Mesoscopic simulator data to perform dynamic origin- destination matrices estimation in urban context

The aim of this paper is to explore a new approach to obtain better traffic demand (Origin-Destination, OD matrices) for dense urban networks using traffic simulation data. From reviewing existing methods, from static to dynamic OD matrix evaluation, possible deficiencies in the approach could be identified. To improve the global process of traffic demand estimation, this paper is focusing on a new methodology to determine dynamic OD matrices for urban areas characterized by complex route choice situation and high level of traffic controls. An iterative bi- level approach will be used to perform the OD estimation. The Lower Level (traffic assignment) problem will determine, dynamically, the utilization of the network by vehicles using heuristic data from mesoscopic traffic simulator particularly adapted for urban context. The Upper Level (matrix adjustment) problem will proceed to an OD estimation using optimization least square techniques. In this way, a full dynamic and continuous estimation of the final OD matrix could be obtained. First evaluation of the proposed approach and conclusions are presented

Updating of travel behavior parameters and estimation of vehicle trip-chain data based on plate scanning

This article proposes a maximum-likelihood method to update travel behavior model parameters and estimate vehicle trip chain based on plate scanning. The information from plate scanning consists of the vehicle passing time and sequence of scanned vehicles along a series of plate scanning locations (sensor locations installed on road network). The article adopts the hierarchical travel behavior decision model, in which the upper tier is an activity pattern generation model, and the lower tier is a destination and route choice model. The activity pattern is an individual profile of daily performed activities. To obtain reliable estimation results, the sensor location schemes for predicting trip chaining are proposed. The maximum-likelihood estimation problem based on plate scanning is formulated to update model parameters. This problem is solved by the expectation-maximization (EM) algorithm. The model and algorithm are then tested with simulated plate scanning data in a modified Sioux Falls network. The results illustrate the efficiency of the model and its potential for an application to large and complex network cases

Estimation of Dynamic Assignment Matrices and OD Demands Using Adaptive Kalman Filtering

[[abstract]]The purpose of this research was to develop a dynamic model for the on-line estimation and prediction of freeway users’ origin-destination (OD) matrices. In this paper, we present a Kalman Filtering algorithm that uses time-varying assignment matrices generated by using a mesoscopic traffic simulator. The use of a traffic simulator to predict time-varying travel time model parameters was shown to be promising for the determination of dynamic OD matrices for a freeway system. Moreover, the issues of using time-varying model parameters, effects of incorporating different sources of measurements and the use of adaptive estimation are addressed and investigated in this research.[[notice]]補正完

Statistical modelling and analysis of traffic: a dynamic approach

In both developed and emerging-economies, major cities continue to experience increasing traffic congestion. To address this issue, complex Traffic Management Systems (TMS) are employed in recent years to help manage traffic. These systems fuse traffic-surveillance-related information from a variety of sensors deployed across traffic networks. A TMS requires real-time information to make effective control decisions and to deliver trustworthy information to users, such as travel time, congestion level, etc. There are three fundamental inputs required by TMS, namely, traffic volume, vehicular speed, and traffic density. Using conventional traffic loop detectors one can directly measure flow and velocity. However, traffic density is more difficult to measure. The situation becomes more difficult for multi-lane motorways due to drivers lane-change behaviour. This research investigates statistical modelling and analysis of traffic flow. It contributes to the literature of transportation and traffic management and research in several aspects. First, it takes into account lane-changes in traffic modelling through incorporating a Markov chain model to describe the drivers lane-change behaviour. Secondly, the lane change probabilities between two adjacent lanes are not assumed to be fixed but rather they depend on the current traffic condition. A discrete choice model is used to capture drivers lane choice behaviour. The drivers choice probabilities are modelled by several traffic-condition related attributes such as vehicle time headway, traffic density and speed. This results in a highly nonlinear state equation for traffic density. To address the issue of high nonlinearity of the state space model, the EKF and UKF is used to estimate the traffic density recursively. In addition, a new transformation approach has been proposed to transform the observation equation from a nonlinear form to a linear one so that the potential approximation in the EKF & UKF can be avoided. Numerical studies have been conducted to investigate the performance of the developed method. The proposed method outperformed the existing methods for traffic density estimation in simulation studies. Furthermore, it is shown that the computational cost for updating the estimate of traffic densities for a multi-lane motorway is kept at a minimum so that online applications are feasible in practice. Consequently the traffic densities can be monitored and the relevant information can be fed into the traffic management system of interest

