Abstract

We consider the problem of allocating bandwidth to competing flows in an MPLS network, subject to constraints on fairness, efficiency, and administrative complexity. The aggregate traffic between a source and a destination, called a flow, is mapped to label switched paths (LSPs) across the network. Each flow is assigned a preferred ('primary') LSP, but traffic may be sent to other ('secondary') LSPs. Within this context, we define objectives for traffic engineering, such as fairness, efficiency, and preferred flow assignment to the primary LSP of a flow ('Primary Path First', PPF). We propose a distributed, feedback-based multipath routing algorithm that attempts to apply additive-increase and multiplicative-decrease (AIMD) to implement our traffic engineering objectives. The new algorithm is referred to as multipath-AIMD. We use ns-2 simulations to illustrate the fairness criteria and PPF property of our multipath-AIMD scheme in an MPLS network. Document type: Part of book or chapter of book

Abstract

During the last years, many speed-up techniques for Dijkstra 's algorithm have been developed. As a result, computing a shortest path in a staticroad network is a matter of microseconds. However, only few of those techniques work in time-dependentnetworks. Unfortunately, such networks appear frequentely in reality: Roads are predictably congestured by traffic jams, and efficient timetable information systems rely on time-dependent networks. Hence, a fast technique for routing in such networks is needed. In this work, we present an exacttime-dependent speed-up technique based on our recent SHARC-algorithm. As a result, we are able to efficiently compute shortest paths in time-dependent continental-sized transportation networks, both of roads and of railways. Document type: Part of book or chapter of book

Abstract

A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction.\\ud \\ud We propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios. Document type: Part of book or chapter of book

Abstract

This chapter traced the evolution of the foundations of transport network equilibrium modeling and analysis, with a focus on the principle methodological advances. In particular, an attempt has been made to set out in accessible fashion rigorous approaches to the formulation of a variety of traffic network equilibrium models and to establish relationships between the models as well as those that are closely linked such as spatial price equilibrium models. It should be emphasized that this topic is a very active area of research as well as practice. It should also be highlighted and further emphasized that traffic network equilibrium modeling and analysis provide a powerful framework for decision-making on complex networks. Indeed, given the interrelationships between telecommunications and transportation network’s in today’s network economy, it can be expected further synergies and advances in the study of such foundational networks. Of particular promise are the areas of multicriteria decision-making on networks, multitiered networks, as well as multilevel networks (in the form of transportation/logistical/financial/informational networks), formally referred to as supernetworks. Document type: Part of book or chapter of book

Abstract

Traditional solutions to shortest path problems on time-varying transportation networks use traffic information only at precise moments regardless of considering the fact that the travel time through any link is dependent on the time entering that link. In this study, travel speed rather than travel time on each link is used as the time period dependent parameter to model time-dependent transportation networks, and a First-In-First-Out (FIFO) condition satisfied computational function of link travel time is then deduced based on kinematics. Finally, a temporally adaptive A* shortest path algorithm on this FIFO network is presented, where the time factor is introduced into the evaluation function, and the Euclidean distance divided by the maximum possible travel speed is used as a heuristic evaluator. An experiment on a real road network shows that the proposed algorithm is capable of foreseeing and bypassing forthcoming traffic congestion, at a cost only about 10% more in computational time than the traditional algorithm. In addition, frequent path reoptimization required with use of the traditional algorithm is effectively avoided. Document type: Part of book or chapter of book

Abstract

This paper explores the inter-connectedness of the evolution of transportation networks and land use through the application of a Markov Chain mode. The model investigates how individual cells, with both land use and transportation network attributes, change over time. While this paper does not consider land use density directly, it does not consider land use type, and as cells change, it can be concluded that some form of development is likely to be occurring. The next section of the paper outlines the Markov Chain model and a discussion of the Twin Cities data used in the study follows. The subsequent section develops the transition probability matrices used by the Markov Chain. Those matrices are analyzed in order to understand the empirical regularities that appear in the data. They are then applied to both assess the predictive ability of the Markov Chain model (comparing what the model would predict with what actually happened), and then applying the mode to forecast future changes the Twin Cities area. The paper concludes with some suggestions for future research. Document type: Part of book or chapter of book

