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Percolation theory has been widely employed in network systems as an effective tool to analyze phase transitions from functional to nonfunctional states. In this paper, we analyze the propagation of congestion on transportation networks and its influence on origin–destination (OD) pairs using the percolation process. This approach allows us to identify the most critical links within the network that, when disrupted due to congestion, significantly impact overall network performance. Understanding the role of these critical links is essential for developing strategies to mitigate congestion effects and enhance network resilience. Building on this analysis, we propose two methods to adjust the capacities of these critical links. First, we introduce a greedy method that incrementally adjusts the capacities based on their individual impact on network connectivity and traffic flow. Second, we employ a Particle Swarm Optimization (PSO) method to strategically increase the capacities of certain critical links, considering the network as a whole. These capacity adjustments are designed to enhance the network's resilience by ensuring it remains functional even under conditions of high demand and congestion. By preventing the propagation of congestion through strategic capacity enhancements, the transportation network can maintain connectivity between OD pairs, reduce travel times, and improve overall efficiency. Our approach provides a systematic method for improving the robustness of transportation networks against congestion propagation. The results demonstrate that both the greedy method and the PSO method effectively enhance network performance, with the PSO method showing superior results in optimizing capacity allocations. This research is crucial for maintaining efficient and reliable mobility in urban areas, where congestion is a persistent challenge, and offers valuable insights for transportation planners and policymakers aiming to design more resilient transportation infrastructures. [ABSTRACT FROM AUTHOR]

• Semi-automatic technique has been utilized using VHR imagery to develop landslides inventory. • Most prone transportation infrastructure has been identified using susceptibility analysis. • Temporal recovery of landslides based on the vegetation change has been evaluated. • Rainfall induced landslides has been identified using timeseries data. In mountainous terrains, the frequent landslides and their associated impacts on human lives and the economy is increasing globally. Development of landslide inventory and afterward landslide susceptibility mapping are the main prerequisites for implementing landslide mitigation measures and protection in mountainous regions. The 2005 Kashmir earthquake induced different small and large landslides and some were active for the long term. So far many studies have used medium and high-resolution data to develop landslide inventory. This study aims to develop a 1st detailed, comprehensive and accurate landslide inventory using a very high-resolution image using a semi-automatic technique. The precise landslide inventory is employed to develop an accurate and comprehensive landslide susceptibility map considering the landslide inventory data using a logistic regression training model. Furthermore, the landslide's temporal recovery from the earthquake and its reactivation due to rainfall in spare vegetation areas have been evaluated. Fine-resolution satellite images of Worldview-2 are applied to develop a detailed landslide inventory using a Support Vector Machine (SVM) classifier. A total of 63,630 landslides were identified using a semi-automatic technique within a study area of 265 km2. From regression modeling, the results show that geology, topography, and road networks have a significant impact on the spatial distribution of landslides. Model performance was evaluated based on the testing data, the model gives an AUC of 0.93 and the kappa value of 0.9353. The spatiotemporal NDVI has been assessed to identify the landslide recovery and its reactivation due to extreme rainfall. The results show that 72.1 % of the landslides occurred in Muzaffarabad formation in the study area. The developed landslide susceptibility map can be further used for land-use planning and implementing mitigation measures for the safety of roads and other infrastructure in the area. [ABSTRACT FROM AUTHOR]

Accurate logistics cost enables efficient and cost-effective planning of transportation systems. It facilitates the fast, on-time, and seamless transportation of goods between the cities. Existing cost models are based on the unit transportation cost of goods, which could be more realistic and accurate. In this work, we introduce a new cost function based on the unit transportation cost of a truck to overcome the undercharging or overcharging issues in the existing cost functions. The proposed cost function best suits the government and industry when developing the facilities. This work also presents the logistic transportation problem, formulation of a cost function using integer programming, and methodology to solve the transportation problem using meta-heuristic algorithms. In addition, we construct a new transportation problem for one district-one product (ODOP) using real-world scenarios and data. ODOP is an initiative of the Uttar Pradesh Government for the balanced economic development of the state in India. We solve this 18 cities problem using eight meta-heuristic algorithms. Our experimental simulation finds an optimal logistics cost of Rs. 2159140.00/- for four logistics hubs and 14 distribution/collection centres. Our observation also identifies the dependency of network configuration over various factors like establishment/rental and operational cost, the unit cost of transportation, and the capacity of vehicles instead of only on product supply and demand of goods/products. Our evaluation also reports an 80% consistent result by the anti-predatory nature-inspired algorithm compared to the other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Recent natural or manmade hazards highlighted the pressing need for allocating enough financial resources for the enhancement of the resilience of infrastructure in our society. In this regard, bridges play a pivotal role in transportation networks, where any reduction in their performance can influence the functionality of the whole network. Bridge retrofitting is a common strategy for enhancing transportation network resilience. Composite reinforcement with different thicknesses and concrete seat extenders with different widths have been used to retrofit an existing curved bridge in Iran. A novel hybrid algorithm called NSGAII-SA was proposed and compared against a simple optimisation strategy, a traditional genetic algorithm solver (NSGA-II), and pure simulated annealing (SA). These optimisation strategies identify the optimal retrofit scenarios set in terms of maximum resilience index, minimum recovery time, and minimum retrofitting cost. For this problem, NSGAII-SA outperforms the traditional NSGA-II. The comparison shows that for this particular application, SA has the best overall performance. However, the proposed NSGAII-SA algorithm has satisfactory performance too, it is a clear improvement over NSGAII and it has the potential to be very useful and better suited than SA for problems with large design spaces and many local minima. [ABSTRACT FROM AUTHOR]

