Transportation System Resilience Research Articles

Resilience assessment framework toward interdependent bus–rail transit network: Structure, critical components, and coupling mechanism

Understanding the interdependent nature of multimodal public transit networks (PTNs) is vital for ensuring the resilience and robustness of transportation systems. However, previous studies have predominantly focused on assessing the vulnerability and characteristics of single-mode PTNs, neglecting the impacts of heterogeneous disturbances and shifts in travel behavior within multimodal PTNs. Therefore, this study introduces a novel resilience assessment framework that comprehensively analyzes the coupling mechanism, structural and functional characteristics of bus–rail transit networks (BRTNs). In this framework, a network performance metric is proposed by considering the passengers’ travel behaviors under various disturbances. Additionally, stations and subnetworks are classified using the k-means algorithm and resilience metric by simulating various disturbances occurring at each station or subnetwork. The proposed framework is validated via a case study of a BRTN in Beijing, China. Results indicate that the rail transit network (RTN) plays a crucial role in maintaining network function and resisting external disturbances in the interdependent BRTN. Furthermore, the coupling interactions between the RTN and bus transit network (BTN) exhibit distinct characteristics under infrastructure component disruption and functional disruption. These findings provide valuable insights into emergency management for PTNs and understanding the coupling relationship between BTN and RTN.

A novel method to assess urban multimodal transportation system resilience considering passenger demand and infrastructure supply

With the continuous construction of urban infrastructure, urban multi-modal transportation system (UMTS) has gradually become complex and intertwined, which makes the system more uncertain and vulnerable, especially under disruptions. Improving the resilience of UMTS is critical to maintaining urban reliability and sustainability. This paper aims to build effective models to assess the resilience of UMTS. To develop the resilience assessment model, we provide a comprehensive, dynamic index that considers passenger demand and infrastructure supply. Three transportation modes are considered: bus, subway and taxi, and a cascading failure model is simulated. Data from Singapore are collected to analyse resilience indicators’ characteristics and explore relationship between resilience indicators and node measures. The results indicate that 1) when stations experience failure, it has the greatest impact on the accessibility of the transportation network, followed by efficiency and connectivity; 2) system resilience is negatively related to the node degree and node betweenness centrality of the attacked station; 3) beyond a certain threshold, continuing to increase stations’ additional capacity does not significantly increase system resilience. The proposed resilience index can be used as an optimization goal during system design and recovery. The conclusions and recommendations can inform the management and improvement of urban transportation system resilience.

Potential of Bike Sharing During Disruptive Public Health Crises: A Review of COVID-19 Impacts

With public transport (PT) continuing to be negatively affected by the coronavirus pandemic and private car usage surging, alternative modes need to be considered. In this study, we review the available evidence (from academic and gray literature sources) on the performance of bike sharing systems (BSSs) during COVID-19 around the world, with the goal of assessing their potential contribution to improving the resilience of transport systems during pandemics and similar disruptive events. We found BSS usage followed a decrease-rebound pattern, with BSSs overall sustaining lower ridership declines and faster recoveries compared with PT. During lockdowns especially, the average duration of BSS trips increased, following a rise in casual users and leisure trips, while commuting trips decreased. Evidence has also been found for a possible modal shift from some PT users to BSSs, with a decline in the share of multimodal trips conducted between PT and BSSs. Bike sharing is perceived as safer than other shared modes (e.g., PT, taxis, and ride-hailing/sharing) but as having a higher infection risk than personal modes (e.g., private car, walking, and personal bike). Moreover, the BSS was an important transport alternative for essential workers, with several operators providing waivers especially to healthcare staff, leading to ridership increases near healthcare facilities and in deprived neighborhoods. Findings from this research support policies for promoting bike sharing, namely through fee reductions, system expansions, and symbiotic integration with PT, as BSSs can increase the sustainability and resilience of transport systems during disruptive public health events like COVID-19.

