Modeling Network-level Traffic Flow Transitions on Sparse Data

• URL: http://arxiv.org/abs/2208.06646v1
• Date: Sat, 13 Aug 2022 13:30:35 GMT
• Title: Modeling Network-level Traffic Flow Transitions on Sparse Data
• Authors: Xiaoliang Lei, Hao Mei, Bin Shi, Hua Wei
• Abstract summary: We present DTIGNN, an approach that can predict network-level traffic flows from sparse data. We demonstrate that our method outperforms state-of-the-art methods and can better support decision-making in transportation.
• Score: 6.756998301171409
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling how network-level traffic flow changes in the urban environment is useful for decision-making in transportation, public safety and urban planning. The traffic flow system can be viewed as a dynamic process that transits between states (e.g., traffic volumes on each road segment) over time. In the real-world traffic system with traffic operation actions like traffic signal control or reversible lane changing, the system's state is influenced by both the historical states and the actions of traffic operations. In this paper, we consider the problem of modeling network-level traffic flow under a real-world setting, where the available data is sparse (i.e., only part of the traffic system is observed). We present DTIGNN, an approach that can predict network-level traffic flows from sparse data. DTIGNN models the traffic system as a dynamic graph influenced by traffic signals, learns the transition models grounded by fundamental transition equations from transportation, and predicts future traffic states with imputation in the process. Through comprehensive experiments, we demonstrate that our method outperforms state-of-the-art methods and can better support decision-making in transportation.

Related papers

• A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation [53.39174966020085]
  Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions. In this study, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. We introduce a holistic traffic simulation framework called TrafficDojo towards vision-based TSC and its benchmarking.
  arXiv  Detail & Related papers  (2024-03-11T16:42:29Z)
• BjTT: A Large-scale Multimodal Dataset for Traffic Prediction [49.93028461584377]
  Traditional traffic prediction methods rely on historical traffic data to predict traffic trends. In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic generation. We propose ChatTraffic, the first diffusion model for text-to-traffic generation.
  arXiv  Detail & Related papers  (2024-03-08T04:19:56Z)
• FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph [10.675666104503119]
  We propose Fine-grained Deep Traffic Inference, as termedI. We construct a fine-grained traffic graph based on traffic signals to model the inter-road relations. We are the first to conduct the city-level fine-grained traffic prediction.
  arXiv  Detail & Related papers  (2023-06-19T14:03:42Z)
• TransWorldNG: Traffic Simulation via Foundation Model [23.16553424318004]
  We present TransWordNG, a traffic simulator that uses Data-driven algorithms and Graph Computing techniques to learn traffic dynamics from real data. The results demonstrate that TransWorldNG can generate more realistic traffic patterns compared to traditional simulators.
  arXiv  Detail & Related papers  (2023-05-25T05:49:30Z)
• TraffNet: Learning Causality of Traffic Generation for What-if Prediction [4.604622556490027]
  Real-time what-if traffic prediction is crucial for decision making in intelligent traffic management and control. Here, we present a simple deep learning framework called TraffNet that learns the mechanisms of traffic generation for what-if pre-diction.
  arXiv  Detail & Related papers  (2023-03-28T13:12:17Z)
• 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)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Integrated Traffic Simulation-Prediction System using Neural Networks with Application to the Los Angeles International Airport Road Network [39.975268616636]
  The proposed system includes an optimization-based OD matrix generation method, a Neural Network (NN) model trained to predict OD matrices via the pattern of traffic flow and a microscopic traffic simulator. We test the proposed system on the road network of the central terminal area (CTA) of the Los Angeles International Airport (LAX)
  arXiv  Detail & Related papers  (2020-08-05T01:41:10Z)
• Spatio-Temporal Point Processes with Attention for Traffic Congestion Event Modeling [28.994426283738363]
  We present a novel framework for modeling traffic congestion events over road networks. Using multi-modal data by combining count data from traffic sensors with police reports that report traffic incidents, we aim to capture two types of triggering effect for congestion events. Current traffic congestion at one location may cause future congestion over the road network, and traffic incidents may cause spread traffic congestion.
  arXiv  Detail & Related papers  (2020-05-15T04:22:18Z)

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.