1,362 research outputs found
Urban traffic flow prediction, a spatial-temporal approach
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesCurrent advances in computational technologies such as machine learning combined with traffic data availability are inspiring the development and growth of intelligent transport Systems (ITS). As urban authorities strive for efficient traffic systems, traffic forecasting is a vital element for effective control and management of traffic networks. Traffic forecasting methods have progressed from traditional statistical techniques to optimized data driven methods eulogised with artificial intelligence. Today, most techniques in traffic forecasting are mainly timeseries methods that ignore the spatial impact of traffic networks in traffic flow modelling. The consideration of both spatial and temporal dimensions in traffic forecasting efforts is key to achieving inclusive traffic forecasts. This research paper presents approaches to analyse spatial temporal patterns existing in networks and goes on to use a machine learning model that integrates both spatial and temporal dependency in traffic flow prediction. The application of the model to a traffic dataset for the city of Singapore shows that we can accurately predict traffic flow up to 15 minutes in advance and also accuracy results obtained outperform other classical traffic prediction methods
Machine learning for early detection of traffic congestion using public transport traffic data
The purpose of this project is to provide better knowledge of how the bus travel times is affected by congestion and other problems in the urban traffic environment. The main source of data for this study is second-level measurements coming from all buses in the Linköping region showing the location of each vehicle.The main goal of this thesis is to propose, implement, test and optimize a machine learning algorithm based on data collected from regional buses from Sweden so that it is able to perform predictions on the future state of the urban traffic.El objetivo principal de este proyecto es proponer, implementar, probar y optimizar un algoritmo de aprendizaje automático basado en datos recopilados de autobuses regionales de Suecia para que poder realizar predicciones sobre el estado futuro del tráfico urbano.L'objectiu principal d'aquest projecte és proposar, implementar, provar i optimitzar un algoritme de machine learning basat en dades recollides a partir d'autobusos regionals de Suècia de manera per poder realitzar prediccions sobre l'estat futur del trànsit urbà
STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph
The key to traffic prediction is to accurately depict the temporal dynamics
of traffic flow traveling in a road network, so it is important to model the
spatial dependence of the road network. The essence of spatial dependence is to
accurately describe how traffic information transmission is affected by other
nodes in the road network, and the GNN-based traffic prediction model, as a
benchmark for traffic prediction, has become the most common method for the
ability to model spatial dependence by transmitting traffic information with
the message passing mechanism. However, existing methods model a local and
static spatial dependence, which cannot transmit the global-dynamic traffic
information (GDTi) required for long-term prediction. The challenge is the
difficulty of detecting the precise transmission of GDTi due to the uncertainty
of individual transport, especially for long-term transmission. In this paper,
we propose a new hypothesis\: GDTi behaves macroscopically as a transmitting
causal relationship (TCR) underlying traffic flow, which remains stable under
dynamic changing traffic flow. We further propose spatial-temporal Granger
causality (STGC) to express TCR, which models global and dynamic spatial
dependence. To model global transmission, we model the causal order and causal
lag of TCRs global transmission by a spatial-temporal alignment algorithm. To
capture dynamic spatial dependence, we approximate the stable TCR underlying
dynamic traffic flow by a Granger causality test. The experimental results on
three backbone models show that using STGC to model the spatial dependence has
better results than the original model for 45 min and 1 h long-term prediction.Comment: 14 pages, 16 figures, 4 table
Temporal Graphs Anomaly Emergence Detection: Benchmarking For Social Media Interactions
Temporal graphs have become an essential tool for analyzing complex dynamic
systems with multiple agents. Detecting anomalies in temporal graphs is crucial
for various applications, including identifying emerging trends, monitoring
network security, understanding social dynamics, tracking disease outbreaks,
and understanding financial dynamics. In this paper, we present a comprehensive
benchmarking study that compares 12 data-driven methods for anomaly detection
in temporal graphs. We conduct experiments on two temporal graphs extracted
from Twitter and Facebook, aiming to identify anomalies in group interactions.
Surprisingly, our study reveals an unclear pattern regarding the best method
for such tasks, highlighting the complexity and challenges involved in anomaly
emergence detection in large and dynamic systems. The results underscore the
need for further research and innovative approaches to effectively detect
emerging anomalies in dynamic systems represented as temporal graphs
Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks
Short-term traffic flow prediction is one of the crucial issues in
intelligent transportation system, which is an important part of smart cities.
Accurate predictions can enable both the drivers and the passengers to make
better decisions about their travel route, departure time and travel origin
selection, which can be helpful in traffic management. Multiple models and
algorithms based on time series prediction and machine learning were applied to
this issue and achieved acceptable results. Recently, the availability of
sufficient data and computational power, motivates us to improve the prediction
accuracy via deep-learning approaches. Recurrent neural networks have become
one of the most popular methods for time series forecasting, however, due to
the variety of these networks, the question that which type is the most
appropriate one for this task remains unsolved. In this paper, we use three
kinds of recurrent neural networks including simple RNN units, GRU and LSTM
neural network to predict short-term traffic flow. The dataset from TAP30
Corporation is used for building the models and comparing RNNs with several
well-known models, such as DEMA, LASSO and XGBoost. The results show that all
three types of RNNs outperform the others, however, more simple RNNs such as
simple recurrent units and GRU perform work better than LSTM in terms of
accuracy and training time.Comment: arXiv admin note: text overlap with arXiv:1706.06279,
arXiv:1804.04176 by other author
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