SWMLP: Shared Weight Multilayer Perceptron for Car Trajectory Speed Prediction using Road Topographical Features

• URL: http://arxiv.org/abs/2310.02282v1
• Date: Mon, 2 Oct 2023 12:39:33 GMT
• Title: SWMLP: Shared Weight Multilayer Perceptron for Car Trajectory Speed Prediction using Road Topographical Features
• Authors: Sarah Almeida Carneiro (LIGM, IFPEN), Giovanni Chierchia (LIGM), Jean Charl\'ety (IFPEN), Aur\'elie Chataignon (IFPEN), Laurent Najman (LIGM)
• Abstract summary: We propose a speed prediction method that is independent of large historical speed data. Our results show significant improvement, both qualitative and quantitative, over standard regression analysis.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although traffic is one of the massively collected data, it is often only available for specific regions. One concern is that, although there are studies that give good results for these data, the data from these regions may not be sufficiently representative to describe all the traffic patterns in the rest of the world. In quest of addressing this concern, we propose a speed prediction method that is independent of large historical speed data. To predict a vehicle's speed, we use the trajectory road topographical features to fit a Shared Weight Multilayer Perceptron learning model. Our results show significant improvement, both qualitative and quantitative, over standard regression analysis. Moreover, the proposed framework sheds new light on the way to design new approaches for traffic analysis.

Related papers

• Strada-LLM: Graph LLM for traffic prediction [62.2015839597764]
  A considerable challenge in traffic prediction lies in handling the diverse data distributions caused by vastly different traffic conditions. We propose a graph-aware LLM for traffic prediction that considers proximal traffic information. We adopt a lightweight approach for efficient domain adaptation when facing new data distributions in few-shot fashion.
  arXiv  Detail & Related papers  (2024-10-28T09:19:29Z)
• IBB Traffic Graph Data: Benchmarking and Road Traffic Prediction Model [0.24999074238880487]
  Road traffic congestion prediction is a crucial component of intelligent transportation systems. IBB Traffic graph dataset covers the sensor data collected at 2451 distinct locations. We propose a novel Road Traffic Prediction Model that strengthens temporal links through feature engineering.
  arXiv  Detail & Related papers  (2024-08-02T05:23:19Z)
• Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline [21.37853568400125]
  This paper extensively analyzes and categorizes existing research in lane-level traffic prediction. It introduces a simple baseline model, GraphMLP, based on graph structure and prediction networks. We have replicated codes not publicly available in existing studies and assessed various models in terms of effectiveness, efficiency, and applicability.
  arXiv  Detail & Related papers  (2024-03-22T04:21:40Z)
• Clustering Dynamics for Improved Speed Prediction Deriving from Topographical GPS Registrations [0.0]
  We propose solutions for speed prediction using sparse GPS data points and their associated topographical and road design features. Our goal is to investigate whether we can use similarities in the terrain and infrastructure to train a machine learning model that can predict speed in regions where we lack transportation data.
  arXiv  Detail & Related papers  (2024-02-12T09:28:16Z)
• MA2GCN: Multi Adjacency relationship Attention Graph Convolutional Networks for Traffic Prediction using Trajectory data [1.147374308875151]
  This paper proposes a new traffic congestion prediction model - Multi Adjacency relationship Attention Graph Convolutional Networks(MA2GCN) It transformed vehicle trajectory data into graph structured data in grid form, and proposed a vehicle entry and exit matrix based on the mobility between different grids. Compared with multiple baselines, our model achieved the best performance on Shanghai taxi GPS trajectory dataset.
  arXiv  Detail & Related papers  (2024-01-16T14:22:44Z)
• 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)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction [110.61383502442598]
  We introduce a novel neural network framework termed Cross-Modal Message Propagation Network (CMMPNet) CMMPNet is composed of two deep Auto-Encoders for modality-specific representation learning and a tailor-designed Dual Enhancement Module for cross-modal representation refinement. Experiments on three real-world benchmarks demonstrate the effectiveness of our CMMPNet for robust road extraction.
  arXiv  Detail & Related papers  (2021-11-30T04:30:10Z)
• Real Time Monocular Vehicle Velocity Estimation using Synthetic Data [78.85123603488664]
  We look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. We propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity.
  arXiv  Detail & Related papers  (2021-09-16T13:10:27Z)
• Graph modelling approaches for motorway traffic flow prediction [6.370406399003785]
  This paper presents two new spatial-temporal approaches for building accurate short-term prediction along a popular motorway in Sydney. The methods are built on proximity-based approaches, denoted backtracking and proximity, which uses the most recent and closest traffic flow information for each of the target counting stations along the motorway. The results indicate that for short-term predictions (less than 10 minutes into the future), the proposed graph-based approaches outperform state-of-the-art deep learning models.
  arXiv  Detail & Related papers  (2020-06-26T06:54:14Z)
• 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.