Citation

Daganzo CF, Lehe LJ, Argote-Cabanero J. Transp. Res. Proc. 2017; 23: 612-623.

Copyright

(Copyright © 2017, Elsevier Publications)

DOI

10.1016/j.trpro.2017.05.034

PMID

unavailable

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 simulataneously, 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 216s to 181s. 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 2037s to 1143s.

Language: en