A motorcycle box intersection is a signalized intersection with advanced stop lines or stopping spaces intended for motorcycles, creating a waiting area in front of other vehicles. This study introduces the External Driver Model (EDM) with microscopic traffic simulation using PTV Vissim 2024 software, which replicates the filtering and stopping behavior of motorcycles in mixed traffic on intersection approaches. This research aims to evaluate the traffic performance of motorcycle boxes with respect to motorcycle departure times, headway intervals, lane-filtering rates, and vehicle movement patterns at 12 signalized urban intersections in Chiang Mai, Thailand. The results show that the motorcycle box intersection has improved traffic efficiency, reduced motorcycle departure time, and maintained a constant distance between cars and other vehicles. Signalized intersections with motorcycle boxes improved traffic flow efficiency by favoring motorcycles without affecting car delays. Spatial-temporal visualization further supported the clustering characteristics of motorcycles in motorcycle-stopping areas, contributing to more orderly and predictable behavior in traffic. Furthermore, the lane-filtering rates demonstrated significant improvement at intersections equipped with motorcycle boxes compared to conventional intersection designs. These findings indicated that motorcycle boxes are valuable for motorcycle traffic management and intersection safety in urban areas with high volumes of motorcycle traffic.

The classification of roadway contexts and speeds is a critical step in the planning, design, and operation of highway infrastructure. In developing countries, road users encounter safety and operational issues due to poorly defined roadway contexts and inappropriately determined target speeds for a highway network. This study developed an expert system for classifying roadway contexts and target speeds of multilane highway segments and applied the classification process to 16,235 km of multilane highways in Thailand’s highway network. The proposed methodology used a fuzzy decision mechanism to deal with subjective and imprecise expert judgment (e.g., low, high), many variables, and a complex evaluation process. This study used the Fuzzy Delphi method to identify the possible important factors influencing contexts and speeds and the Fuzzy Inference System method to reason factors to categorize multilane highway segments in Thailand into different classes of roadway contexts (e.g., rural, low-density suburban, high-density suburban, and urban highways) and target speeds (e.g., ≤50 km/h, 50–60 km/h, 60–70 km/h, 70–80 km/h, 80–90 km/h, 90–100 km/h, and 100 km/h). The study was based on data from questionnaire surveys of experts and field investigations of 120 highway segments. The results showed that roadside environments and activities influence the roadway contexts, while the target speeds are sensitive to the roadway characteristics and contexts. These findings support the need for changes in context-adapted highway design and speed management. The proposed expert system provided high accuracy (90.8%) in classifications of both roadway contexts and target speeds. The fuzzy expert system provides a systematic and structural framework for analyzing imprecise data in highway contextual and speed classifications and improving the clarity and accuracy of the evaluation process. The implementation of the fuzzy expert system has the potential to revolutionize the highway classification decision-making problem under uncertainty.

This study developed and evaluated free flow speed (FFS) estimation models for multilane highways in Thailand by applying multivariable linear regression. Data from 272 highway segments were analyzed considering the percentage of heavy vehicles, left shoulder width, and access point density. Highways were categorized into four-lanes and more-than-four-lanes configurations and further analyzed under different base free flow speeds (BFFS) ranging from 90 to 120 km/h. For four-lane highways, Model 1-2 (BFFS = 100 km/h) achieved the best predictive performance, with an R² of 0.9583, root mean squared error (RMSE) of 1.51, and mean absolute error (MAE) of 1.24. For more-than-four-lanes highways, Model 2-3 (BFFS = 110 km/h) performed best (R² = 0.9387, RMSE = 1.86, MAE = 1.46). All variables showed significant negative effects on FFS. The developed models provide practical tools for assessing roadway performance, thereby regulating speeds, and guiding infrastructure planning, enhancing safety and efficiency on Thai multilane highways.