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Time–space conflicts are one of the most common issues facing construction practices, impacting safety and productivity in several negative ways at construction sites. Therefore, implementing and developing methods to reduce the frequency of such conflicts occurring in activity workspaces can effectively enhance project performance. Space is usually a constrained resource in construction project sites; therefore, in this project, we propose an approach as a method of time–space conflict management in construction project sites. The method implements Informed Rapidly Exploring Random Tree-Star (Informed-RRT*) path planning, Discrete Event Simulation (DES), and geometry to automatically detect and resolve time–space conflicts in construction projects. To evaluate the method's capabilities, it is tested on a case study of an earthwork operation, including the loading, hauling, dumping, and return phases. Finally, our method finds the shortest travel path and duration for each hauling truck between two given starting and end points in each phase without colliding with static obstacles (randomly placed in the site), intersection points of the trucks' path, the start and stop time for the truck serving higher-priority construction activities, and the total duration of each truck's earthwork operation. [ABSTRACT FROM AUTHOR]

Transportation is a fundamental tool to develop communities, cities, and countries on a larger scale, and more extensive transportation networks have developed ubiquitously. However, it is needed to consider the fact that animals also live in the same environment without using the same means, and there is always a chance of colliding with them while driving vehicles. Animal-Vehicle Collision (AVC) is a principal concern for transportation agencies and roadway hazards that influences human safety, property, and wildlife. State of Tennessee animal crash data has been collected for 23 years containing different types of information for each collision. This paper presents and evaluates the performance of five machine learning-based prediction models for animal collisions in the presence of both categorical and non-categorical features. These five models are developed using Logistic Regression, Random Forest, CatBoost, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). The CatBoost model has the highest accuracy level at 78.52%. Therefore, it seems to be the most suitable model to predict animal collisions based on 23-year data from Tennessee. The experimental results demonstrate the potential of leveraging categorical data with CatBoost as a viable solution for creating up-to-date and complete analysis for animal-vehicle collision data. [ABSTRACT FROM AUTHOR]

It is challenging to devise a group decision-making process that supports compromise solutions reflecting diverse stakeholders' preferences about multiple criteria. This study illustrates how the combination of multicriteria decision analysis and a group decision-making technique can be used to arrive at solutions that are mutually acceptable to multiple stakeholders when selecting sustainable transport projects. The proposed methodology, in which the collective preference is determined based on the aggregation of individual preferences from all stakeholders, is illustrated through a case study of the prioritization of nine prospective transport projects affecting three stakeholder groups (decision-makers, project designers, and system users) in Tianjin Binhai New Area, China. Specifically, the various projects were evaluated for their probable sustainability performance according to 14 criteria. The results show that the proposed methodology can enable transportation managers to achieve an acceptable solution to a complex project-prioritization problem by understanding the different needs and concerns of diverse stakeholders. It is hoped that the present research will serve as a practical tool for infrastructure managers seeking to build consensus, and helping them to assess the degree of consensus among stakeholders at various points in the process, thereby facilitating effective communication and the coordination of decisions. [ABSTRACT FROM AUTHOR]