Road Disruption Research Articles

Alternative service network design for bus systems responding to time-varying road disruptions

In practice, road disruptions occur frequently, interrupting multiple bus routes at the same time and causing widespread passenger delays. Typically, these disrupted roads are repaired sequentially and then gradually put into service. In response to such time-varying road disruptions, this paper aims to assist bus operators in developing effective alternative service networks for passengers. The proposed approach involves the joint optimization of service-based route adjustments, bus timetables, and passenger assignment to minimize the total passenger cost and weighted bus operation time. Specifically, a novel service-based adjustment strategy is introduced to flexibly adapt each bus service to time-varying road disruptions. An integer programming model is built for the studied problem based on the set of passengers’ time-space itineraries. To efficiently generate these time-space itineraries and solve models for large-scale problems, this paper develops a hierarchical solution framework. The framework consists of three key parts: (1) a column generation procedure to iteratively explore passengers’ spatial paths; (2) a customized extension algorithm to extend these spatial paths to time-space itineraries; and (3) a tailored adaptive large neighbourhood search heuristic to solve the final itinerary-based model. After that, the overall methodology is tested with both an illustrative example and a real-world example in Beijing. Experimental results show that our methodology produces a high-performance solution with only 7.3% of unserved passengers. Besides, compared to the two benchmark adjustment strategies, our service-based adjustment strategy reduces the average itinerary cost for all passengers by 27.0% and 43.3%, respectively.

A Bayesian updating framework for calibrating the hydrological parameters of road networks using taxi GPS data

Abstract. Hydrological parameters should pass through a careful calibration procedure before being used in a hydrological model that aids decision making. However, significant difficulty is encountered when applying existing calibration methods to regions in which runoff data are inadequate. To achieve accurate hydrological calibration for ungauged road networks, we propose a Bayesian updating framework that calibrates hydrological parameters based on taxi GPS data. Hydrological parameters were calibrated by adjusting their values such that the runoff generated by acceptable parameter sets corresponded to the road disruption periods during which no taxi points are observed. The proposed method was validated on 10 flood-prone roads in Shenzhen and the results revealed that the trends of runoff could be correctly predicted for 8 of 10 roads. This study demonstrates that the integration of hydrological models and taxi GPS data can provide viable alternative measures for model calibration to derive actionable insights for flood hazard mitigation.

Multi-route coordination for bus systems in response to road disruptions

Road disruptions frequently occur in practice, resulting in a productivity loss of bus services and widespread passenger delays. These negative impacts are significant, especially when multiple routes are influenced at the same time. In order to mitigate these impacts, this paper proposes a multi-route coordination approach that collaboratively adjusts multiple bus routes and optimizes bus timetables to provide effective alternative bus services for passengers. Three adjusting strategies are adopted for bus routes with varying passenger demand: detouring, short-running, and cancellation. To address the multi-route coordination problem, a column-generation-based two-stage framework is developed. Concretely, a column generation technique is utilized in the first stage to iteratively generate candidate adaptive bus routes and passenger paths. Following that, an integrated integer programming model is built in the second stage to simultaneously determine bus timetables and the combinations of those adaptive bus routes. After realizing the low computational efficiency for large-scale problems, this paper designs a customized decomposition algorithm based on set partitioning to solve the presented model and obtain near-optimal solutions efficiently. Finally, the proposed methodology is applied to an illustrative Sioux Falls network and a real-world bus network in Beijing to verify its validity and effectiveness. Three comparative analyses are conducted to discuss the advantages of the three adjusting strategies, to investigate the benefits of coordinating bus timetables, and to explore the applicability of different adjusting strategies, respectively.

Landslide risk of regional roads: Consider the road mileage of expected losses

Considering the road disruption caused by landslides, this study develops a quantitative method to evaluate the landslide risk encountered by regional roads. First, a logistic regression model was performed to evaluate the landslide possibility with nine landslide factors and 359 landslide samples. Subsequently, network self-adjustment and three vulnerable factors were integrated to depict road vulnerability. After determining the cost of different road sections, the landslide risk of roads was calculated and expressed as a potentially expected loss. Results show that the study area highlights the road segments with low landslide risk, focusing on the expected loss of 0.0–0.1 million CNY; while lines of Tahe-Great Khingan-Mohe and Mohe-Huma maintain the highest risk, and the expected loss exceeds 0.8 million CNY. Furthermore, the lower the road class, not only the greater the expected loss, but the higher the road mileage exposed to landslides, which facilitates the mitigation strategy for different types of roads in response to landslides.

