Robust constrained control of uncertain macroscopic fundamental diagram networks

This paper considers modeling and control of uncertain Macroscopic Fundamental Diagram (MFD) systems for multiple-region networks. First, the nonlinear vehicle conservation equations based on MFD dynamics, presented in earlier publications, are transformed to linear equations with parameter uncertainties. The parameter uncertainties include the destination decomposition fractions, that are difficult to estimate in reality. Then, the uncertain linear model is utilized to design a robust feedback controller by an interpolation-based approach. This approach (i) guarantees robustness against all parameter uncertainties, (ii) handle control and state constraints, and (iii) present a computationally cheap solution. The main idea is to interpolate between (i) a stabilizing outer controller that respects the control and state constraints and (ii) an inner robustly stable controller designed by any method. The robust control is further challenged to deal with different relative locations of reference accumulation points on the MFD diagrams. Numerical results for a two-region system show that the uncertain linear model can replace the nonlinear model for modeling and control. Moreover, the robust control law is presented as implicit and explicit solutions, where in the implicit case one linear programming (LP) problem is solved at each time instant, while in the explicit case, the control law is shown as a piecewise affine function of state. Finally, a comparison between the interpolating controller and other controllers in the literature is carried out. The results demonstrate the performance advantages from applying the robust interpolating controller.     

Making time count: Traveler activity engagement on urban transit

In practice, travel time is assigned a cost and treated as a disutility to be minimized. There is a growing body of research supporting the hypothesis that travel time has some value of its own, and the proliferation of information and communication technology (ICT) may be contributing to that value. Travelers’ attitudes are confounded with their mode choice, and as telecommunications mediate travel behavior, analysts must recognize the interaction between time use and customer satisfaction for appropriate travel demand management. To that end, this paper presents results from jointly estimated models of travelers’ latent satisfaction and on-board activity engagement using Chicago transit rider data gathered in April 2010. The simple questionnaire and small sample corroborate the findings of past research indicating travel attitudes and activity engagement have potential to influence travelers’ value of time, and many transit riders consider transit a better use of time and/or money than driving. The findings affirm the need for a more holistic understanding of value of time for travel demand management and infrastructure valuation. As time use has an influence on users’ valuation of the transit mode, offering opportunities to conduct certain leisure activities could improve the perceived value of travel time.     

Comparing smart scheme effects for congested highways

A high level objective for many international governments and local operators is that highways should be managed in a way that is sustainable in terms of a Low Carbon Energy future. Recent initiatives such as the Strategic Transport Technology Plan and the policy and legal framework promoted by the European Commissions’ Intelligent Transport System (ITS) Directive and ITS Action Plan may assist with this objective. However, many levels of complexity are inherent within the (ITS) schemes that are now part of highway management, due to the linkage of various technological components to complex systems and services. Maintaining efficient, sustainable co-operative performance is therefore a major task, with inconsistencies between product suppliers, network managers and operators. As a result, it is of considerable interest to the highway operators and high level policy makers to be able to assess the performance of individual ITS schemes and furthermore, to be able to compare performance between ITS schemes. In this paper, an illustration is provided of a methodology that can be used to assess the performance of ITS schemes according to a set of sustainability criteria. A case study is introduced which compares the performance of anticipated Active Traffic Management (ATM) schemes for what the road network operator (Highways England) perceive to be the four most congested highways in England (in terms of annual average daily traffic flows). Appropriate action can then be taken to improve the energy and sustainable management of Information Communication Technology (ICT) and transport systems for the benefit of a smarter, sustainable and efficient future.     

Integrating GIS concepts into transportation network data structures

Transportation network data structures must be designed to meet the requirements of the analyses being conducted and must be compatible with the selected graphical user interface. Increasing interest in geographic information systems (GIS) and intelligent transportation systems (ITS) have further burdened the network data structure. It is possible to implement object oriented programming (OOP) technology to satisfy these needs, without making the data structure excessively complicated.

This paper shows how a well‐developed network data structure can incorporate major capabilities normally associated with stand‐alone GIS's. The design of a network data structure derives from both theoretical and practical considerations. A design of a network data structure, composed entirely of objects, is presented. Examples of its implementation, limitations, advantages, and possible extensions are drawn from experience with the General Network Editor (GNE).     

