Bayesian Networks Methods for Traffic Flow Prediction

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A state of the art of sensor location, flow observability, estimation, and prediction problems in traffic networks

A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations

Estimation of origin-destination matrix from traffic counts: the state of the art

The estimation of up-to-date origin-destination matrix (ODM) from an obsolete trip data, using current available information is essential in transportation planning, traffic management and operations. Researchers from last 2 decades have explored various methods of estimating ODM using traffic count data. There are two categories of ODM; static and dynamic ODM. This paper presents studies on both the issues of static and dynamic ODM estimation, the reliability measures of the estimated matrix and also the issue of determining the set of traffic link count stations required to acquire maximum information to estimate a reliable matrix

Estimation of origin-destination matrix from traffic counts: the state of the art

The estimation of up-to-date origin-destination matrix (ODM) from an obsolete trip data, using current available information is essential in transportation planning, traffic management and operations. Researchers from last 2 decades have explored various methods of estimating ODM using traffic count data. There are two categories of ODM; static and dynamic ODM. This paper presents studies on both the issues of static and dynamic ODM estimation, the reliability measures of the estimated matrix and also the issue of determining the set of traffic link count stations required to acquire maximum information to estimate a reliable matrix

Pushing towards the Limit of Sampling Rate: Adaptive Chasing Sampling

Measurement samples are often taken in various monitoring applications. To reduce the sensing cost, it is desirable to achieve better sensing quality while using fewer samples. Compressive Sensing (CS) technique finds its role when the signal to be sampled meets certain sparsity requirements. In this paper we investigate the possibility and basic techniques that could further reduce the number of samples involved in conventional CS theory by exploiting learning-based non-uniform adaptive sampling. Based on a typical signal sensing application, we illustrate and evaluate the performance of two of our algorithms, Individual Chasing and Centroid Chasing, for signals of different distribution features. Our proposed learning-based adaptive sampling schemes complement existing efforts in CS fields and do not depend on any specific signal reconstruction technique. Compared to conventional sparse sampling methods, the simulation results demonstrate that our algorithms allow $46\%$ less number of samples for accurate signal reconstruction and achieve up to $57\%$ smaller signal reconstruction error under the same noise condition.Comment: 9 pages, IEEE MASS 201

Comparative Assessment on Static O-D Synthesis

Recognizing the benefits of data and the information it provides to travel demand is pertinent to network planning and design. Technological advances have led the ability to produce large quantities and types of data and as a result, many origin-destination (O-D) estimation techniques have been developed to accommodate this data. In contrast to the abundant choices on data types, data quantity and estimation procedures, there lacks a common framework to assess these methods. Without consistency in a baseline foundation, the performances of the methodologies can vary greatly based on each individual assumption. This research addresses the need for techniques to be tested on a common framework by establishing a baseline condition for static O-D estimation through a synthetic Vissim model of the Sioux Falls network as a case study area. The model is used to generate a master dataset, representing the ground-truth, and a subset of the master dataset, emulating the data collected from real world technologies. The subset of data is used as the input for the O-D estimation techniques where the input is varied to evaluate the effects of different levels of coverage/penetration of each data type on estimation results. A total of five estimation techniques developed by Cascetta and Postorino (2001), Castillo et al. (2008b), Parry and Hazelton (2012), Feng et al. (2015) and X. Yang et al. (2017) are tested with three data types (link counts, partial traces, and full traces) and two traffic assignment conditions (all-or-nothing and user equilibrium). The result of this research highlights the uniqueness of each network situation and highlights the outcomes of each approach. The wealth of data does not directly equal better information for every methodology. The insights that each data type provides each estimation technique reveals different results. The findings of this research demonstrate and supports that an established testbed framework supports and enhances future O-D estimation scenarios as it pertains to general O-D estimation and extensions of existing techniques

Estimation of origin-destination matrices from traffic counts: theoretical and operational development