Progressive and sudden deteriorations are the main reasons affecting the functionality of a transportation network. This paper presents a general probabilistic approach in which the ensembles of regression trees (ERT) are innovatively adopted to predict the life-cycle system reliability of asphalt pavements using Monte Carlo simulations, and pavement segments are considered with bridges in the analysis, prediction, and management of the functionality of transportation networks under both progressive and sudden deterioration due to multiple hazards. Four performance indicators of asphalt pavement subjected to multiple hazards were modeled using ERT trained with the Long-Term Pavement Performance database. The specific hazard types corresponding to each pavement performance indicator for the associated ERT model training were identified. The structural performance associated with bridge superstructures and substructures was analyzed by considering corrosion, traffic loading, and seismic hazards. The proposed approach is illustrated on an existing transportation network in Pennsylvania. The essential retrofitting timing, importance measure, and retrofitting priority associated with the individual component were investigated utilizing the calculated time-variant connectivity-based functionality and resilience associated with the network. The results demonstrate that asphalt pavements have a significant impact on the network functionality and should be considered in the postevent decision-making process of retrofitting strategies. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: The quality of the solution in resolving a complex network depends on either the speed or accuracy of the results. While some health studies prioritize high performance, fast algorithms are favored in scenarios requiring rapid decision‐making. A comprehensive understanding of the problem necessitates a detailed analysis of the network and its individual components. Betweenness Centrality (BC) and Closeness Centrality (CC) are commonly employed measures in network studies. This study introduces a new strategy to compute BC and CC that assesses their sensitivity in the scale space while measuring the shortest path. The scale space is generated by incorporating a scale parameter that is shown to achieve up to 60% performance improvements for various datasets. The study provides in‐depth insights into the importance of the scale space analysis. Finally, a flexible measurement tool is provided that is suitable for various types of problems. To demonstrate the flexibility and applicability, we experimented with two methods for 10 different graphs using the proposed approach. [ABSTRACT FROM AUTHOR]

Disruptions in road networks can significantly impact traffic flow, necessitating the identification and mitigation of vulnerable components. This study presents a methodology to assess the vulnerability and robustness of road networks, addressing the research gap in comprehensive approaches that effectively evaluate both controlled intersections and uncontrolled/free-flow highway segments. The proposed framework employs traffic simulation tools to model road closures within designated study areas, focusing on networks in Qatar. Performance measures such as average control delay and volume to capacity ratios are utilized to calculate vulnerability indices for network components. The two-pronged approach quantifies the impact of simulated closures at both the component and network levels, revealing critical intersections and road segments that markedly affect network performance during disruptions. The findings contribute to the field by providing transportation engineers with an effective quantitative tool to detect vulnerabilities in their road network, guiding design plans and actions to mitigate the potential harmful impacts of disruptive events on transportation infrastructure. The methodology's scalability ensures its applicability to various operational contexts, offering significant insights for transportation planning and infrastructure resilience. By establishing a comprehensive vulnerability analysis framework, this study enhances the understanding and management of road network vulnerabilities in complex urban environments. Further research on integrating dynamic traffic assignment and real-time data is recommended to further enhance the predictive accuracy and applicability of the proposed framework, ultimately improving transportation infrastructure resilience.

