4,264 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Adaptive traffic lights based on traffic flow prediction using machine learning models
Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections
Building Transportation Foundation Model via Generative Graph Transformer
Efficient traffic management is crucial for maintaining urban mobility,
especially in densely populated areas where congestion, accidents, and delays
can lead to frustrating and expensive commutes. However, existing prediction
methods face challenges in terms of optimizing a single objective and
understanding the complex composition of the transportation system. Moreover,
they lack the ability to understand the macroscopic system and cannot
efficiently utilize big data. In this paper, we propose a novel approach,
Transportation Foundation Model (TFM), which integrates the principles of
traffic simulation into traffic prediction. TFM uses graph structures and
dynamic graph generation algorithms to capture the participatory behavior and
interaction of transportation system actors. This data-driven and model-free
simulation method addresses the challenges faced by traditional systems in
terms of structural complexity and model accuracy and provides a foundation for
solving complex transportation problems with real data. The proposed approach
shows promising results in accurately predicting traffic outcomes in an urban
transportation setting
Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization
Statistical traffic data analysis is a hot topic in traffic management and
control. In this field, current research progresses focus on analyzing traffic
flows of individual links or local regions in a transportation network. Less
attention are paid to the global view of traffic states over the entire
network, which is important for modeling large-scale traffic scenes. Our aim is
precisely to propose a new methodology for extracting spatio-temporal traffic
patterns, ultimately for modeling large-scale traffic dynamics, and long-term
traffic forecasting. We attack this issue by utilizing Locality-Preserving
Non-negative Matrix Factorization (LPNMF) to derive low-dimensional
representation of network-level traffic states. Clustering is performed on the
compact LPNMF projections to unveil typical spatial patterns and temporal
dynamics of network-level traffic states. We have tested the proposed method on
simulated traffic data generated for a large-scale road network, and reported
experimental results validate the ability of our approach for extracting
meaningful large-scale space-time traffic patterns. Furthermore, the derived
clustering results provide an intuitive understanding of spatial-temporal
characteristics of traffic flows in the large-scale network, and a basis for
potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013
TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks
Road safety is a major global public health concern. Effective traffic crash
prediction can play a critical role in reducing road traffic accidents.
However, Existing machine learning approaches tend to focus on predicting
traffic accidents in isolation, without considering the potential relationships
between different accident locations within road networks. To incorporate graph
structure information, graph-based approaches such as Graph Neural Networks
(GNNs) can be naturally applied. However, applying GNNs to the accident
prediction problem faces challenges due to the lack of suitable
graph-structured traffic accident datasets. To bridge this gap, we have
constructed a real-world graph-based Traffic Accident Prediction (TAP) data
repository, along with two representative tasks: accident occurrence prediction
and accident severity prediction. With nationwide coverage, real-world network
topology, and rich geospatial features, this data repository can be used for a
variety of traffic-related tasks. We further comprehensively evaluate eleven
state-of-the-art GNN variants and two non-graph-based machine learning methods
using the created datasets. Significantly facilitated by the proposed data, we
develop a novel Traffic Accident Vulnerability Estimation via Linkage (TRAVEL)
model, which is designed to capture angular and directional information from
road networks. We demonstrate that the proposed model consistently outperforms
the baselines. The data and code are available on GitHub
(https://github.com/baixianghuang/travel).Comment: 10 pages, 5 figure
- …