A GIS-Based Damage Evaluation Method for Explosives Road Transportation Accidents
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Preprocessing
- Data Transformation: In this step, we perform a feature transition on the road network data. The raw road network data is collected from OpenStreetMap [27] and it is provided in a spatial data format known as a shapefile, which contains geographic feature information. Because each road network edge is represented as a line string, and certain intersections of edges are omitted in the raw data, we employ a GIS tool, ArcMap [28], to convert the edge features into lines. This is achieved by splitting lines at their intersections using the Feature to Line operator in Arcmap. Following the feature transitions of edges, the original 70,723 edges have been divided into 83,727 edges to facilitate subsequent computations. It is worth noting that all of the collected datasets utilize the WGS84 coordinate system, eliminating the need for coordinate transformation.
- Data Completion and Integration: To evaluate the economic cost of building damage, the housing price data of buildings are required. The collected housing price data comprise prices for each 250 m × 250 m spatial grid, as well as the administrative districts of Wuhan. As a result, we need to associate the buildings with either the spatial grids or the administrative districts based on their locations, and supplement the building data with the housing price information. Then, we integrate the geographic data of buildings and housing price data into a building database, called Buildings. This database includes the following attributes: bid, geometry, height, and price.
Datasets | Description | Temporal Information | Data Type | Data Source |
---|---|---|---|---|
Dataset-1 | Road network data | Obtained on 1 June 2023 | Shapefile | OpenStreetMap |
(48,637 nodes and 70,723 edges) | (https://www.openstreetmap.org/) | |||
Dataset-2 | Geographic data of | Obtained on 24 May 2022 | Shapefile | Amap |
buildings (166,062 buildings) | (http://ditu.amap.com/) | |||
Dataset-3 | Housing price data of | By 24 May 2022 | Csv | Amap |
each 250 m × 250 m grid | (http://ditu.amap.com/) | |||
Dataset-4 | Housing price data of | By 24 May 2022 | Csv | City house |
the administrative districts | (https://wh.cityhouse.cn/market/) | |||
Dataset-5 | Population density of each | 24 h on 30 July 2022 | Csv | Baidu Huiyan |
200 m × 200 m grid | (https://huiyan.baidu.com/) |
2.3. Damage Evaluation Methods
2.3.1. Damage Evaluation Scenarios
- Explosion Point-based Damage Evaluation: In the case of a sudden explosion accident or the need to designate a mid-way parking location during explosive transportation, an explosion point-based damage evaluation model is required. As illustrated in Figure 2a, an explosion impact area is defined as a circular region with the explosion point as its center (indicated by a cross symbol), and R representing the radius of this area. The calculation of explosion consequences depends on the distribution of people and building facilities within the explosion impact area.
- Road Segment-based Damage Evaluation: Unlike the static explosion point, the road segment-based damage evaluation is designed for dynamic transportation scenarios, where the explosion consequence is evaluated by the surrounding conditions of the road segment. As depicted in Figure 2b, the explosion impact area is represented by the red region surrounding the road segment, with the minimum distance occurring between any point on the region boundary and the road segment, denoted as R.
- Route-based Damage Evaluation: The route-based damage evaluation is mainly necessary in two situations, i.e., before and after an explosion accident occurs. Pre-accident damage evaluation is crucial for route planning and the approval evaluation of the transport route, whereas post-accident is valuable for emergency response and evacuation route planning, among other applications. The route-based damage evaluation is fundamentally built upon the road segment-based damage evaluation. As Figure 2c shows, the explosion impact area in the route-based scenario encompasses multiple road segments. Furthermore, the damage evaluation result for a given route is a weighted value derived from the damage evaluation results of road segments within the route. We define the weighted function as being either Max or Avg.
