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Climate change is inevitable and intensifying. The consequences are particularly severe for urban areas, which are becoming increasingly populated. This has resulted in the necessity to analyze the effects of climate change on the functioning of urban areas and build and plan strategies for strengthening the resilience of cities and their infrastructures and for predicting climate change and the threats associated with it. This study proposes a multi-criteria model for analyzing and assessing the risk arising from climate change to urban areas by determining the probability of the occurrence of various threats and their potential consequences for urbanization. The model takes into account the exposure and vulnerability of assets, systems, infrastructure, and communities to the significant consequences of climate change and the occurrence of hazardous events. Bayesian probability theory was proposed to predict the probability of hazardous event occurrence, taking into account climate change and the statistical uncertainty in estimating extreme hazard impacts. The proposed model allows us to include vulnerability drivers and resilience factors and their effect on the functioning of a city and its critical infrastructures and, consequently, the lives and well-being of residents. The model can be applied to risk management and planning strategies for urban resilience strengthening. [ABSTRACT FROM AUTHOR]

Common difficulties across industries are discovered in data management, where handling the volume, variety, and quality of data is crucial for informed decisions in uncertain environments. In this context, rail management must navigate complex decision‐making to ensure safety, service continuity, and cost‐effectiveness. The 2020 Stonehaven derailment is an example of the increasing vulnerability of rail infrastructure to environmental factors and systemic failures. It emphasizes the need for resilient systems, proficient at preventative maintenance and adaptable to escalating challenges. These matters further accentuate the need for context‐dependent strategies that bridge theoretical insights and practical applications. This scoping review explores strategies for decision‐making under uncertainty across sectors such as civil infrastructure, agriculture, water management, and emergency response. It unfolds a selection of procedures addressing the impacts of extreme weather and other unexpected disruptions. It also sets a foundation for future research to support rail infrastructure adaptation to climate change by advocating the use of cybernetic principles and artificial intelligence (AI) to enhance decision‐making processes. Cybernetics enables collaborative human‐AI methods, improving adaptability and resilience. However, balancing and incorporating diverse stakeholder viewpoints into decision chains remains difficult. While promising, substantial research and system improvements are needed to fully harness the potential of AI. [ABSTRACT FROM AUTHOR]

Urban agglomerations are pivotal to industrial co-agglomeration, underscoring the significance of efficient road networks and economic growth. This study examines the spatial spillover effects of infrastructure resilience on industrial co-agglomeration at varying mobility levels in the Guanzhong Plain, China, utilizing origin–destination (OD) and traffic flow networks in highways. Guanzhong contributes 88% of the GDP and covers 25% of land, and its prime location was the initial point of the ancient Silk Road in China. Our analysis yields several novel insights. Industrial co-agglomeration displays negative (from −0.175 to −0.207) spatial autocorrelation among neighboring regions, indicating pronounced regional competition. In the OD network, both connectivity and efficiency resilience positively influence (0.189 and 0.397) local industrial co-agglomeration but adversely affect connected regions (−0.383 and –0.915), with the impact of efficiency resilience increasing at higher mobility levels. The highway network intensifies spatial spillover effects and exacerbates competition and disparities in industrial co-agglomeration across counties. The network resilience of highways exhibits distinct spatial distribution patterns, with critical nodes concentrated along the central economic axis of the urban agglomeration. Furthermore, the influence of highway network resilience on industrial co-agglomeration varies between OD and traffic flow networks, which implies different impacts under some mobility scenarios. These findings advance our understanding of the intricate relationship between road networks and industrial co-agglomeration, offering valuable insights for crafting balanced regional development strategies and informing transportation planning to foster local and regional coordination. [ABSTRACT FROM AUTHOR]

Purpose: Over the next decade, humanity is going to face big environmental problems, and considering these serious issues, businesses are adopting environmentally responsible practices. To put forward specific measures to achieve a more prosperous environmental future, this study aims to develop an environment-based perspective framework by integrating the Internet of Things (IoT) technology into a sustainable automotive supply chain (SASC). Design/methodology/approach: The study presents a conceptual environmental framework – based on 29 factors constituting four stakeholders' rectifications – that holistically assess the SASC operations as part of the ReSOLVE model utilizing IoT. Then, experts from the SASC, IoT and sustainability areas participated in two rigorous rounds of a Delphi study to validate the framework. Findings: The results indicate that the conceptual environmental framework proposed would help companies enhance the connectivity between major IoT tools in SASC, which would help develop congruent strategies for inducing sustainable growth. Originality/value: This study adds value to existing knowledge on SASC sustainability and digitalization in the context where the SASC is under enormous pressure, competitiveness and increased variability. [ABSTRACT FROM AUTHOR]

Targeted contingency measures have proven highly effective at reducing the duration and harm caused by incidents. This study utilized the Classification and Regression Trees (CART) data mining technique to predict and quantify the duration of incidents. To achieve this, multisensor data collected from the Hangzhou freeway in China spanning from 2019 to 2021 was utilized to construct a regression tree with eight levels and 14 leaf nodes. By extracting 14 rules from the tree and establishing contingency measures based on these rules, accurate incident assessment and effective implementation of post-incident emergency plans were achieved. In addition, to more accurately apply the research findings to actual incidents, the CART method was compared with XGBoost, Random Forest (RF), and AFT (accelerated failure time) models. The results indicated that the prediction accuracy of the CART model is better than the other three models. Furthermore, the CART method has strong interpretability. Interactions between explanatory variables, up to seven, are captured in the CART method, rather than merely analyzing the effect of individual variables on the incident duration, aligning more closely with actual incidents. This study has important practical implications for advancing the engineering application of machine learning methods and the analysis of sensor data. [ABSTRACT FROM AUTHOR]

