Vulnerability analysis for large-scale and congested road networks with demand uncertainty

To assess the vulnerability of congested road networks, the commonly used full network scan approach is to evaluate all possible scenarios of link closure using a form of traffic assignment. This approach can be computationally burdensome and may not be viable for identifying the most critical links in large-scale networks. In this study, an “impact area” vulnerability analysis approach is proposed to evaluate the consequences of a link closure within its impact area instead of the whole network. The proposed approach can significantly reduce the search space for determining the most critical links in large-scale networks. In addition, a new vulnerability index is introduced to examine properly the consequences of a link closure. The effects of demand uncertainty and heterogeneous travellers’ risk-taking behaviour are explicitly considered. Numerical results for two different road networks show that in practice the proposed approach is more efficient than traditional full scan approach for identifying the same set of critical links. Numerical results also demonstrate that both stochastic demand and travellers’ risk-taking behaviour have significant impacts on network vulnerability analysis, especially under high network congestion and large demand variations. Ignoring their impacts can underestimate the consequences of link closures and misidentify the most critical links.     

Modelling the resilience of rail passenger transport networks affected by large-scale disruptive events: the case of HSR (high speed rail)

This paper deals with modelling the dynamic resilience of rail passenger transport networks affected by large-scale disruptive events whose impacts deteriorate the networks’ planned infrastructural, operational, economic, and social-economic performances represented by the selected indicators. The indicators of infrastructural performances refer to the physical and operational conditions of the networks’ lines and stations, and supportive facilities and equipment. Those of the operational performances include transport services scheduled along particular routes, their seating capacity, and corresponding transport work/capacity. The indicators of economic performances include the costs of cancelled and long-delayed transport services imposed on the main actors/stakeholder involved—the rail operator(s) and users/passengers. The indicators of social-economic performances reflect the compromised accessibility and consequent prevention of the user/passenger trips and their contribution to the local/regional/national Gross Domestic Product. Modeling resulted in developing a methodology including two sets of analytical models for: (1) assessing the dynamic resilience of a given rail network, i.e., before, during, and after the impacts of disruptive event(s); and (2) estimation of the indicators of particular performances as the figures-of-merit for assessing the network’s resilience under the given conditions. As such, the methodology could be used for estimating the resilience of different topologies of rail passenger networks affected by past, current, and future disruptive events, the latest according to the “what-if” scenario approach and after introducing the appropriate assumptions. The methodology has been applied to a past case—the Japanese Shinkansen HSR network affected by a large-scale disruptive event—the Great East Japan Earthquake on 11 March 2011.     

Identification and quantification of link vulnerability in multi-level public transport networks: a passenger perspective

Robust public transport networks are important, since disruptions decrease the public transport accessibility of areas. Despite this importance, the full passenger impacts of public transport network vulnerability have not yet been considered in science and practice. We have developed a methodology to identify the most vulnerable links in the total, multi-level public transport network and to quantify the societal costs of link vulnerability for these identified links. Contrary to traditional single-level network approaches, we consider the integrated, total multi-level PT network in the identification and quantification of link vulnerability, including PT services on other network levels which remain available once a disturbance occurs. We also incorporate both exposure to large, non-recurrent disturbances and the impacts of these disturbances explicitly when identifying and quantifying link vulnerability. This results in complete and realistic insights into the negative accessibility impacts of disturbances. Our methodology is applied to a case study in the Netherlands, using a dataset containing 2.5 years of disturbance information. Our results show that especially crowded links of the light rail/metro network are vulnerable, due to the combination of relatively high disruption exposure and relatively high passenger flows. The proposed methodology allows quantification of robustness benefits of measures, in addition to the costs of these measures. Showing the value of robustness, our work can support and rationalize the decision-making process of public transport operators and authorities regarding the implementation of robustness measures.     

