264 research outputs found
UNCERTAINTY ASSOCIATED WITH TRAVEL TIME PREDICTION: ADVANCED VOLATILITY APPROACHES AND ENSEMBLE METHODS
Travel time effectively measures freeway traffic conditions. Easy access to this information provides the potential to alleviate traffic congestion and to increase the reliability in road networks. Accurate travel time information through Advanced Traveler Information Systems (ATIS) can provide guidance for travelers' decisions on departure time, route, and mode choice, and reduce travelers' stress and anxiety. In addition, travel time information can be used to present the current or future traffic state in a network and provide assistance for transportation agencies in proactively developing Advanced Traffic Management System (ATMS) strategies. Despite its importance, it is still a challenging task to model and estimate travel time, as traffic often has irregular fluctuations. These fluctuations result from the interactions among different vehicle-driver combinations and exogenous factors such as traffic incidents, weather, demand, and roadway conditions. Travel time is especially sensitive to the exogenous factors when operating at or near the roadway's capacity, where congestion occurs. Small changes in traffic demand or the occurrence of an incident can greatly affect the travel time. As it is impossible to take into consideration every impact of these unpredictable exogenous factors in the modeling process, travel time prediction problem is often associated with uncertainty. This research uses innovative data mining approaches such as advanced statistical and machine learning algorithms to study uncertainty associated with travel time prediction. The final objective of this research is to develop more accurate and reliable travel time prediction models
Effect of Time Intervals on K-nearest Neighbors Model for Short-term Traffic Flow Prediction
The accuracy and reliability in predicting short-term traffic flow is important. The K-nearest neighbors (K-NN) approach has been widely used as a nonparametric model for traffic flow prediction. However, the reliability of the K-NN model results is unknown and the uncertainty of traffic flow point prediction needs to be quantified. To this end, we extended the K-NN approach by constructing the prediction interval associated with the point prediction. Recognizing the stochastic nature of traffic, time interval used to measure traffic flow rate is remarkably influential. In this paper, extensive tests have also been conducted after aggregating real traffic flow data into time intervals, ranging from 3 minutes to 30 minutes. The results show that the performance of traffic flow prediction can be improved when the time interval increases. More importantly, when the time interval is shorter than 10 minutes, K-NN can generate higher accuracy of the point prediction than the selected benchmark model. This finding suggests the K-NN model may be more appropriate for traffic flow point and interval prediction at a shorter time interval
Forecasting: theory and practice
Forecasting has always been in the forefront of decision making and planning.
The uncertainty that surrounds the future is both exciting and challenging,
with individuals and organisations seeking to minimise risks and maximise
utilities. The lack of a free-lunch theorem implies the need for a diverse set
of forecasting methods to tackle an array of applications. This unique article
provides a non-systematic review of the theory and the practice of forecasting.
We offer a wide range of theoretical, state-of-the-art models, methods,
principles, and approaches to prepare, produce, organise, and evaluate
forecasts. We then demonstrate how such theoretical concepts are applied in a
variety of real-life contexts, including operations, economics, finance,
energy, environment, and social good. We do not claim that this review is an
exhaustive list of methods and applications. The list was compiled based on the
expertise and interests of the authors. However, we wish that our encyclopedic
presentation will offer a point of reference for the rich work that has been
undertaken over the last decades, with some key insights for the future of the
forecasting theory and practice
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
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
Feature Papers of Forecasting 2021
This book focuses on fundamental and applied research on forecasting methods and analyses on how forecasting can affect a great number of fields, spanning from Computer Science, Engineering, and Economics and Business to natural sciences. Forecasting applications are increasingly important because they allow for improving decision-making processes by providing useful insights about the future. Scientific research is giving unprecedented attention to forecasting applications, with a continuously growing number of articles about novel forecast approaches being publishe
Feature Papers of Forecasting 2021
Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND
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