The role of green mobility as a low-carbon lifestyle in carbon reduction and sustainable development cannot be ignored. The digital economy effectively promotes green mobility for sustainable energy use in the broader setting of the significant data era and sustainable development. This study utilizes the panel data of 264 cities in China from 2011 to 2021 to construct a two-way fixed-effects regression model to analyze the impact of the digital economy on residents' green mobility and the indirect impact mechanism of the two policy tools, a low-carbon transportation pilot and carbon emissions trading, from theoretical and empirical aspects. The results show that digital economic development helps promote residents' green mobility. In addition, the implementation of low-carbon transportation pilots and carbon trading policies has strengthened the role of the digital economy in promoting green mobility. The findings remain after introducing robustness tests such as "smart city" pilots as exogenous shock policies. A heterogeneity study suggests that the effect of the digital economy on green mobility for residents is more significant in economically developed and human capital-rich areas. This study contributes to the literature by providing empirical evidence on the role of the digital economy in promoting sustainable urban transportation and by demonstrating the moderating effects of policy instruments, thereby offering practical insights for policymakers aiming to reduce urban pollution and enhance sustainable development. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) have the potential to revolutionize transportation by offering safer, more efficient, and convenient mobility solutions. As AV technology advances, there is a growing need to understand and model traffic dynamics in environments where AVs interact with human-driven vehicles. This review provides a comprehensive overview of the modeling techniques used to simulate and analyze autonomous vehicle traffic. It covers the fundamental principles of AVs, key factors influencing traffic dynamics, various modeling approaches, their applications, challenges, and future directions in AV traffic modeling. [ABSTRACT FROM AUTHOR]

The platform–train interface (PTI) is one of the most complex spaces in metro stations. At the PTI, the interaction of boarding and alighting causes a high number of passengers to be present, affecting the boarding and alighting time, among other variables related to safety and efficiency. This research was performed to develop a multidimensional level of service for passengers by means of observing existing stations. The methodology included five dimensions of the level of service (passenger behaviour, train design, passenger characteristics, train operation, and platform built environment), and detection techniques based on a tracking system. The stations are operating urban services in Valparaiso Metro (Chile). The results show that there is a relationship between the speed of passengers and the detected passengers, among other variables. In addition, the dispersion of passengers is analysed at the PTI. Further research will include other types of stations and conditions. [ABSTRACT FROM AUTHOR]

This study investigated the interplay between exit selection models and local pedestrian movement patterns within floor field frameworks. Specifically, this investigation analysed the performance of a multinomial logit exit choice model, incorporating both expected utility theory and cumulative prospect theory frameworks when coupled with three distinct local-level pedestrian movement models (FF-Von Neumann, FF-Moore, and NSFF). The expected utility theory framework considers the deterministic component as a linear relationship, while the cumulative prospect theory framework further considers the decision-maker's risky attitudes by transforming objective terms into subjective terms using a power value function. The core objective was to comprehend how local movement dynamics, as represented by the floor field models, influence decision-making during exit selection. Comparative analyses revealed intriguing variations between the three local models, despite their shared expected utility theory-based exit choice framework. These discrepancies stemmed from the diverse pedestrian trajectory behaviours generated by each model. Consequently, these local dynamics impacted the decision-maker's assessment of critical factors, such as the number of evacuees close to the decision-maker ( N C D M ) and the number of evacuees close to an exit ( N C E ), which the exit choice model incorporates. These assessments, in turn, significantly affected higher-level decision-making. The integration of the three models with the multinomial logit exit choice model, using either cumulative prospect theory and expected utility theory frameworks, further strengthened the observed bilateral relationship. While the specific nature of this relationship varied depending on the chosen framework and its implementation details, these consistent findings demonstrate the robustness of the results. This reinforced the influence of local-level pedestrian dynamics on higher-level exit selection, highlighting the importance of accurate crowd dynamics modelling, especially when advanced exit choice models consider local movement factors. [ABSTRACT FROM AUTHOR]

Urban day-to-day travel systems generally exist in various types of cities. Their modeling is difficult due to the uncertainty of individual travelers in micro travel decision-making. Moreover, with the advent of the information age, intelligent connected vehicles, smartphones, and other types of intelligent terminals have placed urban day-to-day travel systems in an information environment. In such an environment, the travel decision-making processes of travelers are significantly affected, making it even more difficult to give theoretical explanations for urban day-to-day travel systems. Considering that analyzing urban day-to-day travel patterns in an information environment is of great significance for governing the constantly developing and changing urban travel system and, thus, of great importance for the sustainable development of cities, this paper gives a systematic review of the theoretical research on urban day-to-day travel and its development in an information environment over the past few decades. More specifically, the basic explanation of an information environment for urban day-to-day travel is given first; subsequently, the theoretical development of micro decision-making related to individual day-to-day travelers in an information environment is discussed, and the theoretical development related to changes in urban macro traffic flow, which can be recognized as the aggregation effect formed by individual micro decision-making, is also discussed; in addition, the development of understanding different types of traffic information that travelers may obtain in an information environment is discussed; finally, some important open issues related to the deep impact of information environment on urban day-to-day travel systems that require further research are presented. These valuable research directions include using information methods to fit day-to-day travel patterns of cities and implementing macro and micro integrated modeling for urban day-to-day travel systems based on complex system dynamics and even quantum mechanics. [ABSTRACT FROM AUTHOR]

Research Problem: Families with children travel by car more frequently than any other household type and hence significantly contribute to transport externalities. Lift-sharing is a potential time-effective and convenient means of mitigating these effects. Whilst some research has been conducted on lift-sharing for the school run, there is little research beyond this context, particularly around lift-sharing for children's activities (e.g., sport). Study Aim: Consequently, the aim of this study was to assess the current prevalence of lift-sharing (for children's activities and other types of trips) in families with young children, the factors influencing its uptake, the experiences and attitudes of regular lift-sharers, and whether previous literature findings on reciprocity applied in this context to gain a deeper understanding of how and why families participate in activity lift-sharing. Research Design: A mixed-methods approach was applied, comprising (1) a travel survey of 474 families to establish socio-demographic and activity factors that influence lift-share prevalence for activity trips; and (2) 15 semi-structured interviews with parents to further explore how and why families participate in activity lift-sharing. Results: Factors influencing lift-sharing decisions for activity travel were number of cars owned, number of seats in the car, settlement type, income, time of day and location of the activity, number of children attending, parking availability, whether the activity is a sporting activity or not, and number of close friends of the child at the same activity. Salient motivating factors triggering lift-sharing included intentions to reduce chauffeuring and parents wanting their children to socialise. Trust was an initial imperative component of lift-share formation, and attitudes towards reciprocity supported previous literature findings relating to the variability of acceptable reciprocation and the role of fairness. [ABSTRACT FROM AUTHOR]

