Real-Time Predictive Control Strategy Optimization
Name
1901.04571.pdf
Description
Submitted version
Size
2.76 MB
Format
Adobe PDF
Checksum (MD5)
cb6d1feb010af8476d89afe89a191408
Author(s)
Gupta, Samarth
Seshadri, Ravi
Atasoy, Bilge
Prakash, A Arun
Pereira, Francisco
Tan, Gary
Ben-Akiva, Moshe
Date Issued
February 24, 2020
Journal
Transportation Research Record
Publisher
SAGE Publications
Citation
Gupta S, Seshadri R, Atasoy B, et al. Real-Time Predictive Control Strategy Optimization. Transportation Research Record. 2020;2674(3):1-11
Version
Original manuscript
Abstract
Urban traffic congestion has led to an increasing emphasis on management measures for more efficient utilization of existing infrastructure. In this context, this paper proposes a novel framework that integrates real-time optimization of control strategies (tolls, ramp metering rates, etc.) with the generation of traffic guidance information using predicted network states for dynamic traffic assignment systems. The efficacy of the framework is demonstrated through a fixed demand dynamic toll optimization problem, which is formulated as a non-linear program to minimize predicted network travel times. A scalable efficient genetic algorithm that exploits parallel computing is applied to solve this problem. Experiments using a closed-loop approach are conducted on a large-scale road network in Singapore to investigate the performance of the proposed methodology. The results indicate significant improvements in network-wide travel time of up to 9% with real-time computational performance.
MIT Department
Massachusetts Institute of Technology. Center for Computational Science and Engineering
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Singapore-MIT Alliance in Research and Technology (SMART)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/0361198120907903
