In the network structure analysis, we explore an underestimated key metric, the Relative Size of Largest Connected Component (RSLCC) and demonstrate its importance in post-disaster network ...connectivity assessment. RSLCC was first investigated in the study of complex network structures, but remains largely unexplored in terms of analysis within a specific application domain such as scenarios in transportation networks, wireless networks, communication networks, power networks, etc. Through the research presented in this paper, we not only prove that this metric is underestimated, but also design 7 methods to predict the value of this metric, with a Deep Neural Network (DNN) prediction accuracy of more than 99%. This study focuses on the assessment and analysis of post-disaster network connectivity, by exploring how the RSLCC, a key metric of network connectivity, can be used to efficiently predict and assess network connectivity in a disaster scenario, specifically, the approximate network connectivity value can be predicted simply by knowing the number of connected edges in the pre-disaster network and the number of connected edges in the post-disaster network. To achieve this, firstly, a sufficiently large-scale 100,000 datasets containing the values of attributes related to the network structure is prepared. Secondly, based on the preprocessing of the data, principal component analysis and variance contribution analysis are carried out, and the metric with the highest contribution to the principal component is approximated as the network connectivity. The next step is the prediction process, Network Disruption Degree (NDD) is chosen as the independent variable. since it is best to choose an extremely simple metric as the independent variable for prediction, rather than all network structure-related metrics, this paper demonstrates that it is possible to get satisfactory prediction results with this metric. It is found that NDD prediction methods have the highest prediction accuracy but take the longest run time and require training data of a sufficiently large size. If the prediction is done in small-size data, then Random Forest Regression (RFR) is proven to have the highest prediction accuracy. Although the network connectivity metric proposed in this paper is only an approximation, it provides good directions for simplifying the network connectivity analysis and the use of this metric for the study of practical modeling problems is also highly interpretable.
Road traffic congestion continues to manifest and propagate in cities around the world. The recent technological advancements in intelligent traveler information have a strong influence on the route ...choice behavior of drivers by enabling them to be more flexible in selecting their routes. Measuring traffic congestion in a city, understanding its spatial dispersion, and investigating whether the congestion patterns are stable (temporally, such as on a day-to-day basis) are critical to developing effective traffic management strategies. In this study, with the help of Google Maps API, we gather traffic speed data of 29 cities across the world over a 40-day period. We present generalized congestion and network stability metrics to compare congestion levels between these cities. We find that (a) traffic congestion is related to macroeconomic characteristics such as per capita income and population density of these cities, (b) congestion patterns are mostly stable on a day-to-day basis, and (c) the rate of spatial dispersion of congestion is smaller in congested cities, i.e. the spatial heterogeneity is less sensitive to increase in delays. This study compares the traffic conditions across global cities on a common datum using crowdsourced data which is becoming readily available for research purposes. This information can potentially assist practitioners to tailor macroscopic network congestion and reliability management policies. The comparison of different cities can also lead to benchmarking and standardization of the policies that have been used to date.
Current transportation management systems rely on physical sensors that use traffic volume and queue-lengths. These physical sensors incur significant capital and maintenance costs. The ubiquity of ...mobile devices has made possible access to accurate and cheap traffic delay data. However, current traffic signal control algorithms do not accommodate the use of such data. In this paper, we propose a novel parsimonious model to utilize real-time crowdsourced delay data for traffic signal management. We demonstrate the versatility and effectiveness of the data and the proposed model on seven different intersections across three cities and two countries. This signal system provides an opportunity to leapfrog from physical sensors to low-cost, reliable crowdsourced data.
The Relief Network Design Problem (RNDP) is particularly important in emergency management. Any uncertain factors caused by natural disasters, the equity measurement in network design, and the ...adequate analysis of relief behavior will affect the efficiency of the relief network. This paper provides a comprehensive basis to support this view. The scope of the review allowed for exploring all existing literature on RNDP, where screening for titles, abstracts, keywords, and main contents, a total of 629 relevant articles are preserved. To construct the review work, existing research perspectives on the Relief Logistics Network Design Problem (RLNDP) as well as the Relief Transport Network Design Problem (RTNDP) are addressed, and their research focus and main research approaches are discussed. The existing studies on RNDP seem to be reached a bottleneck on how to design a humanitarian relief network. Hence, this paper contributes to the existing body of knowledge by summarizing the literature in the field, identifying gaps, analyzing future challenges, and providing solutions for future research. Specifically, this review reveals that while a large number of studies have considered uncertainty in the network design, they have not considered it at both the management level and the residents' level. In addition, equity is often mentioned, but the definition of humanitarian equity is unclear, as most studies consider equity at the management level. In real disaster relief scenarios, people do not only wait for relief, but self-evacuation is also a main behavior in the relief process, yet there are few studies that consider it in the network design. This review also emphasizes the relief network design structure problem, and the interdependence and coupling of the relief infrastructure transport or logistics facility network with other networks, such as the electric network, energy network, etc., deserves to be focused. In summary, five valuable research highlights are proposed based on a review of the existing literature: (1) Explore uncertainties from both the government management and disaster victim perspectives and integrate them into network design approaches. (2) Define and consider relief equity from both the government management and disaster victim perspectives. (3) Analyze self-evacuation behavior in the emergency relief phase and explore how it affects RNDP. (4) Optimize the transfer point location and relief routing from the perspective of management and humanitarian equity. (5) Strengthen the resilience of disaster relief interdependent network.
•The research on RNDP is reviewed and a classification of RNDP is proposed and discussions are given for each classification.•Research gaps are identified in terms of uncertainty, equity, relief behavior, and disaster time phases, respectively.•Research challenges, research opportunities are proposed to lay the foundation for future research.