Currently, there is a growing consensus that the use of more sustainable urban freight transportation has the potential to offer major energy and efficiency benefits which can be achieved through the appropriate combination of cargo bikes and the integration of drones into the urban logistics system. This study presents the results of a stepwise regression analysis that examines the role, benefits, and impact of electric vehicles such as e-bikes, cargo bikes, and drones for intermodal freight transportation in five Greek cities to support the green logistics paradigm. The results show that between routes of almost equal length, the most bicycle-friendly routes, such as routes along pedestrian areas or residential streets, should be avoided, as they reduce delivery speed and increase delivery costs in terms of energy and time expenditure. In addition, priority roads, which usually have higher traffic speeds and more trucks, are preferred by cyclists due to the quality of the road surface, even though the feeling of safety is lower on such roads. Finally, regarding drones, energy consumption is relatively low in the 0–5 mph and 6–10 mph wind speed ranges, indicating efficient energy use. In the 11–15 mph and 16–20 mph wind speed ranges, energy efficiency is significantly lower than the battery capacity, indicating that the cargo drone exhibits excessive energy consumption under these conditions. [ABSTRACT FROM AUTHOR]

Purpose: This paper examines what facilitates the swift reconfiguration of freight movements across transport modes in the wake of a major disaster. Design/methodology/approach: A qualitative research approach focussing on the New Zealand (NZ) domestic freight transport operations in the wake of the 2016 Kaikōura earthquake is used with data collected through 19 interviews with 27 informants. The interviews are thematically analysed by using the framework method. Findings: The paper provides rich and detailed descriptions of the ability of a freight transport system to recover from a disaster through rapid modal shifts. This paper identifies nine factors enabling modular transport operations and highlights the critical role of physical, digital, operational and inter-organisational interconnectivity in the aftermath of a disaster. Originality/value: Although the management of freight disruptions has become a prevalent topic not only in industry and policy-making circles, but also in the academic literature, qualitative research focussing on the ability of commercial freight systems to adapt and recover from a disaster through rapid modal shifts is limited. This qualitative study sheds light on the mechanisms underlying the continuity of freight operations in the wake of a disaster and provides a comprehensive understanding of modular transport operations and the ability of freight systems to keep goods moving. [ABSTRACT FROM AUTHOR]

Optimal planning of Maintenance and Rehabilitation (M&R) treatments due to limited resources and funding is vital. A pavement performance function formula is needed to model the M&R optimisation problem. By considering important features, a pavement deterioration function was developed with an R2 of 0.897. The essence of pavement deterioration is uncertain. Hence, disregarding the uncertainty may result in sub-optimal solutions. Therefore, two approaches were employed to optimise M&R treatments in a large-scale pavement network, including deterministic and risk-based. Both approaches aimed to minimise the required budget in the planning period constrained to acceptable pavement condition. A novel and robust metaheuristic algorithm was adjusted to solve the M&R planning problem deterministically. In the risk-based method, appropriate probability distribution functions were fitted to historical data of uncertain parameters. Monte Carlo simulation was performed to generate probability distributions of the required budget and condition of pavement sections. By using a risk-based strategy instead of the deterministic one, the required budget was reduced by 30%. Fault tree analysis was performed on the solutions obtained from the risk-based method to increase the pavement network's reliability. The results of the mentioned analysis represented that only by spending 2.5% more budget network's reliability could be significantly increased. [ABSTRACT FROM AUTHOR]