Dynamic urban origin-destination matrix estimation methodology

The aim of this thesis is to develop a new methodology to determine dynamic Origin-Destination (OD) matrices for urban networks characterized by a high number of traffic hubs, complex route choice possibilities and a high level of traffic controls. By reviewing existing methods, from static to dynamic OD matrix evaluation, deficiencies in the approaches are identified: mainly, the level of detail of the traffic assignment for complex urban networks and the lack in dynamic approaches. The proposed methodology is comprised of a heuristic bi-level approach. Assignment of the initial demand is performed by mesoscopic simulation based on the Dynamic User Equilibrium to model detailed dynamic traffic patterns without numerous calibration parameters. OD flow adjustment is executed by an efficient least square solution which takes into account dynamic aspects of the flow propagation and traffic counts. For this task, a LSQR algorithm has been selected for its capacities to deal with a large matrix and its ability to constrain outputs. Parallel comparison with the most common approach for OD estimation (sequential static approach) has shown: first, the ability of the method to generate OD flows close to the actual demand, compared to the common practice; second, the utilization of the obtained demand by a dynamic traffic model has established its aptitude to reproduce realistic assignment patterns. Finally, applicability and example of utilization of the proposed method has been presented by solving realistic problems using the simulation software AIMSUN in which the proposed methodology is implemented as a plug-in. This research has shown the importance of input data for the OD estimation process and mainly the detection layout configuration used for traffic count data. Sensitivity analysis has shown that a small number of detectors is usually sufficient for efficient OD estimation in short computation time, if the traffic detectors intercept the most critical flows

Simultaneous calibration of a microscopic traffic simulation model and OD matrix

With the recent widespread deployment of intelligent transportation systems (ITS) in North America there is an abundance of data on traffic systems and thus an opportunity to use these data in the calibration of microscopic traffic simulation models. Even though ITS data have been utilized to some extent in the calibration of microscopic traffic simulation models, efforts have focused on improving the quality of the calibration based on aggregate form of ITS data rather than disaggregate data. In addition, researchers have focused on identifying the parameters associated with car-following and lane-changing behavior models and their impacts on overall calibration performance. Therefore, the estimation of the Origin-Destination (OD) matrix has been considered as a preliminary step rather than as a stage that can be included in the calibration process. This research develops a methodology to calibrate the OD matrix jointly with model behavior parameters using a bi-level calibration framework. The upper level seeks to identify the best model parameters using a genetic algorithm (GA). In this level, a statistically based calibration objective function is introduced to account for disaggregate form of ITS data in the calibration of microscopic traffic simulation models and, thus, accurately replicate dynamics of observed traffic conditions. Specifically, the Kolmogorov-Smirnov test is used to measure the "consistency" between the observed and simulated travel time distributions. The calibration of the OD matrix is performed in the lower level, where observed and simulated travel times are incorporated into the OD estimator for the calibration of the OD matrix. The interdependent relationship between travel time information and the OD matrix is formulated using a Extended Kalman filter (EKF) algorithm, which is selected to quantify the nonlinear dependence of the simulation results (travel time) on the OD matrix. The two test sites are from an urban arterial and a freeway in Houston, Texas. The VISSIM model was used to evaluate the proposed methodologies. It was found that that the accuracy of the calibration can be improved by using disaggregated data and by considering both driver behavior parameters and demand