Lídia Montero

A Kalman filter approach for exploiting bluetooth traffic data when estimating time-dependent OD matrices

Time-dependent origin–destination (OD) matrices are essential input for dynamic traffic models such as microscopic and mesoscopic traffic simulators. Dynamic traffic models also support real-time traffic management decisions, and they are traditionally used in the design and evaluation of advanced traffic traffic management and information systems (ATMS/ATIS). Time-dependent OD estimations are typically based either on Kalman filtering or on bilevel mathematical programming, which can be considered in most cases as ad hoc heuristics. The advent of the new information and communication technologies (ICT) provides new types of traffic data with higher quality and accuracy, which in turn allows new modeling hypotheses that lead to more computationally efficient algorithms. This article presents ad hoc, Kalman filtering procedures that explicitly exploit Bluetooth sensor traffic data, and it reports the numerical results from computational experiments performed at a network test site.

Exploring Link Covering and Node Covering Formulations of Detection Layout Problem

The primary data input used in principal traffic models comes from origin–destination (O-D) trip matrices, which describe patterns of traffic behavior across a network. O-D matrices are a critical requirement in advanced traffic management or information systems that are supported by dynamic traffic assignment models. However, because O-D matrices are not directly observable, current practice adjusts an initial seed matrix from link flow counts that are provided by an existing layout of traffic-counting stations. The adequacy of the detection layout is critical to determining the quality of the adjusted O-D matrix. The usual approaches to the detection layout problem assume that detectors are located at network links. This paper proposes a modified set that formulates the link detection layout problem with side constraints and presents a new metaheuristic tabu search algorithm with high computational efficiency. Emerging information and communication technologies (ICT), especially those based on the detection of the electronic signature of onboard devices (such as Bluetooth devices), allow the location of sensors at intersections. To take into account explicitly how these ICT sensors operate, this paper proposes a new formulation of a node-covering problem with side constraints, which for practical purposes can be efficiently solved with standard professional solvers such as CPLEX.

A combined methodology for transportation planning assessment. Application to a case study

Abstract Traffic assignment models based on the user-equilibrium approach are one of the most widely used tools in transportation planning analysis. Resulting flows offer a static average view of the expected use of the road infrastructure under the modeling hypothesis. This information has usually been enough for the planning decisions. The planned infrastructure is probably sufficient for average demand, but time-varying traffic flows, i.e., at peak periods, combined with the influence of road geometry, can produce undesired congestion that can not be forecasted or analysed with the static tools. There is a clear case for a change in the analysis methodology such as combination of a traffic assignment tool, with a microscopic traffic simulator. This paper illustrates, by means of a case study, the combination of a well-known traffic assignment tool, the EMME/2 model, with a microscopic traffic simulator, Advanced Interactive Microscopic Simulator For Urban And Non-Urban Networks (AIMSUN2) with emphasis on the description of the specific interfaces that make consistent the combination of both tools in the Generic Environment for Traffic Analysis and Modeling (GETRAM) environment. Models for complex transportation systems should be the combination of mathematical models and computer models, to overcome, for example, the difficulties of the integration of modeling tools. GETRAM environment has an open and flexible computer architecture suitable for such purposes.

Using ACCPM in a simplicial decomposition algorithm for the traffic assignment problem

Abstract The purpose of the traffic assignment problem is to obtain a traffic flow pattern given a set of origin-destination travel demands and flow dependent link performance functions of a road network. In the general case, the traffic assignment problem can be formulated as a variational inequality, and several algorithms have been devised for its efficient solution. In this work we propose a new approach that combines two existing procedures: the master problem of a simplicial decomposition algorithm is solved through the analytic center cutting plane method. Four variants are considered for solving the master problem. The third and fourth ones, which heuristically compute an appropriate initial point, provided the best results. The computational experience reported in the solution of real large-scale diagonal and difficult asymmetric problems—including a subset of the transportation networks of Madrid and Barcelona—show the effectiveness of the approach.

A simulation framework for real-time assessment of dynamic ride sharing demand responsive transportation models

Sustainable mobility is not merely a technological question. While automotive technology will be part of the solution, it will also be combined with a paradigm shift from car ownership to vehicle usage, which itself will be facilitated by the application of Information and Communication Technologies that make it possible for a user to have access to a mobility service from anywhere to anywhere at any time. Multiple Passenger Ridesharing and its variants appear to be one of the promising mobility concepts that are emerging. However, in implementing these systems while accounting specifically for time dependencies and time windows that reflect user needs, challenges are raised in terms of real-time fleet dispatching and dynamic route calculation. This paper analyzes and evaluates both aspects through microscopic simulation emulating real-time traffic information while also interacting with a Decision Support System. The paper presents and discusses the obtained results for a Barcelona model.