A method to assess demand growth vulnerability of travel times on road network links

Many national governments around the world have turned their recent focus to monitoring the actual reliability of their road networks. In parallel there have been major research efforts aimed at developing modelling approaches for predicting the potential vulnerability of such networks, and in forecasting the future impact of any mitigating actions. In practice—whether monitoring the past or planning for the future—a confounding factor may arise, namely the potential for systematic growth in demand over a period of years. As this growth occurs the networks will operate in a regime closer to capacity, in which they are more sensitive to any variation in flow or capacity. Such growth will be partially an explanation for trends observed in historic data, and it will have an impact in forecasting too, where we can interpret this as implying that the networks are vulnerable to demand growth. This fact is not reflected in current vulnerability methods which focus almost exclusively on vulnerability to loss in capacity. In the paper, a simple, moment-based method is developed to separate out this effect of demand growth on the distribution of travel times on a network link, the aim being to develop a simple, tractable, analytic method for medium-term planning applications. Thus the impact of demand growth on the mean, variance and skewness in travel times may be isolated. For given critical changes in these summary measures, we are thus able to identify what (location-specific) level of demand growth would cause these critical values to be exceeded, and this level is referred to as Demand Growth Reliability Vulnerability (DGRV). Computing the DGRV index for each link of a network also allows the planner to identify the most vulnerable locations, in terms of their ability to accommodate growth in demand. Numerical examples are used to illustrate the principles and computation of the DGRV measure.     

Large-scale road network vulnerability analysis: a sensitivity analysis based approach

Traditionally, an assessment of transport network vulnerability is a computationally intensive operation. This article proposes a sensitivity analysis-based approach to improve computational efficiency and allow for large-scale applications of road network vulnerability analysis. Various vulnerability measures can be used with the proposed method. For illustrative purposes, this article adopts the relative accessibility index (AI), which follows the Hansen integral index, as the network vulnerability measure for evaluating the socio-economic effects of link (or road segment) capacity degradation or closure. Critical links are ranked according to the differences in the AIs between normal and degraded networks. The proposed method only requires a single computation of the network equilibrium problem. The proposed technique significantly reduces computational burden and memory storage requirements compared with the traditional approach. The road networks of the Sioux Falls city and the Bangkok metropolitan area are used to demonstrate the applicability and efficiency of the proposed method. Network manager(s) or transport planner(s) can use this approach as a decision support tool for identifying critical links in road networks. By improving these critical links or constructing new bypass roads (or parallel paths) to increase capacity redundancy, the overall vulnerability of the networks can be reduced.     

Road network vulnerability analysis of area-covering disruptions: A grid-based approach with case study

We present an approach to systematically analysing the vulnerability of road networks under disruptions covering extended areas. Since various kinds of events including floods, heavy snowfall, storms and wildfires can cause such spatially spread degradations, the analysis method is an important complement to the existing studies of single link failures. The methodology involves covering the study area with grids of uniformly shaped and sized cells, where each cell represents the extent of an event disrupting any intersecting links. We apply the approach to the Swedish road network using travel demand and network data from the Swedish national transport modelling system Sampers. The study shows that the impacts of area-covering disruptions are largely determined by the level of internal, outbound and inbound travel demand of the affected area itself. This is unlike single link failures, where the link flow and the redundancy in the surrounding network determine the impacts. As a result, the vulnerability to spatially spread events shows a markedly different geographical distribution. These findings, which should be universal for most road networks of similar scale, are important in the planning process of resource allocation for mitigation and recovery.     

Characterizing European high speed train stations using intermodal time and entropy metrics

This paper presents a quantitative method for characterizing high speed train stations in terms of passenger intermodality. The aim of the procedure is two-fold: firstly, the method is to be used as an objective measure for comparing stations in order to detect suboptimal points and improve the performance of stations as nodal points; secondly, the method provides a means to embed intermodality into regional accessibility models, allowing comprehensive modelling at lower scales. The empirical base of the work comprises data from 27 European high speed rail stations, which is deemed an appropriate statistical sample for the whole European rail network. Using the entropy metric, we found that several different patterns emerged: there was a clear hierarchy of stations which was linked to their respective roles within the system, while strong constraints impeded stations from performing optimally as true intermodal nodes.     

