Snow removal activities are performed by roadway agencies to enhance winter mobility and safety. Slower travel speeds during these operations, combined with low visibility and reduced pavement friction, mean that safety and collision avoidance remain a persistent concern. Many studies have implemented signing and lighting technologies to improve the visibility of snowplows. Although a few studies have evaluated the use of different colors on snowplows, there is no rigorous study that evaluates the potential impacts of using green warning lights for winter maintenance operations. This study, therefore, investigates the impacts of various warning light configurations on the visibility of snowplows, with the focus on green lights. To this end, 37 warning light configurations are designed using various color combinations (green and amber), and flashing patterns (single and quad) on the back (LED), the top (beacon), or both, of snowplows. These configurations are evaluated to identify the most effective configurations. Three sets of experiments are designed and implemented: static, dynamic, and weather to evaluate the visibility effectiveness in different contexts: day versus night, clear versus snowy weather, and static versus dynamic scenarios. Human subjects are employed to conduct the experiments and the test results are evaluated using statistical analyses. The conspicuity during the day time and glare during the night time are statistically different among various configurations. In addition, adding green lights with a single flash pattern to amber warning lights improves the conspicuity, while keeping the glare at an acceptable level relative to configurations using only amber.

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Rising urban population, aging infrastructure, and increasing capital maintenance costs call for more efficient use of limited available resources. To address these concerns, the use of technology for urban infrastructure management and operational efficiency comes naturally with emerging technological advancements. Although there have been analyses on how to conceptually design a smart city from the ground up, they are often less applicable in transforming existing cities into smart ones. Retrofitting existing infrastructures requires integration and synergies with existing systems. Given the broad scope of smart cities, this paper equips planners with surface-level considerations in adopting smart mobility solutions. This provides an avenue to assess project feasibility, risk management, and investment requirement. The process is presented via a replicable framework with a use case with simplistic approaches that do not require complex constraints or modeling. The framework streamlines how to deduce a feasible set of user-centric smart solutions, which are then ranked according to their impacts for implementation priority. Middle East Technical University campus located in Ankara (Turkey) is considered for the use case. The main outcomes for the use case are deducing high-impact smart solutions based on the proposed framework. Preliminary system design analyses are showcased for three high-ranked solutions: electric vehicle charging station installation and investment optimization, autonomous electric shuttle system design, and bus network electrification strategies.