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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]

High quality education standards and safe learning environments are the fundamental goals of most educational systems. One step towards achieving these goals is to keep existing school facilities in good working conditions through continues repair and maintenance programs. Due to limited resources, efficient use of allocated funds is necessary. This paper presents an optimization tool using Genetic Algorithm (GA) and Dynamic Programming (DP) that manages the expenses of K-12 school rehabilitation projects. These models help to find the optimum solutions among the given data through mutations and crossover in deriving a possible solution to a multi-layered problem. The proposed optimization tools maximize the benefits of K-12 school repair projects, and focus primarily on serving at-risk students, including those who come from low-income families. To demonstrate the use and capabilities of the proposed tool, a case study is presented. The results revealed by the case study show high potential for better management of school rehabilitation projects and better provision of service to disadvantaged students. [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]

Frequent analysis and updating the condition of traffic signs and mile markers are among the most important aspects of a highway asset management system. Today's practices mainly involve manual data collection and analysis, which have to be done for millions of miles of roads and the practice needs to be repeated regularly. While significant progress has been made on improving the data collection practice by leveraging video streams collected from car-mounted cameras, the analysis has primarily remained a manual and labor-intensive process. Automating the analysis from the collected videos is also challenging due to the interclass variability of traffic signs, expected changes in illumination, occlusion, sign position, and orientation. To address these challenges, this paper presents and evaluates the performance of three computer vision algorithms for detection and classification of traffic signs in presence of cluttered backgrounds and static and dynamic occlusions. The task particularly focuses on (1) extracting two-dimensional (2D) candidate windows from already collected video streams that potentially contain traffic signs-without making any prior assumption about their locations; (2) detecting the presence of signs in these 2D candidate windows; and (3) classifying them into warning, regulatory, stop, and yield sign categories based on their shape and color. For validation, a new comprehensive benchmark data set of over 11,000 annotated U.S. traffic sign images with a large range of pose, scale, background, illumination, and occlusion variation is introduced. Experimental results show an average accuracy of 76.20%, 89.31%, and 94.83% for the methods of (1) Haar-like features with Cascade classifiers, (2) histograms of oriented gradients (HOG) with multiple one-versus-all support vector machine (SVM) classifiers, and (3) HOG+C with the SVM classifiers, a variant of the second method with histograms of colors concatenated to HOG. The experimental results demonstrate the potential of leveraging joint representation of texture and color in HOG+C together with SVM discriminative classifiers as a viable solution for creating up-to-date and complete inventories of traffic signs for U.S. roadways. [ABSTRACT FROM AUTHOR]

The problem of selection or ranking of alternatives based on multiple criteria is not an easy problem, neither economically nor mathematically. The recent advancements in various structural systems have made the selection of the appropriate structural system for a specific project a challenging decision-making process. This process involves different economic and technical criteria representing the availability of experienced technicians and engineers and necessary machinery and construction materials. Economic life cycle, environmental protection related issues, safety of the project site, and vulnerability to natural disasters such as earthquakes are also issues related to the country and location in which the construction project takes place, and they should also be addressed in the decision-making process. In this paper, a new algorithm combining two well-known multicriteria decision-making (MCDM) techniques, namely, elimination and choice expressing the reality III (ELECTRE III) and preference ranking organization method for enrich evaluation II (PROMETHEE II), have been specially developed for ranking of structural systems. The proposed algorithm is also applicable to group decision-making cases. In addition, the decision matrix data, criteria weights, preference, indifference, and veto thresholds are set as interval values to consider uncertainties in the decision-making procedure. The proposed methodology considers veto threshold, provide complete ranking, exempts the preference functions and includes conceptions such as risk, uncertainty, and reliability with interval approach. The proposed algorithm has been utilized in selecting the appropriate structural system in a low-rise multihousing project in Iran. In this case study, sixteen criteria have been used to select the best structural system between eight alternatives considering relevant uncertainties in the judgments of three groups of stakeholders (owner, consultants, and contractors). The results of the case study showed that the proposed methodology can be used as a proper tool for decision making in the construction management processes. [ABSTRACT FROM AUTHOR]