Using survival models to estimate bus travel times and associated uncertainties

Transit agencies often provide travelers with point estimates of bus travel times to downstream stops to improve the perceived reliability of bus transit systems. Prediction models that can estimate both point estimates and the level of uncertainty associated with these estimates (e.g., travel time variance) might help to further improve reliability by tempering user expectations. In this paper, accelerated failure time survival models are proposed to provide such simultaneous predictions. Data from a headway-based bus route serving the Pennsylvania State University-University Park campus were used to estimate bus travel times using the proposed survival model and traditional linear regression frameworks for comparison. Overall, the accuracy of point estimates from the two approaches, measured using the root-mean-squared errors (RMSEs) and mean absolute errors (MAEs), was similar. This suggests that both methods predict travel times equally well. However, the survival models were found to more accurately describe the uncertainty associated with the predictions. Furthermore, survival model estimates were found to have smaller uncertainties on average, especially when predicted travel times were small. Tests for transferability over time suggested that the models did not over-fit the dataset and validated the predictive ability of models established with historical data. Overall, the survival model approach appears to be a promising method to predict both expected bus travel times and the uncertainty associated with these travel times.     

Interactive image-based information visualization for aircraft trajectory analysis

Objectives: The objective of the presented work is to present novel methods for big data exploration in the Air Traffic Control (ATC) domain. Data is formed by sets of airplane trajectories, or trails, which in turn records the positions of an aircraft in a given airspace at several time instants, and additional information such as flight height, speed, fuel consumption, and metadata (e.g. flight ID). Analyzing and understanding this time-dependent data poses several non-trivial challenges to information visualization.Materials and methods: To address this Big Data challenge, we present a set of novel methods to analyze aircraft trajectories with interactive image-based information visualization techniques.As a result, we address the scalability challenges in terms of data manipulation and open questions by presenting a set of related visual analysis methods that focus on decision-support in the ATC domain. All methods use image-based techniques, in order to outline the advantages of such techniques in our application context, and illustrated by means of use-cases from the ATC domain.Results: For each considered use-case, we outline the type of questions posed by domain experts, data involved in addressing these questions, and describe the specific image-based techniques we used to address these questions. Further, for each of the proposed techniques, we describe the visual representation and interaction mechanisms that have been used to address the above-mentioned goals. We illustrate these use-cases with real-life datasets from the ATC domain, and show how our techniques can help end-users in the ATC domain discover new insights, and solve problems, involving the presented datasets.     

《铁路信号显示的发展与思考》之二——多种显示制式的混用

由于混运、兼运的需求,再加故障备用模式,导致在同一线路上显示制式常常混用,情况复杂。不同的显示制式有不同显示方式,原则和规定,与行车有关的人员应当熟悉。     

Identifying multiclass vehicles using global positioning system data

It has been previously evidenced that global positioning system (GPS) data can be used to distinguish passenger cars from delivery trucks. In this paper, a machine learning approach is proposed to use GPS data to identify multiclass vehicles, including passenger cars, single unit trucks, and multi-trailer trucks. The method is acceleration and deceleration-based since it considers the variations of acceleration and deceleration as the most effective features to classify vehicles. The overall classification result for the three vehicle classes is about 75%. The major challenge is to distinguish single unit trucks from multitrailer trucks due to their somewhat similar mobility patterns. The paper also explores the impacts of GPS sampling frequency on vehicle classification. It is found that the proposed multiclass vehicle classification can be reasonably conducted if the data are collected frequently enough (i.e., every five seconds or more frequently) to capture the major acceleration and deceleration processes. The proposed method can be considered as a low-cost and non-intrusive approach to collect vehicle class information and to potentially supplement the existing classification schemes in urban areas.     

Development and evaluation of bus lanes with intermittent and dynamic priority in connected vehicle environment

Exclusive bus lanes provide a very high level of priority for transit operations, especially for Bus Rapid Transit and Express service, but these lanes could be underutilized and be a source of extra capacity if they could be shared in an intelligent way. This article explores the benefits of providing intermittent priority, called bus lane with intermittent and dynamic priority, of these exclusive bus lanes. Intermittent and dynamic priority can be implemented by allowing vehicles to use the lane when Bus Rapid Transit or Express bus is not present. Drivers can be alerted when a bus is in the lane using either infrastructure-based signs, or in the future using infrastructure-to-vehicle, or connected vehicle communications. Some critical operating parameters for implementing bus lane with intermittent and dynamic priority system including clear distance, degree of saturation (volume-to-capacity ratio), connected vehicle penetration, and bus departure/headway frequency have been investigated in this paper.