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Updated: 10 min 25 sec ago

Superpave Mix Design for Local Agencies, Feb 21-23

10 min 25 sec ago
The SUPERPAVE mix design method is designed to replace the Hveem method. California Department of Transportation (Caltrans) started implementing the national SUPERPAVE standard for designing, specifying, and accepting pavement projects for all state jobs. The new mix design accounts for traffic loading and environmental conditions and includes a new method of evaluating the asphalt mixture. This course provides an overview of the SUPERPAVE mix design for local agencies and adjustments needed to start transitioning to the new mix design.

NSF CAREER Workshop, Feb 23

10 min 25 sec ago
The NSF Faculty Early Career Development (CAREER) Program is an NSF-wide activity that offers the National Science Foundation's most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations. The CAREER Award program requires an integration of research and education activities beyond the scope of a regular NSF grant.

BRDO offers a free NSF CAREER Award Workshop for early career faculty each spring. This workshop provides information on NSF CAREER requirements and includes concrete suggestions on how to write a competitive proposal. A panel discussion with current CAREER awardees is a centerpiece of the workshop. Lunch will be provided. Registration required.

Modeling and Analysis of Ridesourcing Services, Apr 21

10 min 25 sec ago
Bio: Dr. Yafeng Yin is a Professor at Department of Civil and Environmental Engineering, University of Michigan. He works in the area of transportation systems analysis and modeling, and has published over 90 refereed papers in leading academic journals.. Dr. Yin is the Editor-in-Chief of Transportation Research Part C: Emerging Technologies and serves on the editorial boards for another four transportation journals. He is a member of Transportation Network Modeling Committee and International Cooperation Committee of Transportation Research Board. He is also the Immediate Past President of Chinese Overseas Transportation Association (COTA). Dr. Yin received his Ph.D. from the University of Tokyo, Japan in 2002, his master’s and bachelor’s degrees from Tsinghua University, Beijing, China in 1996 and 1994 respectively. Prior to his current appointment at the University of Michigan, he was a faculty member at University of Florida between 2005 and 2016. He worked as a postdoctoral researcher and then assistant research engineer at University of California at Berkeley between 2002 and 2005. Between 1996 and 1999, he was a lecturer at Tsinghua University.

Increasing Freeway Capacity by Efficiently Timing its Nearby Arterial Traffic Signals, May 5

10 min 25 sec ago
Abstract: The objective of freeway on-ramp metering is to regulate the entry of vehicles to prevent capacity drop on the freeway mainline. However, the nearby arterial traffic signals facilitating freeway access fail to recognize that the metered on-ramps can be oversaturated due to the flow restriction and limited storage. Instead, the arterial traffic signals provide long cycles in order to maximize arterial capacity during peak hours. This often leads to large platoons of arterial traffic advancing to the on-ramps and thus queue spillback on the surface street. As a result, most ramp meters employ a “queue override” feature that is intended to prevent the on-ramp queue from obstructing traffic conditions along the adjacent surface streets. The override is triggered whenever a sensor placed at the entrance of the on-ramp detects a potential queue spillover of the on-ramp vehicles on the adjacent surface streets, and releases the queue into the freeway. The queue override reduces the effectiveness of ramp metering during the time of highest traffic demand, when the ramp metering is most needed. A field test undertaken at a freeway bottleneck in San Jose, California shows that queue override may reduce the freeway capacity by 10%. Significant benefits can be realized by reducing cycle length to prevent on-ramp oversaturation and thereby queue override. A method for determining the appropriate cycle length was developed and the improved signal timing was tested through simulation. The results show that the proposed approach prevented queue override and reduced both freeway and arterial delays.

Multicopter Dynamics and Control: Surviving the complete loss of multiple actuators and quickly generating trajectories, Feb 24

10 min 25 sec ago
Abstract: Flying robots, such as multicopters, are increasingly becoming part of our everyday lives, with current and future applications including personal transportation, delivery services, entertainment, and aerial sensing. These systems are expected to be safe and to have a high degree of autonomy. This talk will discuss the dynamics and control of multicopters, including some research results on trajectory generation for multicopters and fail-safe algorithms. We will also discuss the intersection of drones with personal transportation, and discuss some of the dominant scaling laws affecting the use of multicopters for personal transportation. Finally, we will present the application of a failsafe algorithm to a fleet of drones performing as part of a live theatre performance on New York's Broadway.

Bio: Mark W. Mueller joined the mechanical engineering department at UC Berkeley in September 2016. He completed his PhD studies, advised by Prof. Raffaello D'Andrea, at the Institute for Dynamic Systems and Control at the ETH Zurich at the end of 2015. He received a bachelors degree from the University of Pretoria, and a masters from the ETH Zurich in 2011, both in Mechanical Engineering.

California Transportation Planning Conference, Mar 3-5

10 min 25 sec ago
The California Department of Transportation (Caltrans), in partnership with the Institute of Transportation Studies (ITS) at University of California, Berkeley present the: 2017 California Transportation Planning Conference, Partnering for Sustainable Transportation: Meeting the Challenge Now and Into the Future.