2.3.2. Damage Level and Influence Radius Estimation
2.3.3. Height-Aware Hierarchical Building Damage (HHBD) Model
Algorithm 1: The HHBD algorithm |
|
2.3.4. Shelter-Aware Human Casualty (SHC) Model
2.4. Spatial Queries
- Explosion point-based spatial range query: Given the explosion influence radius , the spatial query of retrieving buildings and population density values within the explosion impact area essentially involves a spatial range query as follows. Initially, we create a buffer region using the ST_Buffer function, which generates a circular region centered at point with a radius of . We subsequently check if a building or a grid containing people falls within this buffer region using the ST_Intersects function. The corresponding spatial query is detailed as follows:SELECT Buildings.*FROM Buildings, ST_Buffer(ST_MakePoint(X,Y)::geography,r_1) as bufferWHERE ST_Intersects(buildings.geom, buffer:geometry)Note that the table name of Buildings is optional; e.g., for human casualty evaluation, the table name becomes People.
- Road segment-based spatial range query: The road segment-based spatial range query is similar to the explosion point-based query, with the primary distinction being the method of buffer region generation. Given a road segment e, the explosion impact area is generated by ST_Buffer based on the geographical information of e, which is retrieved from the Roads table. The road segment-based spatial range query is structured as follows:SELECT Buildings.*FROM Buildings, RoadsWHERE Roads.rid=eAND ST_Intersects(Buildings.geom, ST_Buffer(Roads.geom::geography, r_1)::geometry)
- Human casualty region generation: Based on the defined damage levels of human casualty, i.e., death, serious injury, and slight injury, three distinct human casualty regions should be generated. The overlapping area is calculated by the higher damage level. Taking the circle region as an example, each region is generated using the ST_Buffer function as follows. The explosion influence radius for each damage level is , respectively. The ST_Difference function is used for the partition of the overlapping area.SELECT buffer_death:geometry,ST_Difference(buffer_death::geometry, buffer_serious::geometry),ST_Difference(buffer_slight::geometry, buffer_serious::geometry)FROMST_MakePoint(X, Y) as point,ST_Buffer(point::geography, r_1)as buffer_death,ST_Buffer(point::geography, r_2)as buffer_serious,ST_Buffer(point::geography, r_3)as buffer_slight
- Area computation: Area computation plays a crucial role in the damage evaluation process. For instance, in the SHC model for human casualty evaluation, we need to calculate the explosion impact area and the total building area in the explosion impact area. As an illustration of point-based explosion impact area computation, the ST_Area function is employed in the following manner:SELECT ST_Area(ST_Buffer(ST_MakePoint(X,Y)::geography,R))
3. Experimental Results
3.1. Case Study
3.1.1. Explosion Point-Based Damage Visualization
3.1.2. Route Planning for Explosives Transportation
3.1.3. Human Casualties Varying over Time
3.2. Efficiency Evaluation
4. Discussion
- The interactive visualization of the GIS platform enables real-time explosion damage evaluation through a user-friendly interface and efficient query computation. In the case study, we observed that the execution time for the explosion point-based damage evaluation typically remained below 1 s.
- The scalabilities of the explosion point-based, road segment-based, and route-based damage evaluation methods was verified by increasing the number of explosion points, the number of road segments and the distance of a selected route. Notably, the utilization of R-Tree spatial indexing has proven instrumental in enhancing the efficiency of these damage evaluation methods. Furthermore, it is worth highlighting that the efficiency gains achieved through R-Tree indexing are more pronounced when applied to building data as compared to people density data. This discrepancy is attributed to the fact that the distribution of buildings exhibits a greater irregularity compared to the people density data.
- For route planning, it is crucial to recognize that various strategies are better suited for different scenarios. The Distance-first strategy prioritizes finding the shortest path but does not consider the associated costs of damage to buildings and human casualties. The Human-first strategy performs well on human casualties, albeit at the expense of potentially longer distances. Lastly, the Building-first strategy proves to be most effective when prioritizing the protection of buildings, or during time periods when a significant portion of people tend to remain indoors.
- Firstly, we formalized three typical scenarios of damage evaluation for explosive road transportation and defined the corresponding explosion impact area for each scenario.
- Secondly, we proposed an HHBD model and an SHC model for building damage and human casualty evaluation, respectively. The HHBD model considers different building damages at different heights, while the SHC model considers the impact of building shelters based on real-time population density data.
- We also developed a GIS platform that integrates multi-source geospatial data and that enables efficient geospatial computation and map-based interactive visualization.
