Traffic Evacuation Research Articles

Piston problem for the pressureless hydrodynamic traffic flow model

The hydrodynamic model can be used to describe traffic problems in transport. When the speed of the first car is less than the speed behind it, it leads to traffic jams. When the first car's speed is faster than the cars behind it, it leads to traffic evacuation. If we consider the first car to be a piston, then the speed of the piston will cause traffic jams and traffic evacuation. In this paper, we study the piston problem for the hydrodynamic model. The formation and propagation of shock wave, rarefaction wave, delta-shock wave, and vacuum can describe the phenomena of traffic jams, traffic evacuation, severe traffic jams, and traffic evacuation with traffic volume of zero, respectively. Therefore, for different traffic phenomena, we prove the existence of shock solution, rarefaction solution, delta shock solution, and vacuum solution. In addition, we perform some representative numerical simulations.

The analysis of traffic data of wildfire evacuation: the case study of the 2020 Glass Fire

Evacuation is a crucial policy to mitigate wildfire impacts. Understanding traffic dynamics during a wildfire evacuation can help authorities to improve in improving emergency management plans, thus improving life safety. In this study, we developed a methodology to extract historical traffic data from vehicle detector stations and automate the analysis of traffic dynamics for actual wildfire evacuations. This has been implemented in an open-access tool called Traffic Dynamic Analyser (TDA) which generates speed-density and flow-density relationships from data using both commonly used macroscopic traffic models as well as machine learning techniques (e.g., support vector regression). The use of the methodology is demonstrated with a case study of the 2020 Glass Fire in California, USA. The results from TDA showed a slight reduction in speeds and flows on US Highway 101 during the evacuation scenario, compared with the routine scenario. Moreover, background traffic has been shown to play a key role in the 2020 Glass Fire compared with previous wildfire evacuation scenarios (e.g., the 2019 Kincade fire). The case study showed that the methodology implemented in the TDA can be used to understand traffic evacuation dynamics in wildfire scenarios and to validate evacuation models.

Managing traffic evacuation with multiclass connected and autonomous vehicles

Managing traffic evacuation with multiclass connected and autonomous vehicles

Bimodal Traffic Evacuation Management for Road Tunnels Considering Social Fairness: CTM-Based Model

Bimodal Traffic Evacuation Management for Road Tunnels Considering Social Fairness: CTM-Based Model

Combined Dynamic Route Guidance and Signal Timing Optimization for Urban Traffic Congestion Caused by Accidents

When traffic congestion caused by accidents occurs on urban roads, the current signal control scheme may face failure. It is necessary to establish an effective dynamic route guidance scheme and formulate a reasonable signal timing scheme to alleviate the impact of the accident on urban traffic. In this paper, traffic congestion is divided into three states and the traffic evacuation strategies are formulated, respectively. Then, on the basis of analyzing the internal mechanism and spatiotemporal variation of queuing at intersections, a two-stage model is proposed to optimize the signal timing parameters. In the first stage, the basic timing parameters of each intersection, such as signal cycle and green ratio, are obtained. In the second stage, the offset optimization model is constructed to minimize the total delay time of the coordinated phase of all detour routes in the control scope, considering the early dissipation of queuing vehicles at the entrance of the intersection. This model is solved with a particle swarm optimization algorithm and simulated to the actual road network with VISSIM. As the experimental results show, this control method can greatly reduce the average vehicle delay, stop times, queue length, and the number of vehicles passing.

The verification of wildland–urban interface fire evacuation models

This paper introduces a protocol for the verification of multi-physics wildfire evacuation models, including a set of tests used to ensure that the conceptual modelling representation of each modelling layer is accurately implemented, as well as the interactions between different modelling layers and sub-models (wildfire spread, pedestrian movement, traffic evacuation, and trigger buffers). This work presents a total of 24 verification tests, including (1) 4 tests related to pedestrians, (2) 15 tests for traffic evacuation, (3) 5 tests concerning the interaction between different modelling layers, along with 5 tests for wildfire spread and trigger buffers. The evacuation tests are organized in accordance with different core components related to evacuation modelling, namely Population, Pre-evacuation, Movement, Route/destination selection, Flow constraints, Events, Wildfire spread and Trigger buffers. A reporting template has also been developed to facilitate the application of the verification testing protocol. An example application of the testing protocol has been performed using an open wildfire evacuation modelling platform called WUI-NITY and its associated trigger buffer model k-PERIL. The verification testing protocol is deemed to improve the credibility of wildfire evacuation model results and stimulate future modelling efforts in this domain.