This three-day conference will provide attendees the opportunity to interact with transportation practitioners and decision-makers, exchange ideas and learn about emerging technologies and advancements in transportation planning from national, state, and local experts. The conference will focus on themes around sustainability and how we can partner to meet the challenges facing us now and into the future as required by California legislation and influenced by funding constraints.

Transportation as a Language: Mobility management of China’s urban billion, Mar 10

10 min 25 sec ago
Abstract: The rapid urbanization and economic growth in China uniquely characterize her transportation challenges and corresponding solutions. Extraordinary growth calls for extraordinary measures. Boldness in both infrastructure development and policy design seems commonplace in China’s transportation arena. This talk, however, will present the subtleties in these bold designs through three stories: the rise and decline of bicycles, the high speed rail and mega-regionalization, and contrasting policy models of automobile management. I see urban transportation as a language, to describe a person, to characterize a city, and to understand an institution in contemporary Chinese society. The talk starts and ends with the speculations of the (im)possibility of sustainable transportation in China and a glimpse of hope.

Bio: Jinhua Zhao is the Edward and Joyce Linde Assistant Professor of Urban Planning in the Department of Urban Studies and Planning at MIT. He holds Master of Science, Master of City Planning and Ph.D. degrees from MIT and a Bachelor's degree from Tongji University. Prof. Zhao brings behavioral science and transportation technology together to improve urban mobility systems and policies. He also studies China’s urbanization and urban mobility. Prof. Zhao directs the Urban Mobility Lab at MIT.

Geometric Design for California, Mar 14-16

10 min 25 sec ago
This 3-day course covers the principles and best practices of roadway geometric design for various functional classes of roadways, including local streets, arterials and freeways, intersections and interchanges. This course focuses on practical, real world applications of geometric design methods. Developed with professionals in California in mind, the course will use design standards and guidelines in the Caltrans Highway Design Manual, the AASHTO "Greenbook," and other materials as appropriate. In addition to the geometric design focus, this course also addresses topics related to successful design and re-design practices in California, including stage construction, traffic handling, value analysis, context sensitive approach, owners to designers, etc. This fast-paced, hands-on course combines presentations, case-study examples, problem-solving and class exercises, with ample opportunity for networking and questions.

Pavement Management Systems and Preservation Strategies, Mar 15-16

10 min 25 sec ago
Pavement networks are often the most valuable asset that an agency owns. This asset is not only expensive to replace, but it is an essential component to the traveling public's safety. Agencies are looking for more cost-effective ways to perform engineering, maintenance, management, and rehabilitation of roadways more than ever before to stretch funding allocations. A pavement management system is an essential tool to assist in cost-effective roadway maintenance planning.

Bayesian Optimization and Self Driving Cars, Mar 17

10 min 25 sec ago
Abstract: An important property of embedded learning systems is the ever-changing environment they create for all algorithms operating in the system. Optimizing the performance of those algorithms becomes a perpetual online activity rather than a one-off task. I will review some of these challenges in autonomous vehicles. I will discuss active optimization methods and their application in robotics and scientific applications, focusing on scaling up the dimensionality and managing multi-fidelity evaluations. I will finish with lessons learned and thoughts on future directions as we strive to bring autonomous vehicles into widespread use.

Bio: Dr. Jeff Schneider is the engineering lead for machine learning at Uber's Advanced Technologies Center. He is also a research professor in the Carnegie Mellon University School of Computer Science. He has 20 years experience developing, publishing, and applying machine learning algorithms in government, science, and industry. He has more than 100 publications and regularly gives talks and tutorials on the subject.

Transit-Oriented Development: Putting it all Together, Mar 20-29

10 min 25 sec ago
Transit-oriented development (TOD) has emerged as a powerful, effective way to integrate land use and public transit. TOD done right links smart growth and sustainability with higher capacity rail or bus transit services. This linkage takes place in the environs of the rail passenger station or the bus rapid transit stop. TOD concentrates workplaces, residences, and supporting retail services within convenient walking distance of rail or bus rapid transit service. In doing so, TOD brings customers to public transit services as well as creates vibrant, mixed-use communities. There are many challenges in creating successful TODs. These include building effective public-private partnerships, ensuring multi-modal TOD access for the "last mile" and beyond, "right-sizing parking", and balancing private and public uses to create a unique place identify.