- Finally, a case study of liquefied natural gas (LNG) transportation in Wuhan, China, was demonstrated to verify the effectiveness and efficiency of the proposed methods.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
(m) | 5 | 6 | 7 | 8 | 9 | 10 | 12 | 14 | 16 | 18 | 20 |
---|---|---|---|---|---|---|---|---|---|---|---|
(MPa) | 2.94 | 2.06 | 1.67 | 1.27 | 0.95 | 0.76 | 0.50 | 0.33 | 0.235 | 0.17 | 0.126 |
(m) | 25 | 30 | 35 | 40 | 45 | 50 | 55 | 60 | 65 | 70 | 75 |
(MPa) | 0.079 | 0.057 | 0.043 | 0.033 | 0.027 | 0.0235 | 0.0205 | 0.018 | 0.016 | 0.0143 | 0.013 |
Shock Wave Overpressure (MPa) | Building Damage Degree |
---|---|
Partial crushing of door and window glass. | |
Greatest pressure, resulting in broken door and window glass. | |
Door frame damaged. | |
Cracks appear on the wall. | |
Large cracks appear on the wall or house tiles fall. | |
Broken pillars in wooden building factory buildings | |
or room frames loosen. | |
Brick wall collapses. | |
Shockproof reinforced concrete destroyed | |
or small houses collapse. | |
Large steel frame structures destroyed. |
Shock Wave Overpressure (MPa) | Human Casualty Degree |
---|---|
Slight injury | |
Serious injury | |
Severe visceral injury or death |
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Damage Level | Description | Shock Wave Overpressure (MPa) | Standard Distance (m) | |
---|---|---|---|---|
Building damage | 1 | Complete destruction | ||
2 | Serious damage | |||
3 | Moderate damage | |||
4 | Minor damage | |||
Human casualty | 1 | Death | ||
2 | Serious injury | |||
3 | Slight injury |
Accident Levels | Description | Building Damages (10K) | Human Casualties |
---|---|---|---|
1 | Fatal | Death: | |
Serious injury: | |||
2 | Serious | [5000, 10,000) | Death: |
Serious injury: | |||
3 | Major | [1000, 5000) | Death: |
Serious injury: | |||
4 | Ordinary | Death: | |
Serious injury: |
Routes | Planning Strategy | Distance (km) | Weight (t) | Building Damage (10K CNY) | Human Casualties |
---|---|---|---|---|---|
R-1 | Distance-first | 29.5 | 1 | Max: 93,766 | Max: 1238 |
Avg: 16,895 | Avg: 46 | ||||
16 | Max: 453,287 | Max: 8227 | |||
Avg: 148,932 | Avg: 430 | ||||
R-2 | Building-first | 32.7 | 1 | Max: 41,162 | Max: 473 |
Avg: 3687 | Avg: 38 | ||||
16 | Max: 377,479 | Max: 1127 | |||
Avg: 55,474 | Avg: 236 | ||||
R-3 | Human-first | 36.4 | 1 | Max: 41,162 | Max: 97 |
Avg: 6487 | Avg: 5 | ||||
16 | Max: 377,479 | Max: 570 | |||
Avg: 83,331 | Avg: 117 |
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Zhao, J.; Liu, N.; Li, J.; Guo, X.; Deng, H.; Sun, J. A GIS-Based Damage Evaluation Method for Explosives Road Transportation Accidents. ISPRS Int. J. Geo-Inf. 2023, 12, 470. https://doi.org/10.3390/ijgi12120470
Zhao J, Liu N, Li J, Guo X, Deng H, Sun J. A GIS-Based Damage Evaluation Method for Explosives Road Transportation Accidents. ISPRS International Journal of Geo-Information. 2023; 12(12):470. https://doi.org/10.3390/ijgi12120470
Chicago/Turabian StyleZhao, Jing, Ning Liu, Junhui Li, Xi Guo, Hongtao Deng, and Jinshan Sun. 2023. "A GIS-Based Damage Evaluation Method for Explosives Road Transportation Accidents" ISPRS International Journal of Geo-Information 12, no. 12: 470. https://doi.org/10.3390/ijgi12120470