Traffic resilience modeling for post-earthquake emergency medical response and planning considering disrupted infrastructure and dislocated residents

Quick access to hospitals is essential for severely injured people during post-earthquake emergency medical response. In addition to emergency medical vehicles, most injured residents are often transported to hospitals by private vehicles. Unlike emergency vehicles, private vehicles carrying injured people don't have the driving privilege and therefore become part of regular traffic. Meanwhile, there is usually considerable traffic from dislocated people subjected to various levels of road disruptions following earthquakes. A realistic and efficient assessment of the resilience performance of the disrupted transportation network during the emergency medical response is crucial to saving as many lives as possible through optimal emergency response planning. This study proposes a new methodology to quantify the resilience of disrupted transportation networks during the emergency response stage considering private-vehicle-based injury transport and evacuation traffic. The proposed methodology simulates the traffic during the emergency medical service using the dynamic traffic assignment method by considering various road disruptions. A resilience index is proposed based on the traffic efficiency of the hospitalization trips and uncertainties in earthquakes and infrastructure vulnerability. Finally, the reasonable number of EMS vehicles is investigated from the optimal emergency medical transportation perspective.

Optimal Solution Design for Initial Post-earthquake Casualty Dispatch Considering Road Disruption Scenarios: Based on Network Optimization Model

The optimization of casualty dispatch is a key problem faced in the initial post-earthquake relief process. In view of the urgency and uncertainty of casualty rescue in the early post-earthquake period, this paper investigates the decision optimization problem of casualty dispatching in the early post-earthquake period. Based on the fact that current research on emergency disaster relief in the early post-earthquake period rarely considers the bi-objective optimization problem of transport time and system cost, this paper quantifies the risk of road disruption based on the earthquake risk level and designs a location-routing optimization model with the goal of minimizing the cost of the casualty dispatch system and minimizing the casualty transport time, which provides a solution for casualty dispatch in the initial post-earthquake period. Finally, the example of Sichuan Ya’an Lushan earthquake is used to search for the least costly and most efficient emergency rescue solution. Under this solution, even if the road network in some of the affected sites is severely damaged, timely treatment of casualties and optimal transportation system cost can still be ensured. Moreover, it can adjust measures to local conditions, and decision-makers can make a trend between shortening casualty dispatching time and reducing total system cost based on casualty injuries and road network conditions in the affected sites. Based on the casualty situation and the road network condition in the affected area, we make a trade-off between shortening the casualty dispatch time and reducing the total cost of the system to achieve the optimal casualty dispatch benefit.

A robust model for emergency supplies prepositioning and transportation considering road disruptions

Proper prepositioning of emergency supplies can dramatically improve the efficiency of emergency response work. However, the uncertainties of emergency demands and road conditions bring difficulties to the prepositioning of emergency supplies. This paper proposes a two-stage robust model to locate emergency supply points and preposition the corresponding storage amount of emergency supplies, in which we presented two budget sets to describe the uncertainties of demands and road conditions, respectively. The innovative use of variables in the model to limit road capacities addresses the representation of different road interruption scenarios. We proposed an algorithm based on Benders decomposition by transforming the second-stage model into a binary linear programming model. Computational experiments based on the Sioux Falls network demonstrate the validity of the model and algorithm. We conducted sensitivity analyses for some important parameters in the model, such as two uncertainty control parameters, unit transportation cost, budget for the construction of emergency supply points, etc. We find that the uncertainty of road disruptions has a greater impact on the model than the uncertainty of demands. In addition, when the control parameter of the road disruptions exceeds a certain threshold, its influence on the model remains essentially constant.

Exploring Natural and Social Factors Affecting Road Disruption Patterns and the Duration of Recovery: A Case from Hiroshima, Japan

For the past few decades, the occurrence and severity of disasters have been increasing. This study empirically explores factors affecting road disruption patterns and the duration of road recovery based on the road network disruption and recovery record in Hiroshima, Japan, over the last 19 years, using (1) a binary logit model to identify factors affecting the disruption probability of each road link, and (2) a survival model to identify the factors affecting the recovery duration. We divided the factors into social and natural factors, where the former might be easier for policy makers to control. Results show that not only natural factors, but also social factors, particularly who manages the road, significantly affect both the probability of road disruptions and road recovery duration. This implies that the ability and available resources that each road manager has firstly affects the quality of the road, which in turn affects the probability of it being disrupted, and secondly affects the quickness of taking recovery actions. This points to potential avenues for improving coordination across cities, prefectures, and national road managers in managing roads during disasters.

