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Integrating connected vehicles into urban traffic management systems

Integrating connected vehicles into urban traffic management systems
Integrating connected vehicles into urban traffic management systems
Connected intelligent transport systems contain a wealth of data accessible to traffic signal controllers. However, algorithms that use data from a connected environment do not fully exploit the potential of this new data source. Instead, traffic signal controllers rely on speed and position data to supplement data from infrastructure. This research aims to understand which data that are available from connected vehicles are useful for integrating with existing traffic signal control systems in urban environments. Vehicle positions and speeds fit well into our current understanding of traffic theory, but more abstract data such as passenger counts and stop frequencies may offer new ways to optimise traffic signal controllers to reduce traffic delays.

The contributions of this research include 1) A traffic signal control algorithm which combines position information from connected vehicles with data from existing inductive loops and signal timing plans to perform decentralised traffic signal control to reduce delays at existing urban intersections. The algorithm adapts to scenarios with low numbers of connected vehicles and degraded infrastructure, an area where existing traffic signal control strategies are limited. 2) A framework for testing connected traffic signal controllers based on a large urban corridor in the city of Birmingham, UK. The testing framework overcomes the limitations of existing research by implementing a large-scale, realistic simulation case study, which accounts for mixed-mode traffic, multiple levels of traffic demand, degraded loop detector coverage, non-ideal wireless communications, and a full 24-hour simulation period. 3) A greedy stage sequence optimisation algorithm that abstracts for arbitrary connected vehicle data. 4) A method for introducing implicit stage coordination to greedy stage optimisation paradigms. 5) Insights that show coordination is redundant when signal controllers have accurate data and can react fast enough to traffic changes.

This research shows how data connected vehicles can be exploited to improve urban traffic signal control, and how using connected vehicle data differs from traditional sources. The outcomes of this research have a significant impact on the implementation of connected intelligent transportation systems and policy for the transportation industry.
University of Southampton
Rafter, Craig Benjamin
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Rafter, Craig Benjamin
8f56b72d-8984-47e4-ae2a-f38a68fbad14
Cherrett, Thomas
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Anvari, Bani
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Waterson, Benedict
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Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113

Rafter, Craig Benjamin (2020) Integrating connected vehicles into urban traffic management systems. University of Southampton, Doctoral Thesis, 344pp.

Record type: Thesis (Doctoral)

Abstract

Connected intelligent transport systems contain a wealth of data accessible to traffic signal controllers. However, algorithms that use data from a connected environment do not fully exploit the potential of this new data source. Instead, traffic signal controllers rely on speed and position data to supplement data from infrastructure. This research aims to understand which data that are available from connected vehicles are useful for integrating with existing traffic signal control systems in urban environments. Vehicle positions and speeds fit well into our current understanding of traffic theory, but more abstract data such as passenger counts and stop frequencies may offer new ways to optimise traffic signal controllers to reduce traffic delays.

The contributions of this research include 1) A traffic signal control algorithm which combines position information from connected vehicles with data from existing inductive loops and signal timing plans to perform decentralised traffic signal control to reduce delays at existing urban intersections. The algorithm adapts to scenarios with low numbers of connected vehicles and degraded infrastructure, an area where existing traffic signal control strategies are limited. 2) A framework for testing connected traffic signal controllers based on a large urban corridor in the city of Birmingham, UK. The testing framework overcomes the limitations of existing research by implementing a large-scale, realistic simulation case study, which accounts for mixed-mode traffic, multiple levels of traffic demand, degraded loop detector coverage, non-ideal wireless communications, and a full 24-hour simulation period. 3) A greedy stage sequence optimisation algorithm that abstracts for arbitrary connected vehicle data. 4) A method for introducing implicit stage coordination to greedy stage optimisation paradigms. 5) Insights that show coordination is redundant when signal controllers have accurate data and can react fast enough to traffic changes.

This research shows how data connected vehicles can be exploited to improve urban traffic signal control, and how using connected vehicle data differs from traditional sources. The outcomes of this research have a significant impact on the implementation of connected intelligent transportation systems and policy for the transportation industry.

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Published date: November 2020

Identifiers

Local EPrints ID: 448150
URI: http://eprints.soton.ac.uk/id/eprint/448150
PURE UUID: 3a6e51f9-91e4-4f2d-8a22-8c26de250110
ORCID for Craig Benjamin Rafter: ORCID iD orcid.org/0000-0003-3411-114X
ORCID for Bani Anvari: ORCID iD orcid.org/0000-0001-7916-7636
ORCID for Benedict Waterson: ORCID iD orcid.org/0000-0001-9817-7119

Catalogue record

Date deposited: 13 Apr 2021 16:30
Last modified: 01 Dec 2021 05:01

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Contributors

Author: Craig Benjamin Rafter ORCID iD
Thesis advisor: Thomas Cherrett
Thesis advisor: Bani Anvari ORCID iD
Thesis advisor: Benedict Waterson ORCID iD
Thesis advisor: Simon Box

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