Characterizing metro networks: state,form, and structure

The broad goal of this paper is to characterize the network feature of metro systems. By looking at 33 metro systems in the world, we adapt various concepts of graph theory to describe characteristics of State, Form and Structure; these three characteristics are defined using new or existing network indicators. State measures the complexity of a network; we identify three phases in the development of transit networks, with mature systems being 66% completely connected. Form investigates the link between metro systems and the built environment, distinguishing networks oriented towards regional accessibility, local coverage or regional coverage. Structure examines the intrinsic properties of current networks; indicators of connectivity and directness are formulated. The method presented is this paper should be taken as a supplement to traditional planning factors such as demand, demography, geography, costs, etc. It is particularly useful at the strategic planning phase as it offers information on current and planned systems, which can then be used towards setting a vision, defining new targets and making decision between various scenarios; it can also be used to compare existing systems. We also link the three characteristics to transit line type and land-use; overall the presence of tangential and/or (semi)-circumferential lines may be key. In addition, we have been able to identify paths of development, which should be strongly considered in future projects.     

A family of macroscopic node models

The family of macroscopic node models which comply to a set of basic requirements is presented and analysed. Such models are required in macro-, mesoscopic traffic flow models, including dynamic network loading models for dynamic traffic assignment. Based on the behaviour of drivers approaching and passing through intersections, the model family is presented. The headway and the turn delay of vehicles are key variables. Having demand and supply as input creates a natural connection to macroscopic link models. Properties like the invariance principle and the conservation of turning fractions are satisfied. The inherent non-uniqueness is analysed by providing the complete set of feasible solutions. The node models proposed by Tampère et al. (2011), Flötteröd and Rohde (2011) and Gibb (2011) are members of the family. Furthermore, two new models are added to the family. Solution methods for all family members are presented, as well as a qualitative and quantitative comparison. Finally, an outlook for the future development of empirically verified models is given.     

Assessing network vulnerability of heavy rail systems with the impact of partial node failures

Much of the literature in recent years has examined the vulnerability of transportation networks. To identify appropriate and operational measures of nodal centrality using connectivity in the case of heavy rail systems, this paper presents a set of comprehensive measures in the form of a Degree of Nodal Connection (DNC) index. The DNC index facilitates a reevaluation of nodal criticality among distinct types of transfer stations in heavy rail networks that present a number of multiple lines between stations. Specifically, a new classification of transfer stations—mandatory transfer, non-mandatory transfer, and end transfer—and a new measure for linkages—link degree and total link degree—introduces the characteristics of heavy rail networks when we accurately expose the vulnerability of a node. The concept of partial node failure is also introduced and compare the results of complete node failure scenarios. Four local and global indicators of network vulnerability are derived from the DNC index to assess the vulnerability of major heavy rail networks in the United States. Results indicate that the proposed DNC indexes can inform decision makers or network planners as they explore and compare the resilience of multi-hubs and multi-line networks in a comprehensive but accurate manner regardless of their network sizes.

     

Congestion pricing under operational,supply-side uncertainty

This paper is concerned with finding first-best tolls in static transportation networks with day-to-day variation in network capacity, as accounted for by changes in the volume-delay function. The key question in addressing this problem is that of information, namely, which agents have access to what information when making decisions. In this work, travelers are assumed to be either fully informed about network conditions before embarking on travel, or having no information except the probability distributions; likewise, the network manager (toll-setter) is either able to vary tolls in response to realized network conditions, or must apply the same tolls every day. Further, travelers’ preference for reliable travel is accounted for, representing risk aversion in the face of uncertainty. For each of the scenarios implied by combinations of these assumptions, we present methods to determine system-optimal link prices. A demonstration is provided, using the Sioux Falls test network, suggesting that attempts to incorporate uncertainty into nonresponsive tolls involve significantly higher prices.     

Assessment of policies and detection of unintended effects: Guiding principles for the consideration of methods and tools in policy-packaging

Single policies or entire policy packages are often assessed using different methods aiming at a quantification of effects as well as the detection of undesired outcomes. The knowledge of potential impacts is essential to take informed policy actions. Hence, there is a constant need for efficient assessment approaches to support policy decision-making. A broad range of such assessment methods is used in policymaking. Some of them are using quantitative data; others are characterized by qualitative information, observations or opinions. Practical experiences with transport policy prove that these methods all have their pros and cons, but none of them are able to detect the full range of effects. This leads to important questions this article deals with, such as what are the strengths and limitations of the different tools and methods for assessing impacts, and how should different approaches be integrated into the policymaking processes?We analyze the ability of assessment methods to detect different kinds of intended and unintended effects, and introduce the concepts of structurally open (mainly qualitative) and structurally closed (mainly quantitative) methods. It is argued that these concepts support making the pros and cons of the tools and methods more explicit and, thus, allow integrating the different tools and methods into the process of policy packaging. Based on a policy package example, we provide practical recommendations on how to integrate different assessment methods adequately and show that both quantitative and qualitative tools should be used in different phases of the process. The main recommendation is to alternate the application of assessment methods with structurally open methods used in the beginning and the end of the policymaking process and applying structurally closed methods in between.     