Evaluation of Cooperative Intelligent Transportation System scenarios for resilience in transportation using type-2 neutrosophic fuzzy VIKOR

There is a critical need for research in proactive and predictive management of the resilience of transportation systems implementing new technologies. Cooperative Intelligent Transportation System (C-ITS) uses wireless technology to allow vehicles and infrastructure to talk to each other in real-time. This makes it easier for people to work together on the road and makes it possible to make safer and more efficient traffic flows. Significant progress may be made in the transportation industry as a result of the incorporation of self-powered sensors into C-ITS providing resilience in transportation operation. One advantageous feature is that these sensors, which generate their power, could be deployed in a variety of C-ITS implementation scenarios. To assist decision-makers in making the most informed choice possible concerning investments and implementations, a type-2 neutrosophic number (T2NN) based VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method is used to perform advantage prioritization. To accomplish this goal, a case study is carried out to determine which of the three alternatives is the most suitable based on a set of twelve criteria that is divided into four aspects. According to the findings, the applicability and short-term benefits are the most crucial factors in determining which option is the most advantageous for the use of self-powered sensors in C-ITS. This is because both of these factors have an immediate impact on the system.

Visualizing the intellectual landscape and evolution of transportation system resilience: A bibliometric analysis in CiteSpace

As an emerging concept of a system's ability to resist disasters, transportation system resilience (TSR) has attracted the attention of scholars. In this study, a bibliometric analysis of 303 academic publications from the Web of Science related to TSR is presented to portray the intellectual landscape by visualizing the evolution of the collaboration network, the co-citation network, and keywords co-occurrence. The results showed that the number of publications in the field of TSR exploded after 2014 and attracted multi-disciplinary and cross-disciplinary attention from all over the world. Collaborative efforts among authors in this field tend to confine in small groups, and a large-scale collaborative network has yet to be established. This study also identifies the influential journals, institutions, scholars, and literature and summarizes the hot research topics and future research directions. In addition, it offers valuable references for researchers interested in TSR, and put forward recommendations on the emphasis and orientations of future studies.

Exploring the potential role of bikeshare to complement public transit: The case of San Francisco amid the coronavirus crisis

The recent worldwide SARS-CoV-2 (COVID-19) pandemic has reshaped the way people live, how they access goods and services, and how they perform various activities. For public transit, there have been health concerns over the potential spread to transit users and transit service staff, which prompted transportation agencies to make decisions about the service, e.g., whether to reduce or temporarily shut down services. These decisions had substantial negative consequences, especially for transit-dependent travelers, and prompted transit users to explore alternative transportation modes, e.g., bikeshare. However, local governments and the public in general have limited information about whether and to what extent bikeshare provides adequate accessibility and mobility to those transit-dependent residents. To fill this gap, this study implemented spatial and visual analytics to identify how micro-mobility in the form of bikesharing has addressed travel needs and improved the resilience of transportation systems. The study analyzed the case of San Francisco in California, USA, focusing on three phases of the pandemic, i.e., initial confirmed cases, shelter-in-place, and initial changes in transit service. First, the authors implemented unsupervised machine learning clustering methods to identify different bikesharing trip types. Moreover, through spatiotemporally matching bikeshare ridership data with transit service information (i.e., General Transit Feed Specification, GTFS) using the tool called OpenTripPlanner (OTP), the authors studied the travel behavior changes (e.g., the proportion of bikeshare trips that could be finished by transit) for different bikeshare trip types over the three specified phases. This study revealed that during the pandemic, more casual users joined bikeshare programs; the proportion of recreation-related bikeshare trips increased; and routine trips became more prevalent considering that docking-station-based bikeshare trips increased. More importantly, the analyses also provided insights about mode substitution, because the analyses identified an increase in dockless bikeshare trips in areas with no or limited transit coverage.