Developing an accurate pavement prediction model plays a dominant role in pavement M&R optimization. Despite employing different robust machine learning techniques to predict pavement conditions, these methods have some weaknesses in synchronising with exact optimization algorithms. The main contribution of this study is to propose a novel method for optimizing the pavement M&R plan with high accuracy. Contrary to conventional approaches, a robust prediction algorithm, Random Forest Regression (RFR), is applied to predict the pavement International Roughness Index (IRI). In addition, Multiple Linear Regression (MLR) is employed to assess the performance of the proposed technique in terms of IRI prediction accuracy. Whale Optimization Algorithm (WOA), as a powerful metaheuristic optimization algorithm, is utilised to obtain the optimal solution to the pavement M&R optimization problem. RFR is run as an internal part of the WOA in the introduced method. Furthermore, Genetic Algorithm (GA) is used to examine the performance of the proposed approach in finding the optimal solution. The RFR results conclude a more accurate prediction of IRI than MLR based on all machine learning performance indicators. Furthermore, the newly developed hybrid model significantly outperforms GA in finding the optimal and cost-effective solution to the M&R optimization problem. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to demonstrate the potential of the EXODUS evacuation model in building environments. The latest PC/workstation version of EXODUS is described and is also applied to a large hypothetical supermarket/restaurant complex measuring 50 m x 40 m. A range of scenarios is presented where population characteristics (such as size, individual travel speeds, and individual response times), and enclosure configuration characteristics (such as number of exits, size of exits, and opening times of exits) are varied.The results demonstrate a wide range of occupant behavior including overtaking, queuing, redirection, and conflict avoidance. Evacuation performance is measured by a number of model predicted parameters including individual exit flow rates, overall evacuation flow rates, total evacuation time, average evacuation time per occupant, average travel distance, and average wait time.The simulations highlight the profound impact that variations in individual travel speeds and occupant response times have in determining the overall evacuation performance.1. Jin, T., and Yamada T., "Experimental Study of Human Behavior in Smoke Filled Corridors," Proceedings of The Second International Symposium on Fire Safety Science, 1988, pp. 511-519.2. Galea, E.R., and Galparsoro, J.M.P., "EXODUS: An Evacuation Model for Mass Transport Vehicles," UK CAA Paper 93006 ISBN 086039 543X, CAA London, 1993.3. Galea, E.R., and Galparsoro, J.M.P., "A Computer Based Simulation Model for the Prediction of Evacuation from Mass Transport Vehicles," Fire Safety Journal, Vol. 22, 1994, pp. 341-366.4. Galea, E.R., Owen, M., and Lawrence, P., "Computer Modeling of Human Be havior in Aircraft Fire Accidents," to appear in the Proceedings of Combus tion Toxicology Symposium, CAMI, Oklahoma City, OK, 1995.5. Kisko, T.M. and Francis, R.L., "EVACNET+: A Computer Program to Determine Optimal Building Evacuation Plans," Fire Safety Journal, Vol. 9, 1985, pp. 211-220.6. Levin, B., "EXITT, A Simulation Model of Occupant Decisions and Actions in Residential Fires," Proceedings of The Second International Symposium on Fire Safety Science, 1988, pp. 561-570.7. Fahy, R.F., "EXIT89: An Evacuation Model for High-Rise Buildings," Pro ceedings of The Third International Sym posium on Fire Safety Science, 1991, pp. 815-823.8. Thompson, P.A., and Marchant, E.W., "A Computer Model for the Evacuation of Large Building Populations," Fire Safety Journal, Vol. 24, 1995, pp. 131-148.9. Still, K., "New Computer System Can Predict Human Behavior Response to Building Fires," FIRE 84, 1993, pp. 40-41.10. Ketchell, N., Cole, S.S., Webber, D.M., et.al., "The Egress Code for Human Move ment and Behavior in Emergency Evacu ations," Engineering for Crowd Safety (Smith, R.A., and Dickie, J.F., Eds.), Elsevier, 1993, pp. 361-370.11. Takahashi, K., Tanaka, T. and Kose, S., "An Evacuation Model for Use in Fire Safety Design of Buildings," Proceedings of The Second International Symposium on Fire Safety Science, 1988, pp. 551- 560.12. G2 Reference Manual, Version 3.0, Gensym Corporation, Cambridge, MA.13. XVT Reference Manual, Version 3.0 XVT Software Inc., Boulder, CO.14. Galea, E.R., "On the Field Modeling Approach to the Simulation of Enclosure Fires, Journal of Fire Protection Engineering, Vol. 1, No. 1, 1989, pp. 11-22.15. Purser, D.A., "Toxicity Assessment of Combustion Products," SFPE Handbook of Fire Protection Engineering, National Fire Protection Association, Quincy, MA, pp. 1-200 - 1-245, 1988.16. Hankin, B.D., and Wright, R.A., "Pas senger Flows in Subways," Operational Research Quarterly, Vol. 9, 1958, pp. 81-88.17. HMSO, The Building Regulations 1991 - Approved Document B, section B 1 (1992 edition), HMSO publications, London, pp. 9-40.18. Polus A., Schofer, J.L., and Ushpiz, A., "Pedestrian Flow and Level of Service," Journal of Transportation Engineering, Vol. 109, 1983, pp. 46-47.19. Muir, H., Marrison, C., and Evans, A., "Aircraft Evacuations: the Effect of ... [ABSTRACT FROM PUBLISHER]

Studites on traffic management is crucial for the development of intelligent transportation systems and smart cities. However, identifying the development stages of traffic management field based on bibliometric analysis is still lacking. In this study, CiteSpace and VOSviewer software are used to explore "traffic management" field by summarizing development process and predicting future research trend. A total of 3,028 relevant documents over the past 40 years were collected from Web of Science. Results show that (1) studies on traffic management were mainly published by researchers from USA (30.55%), China (20.90%), and some European countries; (2) the key traffic management research contents can be classified into four categories, that is, background requirements, traffic problems, method models, and control strategies; (3) the evolution process can be divided into four stages, that is, budding stage (1990–1994), development stage (1995–2003), calm stage (2004–2010), and maturation stage (2011–); (4) machine learning, deep learning and other intelligent algorithms have played more important roles in recent years, and connected vehicle management is also a potential development trend. Results suggest that cooperative vehicle‐infrastructure systems or machine learning‐based studies might be the hotspots on traffic management studies. [ABSTRACT FROM AUTHOR]

This paper examines the challenges associated with the efficient planning and operation of an E-grocery delivery system using Autonomous Delivery Robots (ADR) during unforeseen events. The primary objective is to minimize unfulfilled customer demands rather than focusing solely on cost reduction, considering the humanitarian aspect. To address this, a two-echelon vehicle routing problem is formulated, taking into account stochastic service times and demands. Two models, namely a deterministic model and a chance-constraint model, are employed to solve this problem. The results demonstrate that the chance-constraint model significantly reduces unmet demands compared to the deterministic model, particularly when the delivery deadline has a broad time window and the ADR/van speed ratio is moderate. [ABSTRACT FROM AUTHOR]

In traffic flow, the relationship between speed and density exhibits decreasing monotonicity and continuity, which is characterized by various models such as the Greenshields and Greenberg models. However, some existing models, i.e., the Underwood and Northwestern models, introduce bias by incorrectly utilizing linear regression for parameter calibration. Furthermore, the lower bound of the fitting errors for all these models remains unknown. To address above issues, this study first proves the bias associated with using linear regression in handling the Underwood and Northwestern models and corrects it, resulting in a significantly lower mean squared error (MSE). Second, a quadratic programming model is developed to obtain the lower bound of the MSE for these existing models. The relative gaps between the MSEs of existing models and the lower bound indicate that the existing models still have a lot of potential for improvement. [ABSTRACT FROM AUTHOR]

