國立虎尾科技大學 |

Developing a Cyber-Physical System for Real-Time Proactive Traffic Control and Management of Mixed Fleet of Vehicles with Various Levels of Autonomy.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Developing a Cyber-Physical System for Real-Time Proactive Traffic Control and Management of Mixed Fleet of Vehicles with Various Levels of Autonomy./
Author:	Potluri, Viswanath.
Description:	1 online resource (151 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
Subject:	Transportation. -
Online resource:	click for full text (PQDT)
ISBN:	9798379530440

Developing a Cyber-Physical System for Real-Time Proactive Traffic Control and Management of Mixed Fleet of Vehicles with Various Levels of Autonomy.
Potluri, Viswanath.

Developing a Cyber-Physical System for Real-Time Proactive Traffic Control and Management of Mixed Fleet of Vehicles with Various Levels of Autonomy. - 1 online resource (151 pages)

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--Arizona State University, 2023.

Includes bibliographical references

In this dissertation, a cyber-physical system called MIDAS (Managing Interacting Demand And Supply) has been developed, where the "supply" refers to the transportation infrastructure including traffic controls while the "demand" refers to its dynamic traffic loads. The strength of MIDAS lies in its ability to proactively control and manage mixed vehicular traffic, having various levels of autonomy, through traffic intersections. Using real-time traffic control algorithms MIDAS minimizes wait times, congestion, and travel times on existing roadways.For traffic engineers, efficient control of complicated traffic movements used at diamond interchanges (DI), which interface streets with freeways, is challenging for normal human driven vehicular traffic, let alone for communicationally-connected vehicles (CVs) due to stochastic demand and uncertainties. This dissertation first develops a proactive traffic control algorithm, MIDAS, using forward-recursion dynamic programming (DP), for scheduling large set of traffic movements of non-connected vehicles and CVs at the DIs, over a finite-time horizon. MIDAS captures measurements from fixed detectors and captures Lagrangian measurements from CVs, to estimate link travel times, arrival times and turning movements. Simulation study shows MIDAS' outperforms (a) a current optimal state-of-art optimal fixed-cycle time control scheme, and (b) a state-of-art traffic adaptive cycle-free scheme. Subsequently, this dissertation addresses the challenges of improving the road capacity by platooning fully autonomous vehicles (AVs), resulting in smaller headways and greater road utilization. With the MIDAS AI (Autonomous Intersection) control, an effective platooning strategy is developed, and optimal release sequence of AVs is determined using a new forward-recursive DP that minimizes the time-loss delays of AVs. MIDAS AI evaluates the DP decisions every second and communicates optimal actions to the AVs.Although MIDAS AI's exact DP achieves optimal solution in almost real-time compared to other exact algorithms, it suffers from scalability. To address this challenge, the dissertation then develops MIDAS RAIC (Reinforced Autonomous Intersection Control), a deep reinforcement learning based real-time dynamic traffic control system for AVs at an intersection. Simulation results show the proposed deep Q-learning architecture trains MIDAS RAIC to learn a near-optimal policy that minimizes the total cumulative time loss delay and performs nearly as well as the MIDAS AI.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379530440Subjects--Topical Terms:

558117
Transportation.
Subjects--Index Terms:

Connected vehiclesIndex Terms--Genre/Form:

554714
Electronic books.

Developing a Cyber-Physical System for Real-Time Proactive Traffic Control and Management of Mixed Fleet of Vehicles with Various Levels of Autonomy.
LDR:04122ntm a2200421K 4500 001 1141039
005 20231130111554.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379530440
035 $a (MiAaPQ)AAI30250225
035 $a AAI30250225
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Potluri, Viswanath. $3 1464609
245 1 0 $a Developing a Cyber-Physical System for Real-Time Proactive Traffic Control and Management of Mixed Fleet of Vehicles with Various Levels of Autonomy.
264 0 $c 2023
300 $a 1 online resource (151 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
500 $a Advisor: Mirchandani, Pitu.
502 $a Thesis (Ph.D.)--Arizona State University, 2023.
504 $a Includes bibliographical references
520 $a In this dissertation, a cyber-physical system called MIDAS (Managing Interacting Demand And Supply) has been developed, where the "supply" refers to the transportation infrastructure including traffic controls while the "demand" refers to its dynamic traffic loads. The strength of MIDAS lies in its ability to proactively control and manage mixed vehicular traffic, having various levels of autonomy, through traffic intersections. Using real-time traffic control algorithms MIDAS minimizes wait times, congestion, and travel times on existing roadways.For traffic engineers, efficient control of complicated traffic movements used at diamond interchanges (DI), which interface streets with freeways, is challenging for normal human driven vehicular traffic, let alone for communicationally-connected vehicles (CVs) due to stochastic demand and uncertainties. This dissertation first develops a proactive traffic control algorithm, MIDAS, using forward-recursion dynamic programming (DP), for scheduling large set of traffic movements of non-connected vehicles and CVs at the DIs, over a finite-time horizon. MIDAS captures measurements from fixed detectors and captures Lagrangian measurements from CVs, to estimate link travel times, arrival times and turning movements. Simulation study shows MIDAS' outperforms (a) a current optimal state-of-art optimal fixed-cycle time control scheme, and (b) a state-of-art traffic adaptive cycle-free scheme. Subsequently, this dissertation addresses the challenges of improving the road capacity by platooning fully autonomous vehicles (AVs), resulting in smaller headways and greater road utilization. With the MIDAS AI (Autonomous Intersection) control, an effective platooning strategy is developed, and optimal release sequence of AVs is determined using a new forward-recursive DP that minimizes the time-loss delays of AVs. MIDAS AI evaluates the DP decisions every second and communicates optimal actions to the AVs.Although MIDAS AI's exact DP achieves optimal solution in almost real-time compared to other exact algorithms, it suffers from scalability. To address this challenge, the dissertation then develops MIDAS RAIC (Reinforced Autonomous Intersection Control), a deep reinforcement learning based real-time dynamic traffic control system for AVs at an intersection. Simulation results show the proposed deep Q-learning architecture trains MIDAS RAIC to learn a near-optimal policy that minimizes the total cumulative time loss delay and performs nearly as well as the MIDAS AI.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Transportation. $3 558117
653 $a Connected vehicles
653 $a Diamond interchange control
653 $a Dynamic programming
653 $a Real time traffic control
653 $a Reinforcement learning
653 $a Managing Interacting Demand And Supply
653 $a MIDAS Autonomous Intersection
655 7 $a Electronic books. $2 local $3 554714
690 $a 0796
690 $a 0709
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Arizona State University. $b Industrial Engineering. $3 1179835
773 0 $t Dissertations Abstracts International $g 84-11B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30250225 $z click for full text (PQDT)