Search for: All records

A new Research Experience for Teachers (RET) site was established in the Department of Civil, Construction, and Environmental Engineering at North Dakota State University (NDSU) with funding from the National Science Foundation Division of Engineering Education and Centers (NSF Award #1953102). The site focused on civil engineering instruction around the theme of mitigating natural disasters for secondary education (6th to 12th grade) teachers. Eight local teachers and one pre-service teacher (who comprised the first cohort) were provided with a six-week long authentic research experience during the summer, which they translated into a hands-on curriculum for their classrooms during the 2021-2022 academic year. Partnerships were developed between the host institution, area teachers and local partners from civil engineering industries. This paper will summarize the lessons learned by the authors as well as the effectiveness of the program activities to accomplish two objectives: (1) provide a deeper understanding of civil engineering and (2) develop better abilities among secondary education teachers to prepare future science, technology, engineering and mathematics (STEM) leaders. Several strengths were identified by the authors as they reflected on the summer activities including the successes in creating strong connections between the teachers, faculty members and graduate students, and the industry partners as well as the agility of the core research team to overcome unexpected challenges. However, the reflections also revealed several areas for improvement that would increase the accessibility of the site to underserved and/or underrepresented teacher populations, better utilize the resources available and in general, improve the quality of the program and curriculum developed by the teachers. Included within this paper are suggestions that the authors would make to improve current and future RET sites. All of the teachers agreed or strongly agreed that their participation in the RET program increased their knowledge of STEM topics and specifically, civil engineering topics. The participants agreed to varying extents that they will use the information they learned from the program to teach their students and will implement the new strategies they gained to promote increased student learning about STEM topics. Furthermore, the feedback that they provided corroborated some of the same changes the authors plan to implement. 

Recent developments in autonomous vehicle (AV) or connected AVs (CAVs) technology have led to predictions that fully self-driven vehicles could completely change the transportation network over the next decades. However, at this stage, AVs and CAVs are still in the development stage which requires various trails in the field and machine learning through autonomous driving miles on real road networks. Until the complete market adoption of autonomous technology, a long transition period of coexistence between conventional and autonomous cars would exist. It is important to study and develop the expected driving behavior of future autonomous cars and the traffic simulation platforms provide an opportunity for researchers and technology developers to implement and assess the different behaviors of self-driving vehicle technology before launching it to the actual ground. This study utilizes PTV VISSIM microsimulation platform to evaluate the mobility performance of unmanned vehicles at a 4-way signalized traffic intersection. The software contains three different AV-ready driving logics such as AV-cautious, AV-normal, and AV-aggressive which were tested against the performance of the conventional vehicles, and the results of the study revealed that the overall network operational performance improves with the progressive introduction of AVs using AV-normal, and AV-aggressive driving behaviors while the AV-cautious driving behavior stays conservative and deteriorates the traffic performance. 

With the rapid development of smart cities, interest in vehicle automation continues growing. Autonomous vehicles are becoming more and more popular among people and are considered to be the future of ground transportation. Autonomous vehicles, either with adaptive cruise control (ACC) or cooperative adaptive cruise control (CACC), provide many possibilities for smart transportation in a smart city. However, traditional vehicles and autonomous vehicles will have to share the same road systems until autonomous vehicles fully penetrate the market over the next few decades, which leads to conflicts because of the inconsistency of human drivers. In this paper, the performance of autonomous vehicles with ACC/CACC and traditional vehicles in mixed driver environments, at a signalized intersection, were evaluated using the micro-simulator VISSIM. In the simulation, the vehicles controlled by the ACC/CACC and Wiedemann 99 (W99) model represent the behavior of autonomous vehicles and human driver vehicles, respectively. For these two different driver environments, four different transport modes were comprehensively investigated: full light duty cars, full trucks, full motorcycles, and mixed conditions. In addition, ten different seed numbers were applied to each model to avoid coincidence. To evaluate the driving behavior of the human drivers and autonomous vehicles, this paper will compare the total number of stops, average velocity, and vehicle delay of each model at the signalized traffic intersection based on a real road intersection in Minnesota.