Adaptive offsets for signalized streets

Carlos F. Daganzo, Lewis J. Lehe, Juan Argote-Cabanero

Research output: Contribution to journalArticle

Abstract

This paper shows that severe congestion on streets controlled by traffic signals can be reduced by dynamically adapting the signal offsets to the prevailing density with a simple rule that keeps the signals’ green-red ratios invariant. Invariant ratios reduce a control policy's impact on the crossing streets, so a policy can be optimized and evaluated by focusing on the street itself without the confounding factors present in networks. Designed for heavy traffic with spillovers, the proposed policies are adaptive and need little data – they only require average traffic density readings and no demand forecasts. A battery of numerical experiments simulating the dynamics of rush hour traffic on a congested, homogeneous circular street reveals that the proposed form of adaptation reduces the duration of the rush and overall congestion compared with pre-timed control strategies. Eighteen different adaptation policies were considered. All inspect the street densities periodically and simultaneously, and retime the signals immediately thereafter. The period is a fixed multiple of the cycle. The street is evenly divided into sections that contain a set number of consecutive blocks and signals. The offset is the same for all blocks in a section. Three inspection intervals and six section sizes were tested. The latter ranged from a single block/signal to the whole street. It was found that adaptation worked best when sections were large and adaptation frequent. The effects were considerable across all scenarios. For a short street with a short rush and high input flows the probabilistic incidence of gridlock was reduced from 10 to 0%, and the average duration of a trip from 216 to 181 s. For a long street with a long rush and high input flows the gridlock probability was reduced from 23 to 0% and the average trip duration from 2037 to 1143s.

Original languageEnglish (US)
Pages (from-to)926-934
Number of pages9
JournalTransportation Research Part B: Methodological
Volume117
DOIs
StatePublished - Nov 2018

Fingerprint

rush hour traffic
traffic
demand forecast
traffic volume
incidence
scenario
present
experiment
Traffic signals
Inspection
Experiments

Keywords

  • Adaptive offsets
  • Congestion
  • ISTTT22
  • MFD
  • Traffic signals

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation

Cite this

Adaptive offsets for signalized streets. / Daganzo, Carlos F.; Lehe, Lewis J.; Argote-Cabanero, Juan.

In: Transportation Research Part B: Methodological, Vol. 117, 11.2018, p. 926-934.

Research output: Contribution to journalArticle

Daganzo, Carlos F. ; Lehe, Lewis J. ; Argote-Cabanero, Juan. / Adaptive offsets for signalized streets. In: Transportation Research Part B: Methodological. 2018 ; Vol. 117. pp. 926-934.
@article{d58699c326c84ca0982e63a375247f00,
title = "Adaptive offsets for signalized streets",
abstract = "This paper shows that severe congestion on streets controlled by traffic signals can be reduced by dynamically adapting the signal offsets to the prevailing density with a simple rule that keeps the signals’ green-red ratios invariant. Invariant ratios reduce a control policy's impact on the crossing streets, so a policy can be optimized and evaluated by focusing on the street itself without the confounding factors present in networks. Designed for heavy traffic with spillovers, the proposed policies are adaptive and need little data – they only require average traffic density readings and no demand forecasts. A battery of numerical experiments simulating the dynamics of rush hour traffic on a congested, homogeneous circular street reveals that the proposed form of adaptation reduces the duration of the rush and overall congestion compared with pre-timed control strategies. Eighteen different adaptation policies were considered. All inspect the street densities periodically and simultaneously, and retime the signals immediately thereafter. The period is a fixed multiple of the cycle. The street is evenly divided into sections that contain a set number of consecutive blocks and signals. The offset is the same for all blocks in a section. Three inspection intervals and six section sizes were tested. The latter ranged from a single block/signal to the whole street. It was found that adaptation worked best when sections were large and adaptation frequent. The effects were considerable across all scenarios. For a short street with a short rush and high input flows the probabilistic incidence of gridlock was reduced from 10 to 0{\%}, and the average duration of a trip from 216 to 181 s. For a long street with a long rush and high input flows the gridlock probability was reduced from 23 to 0{\%} and the average trip duration from 2037 to 1143s.",
keywords = "Adaptive offsets, Congestion, ISTTT22, MFD, Traffic signals",
author = "Daganzo, {Carlos F.} and Lehe, {Lewis J.} and Juan Argote-Cabanero",
year = "2018",
month = "11",
doi = "10.1016/j.trb.2017.08.011",
language = "English (US)",
volume = "117",
pages = "926--934",
journal = "Transportation Research, Series B: Methodological",
issn = "0191-2615",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Adaptive offsets for signalized streets

AU - Daganzo, Carlos F.