Analysis of Influencing Factors of Urban Community Function Loss in China under Flood Disaster Based on Social Network Analysis Model.

The increasing frequency of floods is causing an increasing impact on urban communities. To identify the key influencing factors of functional loss in Chinese urban communities under floods, this paper explored the influencing factors and factor combinations through a social network analysis approach using the 265 cases of urban communities in China affected by floods collected from 2017–2021 as research data. The key influencing factors and factor combinations were identified comprehensively using multiple indicator analyses such as core-periphery structure, node centrality, and factor pairing. The analysis results showed that “road disruption”, “housing inundation”, and “power interruption” are the three most critical factors affecting the functional loss of urban communities in China under floods, followed by “residents trapped”, “enterprises flooded”, and “silt accumulation”. In addition, “road disruption–housing inundation”, “housing inundation–residents trapped”, and “road disruption–residents trapped” are the most common combinations of influencing factors.

Integrating criticality concepts into road network disruption assessments for volcanic eruptions

Road networks in volcanically active regions can be exposed to various volcanic hazards from multiple volcanoes. Exposure assessments are often used in these environments to prioritise risk management and mitigation efforts towards volcanoes or hazards that present the greatest threat. Typically, road exposure has been assessed by quantifying the amount of road network affected by different hazards and/or hazard intensity. Whilst this approach is computationally efficient, it largely fails to consider the relative importance of road segments within the network (i.e., road criticality). However, road criticality is an important indicator of the disruption that may be caused by an eruption. In this work, we aim to integrate road criticality concepts to enhance typical volcanic eruption road exposure assessments into road disruption assessments. We use three key components to quantify disruption: a) road criticality, b) impact severity, and c) affected road quantity. Two case study eruptions: Merapi 2010 and Kelud 2014, both in Java, Indonesia, are used to demonstrate the usefulness of integrating road criticality into road disruption assessments from volcanic eruptions. We found that disruption of the road network from the Kelud 2014 case study was an order of magnitude greater than the Merapi 2010 case study. This is primarily driven by the more widely dispersed tephra fall from the Kelud 2014 event, which affected nearly 28% of Java’s road network length, compared to Merapi 2010, which affected 1.5%. We also identified potential disruption hotspots that were affected by both of these case study eruptions. At Merapi, roads that carry traffic directly away from the summit, those that cross major valleys, and the major Yogyakarta-Magelang highway were key disruption hotspots, which has implications for moving large volumes of traffic efficiently, such as in an evacuation. The Kelud case study highlighted the potential impacts of widespread tephra falls on socio-economic activity and connectivity of large urban centres. Our approach has been designed such that it can be applied entirely using open-sourced datasets. Therefore, the approach to integrating road criticality in this paper can be used, applied, and adapted to assess road network disruption at any volcano in the world.

Evaluating the Effects of Disruptions on the Behavior of Travelers in a Multimodal Network Utilizing Agent-Based Simulation

Disruptions in transport networks have major adverse implications on passengers and service providers, as they can yield delays, decreased productivity, and inconvenience for travelers. Previous studies have considered the vulnerability of connections and infrastructures. Although such studies provide insights on general disruption management approaches, there is a lack of knowledge concerning integrated multi-level traffic management and its effects on travelers to reduce the impacts of disruptions. Integrated multi-level traffic management refers to coordinating individual network operations to create an interconnected mobility management system. This study sought to assess the management of road disruption utilizing multi-level disruption management. Multi-level disruption management is proposed that integrates an information dissemination strategy and allows changing the functionality of parking spaces to traffic lanes to facilitate the movement of travelers. The capacity/frequency of public transport vehicles is also increased to help travelers reach their destinations by changing to public transport mode. To achieve such goals, an extension to an agent-based simulation was developed. Numerical experiments are applied to a part of the city of Zürich. The results indicate that the proposed approach, multi-level disruption management in a multimodal network, can shorten travelers’ delays, especially comparing the effects of disruption management. Results show heterogeneity of behavior among agents. Adding lanes as a disruption management enhances the usage of car-mode by all agents, whereas it reduces the usage of car-mode by the directly affected agents, those who cannot pass the disrupted roads. In the presence of full information and increased capacity of transit vehicles, delay is reduced by 47%.