Risk of increasing temperature due to climate change on high-speed rail network in Spain

With more than 3,200 km of track, the Spanish high-speed rail network is the longest network in Europe and the second largest in the world after China. Due to its geographical location in southern Europe, the entire network is exposed to periods of elevated temperatures that can cause disturbances and severe disruptions such as rail deformation, or in the worst case, lateral track buckling. In this study, the vulnerability of the current Spanish high-speed rail network is analysed in terms of track buckling failures with a Monte Carlo simulation. Downscaled temperature projections from a range of Global Climate Models (GCMs), under three Representative Concentration Pathways (RCP4.5, RCP6.0 and RCP8.5), were forced in a buckling model and particularized for different segments of the network. With that, the proposed methodology provides the number of rail buckles expected per year by assuming current maintenance standards and procedures. The result reveals significant increase in the occurrence of buckling events for future years, mainly in the central and southern areas of mainland Spain. However, relevant variations are found in different climates and time horizon scenarios in Spain. The anticipated buckling occurrences highlight the vulnerability of the Spanish rail network in the context of global warming scenarios. Overall, the proposed methodology is designed to be applicable in large-scale railway networks to identify potential buckling sites for the purpose of understanding and predicting their behaviour.     

An integrated measure of accessibility and reliability of mass transit systems

The growth of a city or a metropolis requires well-functioning transit systems to accommodate the ensuing increase in travel demand. As a result, mass transit networks have to develop and expand from simple to complex topological systems over time to meet this demand. Such an evolution in the networks’ structure entails not only a change in network accessibility, but also a change in the level of network reliability on the part of stations and the entire system as well. Network accessibility and reliability are popular measures that have been widely applied to evaluate the resilience and vulnerability of a spatially networked system. However, the use of a single measure, either accessibility or reliability, provides different results, which demand an integrated measure to evaluate the network’s performance comprehensively. In this paper, we propose a set of integrated measures, named ACCREL (Integrated Accessibility and Reliability indicators) that considers both metrics in combination to evaluate a network’s performance and vulnerability. We apply the new measures for hypothetical mass transit system topologies, and a case study of the metro transit system in Seoul follows, highlighting the dynamics of network performance with four evolutionary stages. The main contribution of this study lies in the results from the experiments, which can be used to inform how transport network planning can be prepared to enhance the network functionality, thereby achieving a well-balanced, accessible, and reliable system. Insights on network vulnerability are also drawn for public transportation planners and spatial decision makers.     

The implications of differential network flexibility for spatial structures

Transportation and telecommunications networks have profound effects on location decisions of households, firms and of public facilities. Increasingly, such decisions depend on more than one network. Yet, most analyses focus on the land use implications of single networks. This paper suggests that in a multi-network setting' network flexibility is a significant factor in determining the relative importance of a network for the organization of space. To this end, the paper defines node and link flexibility and demonstrates the implications that differences in network flexibility have on location decisions. Counter to some expectations, the spatial effects of the most flexible network, namely telecommunications, may not be substantive, as location decisions seem to be most constrained by the least flexible networks. In response to growing congestion on existing networks, high capacity upper tier networks are increasingly developed. Such networks are generally inflexible. As a result the emerging map is more differentiated than the current one, changing the trend of that prevailed during the last fifty years toward greater equalization of space.     

Reverse time-restricted shortest paths: Application to air traffic management

In this paper, we consider a particular class of network flow problems that seeks a shortest path, if it exists, between a source node s and a destination node d in a connected digraph, such that we arrive at node d at a specified time τ while leaving node s no earlier than a lower-bounding time LB, and where the availability of each network link is time-dependent in the sense that it can be traversed only during specified intervals of time. We refer to this problem as the reverse time-restricted shortest path problem (RTSP), and it arises, for example, in the context of generating flight plans within air traffic management approaches under severe convective weather conditions. We show that this problem is NP-hard in general, but is polynomially solvable under a special regularity condition. A pseudo-polynomial time dynamic programming algorithm is developed to solve Problem RTSP, along with an effective heap implementation strategy. Computational results using real flight generation test cases as well as random simulated problems are presented.     

