EVACUATION ROUTE PLANNING: A CASE STUDY IN SEMANTIC COMPUTING

This paper proposes an implementation of linear programming in tsunami horizontal evacuation routes decision making. A tsunami is a huge wave that travels with high speed towards a coastal area. Padang is the capital city of West Sumatera Province that is located in the coastal area as well as have a history of the tsunami disaster. The main problem is only a few minutes of the interval for the tsunami waves arrive at the coast after tsunami hazard warning. In the evacuation concept, the evacuees from the high-risk zone are moving to the shelter through a safe evacuation route. This research aims to determine the optimal evacuation routes with the constraints of a few minutes of the interval of evacuation time, the capacity of each shelter, and the number of evacuees. The evacuation time is an objective function that depends on the number of population, people density, road width, distance from the high-risk zone to the shelter, and velocity. The numerical simulation is done using linear programming with Matlab. The results show that all evacuees in each high-risk region are proportionally evacuated to available shelter areas within allocated evacuation time. The results of this research can be used for supporting tsunami evacuation plans and disaster resilience management by the authorities in Padang.

Efficient evacuation planning is important for quickly navigating people to shelters during and after an earthquake. Geographical information systems are often used to plan routes that minimize the distance people must walk to reach shelters, but this approach ignores the risk of exposure to hazards such as collapsing buildings. We demonstrate evacuation route assignment approaches that consider both hazard exposure and walking distance, by estimating building collapse hazard zones and incorporating them as travel costs when traversing road networks. We apply our methods to a scenario simulating the 2016 Gyeongju earthquake in South Korea, using the floating population distribution as estimated by a mobile phone network provider. Our results show that balanced routing would allow evacuees to avoid the riskiest districts while walking reasonable distances to open shelters. We discuss the feasibility of the model for balancing both safety and expediency in evacuation route planning.

The spatial–temporal simulation of fire disasters and evacuation route planning are important research fields for urban emergency responses and are primary tasks that answer complex questions after fires break out. The increasing demand for refined building information models will sharply increase the calculated and analyzed quantity. This demand presents a challenge for fire emergency responses based on massive building information. In this paper, the principle of the realistic worst case (RWC) is introduced into fire simulation and evacuation route planning. Taking the library of the Nanjing Forestry University as the study object, the spatial–temporal characteristics of the influential environmental factors of the fire are simulated, such as the meteorological elements, building structure, and building skin. The scenario zones that are relatively prone to fire are selected using an overlay analysis across the four seasons. Then, the risk threshold for evacuating personnel is analyzed in the fire zone according to international standards and firefighting criteria. Specific parameters are determined based on the analysis of the above. The growing trends for fires across the four seasons are simulated with scenario zones as the starting positions and incorporate factors such as temperature, carbon monoxide, and smoke. Lastly, a life safety assurance path (LSAP) for personnel evacuation is designed based on an indoor road network and path search algorithm. The evacuation planning result is compared with the traditional shortest-time path and shortest-distance path. Based on the study results, fire scenario zones can improve the speed and operating efficiency of spatial–temporal simulation models of fire and can also support path planning and design for emergency responses.

We are living in the 21st century in which technological advances have greatly improved the quality of human lives. However nature keeps presenting its challenges in the form of tsunamis, floods, wild fires, hurricanes, volcanoes, etc. Excellent evacuation route planning is of paramount importance to reduce loss of lives during such disasters. The parameters of finite time lined capacity of evacuation paths and thousands to millions of evacuees for the problem of evacuation route planning makes it more complex than the problem of finding the shortest path which has been studied extensively in graph theory. Computation time (execution time hereafter) and evacuation time are two key properties which determine the efficiency and effectiveness of evacuation routing algorithms. This paper proposes a graph theory based evacuation routing algorithm called Forward Backward Shortest Path (FBSP). FBSP uses a novel idea to determine multiple routes by combining multiple paths at a time unlike other evacuation routing algorithms such as Shortest Multiple Path (SMP) that uses just the shortest paths between the source nodes and destination nodes. The experimental analysis on simulated graphs showed that FBSP improves evacuation time over SMP without significantly increasing execution time.

Given spatio-temporal networks (e.g., roadmaps with traffic speed reported as a time-series in 5 min increments over a typical day for each road-segment) and operators (e.g., network snapshot, shortest path or path evaluation), a spatio-temporal network model provides a computer representation to facilitate reasoning, analysis and algorithm design for important societal applications. For example, next generation routing services are estimated to save consumers hundreds of billions of dollars in terms of time and fuel saved by 2020. Developing a model for spatio-temporal networks is challenging due to potentially conflicting requirements of expressiveness and model simplicity. Related work in Time Geography models spatio-temporal movement and relationships via dimension-based representations such as space-time prisms and space-time trajectories. These representations are not adequate for many STN use-cases, such as spatio-temporal routing queries. To address these limitations, we discuss a novel model called time-aggregated graph (TAG) that allows the properties of the network to be modeled as a time series. This model retains spatial network information while reducing the temporal replication needed in other models, thus resulting in a much more efficient model for several computational techniques for routing problems. In this chapter, we discuss spatio-temporal networks as represented by time-aggregated graphs at a conceptual, logical, and physical level. This chapter also focuses on shortest path algorithms for spatio-temporal networks. We develop the topics via case studies using TAGs in context of Lagrangian shortest-path queries and evacuation route planning.

