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Nowadays, the tendency of people using bicycles as the way of transportation has increased as well as the tendency of the bicycle accidents. According to the research of National Highway Traffic Safety Administration (NHTSA), National Survey on Bicyclist and Pedestrian Attitude and Behavior, the major root causes of bicycle accidents are from the road surface condition. Thus, this work has developed the system to detect the road surface condition. The system utilizes the laser and camera to measure the height of road. Then, with the information of the road height and bicycle speed, the road surface condition can be classified into 3 categories due to severe condition of the road. For the secure road, cyclists could safely ride on it. For the warning road, cyclists need to slow down the speed. Lastly, for the dangerous road, cyclists have to stop their bicycles.

According to the recent study, Thailand has the 2nd most dangerous road in the world. Based on many researches, the driver is the main influencers of the traffic fatalities. Since the more dangerous the driver drive, the more chance of accident become. Therefore, driver’s monitoring system become one of the solutions that acceptable and reliable, especially for fleet management and public transportation. This paper’s goal is to find an algorithm that can distinguish driving behaviour based on cars’ acceleration and velocity, calling it as Risk Driving Score (RDS). The algorithm was tested by driving test by volunteers on highways with observers, who were told to rank the drivers in terms of driving risk from the 1-5 point. Meanwhile, the drivers were asked to drive in 3 different styles, normal, safety, and hurry. All drives were recorded by satellite and video data then filtered and used for the algorithm calculation.

The utility of energy efficiency and zero-emission of electric vehicles leads the way to electrify the urban public transport bus networks in many cities around the world. The purpose of this paper is to study the operational feasibility of transition the existing conventional combustion bus fleet to Electric bus fleet. The analysis is based on the data of the bus operation in Bangkok, Thailand. Traffic congestion as occurred in Bangkok is considered in the analysis. This research is focused on Fast charging technology of Electric vehicles. Instead of slow-charging overnight, Fast-charging is used during the regular layover time of the bus operation which is called Opportunity charging. The opportunity charging allows to extend the driving range of the electric buses which is a prominent problem of electric vehicles.

Drowsiness has been one of the major reasons of road fatality over a decade because the effect of drowsiness is trying to shut down both mind and body. That means drivers are unable to completely control the car while trapped in an unconscious state. It will lead to a several number of casualties on road. In this research, two sets of people, partial sleep-deprivation and non-sleep-deprivation, are tested on the driving simulator. Two methods were used in this study to identify driver drowsiness. First, driving behavior focusing on steering pattern. And secondly, vehicle offset from the lane center. The tests were carried out on the designed scenarios in the Driving Simulator. The scenarios are such as following straight lane with vehicle along the road, following curve lane without other cars, car following on a single lane and following straight lane without other cars. From the results, there are significant results that can roughly separate drowsy driver from regular driver.

Urban Mobility is one of the most critical issues at the present. Public transport in connection with feeder bus system is proposed to be one of the main solution. Chulalongkorn University has a fleet of electric feeder bus in operation for a few years now. The fleet service is, however, to be improved because of current limitations in battery energy capacity and long battery charging time. This paper aims to examine the total cost of ownership (TCO) of the electric feeder buses using various types of energy storage. The results on the sensitivity analysis highlight the major parameters that exert strong influence in the TCOs. The fast charging system using supercapacitor battery bus shows the lowest TCO for the present bus fleet. The travel distance (km/year) and operational years were illustrated to be the top two parameters that exert major influence towards the TCO.