Urban Traffic Forecasting with Integrated Travel Time and Data Availability in a Conformal Graph Neural Network Framework
- URL: http://arxiv.org/abs/2407.12238v1
- Date: Wed, 17 Jul 2024 01:11:07 GMT
- Title: Urban Traffic Forecasting with Integrated Travel Time and Data Availability in a Conformal Graph Neural Network Framework
- Authors: Mayur Patil, Qadeer Ahmed, Shawn Midlam-Mohler,
- Abstract summary: We propose a novel framework to incorporate travel times between stations into a weighted adjacency matrix of a Graph Neural Network architecture.
To handle uncertainty, we utilize the Adaptive Conformal Prediction (ACP) method that adjusts prediction intervals based on real-time validation residuals.
Experiments show that the proposed model outperformed the next-best model by approximately 24% in MAE and 8% in RMSE.
- Score: 0.6554326244334868
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Traffic flow prediction is a big challenge for transportation authorities as it helps in planning and developing better infrastructure. State-of-the-art models often struggle to consider the data in the best way possible, intrinsic uncertainties, and the actual physics of the traffic. In this study, we propose a novel framework to incorporate travel times between stations into a weighted adjacency matrix of a Graph Neural Network (GNN) architecture with information from traffic stations based on their data availability. To handle uncertainty, we utilized the Adaptive Conformal Prediction (ACP) method that adjusts prediction intervals based on real-time validation residuals. To validate our results, we model a microscopic traffic scenario and perform a Monte-Carlo simulation to get a travel time distribution for a Vehicle Under Test (VUT) while it is navigating the traffic scenario, and this distribution is compared against the actual data. Experiments show that the proposed model outperformed the next-best model by approximately 24% in MAE and 8% in RMSE and validation showed the simulated travel time closely matches the 95th percentile of the observed travel time value.
Related papers
- 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) - ST-Mamba: Spatial-Temporal Selective State Space Model for Traffic Flow Prediction [32.44888387725925]
The proposed ST-Mamba model is first to leverage the power of spatial-temporal learning in traffic flow prediction without using graph modeling.
The proposed ST-Mamba model achieves a 61.11% improvement in computational speed and increases prediction accuracy by 0.67%.
Experiments with real-world traffic datasets demonstrate that the textsfST-Mamba model sets a new benchmark in traffic flow prediction.
arXiv Detail & Related papers (2024-04-20T03:57:57Z) - 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) - 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) - An Application of Vector Autoregressive Model for Analyzing the Impact
of Weather And Nearby Traffic Flow On The Traffic Volume [0.0]
This paper aims to predict the traffic flow at one road segment based on nearby traffic volume and weather conditions.
Our team also discover the impact of weather conditions and nearby traffic volume on the traffic flow at a target point.
arXiv Detail & Related papers (2023-11-12T16:45:29Z) - Uncertainty Quantification for Image-based Traffic Prediction across
Cities [63.136794104678025]
Uncertainty quantification (UQ) methods provide an approach to induce probabilistic reasoning.
We investigate their application to a large-scale image-based traffic dataset spanning multiple cities.
We find that our approach can capture both temporal and spatial effects on traffic behaviour in a representative case study for the city of Moscow.
arXiv Detail & Related papers (2023-08-11T13:35:52Z) - Predicting traffic signals on transportation networks using
spatio-temporal correlations on graphs [56.48498624951417]
This paper proposes a traffic propagation model that merges multiple heat diffusion kernels into a data-driven prediction model to forecast traffic signals.
We optimize the model parameters using Bayesian inference to minimize the prediction errors and, consequently, determine the mixing ratio of the two approaches.
The proposed model demonstrates prediction accuracy comparable to that of the state-of-the-art deep neural networks with lower computational effort.
arXiv Detail & Related papers (2021-04-27T18:17:42Z) - A model for traffic incident prediction using emergency braking data [77.34726150561087]
We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents.
We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
arXiv Detail & Related papers (2021-02-12T18:17:12Z) - A Graph Convolutional Network with Signal Phasing Information for
Arterial Traffic Prediction [63.470149585093665]
arterial traffic prediction plays a crucial role in the development of modern intelligent transportation systems.
Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors.
We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections.
arXiv Detail & Related papers (2020-12-25T01:40:29Z) - Prediction of Traffic Flow via Connected Vehicles [77.11902188162458]
We propose a Short-term Traffic flow Prediction framework so that transportation authorities take early actions to control flow and prevent congestion.
We anticipate flow at future time frames on a target road segment based on historical flow data and innovative features such as real time feeds and trajectory data provided by Connected Vehicles (CV) technology.
We show how this novel approach allows advanced modelling by integrating into the forecasting of flow, the impact of various events that CV realistically encountered on segments along their trajectory.
arXiv Detail & Related papers (2020-07-10T16:00:44Z) - Large-scale Analysis and Simulation of Traffic Flow using Markov Models [0.0]
A mathematically rigorous model that can be used for traffic analysis was proposed earlier by other researchers.
In this paper, a new parametrization is presented for this model by introducing the concept of two-dimensional stationary distribution.
We have run simulations in medium and large scales and both the model and estimation procedure, based on artificial and real datasets, have been proved satisfactory.
arXiv Detail & Related papers (2020-07-06T12:31:27Z)
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.