A bibliometric analysis and review on reinforcement learning for transportation applications

Transportation is the backbone of the economy and urban development. Improving the efficiency, sustainability, resilience, and intelligence of transportation systems is critical and also challenging. The constantly changing traffic conditions, the uncertain influence of external factors (e.g. weather, accidents), and the interactions among multiple travel modes and multi-type flows result in the dynamic and stochastic natures of transportation systems. The planning, operation, and control of transportation systems require flexible and adaptable strategies in order to deal with uncertainty, non-linearity, variability, and high complexity. In this context, Reinforcement Learning (RL) that enables autonomous decision-makers to interact with the complex environment, learn from the experiences, and select optimal actions has been rapidly emerging as one of the most useful approaches for smart transportation applications. This paper conducts a bibliometric analysis to identify the development of RL-based methods for transportation applications, representative journals/conferences, and leading topics in recent 10 years. Then, this paper presents a comprehensive literature review on applications of RL in transportation based on specific topics. The potential future research directions of RL applications and developments are also discussed.

Managerial decisions to recover from Covid-19 disruption: A multi-objective optimization approach applied to public transport operators

The resilience of transport systems, facing natural or man-made disruptions, has been widely discussed in literature in terms of recovery capabilities concerning infrastructures, suggesting solutions to provide users an acceptable level of service along the interrupted network. However, in the context of the Covid-19 outbreak, the disruption has stressed the resilience of transport systems not on the supply side but rather at organizational level for transport service providers. Indeed, the sudden and drastic decrease in users due to the restrictions imposed by governments to limit the pandemic spread has implicated severe economic consequences in the running of transport companies. In this paper, attention has been focused on the public transport sector to analyse the effects of different initiatives, which companies could undertake in response to the demand shock caused by the Covid-19 emergency. Notably, an optimization procedure has been developed with the aim of determining feasible Pareto-front solutions, which correspond to trade-off conditions for the concurrent maximization of the company profit and the minimization of outsourcing services. The time span necessary to implement the examined recovery measures has been considered together with the limitation to appropriate threshold values for the main cost and income items influencing the company operations management. The proposed approach has been applied to the case study of an Italian public transport company to appraise different post-Covid-19 resilience strategies.

Modeling the resilience of urban mobility when exposed to the COVID-19 pandemic: A qualitative system dynamics approach

In December 2019, coronavirus disease (COVID-19) was detected in Wuhan, China. Due to the rapid spread of the disease, containment measures were adopted, which caused unprecedent shifts in individual mobility. Although some studies explored the impacts of the COVID-19 pandemic on travel patterns and resilience of transport systems based on different analysis techniques, there is a lack of studies addressing the impacts of the pandemic on the sustainability and resilience of urban mobility systems using in-depth and holistic methods, such as system dynamics. This research aims to characterize the dynamics present in urban mobility systems when exposed to pandemics and analyze the changes needed for systems to increase their resilience to pandemics using qualitative system dynamics modeling. The framework comprises the characterization of cause-and-effect relationships and the creation of systems’ causal loop diagrams (CLD) in their basic state of functionality, when affected by pandemics, and still operating owing to its resilience. Our findings indicated that the CLD of a resilient system is driven by strategic preparedness and response plans, as well as research and development, which balance the spread of the pandemic and increase support on technological strengths and the activities performed from home.

Unraveling the Decision-making Process Interpretable Deep Learning IDS for Transportation Network Security