Computer simulations of traffic and driving provide essential solutions to reduce risk and cost in traffic-related studies and research. Through nearly 90 years of simulation development, many research projects have attempted to improve the various aspects of realism through the use of traffic theory, cameras, eye-tracking devices, sensors, etc. However, the previous studies still present limitations, such as not being able to simulate mixed and chaotic traffic flows, as well as limited integration/interoperability with 3D driving simulators. Thus, instead of reusing previous traffic simulators, in this paper, we define relevant concepts and describe the development and testing of a novel traffic generator. First, we introduce realistic aspects to improve traffic generation, including interactive physics (i.e., interactions based on physics among the vehicles, infrastructure, and weather) and natural traffic behaviors (e.g., road user behaviors and traffic rules), allowing the self-driving vehicle behaviors to mimic human behaviors under stochastic factors such as random vehicles and speed. Second, we gain experiences from the technical deficiencies of existing systems. Third, we propose methods for traffic generation based on the action point angle of sight (APAS) formula, which adheres to these constraints and is interoperable with modern driving simulators. We also conducted quantitative evaluations in two experiments (comprising 250 trials), in order to prove that the proposed solution can effectively simulate mixed traffic flows. Moreover, the approaches presented in this study can help self-driving cars to find their way at an intersection/T-junction, as well as allowing them to steer automatically after an accident occurs. The results indicate that traffic generation algorithms based on these new traffic theories can be effectively implemented and used in modern driving simulators and multi-driving simulators, outperforming previous traffic generators based on repurposed technologies. [ABSTRACT FROM AUTHOR]

This article concerns the identification of connections between the energy intensity of rail transport and spatial integration (convergence processes in the EU and spatial proximity) and the economic situation of countries that are in international export–import trade relations. Three research questions are formulated in the article: (1) Does the improvement of the economic situation of the importing country reduce the energy intensity of the Polish export of goods by rail transport to the partner country? (2) Does EU integration contribute to reducing the energy intensity of the export of goods by rail transport? (3) Is there a positive spatial effect (convergence) of reducing the energy intensity of the export of goods by rail transport? This study concerned 14 geographical destinations (export destinations from Poland) during the years 2010–2020. A comprehensive study of convergence was used by building a spatial (club) and type β convergence model and using an unconventional method (panel stationarity analysis). It has been verified that there is a convergence in the energy intensity of goods exportation by rail transport in EU countries. The rate of spatial (club) convergence is 8.66% per year, while the period after which this convergence will be halfway between the initial state and the long-term equilibrium point is 8 years. [ABSTRACT FROM AUTHOR]

The demand-responsive public transport system with multi-vehicles has the potential to efficiently meet real-time and high-volume transportation needs through effective scheduling. This paper focuses on studying the real-time vehicle scheduling problem, which involves dispatching and controlling different model vehicles uniformly based on generated vehicle number tasks at a given point in time. By considering the immediacy of real-time itinerary tasks, this paper optimizes the vehicle scheduling problem at a single time point. The objective function is to minimize the total operating cost of the system while satisfying constraints such as passenger capacity and vehicle transfer time. To achieve this, a vehicle scheduling optimization model is constructed, and a solution approach is proposed by integrating bipartite graph optimal matching theory and the Kuhn–Munkres algorithm. The effectiveness of the proposed approach is demonstrated by comparing it with a traditional greedy algorithm using the same calculation example. The results show that the optimization method has higher solution efficiency and can generate a scheduling scheme that effectively reduces operating costs, improves transportation efficiency, and optimizes the operation organization process for demand-responsive buses. [ABSTRACT FROM AUTHOR]

Capacity drop (CD) at overloaded bottlenecks is a puzzling traffic flow phenomenon with some internal and complicated mechanisms at the microscopic level. Capacity drop is not only important for traffic flow theory and modelling, but also significant for traffic control. A traffic model evaluating traffic control measures needs to be able to reproduce capacity drop in order to deliver reliable evaluation results. This paper delivers a comprehensive overview on the subject from the behavioral mechanism perspective, as well as from microscopic and macroscopic simulation points of view. The paper also conducts comparable studies to replicate capacity drop at freeway ramp merges from both macroscopic and microscopic perspectives. Firstly, the subject is studied using the macroscopic traffic flow model METANET with respect to ramp merging scenarios with and without ramp metering. Secondly, one major weakness of commercial microscopic traffic simulation tools in creating capacity drop at ramp merges is identified and a forced lane changing model for ramp-merging vehicles is studied and incorporated into the commercial traffic simulation tool AIMSUN. The extended AIMSUN carefully calibrated against real data is then examined for its capability of reproducing capacity drop in a complicated traffic scenario with merging bottlenecks. The obtained results demonstrate that reproducible capacity drop can be delivered for the targeted bottlenecks using both macroscopic and microscopic simulation tools. [ABSTRACT FROM AUTHOR]

COVID-19 is a major global crisis affecter, changing global norms and societal behavioral models. Many companies have faced existential crises, but on the other hand, businesses that were and are helping others to boost digitalization, ICT and software solutions deployment, remote communications integration, e-commerce & e-services, and so on, have boosted their businesses, as people shifted online during the global lockdown and international travel restrictions. Our work explores the trend of e-commerce and e-services utilization during the ease of restrictions and the social distancing period to forecast the trend continuation patterns after the pandemic. An online survey was conducted and targeted individuals in Saudi Arabia and Egypt, resulting in 155 participants. The data were analyzed from four perspectives: demographics, COVID-19 health impact, trend analysis, and regression analysis. The results indicate heavy utilization of e-commerce and e-services during the global movement restrictions and travel bans. This trend has, however, significantly reduced during the ease of restrictions and social distancing period. Utilizing e-commerce and e-services in Saudi Arabia and Egypt, based on the research data, is positively correlated to the outbreak conditions. On the other hand, current data still does not give clear indications, and this pattern is going to be mostly, partly, or not at all permanent now as societies are returning to mostly a free movement of people and marginally restricted social distancing times. [ABSTRACT FROM AUTHOR]

It is anticipated that the prevalence of motorcycles in Asian countries will continue to increase, causing congestion and network imbalances concerning the nature of motorcycles. Literature demonstrates Variable Message Signs (VMSs) as an effective measure for addressing this issue. Understanding route choice behavior may thus aid in determining the appropriate traffic information to broadcast. This study aims to identify the impact of VMS messages related to traffic conditions and regulations on the route choice of motorcycle riders. In this instance, the core concept of ramp metering is adapted for non-highways to manage the proportion of motorcycles entering the traffic stream of the mainline. Two predetermined routes were offered through a stated preference survey to capture the responses to VMS. A binary logit model was initially introduced, further improved by including the individual characteristics and accommodating the unobserved factors across a series of observations (panel effects) by applying the mixed binary logit. It was revealed that traffic flow conditions significantly affect route preference; therefore, motorcycles tend to choose routes with lower volumes. However, waiting time at a ramp meter has no impact. The present research is a preliminary investigation for further implications in proposing traffic management strategies under mixed traffic situations. [ABSTRACT FROM AUTHOR]

