixed-time signal settings. However, the reduction depends on
the sum of flow ratios.
Implications:
The proposed control method uses occupancy information obtained from loop detectors to construct
the time-space diagrams using the shockwave theory while most existing signal control algorithms rely on
flow information to calculate signal timing. A simple feedback control policy for signal timing adjustment is presented.
It is found that the proposed algorithm can help reducing emissions at signalized intersections in urban areas.