Neural Networks Model for Travel Time Prediction Based on ODTravel Time Matrix

• URL: http://arxiv.org/abs/2004.04030v1
• Date: Wed, 8 Apr 2020 15:01:13 GMT
• Title: Neural Networks Model for Travel Time Prediction Based on ODTravel Time Matrix
• Authors: Ayobami E. Adewale and Amnir Hadachi
• Abstract summary: Two neural network models namely multi-layer(MLP) perceptron and long short-term model(LSTM) are developed for predicting link travel time of a busy route. The experiment result showed that both models can make near-accurate predictions however, LSTM is more susceptible to noise as time step increases.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Public transportation system commuters are often interested in getting accurate travel time information to plan their daily activities. However, this information is often difficult to predict accurately due to the irregularities of road traffic, caused by factors such as weather conditions, road accidents, and traffic jams. In this study, two neural network models namely multi-layer(MLP) perceptron and long short-term model(LSTM) are developed for predicting link travel time of a busy route with input generated using Origin-Destination travel time matrix derived from a historical GPS dataset. The experiment result showed that both models can make near-accurate predictions however, LSTM is more susceptible to noise as time step increases.

Related papers

• Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies [55.2480439325792]
  This paper introduces the Signal-Enhanced Graph Convolutional Network Long Short Term Memory (SGCN-LSTM) model for predicting traffic speeds across road networks. Experiments on the PEMS-BAY road network traffic dataset demonstrate the SGCN-LSTM model's effectiveness.
  arXiv  Detail & Related papers  (2024-11-01T00:37:00Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Traffic estimation in unobserved network locations using data-driven macroscopic models [2.3543188414616534]
  This paper leverages macroscopic models and multi-source data collected from automatic traffic counters and probe vehicles to accurately estimate traffic flow and travel time in links where these measurements are unavailable. Because MaTE is grounded in macroscopic flow theory, all parameters and variables are interpretable. Experiments on synthetic data show that the model can accurately estimate travel time and traffic flow in out-of-sample links.
  arXiv  Detail & Related papers  (2024-01-30T15:21:50Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• GC-GRU-N for Traffic Prediction using Loop Detector Data [5.735035463793008]
  We use Seattle loop detector data aggregated over 15 minutes and reframe the problem through space time. The model ranked second with the fastest inference time and a very close performance to first place (Transformers)
  arXiv  Detail & Related papers  (2022-11-13T06:32:28Z)
• 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)
• Travel time prediction for congested freeways with a dynamic linear model [10.965065178451104]
  We propose to use dynamic linear models (DLMs) to approximate the non-linear traffic states. We show significant improvements in the accuracy, especially for short-term prediction.
  arXiv  Detail & Related papers  (2020-09-02T12:48:06Z)
• Traffic congestion anomaly detection and prediction using deep learning [6.370406399003785]
  Congestion prediction is a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, but this is a challenging task due to inter-dependencies of traffic flow both in time and space. We show that our deep learning models consistently outperform traditional methods, and we conduct a comparative analysis of the optimal time horizon of historical data required to predict traffic flow at different time points in the future.
  arXiv  Detail & Related papers  (2020-06-23T08:49:46Z)
• FMA-ETA: Estimating Travel Time Entirely Based on FFN With Attention [88.33372574562824]
  We propose a novel framework based on feed-forward network (FFN) for ETA, FFN with Multi-factor self-Attention (FMA-ETA) The novel Multi-factor self-attention mechanism is proposed to deal with different category features and aggregate the information purposefully. Experiments show FMA-ETA is competitive with state-of-the-art methods in terms of the prediction accuracy with significantly better inference speed.
  arXiv  Detail & Related papers  (2020-06-07T08:10:47Z)
• PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction [16.010576606023417]
  We propose a novel method named PCNN based on deep Convolutional Neural Network, modeling Periodic traffic data for short-term traffic congestion prediction. PCNN has two pivotal procedures: time series folding and multi-grained learning. Experimental results on a real-world urban traffic dataset confirm that folding time series data into a two-dimensional matrix is effective.
  arXiv  Detail & Related papers  (2020-03-16T05:36:58Z)

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.