This thesis deals with the o-d estimation problem from indirect measures, addressing two main aspects of the problem: the identification of the set of indirect measures that provide the maximum information with a resulting reduction of the uncertainty on the estimate; once defined the set of measures, the choice of an estimator to identify univocally and as much reliable as possible the estimate. As regards the former aspect, an innovative and theoretically founded methodology is illustrated, explicitly accounting for the reliability of the o-d matrix estimate. The proposed approach is based on a specific measure, named Synthetic Dispersion Measure (SDM), related to the trace of the dispersion matrix of the posterior demand estimate conditioned to a given set of sensors locations. Under the mild assumption of multivariate normal distribution for the prior demand estimate, the proposed SDM does not depend on the specific values of the counted flows – unknown in the planning stage – but just on the locations of such sensors. The proposed approach is applied to real contexts, leading to results outperforming the other methods currently available in the literature. In addition, the proposed methodology allows setting a formal budget allocation problem between surveys and counts in the planning stage, in order to maximize the overall quality of the demand estimation process. As regard the latter aspect, a “quasi-dynamic” framework is proposed, under the assumption that o-d shares are constant across a reference period, whilst total flows leaving each origin vary for each sub-period within the reference period. The advantage of this approach over conventional within-day dynamic estimators is that of reducing drastically the number of unknowns given the same set of observed time-varying traffic counts. The quasi-dynamic assumption is checked by means of empirical and statistical tests and the performances of the quasi-dynamic estimator - whose formulation is also given – are compared with other dynamic estimators

Metodología para modelizar una red de tráfico en la que se van a obtener datos mediante la técnica del escaneo de matrículas

[ES] En el presente artículo se aborda el problema de modelizar una red de tráfico para poder aplicar la técnica del escaneo de matrículas para estimar flujos en ruta, y por tanto obtener la matriz Origen-Destino así como la asignación de la red. Para llevar a cabo dicha modelización se plantea una metodología que trata de manera global la simplificación de la red y que tiene como base la reducción del número de rutas mediante la eliminación de pares Origen-Destino que no tengan una demanda relevante. Dicha simplificación tiene un enfoque práctico muy diferente de la visión tradicional de zonificación y disposición de centroides dentro de la red y que permitirá imbricarla con los modelos de ubicación de dispositivos de escaneo. La metodología permite detectar aquellos arcos de la red que son afectados por la simplificación y las consecuencias sobre la estimación de flujos que puedan derivarse de dicha afección. Con todo ello, se puede establecer una priorización en la ubicación de los equipos de escaneo que permitirá hacer una reconstrucción más fiable de los flujos de la red. Se ha empleado una red basada en la denominada red Nguyen-Dupuis como ejemplo de aplicación de la metodología desarrollada. A través del mismo se irá aclarando paso por paso cada una de las fases del método.Sánchez Cambronero, S.; Rivas Álvarez, A.; Barba Contreras, R.; Ruiz Ripoll, L.; Gallego Giner, M.; Menéndez Martinez, J. (2016). Metodología para modelizar una red de tráfico en la que se van a obtener datos mediante la técnica del escaneo de matrículas. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 1142-1154. https://doi.org/10.4995/CIT2016.2015.4216OCS1142115

Estimation/updating of origin-destination flows: recent trends and opportunities from trajectory data

Understanding the spatial and temporal dynamics of mobility demand is essential for many applications over the entire transport domain, from planning and policy assessment to operation, control, and management. Typically, mobility demand is represented by origin-destination (o-d) flows, each representing the number of trips from one traffic zone to another, for a certain trip purpose and mode of transport, in a given time interval (Cascetta, 2009, Ortuzar and Willumsen, 2011). O-d flows have been generally unobservable for decades, thus the problem of o-d matrix estimation is still one of the most challenging in transportation studies. In recent times, unprecedented tracing and tracking capabilities have become available. The pervasive penetration of sensing devices (smartphones, black boxes, smart cards, ...) adopting a variety of tracing technologies/methods (GPS, Bluetooth, ...) could make in many cases o-d flows now observable. The increasing availability of trajectory data sources has provided new opportunities to enhance observability of human mobility and travel patterns between origins and destinations, recently explored by researchers and practitioners, bringing innovation and new research directions on origin-destination (o-d) matrix estimation. The purpose of this thesis is to develop a deep understanding of the opportunities and the limitations of trajectory data to assess its potential for ameliorating the o-d flows estimation/updating problem and for conducting o-d related analysis. The proposed work involves both real trajectory data analysis and laboratory experiments based on synthetic data to investigate the implications of the trajectory data sample distinctive features (e.g. sample representativeness and bias) on demand flows accuracy. Final considerations and results might provide useful guidelines for researchers and practitioners dealing with various types of trajectory data sample and conducting o-d related applications