arXiv subjects

Cy Chan

Publications and source records attributed to Cy Chan.

6 recordsLinked to original sources

arXiv2022

Simulating the Impact of Dynamic Rerouting on Metropolitan-Scale Traffic Systems

The rapid introduction of mobile navigation aides that use real-time road network information to suggest alternate routes to drivers is making it more difficult for researchers and government transportation agencies to understand and predict the dynamics of congested transportation systems. Computer simulation is a key capability for these organizations to analyze hypothetical scenarios; however, the complexity of transportation systems makes it challenging for them to simulate very large geographical regions, such as multi-city metropolitan areas. In this paper, we describe the Mobiliti traffic simulator, which includes mechanisms to capture congestion delays, timing constraints, and link storage capacity constraints. The simulator is designed to support distributed memory parallel execution and be scalable on high-performance computing platforms. We introduce a method to model dynamic rerouting behavior with the addition of vehicle controller actors and reroute request events. We demonstrate the potential of the simulator by analyzing the impact of varying the population penetration rate of dynamic rerouting on the San Francisco Bay Area road network. Using high-performance parallel computing, we can simulate a day of the San Francisco Bay Area with 19 million vehicle trips with 50 percent dynamic rerouting penetration over a road network with 0.5 million nodes and 1 million links in less than three minutes. We present an analysis of system-level impacts when changing the dynamic rerouting penetration rate and examine the varying effects on different functional classes and geographical regions. Finally, we present a validation of the simulation results compared to real world data.

cs.DC↗

arXiv2021

Quasi-Dynamic Traffic Assignment using High Performance Computing

Traffic assignment methods are some of the key approaches used to model flow patterns that arise in transportation networks. Since static traffic assignment does not have a notion of time, it is not designed to represent temporal dynamics that arise as vehicles flow through the network and demand varies through the day. Dynamic traffic assignment methods attempt to resolve these issues, but require significant computational resources if modeling urban-scale regions (on the order of millions of links and vehicles) and often take days of compute time to complete. The focus of this work is two-fold: 1) to introduce a new traffic assignment approach - a quasi-dynamic traffic assignment (QDTA) model and 2) to describe how we parallelized the QDTA algorithms to leverage High-Performance Computing (HPC) and scale to large metropolitan areas while dramatically reducing compute time. We examine and compare different scenarios, including a baseline static traffic assignment (STA) and a quasi-dynamic scenario inspired by the user-equilibrium (UET). Results are presented for the San Francisco Bay Area which accounts for 19M trips/day and an urban road network of 1M links. We utilize an iterative gradient descent method, where the step size is selected using a Quasi-Newton method with parallelized cost function evaluations and compare it to using pre-defined step sizes (MSA). Using the parallelized line search provides a 16 percent reduction in total execution time due to a reduction in the number of gradient descent iterations required for convergence. The full day QDTA comprising 96 optimization steps over 15 minute intervals runs in about 4 minutes on 1,024 cores of the NERSC Cori computer, which represents a speedup of over 36x versus serial execution. To our knowledge, this compute time is significantly lower than other traffic assignment solutions for a problem of this scale.

cs.DC↗

arXiv2020

Electric Vehicle Charging Infrastructure Planning: A Scalable Computational Framework

The optimal charging infrastructure planning problem over a large geospatial area is challenging due to the increasing network sizes of the transportation system and the electric grid. The coupling between the electric vehicle travel behaviors and charging events is therefore complex. This paper focuses on the demonstration of a scalable computational framework for the electric vehicle charging infrastructure planning over the tightly integrated transportation and electric grid networks. On the transportation side, a charging profile generation strategy is proposed leveraging the EV energy consumption model, trip routing, and charger selection methods. On the grid side, a genetic algorithm is utilized within the optimal power flow program to solve the optimal charger placement problem with integer variables by adaptively evaluating candidate solutions in the current iteration and generating new solutions for the next iterations.

cs.AI↗

arXiv2020

Adaptive Total Variation Stable Local Timestepping for Conservation Laws

This paper proposes a first-order total variation diminishing (TVD) treatment for coarsening and refining of local timestep size in response to dynamic local variations in wave speeds for nonlinear conservation laws. The algorithm is accompanied with a proof of formal correctness showing that given a sufficiently small minimum timestep the algorithm will produce TVD solution for nonlinear scalar conservation laws. A key feature of the algorithm is its formulation as a discrete event simulation, which allows for easy and efficient parallelization using existing software. Numerical results demonstrate the stability and adaptivity of the method for the shallow water equations. We also introduce a performance model to load balance and explain the observed performance gains. Performance results are presented for a single node on Stampede2's Skylake partition using an optimistic parallel discrete event simulator. Results show the proposed algorithm recovering 59%-77% of the theoretically achievable speed-up with the discrepancies being attributed to the cost of computing the CFL condition and load imbalance.

math.NA↗

arXiv2018

Predictive Management of Electric Vehicles in a Community Microgrid

The charging load from Electric vehicles (EVs) is modeled as deferrable load, meaning that the power consumption can be shifted to different time windows to achieve various grid objectives. In local community scenarios, EVs are considered as controllable storage devices in a global optimization problem together with other microgrid components, such as the building load, renewable generations, and battery energy storage system, etc. However, the uncertainties in the driver behaviors have tremendous impact on the cost effectiveness of microgrid operations, which has not been fully explored in previous literature. In this paper, we propose a predictive EV management strategy in a community microgrid, and evaluate it using real-world datasets of system baseload, solar generation and EV charging behaviors. A two-stage operation model is established for cost-effective EV management, i.e. wholesale market participation in the first stage and load profile following in the second stage. Predictive control strategies, including receding horizon control, are adapted to solve the energy allocation problem in a decentralized fashion. The experimental results indicate the proposed approach can considerably reduce the total energy cost and decrease the ramping index of total system load up to 56.3%.

eess.SP↗

arXiv2017

ExaGridPF: A Parallel Power Flow Solver for Transmission and Unbalanced Distribution Systems

This paper investigates parallelization strategies for solving power flow problems in both transmission and unbalanced, three-phase distribution systems by developing a scalable power flow solver, ExaGridPF, which is compatible with existing high-performance computing platforms. Newton-Raphson (NR) and Newton-Krylov (NK) algorithms have been implemented to verify the performance improvement over both standard IEEE test cases and synthesized grid topologies. For three-phase, unbalanced system, we adapt the current injection method (CIM) to model the power flow and utilize SuperLU to parallelize the computing load across multiple threads. The experimental results indicate that more than 5 times speedup ratio can be achieved for synthesized large-scale transmission topologies, and significant efficiency improvements are observed over existing methods for the distribution networks.

cs.CE↗