A bi-level model of dynamic traffic signal control with continuum approximation

This paper proposes a bi-level model for traffic network signal control, which is formulated as a dynamic Stackelberg game and solved as a mathematical program with equilibrium constraints (MPEC). The lower-level problem is a dynamic user equilibrium (DUE) with embedded dynamic network loading (DNL) sub-problem based on the LWR model (Lighthill and Whitham, 1955; Richards, 1956). The upper-level decision variables are (time-varying) signal green splits with the objective of minimizing network-wide travel cost. Unlike most existing literature which mainly use an on-and-off (binary) representation of the signal controls, we employ a continuum signal model recently proposed and analyzed in Han et al. (2014), which aims at describing and predicting the aggregate behavior that exists at signalized intersections without relying on distinct signal phases. Advantages of this continuum signal model include fewer integer variables, less restrictive constraints on the time steps, and higher decision resolution. It simplifies the modeling representation of large-scale urban traffic networks with the benefit of improved computational efficiency in simulation or optimization. We present, for the LWR-based DNL model that explicitly captures vehicle spillback, an in-depth study on the implementation of the continuum signal model, as its approximation accuracy depends on a number of factors and may deteriorate greatly under certain conditions. The proposed MPEC is solved on two test networks with three metaheuristic methods. Parallel computing is employed to significantly accelerate the solution procedure.     

Complementarity formulations for the cell transmission model based dynamic user equilibrium with departure time choice, elastic demand and user heterogeneity

In this paper we formulate the dynamic user equilibrium problem with an embedded cell transmission model on a network with a single OD pair, multiple parallel paths, multiple user classes with elastic demand. The formulation is based on ideas from complementarity theory. The travel time is estimated based on two methods which have different transportation applications: (1) maximum travel time and (2) average travel time. These travel time functions result in linear and non-linear complementarity formulations respectively. Solution existence and the properties of the formulations are rigorously analyzed. Extensive computational experiments are conducted to demonstrate the benefits of the proposed formulations on various test networks.     

Effects of queue spillover in networks considering simultaneous departure time and route choices

This paper explores the effects of queue spillover in transportation networks, in the context of dynamic traffic assignment. A model of spatial queue is defined to characterize dynamic traffic flow and queuing formation in network links. Network users simultaneously choose departure time and travel route to minimize the travel cost including journey time and unpunctuality penalty. Using some necessary conditions of the dynamic user equilibrium, dynamic network flows are obtained exactly on some networks with typical structure. Various effects of queue spillover are discussed based on the results of these networks, and some new paradoxes of link capacity expansion have been found as a result of such effects. Analytical and exact results in these typical networks show that ignoring queuing length may generate biased solutions, and the link storage capacity is a very important factor concerning the performance of networks.     

Pruning and ranking the Pareto optimal set,application for the dynamic multi‐objective network design problem

Solving the multi‐objective network design problem (MONDP) resorts to a Pareto optimal set. This set can provide additional information like trade‐offs between objectives for the decision making process, which is not available if the compensation principle would be chosen in advance. However, the Pareto optimal set of solutions can become large, especially if the objectives are mainly opposed. As a consequence, the Pareto optimal set may become difficult to analyze and to comprehend. In this case, pruning and ranking becomes attractive to reduce the Pareto optimal set and to rank the solutions to assist the decision maker. Because the method used, may influence the eventual decisions taken, it is important to choose a method that corresponds best with the underlying decision process and is in accordance with the qualities of the data used. We provided a review of some methods to prune and rank the Pareto optimal set to illustrate the advantages and disadvantages of these methods. The methods are applied using the outcome of solving the dynamic MONDP in which minimizing externalities of traffic are the objectives, and dynamic traffic management measures are the decision variables. For this, we solved the dynamic MONDP for a realistic network of the city Almelo in the Netherlands using the non‐dominated sorting genetic algorithm II. For ranking, we propose to use a fuzzy outranking method that can take uncertainties regarding the data quality and the perception of decision makers into account; and for pruning, a method that explicitly reckons with significant trade‐offs has been identified as the more suitable method to assist the decision making process. Copyright © 2012 John Wiley & Sons, Ltd.     

