Maintenance of traffic (MOT) during construction periods is critical to the success of project delivery and the overall mission of transportation agencies. MOT plans may include full road closures and coordination of detours near construction areas. Various state DOTs have designed their own manuals for detour mapping and coordination. However, very limited information is provided to select optimal detour routes. Moreover, closures or detours should provide not only measurable consequences, such as vehicle operating costs and added travel time, but also various unforeseen qualitative impacts, such as business impacts and inconvenience to local communities. Since the qualitative aspects are not easily measurable they tend to be neglected in systematic evaluations and decision-making processes. In this study, the current practices obtained based on an extensive literature review, a nation-wide survey, as well as a series of interviews with INDOT and other state DOTs are leveraged to (1) identify a comprehensive set of Key Performance Indicators (KPIs) for detour route mapping, (2) understand how other state DOTs address the qualitative criteria, (3) identify how the involved risks during the planning, service time, and closure of the detour routes are managed, and (4) recommend process improvements for INDOT detour mapping guidelines. As demonstrated by two sample case studies, the proposed KPIs can be taken as a basis for developing a decision-support tool that enables decision-makers to consider both qualitative and quantitative aspects for optimal detour route mapping. In addition, the current INDOT detour policy can be updated based on the proposed process improvements.

Construction projects should be planned and executed in a way that minimizes the inconvenience to the local community. For that, it is crucial to incorporate public opinion by engaging them in the decision-making process. However, the public is generally involved indirectly in the planning of infrastructure projects through information-sharing reports and meetings, which have not shown to be very effective. This paper presents the findings of a case study as a hands-on experience for graduate engineering students toward engaging the public in the feasibility assessment of a real-world rehabilitation project. The case study involves the application of a simple additive weighting (SAW) multi-criteria decision-making (MCDM) approach to the assessment of various dimensions of the proposed rehabilitation alternatives. As a part of the MCDM framework, public opinion is sought to determine the relative importance of various criteria in making the final decision. The steps and processes of the case study are summarized and proposed in the form of a framework for engaging both students and the community members in the planning of construction projects. The case study and the framework serve as a structured introductory exercise for raising awareness in the students about the impact of public opinion on the planning of construction projects, and the existence of methods that can help them articulate participatory processes. This structured exercise is replicable for future researchers. It is expected that the application of the approach pursued in this study will help promote a culture of accommodating public engagement among engineering students as future engineers in the long term.

Greenhouse Gas (GHG) emissions are among the major causes of the rise in the global temperature and global warming. US commercial and residential New York City (NYC) buildings emit a high amount of GHG each year. Several past studies have introduced prediction models for the GHG emission of buildings. However, the factors that contribute to the changes in GHG emission patterns over time are less explored. In this paper, the New York Local Law 84 datasets for four consecutive years from 2017 to 2020 are used to explore which changes in building attributes would increase/decrease GHG emission over time. A Random Forest prediction model in combination with a variable importance analysis is conducted to identify the most important factors contributing to the observed increasing or decreasing GHG emission patterns. The results show that Energy Star Score is a significant factor in explaining an increasing GHG emission pattern. Similarly, the reduction in the GHG emission levels can be explained by the success in maintaining the Energy Star Score and significant reduction in the electricity and gas consumption of the buildings. The findings of the study can be used to prioritize actions and design appropriate policies to reduce the GHG emission of buildings.

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