Traffic Map Movie Forecasting 2019
- Study traffic movies of entire cities
- Predict future traffic flows by high-resolution forecasts
- Discover rules and patterns, new insights and understanding
Solving sustainable mobility
The dense and growing populations of cities face a mobility and sustainability challenge. Superior predictive models could offer a solution, by empowering the planning of Smart Cities and establishing better road networks for more efficient and sustainable mobility. Improved traffic predictions are of great social and environmental value, while also advancing our general ability to capture the simple implicit rules underlying a complex system and model its future states.
The TRAFFIC4CAST competition reaches right to the heart of that issue. It challenges competitors to understand complex traffic systems, to be able to make predictions about future states of such systems
Traffic4cast Challenge 2019
Prediction of Short-term Traffic Flows
Unique high-resolution traffic map movies
Unprecedented scope and detail
Working with HERE Technologies we can provide industrial-scale real-world data for 3 full cities throughout a year. Samples every 5 minutes capture mornings, evenings, and rush-hour. In addition, we can learn weekday and seasonal effects. Overall, the data that we will share with the scientific community is based on the unprecedented number of over 100 billion (1011) probe points. The derived movies have multiple colour channels characterizing traffic volume, speed, and direction.
a special partnership with Here technologies makes this competition possible
Since 1985, HERE Technologies has been dedicated to advancing the science of location intelligence. After collecting over 100 billion probe-points of real-time and historic traffic data, HERE has partnered with IARAI to bring these data to the scientific community. Through efforts like these and their record as a leader in location services, HERE reaffirms their commitment to an autonomous world for everyone.
Benchmark and Robustness
Traffic4cast is a novel challenge that brings together several complementary fields from machine-learning and classical traffic research. The different perspectives in this interdisciplinary community will make for lively debates and lead to new insights.
Join us to explore unanswered questions, like what metrics might best identify award the correct prediction of relevant events, such as traffic jams! We will also jointly set optional additional goals, like journey time prediction, or the identification of simple rules underlying traffic patterns.
Our core metric will be the mean squared error of all predicted pixel values, the widely accepted ‘gold standard’ in video prediction tasks. In addition, we will publish filters applied to distinguish realistic traffic predictions from crude fakes.
We will incentivize and through lively debate in the community jointly develop complementary meaningful metrics and filters. Join our discussion on ideas like using the Frechet Inception Distance from classical image classification networks or a learned loss function from a GAN discriminator.
Traffic4cast is a novel challenge that brings together several complementary fields from machine-learning and classical traffic research. As an interdisciplinary community, we will jointly identify alternative metrics and complementary optional tasks of value that merit extra recognition, such as journey time prediction, or the identification of simple rules underlying traffic patterns.
Winners of these complementary competitions will be honoured at NeurIPS.
Award announcements, invitation to Traffic4cast Symposion