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Highway asset condition is of the utmost importance for transportation maintenance and pedestrian safety. Transportation facility managers must have up-to-date information on the status of all transportation assets to keep the transportation facilities operating at their highest level. Because of the sheer volume of transportation assets, an efficient and affordable data-collection procedure is necessary to gather the as-is status of the assets and create an asset inventory. Some pioneer departments of transportation in the United States use mobile Light Detection and Ranging (LiDAR) to monitor highway assets and pavement condition data. Not only is the laser scanning equipment expensive, but the operator in charge of using the equipment must have special technical knowledge that may not be accessible to every individual. More recently, image-based reconstruction, known as photogrammetry, has emerged as a cheaper and simpler technology than LiDAR. Image-based 3D reconstruction can be done using a digital camera, such as a digital single-lens reflex camera or even a smartphone. This paper presents a full review of various research studies conducted on highway asset management and pavement condition assessment using spatial data modeling by the use of LiDAR and photogrammetry. This paper also presents two case studies to fill the current research gap in highway asset inventorying using photogrammetry. The results show the superiority of mobile LiDAR for highway asset inventorying and the possibility of having photogrammetry as a reliable alternative technology only in favorable illumination conditions.

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Road reconstruction and the resulting work zones are considered as a major source of traffic congestion and delays on freeways. The roadway capacity is decreased as a result of a reduced number of traffic lanes, narrower lanes, and work zone speed limits. Accurate prediction of construction work zone capacity helps traffic engineers to have a better estimation of the traffic flow characteristics. To this end, multiple methodologies have been developed to quantify the impacts of work zones on traffic flow. This paper presents a critical review of the three types of approaches to estimating construction work zone capacities, including parametric, non-parametric, and simulation. Then the most commonly considered factors and their frequency are presented. It also performs a detailed review of the approaches, their objectives, and weaknesses. Lastly, it provides recommendations for future research. The presented work could help researchers in the area of work zone capacity estimation by presenting all the previous methodologies in one place.

PurposeThis study provides an integrated risk-based cost and time estimation approach for deep excavation projects. The purpose is to identify the best practices in recent advances of excavation risk analysis (RA) and integrate them with traditional cost and time estimation methods.Design/methodology/approachThe implemented best practices in this research are as follows: (1) fault-tree analysis (FTA) for risk identification (RI); (2) Bayesian belief networks (BBNs), fuzzy comprehensive analysis and Monte Carlo simulation (MCS) for risk analysis; and (3) sensitivity analysis and root-cause analysis (RCA) for risk response planning (RRP). The proposed approach is applied in an actual deep excavation project in Tehran, Iran.FindingsThe results show that the framework proposes a practical approach for integrating the risk management (RM) best practices in the domain of excavation projects with traditional cost and time estimation approaches. The proposed approach can consider the interrelationships between risk events and identify their root causes. Further, the approach engages different stakeholders in the process of RM, which is beneficial for determining risk owners and responsibilities.Originality/valueThis research contributes to the project management body of knowledge by integrating recent RM best practices in deep excavation projects for probabilistic estimation of project time and cost.