The existence, uniqueness and computation of an arc-based dynamic network user equilibrium formulation

In this paper, a dynamic user equilibrium traffic assignment model with simultaneous departure time/route choices and elastic demands is formulated as an arc-based nonlinear complementarity problem on congested traffic networks. The four objectives of this paper are (1) to develop an arc-based formulation which obviates the use of path-specific variables, (2) to establish existence of a dynamic user equilibrium solution to the model using Brouwer's fixed-point theorem, (3) to show that the vectors of total arc inflows and associated minimum unit travel costs are unique by imposing strict monotonicity conditions on the arc travel cost and demand functions along with a smoothness condition on the equilibria, and (4) to develop a heuristic algorithm that requires neither a path enumeration nor a storage of path-specific flow and cost information. Computational results are presented for a simple test network with 4 arcs, 3 nodes, and 2 origin–destination pairs over the time interval of 120 periods.     

Traffic Performance Models for Dynamic Traffic Assignment: An Assessment of Existing Models

Abstract

This paper provides a review of the traffic performance models for dynamic traffic assignment (DTA) and it identifies the strength and weakness of existing models. Requirements for traffic performance models are identified and various forms of existing traffic performance models for DTA are reviewed and analysed according to the requirements. Non‐linear travel time models are shown to have some deficiencies that make them unsuitable for the analysis of time‐varying transportation networks. Even though linear‐type travel time models are identified as good candidates for the analysis of dynamic transportation networks, they have limitations from the practical point of view that travel time increases only linearly with the amount of traffic on the link. This poses a dilemma and it seems to be one whose resolution is an imminent precondition for DTA modelling to progress in a way that is both theoretically coherent and plausible in practical terms.     

The case for heterodoxy in longitudinal analysis

Longitudinal analysis should be developed with the prime objective of discovering the characteristics of dynamic, disequilibrium processes. This will require a range of different research methods, including qualitative social research, repeated cross-section surveys, panel surveys, aggregate time series analysis, and simulation models. In each case it is essential that models should be formulated in a dynamic way (e.g. with lags, inertia and asymmetry). Examples are given of work using each of these methods, applied to the analysis of habit, aging, market volatility, turnover, long-term demand elasticities, and forecasting using demographic information.     

Developing environmentally sustainable logistics: Exploring themes and challenges from a logistics service providers’ perspective

The purpose of this paper is to explore themes and challenges in developing environmentally sustainable logistical activities.The approach is explorative with a cross sectional design that takes advantage of ten case studies out of selected logistics service providers (LSPs) operating primarily in the Scandinavian countries.The findings illustrate the major themes by analyzing current and future activities in developing environmentally sustainable logistical activities. In addition, four categories of challenges are identified: customer priorities, managerial complexity, network imbalance, and technological and legislative uncertainties. It is concluded that there is a great need for a holistic perspective where LSPs and product owners together analyze and design future logistical setups.The suggested holistic and integrative model, building on a three-dimensional concurrent engineering framework, provides new opportunities for research. Further research is needed to improve the interrelationship between LSPs and their customers in the development of sustainable logistical solutions.This paper puts forward recommendations for the sustainable development of logistics by combining the results from the case studies with a review of related literature. This will be beneficial for managers and policy makers when they approach sustainable logistical challenges. The emergence and synthesis of themes and challenges are critical for a sustainable society.     

Applying multiple kernel learning and support vector machine for solving the multicriteria and nonlinearity problems of traffic flow prediction

This article proposes to develop a prediction model for traffic flow using kernel learning methods such as support vector machine (SVM) and multiple kernel learning (MKL). Traffic flow prediction is a dynamic problem owing to its complex nature of multicriteria and nonlinearity. Influential factors of traffic flow were firstly investigated; five‐point scale and entropy methods were employed to transfer the qualitative factors into quantitative ones and rank these factors, respectively. Then, SVM and MKL‐based prediction models were developed, with the influential factors and the traffic flow as the input and output variables. The prediction capability of MKL was compared with SVM through a case study. It is proved that both the SVM and MKL perform well in prediction with regard to the accuracy rate and efficiency, and MKL is more preferable with a higher accuracy rate when under proper parameters setting. Therefore, MKL can enhance the decision‐making of traffic flow prediction. Copyright © 2012 John Wiley & Sons, Ltd.     

International logistics in Lithuania at a time of transition

This paper examines the current situation in the largest of the Baltic States - Lithuania - with respect to the development of international transport and logistics in the context of the severe political, economic and social changes that have taken place since 1991. Utilizing statistics previously unavailable for the region, the paper examines the impact of transition and the competitive role of East European carriers upon a specific international movement of electronic goods between Frankfurt/Main in Germany and Vilnius in Lithuania, developing a detailed cost model for the alternative modal choices that are available - road and ship/road. Rail transport is excluded as no international rail services between the two countries are available and there are no plans for such services in the near future. From this, a series of issues for the future in the context of potential EU membership, transport infrastructure developments and logistical trends is analysed.     

