Understanding joint and causal relationships among multiple endogenous variables has been of much interest to researchers in the field of activity and travel behavior modeling. Structural equation models have been widely developed for modeling and analyzing the causal relationships among travel time, activity duration, car ownership, trip frequency and activity frequency. In the model, travel time and activity duration are treated as continuous variables, while car ownership, trip frequency and activity frequency as ordered discrete variables. However, many endogenous variables of interest in travel behavior are not continuous or ordered discrete but unordered discrete in nature, such as mode choice, destination choice, trip chaining pattern and time-of-day choice (it can be classified into a few categories such as AM peak, midday, PM peak and off-peak). A modeling methodology with involvement of unordered discrete variables is highly desired for better understanding the causal relationships among these variables. Under this background, the proposed dissertation study will be dedicated into seeking an appropriate modeling methodology which aids in identifying the causal relationships among activity and travel variables including unordered discrete variables. In this dissertation, the proposed modeling methodologies are applied for modeling the causal relationship between three pairs of endogenous variables: trip chaining pattern vs. mode choice, activity timing vs. duration and trip departure time vs.mode choice. The data used for modeling analysis is extracted from Swiss Travel Microcensus 2000. Such models provide us with rigorous criteria in selecting a reasonable application sequence of sub-models in the activity-based travel demand model system.

Artificial Intelligence in form of Machine Learning classifiers is increasingly applied for travel choice modeling issues and therefore constitutes a promising, competitive alternative towards conventional discrete choice models like the Logit approach. In comparison to traditional theory-based models, data-driven Machine Learning generally shows powerful predictive performance, but often lacks in model interpretability, i.e., the provision of comprehensible explanations of individual decision behavior. Consequently, the question about which approach is superior remains unanswered. Thus, this paper performs an in-depth comparison between benchmark Logit models and Artificial Neural Networks and Decision Trees representing two popular algorithms of Artificial Intelligence. The primary focus of the analysis is on the models’ prediction performance and its ability to provide reasonable economic behavioral information such as the value of travel time and demand elasticities. For this purpose, I use crossvalidation and extract behavioral indicators numerically from Machine Learning models by means of post-hoc sensitivity analysis. All models are specified and estimated on synthetic and empirical data. As the results show, Neural Networks provide plausible aggregate value of time and elasticity measures, even though their values are in different regions as those of the Logit models. The simple Classification Tree algorithm, however, appears unsuitable for the applied computation procedure of these indicators, although it provides reasonable interpretable decision rules for travel choice behavior. Consistent with the literature, both Machine Learning methods achieve strong overall predictive performance and therefore outperform the Logit models in this regard. Finally, there is no clear indication of which approach is superior. Rather, there seems to be a methodological tradeoff between Artificial Intelligence and discrete choice models depending on the underlying modeling objective.

Movements of cyclists and m.otor vehicles at intersections involve a wide variety of potential conflicting interactions. In Australia, the high numbers of motor vehicles, particularly light passenger vehicles, mixed with cyclists results in many bicycle-light passengervehicle (LPV) crashes (3,135 crashes during 2002-2014). About 68% of cyclist deaths at Australian intersections in 2016 were due to crashes between bicycles and LPVs (DITRLDG, 2016). The high number ofLPV crashes among fatalities among cyclists is an increasing safety concem. When an LPV collides with a cyclist, the resulting impact forces in.tluence the probability of cyclist injury severity outcom.e. Therefore, the goa1 at intersections should be to understand whether and which particular crash patterns are more injurious, in order to better inform approaches to reduce the impact forces to levels that do not result in severe injury outcomes. To examine how crash pattem (or mechanism) influences the injury severity of cyclists in bicycle-motor vehicle crashes at intersections, researchers typically describe the crash mechanism in terms of crash types, such as angle crashes, head--on crashes, rear-end crashes, and sideswipe crashes (e.g., Kim et al., 2007; Pai, 2011 ). While crash types explain crash mechanisms to some extent, this study hypothesiz.es that the trajectories of the crash involved vehicles may provide additional information because they better capture the movements of the vehicles prior to collision. Furthermore, it is argued that injury pattem might be in.tluenced by vehicle travel direction and manoeuvre (Isaksson-Hellman and Wemeke, 2017). For example, when a car is moving straight ahead it is likely to have a higher speed than when it is turning, and if cyclists are struck at a higher impact speed, they tend to sustain more severe injury (Badea-Romero and Lenard, 2013). While many studies have evaluated the association between cyclist injwy severity and crash types, the factors that might influence cyclist injury severity related to trajectory types (vehicle movement and travel direction) have not yet been thoroughly investigated. This study aims to examine the factors associated with cyclists' injury severity for 'trajectory types• compared with the typically used 'crash types' at intersections.

個體選擇模式(discrete choice model)廣泛應用於國外的交通運輸及行銷領域，而國內交通運輸領域，也長期以此模式分析個體的運具選擇行為。反觀國內的行銷領域，因較難取得消費者的商品品牌購買紀錄，而鮮少應用個體選擇模式分析消費者的選擇行為。有鑒於此，本研究嘗試以問卷收集消費者對三個洗髮精品牌的選擇行為，以個體選擇模式中的多項邏輯模式(multinomial logit model)、巢狀邏輯模式(nested multinomial logit model)、混合多項邏輯模式(mixed logit model)進行分析，檢驗問卷設計中的促銷活動、消費者特性對選擇行為的影響性。 實證分析的結果發現，洗髮精的原價格及促銷折扣、贈品容量、加量不加價等促銷活動，皆對消費者的選擇行為有顯著的影響力，其中促銷折扣與贈品容量影響的程度較大，是較具有效果的促銷活動。而消費者的性別、年齡、職業及品牌更換的頻率，皆影響洗髮精的選擇行為。此外，消費者若固定選擇自己最常購買的洗髮精，此類型的消費者與其他人的品牌選擇行為，也有顯著的不同。 此外，根據本研究樣本，我們也發現海倫仙度絲與潘婷間的替代、互補性較強。 / Discrete choice model has been demonstrated to be a useful tool for analyzing consumers’ choice behavior data in the area of transportation and marketing research. However, since a complete data set containing consumers’ history of purchase behavior was rarely available to public, the model was less popular in the marketing research area than in the transportation research in Taiwan. Based on limited survey data on consumers’ choice among three different brands of shampoo, we applied multinomial logit model、nested multinomial logit model、mixed logit model in this study to understand promotion program’s effect on consumers’ choice behavior , the result showed that shampoos’ original price、discount、volume of hair conditioner bestowal、more volume with the same price all had significant impacts on consumers’ choice behavior, among them, discount and volume of hair conditioner bestowel influenced more .In addition, consumers’ gender、age、occupation and frequency of changing brands also affected consumers on choosing brands of shampoos. The study also found that a consumer who chose the same brand regularly behaved notably differently.