Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications

The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data

A novel approach to dynamic profiling of e-customers considering click stream data and online reviews

In this paper, we present an approach for mining change in customer’s behavior for the purpose of maintaining robust profiling model over time. Most of previous studies leave important questions unanswered: In developing B2C e-commerce strategies, how do managers implicitly load customer’s profiles based on their satisfaction over the online store characteristics? And: What kind of feedback segments do they have? Our proposed approach does not force customers to explicitly express their preference information over the online service but rather capture their preference from their online activities. The challenge does not only lay in analyzing how customer’s classifier model change and when it does so but also to adapt it to the customer’s click stream data using a new decision tree generation algorithm which takes as inputs new set of variables; categorical, continuous and fuzzy variables. Customer’s online reviews rates are considered as classes. Experiments show that this work performed well in identifying relevant customer’s stream data to judge the chinese e-commerce website “Tmall”. The extracted values of the website’s features are also useful to identifying the satisfaction level when the customer’s rate is not available.

Using Smartpens to Examine and Influence the Relationship between Homework Habits and Academic Achievement in Introductory Engineering Courses

This dissertation examines students’ homework behaviors and their relationship to academic achievement in introductory engineering courses. Much of the prior work examining the relationship between homework and achievement has relied on student self-reports of homework effort. Our results demonstrate that such self-reports are problematic. Instead, we avoid this methodological shortcoming by using smartpens to objectively measure students’ learning activities in an unobtrusive manner and with a high level of fidelity. This dissertation examines how much, how frequently, and when students work on their homework assignments, and if these factors are related to achievement. This dissertation also examines if informing students of their homework behavior influences them to change that behavior and improve achievement. This work makes four major contributions. First, we developed quantitative measures of student homework behavior that are related to academic achievement. Second, we demonstrate that self-reported measures of student homework effort are problematic. Third, we show that measures of homework effort early in a course are nearly as effective at predicting achievement as measures from the entire course. This result suggests that student behavior does not change significantly over a course. Finally, we show that informing students of their homework behaviors, and providing suggestions for improving those behaviors, is an insufficient motivator to change behaviors and improve achievement. This result suggests a two-stage model of metacognition for study behaviors, requiring both monitoring (i.e., being aware of how one is studying) and regulation (i.e., adjusting how one studies based on feedback) to affect changes in behavior.This work makes both applied and methodological contributions to educational research. In contrast to existing research, our results demonstrate a strong and consistent relationship between students’ homework behaviors and academic achievement. Additionally, this work shows that students’ homework behaviors are established early in a course, and tend to remain relatively constant throughout a course.This work highlights the potential of educational data mining and smartpen technology for educational research. Our results confirm the unreliability of studies employing self-reports. Our studies also speak to the value of replication in education research

Issues in Process Variants Mining

In today's dynamic business world economic success of an enterprise increasingly depends on its ability to react to internal and external changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, will lead to a large number of process variants, which are created from the same original process model, but might slightly differ from each other. This paper deals with issues related to the mining of such process variant collections. Our overall goal is to learn from process changes and to merge the resulting model variants into a generic process model in the best possible way. By adopting this generic process model in the PAIS, future cost of process change and need for process adaptations will decrease. Finally, we compare our approach with existing process mining techniques, and show that process variants mining is additionally needed to learn from process changes