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.     

Comparisons of the environmental footprint of heavy vehicles in the UK and Switzerland

The European Union project Eureka Logchain Footprint is an ongoing project to identify road and rail vehicles by means of their environmental footprint as characterised by dynamic load, noise, ground borne vibrations and gaseous emissions induced by the vehicle. Part of the project involves the installation of road and rail footprint monitoring stations throughout Europe. This paper presents results of the road stations in Switzerland and the UK. Individual vehicle data from weigh-in-motion and noise are compared. The results indicate that a significant number of vehicles surpass the limits set in both countries. It was shown that the UK sites are generating higher noise levels than their Swiss counterparts; in part due to the much coarser aggregate embedded in the running course of the pavement employed in the UK. Such data can be used to create an incentive for vehicle types with a low footprint and a penalty for vehicles with a large footprint.     

Exposing the role of exposure: Public transport network risk analysis

Network risk assessment takes into consideration the probability that adverse events occur and the impacts of such disruptions on network functionality. In the context of transport networks, most studies have focused on vulnerability, the reduction in performance indicators given that a disruption occurs. This study presents and applies a method to explicitly account for exposure in identifying and evaluating link criticality in public transport networks. The proposed method is compared with conventional measures that lack exposure information. A criticality assessment is performed by accounting for the probability of a certain event occurring and the corresponding welfare loss. The methodology was applied for a multi-modal public transport network in the Netherlands where data concerning disruptions was available. The results expose the role of exposure in determining link criticality and overall network vulnerability. The findings demonstrate that disregarding exposure risks prioritizing links with high passenger volumes over links with a higher failure probability that are significantly more critical to network performance. The inclusion of exposure allows performing a risk analysis and has consequences on assessing mitigation measures and investment priorities.     

Locating rental stations and bikeways in a public bike system

To determine the spatial distribution of rental stations and bikeways in a public bike system, this paper proposes a facility location and network design model. The model is developed as a multi-objective programing problem that considers four objectives (minimizing cyclist risk, maximizing cyclist comfort, minimizing adverse impacts on traffic and maximizing service coverage) and multiple constraints (monetary budget, network connectivity, station spacing, bikeway types, station number and value ranges of decision variables). The ε-constraint method solves the programing problem for the public bike system in Daan District, Taipei City, Taiwan. The nine non-dominated alternatives generated are all markedly better than existing locations of rental stations and bikeways. Scenario analysis results indicate that increasing the construction budget for bikeways significantly improves cyclist safety and comfort whilst increasing the adverse impact on traffic. Planners can use this model to develop public bike systems that spatially integrate rental stations and bikeway networks.     

Identification of communities in urban mobility networks using multi-layer graphs of network traffic

This paper proposes a novel approach to identify the pockets of activity or the community structure in a city network using multi-layer graphs that represent the movement of disparate entities (i.e. private cars, buses and passengers) in the network. First, we process the trip data corresponding to each entity through a Voronoi segmentation procedure which provides a natural null model to compare multiple layers in a real world network. Second, given nodes that represent Voronoi cells and link weights that define the strength of connection between them, we apply a community detection algorithm and partition the network into smaller areas independently at each layer. The partitioning algorithm returns geographically well connected regions in all layers and reveal significant characteristics underlying the spatial structure of our city. Third, we test an algorithm that reveals the unified community structure of multi-layer networks, which are combinations of single-layer networks coupled through links between each node in one network layer to itself in other layers. This approach allows us to directly compare the resulting communities in multiple layers where connection types are categorically different.     