A nuclear emergency partition evacuation framework based on comprehensive risk assessment

Nuclear power has incomparable advantages over traditional fossil energy and has become one of major power sources. Meanwhile, the huge losses in nuclear accidents place higher requirements for emergency response. Nuclear emergency evacuation is an effective protective measure to prevent or reduce the public’s exposure to radioactive materials. However, nuclear emergency evacuation is affected by a variety of factors, including environment, resources, transportation, and radiation, etc. It is difficult for decision makers to analyze a large amount of complex information so as to design effective nuclear emergency evacuation schemes. Considering the potential risk and characteristics of emergency traffic evacuation, a partition evacuation framework based on risk assessment is proposed in this paper. First, to deal with nuclear radiation and traffic evacuation vulnerability, a comprehensive risk assessment method is put forward that can be used for potential risk quantification during nuclear emergency evacuation. Second, according to the risk assessment results, this paper proposes a partition evacuation organization strategy and a cell transmission model (CTM) to minimize the exposure risk of during nuclear emergency evacuation. Finally, taking a coastal nuclear power plant in China as the research object, extensive and in-depth analysis is performed. As suggested by the simulation results, the partition evacuation strategy we proposed could effectively reduce the exposure risk by 36.0% and the average delay is shortened by 50.4%. Compared with traditional evacuation strategies, our framework is useful for increasing the nuclear emergency evacuation efficiency and alleviating traffic congestion.

Modeling pedestrian–vehicle mixed-flow in a complex evacuation scenario

Traffic simulation model has been widely used in urban traffic evacuation and pedestrian evacuation. However, few research efforts have been devoted to a complex evacuation scenario that includes vehicles and pedestrians. Even some of the research studies have focused on pedestrian–vehicle mixed-flow simulation models; they can hardly simulate the pedestrian–vehicle interaction dynamics in complex evacuation scenarios such as intersections, parking lots, and so on. This paper developed a pedestrian–vehicle mixed-flow model based on the Social Force Model (SFM), a car-following model, and a lane change model to solve this problem. Further, the vehicle–pedestrian force and Time to Conflict (TTC) model the interaction between people and vehicles. In the case study of parking lot evacuation, the model is proved to be effective in simulating the behavior of pedestrian–vehicle flow in a complex scenario.

Dynamic Emergency Vehicle Path Planning and Traffic Evacuation Based on Salp Swarm Algorithm

In view of the rescue delay due to traffic congestion in the urban road network, this paper implemented real-time traffic control with congestion index constraints in emergency vehicle dispatching and proposed a two-stage optimization model and algorithm. In the first stage, salp swarm algorithm (SSA) was combined with Dijkstra algorithm, and a novel hybrid algorithm with new updating rules was designed to get the multiple alternative paths. In the second stage, an improved salp swarm algorithm (ISSA) with a population grouping strategy was proposed to obtain the best evacuation schemes and the optimal rescue paths of emergency vehicles. Results of the illustrative examples show that, after evacuation, the average travel time of all alternative paths is reduced by 24.22%, while traffic congestion indexes of the adjacent road sections almost unchanged. The computation time of the hybrid algorithm for obtaining the set number of alternative paths is 56.62% and 50.47% shorter than that of bat algorithm (BA) and SSA. For the solution of the evacuation model, the computation time of the ISSA is 33.51%, 30.15%, and 30.60% shorter than that of particle swarm optimization (PSO), BA, and SSA, and the optimal solution of the ISSA is 25.92%, 10.06%, and 0.97% better than that of PSO, BA, and SSA. That is, we shorten the emergency response time and control the adverse impact of traffic evacuation on background traffic. The improved algorithm has excellent performance. This study provides a new idea and method for emergency rescue of traffic accidents.

