Abstract
Abstract Background In the context of transport systems, traffic congestion is always presented as an operational issue with negative socio-economic impacts. Rarely in engineering research and curricula is congestion portrayed as a public health issue-- a stress causing factor-- as in fact it is. It is accepted in the public health domain that repeated and prolonged driving in congested traffic conditions is a form of chronic stress and a serious health risk. Managing stress in difficult traffic conditions is a multifaceted activity in which proper traffic control can play a significant part. Methods This papers presents the human health side of traffic congestion and connect that to known and quantifiable traffic phenomena that, for modeling purposes, may be used as stress markers. Traffic control can thus be optimized with explicit consideration of stress. A case of a health-sensitive control algorithm for congested signalized systems is presented where control parameters are optimized to explicitly reduce the occurrence and intensity of unhealthy stress-inducing traffic conditions. The traffic signal control algorithm is formulated as a dynamic signal dynamic speed controller that directly and explicitly reduces the known stress-inducing operational traffic conditions. Results The resulting controller is combinatorial optimization problem. The control decision variables are traffic signal control parameters and network link speeds. Genetic Algorithms were used to solve the optimization. The results show that the signal and speed control generated markedly improved traffic flow where stress-inducing conditions were either eliminated or significantly reduced. Conclusions As extended and long-term exposure to congestion and difficult traffic conditions is a documented stressor with negative long term health impacts, current methods of signal control need to be modified to explicitly consider and minimize the impact of known stress-inducing traffic conditions.
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