The growing ubiquity of IoT-enabled devices in recent years emphasizes the critical need to strengthen transportation network safety and dependability. Intrusion detection systems (IDS) are crucial in preventing attacks on transport networks that rely on the Internet of Things (IoT). However, understanding the rationale behind deep learning-based IDS models may be challenging because they do not explain their findings. We offer an interpretable deep learning system that may be used to improve transportation network safety using IoT. To develop naturally accessible explanations for IDS projections, we integrate deep learning models with the Shapley Additive Reasons (SHAP) approach. By adding weight to distinct elements of the input data needed to develop the model, we increase the readability of so-called black box processes. We use the ToN_IoT dataset, which provides statistics on the volume of network traffic created by IoT-enabled transport systems, to assess the success of our strategy. We use a tool called CICFlowMeter to create network flows and collect data. The regularity of the flows, as well as their correlation with specific assaults, has been documented, allowing us to train and evaluate the IDS model. The experiment findings show that our explainable deep learning system is extremely accurate at detecting and categorising intrusions in IoT-enabled transportation networks. By examining data using the SHAP approach, cybersecurity specialists may learn more about the IDS's decision-making process. This enables the development of robust solutions, which improves the overall security of the Internet of Things. Aside from simplifying IDS predictions, the proposed technique provides useful recommendations for strengthening the resilience of IoT-enabled transportation systems against cyberattacks. The usefulness of IDS in defending mission critical IoT infrastructure has been questioned by security experts in the Internet of Vehicles (IoV) industry. The IoV is the primary research object in this case. Deep learning algorithms' versatility in processing many forms of data has contributed to their growing prominence in the field of anomaly detection in intrusion detection systems. Although machine learning models may be highly useful, they frequently yield false positives, and the path they follow to their conclusions is not always obvious to humans. Cybersecurity experts who want to evaluate the performance of a system or design more secure solutions need to understand the thinking process behind an IDS's results. The SHAP approach is employed in our proposed framework to give greater insight into the decisions made by IDSs that depend on deep learning. As a result, IoT network security is strengthened, and more cyber-resilient systems are developed. We demonstrate the effectiveness of our technique by comparing it to other credible methods and utilising the ToN_IoT dataset. Our framework has the best success rate when compared to other frameworks, as evidenced by testing results showing an F1 score of 98.83 percent and an accuracy of 99.15 percent. These findings demonstrate that the architecture successfully resists a variety of destructive assaults on IoT networks. By integrating deep learning and methodologies with an emphasis on explainability, our approach significantly enhances network security in IoT use scenarios. The ability to assess and grasp IDS options provides the path for cybersecurity experts to design and construct more secure IoT systems.

Urban resilience centered on transport systems

Cities are susceptible to events that can change its normal functioning, causing disturbances to the population's well-being and financial impacts for the government to repair to return to the original situation. In the concept of transport systems, these events can range from environmental disasters to disruptions by human causes. With the population increase, cities became even more vulnerable to disturbances, therefore, one way to assess the system's resilience potential is through metrics that can quantify its performance.The transport system resilience of an area can be influenced by several aspects of neighbour's areas, like, purchasing power and unemployment rate. Therefore, in order to identify the existence of patterns, the concept of spatial analysis is used, which allows comparing the calculated potential resilience score with socio-economic indicators.

The strengths and weaknesses of bike sharing as an alternative mode during disruptive public health crisis: A qualitative analysis on the users’ motivations during COVID-19

The coronavirus pandemic may provoke an increase on our overreliance on private car usage due to a permanent loss of confidence on public transport (PT), threatening current decarbonization efforts of the transport sector. Thus, alternative modes like bike sharing systems (BSS) must be considered.In this study, through conducting 16 semi-structured interviews and by employing thematic analysis, we explore the users' perceptions of using Lisbon's BSS during this pandemic. Our findings show that the observed decrease on BSS usage during the COVID-19 lockdowns was mostly due to mandatory teleworking than to a perceived infection risk. Even during the height of the pandemic, users still turned to BSS to fulfil their essential trip needs. Users considered bike sharing to have a lower infection risk comparatively to PT, with some users joining BSS during the pandemic to specifically avoid using PT. Furthermore, users associate riding a shared bicycle with a pleasant activity that reduces their travel times and costs, while also providing health and environmental benefits. Consequently, bike sharing contributes to the resilience of transport systems by providing its users with a transport alternative perceived to have a low infection risk, ensuring their mobility needs during disruptive events.Findings from this research provide evidence that support policies, such as, expanding BSS coverage areas, optimizing rebalancing operations, introducing shared e-bikes, and implementing segregated cycling lanes alongside BSS. These policies may be particularly effective at increasing the competitiveness of BSS as an alternative mode during disruptive public health crises and beyond.

Stability and Resilience of Transportation Systems: Is a Traffic Jam About to Occur?