Lane-based multi-class vehicle collaborative evacuation management

Mixed traffic evacuation management is recognized as a critical and challenging task. This paper develops a bi-level model to address the multi-class vehicle collaborative evacuation problem. The upper-level program is a mixed-integer nonlinear programming (MINLP) model to determine the optimal lane allocation scheme according to the fleet size. The lower-level program is formulated as multiple independent system optimal dynamic traffic assignment (SO-DTA) problems to load mixed traffic flow. A two-layer solution method integrating branch-and-bound algorithm is developed to solve the model. Finally, the optimization model is applied in a small network and a real-world network. Results demonstrate that (1) the evacuation performance of multi-class vehicle collaboration is better than that of single-class vehicles under the medium demand levels; (2) the proposed model realizes the coupling among evacuation demand, fleet size, and road network capacity; (3) the shortest path should first be allocated to the bus during a mass evacuation.

Developing Transportation Response Strategies for Wildfire Evacuations via an Empirically Supported Traffic Simulation of Berkeley, California

Government agencies must make rapid and informed decisions in wildfires to evacuate people safely. However, current evacuation simulation tools for resource-strapped agencies largely fail to compare possible transportation responses or incorporate empirical evidence from past wildfires. Consequently, this study employs online survey data from evacuees of the 2017 Northern California Wildfires ( n = 37), the 2017 Southern California Wildfires ( n = 175), and the 2018 Carr Wildfire ( n = 254) to inform a policy-oriented traffic evacuation simulation model. The simulation is tested for a hypothetical wildfire evacuation in the wildland-urban interface of Berkeley, California. The study focuses on variables including fire speed, departure time distribution, towing of items, transportation mode, GPS-enabled rerouting, phased evacuations (i.e., allowing higher-risk residents to leave earlier), and contraflow (i.e., switching all lanes away from danger). It was found that reducing evacuating household vehicles (i.e., to one vehicle per household) and increasing GPS-enabled rerouting (e.g., 50% participation) lowered exposed vehicles (i.e., total vehicles in the fire frontier) by over 50% and evacuation time estimates (ETEs) by about 30% from baseline. Phased evacuations with a suitable time interval reduced exposed vehicles most significantly (over 90%) but produced slightly longer ETEs. Both contraflow (on limited links because of resource constraints) and slowing fire speed were effective in lowering number of exposed vehicles (around 50%), but not ETEs. Extended contraflow can reduce both exposed vehicles and ETEs. It is recommended that agencies develop a communication and parking plan to reduce the number of evacuating vehicles, create and communicate a phased evacuation plan, and build partnerships with GPS-routing services.

Bp-ant colony algorithm: escape the Louvre

With the spread of terrorism in the new era, France has suffered many terrorist attacks in recent years. The new situation calls for a review of emergency evacuation plans for popular destinations. Taking the Louvre Museum as the research object, this paper discusses the emergency evacuation plan in detail. At the same time, in order to make effective planning, this study selects the day with the largest number of visitors to the Louvre Museum as the sample. In the establishment of emergency evacuation model, ant colony algorithm is introduced. The training time of two kinds of traffic evacuation algorithm is given, and BP neural network is used for training. In addition, the dynamic Bottleneck Identification Method Based on fuzzy set theory is used in this study. Through model optimization, the possible bottleneck location is accurately determined. Finally, this paper analyzes the advantages and disadvantages of the model, and gives the specific evacuation routes.