Abstract The utilization of autonomous vehicles (AVs) has emerged as a pivotal factor in addressing the rising costs and safety concerns associated with modern travel. As technology advances and traffic challenges intensify, enhancing accessibility stands out as a critical goal for transportation experts. Accessibility, constrained by factors like travel time, underscores the increasing need for AVs to mitigate these limitations. This study aimed to model the influence of AVs on the accessibility index within transportation networks and discuss system optimization based on user equilibrium (UE) and system optimum (SO) outcomes. The research conducted numerical analysis employing the Hearn network as a fundamental system to validate a mixed assignment model and ascertain baseline accessibility. Additionally, the Sioux Falls network, a medium-sized network, was employed for analysis. A hybrid heuristic assignment algorithm was introduced, concurrently assigning different percentages of AV presence alongside the remaining non-AV percentage in three distinct scenarios. These scenarios ranged from 0 to 100% AV presence: the first scenario maintained constant network capacity, the second scenario adjusted network capacity based on AV presence, and the third scenario incorporated capacity adjustments in the assignment stage. In all three scenarios, network accessibility was evaluated using gravity and accessibility index methods derived from the hybrid assignment model output. The findings demonstrated that as the percentage of AVs increased, accessibility improved in both Hearn and Sioux Falls networks across all scenarios. The second and third scenarios exhibited higher accessibility increases compared to the first, attributable to augmented capacity resulting from increased AV presence. In the Sioux Falls network, the first scenario showed enhanced SO and UE due to increased AV presence and enhanced system operator management. Conversely, the second and third scenarios, with increased AVs and subsequent capacity increments, displayed reduced UE and SO results. Despite the decline in UE and SO, traffic flow assignment and overall network accessibility improved. These findings highlight the positive correlation between AV presence, network capacity, and enhanced accessibility. The study underscores the potential benefits of AV integration in optimizing transportation networks and improving overall accessibility, albeit with nuances in capacity adjustments impacting traffic flow dynamics. Further research avenues could explore complex traffic flow scenarios and delve into more specific optimization strategies.

Copyright of Journal of Tikrit University for Humanities is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The utilization of autonomous vehicles (AVs) has emerged as a pivotal factor in addressing the rising costs and safety concerns associated with modern travel. As technology advances and traffic challenges intensify, enhancing accessibility stands out as a critical goal for transportation experts. Accessibility, constrained by factors like travel time, underscores the increasing need for AVs to mitigate these limitations. This study aimed to model the influence of AVs on the accessibility index within transportation networks and discuss system optimization based on user equilibrium (UE) and system optimum (SO) outcomes. The research conducted numerical analysis employing the Hearn network as a fundamental system to validate a mixed assignment model and ascertain baseline accessibility. Additionally, the Sioux Falls network, a medium-sized network, was employed for analysis. A hybrid heuristic assignment algorithm was introduced, concurrently assigning different percentages of AV presence alongside the remaining non-AV percentage in three distinct scenarios. These scenarios ranged from 0 to 100% AV presence: the first scenario maintained constant network capacity, the second scenario adjusted network capacity based on AV presence, and the third scenario incorporated capacity adjustments in the assignment stage. In all three scenarios, network accessibility was evaluated using gravity and accessibility index methods derived from the hybrid assignment model output. The findings demonstrated that as the percentage of AVs increased, accessibility improved in both Hearn and Sioux Falls networks across all scenarios. The second and third scenarios exhibited higher accessibility increases compared to the first, attributable to augmented capacity resulting from increased AV presence. In the Sioux Falls network, the first scenario showed enhanced SO and UE due to increased AV presence and enhanced system operator management. Conversely, the second and third scenarios, with increased AVs and subsequent capacity increments, displayed reduced UE and SO results. Despite the decline in UE and SO, traffic flow assignment and overall network accessibility improved. These findings highlight the positive correlation between AV presence, network capacity, and enhanced accessibility. The study underscores the potential benefits of AV integration in optimizing transportation networks and improving overall accessibility, albeit with nuances in capacity adjustments impacting traffic flow dynamics. Further research avenues could explore complex traffic flow scenarios and delve into more specific optimization strategies. [ABSTRACT FROM AUTHOR]

The prediction of delays and their reduction in all modes of passenger transportation, especially rail transportation, is of great importance and annually attracts the attention of many researchers. Train delays can be anticipated by predicting the arrival times of trains at stations. In this paper, a train operated by Raja Company, which travels daily on the Tehran-Mashhad route, has been investigated. This train route consists of 50 stations, of which five main stations, including Tehran, Garmsar, Semnan, Shahrud, and Mashhad, have been selected to predict the train's arrival and departure times at each of these stations. For this purpose, data related to the train timetable and the operations carried out at these five main stations over three years from 2018 to 2020 have been collected. Then, modeling was conducted to predict real-time arrival and departure times for each of these stations. Artificial neural networks, random forest regression, gradient boosting regression, and extreme gradient boosting regression were used for prediction modeling. After evaluating these models, the approach that yielded the best results based on the experimental data was selected for predicting the arrival and departure times at each station.