AU - Lehe, Lewis J.

AU - Argote-Cabanero, Juan

PY - 2018/11

Y1 - 2018/11

N2 - This paper shows that severe congestion on streets controlled by traffic signals can be reduced by dynamically adapting the signal offsets to the prevailing density with a simple rule that keeps the signals’ green-red ratios invariant. Invariant ratios reduce a control policy's impact on the crossing streets, so a policy can be optimized and evaluated by focusing on the street itself without the confounding factors present in networks. Designed for heavy traffic with spillovers, the proposed policies are adaptive and need little data – they only require average traffic density readings and no demand forecasts. A battery of numerical experiments simulating the dynamics of rush hour traffic on a congested, homogeneous circular street reveals that the proposed form of adaptation reduces the duration of the rush and overall congestion compared with pre-timed control strategies. Eighteen different adaptation policies were considered. All inspect the street densities periodically and simultaneously, and retime the signals immediately thereafter. The period is a fixed multiple of the cycle. The street is evenly divided into sections that contain a set number of consecutive blocks and signals. The offset is the same for all blocks in a section. Three inspection intervals and six section sizes were tested. The latter ranged from a single block/signal to the whole street. It was found that adaptation worked best when sections were large and adaptation frequent. The effects were considerable across all scenarios. For a short street with a short rush and high input flows the probabilistic incidence of gridlock was reduced from 10 to 0%, and the average duration of a trip from 216 to 181 s. For a long street with a long rush and high input flows the gridlock probability was reduced from 23 to 0% and the average trip duration from 2037 to 1143s.

AB - This paper shows that severe congestion on streets controlled by traffic signals can be reduced by dynamically adapting the signal offsets to the prevailing density with a simple rule that keeps the signals’ green-red ratios invariant. Invariant ratios reduce a control policy's impact on the crossing streets, so a policy can be optimized and evaluated by focusing on the street itself without the confounding factors present in networks. Designed for heavy traffic with spillovers, the proposed policies are adaptive and need little data – they only require average traffic density readings and no demand forecasts. A battery of numerical experiments simulating the dynamics of rush hour traffic on a congested, homogeneous circular street reveals that the proposed form of adaptation reduces the duration of the rush and overall congestion compared with pre-timed control strategies. Eighteen different adaptation policies were considered. All inspect the street densities periodically and simultaneously, and retime the signals immediately thereafter. The period is a fixed multiple of the cycle. The street is evenly divided into sections that contain a set number of consecutive blocks and signals. The offset is the same for all blocks in a section. Three inspection intervals and six section sizes were tested. The latter ranged from a single block/signal to the whole street. It was found that adaptation worked best when sections were large and adaptation frequent. The effects were considerable across all scenarios. For a short street with a short rush and high input flows the probabilistic incidence of gridlock was reduced from 10 to 0%, and the average duration of a trip from 216 to 181 s. For a long street with a long rush and high input flows the gridlock probability was reduced from 23 to 0% and the average trip duration from 2037 to 1143s.

KW - Adaptive offsets

KW - Congestion

KW - ISTTT22

KW - MFD

KW - Traffic signals

UR - http://www.scopus.com/inward/record.url?scp=85031329568&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85031329568&partnerID=8YFLogxK

U2 - 10.1016/j.trb.2017.08.011

DO - 10.1016/j.trb.2017.08.011

M3 - Article

AN - SCOPUS:85031329568

VL - 117

SP - 926

EP - 934

JO - Transportation Research, Series B: Methodological

JF - Transportation Research, Series B: Methodological

SN - 0191-2615

ER -