Measurement of traffic flow stability and resilience is a critical step toward evaluating the performance of transportation systems and implementing appropriate management strategies. Quantifying changes in the stability and resilience of transportation systems, however, is hampered by the complexity of real traffic dynamics and the diversity of infrastructures. Here, we demonstrate that changes in traffic flow stability and resilience are signaled by generic features, known as early warning signals in the theory of critical slowing down, observed before traffic instabilities occur. This finding is incorporated in an operational data-driven algorithm to evaluate the risk of traffic jams on highways. Theoretical findings and tests on simulated and empirical case studies support the premise of this approach and identify candidate statistical measures that are sensitive to changes in the stability and resilience of transportation systems. Our use of universal measures advances the monitoring capability, prediction and control of complex transportation systems.

Static and dynamic resilience assessment for sustainable urban transportation systems: A case study of Xi 'an, China

Resilient transportation provides a new perspective for constructing sustainable cities. Although current studies have analyzed transportation resilience from different perspectives, accurately assessing the resilience of urban transport systems remains a significant challenge. This paper proposes a framework to evaluate the static and dynamic resilience of urban transportation systems. Based on systemic thinking and sustainability perspectives, we establish a Bayesian network model that comprehensively considers social, economic, organizational, and technological dimensions to assess the static resilience of urban transportation systems. We adopt percolation theory to determine the minimum required performance (MRP) of road network and explore the spatial distribution of dynamic resilience based on traffic performance under traffic congestion. Results show that the transportation resilience of Xi'an showed a continuous growth trend between 2010 and 2020, from 0.37 to 0.64. In addition, the primary task of enhancing resilience is to improve the system's restorative capacity, adaptive capacity, and resourcefulness. The MRP of the road network is 0.37. The areas with larger resilience loss and percolation time are concentrated along the Second and Third Ring Expressway and in the south and southwest areas. The resilience of the urban center area is more potent. The time of the response process is twice that of the recovery process. The above results could help managers and decision-makers have a comprehensive and clear understanding of the transportation resilience and have practical significance for constructing a sustainable urban transportation system.

Reliability and Resource Allocation and Recovery of Urban Transportation System Considering the Virus Transmission

The urban transport system is an integral part of a city and is essential for the proper functioning of other urban functional systems. To improve the resilience of urban transport systems under the background of the spreading COVID-19 epidemic, this paper predicts the number of patients of various types at each stage of epidemic development based on an improved infectious disease model for Wuhan and verifies the validity of the model using statistical methods. Then, a system reliability model is developed from the perspective of controlling the spread of the virus and reducing economic losses, and the optimal time points for urban traffic closure and recovery are determined. Finally, a resource allocation optimization model was developed to determine the number and location of resource allocation points which based on 19 hospitals to avoid the further spread of the virus. The results give a valuable reference for enhancing the resilience of urban transport systems and improving their performance in all phases.

The impact of the COVID-19 pandemic on the behaviour of bike sharing users.

Globally most governments implemented a ‘Working from Home’ (home office) strategy to contain the spread of the coronavirus in 2020 in order to ensure public safety and minimize the transmission of the virus. Unsurprisingly studies have found that COVID-19 has had a detrimental impact on urban transportation systems; however, the number of shared bicycle riders is progressively growing compared to other modes of public transit. The aim of this study is to investigate the influence of COVID-19 on the usage of shared bicycle systems in order to identify passenger travel patterns and habits. In addition, bicycle rentals are becoming more popular in some locations. This demonstrates that bike sharing as a transport option has a high level of social adaptability and is progressively being adopted by the general population in a fashion that promotes the resilience of transport systems.

The COVID-19 economic shutdown and the future of flexible workplace practices in the South Bay region of Los Angeles County