With the rapid advancement of transportation electrification, especially dynamic wireless charging technology, the interconnection between the transportation network and the power distribution network has become increasingly evident at both the informational and physical levels. Consequently, the emergence of integrated transportation-power energy systems has garnered considerable attention. It is urgent to realize a coordinated operation of the energy systems. In this paper, considering the elastic charging behavior and mileage limitation of electric vehicles, a modeling framework and corresponding solution algorithms are proposed to coordinate transportation and power distribution networks operation. In the transportation network, a traffic assignment model with the elastic charging demand and effective path generation models with the mileage limitation of electric vehicles are established to calculate the actual charging demand of electric vehicles and traffic flow distribution. In the power distribution network, an optimal power flow model based on second-order cone relaxation is established to calculate the power flow distribution and scheduling plans of generators. And then, based on the idea of alternating direction multiplier method, a distributed coordinated operation method and solution algorithm for the integrated transportation-power energy systems are proposed. Finally, a case study is performed to verify the effectiveness of the proposed model and algorithm.

Urbanization and the increasing frequency of extreme climates affect the sustainability of urban public transportation systems, and improving resilience is one of the primary directions for sustainable development. To scientifically assess the resilience of urban public transportation systems, a resilience assessment model based on structure and function is established in this study. This model mathematically quantifies and simulates the structural and functional changes in public transportation systems under disruption scenarios and provides a comprehensive assessment of six abilities: 1) structural resistance, 2) structural recoverability, 3) functional resistance, 4) functional recoverability, 5) passenger adaptability, and 6) management adaptability. Depending on the initial failure stations, this model can simulate the resilience of a public transportation system under various scenarios. This model is applied to assess the resilience of public transportation systems in a provincial capital city under an equipment failure scenario. The results show that the impact of equipment failure on resilience varies according to the metro lines, and improvement strategies for functional recoverability and management adaptability are proposed. The weaknesses in the resilience of urban public transportation systems can be identified using the proposed model, which helps provide strategies for improving the capacity to face perturbations.

Percolation theory has been widely employed in network systems as an effective tool to analyze phase transitions from functional to nonfunctional states. In this paper, we analyze the propagation of congestion on transportation networks and its influence on origin–destination (OD) pairs using the percolation process. This approach allows us to identify the most critical links within the network that, when disrupted due to congestion, significantly impact overall network performance. Understanding the role of these critical links is essential for developing strategies to mitigate congestion effects and enhance network resilience. Building on this analysis, we propose two methods to adjust the capacities of these critical links. First, we introduce a greedy method that incrementally adjusts the capacities based on their individual impact on network connectivity and traffic flow. Second, we employ a Particle Swarm Optimization (PSO) method to strategically increase the capacities of certain critical links, considering the network as a whole. These capacity adjustments are designed to enhance the network’s resilience by ensuring it remains functional even under conditions of high demand and congestion. By preventing the propagation of congestion through strategic capacity enhancements, the transportation network can maintain connectivity between OD pairs, reduce travel times, and improve overall efficiency. Our approach provides a systematic method for improving the robustness of transportation networks against congestion propagation. The results demonstrate that both the greedy method and the PSO method effectively enhance network performance, with the PSO method showing superior results in optimizing capacity allocations. This research is crucial for maintaining efficient and reliable mobility in urban areas, where congestion is a persistent challenge, and offers valuable insights for transportation planners and policymakers aiming to design more resilient transportation infrastructures.

This research article examines the relationship between transportation networks and urban vitality in the Lanzhou-Xining urban agglomeration in China. The study finds that while the transportation network accessibility and vitality of nodal cities have increased, there is still a prominent polarization phenomenon and a need for more balanced development within the agglomeration. The research highlights the importance of coordinating transportation and urban development for sustainable growth. Additionally, the article discusses the Chinese government's support for new urbanization and rural revitalization, particularly in county-level towns, and emphasizes the need to consider additional factors such as the urban ecological environment and population mobility in assessing urban vitality. The document also explores the influence of street network structure on spatial vitality in various cities in China, emphasizing the importance of considering urban form, land use, and urban planning in promoting urban vitality. [Extracted from the article]

The couple between the power network and the transportation network (TN) is deepening gradually with the increasing penetration rate of electric vehicles (EV), which also poses a great challenge to the traditional voltage control scheme. In this paper, we propose a coordinated voltage control strategy for the active distribution networks considering multiple types of EV. In the first stage, the action of on-load tap changer and capacitor banks, etc., are determined by optimal power f low calculation, and the node electricity price is also determined based on dynamic time-of-use tariff mechanism. In the second stage, multiple operating scenarios of multiple types of EVs such as cabs, private cars and buses are considered, and the scheduling results of each EV are solved by building an optimization model based on constraints such as queuing theory, Floyd-Warshall algorithm and traffic flow information. In the third stage, the output power of photovoltaic and energy storage systems is fine-tuned in the normal control mode. The charging power of EVs is also regulated in the emergency control mode to reduce the voltage deviation, and the amount of regulation is calculated based on the fair voltage control mode of EVs. Finally, we test the modified IEEE 33-bus distribution system coupled with the 24-bus Beijing TN. The simulation results show that the proposed scheme can mitigate voltage violations well. [ABSTRACT FROM AUTHOR]

