Introduction



Abstract

Traffic congestion is a growing and global problem, impacting the majority of people traveling and goods moving in urban areas around the world. Cities are challenged to keep traffic flowing, reduce pollution and decrease the economic damage caused by congestion. Predictive technology will play a significant role in the cities of the future. An era of solutions using the wide range of collected data will bring new opportunities and efficiencies. Next-generation technology for Traffic Light Controllers is based on real-time data fusion through a real-time traffic model. Rather than emptying queues like traditional controllers, each vehicle approaching the intersection is detected and its arrival time forecasted. Based on these forecasts the most efficient schedule is calculated, increasing the throughput significantly. Along with the optimisation it is possible to prioritise bicycles or trucks, as part of road authority policies and objectives to reduce pollution or encourage the use of certain modes of transportation. This paper describes on-street results and the experiences with various use cases.

Keywords

Traffic Light Controllers, Real-Time Traffic Model, Traffic Optimisation, Forecasting.

Introduction

Urban areas experience a continuing growth in inhabitants. Today around 60 percent of European citizens live in urban areas (cities with over 100.000 inhabitants). Due to further urbanisation this number is expected to rise to 70 percent or more, while the total number of EU citizens will increase as well. Due to this the liveability in cities will be under pressure, while there is an ongoing focus on a cleaner and more sustainable city. Considering mobility alone, more inhabitants means more traffic, more congestion and a higher risk of accidents, resulting in even more congestion. This results in more pollution, decreased safety, higher costs and hence reduced liveability. Solving these issues is the main priority for Smart Mobility1.

Collecting data (Big Data) is an ongoing hype in urban areas. However, the number of applications that actually use the data is rather limited. This is unfortunate as combining the collected data for use in applications has the potential to improve liveability. There is a movement needed that does not think in data but in solutions. The urban area is covered with millions of traffic sensors ranging from induction loops, cameras, Wi-Fi sniffers, Floating Car Data (FCD), etc. Most of the time these sensors are deployed with a very specific and limited use case. An example is the usage of loop detectors for traffic lights. With the movement of making this type of data available in real time new opportunities arise. In this paper we discuss the new generation of traffic control software that is already doing this and its effects.

JEROEN BROUWER

Manager Mobility Solutions

HANS DOMBEEK

Smart Traffic deployment engineer