Anticipatory Adaptation of Signalisation Based on Traffic Flow Forecasts within a Self-organised Traffic Control System

Matthias Sommer,Jörg Hähner

doi:10.1051/matecconf/20168103006

Abstract

Autonomously adapting signalling strategies to changing traffic demand in urban areas have been frequently used as application scenario for self-adapting systems. Striving for the ability to cope with the dynamic behaviour of traffic and to react appropriately to unforeseen conditions, such solutions dynamically adapt the signalisation to the monitored traffic demands. The Organic Traffic Control (OTC) system is one of the most prominent representatives in this domain. OTC implements a multi-layered observer-/controller architecture. In this paper, we extend OTC’s observer with a time series forecast component to create forecasts of future traffic developments for turning movements. These forecasts are then used to proactively adapt signalisation parameters. We demonstrate the benefit of the developed approach in terms of reduced travel times, and vehicle emissions within near-to-reality simulations of realistic traffic conditions from Hamburg, Germany.

Highlights

The vehicular traffic domain is a vivid research field, both for industrial and academic research institutions
We evaluated our approach by means of several metrics: x Mean queue of vehicles in front of red traffic lights. x Average stop time in seconds per kilometre. x Vehicle’s emissions: mono-nitrogen oxide (NOx), carbon dioxide (CO2), and particulate matter (PM)
Compared to the standard Organic Traffic Control (OTC), the forecast-augmented OTCcontrol significantly reduces the mean queue by 5.3% (16.3%), and the average stop time by 3.4% (12.0%)

Summary

Introduction

The vehicular traffic domain is a vivid research field, both for industrial and academic research institutions. Novel trends, such as self-driving cars [1], car-to-car communication [2], and traffic-adaptive control systems [3, 4], have the overall goal to optimise the existing traffic infrastructure towards a more efficient utilisation of road networks. We have to face certain negative impacts on the environment due to the increase of mobility, especially in urban areas. This leads to an increase in pollution, a rising number of incidents, and an inefficient use of the transportation system. The dynamic characteristics of traffic, the unpredictable behaviour of humans, and the highly complex dependencies between several streams throughout the road network make it an interesting and challenging field for self-adapting and self-organising solutions

Methods

Results

Conclusion