Since disruptive events can cause negative impacts on a city's regular traffic order and economic activities, it is crucial that a transport network is resilient against disaster to prevent ...significant economic losses and ensure regular social, economic, and traffic order. However, using the transport metric for resilience improvement can only provide a limited view of transport pre-investments. This study develops an optimization framework to tackle the problem of resilient road pre-investment with the aim of resilience enhancement of traffic systems from an economic perspective by applying the integrated computable general equilibrium (CGE) model. First, we use the Shapley value, which considers road links’ interact cooperation, to determine critical candidate links that need to be upgraded. Second, we propose the Economic-based Network Resilience Measure (ENRM) as a performance indicator to evaluate network-level resilience from the economic perspective. Third, a bi-level multi-objective optimization model is formulated to identify the optimal capacity improvement for candidate critical links, where the objectives of the upper-level model are to minimize the ENRM and pre-enhancement budget. The lower-level model is built on the integrated CGE model. The genetic algorithm approach is used to solve the proposed bi-level model. A case study of the optimization framework is presented using a simplified Sydney network. Results suggest that a higher budget can help promote people's social welfare and improve transportation resilience. However, the Pareto-optimality is observed, and the marginal utility decreases with an increase in the investment budget. Further, the results also show that investment returns are higher in severe disasters. This study will help transport planners and practitioners optimize resilience pre-event investment strategies by capturing a wider range of project impacts and evaluating their economic impacts under general equilibrium rather than partial economic equilibrium, which is often assumed in traditional four-step transport planning.
•We identify eight model-amenable equity metrics which are divided into three categories based on their formulations.•Properties of each model-amenable equity metric in the context of resilience ...optimization are investigated.•Results can help decision-makers or practitioners recognize the implications, and limitations of each equity improvement approach.
It is broadly accepted that transportation planning models must evolve to consider equity and fairness in their role of supporting the design of society's future mobility, as a lack of equity or fairness can directly impact residents’ social wellbeing. However, social equity is seldom considered in resilience-related improvement that is crucial for transport networks to protect against recurring natural and man-made disasters. In addition, there is no single universally accepted definition for equity/fairness which, as a result, significantly complicates quantification. Therefore, this research identifies multiple frameworks for the quantification of model-amenable metrics so that each of the identified potential approaches can be further examined and trade-offs considered. Results suggest that differences in inequity reduction are observed among different equity mechanisms for a given level of investment. In addition, the results demonstrate that a pure focus on the most vulnerable populations is not necessarily to promote equitable development.
Wildfires are becoming more frequent and increasing in intensity, which results in significant threats to human life and property. Road networks play an important role in emergency activities. It is ...reasonable that robust road connectivity will give evacuees and emergency services the ability to respond more effectively, which may lead to a reduction in casualties. This study explores a novel graph-based connectivity index for road networks that considers different analysis scales to measure the impact on global wildfire fatality events in past decades. We find a significant and systematic relationship between fatalities and a calibrated connectivity index across different wildfire events. This parsimonious and simple graph theoretic measure can provide planners a useful metric to reduce vulnerability and increase resilience among areas that are prone to wildfires.
A number of recent disasters have challenged the functionality of transport networks. The significance of road transport infrastructure to the functioning means that systems need to be able to ...operate under undesirable conditions, and quickly return to acceptable levels of service. The objective of the study is to analyze real-world networks speed fluctuation and evaluate the quantitative relationship between resilience and graph-based metrics, and link attributes using crowd-sourced data. We measure resilience in terms of the rate (vehicle speed) at which the road network recovers from a disruptive event and define five metrics to quantify network resilience. We analyze more than 500 links affected by disruptions in multiple cities with more than millions of crowd-sourced data. Using changes in link speed before, during, and after the disruption, the resilience metrics are applied to three case studies that are categorized as no-notice disruption, notice disruption, and disruption caused by continuous events. The results indicate that link graph-based metrics and attributes have a high impact on network resilience. However, the relevance of different metrics and attributes to the link resilience is different. Population density, predictability of disasters, and human factors have a significant impact on the reduction and recovery phases.
Food Rescue and Delivery Nair, Divya Jayakumar; Grzybowska, Hanna; Rey, David ...
Transportation research record,
01/2016, Letnik:
2548, Številka:
1
Journal Article
Recenzirano
This paper addresses a special case of periodic vehicle routing problem in which each node has a nonnegative supply or demand of a single product that is unpaired. The product collected from a pickup ...node can be delivered to any one node or multiple delivery nodes, and the demand of a delivery node can be met by the product collected from any one node or multiple pickup nodes. This periodic unpaired pickup and delivery vehicle-routing problem is a novel variant of the periodic vehicle routing problem. The objective of the problem was to design the pickup and delivery vehicle routes to meet required service levels of delivery nodes, minimizing the total transportation cost while satisfying certain operational constraints. This problem was driven by food relief operations in Sydney, Australia. The logistics aspect of the approach was to design and execute a vehicle routing problem for a food rescue and delivery network. The specific goals were to develop an integrated linear programming model for this new variant of the periodic vehicle routing problem and to propose an integer programming–based heuristic solution approach to solve the problem introduced in the paper. The heuristic algorithm was tested with small instances created from Cordeau's benchmark instances, and the solution approach was validated against optimal solutions obtained through the exact method before implementation on a food rescue and delivery network. The heuristic approach was found to be comparable with the optimal solution and can solve the real-world scenarios with significantly fewer resources than are used in practice.
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