Resilient Transportation Systems Research Articles

Resilient Transportation Systems: Strategies for Mitigating Climate Change Impacts and Enhancing Infrastructure Stability

The transportation sector plays a crucial role in both contributing to climate change and facing its impacts. As extreme weather events become more frequent and severe, transportation infrastructure is increasingly vulnerable to disruptions, posing significant challenges to mobility, safety, and economic stability. strategies for building resilient transportation systems capable of mitigating climate change impacts and enhancing infrastructure stability. Drawing on interdisciplinary research and real-world case studies, this paper explores a range of resilience-enhancing measures applicable to various modes of transportation, including roads, bridges, railways, ports, and airports. These measures encompass both physical infrastructure improvements and operational strategies aimed at reducing vulnerability, enhancing adaptability, and ensuring continuity of service in the face of climate-related hazards.

Extreme Weather Events: The Impact of Flooding on Transportation Network: Case Study of Buyukcekmece Basin

Transportation is a critical sector which is vulnerable to hazardous events like urban floods. Thedisruption in transportation infrastructure affects the whole community in terms of loss of life and property.A resilient transport system is fundamental for resilient communities, and considering the adverse impactsof climate change, it becomes even more important. The Büyükçekmece Basin is a densely populated andeconomically significant area that also includes regions of natural importance requiring preservation. Thus,a flood hazard can affect more people, cause greater monetary losses and have negative impact on theenvironment and nature.The purpose of this research is to present the disruption of road network in Büyükçekmece Basin due toflooding and create an analytical framework. Vulnerability of road systems to rainfall-induced floods isquantified by integrating different models. An integrated framework linking hydrological model andinundation model is established and the relation between flood depth and road network vulnerability isdetermined. Flood hazard maps are created for two different rainfall events with 1 in 50 year and 1 in 100-year return period and flooded road lengths are determined. The flood levels have shown that there are roadsegments that will face moderate and major flooding. In Büyükçekmece Basin where serious floods haveoccurred in the past, measures need to be taken regarding the road network for possible flood risks.

Sustainability of Resilient Transport System

This article represents an additional contribution to our research on transport resilience. Our project's purpose is to create a methodological procedure for transport resilience measurement. In our previous work, we identified four core areas of measurement. These areas are adaptability, absorption (robustness), human factors and recovery, and the fourth one – sustainability. As an objective of this paper, we are going to describe the relationship between this last area and transport resilience. Such knowledge is needed so that we can better understand what parts of sustainability can affect the level of resilience and how to approach the identification of resilience indicators within sustainability. Hence, we divided sustainability into four dimensions. In each of them, we explained what aim of transport sustainability might be achieved and what risk can be mitigated by various elements of the given sustainability dimension. We have included the definition of transport resilience and an explanation for all its partial aims in the introduction and second part of this paper.

Building resilience into smart mobility for urban cities: an emerging economy perspective

Population growth in urban cities has created transport problems such as traffic congestion, pollution, accidents, and infrastructure failures. These problems often lead to disruption of mobility, especially in emerging economies. The goal of any city is to have a resilient transport system that is free from frequent and severe disruptions. Resilience can be incorporated into smart mobility systems to create a robust, integrated and intelligent transport system. Therefore, this study sought to determine resilience factors required for a resilient smart mobility system in emerging economies. The study used interpretive structural modelling (ISM) to establish the causal interrelationships among the factors drawing from data from experts in smart cities, city transport and academia. It was found that the most critical factor to drive resilience is foresight – the ability to anticipate disruptions. Three other resilience factor categories identified were dynamism, certainty and optimisation, which all point to the importance of building dynamic capabilities. The key implication for practice highlights the role that human and artificial intelligence can play in anticipating natural, social and infrastructure disruptions. ISM offered a mechanism to determine the critical causal resilience factors to be prioritised amongst the competing demands on the limited resources of emerging economies. Apart from foresight, which was found to be a driving factor, the other resilience factors are equally strong linkage factors (influence each other), thus further revealing the complexities that decision-makers in urban cities of emerging economies constantly face. A research agenda for resilience in smart mobility in emerging economies was developed.

