DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction

• URL: http://arxiv.org/abs/2108.09091v1
• Date: Fri, 20 Aug 2021 10:08:26 GMT
• Title: DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
• Authors: Renhe Jiang, Du Yin, Zhaonan Wang, Yizhuo Wang, Jiewen Deng, Hangchen Liu, Zekun Cai, Jinliang Deng, Xuan Song, Ryosuke Shibasaki
• Abstract summary: By leveraging state-of-the-art deep learning technologies on such data, urban traffic prediction has drawn a lot attention in AI and Intelligent Transportation System community. According to the specific modeling strategy, the state-of-the-art deep learning models can be divided into three categories: grid-based, graph-based, and time-series models.
• Score: 7.476566278759198
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors. By leveraging state-of-the-art deep learning technologies on such data, urban traffic prediction has drawn a lot of attention in AI and Intelligent Transportation System community. The problem can be uniformly modeled with a 3D tensor (T, N, C), where T denotes the total time steps, N denotes the size of the spatial domain (i.e., mesh-grids or graph-nodes), and C denotes the channels of information. According to the specific modeling strategy, the state-of-the-art deep learning models can be divided into three categories: grid-based, graph-based, and multivariate time-series models. In this study, we first synthetically review the deep traffic models as well as the widely used datasets, then build a standard benchmark to comprehensively evaluate their performances with the same settings and metrics. Our study named DL-Traff is implemented with two most popular deep learning frameworks, i.e., TensorFlow and PyTorch, which is already publicly available as two GitHub repositories https://github.com/deepkashiwa20/DL-Traff-Grid and https://github.com/deepkashiwa20/DL-Traff-Graph. With DL-Traff, we hope to deliver a useful resource to researchers who are interested in spatiotemporal data analysis.

Related papers

• 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)
• STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction [9.467593700532401]
  This paper provides a systematic review of graph learning strategies and commonly used graph convolution algorithms. We then conduct a comprehensive analysis of the strengths and weaknesses of recently proposed spatial-temporal graph network models. We build a study called STG4Traffic using the deep learning framework PyTorch to establish a standardized and scalable benchmark on two types of traffic datasets.
  arXiv  Detail & Related papers  (2023-07-02T06:56:52Z)
• Inverting the Fundamental Diagram and Forecasting Boundary Conditions: How Machine Learning Can Improve Macroscopic Models for Traffic Flow [0.0]
  We consider a dataset with flux and velocity data of vehicles moving on a highway, collected by fixed sensors and classified by lane and by class of vehicle. We extrapolate two important pieces of information: 1) if congestion is appearing under the sensor, and 2) the total amount of vehicles which is going to pass under the sensor in the next future. These pieces of information are then used to improve the accuracy of an LWR-based first-order multi-class model describing the dynamics of traffic flow between sensors.
  arXiv  Detail & Related papers  (2023-03-21T11:07:19Z)
• Spatio-Temporal Meta-Graph Learning for Traffic Forecasting [7.406501288721471]
  We propose Meta-Temporal Learning as a novel Graph Learning mechanism. We implement this idea into Meta-Graph Learner powered by a Meta-Node encoder into GCRN-decoder. Our model can explicitly disentangle time slots with different patterns and be robustly adaptive to any anomalous traffic situations.
  arXiv  Detail & Related papers  (2022-11-27T01:56:58Z)
• IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes [79.18349050238413]
  Preparation and training of deploy-able deep learning architectures require the models to be suited to different traffic scenarios. An unstructured and complex driving layout found in several developing countries such as India poses a challenge to these models. We build a new dataset, IDD-3D, which consists of multi-modal data from multiple cameras and LiDAR sensors with 12k annotated driving LiDAR frames.
  arXiv  Detail & Related papers  (2022-10-23T23:03:17Z)
• Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction [52.0977259978343]
  This paper proposes a Continuous-time and Multi-level dynamic graph representation learning method for Origin-Destination demand prediction (CMOD) The state vectors keep historical transaction information and are continuously updated according to the most recently happened transactions. Experiments are conducted on two real-world datasets from Beijing Subway and New York Taxi, and the results demonstrate the superiority of our model against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-06-30T03:37:50Z)
• 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)
• A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classification [68.11005070665364]
  We explore Incremental Learning (IL) techniques to add new classes to models without a full retraining, hence speeding up model's updates cycle. We consider iCarl, a state of the art IL method, and MIRAGE-2019, a public dataset with traffic from 40 Android apps. Despite our analysis reveals their infancy, IL techniques are a promising research area on the roadmap towards automated DL-based traffic analysis systems.
  arXiv  Detail & Related papers  (2021-07-09T14:28:16Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• Short-term Traffic Prediction with Deep Neural Networks: A Survey [2.9849405664643585]
  In modern transportation systems, an enormous amount of traffic data is generated every day. This has led to rapid progress in short-term traffic prediction (STTP) In traffic networks with complex relationships, deep neural networks (DNNs) often perform well because they are capable of automatically extracting the most important features and patterns.
  arXiv  Detail & Related papers  (2020-08-28T15:06:06Z)
• PerMO: Perceiving More at Once from a Single Image for Autonomous Driving [76.35684439949094]
  We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image. Our approach combines the strengths of deep learning and the elegance of traditional techniques. We have integrated these algorithms with an autonomous driving system.
  arXiv  Detail & Related papers  (2020-07-16T05:02:45Z)

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.