Recently, emergency evacuation management, which is a social work around the world, has been getting lots of attentions due to its importance and necessity. The primary task of emergency evacuation management is evacuation route planning. Considering the particularity of restrict space scenarios, it is more important to guarantee the security and promptness of evacuation routes than that in open space scenarios. In this paper, we introduce a new evacuation route planning problem in restricted spaces, namely Congestion-Avoidable Evacuation Route Network Planning (CA-ERNP) problem. Based on the minimum cost maximum flow (Min-Max Flow) problem, we propose a batch scheduling algorithm based on node-slitting transformation. In addition, we evaluate the average performance of the algorithms via simulation and the results indicate the proposed algorithm outperforms the existing alternatives in terms of efficiency and time cost.

Emergency evacuation in public places becomes a research focus in recent decades. One major objective of evacuation is to maximize the efficiency of the whole evacuation system. The paper proposes a multiobjective evacuation route assignment model to plan an optimal egress route set for the individual evacuees. The three objectives in the proposed model are to minimize the total evacuation time, minimize the total travel distance of all the evacuees and minimize the congestion during the evacuation process. These objectives need to be satisfied simultaneously while some of them conflict with each other. The travel speed on each road segment is related with the time and the number of evacuees on it during a certain time period. The congestion of a road segment is modeled as the density of evacuees passing it in time and space dimensions. The evacuation route assignment problem can be treated as a combinatorial optimization problem. A multiobjective optimization model based genetic algorithm is adopted to solve the proposed evacuation routing problem. Wuhan Sport Center in Wuhan city of China was taken as the experiment scenario to test the performance of the proposed algorithm. The results showed that it can provide some system optimal evacuation plans. Meanwhile, the multi-objective optimization model based genetic algorithm can produce a pareto optimal set rather than single optimal point, thus the model can give alternative strategies for the evacuation policy makers.

Quantified Linear Programming is the problem of checking whether a polyhedron specified by a linear system of inequalities is nonempty, with respect to a specified quantifier string. Quantified Linear Programming subsumes traditional Linear Programming, since in traditional Linear Programming, all the program variables are existentially quantified (implicitly), whereas, in Quantified Linear Programming, a program variable may be existentially quantified or universally quantified over a continuous range. On account of the alternation of quantifiers in the specification of a Quantified Linear Program (QLP), this problem is non-trivial.KeywordsPolynomial TimeTuring MachineConstraint SystemWinning StrategyProgram VariableThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

First-order primal-dual methods are appealing for their low memory overhead, fast iterations, and effective parallelization. However, they are often slow at finding high accuracy solutions, which creates a barrier to their use in traditional linear programming (LP) applications. This paper exploits the sharpness of primal-dual formulations of LP instances to achieve linear convergence using restarts in a general setting that applies to alternating direction method of multipliers (ADMM), primal-dual hybrid gradient method (PDHG) and extragradient method (EGM). In the special case of PDHG, without restarts we show an iteration count lower bound of \(\Omega (\kappa ^2 \log (1/\epsilon ))\), while with restarts we show an iteration count upper bound of \(O(\kappa \log (1/\epsilon ))\), where \(\kappa \) is a condition number and \(\epsilon \) is the desired accuracy. Moreover, the upper bound is optimal for a wide class of primal-dual methods, and applies to the strictly more general class of sharp primal-dual problems. We develop an adaptive restart scheme and verify that restarts significantly improve the ability of PDHG, EGM, and ADMM to find high accuracy solutions to LP problems.

In real-world decision-making, uncertainty plays a crucial role, especially when dealing with complex, multi-objective optimization problems. Traditional linear programming (LP) models often have the assumption that the data is precise and deterministic. However, this ‎assumption is often not realistic for many applications, as imprecision will always be present. This paper presents a fuzzy solution to ‎Z-Z-number-based multi-objective linear programming (ZMOLP) models. Z-numbers can combine fuzzy logic and Z-numbers to manage ‎uncertainty in (multi-objective) decision dilemmas. A Z-number consists of two parts: a fuzzy number for the uncertainty in the ‎data and a reliability score that indicates the degree of confidence in the data. The two aspects of Z-numbers make them ef‎fective for modeling imprecise data in multi-objective positioning. The effectiveness and overall computational efficiency of different ‎fuzzy membership functions (triangular, trapezoidal, Gaussian, etc.) were also explored to understand their impact on optimal solu‎tions. Z-numbers, through fuzzy numbers, provide a flexible and adaptable decision-making solution far superior to traditional methods ‎for managing imprecision that leads to a better representation of uncertainty related to objectives and constraints. To demonstrate the viability of Z-number-based approaches, practical applications were employed to illustrate their usage in healthcare decision-making and ‎engineering optimization‎.

