5 research outputs found
Big Data Analytics for Network Level Short-Term Travel Time Prediction with Hierarchical LSTM and Attention
The travel time data collected from widespread traffic monitoring sensors
necessitate big data analytic tools for querying, visualization, and
identifying meaningful traffic patterns. This paper utilizes a large-scale
travel time dataset from Caltrans Performance Measurement System (PeMS) system
that is an overflow for traditional data processing and modeling tools. To
overcome the challenges of the massive amount of data, the big data analytic
engines Apache Spark and Apache MXNet are applied for data wrangling and
modeling. Seasonality and autocorrelation were performed to explore and
visualize the trend of time-varying data. Inspired by the success of the
hierarchical architecture for many Artificial Intelligent (AI) tasks, we
consolidate the cell and hidden states passed from low-level to the high-level
LSTM with an attention pooling similar to how the human perception system
operates. The designed hierarchical LSTM model can consider the dependencies at
different time scales to capture the spatial-temporal correlations of
network-level travel time. Another self-attention module is then devised to
connect LSTM extracted features to the fully connected layers, predicting
travel time for all corridors instead of a single link/route. The comparison
results show that the Hierarchical LSTM with Attention (HierLSTMat) model gives
the best prediction results at 30-minute and 45-min horizons and can
successfully forecast unusual congestion. The efficiency gained from big data
analytic tools was evaluated by comparing them with popular data science and
deep learning frameworks
Travel time prediction for congested freeways with a dynamic linear model
Accurate prediction of travel time is an essential feature to support
Intelligent Transportation Systems (ITS). The non-linearity of traffic states,
however, makes this prediction a challenging task. Here we propose to use
dynamic linear models (DLMs) to approximate the non-linear traffic states.
Unlike a static linear regression model, the DLMs assume that their parameters
are changing across time. We design a DLM with model parameters defined at each
time unit to describe the spatio-temporal characteristics of time-series
traffic data. Based on our DLM and its model parameters analytically trained
using historical data, we suggest an optimal linear predictor in the minimum
mean square error (MMSE) sense. We compare our prediction accuracy of travel
time for freeways in California (I210-E and I5-S) under highly congested
traffic conditions with those of other methods: the instantaneous travel time,
k-nearest neighbor, support vector regression, and artificial neural network.
We show significant improvements in the accuracy, especially for short-term
prediction.Comment: in IEEE Transactions on Intelligent Transportation Systems, 202
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
Redefine time series models for transportation planning use
Time series models are used to model, simulate, and forecast the behaviour of a phenomenon over time based on data recorded over consistent intervals. The digital era has resulted in data being captured and archived in unprecedented amounts, such that vast amounts of information are available for analysis. Feature-rich time-series datasets are one of the data sets that have become available due to the expanding trend of data collection technologies worldwide. With the application of time series analysis to support financial and managerial decision-making, the development and advancement of time series models in the transportation domain are unavoidable. As a result, this thesis redefines time series models for transportation planning use with the following three aims:
(1) To combine parametric and bootstrapping techniques within time series models;
(2) to develop a time series model capable of modelling both temporal and spatial dependencies in time-series data; and
(3) to leverage the hierarchical Bayesian modelling paradigm to accommodate flexible representations of heterogeneity in data.
The first main chapter introduces an ensemble of ARIMA models. It compares its performance against conventional ARIMA (a parametric method) and LSTM models (a non-parametric method) for short-term traffic volume prediction. The second main chapter introduces a copula time series model that describes correlations between variables through time and space. Temporal correlations are modelled by an ARMA-GARCH model which enables a modeller to describe heteroscedastic data. The copula model has a flexible correlation structure and is used to model spatial correlations with the ability to model nonlinear, tailed and asymmetric correlations. The third main chapter provides a Bayesian modelling framework to raise awareness about using hierarchical Bayesian approaches for transport time series data. In addition, this chapter presents a Bayesian copula model. The combination of the two models provides a fully Bayesian approach to modelling both temporal and spatial correlations. Compared with frequentist models, the proposed modelling structures can incorporate prior knowledge. In the fourth main chapter, the fully Bayesian model is used to investigate mobility patterns before, during and after the COVID-19 pandemic using social media data. A more focused analysis is conducted on the mobility patterns of Twitter users from different zones and land use types