Abstract

The history of demand modeling for person travel has been dominated by the modeling approach that has come to be referred to as the four step model (FSM) (see Chapter 2). Travel, always viewed in theory as derived from the demand for activity participation, in practice has been modeled with trip-based rather than activity-based methods (as presented in Chapter 4). Trip origin-destination (O-D) rather than activity surveys form the principle database. The influence of activity characteristics decreases, and that of trip characteristics increases, as the conventional forecasting sequence proceeds. The application of this modeling approach is near universal, as in large measure are its criticisms (these inadequacies are well documented, e.g., by McNally and Recker (1986)). The current FSM might best be viewed in two stages. In the first stage, various characteristics of the traveler and the land use - activity system (and to a varying degree, the transportation system) are "evaluated, calibrated, and validated" to produce a non-equilibrated measure of travel demand (or trip tables). In the second stage, this demand is loaded onto the transportation network in a process than amounts to formal equilibration of route choice only, not of other choice dimensions such as destination, mode, time-of-day, or whether to travel at all (feedback to prior stages has often been introduced, but not in a consistent and convergent manner). Although this approach has been moderately successful in the aggregate, it has failed to perform in most relevant policy tests, whether on the demand or supply side. This chapter extends the material in Chapter 2 by providing a concise overview of the mechanics of the FSM, illustrated with a hypothetical case study. The discussion in this chapter, however, will focus on U.S. modeling practice. Transportation modeling developed as a component of the process of transportation analysis that came to be established in the U.S.A. during the era of post-war development and economic growth. Initial application of analytical methods began in the 1950s. The landmark study of Mitchell and Rapkin (1954) not only established the link of travel and activities (or land use) but called for a comprehensive framework and inquiries into travel behavior. The initial development of models of trip generation, distribution, and diversion in the early 1950s lead to the first comprehensive application of the four-step model system in the Chicago Area Transportation Study (see Weiner, 1997) with the model sandwiched by land use projection and economic evaluation. The focus was decidedly highway-oriented with new facilities being evaluated versus traffic engineering improvements. The 1960s brought federal legislation requiring "continuous, comprehensive, and cooperative" urban transportation planning, fully institutionalizing the FSM. Further legislation in the 1970s brought environmental concerns to planning and modeling, as well as the need for multimodal planning. It was recognized that the existing model system may not be appropriate for application to emerging policy concerns and, in what might be referred to as the "first travel model improvement program", a call for improved models led to research and the development of disaggregate travel demand forecasting and equilibrium assignment methods that integrated well with the FSM and have greatly directed modeling approaches for most of the last 30 years. The late 1970s brought "quick response" approaches to travel forecasting (Sosslau et al., 1978; Martin and McGuckin, 1998) and independently the start of what has grown to become the activity-based approach (see Chapter 4). The growing recognition of the misfit of the FSM and relevant policy questions in the 1980s led to the (second, but formal) Travel Model Improvement Program in 1991; much of the subsequent period has been directed at improving the state-of-thepractice relative to the conventional model while fostering research and development in new methodologies to further the state-of-the-art (see Chapter 4). The FSM is best seen as a particular application of transportation systems analysis (TSA), a framework due to Manheim (1979) and Florian et al. (1988), which positions the model well to view its strengths and weaknesses. A brief presentation of this TSA framework introduces the FSM context and leads to a discussion of problem and study area definition, model application, and data requirements. The models that are perhaps most commonly utilized in the FSM are then presented in the form of a sample application. Document type: Part of book or chapter of book

Abstract

International audience; The annual number of flights in Western Europe has increased from about 2.6 million in 1982 to about 4.5 million in 1992, an increase of 73%. Acute congestion of the Air Traffic Control system has been the result. One way to reduce this congestion is to modify the flight plans (slot of departure and route) in order to adapt the demand to the available capacity. This paper addresses the general time-route assignment problem. A state of the art of the existing methods shows that this problem is usually partially treated and the whole problem remains unsolved due to the complexity induced. We perform our research on the application of stochastic methods on real traffic data, and without using the flow network concept, but by simulating the flight of each aircraft. The first results shows that our Genetic Algorithms based method is able to reduce congestion of the french airspace by a factor 2. Special coding techniques and operators are used to improve the quality of the genetic search. Document type: Part of book or chapter of book

Abstract

It is widely acknowledged that, to create models for transportation planning that recognize the essential dynamic character of passenger network flows, one must consider two time scales: the so-called within-day time scale and the day-to-day time scale. Substantial progress has been made in modelling within-day dynamic flows for fixed trip matrices; one of the most widely acknowledged models for this purpose is the dynamic user equilibrium model proposed by Friesz et al. (1993) and studied by Xu et al. (1999), Wu et al. (1998), Friesz et al. (2001), Bliemer and Bovy (2003), and Friesz and Mookherjee (2006). In this chapter we propose two day-to-day models of demand growth compatible with a differential variational inequality formulation of the Friesz et al. (1993) model. The first of these employs dynamics inspired by evolutionary game theory, while the second uses the perspective of preferential attachment familiar from the network science and social network literature to create a model of demand growth. Additionally, numerical experiments to compare and contrast the two proposed theories of demand growth are described, along with hypotheses that one might address via such experiments. Document type: Part of book or chapter of book

Abstract

The objective of traffic engineering (TE) is to optimize network resources, while satisfying traffic-oriented performance requirements. As a technology for TE, we focus on a method by which to avoid congestion on some links and balance the traffic load over the network. Given a traffic matrix representing traffic demands, we allocate their traffic flows over the network. In this paper, we propose a number of flow allocation algorithms for TE in ISP backbone networks. As an objective function, we employ the maximum link utilization. We then evaluate the effect of the proposed algorithms with respect to load balancing and compare the performance of these algorithms. Document type: Part of book or chapter of book