PURPOSES: The purpose of this study is to compare the concentrations of fine particulate matter (PM2.5) at different types of roadside bus stops in an urban environment, and analyze the tendencies in PM2.5 concentrations according to the air quality index. METHODS : To compare and analyze the characteristics of fine particulate matter at roadside bus stops, we collected data such as PM2.5 concentration, temperature, humidity etc., and performed a comparative analysis of their concentration levels at different types of bus stops (a partially closed bus stop with a front and back partition, a partially closed bus stop with only a back partition, and a bus stop with an open space). In addition, the daily variation in fine particulate matter concentration was analyzed. RESULTS: The average daily concentration levels of fine PM2.5 in the target area for a partially closed bus stop with a front and back partition, a partially closed bus stop with a back partition, and a bus stop with an open space were 18.40㎍/㎥ to 108.27㎍/㎥, 22.81㎍/㎥ to 135.51㎍/㎥, and 16.62㎍/㎥ to 81.52㎍/㎥, respectively. According to air quality index levels during the target measurement period, the bus stop with an open space had the least concentration levels of PM2.5 compared to the other bus stops. Furthermore, this study revealed that the PM2.5 concentration levels usually increased during the peak hour period in the morning and gradually increased after 2 pm until the end of the peak hour period at night, regardless of the bus stop type. CONCLUSIONS: Based on the results of this study, we demonstrated the effect of PM2.5 concentration levels on the atmospheric, weather, environmental, and transportation conditions in a target area, and the variation in concentration levels depending on the type of bus stop.

PURPOSES: This study was initiated to analyze the characteristics of bus traffic accidents, by bus types, using the decision tree in order to establish customized safety alternatives by bus types, including the intra-city bus, rural area bus, and inter-city bus. METHODS: In this study, the major elements involved in bus traffic accidents were identified using decision trees and CHAID algorithm. The decision tree was used to identify the characteristics of major elements influencing bus traffic accidents. In addition, the CHAID algorithm was applied to branch the decision trees. RESULTS : The number of casualties and severe injuries are high in bus accidents involving pedestrians, bicycles, motorcycles, etc. In the case of light injury caused by bus accidents, different results are found. In the case of intra-city bus accidents, the probability of light injury is of 77.2% when boarding a non-owned car and breaching of duty to drive safely are involved. In the case of rural area bus accidents, the elements showing the highest probability of light injury are boarding an owned car, vehicle-to-vehicle accidents, and breaching of duty to drive safely. In the case of intra-city bus accidents, boarding owned car, streets, and vehicle-to-vehicle accidents work as the critical elements. CONCLUSIONS: In this study, the bus accident data were categorized by bus types, and then the influential elements were identified using decision trees. As a result, the characteristics of bus accidents were found to be different depending on bus types. The findings in this study are expected to be utilized in establishing effective alternatives to reduce bus accidents.