• Korean Journal of Construction Engineering and Management
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• v.23 no.2
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• pp.33-44
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• 2022

The simulation function by the 4D system is a representative BIM function in the construction stage. For the 4D simulation, schedule information for each activity must be created and then linked with the 3D model. Since the 3D model created in the design stage does not consider schedule information, there are practical difficulties in the process of creating schedule information for application to the construction stage and linking the 3D model. In this study, after extracting the schedule information of the construction stage using the HDBSCAN algorithm from the 3D model in the design stage, authors propose a methodology for automatically generating schedule information by identifying precedence and sequencing relationships by applying the topological alignment algorithm. Since the generated schedule information is created based on the 3D model, it can be used as information that is automatically linked by the common parameters between the schedule and the 3D model in the 4D system, and the practical utility of the 4D system can be increased. The proposed methodology was applied to the four bridge projects to confirm the schedule information generation, and applied to the 4D system to confirm the simplification of the link process between schedule and 3D model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.118-133
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• 2020

Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.