Optimal highway incident operation with active traffic management and connected automated vehicle

Jeon, Hong Jae

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https://hdl.handle.net/2142/122041 Copy

Description

Title

Optimal highway incident operation with active traffic management and connected automated vehicle

Author(s)

Jeon, Hong Jae

Issue Date

2023-12-01

Director of Research (if dissertation) or Advisor (if thesis)

Benekohal, Rahim F.

Doctoral Committee Chair(s)

Benekohal, Rahim F.

Committee Member(s)

• Ouyang, Yanfeng
• Meidani, Hadi
• Beck, Carolyn L.

Department of Study

Civil & Environmental Eng

Discipline

Civil Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Incident
• Low-speed duration
• Recovery attempts
• Low-speed duration Prediction
• Incident management strategy
• VSL on incident
• Early merge
• Remain in open lanes

Abstract

This study focuses on the mitigating impact of traffic incidents on highway operations with variable speed limits (VSL) and traffic turbulence reduction strategies to alleviate incident-induced congestion. The emergence of connected autonomous vehicles (CAV) and smart infrastructure, proposed management strategies (MS) aim to enhance operating speed at the bottleneck, reduce arrival volume, and control arrival speed during incident response time periods to improve traffic operation. Responders often need to temporarily close lanes to ensure the safety of all involved parties during incidents, resulting in a time gap between incident clearance and full traffic restoration. This temporal difference is measured through recovery attempts at individual sensor locations, with an average of 4.7 attempts contributing approximately 11.6 minutes to low-speed duration for the entire dataset. Recovery attempts follow specific patterns (U type, nV type, UV type), influencing the number of attempts needed to restore normal operating speeds. U type recovery averages 4.3 attempts, contributing 13.9 minutes, while nV type sees 4.4 attempts, adding about 9.1 minutes. UV type recovery requires an average of 6.9 attempts, with each attempt increasing low-speed duration between 3.9 to 18.1 minutes; a low value corresponds to a high number of attempts, while a high value corresponds to a low number of attempts. Consequently, low-speed duration can be accurately predicted through rigorous lasso regression analysis and fully connected neural networks (FCNN), with the potential for mitigation through the integration of ATM and CAV technologies, emphasizes the importance of addressing key influential variables. Variables such as speed drop, arrival volume, and arrival speed present opportunities for refining incident MS. The proposed incident MS are VSL, CAVs traveling on open lanes early, and CAVs remaining their open lane near the incident site, and they aim to reduce low-speed duration by improving operating speed, minimizing arrival volume, and controlling arrival speed. The effectiveness of these strategies is assessed using Vissim simulation software due to challenges in their field implementation. The effectiveness of the first MS, VSL is contingent upon critical factors such as activation distance, compliance rates, and posted speed limits. VSL demonstrates the potential to mitigate shockwave propagation by reducing both arrival speed and arrival rate. This effectiveness is evident in Vissim simulation results, particularly in terms of travel time, where a consistent reduction is observed as activation distance increases in all CAV market penetration. An increase in compliance rates of human-driven vehicles further contributes to a reduction in travel time. The second MS involves CAVs traveling on open lanes early, aiming to minimize turbulence near the incident site. This strategy is most effective when implemented from 1 mile upstream of the incident site. The third MS, involving CAVs remaining their open lane near the incident site, is not universally beneficial across all CAV market penetrations but becomes advantageous as CAV market penetration and volume increase. In tackling the intricate challenges of finding combination of optimal incident MS, this study introduces innovative data-driven methodologies, decision trees and random forests. These methodologies provide a transparent and interpretable framework for data-driven decision-making, leveraging crucial variables like traffic volume, CAV market penetration, VSL activation distance, and VSL compliance rate. The extensive dataset serves as the foundation for identifying optimal traffic management strategies across diverse incident scenarios, utilizing a meticulous ranking procedure that considers factors such as maximum queue length, total travel time, and average throughput during incident. Rankings are capped at 10 for a focused evaluation, with tied ranks resolved based on priorities like maximum queue length, total travel time, and average throughput during incident. Results from both decision trees and random forests show promise, revealing a consistent trend of improvement across all CAV market penetration, including low levels like 10%. These data-driven strategies contribute to a reduction in queue length (19.24% and 19.63%), travel time (17.86% and 17.76%), and an increase in average throughput during incident (5.74% and 5.70%). Even at low CAV market penetration 10%, the MS obtained from data-driven strategies showed significant improvements in queue length, travel time, and throughput, which is a promising for future incident management.

Graduation Semester

2023-12

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Hong Jae Jeon

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