the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Dec 2021
The Emergency Accessibility Analysis based on Traffic Big Data and Flood Scenario Simulation in the context of Shanghai Hotel industry
Abstract. Underlying the impact of global warming and rapid growth of the tourism industry, the increasing frequency of flood post threats to the sustainable development of the coastal cities in China. The article proposes a methodological approach to evaluate the emergence response capability. This approach combines the flood simulation scenario method, traffic big data with the path navigation interface of the web. This article provides an empirical study to evaluate the emergency response from Fire & Rescue Service (FRS) to the tourist hotel in Shanghai from spatial accessibility perspective. The findings show that (1). The emergency response from FRS has significant relationships with the situation of transportation, the location of hotels, the intensity of flood inundation and the number, location of the urban FRS. (2). The emergency accessibility of a city caused by floods depends on the prevailing traffic conditions. The more severe traffic congestion has a significant impact on the spatial accessibility. (3) Flooding and real-time traffic conditions can change the fastest path from FRS to tourist hotels, resulting in delays in emergency response times, and the selection of the most appropriate travel routes is critical to improving the emergency response capability of cities. The results proved the validity of this proposed approach. Consequently, the approach contributes to the enhancement of the level of emergence response ability of urban tourism when they encounter disasters.
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Interactive discussion
Status: closed
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RC1: 'Comment on nhess-2021-368', Anonymous Referee #1, 14 Jan 2022
This article proposes a methodological approach to assess how flooding can affect the accessibility of rescue services to hotels. The approach is then applied to the city centre of Shanghai, China. The topic of emergency accessibility analysis in urban areas following natural hazards is certainly a relevant one. However, I do not think that this article provides a meaningful scientific contribution to the field, as detailed below. My recommendation is that this article is not accepted for publication in NHESS.
In short, the proposed methodology is based on obtaining travel paths, and respective distances and durations, from each fire station to each hotel in a city, using a web service such as Google Maps (in this case, Gaode Map). The variables of interest are collected for different times of the day to reflect different traffic conditions. A 100-year flood extent map is then overlapped on the road network, and affected roads are marked as impassable. The web service is queried again to find alternative routes from the fire stations to the hotels, and the results between normal and flood conditions are compared. The article does not actually propose a new model or method, instead relying on results obtained from a web service whose underlying models and assumptions are not adequately described.
A core issue with the approach is that the post-event estimation of travel distances and durations for rescue services to reach hotels does not actually take into consideration the post-event traffic conditions in the city. This would require a traffic model to estimate how overall traffic would change during and/or after a flood event in a given city, which is a more complex problem - and arguably a more interesting one from a scientific viewpoint. Simply relying on real-time traffic and calculating routes that avoid certain roads assumed to be blocked by a flood is not a reliable approach, as overall traffic conditions in the flood situation - which will naturally influence the arrival time of rescue services - are not captured.
Adding to this, the case study itself is quite narrow in scope, and of very limited practical use. A large number of hotels exist in the city centre and are included in the analysis. Out of these, directly affected hotels are few, and they are considered inaccessible (note that this has nothing to do with traffic modelling, but simply results from spatially overlapping hotel locations and the flood map). Thus, the flood-related traffic results are mostly focused on the additional time that fire rescue services would take to reach hotels that are not affected by the event, due to road blockages caused by the event, but without considering overall traffic changes due to the event. The relevance of this analysis is questionable at best.
A number of other more minor issues are present throughout the article. For example, the authors state that they obtained their variables of interest from the web service “several times in January 2021”. This is too vague: the reader understands that the data was collected in January 2021, but how? Were travel routes calculated only once for each hotel/fire station, or was this done multiple times in order to get a representative sample? If so, how were those numbers combined? More information is necessary.
Section 4.4 concerning “Disaster Management Strategy” is essentially a filler subsection without a specific connection with the rest of the article, and contains many with unreferenced generalities regarding flood, e.g. “the city's emergency response department should be equipped with some special vehicles and hovercraft with better water wading capabilities to ensure that emergency rescue missions are completed”; “tourism enterprises located in areas of high flood risk should improve their own capacity to prevent flooding and drainage. This can be done by building waterproof walls and drains, constructing steel gates, installing flood-proof glass and customising their own temporary defences”; and others.
The article, although understandable, is not particularly well-written, and the level of English is below-average, with many typos and sentences that are difficult to follow.
In view of this, I suggest that the authors critically rethink their methodological approach and case study application, as well as significantly improve the quality of the text, before considering a resubmission of this article.
Citation: https://doi.org/10.5194/nhess-2021-368-RC1 -
AC1: 'Reply on RC1', qian yao, 20 Mar 2022
Thank you for your suggestion and we decided to withdraw the manuscript and make major changes.
A core issue with the approach is that the post-event estimation of travel distances and durations for rescue services to reach hotels does not take into consideration the post-event traffic conditions in the city. However, this study relies only on real-time traffic and calculations to avoid routes that are assumed to be blocked by flooding to measure accessibility, which is not a reliable method. This is because the overall traffic conditions under flooding conditions - which naturally affect the arrival time of rescue services - are not captured.
To address this issue, we will redesign the methodology, select a more general population, and try to collect real-life emergency response times in Shanghai when faced with flooding.
