developing next-generation work zone merge system enabled by … · 2017. 2. 2. · connected...
TRANSCRIPT
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Developing Next-Generation Work Zone Merge System Enabled by Connected/Autonomous Vehicle Technologies
Majeed Algomaiah
Department of Civil & Environmental Engineering
University of Louisville
2016 International Highway Engineering Exchange
Program (IHEEP 2016)
September 11 - September 15, 2016 Helena, Montana
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Introduction
• The Problem
• Proposed Solution
• Benefits
• Objectives
• Methodology
• Expected Results
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The Problem
• Work Zone
• Early Merge
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The Problem
• Late Merge
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The Problem
• Compliance Rate!
• Trust!
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The Problem
COMMUNICATION!
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Promising Technology
• Connected Vehicles (CV)
• Vehicle-to-Infrastructure (V2I)
• Vehicle-to-Vehicle (V2V)
• Autonomous Vehicles (AV)
• Cooperative Late Merge
• Zipper Merge
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• Utilizes closed lane
• Reduce the length of traffic backup by
• Reduces differences in speeds between lanes
• Increases throughput
Benefits
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Objectives
• Develop late merge system that implements CAV and updated lane-changing and car-following microscopic models
• Test different compliance rate and market penetration rate to identify the sweet spots
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Considered Control Systems
1. Decentralized control via V2I broadcasting and V2V
2. Centralized control via V2I
3. Centralized control via V2I and V2V
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1. Decentralized control via V2I broadcasting and V2V
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2. Centralized control via V2I
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3. Centralized control via V2I and AV
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• 100% Compliance Rate
• Perception-reaction time (PRT)
• Control of speed and headway
• Cooperative Adaptive Cruise Control (CACC)
3. Centralized control via V2I and AV
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• Unequipped Vehicles!
• Compliance Rate!
• Market Penetration Rate!
Challenges
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General Framework
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Modeling
• Time-to-collision (TTC)
𝑇𝑇𝐶 = ൝𝑠
∆𝑉, 𝑣𝐿 < 𝑣𝑆
∞, otherwiseGettman et al. (2008)
• Time-to-end-of-lane (TEOL)
𝑇𝐸𝑂𝐿 =𝑠
𝑣𝑆
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• Lane-changing Hidas (2006)
𝑔𝑙 ≥ 𝑔𝑙,𝑚𝑖𝑛 and 𝑔𝑓 ≥ 𝑔𝑓,𝑚𝑖𝑛
𝑔𝑙,𝑚𝑖𝑛 = 𝑔𝑚𝑖𝑛 + ቊ𝑐𝑙 𝑣𝑠 − 𝑣𝑙 if 𝑣𝑠 > 𝑣𝑙0 otherwise
𝑔𝑓,𝑚𝑖𝑛 = 𝑔𝑚𝑖𝑛 + ቊ𝑐𝑓 𝑣𝑓 − 𝑣𝑠 if 𝑣𝑓 > 𝑣𝑠0 otherwise
Modeling
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• Lane-changing Hidas (2006)
𝑔𝑙 = 𝑔0𝑙 − 𝑣𝑠𝐷𝑡 −𝑏𝑠2𝐷𝑡
+ (𝑣𝑙𝐷𝑡)
𝑔𝑓 = 𝑔0𝑓 − 𝑣𝑓𝐷𝑡 −𝑏𝑓
2𝐷𝑡+ (𝑣𝑠𝐷𝑡)
𝐷𝑡 = 𝐷𝑣/𝑏𝑓,𝑠
Modeling
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• Perception-reaction time (PRT) Adel et al. (2011)
𝑃𝑅𝑇𝐷 =
𝑃𝑅𝑇1 = 𝑃𝑅𝑇0 1 − 0.1 − 0.4σ
100, 𝑡 < ∆𝑡1
𝑃𝑅𝑇2 = 𝑃𝑅𝑇1 + 𝑃𝑅𝑇0 − 𝑃𝑅𝑇1 𝑡 −∆𝑡1∆𝑡2
, ∆𝑡1 ≤ 𝑡 < ∆𝑡1 + ∆𝑡2
𝑃𝑅𝑇0 otherwise
Modeling
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• Desired Speed (DS) Adel et al. (2011)
𝐷𝑆𝐷 =
𝐷𝑆1 = 𝐷𝑆0 + 𝑉𝐶𝑉 − 𝐷𝑆0σ
100, 𝑡 < ∆𝑡1
𝐷𝑆2 = 𝐷𝑆1 + 𝑉𝐶𝑉 − 𝐷𝑆0𝑡 − ∆𝑡1∆𝑡2
, ∆𝑡1 ≤ 𝑡 < ∆𝑡1 + ∆𝑡2
𝐷𝑆0 otherwise
Modeling
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• Desired Headway (DH) Adel et al. (2011)
𝐷𝐻𝐷 =
𝐷𝐻1 = 𝐷𝐻0 + 𝐻𝑊𝐶𝑉 − 𝐷𝐻0
σ
100, 𝑡 < ∆𝑡1
𝐷𝐻2 = 𝐷𝐻1 + 𝐷𝐻0 − 𝐷𝐻1𝑡 − ∆𝑡1∆𝑡2
, ∆𝑡1 ≤ 𝑡 < ∆𝑡1 + ∆𝑡2
𝐷𝐻0 otherwise
Modeling
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• Car-following (Treiber, 2006)
ሶ𝜈 𝑡 = 𝑎 [ 1 −𝑣 𝑡
𝐷𝑆𝐷
𝛼
−𝑠 ∗ (𝑣 𝑡 , ∆𝑣 𝑡 − 𝑃𝑅𝑇𝐷 )
𝑠(𝑡 − 𝑃𝑅𝑇𝐷)
𝛽
𝑠 ∗ 𝑣, ∆𝑣 = 𝑠0 +max (0, 𝑣 ∙ 𝐷𝐻𝐷 +𝑣Δ𝑣
2 𝑎𝑏
Modeling
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• Using VISSIM
• Microscopic Simulation
• Combinations of different scenarios:
• Compliance Rates (e.g. 90%, 75%, 50% and 25%)
• Market Penetration Rates (e.g. 90%, 75%, 50% and 25%)
Simulation
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Simulation
• Simulation running example
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Expected Results
• Increased throughput
• Optimal parameters
• A sweet spot of market penetration rate and compliance rate among 3 control systems
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The End
Any Questions?