Metropolitan MaaS and DRT Schemes: Are They Paving the Way Towards a More Inclusive and Resilient Urban Environment?

Mobility-as-a-Service and Demand-Responsive-Transport schemes are promoting progressively a user-centered approach, made of modularity, flexibility and tailor-made travel experience, and pandemic emergency has furthermore enhanced this new way of thinking, thus representing an unprecedented occasion to develop a new paradigm for a more sustainable and resilient transport system, thus ensuring a greater level of social and territorial inclusion beyond traditional urban borders and outdated distinctions of targeted services for particular users' categories. This paper discusses the main features of MaaS and DRT schemes in order to assess if they could be able to cope with Universal Design principles and to improve metropolitan accessibility accordingly to the urgent request for social and territorial inclusion as sustainable development pre-requisites, made by different stakeholders in the international and European debate (see UNO SDGs or EU Cork Declaration 2.0), and re-launched by many national initiatives (SNAI for Italy, Espana Vacìa for Spain…). To re-think metropolitan mobility as a service that can be shaped accordingly to user's needs and to redefine transport supply as a complex puzzle made by different and complementary services could represent a unique opportunity to overcome one of traditional public transport dramatic problems: low mobility demand, whether it be due to sparsely populated areas or connected with specific demands of targeted population categories. Hence this paper recalls some of the recent DRT experiences already active in Genova Metropolitan Area -the so-called DRINBUS above all- along with the on-demand mobility strategy for Ligurian internal areas in order to discuss how this new user-centered approach is acting on the marginalization of remote territories and fragile user categories. The choice to develop a MaaS scheme could re-shape metropolitan mobility as a comprehensive and global mosaic made by multiple pieces, thus making more resilient the entire system thanks to its modularity and redundancy. This allows to make more sustainable "niche" services as well, according to the systemic nature of this mobility platform, thus opposing the present unsuccessful approach of creating ad-hoc options, focusing indeed on the user's request to travel from point A to point B, without the need to define him as urban resident, commuter, disabled or not, towards a greater social inclusion and territorial cohesion.

Estimating the influence of disruption on highway networks using GPS data

Incidents, such as natural disasters, public events, and holidays, often cause problems to highways, even paralyze the operation of the whole networks, leading to a serious threat to travel efficiency and safety of the public. To provide better transport management and plans for emergencies, it is important to quickly and accurately identify such incidents and estimate their disruptive effects on the networks. To this end, a novel approach has been proposed in this paper, which is based on the Bayesian theory and thrice-standard-error principle while utilizing vehicle GPS data. Two important indicators, including traffic flows and congestion indexes, along with their change ratios, are built to detect the incidents and evaluate the extent of the disruption. The specific disrupted and detour roads are further determined. The proposed method has been tested using two real-world events in China, and the potential and effectiveness of this technique are demonstrated. With more and more vehicles being equipped with GPS devices worldwide, the designed method can be easily transferable to other countries, paving a way for the adoption of the approach for a more spatial–temporal sensitive highway network disruption analysis method that supports the establishment of a more resilient transport system for emergencies.

Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities

The adoption of Unmanned Aerial Vehicles (UAVs) in numerous sectors is projected to grow exponentially in the future as technology advances and regulation evolves. One of the promising applications of UAVs is in transportation systems. As the current transportation system is moving towards Intelligent Transportation Systems (ITS), UAVs will play a significant role in the functioning of ITS. This paper presents a survey on the recent advances of UAVs and their roles in current and future transportation systems. Moreover, the emerging technologies of UAVs in the transportation section and the current research areas are summarized. From the discussion, the challenges and opportunities of integrating UAVs towards future ITS are highlighted. In addition, some of the potential research areas involving UAVs in future ITS are also identified. This study aims to lay a foundation for the development of future intelligent and resilient transportation systems.

