Engaging Undergraduate Students in Transportation Studies through Simulating Transportation for Realistic Engineering Education and Training (STREET)

The practice of transportation engineering and planning has evolved substantially over the past several decades. A new paradigm for transportation engineering education is required to better engage students and deliver knowledge. Simulation tools have been used by transportation professionals to evaluate and analyze the potential impact of design or control strategy changes. Conveying complex transportation concepts can be effectively achieved by exploring them through simulation. Simulation is particularly valuable in transportation education because most transportation policies and strategies in the real world take years to implement with a prohibitively high cost. Transportation simulation allows learners to apply different control strategies in a risk-free environment and to expose themselves to transportation engineering methodologies that are currently in practice. Despite the advantages, simulation, however, has not been widely adopted in the education of transportation engineering. Using simulation in undergraduate transportation courses is sporadic and reported efforts have been focused on the upper-level technical elective courses. A suite of web-based simulation modules was developed and incorporated in the undergraduate transportation courses at University of Minnesota. The STREET (Simulating Transportation for Realistic Engineering Education and Training) research project was recently awarded by NSF (National Science Foundation) to develop web-based simulation modules to improve instruction in transportation engineering courses and evaluate their effectiveness. Our ultimate goal is to become the epicenter for developing simulation-based teaching materials, an active textbook, which offers an interactive learning environment to undergraduate students. With the hand-on nature of simulation, we hope to improve student understanding of critical concepts in transportation engineering and student motivation toward transportation engineering, and improve student retention in the field. We also would like to disseminate the results and teaching materials to other colleges to integrate the simulation modules in their curricula.Transportation Education and Training, Transportation Simulation, Roadway Geometry Design

Paving The Way: Recruiting Students into the Transportation Professions, MTI Report 08-03

The transportation industry faces a growing shortage of professional engineers and planners. One key strategy in solving this problem will be to encourage more civil engineering and urban planning students to specialize in transportation while completing their degrees, so that employers have a larger pool of likely recruits. However, very little is known about how these students choose a specialization. To help fill that gap, this report examines the factors that lead civil engineering undergraduates and urban planning masters students to specialize in transportation, as opposed to other sub-disciplines within the two fields. The primary data collection methods were web-based surveys of 1,852 civil engineering undergraduates and 869 planning masters students. The study results suggest steps the transportation community can take to increase the number of civil engineering and planning students who choose to specialize in transportation

A Cyberinfrastructure for BigData Transportation Engineering

Big Data-driven transportation engineering has the potential to improve utilization of road infrastructure, decrease traffic fatalities, improve fuel consumption, decrease construction worker injuries, among others. Despite these benefits, research on Big Data-driven transportation engineering is difficult today due to the computational expertise required to get started. This work proposes BoaT, a transportation-specific programming language, and it's Big Data infrastructure that is aimed at decreasing this barrier to entry. Our evaluation that uses over two dozen research questions from six categories show that research is easier to realize as a BoaT computer program, an order of magnitude faster when this program is run, and exhibits 12-14x decrease in storage requirements

Attracting and Retaining Women in the Transportation Industry

This study synthesized previously conducted research and identified additional research needed to attract, promote, and retain women in the transportation industry. This study will detail major findings and subsequent recommendations, based on the annotated bibliography, of the current atmosphere and the most successful ways to attract and retain young women in the transportation industry in the future. Oftentimes, it is perception that drives women away from the transportation industry, as communal goals are not emphasized in transportation. Men are attracted to agentic goals, whereas women tend to be more attracted to communal goals (Diekman et al., 2011). While this misalignment of goals has been found to be one reason that women tend to avoid the transportation industry, there are ways to highlight the goal congruity processes that contribute to transportation engineering, planning, operations, maintenance, and decisions—thus attracting the most talented individuals, regardless of gender. Other literature has pointed to the lack of female role models and mentors as one reason that it is difficult to attract women to transportation (Dennehy & Dasgupta, 2017). It is encouraging to know that attention is being placed on the attraction and retention of women in all fields, as it will increase the probability that the best individual is attracted to the career that best fits their abilities, regardless of gender

Ethical and Social Aspects of Self-Driving Cars

As an envisaged future of transportation, self-driving cars are being discussed from various perspectives, including social, economical, engineering, computer science, design, and ethics. On the one hand, self-driving cars present new engineering problems that are being gradually successfully solved. On the other hand, social and ethical problems are typically being presented in the form of an idealized unsolvable decision-making problem, the so-called trolley problem, which is grossly misleading. We argue that an applied engineering ethical approach for the development of new technology is what is needed; the approach should be applied, meaning that it should focus on the analysis of complex real-world engineering problems. Software plays a crucial role for the control of self-driving cars; therefore, software engineering solutions should seriously handle ethical and social considerations. In this paper we take a closer look at the regulative instruments, standards, design, and implementations of components, systems, and services and we present practical social and ethical challenges that have to be met, as well as novel expectations for software engineering.Comment: 11 pages, 3 figures, 2 table

Work Organisation and Innovation - Case Study: Bombardier, Belgium

[Excerpt] Bombardier Inc. is headquartered in Montréal, Canada and is structured around two businesses of almost the same size: aerospace and transportation. It has 76 production and engineering sites in more than 60 countries, and employs 65,400 people. Bombardier Aerospace designs, manufactures and supports innovative aviation products for the business, commercial, specialised and amphibious aircraft markets. Bombardier Transportation is the global leader in the rail industry. It covers the full spectrum of rail solutions, ranging from complete trains to subsystems, maintenance services, system integration and signalling. Bombardier Transportation is headquartered in Berlin

Digital Dissemination Platform of Transportation Engineering Education Materials Founded in Adoption Research

INE/AUTC 14.0

Inverse optimal transport

Discrete optimal transportation problems arise in various contexts in engineering, the sciences and the social sciences. Often the underlying cost criterion is unknown, or only partly known, and the observed optimal solutions are corrupted by noise. In this paper we propose a systematic approach to infer unknown costs from noisy observations of optimal transportation plans. The algorithm requires only the ability to solve the forward optimal transport problem, which is a linear program, and to generate random numbers. It has a Bayesian interpretation, and may also be viewed as a form of stochastic optimization. We illustrate the developed methodologies using the example of international migration flows. Reported migration flow data captures (noisily) the number of individuals moving from one country to another in a given period of time. It can be interpreted as a noisy observation of an optimal transportation map, with costs related to the geographical position of countries. We use a graph-based formulation of the problem, with countries at the nodes of graphs and non-zero weighted adjacencies only on edges between countries which share a border. We use the proposed algorithm to estimate the weights, which represent cost of transition, and to quantify uncertainty in these weights