Analyzing trade-offs between transportation,inventory and production costs on freight networks

The purpose of this paper is to determine optimal shipping strategies (i.e. routes and shipment sizes) on freight networks by analyzing trade-offs between transportation, inventory, and production set-up costs. Networks involving direct shipping, shipping via a consolidation terminal, and a combination of terminal and direct shipping are considered. This paper makes three main contributions. First, an understanding is provided of the interface between transportation and production set-up costs, and of how these costs both affect inventory. Second, conditions are identified that indicate when networks involving direct shipments between many origins and destinations can be analyzed on a link-by-link basis. Finally, a simple optimization method is developed that simultaneously determines optimal routes and shipment sizes for networks with a consolidation terminal and concave cost functions. This method decomposes the network into separate sub-networks, and determines the optimum analytically without the need for mathematical programming techniques.     

Distributed optimization and coordination algorithms for dynamic speed optimization of connected and autonomous vehicles in urban street networks

Dynamic speed harmonization has shown great potential to smoothen the flow of traffic and reduce travel time in urban street networks. The existing methods, while providing great insights, are neither scalable nor real-time. This paper develops Distributed Optimization and Coordination Algorithms (DOCA) for dynamic speed optimization of connected and autonomous vehicles in urban street networks to address this gap. DOCA decomposes the nonlinear network-level speed optimization problem into several sub-network-level nonlinear problems thus, it significantly reduces the problem complexity and ensures scalability and real-time runtime constraints. DOCA creates effective coordination in decision making between each two sub-network-level nonlinear problems to push solutions towards optimality and guarantee attaining near-optimal solutions. DOCA is incorporated into a model predictive control approach to allow for additional consensus between sub-network-level problems and reduce the computational complexity further. We applied the proposed solution technique to a real-world network in downtown Springfield, Illinois and observed that it was scalable and real-time while finding solutions that were at most 2.7% different from the optimal solution of the problem. We found significant improvements in network operations and considerable reductions in speed variance as a result of dynamic speed harmonization.     

Integrated planning of static and dynamic charging infrastructure for electric vehicles

This paper investigates the optimal deployment of static and dynamic charging infrastructure considering the interdependency between transportation and power networks. Static infrastructure means plug-in charging stations, while the dynamic counterpart refers to electrified roads or charging lanes enabled by charging-while-driving technology. A network equilibrium model is first developed to capture the interactions among battery electric vehicles’ (BEVs) route choices, charging plans, and the prices of electricity. A mixed-integer bi-level program is then formulated to determine the deployment plan of charging infrastructure to minimize the total social cost of the coupled networks. Numerical examples are provided to demonstrate travel and charging plans of BEV drivers and the competitiveness of static and dynamic charging infrastructure. The numerical results on three networks suggest that (1) for individual BEV drivers, the choice between using charging lanes and charging stations is more sensitive to parameters including value of travel time, service fee markup, and battery size, but less sensitive to the charging rates and travel demand; (2) deploying more charging lanes is favorable for transportation networks with sparser topology while more charging stations can be more preferable for those denser networks.     

红外及数据库技术在热网管线监管中的应用

近年来,红外热成像技术在石化行业得到了广泛的应用,如热设备的损伤评估、保温评估等,但对于热设备的保温效果始终没有一套统一的评估标准和方法。一般地,石化行业热网管线的检测与评估需要记录并管理大量数据。数据库能够快速存储大量数据,利于数据的规范化管理,但在热网管线方面的检测与评估应用不多。基于分析提出将数据库技术与红外检测技术相结合,描述了一套实用的热网管线的检测与评估方法,为热网管线的监管与数据库技术的应用开辟了一条新的道路。     

Decision makers and socializers, social networks and the role of individuals as participants

Models explaining and predicting human travel behavior have gone through many changes in the past few decades. As researchers attempt to explain more and predict with more accuracy, the inclusion of social interactions in modeling and simulation is being recognized as a necessity. Among these efforts, researchers have focused on issues such as the composition of social networks, and the constraints and influences that others have on spatial decisions. An important aspect that has been understudied however is the variability or heterogeneity of individuals both as social network members and as participants in these social networks. Understanding the role individuals play in decision-making in different social networks can further define our models to include more accurate representations of human behavior. This research explores the differences between social network composition, and the decision roles members play within different social networks specifically when deciding where to participate in activities. A survey was conducted in Santa Barbara, California on social network involvement, network attributes and decision-making roles within each network. Two separate latent class cluster analysis models were developed to classify social network involvement and roles. Results show that there are clearly different types of social involvement and roles within networks. Further data collection and analysis will be used to better understand how these decision-making roles manifest themselves in activity decision-making.