Resilience of the Smart Transport System – Risks and Aims

In our previous research, me and my co-authors explained the Smart City and Safe City concepts and their common systems, with transport as one of them. As we continue with city systematization, our current focus is on defining resilience, its place in the systematization and deeper understanding of resilience within individual systems. In this article, I answered some of the questions with focus on the system of smart transport. What are the risks, threatening the resilience of transport system of the city? What are the aims when building resilient transport system? My answers to these questions, delivered for different levels of the system, are meant to set a base ground for further research of the topic, along with definitions of transport resilience, resilient city, and resilience as an ability itself. As an important part I consider differentiation between aims of resilience and of crisis management, as I encountered unclear, even replacing views on the topic.

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems.

Stream processing data decision model for higher environmental performance and resilience in sustainable logistics infrastructure

PurposeAs the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains.Design/methodology/approachA causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted.FindingsThe result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure.Originality/valueIn this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data.

Weather, travel mode choice, and impacts on subway ridership in Beijing

Abstract Understanding the impact of weather on travel behavior provides insight into building a reliable and resilient transport system. This study uses a survey on residents and subway ridership data from 2014 to 2018 in Beijing to explore the relationship between weather and travel behavior. The results indicate that extreme weather events can affect recreational travel greatly, reduce travel demand, and change travel modes as well. The respondents are inclined to choose subway or cars rather than buses and bicycles under inclement weather days. The analysis of subway ridership confirms that weekends’ trips are more sensitive to weather conditions. Monthly temperature change shows a bigger effect on ridership than daily temperature change. A one-degree increase in effective temperature increases ridership by about 0.5% on weekends, while heavy rain reduces ridership by about 8%. Wind speed and air pollution show significantly negative but small effects on ridership on weekends. Besides, there is a non-linear relationship between temperature and ridership on weekends. These findings suggest that subway is less vulnerable to inclement weather and can be complementary to other travel modes. However, prevention measures are necessary for the subway system to face threats from heavy rain.

Analysis of transit users’ waiting tolerance in response to unplanned service disruptions

Public transit not only provides an affordable, efficient, and green service but also plays a critical role in the development of resilient transportation systems in urban areas. Transit disruption as a common incident in transit service operation can severely affect the resiliency of the transportation system as well as users’ satisfaction. While it is of great interest to transportation authorities to understand passengers’ decision behavior during unplanned transit disruptions in order to implement efficacious recovery strategies, still little is known about users’ behavior in case of such incidents. The scarcity of available data is a major underlying factor for this gap. Utilizing a recently collected data of transit users in the Chicago metropolitan area, the current study investigates transit users’ waiting tolerance during unplanned service disruptions and disclose the factors that affect their behavior. A set of interval-censored accelerated failure time models using different survival distributions are developed, compared, and the factors influencing the survival functions of the waiting tolerance are identified. The results of the analysis reveal that, for instance, having experience of using ridesharing services decreases users’ waiting tolerance during a disruption. Further, built-environment attributes (such as the density of pedestrian-oriented links and transit service frequency), availability of alternative modes, transit service type, user’s attitudes, and trip characteristics turn to be significant in users’ decision behavior.

Road Planning and Route Alignment Selection Criteria in the Norwegian Context

This paper reveals the main factors that guide road alignment design process in Norway. The goal is to discover what constitutes the main priorities for road planners, how these priorities are ranked when it comes to alignment selection, and how they are related to guiding factors identified in official planning documents and government transport plans throughout the life cycle of a road. This is done through a comprehensive literature and data search, involving published academic research in the road alignment design field, and by exploring Norwegian road planning documents and guidelines. Examples from a recently implemented road project are also included as a way to illustrate alignment priorities in theory versus how alignment decisions are made in practice. Particular attention is paid to how key factors influence environmental and social dimensions and how much importance these dimensions are given in the overall decision-making process. The focus on the Norwegian case is relevant in that it will identify which knowledge gaps need to be filled based on actual practices in the Norwegian road sector. The results of this study found that the dominating factors in road planning and alignment selection are the user cost and the environmental and socio-economic as they are directly related to the main national transport strategy of developing a carbon-neutral and resilient transport system. These results can be used to reinforce and amplify existing road planning strategies and to understand where challenges for environmental and social responsible road planning and alignment selection are found.

