## 5 Conclusions  
There are three important findings in this paper. First is that under bad weather conditions, the critical density that is explained to trigger the capacity drop significantly decreases from an average of 78 veh/km to 65 veh/km, which was discovered through empirical analysis. This is an important discovery since this is the first time weather considerations are included in the capacity drop analysis. There are existing traffic management strategies that avert the capacity drop by controlling density, although measuring densities over extended lengths can be costly as it will involve the installation of closer detectors every few hundred meters, which might be impractical. This paper shows that a  
Figure 4: Relationship between queue discharge rate and speed in congestion (clear and bad weather conditions separated). Figure 5: Relationship between the capacity drop and speed in congestion. single density value can be used to infer the capacity drop on an on-ramp bottleneck. This finding needs to be verified and calibrated for other bottlenecks with the consideration of on-site-specific characteristics. This also exhibits that detector locations are important for the validation. The second important finding of this study is the validation of previous studies about the correlation of the speed in congestion with that of the queue discharge rate. However, this current research shows that the relationship can be extended under different rainfall conditions. This shows that weather does not directly become the influencing factor of the queue discharge rate but that of the speed in congestion. This exhibits that the speed in congestion is still the strong explaining factor of the queue discharge rates regardless of the weather condition. The third finding of this research is the negative linear relationship between the percentage of the capacity drop and the speed in congestion shown for the first time. As the speed in congestion increases, the percentage in the capacity drop between the pre-queue capacity and the queue discharge rate decreases. The observed differences between the queue discharge rate accompanying the speed in congestion can be explained by site-specific characteristics such as the number of lanes, positions of traffic detectors, and vehicle composition. Furthermore, driver behavior from different study areas can also have an impact on these values and must be taken into consideration. Rainfall is the most important climatic element, which happens all throughout the year in many countries, most especially in monsoon seasons. Unlike other events known to reduce capacities, such as road incidents, rainfall conditions are predictable through weather forecasts. With this, transportation system facilities can be efficiently improved if weather data can be incorporated into traffic management techniques, especially in consideration of the capacity drop. Such strategies would be important since higher queue discharge rates mean lower overall system delay. On-going follow-up studies of this paper include incorporating these findings into traffic flow models and using those for active traffic management strategies through simulations. Conceptualization, H.M., A.K. and K.K. ; methodology, H.M. and A.K. ; formal analysis, H.M. and A.K. ; investigation, H.M., A.K. and K.K. ; resources, H.M., A.K. and K.K. ; data curation, H.M., A.K. and K.K. ; writing--original draft preparation, H.M.; writing--review and editing, H.M., A.K. and K.K. ; visualization, H.M., A.K. and K.K. ; supervision, A.K. and K.K. ; funding acquisition, H.M., A.K. and K.K. All authors have read and agreed to the published version of the manuscript. This research project is supported by the Thailand Science Research and Innovation (TSRI), Basic Research Fund: Fiscal year 2023 under project number FRB660073/0164 (Sustainable Mobility: Frontier Research and Innovation on Sustainable Mobility) and the Philippine Department of Science and Technology- Science Education Institute. Not applicable.