Previous studies have shown that bus drivers are a major contributing factor to bus accidents. The aim of this study is to explore the factors that contribute to the presence of accident propensity among bus drivers, as well as the relative importance of each influencing factor and the mechanism of influence. To this end, a C5.0 decision tree model was developed to determine the relative importance as well as rank the importance of the impact of poor driving records and demographic factors on accident propensity, and a binary logistic regression model was developed to analyze the relationship between accident propensity and the different values of each essential influencing factor. Based on our results, we found that: (1) the number of violations had the most significant effect on bus drivers' accident propensity, followed by age, driving age, and number of alarms; (2) violations and alarms are positively related to bus driver accident propensity; age and driving age are inversely related to bus driver accident propensity; and (3) men have a higher accident risk probability than women. This study's findings will help bus companies and traffic management authorities to implement more targeted improvements to their bus driver management programs. [ABSTRACT FROM AUTHOR]

Maintenance and Rehabilitation (M&R) scheduling is one of the vital aspects of a pavement management system (PMS). This study aims to establish accurate M&R plans for a large-scale pavement network. To this intent, parameters affecting pavement deterioration were identified from the literature, then Random Forest Regression was employed to determine the effective features for pavement deterioration modelling. An accurate pavement deterioration function was generated by applying significant features. The most robust metaheuristic and evolutionary algorithms were selected and adjusted to solve the M&R scheduling optimisation problem, including the Water Cycle Algorithm (WCA), Arithmetic Optimisation Algorithm (AOA), Differential Evolutionary (DE), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO), and Genetic Algorithm (GA). The performance of the mentioned algorithms was compared to help researchers and decision-makers to select the appropriate algorithm for M&R scheduling optimisation. WCA and AOA showed to have the best performance among the adapted algorithms. Compared to AOA, DE, ACO, PSO, and GA, WCA's objective function was calculated to be 45%, 74%, 74%, 77% and 83% less, while its M&R cost was cheaper by 13%, 16%, 27%, 19%, and 18%, respectively. [ABSTRACT FROM AUTHOR]

With the rapid increment of power outages related to extreme natural disasters such as wildfires and severe storms, microgrids have the potential to enhance resilience locally. Traditionally, grid-connected microgrids are investigated from an economic perspective only, without focusing on resilience solutions benefits during grid interruptions. Hence, the presented work proposes a technical and economic evaluation of an airport grid-connected microgrid consisting of solar photovoltaic (PV), energy storage system, and diesel generator to enhance airport power resilience under different power interruption scenarios. A modified mixed-integer linear programming scheme was introduced to minimize the total annual operating cost of the proposed resilient system. The optimal resilient microgrid components sizing and dispatching were investigated with and without a monetary assigned value for resilience as a service. Moreover, the microgrid survivability during solar performance change was investigated. The possible load increment from electric ground support equipment deployment was considered. The results show that the proposed microgrid can achieve an annual operational cost reduction while ensuring a continuous power supply for all considered outage scenarios. The operational cost saving varies between 20% and 22%. The duration of the outage and critical load level have a higher impact on microgrid sizing and dispatching. [ABSTRACT FROM AUTHOR]

This study aims to examine the role of social media use in reducing household cyclone-induced disaster loss by obtaining warning and preparedness information. Data were collected following the aftermath of cyclone Amphan in affected households of the Koyra sub-district, Khulna district of Bangladesh using random sampling. Data from 150 households using only traditional media and 150 from those using both social and traditional media were collected through semi-structured questionnaires. Using propensity score matching, it was found that households using both social media and traditional media were able to reduce loss by more than 47,494.31 Bangladeshi Taka compared to households using traditional media. Moreover, households using both media received more important timely information than households using only traditional media. This helped them to work more efficiently and timely, such as cutting ripe crops from fields and keeping them in a safe place, taking domestic animals and poultry to the nearest safe places, putting higher fences using necessary material around ponds to prevent fish being washed away, and so on, to reduce cyclone-induce disaster loss. For obtaining information, problems coastal people faced included less internet bandwidth, the absence of a unified Facebook page and a common YouTube channel, and rumors. Possible solutions for these problems, stated by the respondents, were government-managed free internet before cyclones, a single Facebook page and a single YouTube channel for this purpose. Thus, this research shows social media as a good tool for reducing cyclone-induced disaster loss in Bangladesh coastal areas. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has led to thinking about the response to sudden-onset disasters, for which the transportation resilience of urban areas is crucial. The purpose of paper is to provide a graded assessment of urban transportation resilience to help city managers target policies and plans. Wuhan, the first city in China to be severely hit by COVID-19, was selected as the case study for this research. Based on an extensive survey of the travel characteristics of residents in central urban areas, the concept of "travel mode shift" was combined to classify residents into four modes, including non-motorized conventional travel, non-motorized over-distance travel, motorized adaptable travel and motorized non-substitutable travel. The potential transportation stoppages in different levels of epidemic impact were then divided into three scenarios, corresponding to each of the city's three levels of transportation resilience. The concept of MWD (Maximum Willingness Distance) in active travel mode was further developed, which was divided into WMWD (Walking Maximum Willingness Distance) and RMWD (Riding Maximum Willingness Distance). Finally, a hierarchical assessment model of urban transportation resilience is developed based on the MWD distance threshold. Besides, the average income level of urban residents was also included in the assessment system. The following research conclusions were drawn: (1) The degree of transportation resilience in Wuhan showed an "S-curve" relationship with RMWD, with thresholds at RMWD = 2.5 km, 11 km and 23 km respectively. (2) The resilience of transportation in the suburbs of the city was weaker than in the city center, and the gap between the two increases as the RMWD increases, but the share of motorized transportation in short distance trips in the city center was still higher than desirable. (3) The upper-income groups in the city had more flexible travel options, while the lower income groups were less resilient to travel. Based on the results of the study, it is recommended that city managers can identify areas of low resilience and critical distance thresholds that may lead to sudden changes in transportation resilience in the event of a sudden disaster. This will lead to the development of improved policies. The special needs of socially disadvantaged groups should also be taken more into account in this process. [ABSTRACT FROM AUTHOR]

