In the road transport network, intersections are among the most critical locations leading to a risk of death and serious injury. The traditional methods to assess the safety of intersections are based on statistical analyses that require crash data. However, such data may be under-reported and omit important crash-related factors. The conventional approaches, therefore, are not easily applied to making comparisons of intersection designs under different road classifications. This study developed a risk-based approach that incorporates video-based traffic conflict analysis to investigate vehicle conflicts under mixed traffic conditions including motorcycles and cars in Thailand. The study applied such conflict data to assess the risk of intersections in terms of time-to-collision and conflict speed. Five functional classes of intersections were investigated, including local-road/local-road, local-road/collector, collector/arterial, collector/collector, and arterial/arterial intersections. The results showed that intersection classes, characteristics, and control affect the behavior of motorists and the safety of intersections. The results found that the low-order intersections with stop/no control are high risks due to the short time-to-collision of motorcycle-related conflicts. They generate frequent conflicts with low chance of injury. The high-order intersections with signal control are high risks due to high conflicting speeds of motorcycle–car conflicts. They generate few conflicts but at a high chance of injury. The study presents the applicability of video-based traffic conflict analysis for systematically estimating the crash risk of intersections. The risk-based approach can be deemed as a supplement indicator in addition to limited crash data to evaluate the safety of intersections. However, future research is needed to explore the potential of other road infrastructure under different circumstances.

Objective: Many developing countries experience a high death toll among motorcycle users (both drivers and passengers), primarily due to a relatively low rate of helmet use resulting from ineffective helmet law enforcement. The objectives of this study are to explore the change in helmet use behavior due to helmet use enforcement by closed-circuit television (CCTV) camera technology with an automatic helmet use detection system and to identify the factors associated with helmet use along an urban arterial road in the city of Khon Kaen, Thailand. Methods: Data collection was carried out on 49,128 samples by video cameras installed at 5 signalized intersections during 2 periods, namely, before and during the CCTV camera enforcement. The study applied logistic regression analysis to determine factors associated with helmet use and to compare the ratio of helmet use for each variable according to the odds ratio. Results: The study found that CCTV camera enforcement could increase helmet usage at all study intersections by 5.3%. The results imply that 4 factors, including riding status, number of passengers, day of week, and traffic conditions, significantly affected helmet use both before and during the CCTV camera enforcement. Remarkably, 2 more variables, age and police inspection, significantly affected helmet use during the CCTV camera enforcement period. Conclusions: This study confirms that CCTV camera enforcement can be an important driving force for changing helmet use behavior, particularly for child passengers. Moreover, CCTV camera enforcement can support enforcement by extending coverage to a 24-h period and to intersections without police inspection.

This study aims to find the on-road driving parameters influencing fuel consumption and emissions of motorcycle driving on a congested signalized urban corridor. A motorcycle onboard measurement system was developed to measure instantaneously and continuously record on-road driving data, including speed-time profile, emissions, and fuel consumption, by the second. The test motorcycles were driven by 30 sample motorcyclists on a signalized urban corridor in Khon Kaen City, Thailand, to collect their on-road driving behavior during the morning peak period. Cluster analysis was applied to analyze collected driving data and to categorize the drivers by level of fuel consumption and on-road driver behavior. The on-road driving parameter influencing fuel consumption and emissions was then determined. Results revealed that proportion of idle time significantly influenced fuel consumption and emissions of motorcycle driving on a congested signalized urban corridor, though aggressive driving behavior, hard acceleration and deceleration, did not have the same kind of influence.

Automated vehicles (AVs) provide several advantages in solving issues of road traffic; including enhanced safety, reduced greenhouse gas emissions, and reduced traffic congestion. As AVs are still relatively new developments in developing countries, AV adoption faces challenges from both technological and psychological issues. Therefore, our initial research focus is on identifying the factors that influence the intention to use conditionally automated vehicles (CAVs; SAE Level 3). An extended technology acceptance model (TAM), which includes Trust, Perceived Risks, and Environmental concerns, is proposed as the predictor model in this study. The 299 participants gathered through online surveys in Thailand were examined using the Structural Equation Model (SEM) technique. In this study, Trust was shown to be the strongest predictor of Intention, followed by Perceived Ease of Use, whereas Perceived Usefulness had no impact on intention to use the SAE Level 3. The results of this study will be able to guide the forming of future policies that aim at promoting the use of AVs and helping technology developers create systems to better meet the needs of users in developing nations.

This study developed on-road exhaust emission and fuel consumption models for application in traffic microsimulations to estimate motorcycle emissions and fuel consumption in an Asian developing city. The motorcycle onboard measurement system was developed to instantaneously measure and continuously record on-road driving data, including the speed-time profile, exhaust emissions, and fuel consumption per second. The test motorcycle was driven on roads around Khon Kaen City, Thailand, to collect on-road driving data during the morning peak hours for a total of 112 hours. The collected on-road driving data were applied to develop on-road exhaust emission and fuel consumption models using regression analysis. The models were developed with high correlations among the amount of exhaust emissions and fuel consumption and the instantaneous speed and acceleration rate. The developed models were applied with a traffic microsimulation to evaluate the exclusive zone for motorcycles stopping at a signalized intersection. The evaluation results reveal that it could improve the level of intersection service by decreasing travel times, delays, and queue lengths at intersections, as well as by reducing the fuel consumption and emissions of vehicles travelling through intersections compared with these values under the existing conditions.