Abstract
Balancing traffic flow by influencing drivers' route choices to alleviate congestion is becoming increasingly more appealing in urban traffic planning. Here, we introduce a discrete dynamical model comprising users who make their own routing choices on the basis of local information and those who consider routing advice based on localized inducement. We identify the formation of traffic patterns, develop a scalable optimization method for identifying control values used for user guidance, and test the effectiveness of these measures on synthetic and real-world road networks.
Highlights
Many of the world’s major cities are increasingly gridlocked with a staggering estimated annual cost of $166B in the United States alone [1]
Recent simulation results demonstrate the potential of having a mixed environment, of drivers who make their own route choices en route and those who follow routing advice that is centrally optimized, in reducing congestion [13]; this scenario is inherently accommodated within the framework presented here
Drivers do not have full information of the traffic flow and unbounded computational capacity to determine the rational route choices [19,20]. They typically adjust their route choice, especially in urban settings, en route according to the traffic conditions in downstream junctions, which has been investigated in some dynamic traffic assignment problems [21,22,23]
Summary
Many of the world’s major cities are increasingly gridlocked with a staggering estimated annual cost of $166B in the United States alone [1]. Advice-susceptible users are incentivized to follow centrally optimized routing suggestions that benefit traffic globally.
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