The Green Wave Buddy- Cycling the Green Wave



The Green Wave Buddy- Cycling the Green Wave

Authors

Daniel Kofler, BikeCityGuide, Robert Schönauer, Mobimera Fairkehrstechnologien, Markus Straub, Gerald Richter, AIT Austrian Institute of Technology GmbH

Description

Signal programs of traffic light sequences are reconstructed using cyclists' GPS tracks. A novel feature in a smartphone navigation app assists in surfing the "green wave" derived from that data.

Abstract

Global smartphone penetration of around 30% [1] and the increasing use of navigation devices [2] which incorporate real time information show the potential of cyclists using digital navigation to reach familiar destinations. Ideally real time information incorporated into navigation devices helps finding the least costly route (in terms of time and energy requirements).
We identify two basic requirements of a cycling navigation assistant which takes into account waiting time at intersections:

1. Advanced routing and cycling assistants require profound knowledge about expected waiting times at traffic signals.
2. A user interface has to be designed to let cyclists adequately use the information for speed and route choice.

The first requirement is met by analysing users' GPS trajectories and inferring traffic signal programs. An algorithm to identify signal periodicity and green light patterns for origin-destination relationships at intersections has already been introduced [3]. Signal control cycle lengths are identified and offset times and green-light duration are estimated. The second requirement - the interface for cyclists – is implemented in BikeCityGuide, a navigation app for smartphones specifically designed for the needs of cyclists in cities. The app provides offline routing, navigation and accurate vocal instructions even in difficult turning situations. Additionally, the app uses the green light patterns to augment the user's context, provide hints and allow waiting times at traffic lights to be minimized.

This contribution presents the novel approach, briefly describes the methods and first results within its application on smartphones, with a focus on the implementation in BikeCityGuide. The methods are applied to GPS tracks acquired in the city of Vienna. It is demonstrated how the recommended route choices as well as the green wave assistant allow minimal waiting times at traffic lights and a comfortable ride.

The use of OpenStreetMap, GPS trajectories and self-learning waiting time models makes the prototype independently usable in any city. The app thus promotes bicycling as an elegant, efficient and environmentally friendly mode of transport.

Publisher

Association for European Transport