Characteristics of Optimal Solutions to the Sensor Location Problem

In [Bianco, L., Giuseppe C., and P. Reverberi. 2001. "A network based model for traffic sensor location with implications on O/D matrix estimates". Transportation Science 35(1):50-60.], the authors present the Sensor Location Problem: that of locating the minimum number of traffic sensors at intersections of a road network such that the traffic flow on the entire network can be determined. They offer a necessary and sufficient condition on the set of monitored nodes in order for the flow everywhere to be determined. In this paper, we present a counterexample that demonstrates that the condition is not actually sufficient (though it is still necessary). We present a stronger necessary condition for flow calculability, and show that it is a sufficient condition in a large class of graphs in which a particular subgraph is a tree. Many typical road networks are included in this category, and we show how our condition can be used to inform traffic sensor placement.Comment: Submitted for peer review on October 3, 201

The sensor location problem: methodological approach and application

The sensor location problem is of particular importance when planning the allocation of limited field equipment intended to be used for advanced traffic management systems and traveller information services. The locations within a network that satisfy specific goals need to be carefully selected, based on predefined goals related to the effective collection of data and the subsequent estimation of traffic related information. The detection of traffic volumes is mainly associated with two purposes, the travel time and the Origin–Destination (O–D) trip matrix estimation. In this context, this paper presents a quadratic programing model, able to determine the optimal location of tracking sensors. The model is implemented in the urban road network of the city of Thessaloniki (Greece) in which specific number of sensors is installed and utilized for real-time travel time information provision. The proposed methodology models the sensor location problem under the general framework of a set covering problem, which is one of the most popular optimization problems and has been applied in many industrial problems. The results of the case study in Thessaloniki reveal that the proposed model defines the optimal location of the limited number of sensors in such a way that the network, which is created having all sensors as origin or destination of all possible paths, represents to great extent (87% of the traffic flow along the major paths) the traffic volumes of the whole road network of the city. First published online: 11 Jan 201

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

Joint Flow and Density Reconstruction in Large Traffic Networks UsingPartial Turning Ratio Information

International audienceWe address the recent problem of state reconstruction in large scale traffic networks using heterogeneous sensor data. First, we deal with the conditions imposed on the number and location of fixed sensors such that all flows in the network can be uniquely reconstructed. We determine the minimum number of sensors needed to solve the problem given partial information of turning ratios, and then we propose a linear time algorithm for their allocation in a network. Using these results in addition to floating car data, we propose a method to reconstruct all traffic density and flow. Finally, the algorithms are tested in a simulated Manhattan-like network

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

Sensor Location For Network Flow And Origin-Destination Estimation With Multiple Vehicle Classes

The need for multi-class origin-destination (O-D) estimation and link volume estimation requires multi-class observations from sensors. This dissertation has established a new sensor location model that includes: 1) multiple vehicle classes; 2) a variety of data types from different types of sensors; and 3) a focus on both link-based and O-D based flow estimation. The model seeks a solution that maximizes the overall information content from sensors, subject to a budget constraint. An efficient twophase metaheuristic algorithm is developed to solve the problem. The model is based on a set of linear equations that connect O-D flows, link flows and sensor observations. Concepts from Kalman filtering are used to define the information content from a set of sensors as the trace of the posterior covariance matrix of flow estimates, and to create a linear update mechanism for the precision matrix as new sensors are added or deleted from the solution set. Sensor location decisions are nonlinearly related to information content because the precision matrix must be inverted to construct the covariance matrix which is the basis for measuring information. The resulting model is a nonlinear knapsack problem. The two-phase search algorithm proposed addresses this nonlinear, nonseparable integer sensor location problem. A greedy phase generates an initial solution, feeding into a Tabu Search phase which swaps sensors along the budget constraint. The neighbor generation in Tabu search is a combination of a fixed swapout strategy with a guided random swap-in strategy. Extensive computational experiments have been performed on a standard test network. These tests verify the effectiveness of the problem formulation and solution algorithm. A case study on Rockland County, NY demonstrates that the sensor location method developed in this dissertation can successfully allocate sensors in realistic networks, and thus has significant practical value

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