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Connected vehicle technology, the Internet of Things, and other advanced communication technologies create possibilities to facilitate the movement of vehicles through transportation networks and reduce their travel time. Harmonizing the speed of vehicles in different network links not only yields a more efficient network capacity utilization, but also regulates the movement of vehicles to achieve a “smoother” flow of traffic. This study develops a mathematical nonlinear formulation for dynamic speed harmonization in urban street networks aiming at improving traffic operations. We have converted the nonlinear problem into a linear program utilizing the fundamental flow–density relationship and developed a model predictive control approach to account for stochastic changes in traffic demand and further improve the efficiency of the developed solution algorithm. Results showed that the algorithm efficiently found dynamic optimal advisory speeds on various network links, and speed harmonization significantly reduced the travel time (up to 5.4%), speed variance (19.8%–29.4%), and the number of stops (8.3%–18.5%), while increasing the average speed (up to 5.9%) and the number of completed trips (up to 4%) in our case study network under all tested demand patterns.

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