In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.

Traffic Flow Prediction With Big Data: A Deep Learning Approach

Introduction to linear and nonlinear programming

SUMO is an open source traffic simulation package including the simulation application itself as well as supporting tools, mainly for network import and demand modeling. SUMO helps to investigate a large variety of research topics, mainly in the context of traffic management and vehicular communications. We describe the current state of the package, its major applications, both by research topic and by example, as well as future developments and extensions. Keywords-microscopic traffic simulation; traffic management; open source; software

https://www.thinkmind.org/articles/sysmea_v5_n34_2012_4.pdf

Recent Development and Applications of SUMO - Simulation of Urban MObility

Modeling travel behavior is a key aspect of demand analysis, where aggregate demand is the accumulation of individuals’ decisions. In this chapter, we focus on “short-term” travel decisions. The most important short-term travel decisions include choice of destination for a non-work trip, choice of travel mode, choice of departure time and choice of route. It is important to note that short-term decisions are conditional on long-term travel and mobility decisions such as car ownership and residential and work locations.

/pdf/discrete-choice-methods-and-their-applications-to-short-term-1q7mp179vp.pdf

Discrete Choice Methods and their Applications to Short Term Travel Decisions

Random utility route choice models play a central role in stochastic assignment, to road networks The usually adopted specifications for such models are Multinomial Logit (MNL) and Probit, both having pros and cons In this paper a modified specification of the Logit model, named C-Logit, is proposed The C-Logit overcomes the main shortcoming of MNL, ie unrealistic choice probabilities for paths sharing a number of links, while keeping a closed analytical structure allowing calibration on disaggregate data and efficient path flow computations when paths are explicitly enumerated A behavioural interpretation of the C-Logit model is proposed by presenting it as a particular case of a wider class of implicit joint models of route perception-choice In the second part of the paper a procedure for path enumeration is proposed, and MNL and C-Logit models are specified and calibrated from a sample of 1471 paths chosen by truck drivers on the Italian inter-city road network The preliminary calibration results are promising and show significant improvements induced by both the commonality factor characterizing the C-Logit model and by label variables which can be seen as attributes explaining path perception (A) For the covering abstract see IRRD 886400

A modified logit route choice model overcoming path overlapping problems. specification and some calibration results for interurban networks

Introduction. 1. Transportation Systems. 2. Transportation Supply Models. 3. Random Utility Theory. 4. Transportation Demand Models. 5. Models for Traffic Assignment to Transportation Networks. 6. Intra-Period (Within-Day) Dynamic Models*. 7. Algorithms for Traffic Assignment to Transportation Networks. 8. Estimation of Travel Demand Flows. 9. Transportation Supply Design Models. 10. Transportation Systems Engineering for Planning and Evaluation. A. Review of Numerical Analysis. References. Index. Main Variables.

Transportation Systems Engineering: Theory and Methods

Methods commonly used for estimating origin-destination (O-D) matrices can be divided into three categories: direct sample estimation, model estimation and estimation from traffic counts. In this paper a generalized least squares estimator of the O-D matrix is proposed combining direct or model estimators with traffic counts via an assignment model. The presence of measurement errors and time variability in the observed flows is explicitly considered. A special case is also presented in which the flows are assumed to be deterministically known. For the proposed estimators, means and dispersion matrices are expressed in function of the possible bias in the direct or model estimators and assignment model misspecification. The variance of the O-D matrix obtained with the generalized least squares (GLS) estimators is proved to be lower than that obtained with direct or model estimators but, because of possible biases and misspecifications, it is suggested that their performances have to be compared by using risk or generalized mean square error criteria.

Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator

Modeling Transportation Systems: Preliminary Concepts and Application Areas.- Transportation Supply Models.- Random Utility Theory.- Travel-Demand Models.- Basic Static Assignment to Transportation Networks.- Advanced Models for Traffic Assignment to Transportation Networks.- Intraperiod (Within-Day) Dynamic Models.- Estimation of Travel Demand Flows.- Transportation Supply Design Models.- Methods for the Evaluation and Comparison of Transportation System Projects.

Transportation Systems Analysis: Models and Applications

This paper proposes different “dynamic” estimators using time-varying traffic counts to obtain (discrete) time-varying OD flows or average OD flows. All the estimators can combine counts with other available information, e.g., out-dated matrices and surveys, on a general network and can be formulated as optimization problems. In the case of time-varying OD flows, two types of estimators are proposed. Simultaneous estimators produce joint estimates of the whole set of OD matrices, one for each time slice, whereas sequential estimators produce a sequence of OD estimates for successive time slices. A particular type of simultaneous and sequential estimator (namely Generalized Least Squares) was tested on the Italian Brescia–Padua motorway for which “true” OD flows over a whole day were available. Results were generally satisfactory, showing that also in the “no a priori information” case significant estimates could be obtained.

Ennio Cascetta

Papers

A modified logit route choice model overcoming path overlapping problems. specification and some calibration results for interurban networks

Transportation Systems Engineering: Theory and Methods

Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator

Transportation Systems Analysis: Models and Applications

Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts