Jennifer T. Wong

Analyzing heterogeneous accident data from the perspective of accident occurrence

Clustering and classification approaches have been commonly applied in reducing the heterogeneity in accident data. As part of an effort to understand the features of the heterogeneity, this study assessed accident data from the perspective of accident occurrences. Using the rule-based classification method, rough set theory, rules were derived which consisted of indispensable factors to certain accident outcomes and reflected the process of accident occurrences. The occurring frequency of each derived rule was then adopted as the basis for grouping accidents for further analyses. Empirical results showed that rules with high occurring frequencies were largely related to drivers with high-risk characteristics. On the other hand, road facilities played a key role in rules with low-occurring frequencies. The distinctive features indicated the essential differences between the frequently repeated and the sparsely unique processes of accident occurrences. This suggests that the heterogeneity of accident data is not limited to one single factor, such as age, gender or area. Thus, the proposed approach, which takes the process of accident occurrences into consideration, can be a potential alternative to more comprehensively analyze the heterogeneity in accident data.

Comparison of Methodology Approach to Identify Causal Factors of Accident Severity

Identifying the factors that significantly affect accident severity has become one of the many ways to reduce it. While many accident database studies have reported associations between factors and severities, few of them could assert causality, primarily because of uncontrolled confounding effects. This research is an attempt to resolve the issue by comparing the difference between what happened and what would have happened in different circumstances. Data on accidents were analyzed first with rough set theory to determine whether they included complete information about the circumstances of their occurrence by an accident database. The derived circumstances were then compared with each other. For those remaining accidents without sufficient information, logistic regression models were employed to investigate possible associations. Adopting the 2005 Taiwan single-auto-vehicle accident data set, the empirical study showed that an accident could be fatal mainly because of a combination of unfavorable factors instead of a single unfavorable factor. Moreover, the accidents related to rules with high support and those with low support showed distinct features.

Developing effective professional bus driver health programs: An investigation of self-rated health

The health of professional bus drivers is a critical factor in their driving performance; any impairment may lead to undesired consequences. In an attempt to develop and prioritize health and wellness programs, this study investigates the factors significantly affecting the health conditions of professional bus drivers, as well as the strength of these factors. This study uses self-rated health as the examination measurement. This simple assessment is an inclusive measure of health status for judging health trajectory, and is highly associated with changes in functional ability, including perceived control over driving. This study evaluates driver responses of self-rated health with ordered response models that consider factors such as the driver reported health problems, physical and psychological conditions, demographic factors, driving experience, and working environment. Analysis of a sample of 785 drivers shows that age, body mass index, depression, daily working hours, perceived company safety culture, and health problems are the factors significantly affecting self-rated health. Depression has the greatest effect among all factors except health problems. Unlike the linear relationships for the other factors, the relationships between depression levels and perceived health are S-shaped. The results of ordered response models suggest that these influential factors have distinct effects on the self-rated health of individual drivers and on the different levels of self-rated health.

Development and application of an airport terminal simulation model : A case study of CKS airport

Technology advancement, terminal user behavior and changes of service characteristics of terminal facilities all have great impact on airport terminal operations. Consequently, the adequacy of traditional airport terminal planning concepts and standards have recently been challenged and are worth being explored. To investigate the associated impact of environmental changes on terminal operations, a simulation model which takes into consideration air travel patterns, facility operational characteristics, flight delays, passenger behavior and needs, etc. is developed and verified. The simulation logic and results show that the model works well for the case of CKS (Chiang Kai‐Shek International Airport, Taipei) operations. Also, the exploration demonstrates that passenger arrival patterns, numbers of group passengers, flight delays and load factors all have very significant influences on terminal space requirements. This result strongly suggests that local characteristics should not be neglected in planning ...