Multi-Route Coordination for Bus Systems in Response to Road Disruptions

Multi-Route Coordination for Bus Systems in Response to Road Disruptions

Planning road network restoration and relief distribution under heterogeneous road disruptions

Planning road network restoration and relief distribution under heterogeneous road disruptions

Dynamic network flow optimization for real-time evacuation reroute planning under multiple road disruptions

During the course of an evacuation, evacuees often encounter unexpected incidents interrupting their plans for evacuation. Roads may not be accessible due to flooding, wild-fire propagation, accidents, the collapse of highway structures, and various other reasons. The evolving disturbances to the evacuation plan due to road disruptions may prolong the evacuation process and lead to chaos, injuries, and loss of life unless a quick, efficient recovery plan is implemented. In this work, we aim to provide a rerouting approach for an evacuation network that undergoes road disruptions. Unlike previous studies, it is assumed that incidents can occur on multiple roads and that the time of each occurrence can differ from the time of other occurrences. Flow optimization techniques are used to represent evacuation traffic flow on the transportation network. A dynamic traffic flow rate is considered in which the evacuation flow rate can change over time during the planning horizon. The variation in the flow rates enables a better projection of the traffic dynamics and consequences caused by disturbances. Furthermore, a path-based dynamic network flow optimization formulation is proposed to make the model scalable for large evacuation networks. Two preprocessing algorithms are introduced to calculate specific parameters associated with road disruptions and topology of the evacuation network. The use of these parameters enables us to transform the original optimization model into a linear model to reduce the computational burden. Numerical experiments are made to show the performance of the proposed model. Furthermore, the effects of specific features such as disruption time, disturbance location, and the plan updating time on the evacuation process are investigated. Results indicate that when more incidents occur later or when incident information is received earlier, the magnitude of the rerouting completion time is lessened.

A Study of Evacuation Plan considering Road Disruption Measures in Densely Built-up Wooden Areas in a Large-Scale Earthquake

A Study of Evacuation Plan considering Road Disruption Measures in Densely Built-up Wooden Areas in a Large-Scale Earthquake

Multi-modal transportation planning for multi-commodity rebalancing under uncertainty in humanitarian logistics

Multi-commodity rebalancing plays a critical role before and during the attack of large-scale disasters. In practice, some relief centers can be out of reach from the ground for vehicles due to the road disruption. Accordingly, alternative transportation systems are essential to maximize fairness and minimize the total transportation time, simultaneously. However, little study has reported on this issue for humanitarian logistics. To address it, a bi-objective stochastic optimization model is proposed to rebalance and transport commodities with the multi-modal transportation system. This work first linearizes the model and then applies an adaptive augmented E-constraint method to obtain a number of Pareto-optimal solutions. Furthermore, a case study of an emergency event is carried out, of which the computational results indicate its decision making effectiveness. Lastly, sensitivity analysis on critical parameters is conducted and the trade-off between the objectives is also analyzed to provide valuable managerial insights.

Fleet routing and scheduling in bushfire emergency evacuation: A regional case study of the Black Saturday bushfires in Australia

Evacuation planning provides evidence base for effective decision-making in various aspects of disaster response, such as the selection of safe shelters, dynamic assignment of rescue vehicles, and the identification of safest routes and vehicle scheduling. However, an efficient emergency service response to a short-notice bushfire evacuation is a complicated procedure. Any failure to response promptly to short notice evacuation can adversely affect the effectiveness of operations and hence potentially contribute to increased fatalities. This study develops a Vehicle Routing Problem (VRP) model to enhance the efficiency and effectiveness of short-notice emergency response and rescue operations. This model computes the required number of vehicles and identifies the safest routes and schedules for late evacuees under various time windows and road disruption risk. A heuristic solution method is developed to tackle this highly complex nature of VRP due to operational interdependencies. The model parameters are realistically derived from the 2009 Black Saturday bushfires in Victoria, Australia. The results indicate the feasibility of evacuating 1100 late evacuees via low risk routes within the available resources of four shelters and seven rescue vehicles. However, the evacuation of late evacuees from the bushfire affected areas to safe shelters could be potentially affected when the hard constraints such as the time-window are changed. Nevertheless, the model outputs are useful in the development of emergency evacuation plans to mitigate the potential risk of fatality or injury during a bushfire.

