The "capacity drop (CD)" phenomenon, which means the queue discharge flow rate is less than the traffic capacity before congestion, is observed at freeway bottlenecks in general. Although this phenomenon has been known for a long time, its precise mechanism is not necessarily clear. This study conducts an empirical analysis of the CD phenomenon based on the recent advanced continuum traffic flow theory (Jin, 2018; Wada et al., 2020) that describes the CD endogenously for freeway sag and tunnel bottlenecks. Specifically, we first show the ideas, components, and consequences of the theory while reviewing the conventional hypotheses for the CD and their issues. We then compare several theoretical predictions with real data (i.e., detector data and ETC2.0 probe data), qualitatively and quantitatively. The results validate the theory and bring us new interpretations of observations, which will help to advance various traffic control methods for mitigating the CD in the future.

In recent years, advances in technological innovations realized in the field of transportation, such as Mobility as a Service (MaaS) and Automated Driving Vehicle with Utility Services (ADVUS), and innovations realized in the medical field, such as wearable terminals, have led to the introduction of connected medicine, linking diverse entities such as places, people, and data related to medical care. Connected medicine can be expected to become an important solution under pressure from declining birthrates and aging populations. In that context, unregulated introduction of a system might engender inefficient use of medical resources, which in turn might engender a decline in local medical services. Therefore, connected medicine systems must be introduced based on an understanding of preferences for medical treatment methods included in connected medicine. For this study, an original questionnaire survey was administered to elicit respondents' willingness to use medical treatment methods and to clarify factors that affect the preferences.