Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

• URL: http://arxiv.org/abs/2305.19591v2
• Date: Sun, 4 Jun 2023 10:54:15 GMT
• Title: Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
• Authors: Maryam Shaygan, Collin Meese, Wanxin Li, Xiaolong Zhao, Mark Nejad
• Abstract summary: This survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods.
• Score: 2.5199066832791535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.

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)
• Causally-Aware Spatio-Temporal Multi-Graph Convolution Network for Accurate and Reliable Traffic Prediction [5.200012764049096]
  This study focuses on an instance of--temporal problem--traffic prediction--to demonstrate an advanced deep learning model for making accurate and reliable forecast. We propose an end-to-end traffic prediction framework that leverages three primary components to accurate and reliable traffic predictions. Experimental results on two real-world traffic datasets demonstrate that the method outperforms several state-of-the-art models in prediction accuracy.
  arXiv  Detail & Related papers  (2024-08-23T14:35:54Z)
• TrafficGPT: Towards Multi-Scale Traffic Analysis and Generation with Spatial-Temporal Agent Framework [3.947797359736224]
  We have designed a multi-scale traffic generation system, TrafficGPT, using three AI agents to process multi-scale traffic data. TrafficGPT consists of three essential AI agents: 1) a text-to-demand agent to interact with users and extract prediction tasks through texts; 2) a traffic prediction agent that leverages multi-scale traffic data to generate temporal features and similarity; and 3) a suggestion and visualization agent that uses the prediction results to generate suggestions and visualizations.
  arXiv  Detail & Related papers  (2024-05-08T07:48:40Z)
• Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis [6.8775337739726226]
  We propose an improved traffic prediction method based on graph convolution deep learning algorithms. We leverage human activity frequency data from National Household Travel Survey to enhance the inference capability of a causal relationship between activity and traffic patterns.
  arXiv  Detail & Related papers  (2023-08-20T14:31:55Z)
• 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)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions [3.655021726150368]
  We have examined over two hundred studies related to trajectory prediction in the context of AVs. This review conducts a comprehensive evaluation of several deep learning-based techniques. By identifying challenges in the existing literature and outlining potential research directions, this review significantly contributes to the advancement of knowledge in the domain of AV trajectory prediction.
  arXiv  Detail & Related papers  (2023-07-12T10:20:19Z)
• Behavioral Intention Prediction in Driving Scenes: A Survey [70.53285924851767]
  Behavioral Intention Prediction (BIP) simulates a human consideration process and fulfills the early prediction of specific behaviors. This work provides a comprehensive review of BIP from the available datasets, key factors and challenges, pedestrian-centric and vehicle-centric BIP approaches, and BIP-aware applications.
  arXiv  Detail & Related papers  (2022-11-01T11:07:37Z)
• 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)
• 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)
• Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions [32.25707921285397]
  This paper provides a comprehensive survey on deep learning-based approaches in traffic prediction from multiple perspectives. We first summarize the existing traffic prediction methods, and give a taxonomy. Second, we list the state-of-the-art approaches in different traffic prediction applications.
  arXiv  Detail & Related papers  (2020-04-18T08:28:10Z)

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.