What determines rail transit passenger volume? Implications for transit oriented development planning

Transit oriented development (TOD) has been an important topic for urban transportation planning research and practice. This paper is aimed at empirically examining the effect of rail transit station-based TOD on daily station passenger volume. Using integrated circuit (IC) card data on metro passenger volumes and cellular signaling data on the spatial distribution of human activities in Shanghai, the research identifies variations in ridership among rail transit stations. Then, regression analysis is performed using passenger volume in each station as the dependent variable. Explanatory variables include station area employment and population, residents’ commuting distances, metro network accessibility, status as interchange station, and coupling with commercial activity centers. The main findings are: (1) Passenger volume is positively associated with employment density and residents’ commuting distance around station; (2) stations with earlier opening dates and serving as transfer nodes tend to have positive association with passenger volumes; (3) metro stations better integrated with nearby commercial development tend to have larger passenger volumes. Several implications are drawn for TOD planning: (1) TOD planning should be integrated with rail transit network planning; (2) location of metro stations should be coupled with commercial development; (3) high employment densities should be especially encouraged as a key TOD feature; and (4) interchange stations should be more strategically positioned in the planning for rail transit network.     

Land use around suburban transit stations

Land use change in some form is cited by both supporters and critics of rapid transit deployment. This paper examines and categorizes land use around twenty stations located in suburban Washington, D.C. and San Francisco/Oakland through the use of aerial photographs and field investigations. As a case study of local economic development, it documents the land use pattern associated with two modern heavy rail, rapid transit networks — BART and METRO.Both BART and METRO impact land use around suburban stations. The primary contributors to station area development are residential and commercial developers in addition to the transportation providers themselves. The trend toward more intense development away from the regional CBD toward suburban station areas indicates a wave of influence moving into the hinterland via transit lines. While trends of land use are apparent, individual station areas seem to be dictated by local conditions such as markets, land use restrictions, accessibility, population, and physical geography.     

A network‐based model for estimating the market share of a new high‐speed rail system

This paper presents a novel application of static traffic assignment methods, but with a variable time value, for estimating the market share of high‐speed rail (HSR) in the northwest–southeast (NW–SE) corridor of Korea currently served by air, conventional rail and highway modes. The proposed model employs a time–space network structure to capture the interrelations among competing transportation modes, and to reflect their supply‐ and demand‐side constraints as well as interactions through properly formulated link‐node structures. The embedded cost function for each network link offers the flexibility for incorporating all associated factors, such as travel time and fare, in the model computation, and enables the use of a distribution rather than a constant to represent the time–value variation among all transportation mode users. To capture the value‐of‐time (VOT) of tripmakers along the target corridor realistically, this study has developed a calibration method with aggregate demand information and key system performance data from the NW–SE corridor.     

Relationship between proximity to transit and ridership for journey-to-work trips in Chicago

The use of privately owned vehicles (POVs) contributes significantly to US energy consumption (EC) and greenhouse gas emissions (GHGe). Strategies for reducing POV use include shifting trips to other modes, particularly public transit. Choices to use transit are based on characteristics of travelers, their trips, and the quality of competing transportation services. Here we focus on the proximity of rail stations to trip origins/destinations as a factor affecting mode choice for work trips. Using household travel survey data from Chicago, we evaluate the profile of journey-to-work (JTW) trips, assessing mode share and potential for more travelers to use rail. For work trips having the origin/destination as close as 1 mile from rail transit stations, POVs were still the dominant travel mode, capturing as much as 61%, followed by rail use at 14%. This high degree of POV use coupled with the proportion of JTW trips within close proximity to rail stations indicated that at least some of these trips may be candidates for shifting from POV to rail. For example, shifting all work trips with both the origin/destination within 1 mile of commuter rail stations would potentially reduce the energy associated with all work-related POV driving trips by a maximum of 24%. Based on the analysis of trips having the origin and destination closest to train stations, a complete shift in mode from POV to train could exceed CO₂ reduction goals targeted in the Chicago Climate Action Plan. This could occur with current settlement patterns and the use of existing infrastructure. However, changes in traveler behavior and possibly rail operation would be necessary, making policy to motivate this change essential.