Urban color, primarily emanating from building façades and roofs, plays a pivotal role in shaping a city's image and influencing people's overall impression. Understanding the nuances of color patterns contributes significantly to unraveling the uniqueness and identity of a city. This study introduces a statistical method for the systematic analysis of urban color and macroscopic urban structure. Specifically, we employ drones to collect and extract building roof and façade colors in the main urban area of Hangzhou, mapping these colors to the HSV color space. Subsequently, we establish a random walk model and an origin–destination trip model within the urban transportation network to simulate the movement of people. Our experiments reveal robust correlations between façade and roof values and passing frequency (with the Pearson correlations reaching 0.70). Through a rigorous statistical analysis, we gain insights into the distribution of urban color and the impact of architectural structures on color variations, identifying potential patterns or trends. By integrating color data with architectural structure data, our systematic research method deepens the understanding of the visual features that define cities. Beyond theoretical exploration, this approach offers practical insights for building planning and design. This study not only sheds light on the relationship between architectural structures and urban color but also provides valuable guidance for future urban development initiatives. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objectives of this research are, (1) To determine the connectivity index of the transportation network in the Undana campus environment, and (2) To determine and increase the level of efficiency of traffic movements in supporting the accessibility needs of transportation users in the Undana campus environment. campus environment. The method used is a quantitative descriptive method which is used to analyze data by describing or illustrating the data that has been collected as it is. The results of the analysis in this research are, with the beta index value (B) of 1.05 indicates that road network connectivity is not arranged linearly or simply. Meanwhile, the gamma index (y) value of 0.38 indicates that the condition of transportation network connectivity is still low. Therefore, infrastructure planning is proposed as a solution to increase the mobility and connectivity index values. Several traffic engineering management scenarios are proposed based on the results of the analysis, namely widening roads, limiting public transportation, scheduling traffic movement activities, improving the safety of traffic signs, encouraging the use of public transportation by providing temporary stops, and monitoring traffic. engineering management. [ABSTRACT FROM AUTHOR]

Accelerating the construction of transportation networks has become an important bridge to promote the urbanization of heritage tourism destinations. Based on the development dilemma of tourism towns in northwestern China, this study collected the panel data of 10 cities in Shaanxi Province from 2006 to 2019 and used a panel error correction model to investigate the long-term and short-term relationships between heritage tourism and new urbanization. In addition, this paper establishes a regression model of the transportation network's regulatory effect, revealing the extent of the impact of the transportation network on heritage tourism and new urbanization. The results show that (1) in the short term, the influence of heritage tourism on new urbanization is not remarkable, but in the long term, heritage tourism plays a positive role in facilitating new urbanization; (2) the short-term contribution elasticity of new urbanization to heritage tourism is less than that in the long term; (3) the regulatory effect of the transportation network rectifies the deviation caused by overdeveloped urbanization to moderate development in heritage tourism destinations. [ABSTRACT FROM AUTHOR]

An intercity bus service includes several bus stations for passenger access and bus service routes for bus operating between designated pairs of stations. Fundamentally, whether stations and bus service routes are operating normally or not has a direct impact on profits and the passenger service rate. However, changes in passenger demand patterns over time can lead to changes in the importance of stations and bus service routes, a problem that has rarely been discussed in the literature. This study applies a scheduling model to perform vulnerability analyses related to intercity bus schedules and routes in short-term operations. A series of analyses were conducted to observe the changes in the optimization results by changing its configurations. The results illustrate the vulnerability of the transportation system components under different scenarios and operating strategies, which could help an intercity bus carrier respond early to effectively maintain its operations. [ABSTRACT FROM AUTHOR]

With the rapid development of the global economy and trade, the number of ships serving ports in China is increasing continuously. Port traffic is becoming busier, and ship behavior is more complex and changeable. The analysis of ship behavior patterns in port waters has become an urgent problem to improve the efficiency and safety of port areas. In this paper, through the full integration of ship trajectory and port geographic information, the behavior chain of a single ship across the whole process of entering and exiting the port is identified. The traffic complexities and dynamics can be further analyzed by grouping the movement patterns of large ships. Based on graph theory, the port areas can be described as a transportation network in which functional areas are nodes and fairways between different areas are edges. The traffic can be analyzed through the network structure characteristics, such as node degree, node weight, and edge weight, and by their similarities and differences. This methodology provides a quantitative analysis for exploring the behavior patterns of large ships as well as the various traffic complexities. A case study in Tianjin Port has been conducted to verify the proposed model. The results show that it can accurately analyze a ship behavior's regularity, occasion, and correlation. It provides a theoretical reference for the port to schedule and formulate emergency plans. [ABSTRACT FROM AUTHOR]