Changing Fuel Loading Behavior to Improve Airline Fuel Efficiency, Mar 24

10 min 25 sec ago
Abstract: Airlines rely on flight dispatchers to perform the duty of fuel planning. The required trip fuel is calculated by airlines’ Flight Planning Systems (FPS). However, the FPS trip fuel predictions are not always accurate. If planned trip fuel is higher than actual trip fuel, then a flight will waste fuel by carrying excess fuel weight. On the other hand, if trip fuel is under-estimated, then a flight might run into fuel emergency. In practice, dispatchers may also load contingency fuel to mitigate the risks of under-prediction. FPS also calculates recommended contingency fuel quantity for dispatchers called statistical contingency fuel (SCF). However, dispatchers will almost always load extra fuel above suggested SCF values. Therefore, airline fuel efficiency can be improved by more accurate fuel predictions, a deeper understanding of dispatchers’ fuel loading behavior, and more reliable SCF recommendations. Based on a large scale flight fuel loading dataset provided by a US major airline, an ensemble learning algorithm is proposed to improve fuel burn prediction. This method is found to reduce prediction error by over 50% compared to airline’s own predictions. By merging with a dispatcher survey, we are able to integrate dispatchers’ latent attributes into contingency fuel loading modeling. Furthermore, random quantile forests method will also be discussed in improving SCF recommendations. The benefit of improved fuel efficiency will be measured by estimating cost-to-carry reduced unnecessary fuel loading.

Bio: Lei Kang is a Ph.D. candidate of the Institute of Transportation Studies in the Department of Civil and Environmental Engineering, University of California, Berkeley. He received a Master of Arts degree in Biostatistics from the Division of Biostatistics at University of California, Berkeley. He also obtained his Master’s degree in Transportation and Infrastructure Systems Engineering from Purdue University. Lei's Bachelor’s degree is in Transportation Engineering from Tongji University in Shanghai, China. He is a member of the Committee on Airfield and Airspace Capacity and Delay, Transportation Research Board. His research interests are in the application of statistical methods and machine learning techniques to air traffic management and airline fuel loading decisions. He is also interested in causal inference in the area of traffic safety.

Traffic Signal Design: Engineering Concepts, Mar 29-30

10 min 25 sec ago
This newly updated course covers basic concepts, standards, and practices related to the design and installation of traffic signals. Within the framework of the California Vehicle Code, California Manual on Uniform Traffic Control Devices (CA MUTCD), and Chapter 9 on Highway Lighting from Caltrans Traffic Manual, this course will explore the relationship among various engineering disciplines as foundations for signal design; introduce signal phasing diagrams, signal controllers and cabinets; explain the layouts of signal heads, signal poles, conductor schedule, and associated signal conduits, pullboxes, wiring, interconnects, detection and safety lighting. The course includes lectures, sample problems, and exercise projects that will familiarize the course participant with the design process for a simple signal design plan, and to provide for a unit-price-based cost estimate. While this course will focus only on the introductory engineering aspects in signal design and introduce some local agencies' equivalent standards and specifications that vary from Caltrans, the goal is for the course participants to become familiar with standards and specifications that guide the design and lead to successful project delivery of an operational traffic signal.

Commercial Development Site Design and Traffic Impact Analysis, Apr 6-7

10 min 25 sec ago
This new online course is about examining the key components that result in effective internal circulation for commercial land development projects. The course will also focus on why earlier designs have failed to provide good circulation and the resulting impacts on the tenants of shopping centers and business parks. It will discuss the problem of designing commercial development projects for safe access and minimizing traffic impacts on the neighboring roads. It will also discuss the preparation of traffic impact studies for new development projects to make sure impacts are properly addressed and cases studies of projects where studies failed to do so.

Airport Capacity Prediction Using Machine Learning and its Applications, Apr 7

10 min 25 sec ago
Abstract: Air traffic managers and flight operators are faced with challenging decisions due to the uncertainty in capacity stemming from variability in weather, demand and human factors. Accurate airport capacity predictions are necessary to develop efficient decision-support tools for air traffic control and for planning effective traffic management initiatives. Capacity of an airport can be observed only at sufficiently large demand. However, if the throughput of an airport is limited by the demand, we can only conclude that the capacity is larger than or equal to the observed throughput. This inability to directly observe capacity makes capacity prediction a challenging and less explored problem.

This work applies machine-learning methods that incorporate observations censored by insufficient demand to develop an airport capacity prediction model. The model predicts a capacity distribution rather than a single capacity value for an hour of interest at an airport using its weather and scheduled demand data. We also discuss validation measures that account for the presence of censored observations. This work explores an important application of the estimated model: to develop capacity-based distance metric between two days using their predicted hourly capacity distributions. For a given reference day, the capacity-based distance can be used to identify similar historical days. The traffic management initiatives taken on past similar days and their resulting outcomes can augment controller experience to guide decision-making on the reference day at an airport.

Bio: Sreeta Gorripaty is a doctoral candidate in the Transportation Engineering program at UC Berkeley. Sreeta received her MS in Transportation Engineering at UC Berkeley and did her undergraduate in Civil Engineering from IIT Bombay. Her research focuses on applying machine learning and statistical methods to improve air traffic management and airport planning. Sreeta received the Graduate Research Award from Airport Cooperative Research Program in 2015 and also won Women's Transportation Seminar (WTS) Legacy Scholarship in 2015.