The decisions managing all modes of transportation are currently based on the traffic rate and travel time. However, other factors such as Green House Gas (GHG) emissions, the sustainability index, fuel consumption, and travel costs are not considered. Therefore, more comprehensive methods need to be implemented to improve transportation systems and support users' decision making in their daily commute. This paper addresses current challenges by utilizing data analytics derived from our proposed mobile application. The proposed application quantifies various factors of each transportation mode including but not limited to the cost, trip duration, fuel consumption, and Carbon Dioxide (CO2) emissions. All calculated travel costs are based on the real-time gas prices and toll fees. The users are also able to navigate to their destination and update the total travel costs in real-time. The emissions data per trip basis are aggregated to provide analytics of emissions usage. The traffic data is collected for the Southern California region and the effectiveness of the application is evaluated by twenty participants from California State University, Long Beach. The results demonstrate the application's impacts on users' decision-making and the propriety of the factors used in route selection. The proposed application can foster urban planning and operations vis-à-vis daily commutes, and as a result improve the citizens' quality of life in various aspects. [ABSTRACT FROM AUTHOR]

Any change in the geometric configuration of the existing road affects other dependent elements such as travel time, fuel consumption, and existing pavement. This paper quantifies these effects and finds an optimum geometric configuration. Three separate models are developed in this research: (1) the travel time model which predicts vehicles flow speed on the specified geometric condition, traffic composure, and evaluates the imposed costs to the road users; (2) the fuel consumption model, which estimates needed propulsive force and anticipated fuel for vehicles passing through the road; and (3) the pavement rehabilitation cost model considering two main constraints of existing pavement condition and project line elevation. The developed pavement rehabilitation model proposes the best solution for pavement rehabilitation practice and computes associated costs. Particle Swarm Optimization (PSO) is used to find the optimum solution in a minimization problem search space. The proposed model is applied to a real-world case study. Results show that there is an extremely less tendency for change, even with the existence of adverse geometric conditions, when there is a relatively good pavement condition. In the case of deteriorated pavement conditions, economic justification for geometric modification is required. [ABSTRACT FROM AUTHOR]

Efficient data collection of high-quantity and low-cost highway assets such as road signs, traffic signals, light poles, and guardrails is a critical element to the operation, maintenance, and preservation of transportation infrastructure systems. Despite its importance, current practice of highway asset data collection is time-consuming, subjective, and potentially unsafe. The high volume of the data that needs to be collected can also negatively impact the quality of the analysis. To address these limitations, this paper proposes a new algorithm for semantic segmentation and recognition of highway assets using video frames collected from a car-mounted camera. The proposed set of algorithms (1) takes the captured frames and using a pipeline of structure from motion and multiview stereo reconstructs a three-dimensional (3D) point cloud model of the highway and surrounding assets; (2) using a Semantic Texton Forest classifier, each geo-registered two-dimensional (2D) video frame at the pixel-level is segmented based on shape, texture, and color of the highway assets; and finally, (3) based on the results of the 2D segmentation and a new voting scheme, each reconstructed 3D point in the cloud is also categorized for one type of asset and is color coded accordingly. The resulting augmented reality environment that integrates the color-coded point clouds with the geo-registered video frames enables a user to conduct visual walk through and query different categories of assets. Experiments were performed on a challenging video data set containing sequences filmed from a moving car on a 2.2-mi-long, two-lane highway research facility. Experimental results with an average accuracy of 76.50 and 86.75% in segmentation and pixel-level recognition of 12 types of asset categories reflect the promise of the applicability of this approach for segmentation and recognition of highway assets from image-based 3D point clouds. It also enables future algorithmic developments for 3D localization of traffic signs and other assets that are detected using the state-of-the-art vision-based methods. [ABSTRACT FROM AUTHOR]