Emergency services play a crucial role in safeguarding human life and property within society. In this paper, we propose a network-based methodology for calculating transportation access between emergency services and the broader community. Using New York City as a case study, this study identifies ‘emergency service deserts’ based on the National Fire Protection Association (NFPA) guidelines, where accessibility to Fire, Emergency Medical Services, Police, and Hospitals are compromised. The results show that while 95% of NYC residents are well-served by emergency services, the residents of Staten Island are disproportionately underserved. By quantifying the relationship between first responder travel time, Emergency Services Sector (ESS) site density, and population density, we discovered a negative power law relationship between travel time and ESS site density. This relationship can be used directly by policymakers to determine which parts of a community would benefit the most from providing new ESS locations. Furthermore, this methodology can be used to quantify the resilience of emergency service infrastructure by observing changes in accessibility in communities facing threats.

Accelerating the construction of transportation networks has become an important bridge to promote the urbanization of heritage tourism destinations. Based on the development dilemma of tourism towns in northwestern China, this study collected the panel data of 10 cities in Shaanxi Province from 2006 to 2019 and used a panel error correction model to investigate the long-term and short-term relationships between heritage tourism and new urbanization. In addition, this paper establishes a regression model of the transportation network’s regulatory effect, revealing the extent of the impact of the transportation network on heritage tourism and new urbanization. The results show that (1) in the short term, the influence of heritage tourism on new urbanization is not remarkable, but in the long term, heritage tourism plays a positive role in facilitating new urbanization; (2) the short-term contribution elasticity of new urbanization to heritage tourism is less than that in the long term; (3) the regulatory effect of the transportation network rectifies the deviation caused by overdeveloped urbanization to moderate development in heritage tourism destinations.

Abstract In recent years, extreme weather events, such as typhoons, have led to large‐scale power outages in distribution systems. As a result, developing strategies to bolster distribution system resilience has become imperative. This paper proposes a two‐stage stochastic programming model aimed at enhancing this resilience. Prior to a typhoon, the first stage establishes a comprehensive wind field model based on extreme value distribution for accurate wind speed predictions. Simultaneously, a refined stress–strength interference model is used to determine the likelihood of distribution line failures. Taking into account the uncertainty of line damage, repair crews and mobile emergency generators are then strategically positioned at staging depots. Following the typhoon, the second stage coordinates network reconfiguration, dispatches repair crews, and mobilizes mobile emergency generators to minimize load shedding and expedite repairs. This model was validated on the IEEE 33‐bus distribution system, coupled with a corresponding transportation network, utilizing data from the 2018 super typhoon Mangkhut'' in China. Simulations indicate that our approach can effectively reduce load shedding and power outage durations, thereby enhancing the resilience of distribution systems.

Shortest path problem (SPP) has various applications in areas such as telecommunications, transportation and emergency services, and postal services among others. As a result, several algorithms have been developed to solve the SPP and related problems. The current paper extends the TANYAKUMU labelling method for solving the Travelling salesman problem (TSP) to solve SPP in directed transportation networks. Numerical illustrations are used to prove the validity of the proposed method. The main contributions of this paper are as follows: (i) modification of TSP algorithm to solve single source SPP, (ii) the developed method numerically evaluated on four increasingly complex problems of sizes 11×11, 21×21, 23×23 and 26×26 and lastly (iii) the solutions obtained from solving these four problems are compared with those obtained by Minimum incoming weight label (MIWL) algorithm. The proposed algorithm computed the same shortest paths as the MIWL algorithm on all four problems.

The emerging photovoltaic (PV) integration technology called solar road (SR) gives road infrastructure the possibility of not only carrying traffic loads but also generating electricity. The versatility of SR leads to the nexus between transportation network (TN) and power distribution network (PDN). Along with the increasing penetration of electric vehicles (EVs), the installation of SR can promote the reliance of transportation and electrical networks to a higher degree. In order to cope with the severe challenges brought by SR and EV to the TN-PDN coordinated operation, this paper suggests a traffic-power coordinated operation model incorporating road solar generation (TPOSR), where the impact of the traffic flow and SR on the coupled network is studied. Specifically, the user equilibrium (UE) principle is adopted to describe the traffic flow distribution in TN, while the Distflow model is employed to model the power flow in PDN. In TN, the SRs serve as links that let the vehicles through. Moreover, from the perspective of PDN, the SR output model considering traffic shading effect is proposed, based on traffic assignment problem (TAP) and traffic flow theory. In the proposed TPOSR model, we assume the independent system operator, a public organization, can imposes road tolls and dispatch generators to promote coordinated operation of the coupled network. Numerical results demonstrate the effectiveness of the proposed TPOSR for reducing social costs and improving SR performance.