Mass evacuation microsimulation modeling considering traffic disruptions

This study presents a framework of traffic evacuation microsimulation modeling that accounts for uncertain network disruptions endogenous to traffic operations. While evacuation modeling considers external stresses such as flooding-related network disruptions, the risks inherent to the transport operations, particularly vehicle collisions may also cause disruptions to evacuation traffic flows. This study adopts a combined Bayes theory and Monte Carlo simulation approach to identify collision hotspots and their occurrence over different times of an evacuation day. A traffic evacuation microsimulation model is developed which explicitly incorporates vehicle collision-related disruptions at the hotspots identified by this probabilistic model. The proposed probabilistic approach identifies 128 candidate collision locations within the study area. The probabilities of candidate locations to anticipate a vehicle collision range between 0.21 and 7.0%. Based on the probabilities, the Monte Carlo simulation approach identifies five hotspots for traffic microsimulation modeling of vehicle collisions during the evacuation. The results from the traffic simulation reveal that due to concurrent collision occurrence, evacuation times vary within 23–31 h depending on the time required to remove traffic disruptions from the network. On the other hand, the concurrent collision occurrence at the hotspots increases the complete evacuation time by almost 11 h if the disruption is not removed from the network, an increase of 50%, compared to an evacuation scenario without disruptions. The analysis of simulated queue length reveals that the hotspots’ traffic queues range from 0.28 to 2.06 km depending on their locations in the study area. The study asserts that an evacuation model without the consideration of the network disruptions due to endogenous risks may underestimate the traffic impacts and network clearance time for an evacuation. These results will provide emergency professionals with insights into managing emergency traffic operation subjected to uncertainties.

The simulation of wildland-urban interface fire evacuation: The WUI-NITY platform

Wildfires are a significant safety risk to populations adjacent to wildland areas, known as the wildland-urban interface (WUI). This paper introduces a modelling platform called WUI-NITY. The platform is built on the Unity3D game engine and simulates and visualises human behaviour and wildfire spread during an evacuation of WUI communities. The purpose of this platform is to enhance the situational awareness of responders and residents during evacuation scenarios by providing information on the dynamic evolution of the emergency. WUI-NITY represents current and predicted conditions by coupling the three key modelling layers of wildfire evacuation, namely the fire, pedestrian, and traffic movement. This allows predictions of evacuation behaviour over time. The current version of WUI-NITY demonstrates the feasibility and advantages of coupling the modelling layers. Its wildfire modelling layer is based on FARSITE, the pedestrian layer implements a dedicated pedestrian response and movement model, and the traffic layer includes a traffic evacuation model based on the Lighthill-Whitham-Richards model. The platform also includes a sub-model called PERIL that designs the spatial location of trigger buffers. The main contribution of this work is in the development of a modular and model-agnostic (i.e., not linked to a specific model) platform with consistent levels of granularity (allowing a comparable modelling resolution in the representation of each layer) in all three modelling layers. WUI-NITY is a powerful tool to protect against wildfires; it can enable education and training of communities, forensic studies of past evacuations and dynamic vulnerability assessment of ongoing emergencies.

Modeling transit-assisted hurricane evacuation through socio-spatial networks

ABSTRACT Increasing intensity and frequency of hurricane events underscores the need for efficient and inclusive evacuation plans, particularly for carless and disabled populations. Hurricane evacuation intrinsically involves both social and spatial processes. People’s decision to evacuate spreads over social networks; once their decisions are made, they flee through spatial transportation networks. This article describes a novel effort to integrate socio-spatial networks into an agent-based evacuation simulation model, taking the Florida Keys in the USA as a study area. In the model, households, as agents, were synthesized from Census data, then connected by a ‘home-workplace-neighborhood’ social network, and registered to a spatial road network. A threshold decision model was used to simulate social contagion of households’ decision to evacuate. The resulting travel demands were input into the TRANSIMS platform to generate on-road traffic. The model analyzed scenarios of automobile-only and public transit-assisted evacuation. The results show that the simulated traffic under the automobile-only scenario aligns with the observed traffic dynamics, which validates our socio-spatially integrated model. Adding public transportation capacity significantly reduces the traffic load and evacuation time, and provides a practical, accessible, and equitable route to safety for low mobility populations.

