The orthopedic outpatient department (OPD) ward in a large Thai public hospital is modeled using Discrete‐Event Stochastic (DES) simulation. Key Performance Indicators (KPIs) are used to measure effects across various clinical operations during different shifts throughout the day. By considering various KPIs such as wait times to see doctors, percentage of patients who can see a doctor within a target time frame, and the time that the last patient completes their doctor consultation, bottlenecks are identified and resource‐critical clinics can be prioritized. The simulation model quantifies the chronic, high patient congestion that is prevalent amongst Thai public hospitals with very high patient‐to‐doctor ratios. Our model can be applied across five different OPD wards by modifying the model parameters. Throughout this work, we show how DES models can be used as decision‐support tools for hospital management.

The rapid expansion of new metro systems in the last two decades shows a globally growing interest in urban rail transit infrastructure. Every time a new metro line is opened, it has the potential to change the existing modal split, which is usually dominated by private (car or motorcycle) transport users. For each new or expanding metro system, a feasibility study is conducted, which focusses mainly on existing mobility issues, travel demand, new infrastructure proposed, and environmental and financial aspects of a new metro project. In these documents, specific methodologies are applied, using local parameters and suitable software, for demand forecasting purposes. However, not much attention is usually dedicated to potential users, their modal choice characteristics, willingness to shift to metro and conditions under which such a shift would be encouraging. Therefore, it is often unclear what the actual public perception of a new service is and their interest in using it in the future. As a consequence of various shortcomings in feasibility studies, the actual demand for metro services is often much lower than originally predicted. Hence, a survey targeting a group of potential metro users located close to a planned metro line was designed to investigate people’s willingness to shift to metro and include soft factors that could facilitate such a change. Next, a case study of the Red Line whose final metro stop is at Salaya, Thailand, was selected, and a sample of 667 respondents, both staff and students based at a nearby university campus, was investigated. Results revealed that 90% of the sample is willing to shift to metro (easy and medium shifters) for a future 20-km trip between Salaya and Bangkok, while only 10% remains sceptic (difficult shifters). Moreover, there are some differences in shifters’ priorities, where easy shifters see factors such as reliable timetable, good mode connectivity and seamless station–campus connection as significantly more important than medium or difficult shifters do. Also, there is a significant association between a mode type currently used for Salaya and Salaya–Bangkok commutes, where private transport users (51% of staff and 43% of students) have already shifted to public transport. Based on the outcomes of the study, recommendations are addressed to stakeholders involved in the Red Line, as well as in other new metro projects in Thailand and internationally, and to those who aim to better understand the needs of potential customers in order to facilitate their shift to metro as a preferred mode of transport in the future.

The airport rail link (ARL) was launched in 2010 as a premium rail transit service between the inner city of Bangkok and the airport. In 2014, the express service was canceled due to its unpopularity and transformed into the commuter service. In 2017, the new extended service concept was introduced under the three airport links project. Under this new concept, both ARL and high-speed trains will run on the city line section between Don Mueang and Suvarnabhumi Airports. This paper focuses on the use of a microsimulation model to identify the challenge of mixed-service operations, with regular and express trains running on the city line section. The simulation model allows investigation of hypothetical situations and construction of feasible timetables. The model can identify sections where special attention is needed, such as speed reductions, extended dwell times, or delayed departures. During the peak hour, the results suggest that the regular ARL should run with a 10-min headway and the high-speed one line with a 20-min headway, as an alternative solution. This results in fewer train numbers and less additional running time. Simulations indicate that the high-speed train is not efficient, as it consumes 2–3 times higher energy, while offering little time saving compared with the ARL city train. In addition, the Suvarnabhumi Airport extension track layout needs to be carefully considered, since the Suvarnabhumi Station area is a bottleneck liable to disruption.