Network State Estimation and Prediction for Real-Time Traffic Management

Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) have the potential to contribute to the solution of the traffic congestion problem. DynaMIT is a real-time system that can be used to generate guidance for travelers. The main principle on which DynaMIT is based is that information should be consistent, and user optimal. Consistency implies that the traffic conditions experienced by the travelers are consistent with the condition assumed in generating the guidance. To generate consistent user optimal information, DynaMIT performs two main functions: state estimation and prediction. A demand simulator and a supply simulator interact to perform these tasks. A case study demonstrates the value of the system.     

Nonlinear Kalman Filtering Algorithms for On-Line Calibration of Dynamic Traffic Assignment Models

An online calibration approach that jointly estimates demand and supply parameters of dynamic traffic assignment (DTA) systems is presented and empirically validated through an extensive application. The problem can be formulated as a nonlinear state-space model. Because of its nonlinear nature, the resulting model cannot be solved by the Kalman filter, and therefore, nonlinear extensions need to be considered. The following three extensions to the Kalman filtering algorithm are presented: 1) the extended Kalman filter (EKF); 2) the limiting EKF (LimEKF); and 3) the unscented Kalman filter. The solution algorithms are applied to the on-line calibration of the state-of-the-art DynaMIT DTA model, and their use is demonstrated in a freeway network in Southampton, U.K. The LimEKF shows accuracy that is comparable to that of the best algorithm but with vastly superior computational performance. The robustness of the approach to varying weather conditions is demonstrated, and practical aspects are discussed.     

Technology limits for reducing EU transport sector CO2 emissions

Using a new data set describing the techno-economic characteristics of current and projected future transport technologies and a synthesis of existing transport demand models, lifecycle CO(2) emissions from 27 EU countries (EU27) were estimated in the absence and presence of new policy interventions to 2050. Future CO(2) emissions are strongly dependent on geographical scope and economic growth assumptions, and to a lesser extent on uncertainties in technology characteristics, but in the absence of new policy intervention they continue to rise from present-day values in all three scenarios examined. Consequently, EU27 emissions goals, which may require a 60% decrease in transport sector greenhouse gas emissions from year-1990 values by 2050, will be difficult to meet. This is even the case under widespread adoption of the most promising technologies for all modes, due primarily to limitations in biofuel production capacity and a lack of technologies that would drastically reduce CO(2) emissions from heavy trucks and intercontinental aviation.     

Simulation-based dynamic traffic assignment for short-term planning applications

Evaluation of Intelligent Transportation Systems (ITS) at the planning level requires the use of appropriate tools that can capture the dynamic and stochastic interactions between demand and supply. The objective of this paper is to present a methodological simulation-based framework for such applications and implement it in the context of dynamic traffic assignment. The framework consists of a mesoscopic supply simulator and a demand simulator that combines OD estimation capabilities with discrete travel behavior models. Simulation-based DTA systems are particularly suited to evaluate a wide range of Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS). The simulation model performance is illustrated through two large-scale case studies in Irvine, California, and Lower Westchester County, NY.     

A practical policy-sensitive,activity-based,travel-demand model

The development of activity-based models as a tool to analyse travel behaviour and forecast transport demand has been motivated by the growing complexity in activity patterns resulting from socio-economic changes, growing congestion, and negative externalities, as well as the need to estimate changes in travel behaviour in response to innovative policies designed to achieve sustainability. This paper reviews how the trade-off between behavioural realism and complexity, one of the main challenges facing the travel-demand modeler, is made in the best practical activity-based models. It proposes an approach that captures key behavioural aspects and policy sensitivities, while remaining practical with reasonable requirements of computational resources. The three main model elements in this trade-off—model structure, data, and application method—are analysed. Drawing on examples from a model developed for Tel Aviv and from existing US models, this paper shows that behavioural realism and policy sensitivity can be achieved with a reasonable level of model complexity.     

Replica Management in the European DataGrid Project

Within the European DataGrid project, Work Package 2 has designed and implemented a set of integrated replica management services for use by data intensive scientific applications. These services, based on the web services model, enable movement and replication of data at high speed from one geographical site to another, management of distributed replicated data, optimization of access to data, and the provision of a metadata management tool. In this paper we describe the architecture and implementation of these services and evaluate their performance under demanding Grid conditions.This work was partially funded by the European Commission program IST- 2000-25182 through the European DataGrid Project.     

Competing risks mixture model for traffic incident duration prediction

Traffic incident duration is known to result from a combination of multiple factors, including covariates such as spatial and temporal characteristics, traffic conditions, and existence of secondary accidents but also the clearance method itself. In this paper, a competing risks mixture model is used to investigate the influence of clearance methods and various covariates on the duration of traffic incidents and predict traffic incident duration. The proposed mixture model considers the uncertainty in any of five clearance methods that occurred. The probability of the clearance method is specified in the mixture by using a multinomial logistic model. Three candidate distributions, namely, generalized gamma, Weibull, and log-logistic are tested to determine the most appropriate probability density function of the parametric survival analysis model. The unobserved heterogeneity is also incorporated into the mixture model in a way that allows parameters to vary across observations based on the three candidate distributions. The methods are illustrated with incident data from Singaporean expressways from January 2010 to December 2011. Regression analysis reveals that the probability of different clearance methods and the duration of traffic incidents are both significantly affected by various factors, such as traffic conditions and incident characteristics. Results show that the proposed mixture model is better than the traditional accelerated failure time model, and it predicts traffic incident duration with reasonable accuracy, as shown by the mean average percent error.