Autonomous cars will reduce the quantity of street injuries because of human mistakes. Traditionally, a step by step advancement has been introduced in intelligent vehicles. These advancements boom the automation stage in motors with systems that assist the driving force in order to keep a steady speed, comply with a lane, or perform vehicle– driver control handover. Autonomous cars require deep information about their surroundings. Thus it's very vital to carry out road segmentation and object detection for identifying the drivable area in route planning. A portion of road with uniform characteristics can be represented specifically as segments. Existing road segmentation algorithms basically depends on quality of image from camera due to light conditions. As well one more issue for existing road segmentation depends on high quality of lane markings. LiDAR sensors can attain the 3D geometry information of the vehicle surroundings with excessive precision. The information collected by LiDAR will increase precision at the cost of increased memory usage and hence computational overhead. A brand new method using both the Camera and LiDAR data fusion for road detection is discussed. It decreases the gap among the point cloud of 3-D LiDAR information.

In this paper, the linear fractional programming problem subject to a system of fuzzy relation inequalities (FRI) with the max-Hamacher composition operator is studied. First, the structure of its feasible domain is investigated, and its feasible solution set determined. Then, some sufficient conditions are given that under them, some of the optimal components of the problem are directly determined. The optimal solution of the linear fractional programming problem might not be any of the minimal solutions. However, in the process of obtaining the optimal solution, we need to compute the minimal solutions. Therefore, some reductions are presented that under them, we can compute the minimal solutions fast. The original problem can be transformed into some traditional linear fractional programming subproblems and eventually optimized in a small search space. Finally, an algorithm is designed to solve the problem based on the above reductions. An application and some numerical examples are provided to illustrate the procedure.

The prevalence of GPS applications and other mobile devices has led to the accumulation of a large amount of trajectory data that contains valuable information for intelligent transportation, route planning, city computing etc. However, massive data not only brings new challenges to data storage and retrieval but also leads to serious privacy risks because of the abundant spatiotemporal information. In this paper, we propose a storage scheme that strikes a balance between the compression ratio and precision. We then introduce a road segment generalization method to address privacy issues stemming from sensitive places. Next, we design a two-layer index mechanism to provide an effective retrieval. Furthermore, a privacy preserving storage system PP-TrajStore is implemented. It provides efficient storage based on a road segment compression scheme, preserves privacy by employing sensitive segment generalization technologies, and achieves rapid retrieval by a two-layer index strategy. Finally, a realworld dataset is utilized to demonstrate the performance of PP-TrajStore

Traffic congestion wastes time, energy, and causes pollution. In this paper, We propose an application taking advantage of vehicular communications, namely dynamic route planning. In dynamic route planning, the route from a travel origin to its destination, instead of being statically determined at the travel starting time, is periodically recomputed according to real-time traffic information. Estimating future travel time is indeed central to the dynamic route planning problem. Since knowing future travel times cannot be achieved, a major challenge its estimation. In this paper, we consider three estimates for the future travel time: the latest travel time heuristic commonly used in the literature, an improvement of the latest travel time heuristic, and a novel approach based on exploiting the observed correlation between vehicle density in a road segment and travel time. We show through accurate simulation that all these heuristics are able to considerably improve traffic efficiency, with up to 60% traveling time reduction with respect to the case of the static route planning. Among the considered heuristics, the one based on vehicle density is consistently outperforming the others, especially in presence of traffic build up/decongestion situations (e.g., accidents).

Emergency evacuation on freeways is a process aiming to transfer people from dangerous area to the safe area as quickly as possible. Route planning, therefore, plays an important role during this process. This paper proposes a systematic method to generate optimized routes for rescue vehicles step by step. First, the free flow traveling time and historical traffic volume is calculated from large regional toll collection data. Then the Temporal Convolutional Network (TCN) is adopted to generate real-time ratio between road segments and toll gates. Finally, the Dynamic Bureau of Public Road (DBPR) function and Dijkstra algorithm are used to obtain real-time optimized routes for rescue vehicles. The proposed algorithms are tested on a hypothetical emergency event taking place on the Shantou-Kunming expressway in Xingyi, Anhui Province. The computational results show that the generated rescue routes are helpful for rescue vehicles and can save plenty of time. Generate rescue routes rapidly and accurately may provide a practical method for emergency evacuation without expensive facilities and can be a guide for further rescue operations.