Student Symposium

Mogeng Yin

Conditional Signal Priority: Its Effect on Transit Reliability, Transfers and Traffic

Zahra Amini

Activity-Based Travel Demand Model from Cellular Data

Colin Sheppard

The Value Proposition of Vehicle Grid Integration

May 11, 2018

Paul AndersonJoin us for our student symposium, where five doctoral students presented their work from 1-4 p.m., reception to follow, in 290 Hearst Memorial Mining Building, May 11, 2018.

1:00  Paul Anderson 

Conditional Signal Priority: Its Effect on Transit Reliability, Transfers and Traffic

Abstract: Travel time variability is a major problem in bus operations which increases costs for transit agencies and travel times for passengers. In current practice, infrastructure changes can reduce delays to buses and early buses can be returned to schedule by holding, but there are few good options for late buses. A strategy of conditional signal priority is proposed where buses use their current position with respect to the schedule and/or other buses to decide whether or not to request priority. In isolation, this strategy is shown to return both early and late buses to schedule, leading to a steady state where performance is accurately predicted by a Brownian motion with two-valued drift. Used in conjunction with driver holding or anti-bunching strategies, conditional signal priority can achieve the same performance as transit signal priority with fewer requests and therefore less impact on car traffic. Moreover, this strategy can be implemented with existing automatic vehicle location and bus-to-signal communications technology.

Bio: Dr. Paul Anderson is a Postdoctoral Researcher in the Department of Civil & Environmental Engineering at the University of California, Berkeley. His research focuses on the needs of people and on developing robust and scalable control strategies for public transit and traffic operations. Other recent work includes transfer coordination, dynamic bus lanes, cooperative ramp metering, and graph theory methods for data association.

1:30 Mogeng Yin

Activity-Based Travel Demand Model from Cellular Data

Abstract: Activity-based travel demand models are becoming essential tools used in transportation planning and regional development scenario evaluation. However, data collection for activity based models is performed through travel surveys that are infrequent, expensive, and reflect the changes in transportation with significant delays. Thanks to the ubiquitous cell phone data, we see an opportunity to substantially complement these surveys with data extracted from network carrier mobile phone usage logs, such as call detail records (CDRs). In this presentation, we explore a framework that develops the state-of-the-art generative activity-based urban mobility models from raw cellular data, with the capability of recognizing activity patterns for complementing activity-based travel demand modeling. 

Bio: Mogeng Yin received the B.E. degree in civil engineering from Tsinghua University, Beijing, China, in 2013, and the M.S. degree in transportation engineering from University of California at Berkeley, Berkeley, CA, USA, in 2014. His research interests include spatial data mining, urban computing, smart city, and machine learning.

2:00 Zahra Amini

Data-Driven Approaches for Robust Timing Plan in Urban Transportation Networks

Abstract: Considerable attention has been given to new approaches for improving the transportation system because of limited funding and environmental concerns for constructing new highway facilities.  One promising approach is implementation of advanced signal control strategies along arterials. This would reduce unnecessary delays and stops at traffic signals, improve travel times and cut fuel consumption and emissions.  In many instances, these arterial facilities also serve as reliever routes for congested freeways especially under incident conditions. Thus, their efficient operation could be significant for the traffic performance along the entire travel corridors. The main reason for the poor performance is the lack of systematic data collection to estimate performance measures and the development and implementation of control strategies that are responsive to real-time changes to traffic patterns and at the same time are simple and effective. High Resolution (HR) data presents a game-changing opportunity in traffic management. In this talk, I will first discuss characteristics of the HR data and the collection and filtering process of these data sets. Next, I present procedures to analysis HR data and calculate performance measures at traffic signals, and use these measures to develop a robust predesigned timing plan for addressing traffic demand variability. Further, I introduce a data-driven approach for modeling traffic profile in arterial and optimizing signals offset that improves stop delay at intersections and number of vehicle stop over the corridor.

Bio: Zahra Amini is a Ph.D. candidate in Civil and Environmental Engineering in the Transportation Engineering program at University of California (UC) at Berkeley, working under the supervision of Professor Alexander Skabardonis. She received her M.S. in Civil and Environmental Engineering at UC Berkeley in May 2015, and her B.S. in Civil and Environmental Engineering at UC Berkeley in December 2013. Her research interests include traffic operations, public transportation systems operation, intelligent transportation system, and data-driven control system.

 

2:30  --- Coffee Break ---

 

2:50 Wei Ni

Managing City Traffic with Boundary Flow Control

Abstract: Boundary flow control is potentially one of the most effective approaches for city traffic management. Under the control scheme, a city is partitioned into multiple, properly-sized neighborhoods and the transfer flows crossing neighborhood boundaries are metered. Optimal boundary-metering rates are obtained using Macroscopic Fundamental Diagrams in combination with flow conservation laws. A model-predictive control algorithm is utilized so that time-varying metering rates are generated based on their forecasted impacts. Unlike its predecessors, the proposed model accounts for the constraining effects that boundary queues impose on a neighborhood's circulating traffic. It does so at every time step by approximating a neighborhood's street space occupied by boundary queues, and re-scaling the MFD downward to describe the state of circulating traffic that results. The model is also unique in that it differentiates between saturated and under-saturated metering operations. Computer simulations show that these enhancements can substantially improve the predictions of both, the trip completion rates in a neighborhood and the rates that vehicles cross metered boundaries. Optimal metering policies generated as a result are similarly shown to do a better job in reducing the Vehicle Hours Traveled in a city. The VHT reductions stemming from the proposed model and from its predecessors differed by as much as 18%.

Bio: Wei Ni is a PhD student at UC Berkeley doing his research on urban traffic modeling, control and simulation.

 

3:20 Colin SheppardThe Value Proposition of Vehicle Grid Integration

Abstract: Transportation electrification and emerging forms of mobility are bringing dramatic changes to how the transportation system is planned, operated, and analyzed. Plug-in electric vehicles (PEVs) present new challenges and constraints around the siting and operation of refueling infrastructure. Electric load from PEVs can exacerbate grid congestion at either transmission or distribution scales if left unmanaged. In addition, sharing and autonomy are changing mobility which will have unique implications for the grid integration of PEVs. This body of work is an attempt to advance our understanding of the technical and economic potential for PEVs to supply flexibility services to the electric grid based on a variety of methodological approaches that quantify the opportunity at multiple scales, across multiple geographies, and that cover scenarios with both personally owned and shared autonomous PEVs. The approaches range from charging infrastructure siting algorithms, to reduced-form optimization models that schedule PEV operations, to detailed agent-based models that simulate context-specific traveler behaviors and the dynamics of resource-constrained charging infrastructure.  

Bio: Colin Sheppard is a Transportation Scientific Engineering Associate at Lawrence Berkeley National Lab and a PhD Student in Transportation Engineering. His expertise lies in energy and transportation systems engineering. For ten years he has been working in the spaces of sustainable transportation, renewable energy resources development, and energy efficiency. Mr. Sheppard’s role at LBNL under the DOE SMART Mobility Initiative is leading the development of the BEAM Framework (Behavior, Energy, Autonomy, and Mobility), an integrated systems approach to sustainable transportation analysis. BEAM involves agent-based simulation modeling of a fully multi-modal transportation system that includes public transit and shared/autonomous mobility services in addition to traditional modes. In addition, Mr. Sheppard develops vehicle grid integration analysis capabilities at LBNL, which are the primary subject of his PhD research.