Simulation pipeline for traffic evacuation in urban areas and emergency traffic management policy improvements through case studies

Traffic evacuation plays a critical role in saving lives in devastating disasters such as hurricanes, wildfires, floods, etc. An ability to evaluate evacuation plans in advance for these rare events, including identifying traffic flow bottlenecks, improving traffic management policies, and understanding the robustness of the traffic management policy are critical for emergency management. Given the rareness of such events and the corresponding lack of real data, traffic simulation provides a flexible and versatile approach for such scenarios, and furthermore allows dynamic interaction with the simulated evacuation. In this paper, we build a traffic simulation pipeline to explore the above problems, covering many aspects of evacuation, including map creation, demand generation, vehicle behavior, bottleneck identification, traffic management policy improvement, and results analysis. We apply the pipeline to two cases studies in California. The first is Paradise, which was destroyed by a large wildfire in 2018 and experienced catastrophic traffic jams during the evacuation. The second is Mill Valley, which has high risk of wildfire and potential traffic issues since the city is situated in a narrow valley.

Individual Driving Behaviour in Wildfire Smoke

This work presents the results of a virtual reality (VR) experiment aiming at investigating how individual driving behaviour is affected by the presence of wildfire smoke. The experiment included a driving simulation task to study the chosen driving speed at different smoke densities and the lateral position of the driven car on the road cross section. During the VR experiment, participants were presented with a simulated wildfire evacuation scenario including the presence of smoke through a head mounted display and were given a task to evacuate via car using a steering wheel and pedals on a single carriageway road with two lanes. A total of 46 participants took part in the experiments and their driven trajectories along with their instantaneous speed were collected in 5 different visibility conditions. Driving speed decreased with increasing smoke density. No difference in choice of speed was found in relation to the smoke density in the previously driven road segment (in thicker or thinner smoke). No difference in lateral position (closer to or further from the centreline of the road) at different smoke densities was found. Suggested correlations between driving speed and wildfire smoke density are provided in this paper, referring to either a fractional reduction of the speed in smoke-free conditions or an absolute choice of speed at a given visibility condition. These correlations are useful to provide more accurate estimation of evacuation times with traffic evacuation modelling tools in case of wildfire and wildland-urban interface fire scenarios.

Design and Development of Traffic Evacuation Software Based on Micro Simulation

In order to better evacuate residents in the case of regional disasters and ensure the safety of the lives and properties of regional residents, we designed and developed traffic evacuation simulation software under various disasters based on microscopic simulation. The system adopts C / S architecture, MVC design mode, and has the functions of road network simulation, intersection simulation, vehicle operation simulation and signal control simulation. It is loaded through the simulation program, compares different evacuation schemes, optimizes the setting of space division, path division, signal Phase and signal phase. Provide reference basis for the relevant departments to evacuate residents.

Developing and validating evacuation models for fire safety engineering

Evacuation models can adopt different approaches for the simulation of human behaviour in fire. This paper provides an overview of the most commonly used modelling methods to represent the evacuation process in a fire scenario. This is presented through a structure matching the engineering time-line model of evacuation. The evacuation model development process is discussed considering both data-driven empirical correlations as well as theory-based modelling approaches. Examples of alternative methods to the currently used evacuation modelling assumptions are also presented. These methods have been chosen to provide examples of cases in which revisions of well-established assumptions may be needed. This review mainly focuses on buildings and pedestrian evacuation scenarios. Nevertheless, many concepts presented are potentially applicable to traffic evacuation. Particular attention is given to the representation of the impact of smoke on the evacuation process, as this is an important issue for fire safety engineering. Finally, a discussion on existing methods and procedures for the verification and validation of evacuation models is presented and the need for their standardization is advocated.

Modeling evacuation route choices under influence of variable message signs

AbstractThis work develops a novel methodology for investigating dynamic evacuation route switching behavior and its influence on evacuation traffic assignment considering evacuees’ intuitionistic fuzzy perception and cognition of multitype multiattribute real‐time variable message sign (VMS) information. The methodology has three distinctive features. First, a link‐based VMS information utility model is developed. Second, two link‐specific dynamic escape speed models (DESMs), characterized by VMS information, evacuee attributes, and hazard‐related information, are proposed. Third, VMS information characteristics and the intuitionistic fuzzy method are integrated into route choice behavior models. The link‐specific DESMs are used to adjust the reactive DYNASTOCH algorithm to simulate emergency traffic evacuation. Results demonstrate that the proposed models are effective in characterizing not only dynamic evacuation route switching behavior (microscopic) but also network‐wide evacuation traffic performances (macro) under behavioral and information uncertainties.