The COVID-19 lockdown has increased the use of flexible workplace practices (FWP) especially work from home, demonstrating their importance to the resilience of transportation systems and regional economies. This study compares experiences and perceptions of FWP and related policy interventions before and during the COVID-19 shutdown, using a mixed-methods approach focusing on the South Bay region of Los Angeles County, to inform projections about the use of FWP and policy implications post-COVID. Pre-shutdown surveys and focus groups interviews confirmed that major obstacles to FWP expansion were a combination of managerial and executive resistance, alongside occupational constraints. Pre-shutdown interviews suggested that costs associated with manager training and cultural transition are major concerns for executives. A small sample of follow-up interviews with executives, managers, and staff, conducted during the shutdown period has revealed some of the practical issues with full-time FWP such as work-life balance, childcare, productivity, IT hardware and software, and network connectivity. Although organizations have been forced into flexible arrangements, many are considering continuing to utilize the practices after the pandemic settles down. In terms of policy interventions, pre-COVID participants perceived government subsidies and incentives as the most desirable government programs. However, in a resource-constrained post-COVID world, policy makers might instead focus on training programs and promotional campaigns tied to public health messaging, and the implications of reduced commuting for transportation system design and commercial zoning and land use.

Performance Dashboard Tool to Visualize Adaptive Resilience Maturity of Transportation Agencies

Incorporating adaptive resilience as a core value of transportation systems will equip transportation agencies to better address the increasing pace of future changes—both in climate and transportation system performance demands. Quantifiable measures are instrumental in incorporating adaptive resilience in agency organizational frameworks. This paper presents a dashboard tool that evaluates the maturity of adaptive resilience capabilities of a transportation agency, using an Adaptive Resilience (AR) Capability Maturity Model. A demonstrative example is presented to show the potential application of the dashboard tool and its implications for transportation system resilience planning. The example shows how the tool can facilitate identification of adaptive resilience capabilities that are at low maturity and thus may need prioritization for future resilience investments, and capabilities that are at a high maturity level and thus can be leveraged to enhance the lower-level capabilities. The AR performance dashboard tool may be used by transportation agencies to communicate their adaptive resilience efforts and maturity to stakeholders. It can also be used by practitioners to streamline and prioritize process improvements in adaptive resilience efforts.

Operational Efficiency Comparison and Transportation Resilience: Case Study of Nanjing, China

With the changes in global climate, adverse weather events appear to be more frequent. The efficiency, reliability, and safety of transportation operations can be compromised by extreme weather conditions. It is critical to analyse the spatiotemporal effects of extreme weather on the operational efficiency of transportation operational efficiency for meeting people’s travel and transport needs and formulating emergency management operation to prevent road congestion. It is also important to improve the resilience of transportation system. We compared the changes in transportation operational efficiency in Nanjing during normal weather on January 23 and snowstorm on January 25, 2018, using real-time travel data from Gaode Map. The results indicate that there are differences in the distribution of accessibility changes from different residential areas to the main city area in the morning peak. Overall, 90% of the accessibility change values are concentrated in −10 to 10 minutes, while 65% are concentrated in −5 to 5 minutes, and 70% of the rangeability of accessibility is concentrated in −20% to 20%. In a word, the travel in Nanjing in the snowstorm weather is basically normal. This observation is mainly due to the overnight snow removal and the reduction in the number of small car trips. In addition to the Xinjiekou area, the Hexi business district and the exit area of the cross-river tunnel should also be included in the key management areas for taking emergency management operation in the future.

Connectedness Efficiency Analysis of Weighted U.S. Freight Railroad Networks

Abstract Freight rail networks serve a key role in transporting bulk goods to accommodate changing market demands and to serve public needs. Network analyses of such systems can provide important insights into enhancing transportation efficiency and system resilience. This paper develops and investigates a topological analysis model for network efficiency, which is associated with the connectedness of a network's nodes by its links and their corresponding network attributes. This model allows analyzing network topologies with or without assigned weights to their nodes and links based on different attributes. Key attributes may include physical length of links, commodity types and volume of goods transported through links, and dwell-time at nodes, and origination termination volumes of goods, types of goods moved, and origins and destination of goods. The model presented here enables (1) defining distinctions that may be employed for the assignment of node and link weights, (2) gaining an understanding of node and link criticality, and (3) providing methods for objectively maintaining and enhancing network performance. Such analyses can inform rail managers and executives in planning expansions, route or freight changes, or preparations for potential node or link failures. A case study of an aggregate U.S. freight rail network along with other example topologies is presented to demonstrate the use of selected network attributes and their influence on connectedness efficiency and the impacts of node and link failures on the overall transport efficiency.