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Building on previous work to implement problem-based learning, a mechatronic design course was recast as a semester-long activity culminating in the collaborative design and fabrication of an autonomous vehicle. Students were provided a realistic design scenario early in the course, with subsequent lecture and laboratory activities tying directly to the proposed problem. Following the submission of student design work, and demonstration of their mechatronic devices, student learning outcomes were assessed both indirectly and directly. Indirect assessment implied both the course content and collaborative design project contributed to student learning. Direct assessment of student designs showed improvement from previous semesters.

The objective of this study was to develop a methodology for assessing the impact of road construction that could be used to (a) predict the network-level impact of road construction projects, (b) identify critical roadway segments and corridors in which the impacts of construction are expected to be the most severe, and (c) compare alternative construction scenarios and schedules. Dynamic traffic assignment formed the basis of an approach to assess the regional impact of road construction and compare alternative construction schedule scenarios. The application of the model was illustrated with the use of a hypothetical case of two road construction projects in the roadway system of El Paso, Texas.

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active traffic and demand management (ATDM); behaviorally induced system optimal; dynamic traffic assignment; system optimal; travel behaviour; travel demand management (TDM) ABSTRACTThe basic design concept of most advanced traveler information systems (ATIS) is to present generic information to travelers, leaving travelers to react to the information in their own way. This "passive" way of managing traffic by providing generic traffic information makes it difficult to predict the outcome and may even incur an adverse effect, such as overreaction (also referred to as the herding effect). Active traffic and demand management (ATDM) is another approach that has received continual attention from both academic research and real-world practice, aiming to effectively influence people's travel demand, provide more travel options, coordinate between travelers, and reduce the need for travel. The research discussed in this article deals with how to provide users with a travel option that aims to minimize the marginal system impact that results from this routing. The goal of this research is to take better advantage of the available real-time traffic information provided by ATIS, to further improve the system level traffic condition from User Equilibrium (UE), or a real-world traffic system that is worse than UE, toward System Optimal (SO), and avoid passively managing traffic. A behaviorally induced, system optimal travel demand management model is presented to achieve this goal through incremental routing. Both analytical derivation and numerical analysis have been conducted on Tucson network in Arizona, as well as on the Capital Area Metropolitan Planning Organization (CAMPO) network in Austin, TX. The outcomes of both studies show that our proposed modeling framework is promising for improving network traffic conditions toward SO, and results in substantial economic savings.