Spatiotemporal Representation Learning for Driving Behavior Analysis: A Joint Perspective of Peer and Temporal Dependencies

Driving is a complex activity that requires multi-level skilled operations (e.g., acceleration, braking, and turning). Analyzing driving behaviors can help us assess driver performances, improve traffic safety, and, ultimately, promote the development of intelligent and resilient transportation systems. While some efforts have been made for analyzing driving behaviors, existing methods can be improved via representation learning by jointly exploring the peer and temporal dependencies of driving behaviors. To that end, in this paper, we develop a P eer and T emporal- A ware R epresentation L earning based framework (PTARL) for driving behavior analysis with GPS trajectory data. Specifically, we first detect the driving operations and states of each driver from their GPS traces. Then, we derive a sequence of multi-view driving state transition graphs from the driving state sequences, in order to characterize a driver's driving behaviors that vary over time. In addition, we develop a peer and temporal-aware representation learning method to learn a sequence of time-varying yet relational vectorized representations from the driving state transition graphs. The proposed method can simultaneously model both the graph-graph peer dependency and the current-past temporal dependency in a unified optimization framework. Also, we provide two effective solutions for the optimization problem: (i) a joint optimization solution of representation learning and prediction; and (ii) a step-by-step solution of representation learning and prediction. Besides, we explore two strategies to fuse the learned representations from multi-view transition graphs: (i) simple alignment and (ii) collective fusion. Moreover, we apply the developed framework to the two applications of quantitative transportation safety: (i) scoring of driving performances, and (ii) detection of dangerous regions. Finally, we present extensive experimental results with big trajectory data to demonstrate the enhanced performances of the proposed method for quantitative transportation safety.

Resilient transport systems to reduce urban vulnerability to floods in emerging-coastal cities: A case study of Ho Chi Minh City, Vietnam

The role of transportation is becoming ever more important as cities are spatially expanded along with urban development, yet highly agglomerated by complex activities over their geographic territory. Flooding incidents in cities across the world have exposed the vulnerability of transport networks which can result in significant, lasting disruption. This appears to be overlooked in emerging-coastal cities, especially prone to flooding as plans for transport development, are ultimately driven by economic drivers resulting in uncontrolled urbanisation. By using a combined method of hydrological flood modeling and GIS analysis, this paper demonstrates the increasing vulnerability of the transport system in Ho Chi Minh City based on current plans for transport development. The paper highlights the need to consider a new approach to transport planning and advocates the application of a Resilient Transport System (RTS) which can be integrated into potential revisions of city master plans to help Ho Chi Minh City (HCMC) shift from resistance to resilience to extreme floods. The focal point of the concept is the spatial transfer of transport flows based on a pre-organised structural and flexible transport system constituted by links, nodes and relevant services in terms of different levels of elevation and locations. The intrinsic value of its application to the transport plan is that a city can retain a certain capacity (probably on a smaller scale) of its transport system in order to mitigate potential impacts on urban activities resulting from flood disruptions.

On an integrated approach to resilient transportation systems in emergency situations

In this paper we present an integrated approach to the evacuation problem under an emergency situation for transportation systems. The approach is based on a view that a service system has two subsystems: infrastructure and substance. The approach attempts to integrate infrastructure design and substance flow planning to improve the evacuation performance. Without loss of generality, we restrict infrastructure design to reconstruction of a damaged road with two attributes of the road: capacity and travel time, we restrict substance flow planning to the contraflow method, and we consider the evacuation problem with single source and single destination. Further, we apply the discrete variable Particle Swarm Optimization and RelaxIV to solve the problem model. The overall objective function in the problem model is a minimum transportation time. Since recovery of a damaged transportation (damaged road in this case) is implied in our problem, the proposed approach has some significant implication to resilience engineering of a service system as well. An example is studied to show the effectiveness of our approach; in particular it is shown that an integrated solution is significantly better than the solution with only the contraflow method.

