Several converging trends appear to reshape the way citizens and goods move about. These trends are social, including urbanization and population growth, and technological, such as increased automation and connectivity. All these factors influence the market for connected, automated, shared and electric (CASE) vehicles, which presents many opportunities and challenges. The pace of the shift to a profoundly penetrated market for CASE vehicles is far from secure. Such transformation depends on the development of technologies, consumer attitudes, and policies. An expanding body of research has investigated the potential social and behavioral results of deploying CASE vehicles. However, most academic literature to date concentrates on technological issues linked to these vehicles. There are several teams from federal and state agencies, OEMs, academia, startups, and consortiums working on this complex subject. This study investigates several academic papers, as well as federal and industry reports, considering all the stakeholders mentioned above. Its aim is to present a comprehensive picture of the implementation barriers and drivers of CASE vehicle usage and provide suggestions to solve them. The findings confirm that several issues are currently affecting the implementation of CASE vehicles on the road. Although there have been significant partnerships and collaborations between CASE vehicle stakeholders, namely technology companies, federal-state agencies, and academic scholars, considerable work is still required to solve the remaining barriers facing CASE-related technologies. This would enable decision-makers to create effective policies for future transportation networks and increase the speed of CASE vehicle market penetration to enhance road network's level of service.

In construction projects, change orders are commonly issued by the owner and may cause interruption to the contractor’s work, resulting in loss of labor productivity, delay, and cost overruns, which may lead to claims. Because of the complexity in linking the cause of the productivity loss to the change order, the relationship is not well understood. The contractor must prove with a credible calculation that the productivity loss was a result of the change order issued by the owner to be able to receive compensation. Compared to all available productivity loss quantification methods, the measured mile approach is considered the most acceptable and popular in litigation. This study is a discourse on the understanding, use, and potential application of system dynamics within quantifying change orders’ impact on labor productivity. The model boundary, including Endogenous and Exogenous variables, are identified. After determining the parameters used in modeling, the CLD is constructed to understand the system structure better. A system dynamics model is developed using Vensim software, verified, validated, and utilized to quantitatively measure the influence of the change in the project scope on labor productivity. To examine the applicability of the proposed model, it has been implemented to a real highway construction case study, and the impacts of various variables on labor productivity loss have been studied using the data for two completed highway construction projects from FDOT. The study can be applied to construction projects that faced change orders, and as a result of these changes, the contractor’s performance was affected. The proposed model can be used to determine the primary sources that result in labor productivity loss on a specific type of task.

The highway network is an economically necessary form of transportation that has a significant impact on the quality of the life of the citizens who use it. Cost overruns in highway projects have been a universal occurrence that jeopardize the development, maintenance, and expansion of this vital infrastructure. Incorrect cost estimations can drive decision makers to pass ineffective policies that have played a large role in the cost overruns of transportation construction projects. The existing prediction models in the literature are limited in one or multiple areas of modeling approach, inputs, and model development robustness. In this research, a model was developed to accurately predict the total construction cost of highway projects by utilizing machine learning algorithms. This study developed a modeling pipeline to automate much of the cost forecasting process, reducing the amount of manual work and dependence on skilled data scientists. This study used the Florida Department of Transportation’s (FDOT’s) critical highway construction cost items between 2001 and 2017 to test the model. The highways of Florida were selected for testing due to the states’ population growth, high immigrant population, logistics, and hurricane frequency. This study used a pool of five categories of independent variables (69 variables total), including the construction market, energy market, socioeconomics, US economy, and temporal variables, which were compiled from relevant sources and existing literature. The results revealed that our linear model exhibits superiority in generalization and prediction of cost items over nonlinear models and is capable of accurately forecasting highway construction costs. Our suggested approach in this study also provides more accurate forecasts for the detailed cost estimation by considering the monthly historical information for the average 92.6% of the six highway construction types mentioned with a 92.51% prediction accuracy. By employing our developed model, local governments, network operators, contractors, and logistics sectors would be capable of a more exact prediction of highway construction costs.

Construction management is considered a hands-on field of study which requires good spatial and visual cognitive ability. Virtual reality and other innovative immersive technologies have been used to facilitate experiential learning and to improve students’ spatial cognitive abilities. Virtual environments have been criticized due to the gamified look of the environment. Static panorama pictures have been previously used to bring a better sense of reality and immersion at the same time in construction education. However, they cannot provide a continuous experience, and the sense of presence (immersion) is not ideal either. Immersive videos such as 360-degree videos can address this shortfall by providing a continuous experience and a better sense of presence. The use of this technology in construction education field is very limited. As a result, this study investigated a pilot experiment where a combination of 360, 180 3D, and flat videos was incorporated as an educational instrument in delivering construction management content. The content was recorded using different configurations from different body postures to further investigate the optimal way of utilizing this technology for content delivery. The content of the videos was focused on construction means and methods. Students reviewed the content using head-mounted display devices and laptop screens and answered a survey designed to capture their perception and experience of using this technology as an educational tool in the construction management field. The results show a positive perception toward using immersive videos in construction education. Furthermore, the students preferred the head-mounted display as their favorite delivery method. As a result, the prospect of incorporating immersive videos to enhance construction management education is promising.

Knowledge of the truck traffic volumes on state and interstate highways is critical for highway authorities and federal organizations. Increased urbanization, population growth, and economic development have led to an increased demand for freight travel. Several planning applications demand reliable and accurate truck traffic prediction. A review of the available literature indicated that limited research had been performed on the development and utilization of a universal automatic framework for truck traffic volume prediction. As a result, there is a gap to incorporate inclusive predictors, a broad dataset, a comprehensive feature selection approach, and a robust cross-validation method that utilizes both linear and non-linear algorithms. The present study uses a hyperparameter optimization framework to select the appropriate feature selection method and modeling approach among a comprehensive list of available state of the art approaches. Distinct from models based on individual case studies, the proposed framework allows for greater customization and minimized MAPE error. The developed framework automates much of the traffic count forecasting process, and the resulting method is less labor-intensive and may be utilized without the need for experienced data analysts. Florida’s interstate highways historical traffic data were used to test the feasibility of the proposed framework. The results of the Florida Case Study revealed the superiority of non-linear models in the generalization and prediction of traffic volumes over linear models. The random forest algorithm results on the test dataset in this study demonstrate this model’s ability to predict truck traffic with 86% accuracy. Spatial variables were the most significant variable group, followed by road characteristics.

In the construction sector, the absence of a framework to incorporate and synchronize the resources required for activities means that projects risk incurring cost and time overruns. In this study, a framework was generated and tested to optimize resource allocation. The framework considers financial constraints through a genetic algorithm process and constraint-based simulation. The simulation method was developed based on the critical path method relation-defining system, with a dynamic prioritizing system automatically creating the simulation network. By introducing a standard cost database to the data management section of the framework, the total work required to insert data manually is minimized. For the majority of the activities, the user simply chooses an activity and determines its relationships with other activities. The rest of the initial required data are automatically pulled from the imported databases. The validity of the developed framework was demonstrated through a case study on constructing the structure of a residential building in Tehran, including four structural systems. The four systems were compared regarding time and cost of execution to illustrate the relevance of the framework. The framework decreases the user’s manual work while producing outputs of time, cost, and resources that are compatible with building information modeling (BIM) software. Overall, the result is an enhanced quality of project management plans. Moreover, the framework can be used as a planning tool at the preconstruction stage or as a control tool during the construction phase. The proposed approach provides a realistic project management plan, with the potential for substantial savings in finances and resources.