A great deal of research has examined the efficacy of variable message signs (VMS) to induce driver behavior changes, improve safety conditions, and decongest the traffic network. However, there is little literature regarding the most effective ways to display this information on VMS. Furthermore, none of the previous contributions have concentrated on analyzing what impact flashing VMS have on drivers by using real traffic data. This article seeks to bridge this gap, analyzing the effect of incorporating intermittent light stimulation to messages on drivers' behavior on a Chilean highway, using vehicle-by-vehicle data obtained in a non-intrusive way. In order to do so, an experiment was carried out to measure the responses of drivers when faced with two types of messages: (1) those intended to induce a speed reduction and (2) those aimed at generating lane changes. From the statistical models we obtained several insights. Our results show that flashing messages may increase the effectiveness of VMS depending on environmental and traffic conditions. In particular, for speed moderation messages, we found 12 significant effects, showing, for example, that a flashing message is most effective in the hours of darkness, with low congestion, small spacing, and low average speeds. Additionally, it has a more significant impact on experienced drivers. On the other hand, for lane change messages, we found five significant effects, showing that flashing messaging reduces its effectiveness in situations where a high cognitive load is required, such as in high flow and high average speeds. No particular effects were identified in either case for specific vehicle types. [ABSTRACT FROM AUTHOR]

CO2 emission reduction in large-scale pavement network maintenance planning has been an immense concern. The conventional single-objective optimisation modelsoverlook environmental issues such as CO2 emission. However, the introduced multi-objective optimisation aims to enhance the network condition and minimise CO2 emissions simultaneously. Two single-objective (coyote optimisation algorithm and genetic algorithm) and two multi-objective metaheuristic algorithms (multi-objective coyote optimisation algorithm and non-dominated sorting genetic algorithm) are employed to assess the effectiveness of the introduced environmental approach. Pavement maintenance planning optimisation requires the deterioration function formula and treatment improvement equation to be modelled. Hence, a new machine learning method called 'differential evolutionary programming' is introduced, which can provide the output-input formula. Differential evolutionary programming predicts the pavement deterioration value and overlay improvement with R2 of 0.992 and 0.970, respectively. The results indicate that the coyote optimisation algorithm's objective function is 66% lower than that of the genetic algorithm. Likewise, the multi-objective coyote optimisation algorithm reduces the first objective function by 72% on average compared to the non-dominated sorting genetic algorithm. The grey relational analysis is performed to compare single-objective and multi-objective optimal solutions. All optimal solutions presented by multi-objective modelling dominates the single-objective optimisation optimal solution based on the grey relational grade. [ABSTRACT FROM AUTHOR]

This study approaches a multi-stage stochastic mixed-integer programming model for the high-level complexity of large-scale pavement maintenance scheduling problems. The substance of some parameters in the mentioned problems is uncertain. Ignoring the uncertainty of these parameters in the pavement maintenance scheduling problems may lead to suboptimal solutions and unstable pavement conditions. In this study, annual budget and pavement deterioration rate are considered uncertain parameters. On the other hand, pavement agencies generally face large-scale pavement networks. The complexity of the proposed stochastic model increases exponentially with the number of network sections and scenarios. The problem is solved using the Progressive Hedging Algorithm (PHA), which is suitable for large-scale stochastic programming problems, by achieving an effective decomposition over scenarios. A modified adaptive strategy for choosing the penalty parameter value is applied that aims to improve the solution process. A pavement network including 251 sections is considered the case study for this investigation, and the current study seeks optimal maintenance scheduling over a finite analysis period. The performance of the stochastic model is compared with that of the deterministic model. The results indicate that the introduced approach is competent to address uncertainty in maintenance and rehabilitation problems. [ABSTRACT FROM AUTHOR]

This study examines the applicability to developing countries of a pavement management system (PMS) with less data (two-point) requirement to complement current practice. Due to limited capacity, many developing countries like Uganda have adopted a PMS after specified intervals regardless of deterioration rate (i.e. time-dependent policy). Maintenance planning based on deterioration rate (i.e. condition-dependent policy), as proposed, would be more ideal because pavement deterioration is stochastic. A PMS was developed using the Markov hazard model and then an empirical study was conducted for surveyed Ugandan national road network comparing both policies considering a basic plan and a fixed budget. A greedy algorithm was used to select sections for repair based on worst condition first (for time-dependent) or fastest deterioration first (for condition-dependent). A shift to the condition-dependent policy increased percentages of network in good and fair condition by 8.6% and 2.5%, respectively, and reduced percentages in poor and bad condition by 8.5% and 2.6%, respectively. Additionally, maintenance plans with and without preventive maintenance were compared considering a fixed budget. Preventive maintenance further increased percentage in good condition by 27.4% and reduced percentages in poor and bad condition by 11.6% and 4.2%, respectively, with a 53.5% reduction in life cycle costs. [ABSTRACT FROM AUTHOR]

Critical infrastructures such as transportation, power, telecommunication, water supply, and hospitals play a vital role in effectively managing post-disaster responses. The resilience of critical infrastructures should be incorporated in the planning and designing phase based on the risk assessment in a particular geographic area. However, the framework to assess critical infrastructure resilience (CIR) is variably conceptualised. Therefore, the objective of this study was to critically appraise the existing CIR assessment frameworks developed since the adoption of the Sendai Framework in 2015 with the hazard focus on earthquakes. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was used for the selection of the 24 most relevant studies, and these were analysed to delineate existing frameworks, models, and concepts. The study found that there are wide-ranging disparities among the existing frameworks to assess the infrastructure resilience, and it has become a key challenge to prioritise resilience-based investment in the infrastructure sector. Furthermore, key attributes such as performance indicators, emergency aspects, and damage assessment need to be considered for different disaster phases—ex-ante, during, and ex-post—to improve the long-term resilience of critical infrastructure. Subsequently, an integrated and adaptable infrastructure resilience assessment framework is proposed for proper critical infrastructure planning and resilience-based investment decision making. [ABSTRACT FROM AUTHOR]

