Scalable Learning of Segment-Level Traffic Congestion Functions

• URL: http://arxiv.org/abs/2405.06080v1
• Date: Thu, 9 May 2024 20:12:46 GMT
• Title: Scalable Learning of Segment-Level Traffic Congestion Functions
• Authors: Shushman Choudhury, Abdul Rahman Kreidieh, Iveel Tsogsuren, Neha Arora, Carolina Osorio, Alexandre Bayen,
• Abstract summary: We propose and study a data-driven framework for identifying traffic congestion functions at global scale and segment-level granularity. We pool traffic data from all segments into one dataset, combining static attributes with dynamic time-dependent features. We evaluate how well our framework identifies congestion functions on observed segments and how it generalizes to unobserved segments and predicts segment attributes on a large dataset covering multiple cities worldwide.
• Score: 40.26253453756835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose and study a data-driven framework for identifying traffic congestion functions (numerical relationships between observations of macroscopic traffic variables) at global scale and segment-level granularity. In contrast to methods that estimate a separate set of parameters for each roadway, ours learns a single black-box function over all roadways in a metropolitan area. First, we pool traffic data from all segments into one dataset, combining static attributes with dynamic time-dependent features. Second, we train a feed-forward neural network on this dataset, which we can then use on any segment in the area. We evaluate how well our framework identifies congestion functions on observed segments and how it generalizes to unobserved segments and predicts segment attributes on a large dataset covering multiple cities worldwide. For identification error on observed segments, our single data-driven congestion function compares favorably to segment-specific model-based functions on highway roads, but has room to improve on arterial roads. For generalization, our approach shows strong performance across cities and road types: both on unobserved segments in the same city and on zero-shot transfer learning between cities. Finally, for predicting segment attributes, we find that our approach can approximate critical densities for individual segments using their static properties.

Related papers

• TopoStreamer: Temporal Lane Segment Topology Reasoning in Autonomous Driving [52.25176274203747]
  TopoStreamer is an end-to-end temporal perception model for lane segment topology reasoning.<n>TopoStreamer introduces three key improvements: streaming attribute constraints, dynamic lane boundary positional encoding, and lane segment denoising.<n>On the Open-Lane-V2 dataset, TopoStreamer demonstrates significant improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-07-01T12:10:46Z)
• SARN: Structurally-Aware Recurrent Network for Spatio-Temporal Disaggregation [8.636014676778682]
  Open data is frequently released spatially aggregated, usually to comply with privacy policies. But coarse, heterogeneous aggregations complicate coherent learning and integration for downstream AI/ML systems. We propose an overarching model named Structurally-Aware Recurrent Network (SARN), which integrates structurally-aware spatial attention layers into the Gated Recurrent Unit (GRU) model. For scenarios with limited historical training data, we show that a model pre-trained on one city variable can be fine-tuned for another city variable using only a few hundred samples.
  arXiv  Detail & Related papers  (2023-06-09T21:01:29Z)
• Traffic Scene Parsing through the TSP6K Dataset [109.69836680564616]
  We introduce a specialized traffic monitoring dataset, termed TSP6K, with high-quality pixel-level and instance-level annotations. The dataset captures more crowded traffic scenes with several times more traffic participants than the existing driving scenes. We propose a detail refining decoder for scene parsing, which recovers the details of different semantic regions in traffic scenes.
  arXiv  Detail & Related papers  (2023-03-06T02:05:14Z)
• A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data [0.822021749810331]
  We consider the task of classifying the activities of moving objects from their noisy indoor trajectory data in a collaborative manufacturing environment. We present a semi-supervised machine learning approach that first applies an information theoretic criterion to partition a long trajectory into a set of segments. The segments are then labelled automatically based on a constrained hierarchical clustering method.
  arXiv  Detail & Related papers  (2023-01-09T01:20:50Z)
• An Automated Analysis Framework for Trajectory Datasets [0.0]
  Trajectory datasets of road users have become more important in the last years for safety validation of automated vehicles. Several naturalistic trajectory datasets with each more than 10.000 tracks were released and others will follow. Considering this amount of data, it is necessary to be able to compare these datasets in-depth with ease.
  arXiv  Detail & Related papers  (2022-02-12T10:55:53Z)
• Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings [81.09026586111811]
  We propose an approach to semantic segmentation that achieves state-of-the-art supervised performance when applied in a zero-shot setting. This is achieved by replacing each class label with a vector-valued embedding of a short paragraph that describes the class. The resulting merged semantic segmentation dataset of over 2 Million images enables training a model that achieves performance equal to that of state-of-the-art supervised methods on 7 benchmark datasets.
  arXiv  Detail & Related papers  (2022-02-04T07:19:09Z)
• Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection [12.786648212233116]
  We introduce a new metric to assess region-based over- and under-segmentation. We analyze and compare it to other metrics, demonstrating that the use of our metric lends greater explainability to semantic segmentation model performance in real-world applications.
  arXiv  Detail & Related papers  (2021-01-21T03:12:43Z)
• Detecting 32 Pedestrian Attributes for Autonomous Vehicles [103.87351701138554]
  In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes. We introduce a Multi-Task Learning (MTL) model relying on a composite field framework, which achieves both goals in an efficient way. We show competitive detection and attribute recognition results, as well as a more stable MTL training.
  arXiv  Detail & Related papers  (2020-12-04T15:10:12Z)
• Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs [72.67604044776662]
  We tackle the problem of of-temporal tagging of self-driving scenes from raw sensor data. Our approach learns a universal embedding for all tags, enabling efficient tagging of many attributes and faster learning of new attributes with limited data.
  arXiv  Detail & Related papers  (2020-11-12T02:18:16Z)
• Learning to Predict Context-adaptive Convolution for Semantic Segmentation [66.27139797427147]
  Long-range contextual information is essential for achieving high-performance semantic segmentation. We propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector. Our CaC-Net achieves superior segmentation performance on three public datasets.
  arXiv  Detail & Related papers  (2020-04-17T13:09:17Z)
• Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation [11.174536284571802]
  We propose a supervised trajectory segmentation algorithm called Wise Sliding Window (WS-II) WS-II processes the trajectory coordinates to find behavioral changes in space and time, generating an error signal. We evaluate our method over three real datasets from different domains.
  arXiv  Detail & Related papers  (2020-03-23T12:55:40Z)

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.