Transportation System Resilience

The research and the practice associated with the development of resilient transportation systems have, to date, focused largely on the characterization and reduction of the vulnerability of transportation systems and the impacts of disruptive events on system users. Emerging evidence, however, suggests that a move from a technical focus to a sociotechnical focus has the potential to enhance the resilience of built systems and the societies that they serve. This paper first reviews the literature on the resilience of the transportation system to characterize the nature of approaches to the development of resilience in the transportation system. The paper offers examples of cases that provide evidence of the added benefits associated with a move from a largely technical approach to a sociotechnical approach. Subsequently, the paper reviews the literature on resilience in social, ecological, and economic systems to identify opportunities for expanding the scope of existing approaches to develop resilience in the transportation system and offer operational pathways forward. The review findings indicate that the following concepts may be applied to enhance the development of resilience in the transportation system: adaptive behavior and multiple equilibrium states from resilience in ecological systems; vulnerability, adaptive capacity, and participative capacity from resilience in social systems; a multiscale approach to resilience; and static and dynamic resilience in economic systems. Overall systematic efforts toward the development of institutional, organizational, social, and financial capital with influences on the resilience of the transportation system would also enhance the development of resilience in the transportation system.

Research-informed design, management and maintenance of infrastructure slopes: development of a multi-scalar approach

The UK's transport infrastructure is one of the most heavily used in the world. The performance of these networks is critically dependent on the performance of cutting and embankment slopes which make up £20B of the £60B asset value of major highway infrastructure alone. The rail network in particular is also one of the oldest in the world: many of these slopes are suffering high incidents of instability (increasing with time). This paper describes the development of a fundamental understanding of earthwork material and system behaviour, through the systematic integration of research across a range of spatial and temporal scales. Spatially these range from microscopic studies of soil fabric, through elemental materials behaviour to whole slope modelling and monitoring and scaling up to transport networks. Temporally, historical and current weather event sequences are being used to understand and model soil deterioration processes, and climate change scenarios to examine their potential effects on slope performance in futures up to and including the 2080s. The outputs of this research are being mapped onto the different spatial and temporal scales of infrastructure slope asset management to inform the design of new slopes through to changing the way in which investment is made into aging assets. The aim ultimately is to help create a more reliable, cost effective, safer and more resilient transport system.

Toward simulating entire cities with behavioral models of traffic

Resilient transportation systems enable quick evacuation, rescue, distribution of relief supplies, and other activities for reducing the impact of natural disasters and for accelerating the recovery from them. The resilience of a transportation system largely relies on the decisions made during a natural disaster. We developed an agent-based traffic simulator for predicting the results of potential actions taken with respect to the transportation system to quickly make appropriate decisions. For realistic simulation, we govern the behavior of individual drivers of vehicles with foundational principles learned from probe-car data. For example, we used the probe-car data to estimate the personality of individual drivers of vehicles in selecting their routes, taking into account various metrics of routes such as travel time, travel distance, and the number of turns. This behavioral model, which was constructed from actual data, constitutes a special feature of our simulator. We built this simulator using the X10 language, which enables massively parallel execution for simulating traffic in a large metropolitan area. We report the use cases of the simulator in three major cities in the context of disaster recovery and resilient transportation.

Resiliency of Transportation Network of Santo Domingo, Dominican Republic

Every day dependence on transportation grows as local, regional, national, and international independence increases. Resilient transportation systems are needed to secure the highest possible level of service during disruptive events, including natural disasters and those caused by humans. To prepare for these events, decision makers need guidance to determine what investments are likely to improve the resiliency of their networks, which are often hampered by limited resources. To date, such guidance has been primarily qualitative. This paper presents a methodology to quantify resiliency, under preevent conditions, by use of a fuzzy inference approach. This methodology expands on previous work by the authors and others, by refining the definitions of key variables, adjusting model interactions, and increasing transparency between metrics. The paper includes a case study in which the methodology is applied to a disruptive event in Santo Domingo, Dominican Republic. The case study illustrates the methodology's ability to (a) evaluate the extent to which the Dominican Republic's transportation network is prepared for a disruptive event, (b) help select investments that have the potential to increase the resiliency of the network, and (c) provide outputs that will support a variety of current economic analysis strategies, allow comparison of different investment scenarios, and facilitate decision making. The paper concludes with a sensitivity analysis that shows the effects of alternative investments on the network.