Dynamic Route Flow Estimation in Road Networks Using Data from Automatic Number of Plate Recognition Sensors

The traffic flow on road networks is dynamic in nature. Hence, a model for dynamic traffic flow estimation should be a very useful tool for administrations to make decisions aimed at better management of traffic. In fact, these decisions may in turn improve people’s quality of life and help to implement good sustainable policies to reduce the external transportation costs (congestion, accidents, travel time, etc.). Therefore, this paper deals with the problem of estimating dynamic traffic flows in road networks by proposing a model which is continuous in the time variable and that assumes the first-in-first-out (FIFO) hypothesis. In addition, the data used as model inputs come from Automatic Number of Plate Recognition (ANPR) sensors. This powerful data permits not only to directly reconstruct the route followed by each registered vehicle but also to evaluate its travel time, which in turn is also used for the flow estimation. In addition, the fundamental variable of the model is the route flow, which is a great advantage since the rest of the flows can be obtained using the conservation laws. A synthetic network is used to illustrate the proposed method, and then it is applied to the well-known Nguyen-Dupuis and Eastern Massachusetts networks to prove its usefulness and feasibility. The results on all the tested networks are very positive and the estimated flows reproduce the simulated real flows fairly well.El flujo de tráfico en las redes viales es de naturaleza dinámica. Por lo tanto, un modelo para la estimación dinámica del flujo de tráfico debería ser una herramienta muy útil para que las administraciones tomen decisiones encaminadas a una mejor gestión del tráfico. De hecho, estas decisiones pueden a su vez mejorar la calidad de vida de las personas y ayudar a implementar buenas políticas sostenibles para reducir los costos externos de transporte (congestión, accidentes, tiempo de viaje, etc.). Por tanto, este artículo aborda el problema de la estimación de flujos dinámicos de tráfico en redes viales proponiendo un modelo continuo en la variable temporal y que asume la hipótesis de primero en entrar, primero en salir (FIFO). Además, los datos utilizados como entradas del modelo provienen de sensores de reconocimiento automático del número de placas (ANPR). Estos potentes datos permiten no solo reconstruir directamente la ruta seguida por cada vehículo registrado, sino también evaluar su tiempo de viaje, que a su vez también se utiliza para la estimación del flujo. Además, la variable fundamental del modelo es el caudal de ruta, lo que supone una gran ventaja ya que el resto de caudales se pueden obtener mediante las leyes de conservación. Se utiliza una red sintética para ilustrar el método propuesto, y luego se aplica a las conocidas redes Nguyen-Dupuis y Eastern Massachusetts para probar su utilidad y factibilidad. Los resultados en todas las redes probadas son muy positivos y los caudales estimados reproducen bastante bien los caudales reales simulados. lo cual es una gran ventaja ya que el resto de los flujos se pueden obtener usando las leyes de conservación. Se utiliza una red sintética para ilustrar el método propuesto, y luego se aplica a las conocidas redes Nguyen-Dupuis y Eastern Massachusetts para probar su utilidad y factibilidad. Los resultados en todas las redes probadas son muy positivos y los caudales estimados reproducen bastante bien los caudales reales simulados. lo cual es una gran ventaja ya que el resto de los flujos se pueden obtener usando las leyes de conservación. Se utiliza una red sintética para ilustrar el método propuesto, y luego se aplica a las conocidas redes Nguyen-Dupuis y Eastern Massachusetts para probar su utilidad y factibilidad. Los resultados en todas las redes probadas son muy positivos y los caudales estimados reproducen bastante bien los caudales reales simulados

Optimization with interval data: new problems, algorithms, and applications.

The parameters of real-world optimization problems are often uncertain due to the failure of exact estimation of data entries. Throughout the years, several approaches have been developed to cope with uncertainty in the input parameters of optimization problems, such as robust optimization, stochastic optimization, fuzzy programming, parametric programming, and interval optimization. Each of these approaches tackles the uncertainty in the input data with different assumptions on the source of uncertainty and imposes different requirements on the returned solutions. In this dissertation, the approach we take is that of interval optimization, and more specifically, interval linear programming. The two main problems we consider in this context are, considering all realizations of the interval data, the problems of finding the range of the optimal values and determining the set of all possible optimal solutions. While the theoretical aspects of these problems are well-studied, the algorithmic aspects and the engineering implications have not been explored. In this dissertation, we partially fill these voids. In the first part of the dissertation, we present and test three heuristics to find bounds on the worst optimal value of the equality-constrained interval linear program, which is known to be an intractable problem. In the second part of the dissertation, motivated by a real-case problem in the healthcare context, we define and analyze a new problem, the outcome range problem, in interval linear programming. The solution to the problem would help decision-makers quantify unintended/further consequences of optimal decisions made under uncertainty. Basically, the problem finds the range of an extra function of interest (different from the objective function) over all possible optimal solutions of an interval linear program. We analyze the problem from the theoretical and algorithmic perspectives. We evaluate the performance of our algorithms on simulated problem instances and on a real-world healthcare application. In the third part of the dissertation, we extend our analysis of the outcome range problem, exploring different theoretical properties and designing several new solution algorithms. We also test our solution approaches on different datasets, highlighting the strengths and weaknesses of each approach. Finally, in the last part of the dissertation, we discuss an application of interval optimization in the sensor location problem in the traffic management context. Particularly, we propose an optimization approach to handle the measurement errors in the full link flow observability problem. We show the applicability of our approach on several real-world traffic networks

Robust Observability, Control, & Economics of Complex Cyber-Physical Networks

This dissertation deals with various aspects of cyber-physical system. As an example of cyber physical systems, we take transportation networks and solve various problems, namely: 1) Network Observability Problem, 2) Network Control Problem, and 3) Network Economics Problem. We have divided the dissertation into three parts which solve these three problems separately. First part of the dissertation presents a novel approach for studying the observability problem on a general network topology of a traffic network. We develop a new framework which investigates observability in terms of flow information on arcs and the routing information. Second part of the dissertation presents a feedback control design for a coordinated ramp metering problem for two consecutive on-ramps. We design a traffic allocation scheme for ramps based on Godunov’s numerical method and using distributed model. Third part of the dissertation presents a novel approach to model Vehicle Miles Traveled (VMT) dynamics and establish a methodology for designing an optimal VMT tax rate. An Optimal control problem is formulated by designing a cost function which aims to maximize the generated revenue while keeping the tax rate as low as possible. Using optimal control theory, a solution is provided to this problem. To the best knowledge of authors all the three problems have not been solved using the methods proposed in this dissertation, and hence they are a novel contribution to the field

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