This study investigated drivers' perceptions of pedestrian crossing behavior at unsignalized crosswalks, which was less fruitful in quantitative and qualitative traffic research. Subjective and snow-ball sampling were used to conduct semi-structured in-depth interviews based on drivers' daily driving experience from qualitative research. A theoretical model of pedestrian behavior at unsignalized crosswalks was constructed using the grounded theory and the theoretical saturation test. The model involved 4 three-level codes and 13 two-level codes (main category) used to obtain seven subcategories. The results show that drivers believe that pedestrian characteristics, driver characteristics, and age factors are the three factors that affect pedestrian crossing safety. Targeted improvement measures are put forward to guide the design of pedestrian crossing facilities, pedestrian management and guidance, and future research on conflicts between autonomous vehicles and pedestrians. [ABSTRACT FROM AUTHOR]

Global warming, sea-level rise, and rapid urbanization all increase the risk of compound extreme weather events, presenting challenges for the operation of urban-related infrastructure, including transportation infrastructure. In this context, some questions become important. For example, what are the temporal and spatial distribution and development trends of transportation resilience when considering the impact of multilpe extreme weather events on the urban transportation system? What is the degree of loss of urban transportation resilience (UT resilience) under different extreme event intensities, and how long will it take for the entire system to restore balance? In the future, if extreme weather events become more frequent and intense, what trends will urban transportation resilience show? Considering these problems, the current monitoring methods for transportation resilience under the influence of extreme events are lacking, especially the monitoring of the temporal and spatial dynamic changes of transportation resilience under the influence of compined extreme events. The development of big data mining technology and deep learning methods for spatiotemporal predictions made the construction of spatiotemporal data sets for evaluating and predicting UT resilience-intensity indicators possible. Such data sets reveal the temporal and spatial features and evolution of UT resilience intensity under the influence of compound extreme weather events, as well as the related future change trends. They indicate the key research areas that should be focused on, namely, the transportation resilience under climate warming. This work is especially important in planning efforts to adapt to climate change and rising sea levels and is relevant to policymakers, traffic managers, civil protection managers, and the general public. [ABSTRACT FROM AUTHOR]

Appropriate pavement maintenance is of great importance due to the increasing deterioration of pavements and limited resources. Nowadays, highway agencies face large-scale networks. The management of large-scale networks is a challenge concerning the computational complexity, which typically increases exponentially with the dimension of the network. Water cycle and genetic algorithms are utilised in this paper to find the optimal maintenance schedule of large-scale pavement networks. A new practical constraint is introduced in the mathematical formulation that the total annual costs should not fluctuate more than a predefined limit over the planning horizon. Moreover, a novel index is developed to calculate the equity level in pavement maintenance scheduling, and the outcomes of the algorithms are compared based on this equation. A real road network with 103 pavement sections is the case study of this paper. The results show that 'Equity index' is reduced by 94% and 48% during the analysis period by WCA and GA, respectively. Drawing on WCA and GA optimal solutions, the average international roughness index of the network is decreased by 35% and 31% respectively in a 5-year horizon. Moreover, the variance of the maximum and minimum allocated budget in the analysis period is less than 15%. [ABSTRACT FROM AUTHOR]

Transportation systems help in shaping an area's economic health and quality of life, providing the infrastructure for the mobility of people and goods. Nevertheless, the negative externalities of car-oriented urban-metropolitan planning have heightened awareness for the need of urban planning approaches that incorporate sustainable mobility. Consequently, cities worldwide have increasingly produced sustainable mobility plans. This points to the need of creating mechanisms to implement these sustainable plans, particularly in large, complex, and fast-growing cities. This paper provides guidelines to facilitate the implementation of Sustainable Mobility Plans by focusing on the case of Mexico City. This is achieved by applying the complex large-scale integrated open systems (CLIOS) systemic analysis, in two steps: first, we facilitate the identification of the complexities and relationships among the essential systems of Mexico City's urban structure, along with the recognition of their most important components and the institutions involved within the urban planning process. Second, we assess the effectiveness of the public policies–strategies that form part of Mexico City's Sustainable Mobility Plan and organize them in order of importance. The results show which principal subsystems should be considered for sustainable mobility and which public policies–strategies should be prioritized in order to implement the aforementioned plan effectively. [ABSTRACT FROM AUTHOR]

This study examined self-reported supervisory behaviors of caregivers at beaches and ascertained whether self-reported supervision reflects observed behavior. Observations were conducted of caregiver/child pairs at 18 Australian beaches, with questionnaires subsequently completed by caregivers. Caregivers identified visual contact as essential for close supervision and proximity a key determinant in distinguishing supervision and close supervision. Supervisory behavior was associated with child age, while lifeguard patrol had no effect on supervision. All supervision statements from the PSAPQ-BEACH were associated with supervision. Only three statements were significant independent predictors of supervision. Comparisons suggest caregivers' self-reported supervisory behavior reflects actual supervision. As this is the first study of its kind, it is essential that further prospective research using mixed-method approaches build on this information. [ABSTRACT FROM AUTHOR]

Decision Support Systems (DSS) are now an established part of the information systems mainstream, both in research and in practice. The DSS development approach differs from traditional systems development not only with regard to its associated paradigms and orientation, but also in the methods of requirements analysis it employs. This paper surveys the major methodologies used for DSS development. It analyzes them by structure, paradigm, and orientation, and discusses their underlying assumptions. A research model is then developed and used to relate the functions of DSS methodology to decision-making environments and the relevant processes within them. Finally, a contingency theory is presented to show how each of the surveyed methodologies reduces the lack of structure in the decision-making environment, and bow this may be used by DSS developers for selecting a DSS methodology. Other components of the research model are discussed, to identify further prerequisites for improved understanding of the DSS role and application. [ABSTRACT FROM AUTHOR]