TY - CHAP
T1 - Controlling Mixed Connected and Non-Connected Vehicle Traffic Through a Diamond Interchange
AU - Potluri, Viswanath
AU - Mirchandani, Pitu
N1 - Publisher Copyright:
© SAGE Publications Ltd. All rights reserved.
PY - 2022/12
Y1 - 2022/12
N2 - Diamond interchanges (DIs) allow movement of vehicles between surface streets and freeways for all types of vehicles, including normal non-connected human-driven vehicle (NHDV) traffic and the connected vehicles (CVs). Unlike simple intersections, DIs consist of a pair of closely spaced intersections that are controlled together with complicated traffic movements and heavy demand fluctuations. This paper reviews the movements being controlled at DIs and presents a dynamic programming (DP)-based real-time proactive traffic control algorithm called MIDAS, to control both NHDVs and CVs. Like seminal cycle-free adaptive control methods such as OPAC and RHODES, MIDAS uses a forward recursion DP approach with efficient data structures for any large set of phase movements being controlled at DIs, over a finite-time horizon that rolls forward, and then uses a backward recursion to retrieve the optimal phase sequence and duration of phases. MIDAS captures Eulerian measurements from fixed loop detectors for all vehicles, and also captures Lagrangian measurements like in-vehicle GPS from CVs to estimate link travel times, arrival times, turning movements, etc. For every time horizon MIDAS predicts future arrivals, estimates queues at the interchange, and then minimizes a user-defined metric like delays, stops, or queues at an interchange. The paper compares performances of MIDAS with those of an optimal fixed cycle time signal control (OFTC) scheme and RHODES control on a simulated DI. The simulation is of Phoenix, AZ, DI (on I-17/19th Ave.) that uses the VISSIM micro-simulation platform. Performance is evaluated for various traffic loads and various CV market penetrations. Results show that MIDAS control outperforms RHODES and OFTC.
AB - Diamond interchanges (DIs) allow movement of vehicles between surface streets and freeways for all types of vehicles, including normal non-connected human-driven vehicle (NHDV) traffic and the connected vehicles (CVs). Unlike simple intersections, DIs consist of a pair of closely spaced intersections that are controlled together with complicated traffic movements and heavy demand fluctuations. This paper reviews the movements being controlled at DIs and presents a dynamic programming (DP)-based real-time proactive traffic control algorithm called MIDAS, to control both NHDVs and CVs. Like seminal cycle-free adaptive control methods such as OPAC and RHODES, MIDAS uses a forward recursion DP approach with efficient data structures for any large set of phase movements being controlled at DIs, over a finite-time horizon that rolls forward, and then uses a backward recursion to retrieve the optimal phase sequence and duration of phases. MIDAS captures Eulerian measurements from fixed loop detectors for all vehicles, and also captures Lagrangian measurements like in-vehicle GPS from CVs to estimate link travel times, arrival times, turning movements, etc. For every time horizon MIDAS predicts future arrivals, estimates queues at the interchange, and then minimizes a user-defined metric like delays, stops, or queues at an interchange. The paper compares performances of MIDAS with those of an optimal fixed cycle time signal control (OFTC) scheme and RHODES control on a simulated DI. The simulation is of Phoenix, AZ, DI (on I-17/19th Ave.) that uses the VISSIM micro-simulation platform. Performance is evaluated for various traffic loads and various CV market penetrations. Results show that MIDAS control outperforms RHODES and OFTC.
KW - advanced traffic management systems
KW - automated/autonomous/connected vehicles
KW - connected vehicle data applications
KW - data and data science
KW - dynamic traffic assignment
KW - information systems and technology
KW - intelligent traffic system
KW - microscopic traffic simulation
KW - operations
KW - optimization
KW - regional transportation systems management and operations
KW - traffic control devices
KW - traffic management and control
KW - traffic predication
KW - traffic signal systems
KW - traffic signals
KW - traffic signals
KW - traffic simulation
KW - urban transportation data and information systems
UR - http://www.scopus.com/inward/record.url?scp=85144598748&partnerID=8YFLogxK
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U2 - 10.1177/03611981211062217
DO - 10.1177/03611981211062217
M3 - Chapter
AN - SCOPUS:85144598748
T3 - Transportation Research Record
SP - 16
EP - 31
BT - Transportation Research Record
PB - SAGE Publications Ltd
ER -