Case Study on Cooperative Car Data for Estimating Traffic States in an Urban Network

The use of floating car data as a particular case of probe vehicle data has been the object of extensive research for estimating traffic conditions, travel times, and origin-to-destination trip matrices. It is based on data collected from a GPS-equipped vehicle fleet or available cell phones. Cooperative cars with vehicle-to-vehicle and vehicle-to-infrastructure communication capabilities represent a step forward, as they also allow tracking of vehicles surrounding the equipped car. This paper presents the results of a limited experiment with a small fleet of cooperative cars in the central business district of Barcelona, Spain, known as L’Eixample District. Data collected from the experiment were used to build and calibrate the emulation of cooperative functions in a microscopic simulation model that captured the behavior of vehicle sensors in Barcelona’s central business district. Such a calibrated model allows emulating fleet data on a large scale that goes far beyond what a small fleet of cooperative vehicles could capture. To determine the traffic state, several approaches were developed for estimating traffic variables—whose accuracy depends on the penetration level of the technology—on the basis of extensions of Edie’s generalized definitions of the fundamental traffic variables with the emulated data.

Robustness and Computational Efficiency of Kalman Filter Estimator of Time-Dependent Origin-Destination Matrices

Origin–destination (O-D) trip matrices that describe the patterns of traffic behavior across a network are the primary data input used in principal traffic models and, therefore, a critical requirement in all advanced systems supported by dynamic traffic assignment models. However, because O-D matrices are not directly observable, the current practice consists of adjusting an initial or seed matrix from link flow counts that are provided by an existing layout of traffic-counting stations. The availability of new traffic measurements provided by information and communication technologies (ICT) allows more efficient algorithms, namely for real-time estimation of O-D matrices that are based on modified Kalman filtering approaches to exploit the new data. The quality of the estimations depends on various factors such as the penetration of the ICT devices, the detection layout, and the quality of the initial information. The feasibility of real-time applications depends on the computational performance of the proposed algorithms for urban networks of sensitive size. This paper presents the results of a set of computational experiments with a microscopic simulation of the network of Barcelonas central business district that explore the sensitivity of the Kalman filter estimates in relation to design factor values.

Dynamic OD transit matrix estimation: formulation and model-building environment

The aim of this paper is to provide a detailed description of a framework for the estimation of time-sliced origin-destination (OD) trip matrices in a transit network using counts and travel time data of Bluetooth Smartphone devices carried by passengers at equipped transit-stops. A Kalman filtering formulation defined by the authors has been included in the application. The definition of the input for building the space-state model is linked to network scenarios modeled with the transportation planning platform EMME. The transit assignment framework is optimal strategy-based, which determines the subset of paths related to the optimal strategies between all OD pairs.

Impact on Network Performance of Probe Vehicle Data Usage: An Experimental Design for Simulation Assessment

Probe-based technologies are proliferating as a means of inferring traffic states. Technological companies are interested in traffic data for computing the best routes in a traffic-aware manner and they also provide real-time traffic information with certain temporal accuracy. This paper analyses and evaluates how data provided by a fleet of probe cars can be used to develop a navigation service and how the penetration rate of this service affects a set of city-scale KPIs (Key Performance Indicators) and driver KPIs. The case study adopts a model-driven approach in which microscopic simulation emulates real-size fleets of probe vehicles that provide positions and speed data. What is noteworthy about the modelling behaviour is that drivers are segmented according to their knowledge of network conditions for selected trips: experts, regular drivers, and tourists. The paper presents and discusses the modelling approach and the results obtained from an experimental Barcelona CBD model designed to evaluate the penetration rates of probe vehicles and route guidance. An analysis of the simulation experiments reveals remarkable links among city-scale KPIs, which—from a multivariate point of view—is a novelty. A simulation-based framework for results analysis and visualization is also introduced in order to simplify the simulation results analysis and easily visualize OD paths for driver segments.

A visualization tool based on traffic simulation for the analysis and evaluation of smart city policies, innovative vehicles and mobility concepts

The CitScale tool is a software platform for visualizing, analyzing and comparing the impacts of smart city policies based on innovative mobility concepts in urban areas. It places emphasis on new automotive vehicles aimed at reducing traffic or environmental impacts. This paper introduces this traffic simulation-based tool, and two case studies developed for different scenarios in Barcelona City are briefly presented to demonstrate the capabilities of the tool when it is combined with microscopic traffic simulation software. The first case presents an extensive evaluation of new innovative vehicles (electric vehicles, bikes and three-wheeled scooters) and mobility concepts (trip-sharing). In the second one, data provided by connected cars is analyzed in order to compare different developed navigation strategies and how they affect the city. Finally, some of the obtained results from both cases are concisely presented in order to show the potential of the proposed tool.