The frequency of natural and man-made disasters has increased over the past few decades, which has doubled the significance of evacuation planning because it directly affects people's lives and properties. How evacuees behave during a disaster and the methodologies to assess their behaviour are vital factors in managing any emergency scenario. For example, during the evacuation of a transportation network, users' amount of information and how they react are imperative to achieve a resilient response to a disaster. Even though there are numerous approaches for assessing evacuee behaviour, further research is needed to determine how and when each methodology should be employed. This paper intends to evaluate and classify the methodologies that have been presented up to date to create a more consistent approach to interpreting human behaviour during an evacuation process. The majority of studies focus on how people behave during an evacuation of a building, with less attention paid to how they behave during a transport evacuation. Among the proposed methodologies in this paper, the virtual reality approach, in individual behaviour, and agent-based models, in crowd behaviour, have more advantages than other approaches. Overall, a comparison between the proposed approaches is made in the discussion part. The output of this study provides the classifications and suggestions for researchers to pick an appropriate approach based on the types of problems, and some direction for future studies are introduced. To reach the research goal, 177 papers have been reviewed between 1954 and 2022.

Hurricane-induced hazards can result in significant damage to the built environment cascading into major impacts to the households, social institutions, and local economy. Although quantifying physical impacts of hurricane-induced hazards is essential for risk analysis, it is necessary but not sufficient for community resilience planning. While there have been several studies on hurricane risk and recovery assessment at the building- and community-level, few studies have focused on the nexus of coupled physical and social disruptions, particularly when characterizing recovery in the face of coastal multi-hazards. Therefore, this study presents an integrated approach to quantify the socio-physical disruption following hurricane-induced multi-hazards (e.g., wind, storm surge, wave) by considering the physical damage and functionality of the built environment along with the population dynamics over time. Specifically, high-resolution fragility models of buildings, and power and transportation infrastructures capture the combined impacts of hurricane loading on the built environment. Beyond simulating recovery by tracking infrastructure network performance metrics, such as access to essential facilities, this coupled socio-physical approach affords projection of post-hazard population dislocation and temporal evolution of housing and household recovery constrained by the building and infrastructure recovery. The results reveal the relative importance of multi-hazard consideration in the damage and recovery assessment of communities, along with the role of interdependent socio-physical system modeling when evaluating metrics such as housing recovery or the need for emergency shelter. Furthermore, the methodology presented here provides a foundation for resilience-informed decisions for coastal communities.

Can we color the n³ cells of an n x n x n cube L with n² colors in such a way that each layer parallel to each face contains each color exactly once and that the coloring is symmetric so that Lijl = Lijl i = Lijlij for distinct i, j,\ell{ 1, . . .,n}, and Lijl = Lijl,Lijl = Lijl,Lijl = Ljii for i, j in { 1, . . .,n} Using transportation networks, we show that such a coloring is possible if and only if n equiv 0, 2 (mod 3) (with two exceptions: n can be 1, but n cannot be 3). Motivated by the designs of experiments, the study of these objects (without symmetry) was initiated by Kishen and Fisher in the 1940s. These objects are also closely related to orthogonal arrays whose existence has been extensively investigated, and they are natural three-dimensional analogues of symmetric latin squares. [ABSTRACT FROM AUTHOR]

Adding transportation network redundancy is an effective strategy to hedge against disruptions in the pre-disaster planning stage. However, given the low frequency of disruptions and large financial cost of redundancy design, the economic feasibility of redundancy design has always been challenged. This paper develops a framework to investigate the economic feasibility of adding redundancy to road networks in the pre-disaster planning stage by considering network-wide benefits and stochastic disruption scenarios. Specifically, the redundancy of road networks is defined as route diversity, which refers to the number of behaviorally alternative routes between each origin-destination pair. The redundancy design schemes are formulated by adding parallel links to critical links in current road networks, as these added links provide alternative routes for numerous travelers when the critical links are disrupted. As the redundancy of a transportation system is not only accessible under disruptions but also on duty in normal states, this paper estimates the benefits of redundancy design for road networks in both normal and disruptive states. The Winnipeg network is then used in the case study with the focus on adding redundant bridges. Results indicate that the benefit-cost ratio (BCR) of adding one redundant bridge is 3.51 and redundancy design in road networks is economically feasible. Since the value of BCR is sensitive to the discount rate and the performance of disruptive events, the related data should be collected cautiously when estimating the benefits of redundancy design in practice. Meanwhile, we observe the diminishing marginal effect in the addition of multiple redundant links, which provides insights into the decision-making regarding the optimal amount of redundancy investment in road networks. The proposed framework can be considered as a decision support tool to justify and rank alternative projects from the new perspective of redundancy and resilience. • Develop a framework to investigate the economic feasibility of redundancy design. • Consider network-wide normal and resilience benefits under stochastic disruptions. • Use the Winnipeg network as a case study focusing on adding redundant bridges. • Observe the diminishing marginal effect in the addition of multiple redundant links. [ABSTRACT FROM AUTHOR]

