CO2 emission of rail transportation system plays more important role including the expansion of BTS route that consume the energy and emit CO2 gas more and more. The purpose of this research was to find out amount of CO2 emission from travelling by BTS train and determine specific CO2 emission per product in the form of gCO2e/passenger/km then forecasting on CO2 emission from BTS operations. Research process was initiated from collecting monthly data on ridership distance and electricity for 55 months continuously then regression analysis CUSUM and time series analysis were used for prediction and analysis of CO2 emission. The finding indicated that BTS’s specific CO2 emission was 24.7 gCO2e/passenger/km. Forecasting CO2 Emission from BTS result that Winters Exponential Smoothing Method is most accuracy then Double Exponential Smoothing Method Trend Analysis Method and Decomposition Method respectively. The result of these analysis could be used to calculate carbon footprint from staff travelling by BTS train and promote people to use public transport by government as sustainable transportation concept.

Accurate efficient and robust travel time prediction is crucial to the development of advanced traveler information systems for providing route guidance information. To achieve this goal this paper proposed a travel time prediction through the matching of the current speed interval pattern to that in a historical database. Speed intervals instead of speeds are considered in this study to simplify the structure of matching patterns for improving matching efficiency. In this study speed interval patterns are defined by sets of link speed intervals that are either spatially or temporarily correlated with the link considered. With the speed interval patterns the algorithm is developed for searching the historical pattern(s) that is/are the closest match with the current one. Then link speeds from these matched patterns are combined for travel time prediction. By using the GPS probe taxi data which the collected speeds are aggregated in every 5 minutes the proposed travel time prediction system is implemented in Bangkok. With the speed data from probe taxi this paper has chosen four links/paths with different geometric and flow characteristics for testing the performance of the proposed travel time prediction system. From these tests it is found that the optimal speed interval pattern should include: 1) speeds of the studied link within three preceding time intervals and; 2) speeds of links in the first connection level of the studied link. Also while the computational time is capable of real-time application the proposed prediction algorithm is more accurate under uninterrupted flow conditions.

The purpose of this independent study was to investigate factors influencing motivation of Thai tourists visiting Black Sand Beach Laem Ngop District Trat Province. The samples used in this study were 400 Thai tourists who came to visit at Black Sand Beach Laem Ngop District Trat Province. The research instrument was a questionnaire to collect data which were analyzed through frequency percentage average values standard deviation t-test and F-test (One Way ANOVA). The study result indicated that there were more male tourists traveling to Black Sand Beach Laem Ngop District Trat than female. Most of them were single and 21-30 years old earning 15000-30000 baht per month. They lived in other central provinces rather than Bangkok and habitually traveled with the families once or twice. They obtained information from the internet and came for leisure activities that had attractively motivated them to find the adventure visit the natural tourist attractions which were friendly and quiet. In conclusion Thai tourists differed in terms of their demographic characteristics such as gender age occupation income educational level residence and status at the 0.05 level of significance; however their traveling motivation was insignificantly different