Large scale traffic forecasting with gradient boosting, Traffic4cast 2022 challenge

• URL: http://arxiv.org/abs/2211.00157v1
• Date: Mon, 31 Oct 2022 21:52:19 GMT
• Title: Large scale traffic forecasting with gradient boosting, Traffic4cast 2022 challenge
• Authors: Martin Lumiste (1), Andrei Ilie (1 and 2) ((1) Bolt Technology, (2) University of Bucharest)
• Abstract summary: We present our solution to the IARAI Traffic4cast 2022 competition. The goal is to develop algorithms for predicting road graph edge congestion classes and supersegment-level travel times. This simple, fast, and scalable technique allowed us to win second place in the core competition.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate traffic forecasting is of the utmost importance for optimal travel planning and for efficient city mobility. IARAI (The Institute of Advanced Research in Artificial Intelligence) organizes Traffic4cast, a yearly traffic prediction competition based on real-life data [https://www.iarai.ac.at/traffic4cast/], aiming to leverage artificial intelligence advances for producing accurate traffic estimates. We present our solution to the IARAI Traffic4cast 2022 competition, in which the goal is to develop algorithms for predicting road graph edge congestion classes and supersegment-level travel times. In contrast to the previous years, this year's competition focuses on modelling graph edge level behaviour, rather than more coarse aggregated grid-based traffic movies. Due to this, we leverage a method familiar from tabular data modelling -- gradient-boosted decision tree ensembles. We reduce the dimensionality of the input data representing traffic counters with the help of the classic PCA method and feed it as input to a LightGBM model. This simple, fast, and scalable technique allowed us to win second place in the core competition. The source code and references to trained model files and submissions are available at https://github.com/skandium/t4c22 .

Related papers

• Valeo4Cast: A Modular Approach to End-to-End Forecasting [93.86257326005726]
  Our solution ranks first in the Argoverse 2 End-to-end Forecasting Challenge, with 63.82 mAPf. We depart from the current trend of tackling this task via end-to-end training from perception to forecasting, and instead use a modular approach. We surpass forecasting results by +17.1 points over last year's winner and by +13.3 points over this year's runner-up.
  arXiv  Detail & Related papers  (2024-06-12T11:50:51Z)
• BjTT: A Large-scale Multimodal Dataset for Traffic Prediction [49.93028461584377]
  Traditional traffic prediction methods rely on historical traffic data to predict traffic trends. In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic generation. We propose ChatTraffic, the first diffusion model for text-to-traffic generation.
  arXiv  Detail & Related papers  (2024-03-08T04:19:56Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• Multi-task Learning for Sparse Traffic Forecasting [13.359590890052454]
  We propose a multi-task learning network that can simultaneously predict the congestion classes and the speed of each road segment. Our method achieved excellent results on the dataset provided by the Traffic4cast Competition 2022, source code is available at https://github.com/OctopusLi/NeurIPS2022-traffic4cast.
  arXiv  Detail & Related papers  (2022-11-18T02:10:40Z)
• Transposed Variational Auto-encoder with Intrinsic Feature Learning for Traffic Forecasting [23.120977536899424]
  We present our solutions to the Traffic4cast 2022 core challenge and extended challenge. In this competition, the participants are required to predict the traffic states for the future 15-minute based on the vehicle counter data in the previous hour. Our solutions have ranked first in both challenges on the final leaderboard.
  arXiv  Detail & Related papers  (2022-10-30T13:15:19Z)
• Motion Transformer with Global Intention Localization and Local Movement Refinement [103.75625476231401]
  Motion TRansformer (MTR) models motion prediction as the joint optimization of global intention localization and local movement refinement. MTR achieves state-of-the-art performance on both the marginal and joint motion prediction challenges.
  arXiv  Detail & Related papers  (2022-09-27T16:23:14Z)
• Traffic4cast -- Large-scale Traffic Prediction using 3DResNet and Sparse-UNet [2.568084386350801]
  The aim is to build a machine learning model for predicting the normalized average traffic speed and flow of subregions of multiple-scale cities using historical data points. We explore 3DRparseNetes and Sparse-UNet approaches for the tasks in this competition. Our results show that both of the proposed models achieve much better performance than the baseline algorithms.
  arXiv  Detail & Related papers  (2021-11-10T23:40:52Z)
• Attention Gate in Traffic Forecasting [16.153263073898746]
  Traffic Map Movie Forecasting Challenge 2020 is secondly held in the competition track of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) The task is to predict traffic flow volume, average speed in major directions on the geographical area of three big cities: Berlin, Istanbul, and Moscow. In this paper, we apply the attention mechanism on U-Net based model, especially we add an attention gate on the skip-connection between contraction path and expansion path.
  arXiv  Detail & Related papers  (2021-09-27T12:58:12Z)
• Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction [71.97877759413272]
  Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. Our work addresses two key challenges in trajectory prediction, learning outputs, and better predictions by imposing constraints using driving knowledge.
  arXiv  Detail & Related papers  (2021-04-16T17:58:56Z)
• TLab: Traffic Map Movie Forecasting Based on HR-NET [23.40323690536007]
  In our solution, hand-crafted features are input into the model in a form of channels. In terms of prediction accuracy, our solution has won 2nd place in NeurIPS 2020, Traffic4cast Challenge.
  arXiv  Detail & Related papers  (2020-11-13T18:48:13Z)
• TPNet: Trajectory Proposal Network for Motion Prediction [81.28716372763128]
  Trajectory Proposal Network (TPNet) is a novel two-stage motion prediction framework. TPNet first generates a candidate set of future trajectories as hypothesis proposals, then makes the final predictions by classifying and refining the proposals. Experiments on four large-scale trajectory prediction datasets, show that TPNet achieves the state-of-the-art results both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2020-04-26T00:01:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.