Shortest path problem (SPP) has various applications in areas such as telecommunications, transportation and emergency services, and postal services among others. As a result, several algorithms have been developed to solve the SPP and related problems. The current paper extends the TANYAKUMU labelling method for solving the Travelling salesman problem (TSP) to solve SPP in directed transportation networks. Numerical illustrations are used to prove the validity of the proposed method. The main contributions of this paper are as follows: (i) modification of TSP algorithm to solve single source SPP, (ii) the developed method numerically evaluated on four increasingly complex problems of sizes 11×11, 21×21, 23×23 and 26×26 and lastly (iii) the solutions obtained from solving these four problems are compared with those obtained by Minimum incoming weight label (MIWL) algorithm. The proposed algorithm computed the same shortest paths as the MIWL algorithm on all four problems. [ABSTRACT FROM AUTHOR]

The nation's transportation systems are complex and are some of the highest valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and the socioeconomic health of communities, transportation resilience has gained increasing attention in recent years. Previous studies on transportation resilience have heavily emphasized network functionality during and/or following a scenario hazard event by implicitly assuming that sufficient knowledge of structural capacity and environmental/service conditions is available at the time of an extreme event. However, such assumptions often fail to consider uncertainties that arise when an extreme hazard event occurs in the future. Thus, it is essential to quantify and reduce uncertainties to better prepare for extreme events and accurately assess transportation resilience. To this end, this paper proposes a dynamic Bayesian network-based resilience assessment model for a large-scale roadway network that can explicitly quantify uncertainties in all phases of the assessment and investigate the role of inspection and monitoring programs in uncertainty reduction. Specifically, the significance of data reliability is investigated through a sensitivity analysis, where various sets of data having different reliabilities are used in updating system resilience. To evaluate the effectiveness of the model, a benchmark problem involving a highway network in South Carolina, USA is utilized, showcasing the systematic quantification and reduction of uncertainties in the proposed model. The benchmark problem result shows that incorporating monitoring and inspection data on important variables could improve the accuracy of predicting the seismic resilience of the network. It also suggests the need to consider equipment reliability when designing monitoring and inspection programs. With the recent development of a wide range of monitoring and inspection techniques, including nondestructive testing, health monitoring equipment, satellite imagery, LiDAR, etc., these findings can be useful in assisting transportation managers in identifying necessary equipment reliability levels and prioritizing inspection and monitoring efforts. [ABSTRACT FROM AUTHOR]

Bridges and pavements are two major infrastructure components of a transportation network providing mobility of freight and commodities for economic vitality and access to a range of users as social benefits. However, the lack of a comprehensive infrastructure management system incorporating bridges and pavements inhibits accurate performance prediction, optimal maintenance actions, and the associated use of shrinking budgets. This paper presents an integrated probabilistic life-cycle connectivity framework for the performance analysis of transportation networks containing bridges and asphalt pavements by considering flexural and shear failure modes for prestressed concrete and steel bridges and four failure modes, including international roughness index, rut depth, alligator cracking, and transverse cracking, for asphalt pavements. In this framework, neural network–based deep learning models are used to assess the probabilistic performance of transportation networks and to provide guidance for the associated maintenance strategies. An existing transportation network consisting of bridges and asphalt pavement segments is selected to investigate its life-cycle connectivity reliability and component importance using the matrix-based system reliability method. Results show that the consideration of asphalt pavement failure probability has a significant effect on the probability of transportation network connectivity. [ABSTRACT FROM AUTHOR]

After major earthquakes, communities may experience time-evolving population in terms of size and distribution, and varying travel demands, along with the displacement and recovery of residents caused by the damage and restoration of dwelling units. Community transportation can be significantly affected if the changes in population size and distribution are considerable. As a result, the post-earthquake infrastructure reconstruction process is essentially like urban replanning to meet the realistic traffic needs of the remaining and recovering residents and further maximize the sustainability of the community. To fill the gap in existing studies that considered the travel demand as fixed during the long-term recovery stage, it is important to investigate the effects of time-evolving travel demand on transportation resilience modeling and bridge reconstruction planning during the post-earthquake recovery period. A new methodology is proposed to analyze such impact by assessing the time-dependent resilience performance of transportation networks during the post-earthquake recovery stage. Traffic efficiency and safety are the two resilience performance indicators used to evaluate the transportation network. Post-earthquake infrastructure restoration planning is conducted using a heuristic algorithm based on the time-dependent resilience performance indicator. A demonstrative case study is carried out at Shelby County, Tennessee. [ABSTRACT FROM AUTHOR]