Enhancing emergency evacuation response of late evacuees: Revisiting the case of Australian Black Saturday bushfire

This paper develops a multi-objective integer programming model to support tactical planning decision-making during a short-notice evacuation using the situated context of the 2009 Black Saturday bushfires in Victoria. Various bushfire scenarios and sensitivity analysis considering short time windows, availability of resources and road disruptions were implemented to demonstrate the robustness and reliability of the model. The ε-constraint technique was applied to solve the problem. Results showed that it would be possible to evacuate all late evacuees during the Black Saturday bushfire events, even if one or two resources are disrupted within the hard time window constraint.

Systemic Vulnerability and Risk Assessment of Transportation Systems Under Natural Hazards Towards More Resilient and Robust Infrastructures

Transportation infrastructures are complex systems of various connected components like bridges, roads, tunnels, embankments, retaining walls in case of a highway system or wharfs, cranes, buildings, utility systems in case of port facilities. Due to their spatial extent, they are exposed to variable natural hazards such as earthquakes, tsunami or landslides. Experience from past disastrous events shows that transportation infrastructures are quite vulnerable due to the lack of redundancy, the lengthy repair time, the rerouting difficulties or the cascading failures and interdependencies. Their damage could be greatly disruptive in terms of safety of life, business disruption, access to emergency services and key lifelines utilities, rescue operations and socio-economic impacts. Therefore, in terms of resilience it is important to recognize and quantify the risks and global losses associated to damages of transportation systems and to establish efficient risk mitigation strategies. These include, among others, enhancement of emergency preparedness, strengthening of existing structures and improvement of the recovery planning.Herein an integrated framework for the probabilistic systemic vulnerability and risk assessment of transportation and utility networks is presented, based on the achievements of the recently completed EC project SYNER-G (www.syner-g.eu) and the ongoing EC project STREST (www.strest-eu.org). The infrastructure is modeled according to a detailed taxonomy. The framework encompasses in an integrated fashion all aspects in the chain, from regional hazard to fragility assessment of components to the socio-economic impacts of a natural disaster, accounting for relevant uncertainties within an efficient quantitative simulation scheme, and modeling interactions between multiple component systems in the taxonomy. Selected Performance Indicators (PIs) are calculated for each network based on the estimated damages and functionality losses of the different components under the given hazards.The methodology and tools are demonstrated through case studies in the road network and the harbor of Thessaloniki city, Greece, under seismic hazard and associated geotechnical hazards (i.e. soil liquefaction). The applications include assessments of systems’ performance considering the spatial seismic hazard with correlation of ground motion intensities, the vulnerability of the network components, and the effect of interactions within the system, as well as, between components of different systems. In particular, road disruptions can be caused due to direct damage of road segments and bridges, as well as building and overpass collapses. Harbor operations can be disturbed due to failures of waterfront structures and cargo handling equipment, as well as disruptions to the electric power supply and building collapses. The systemic risk for the road network and harbor is calculated, specifically focusing on the short-term impact of seismic events (just after the earthquake) and the risk curves (i.e. mean annual rates of exceedance for loss in performance of the infrastructures) are provided. The significant elements for the functionality of each system are defined through correlation factors to the system PIs. Such results can contribute to the decision-making regarding the enhancement of existing and the robust development of new infrastructures in the frame of safety and resiliency.

The multiscale importance of road segments in a network disruption scenario: a risk-based approach.

This article addresses the problem of the multiscale importance of road networks, with the aim of helping to establish a more resilient network in the event of a road disruption scenario. A new model for identifying the most important roads is described and applied on a local and regional scale. The work presented here represents a step forward, since it focuses on the interaction between identifying the most important roads in a network that connect people and health services, the specificity of the natural hazards that threaten the normal functioning of the network, and an assessment of the consequences of three real-world interruptions from a multiscale perspective. The case studies concern three different past events: road interruptions due to a flood, a forest fire, and a mass movement. On the basis of the results obtained, it is possible to establish the roads for which risk management should be a priority. The multiscale perspective shows that in a road interruption the regional system may have the capacity to reorganize itself, although the interruption may have consequences for local dynamics. Coordination between local and regional scales is therefore important. The model proposed here allows for the scaling of emergency response facilities and human and physical resources. It represents an innovative approach to defining priorities, not only in the prevention phase but also in terms of the response to natural disasters, such as awareness of the consequences of road disruption for the rescue services sent out to local communities.