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Construction industry is claimed to be the fourth most dangerous sector by number of fatalities. In complex construction sites, emergency evacuation risk assessment is a challenging task due to their ever-changing nature. This study developed a model to analyze the risk of fire emergency occurrence, and risks which are associated with evacuation performance (in response to that emergency) through an integrated approach in complex construction sites. To analyze the evacuation scenarios more realistically, we utilized Social Force Model (SFM) simulation engine. Using SFM for simulating the evacuation of complex construction sites has not been adequately addressed in the literature. Microscopically simulating the evacuation scenarios for all workdays of the studied complex project required high computation efforts. To tackle this computation challenge, a parallel computing technique was coupled with SFM simulation engine. More importantly, in this paper site's evacuation performance was evaluated multi-objectively considering evacuation time and evacuation safety. The construction site's emergency scenarios were modeled by 4D-BIM, potential for trigger fire emergency was determined by a fire ignition Quantitative Risk Assessment (QRA) module, and site evacuation was simulated by SFM simulation engine. The proposed framework handled the collaboration of 4D-BIM, fire QRA module, and SFM engine. This research study benefited from data driven from a real mega project. The findings demonstrated that analyzing the risk of evacuation through an integrated approach by the proposed model could render more realistic results. The results also provided the project managers with a reliable safety decision-making support. [ABSTRACT FROM AUTHOR]

• Investigating a dataset of UAV accidents and incidents in Australia. • Using univariate and bivariate distributions to identify incident patterns. • The main causes of the accidents are equipment issues or/and lack of coordination. • The findings support that necessitating regular safety inspections for UAVs. Unmanned aerial vehicles (UAV) have made life easier in many ways, and their applications in civil practice are increasing rapidly. However, this benefit is not entirely risk-free, as unwanted accidents and incidents can cause serious harm and interrupt other aerial activities. In this paper, we investigate a dataset of UAV accidents and incidents in Australia and put up some precautionary exercises to reduce the risk of future events. To that end, univariate and bivariate distributions of past events are analysed, and the exploratory factor analysis technique is used to identify frequent accident and incident patterns. The findings show that equipment issues or/and lack of coordination between aerial activities are two of the accidents and incidents categories; therefore, necessitating regular safety inspections for UAVs and establishing an integrated monitoring system for aerial activities are expected to reduce the risk of accidents and incidents. [ABSTRACT FROM AUTHOR]

• Introducing a model to automatically design optimized staged-evacuation plan. • Total Evacuation Time, fire exposure risk for evacuees, and congestion severity are considered to evaluate the scenarios. • GA used to search for the optimized departure time and SFM utilized to simulate the suggested staged-evacuation scenarios. • A parallel computing technique is hired to handle high number of SFM simulations. • Results show that the congestion severity was considerably mitigated. For many years, simultaneous evacuation of the entire facility was a common approach. The deficiencies of this approach were revealed after many cases of casualties and evacuation efficiency drop that were observed during some evacuations caused by crowd congestion. To tackle this issue, staged-evacuation process (also called phased-evacuation), which was mainly aimed to mitigate the congestions, was introduced. In this study, a bi-level framework consisted of a pedestrian simulation model (Social Force Model) and a metaheuristic optimization model (GA) was developed to automatically determine the optimum schedule for a staged-evacuation process. In this platform, metaheuristic algorithm iteratively searches for a dominant evacuation schedule, and SFM simulates the evacuees' attributes and evaluates the safety of the evacuation scenario which was proposed by the metaheuristic algorithm. Total Evacuation Time (TET), fire exposure threat, and congestion severity were also considered as the safety objectives which were tried to be minimized. An opportunity was also provided to cover the effects of many pedestrians' exceptional behaviors by executing a high number of microscopic simulations during the metaheuristic iterations. Moreover, more reliable input materials were provided by the microscopic simulator for the optimization algorithm. In this regard, executing multiple simulations in every metaheuristic search demands significant computation efforts. To overcome this challenge, a parallel computing technique was hired which resulted in a considerable reduction in the computation time. Finally, an MCDM method was employed to select the best-satisfying evacuation scenario among the Pareto-frontier solutions. Implementing the proposed framework in a case study enabled us to evacuate the examined facility based on the achieved optimized schedule so that the congestion severity tremendously mitigated while fire exposure threat and TET remained compromisingly at their lowest. [ABSTRACT FROM AUTHOR]

In the past few years, the social science literature has shown significance attention to extracting information from social media to track and analyse human movements. In this paper the transportation aspect of social media is investigated and reviewed. A detailed discussion is provided about how social media data from different sources can be used to indirectly and with minimal cost extract travel attributes such as trip purpose, mode of transport, activity duration and destination choice, as well as land use variables such as home, job and school location and socio-demographic attributes including gender, age and income. The evolution of the field of transport and travel behaviour around applications of social media over the last few years is studied. Further, this paper presents results of a qualitative survey from travel demand modelling experts around the world on applicability of social media data for modelling daily travel behaviour. The result of the survey reveals positive view of the experts about usefulness of such data sources. [ABSTRACT FROM AUTHOR]

• Developed an approach to approximate the demand of a carsharing station. • Improved the estimation accuracy of the model by including social media data and spatio-temporal correlated data. • Applied a spatio-temporal autocorrelation model. • Developed a correlation analysis to account for the interactions among variables. As an emerging urban mobility service, carsharing has become increasingly popular worldwide. To understand customers' needs and optimize the design of service networks, the usage of carsharing vehicles whose trips are recorded by the operators has been applied in research estimating carsharing demand. However, as a form of spatio-temporal correlated data, the underlying spatio-temporal information included in such carsharing records has not been investigated in existing models of carsharing demand. Meanwhile, due to the supply limitation of carsharing stations, some demand cannot be fulfilled and thus remains unrecorded in the operational data. Unrealized demand may lead to underestimation of carsharing demand and therefore an incorrect vehicle deployment strategy by the service providers. In view of these issues, this paper develops an innovative approach to estimating the actual demand at a carsharing station with operational data from GoGet , the largest carsharing company in Australia. The accuracy of the estimation is improved by adding spatio-temporal correlated variables as well as variables from emerging data sources such as social media. To explore the latent space-and-time correlated information, spatio-temporal autoregressive and moving-average models have been applied. Based on the results of the analysis, the paper also provides recommendations related to the operation policies of the service providers. [ABSTRACT FROM AUTHOR]