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PhD Final Defense – Hongjae Jeon

Event Type
Civil and Environmental Engineering
Newmark 3350 Conference Room
Nov 20, 2023   10:00 am  

Optimal Highway Bottleneck Operation with Active Traffic Management and Connected Automated Vehicle

Advisor: Rahim F. Benekohal

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 5.3 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 linear 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 maintaining 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 humandriven

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 maintaining

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 queues. 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 queues.

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 queues (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.

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