As for your comment about English proficiency, we will check the English language first when submitting papers in the future to improve the quality of the text. Although this article cannot be published in the HNESS journal, your suggestion is of great importance to the improvement of this paper, and I thank you again.
Citation: https://doi.org/10.5194/nhess-2021-368-AC1
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AC1: 'Reply on RC1', qian yao, 20 Mar 2022
-
RC2: 'Review on nhess-2021-368', Anonymous Referee #2, 08 Feb 2022
The Emergency Accessibility Analysis based on Traffic Big Data and Flood Scenario Simulation in the context of Shanghai Hotel industry
By Qian Yao, Jiangyang Lin, Yong Shi, Zhihao Chen, and Qingwei Wang
Recommendation: Major revision
Summary:
The study performs an analysis of the accessibility of fire and rescue services (FRS) for hotels in Shanghai in case of a flood event. The authors combine flood hazard maps of a 1 in 100 year flood event with a commercial Big data based routing service to estimate travel times between FRS locations and affected hotels. The model considers in their travel time estimates the inaccessibility for FRS vehicles in flooded areas as well as delays due to traffic at different times during the day. The analysis for Shanghai hotels shows that the main delays from FRS come from traffic congestions even in absence of a flood event. Flood events cause additional delays, which become larger during times when there is more traffic congestion. The paper discusses the results and some of the limitations of the study and outlines future research questions that follow from this analysis.
General comments:
The study is interesting and covers a relevant topic. The methodology using a commercial Big data based routing service is clever and produces interesting results. My two main points of criticism are the unclear framing and research question of the study as well as the lack of some essential information, which makes it difficult to fully follow the argument the authors are making. I would also recommend restructuring the paper to introduce the Shanghai case study earlier in the manuscript. The paper would also benefit from a thorough English copy-editing.
Framing and Research Question
The paper addresses an interesting and relevant research question, namely “how long does it take an FRS team to arrive at a hotel during a flood in Shanghai?”. However, the way the authors frame this question in the introduction and throughout the text makes it difficult to understand the goal of their analysis. Some key questions that should be addressed in the manuscript:
- “Why is it important to know the emergency response time for a hotel?” The authors provide some general statements on avoiding damages, general decline in tourism etc. in the introduction, but I would argue that this information is most relevant for a hotel’s own emergency planning and when they can expect emergency responders to arrive. I am not sure if this can be really framed as an economic question as it is unclear to me how this information would be able to a) reduce direct damages as in most cases FRS would be called when e.g. water has already entered a building b) to reduce indirect damages such as higher cancellation rates of hotel bookings/a general loss of attractivity of a tourist destination after a flood event.
- “What scenarios are considered in the analysis” From reading the manuscript I was not able to fully understand whether the analysis focusses on a case where the FRS route to a hotel is restricted because of a flood but the hotel is not directly affected by the flood (i.e. the hotel has a non-flood related emergency) or a case where the hotel itself is flooded and the access to the hotel is restricted.
- What type of flood events are considered? Figure 1 mentions a 100-y pluvial flood, but most of the maps in the other figures look like the authors use flood maps for a river flood. The choice of flood event has important implications as river floods often have a slower onset that pluvial or surface water floods, which means different emergency planning approaches for FRS. River floods of large rivers often have warning lead times of several days which would mean that FRS would be able to plan ahead while pluvial floods often have very short to no warning lead times. The authors should clarify what type of flood event they are considering and why.
Missing information
The manuscript lacks a few critical information, which should be added to better understand the analysis.
- Chapter 3.1. very briefly describes the flood maps that have been used and while the authors refer to other publications for details, they should at least provide information on the type of flood considered (see previous comment) and the inputs they have used such as the drainage network, rainfall data etc.
- The authors should also provide more detailed information about how the flood maps are considered by the route planning algorithm. Is there any cut-off value for the flood depth that would consider a road as inaccessible or is any flooded street excluded from the route calculation?
- Chapter 4.1. explains how the authors have identified hotels that are located in a flood hazard area. However, it is not described how this information is used in the response time analysis. Is there a distinction in the analysis between hotels that are expected to be flooded in an event and hotels that are only affected by flood-related delays of the FRS?
- In the discussion several limitations of the study are discussed. One aspect that seems to be missing is the interaction between flood events and traffic congestion. Flooding in one area would directly cause more traffic congestion in another area because the same amount of vehicles have less roads they can access. It seems the model the authors use, cannot account for that. Including this interaction is very challenging and I would not expect the authors to include that in their model but would be good if they could discuss this limitation in their study.
Other comments:
P1 L28: The numbers from Hurricane Katrina and the tsunami in Thailand are really interesting and impressive, but I am wondering how that links to your analysis, as none of these damages could have been prevented from FRS arriving faster.
P10 L22: “The statistics show that Shanghai had 361,405,100 of domestic tourists, 8,972,300 inbound tourists and generated over 23 million yuan in 2019.“ Are these number correct? That would mean each tourist has spent less than 0.07 Yuan (0.01 USD) in 2019.
Chapter 4.1: Most of the information should be provided earlier in the manuscript as they provide important context to the motivation of the study.
Figure 7: Please provide access labels with the units that are used here.
P15 L333ff: The changes in arrival time due to flooding are quite small compared to the changes caused by traffic congestion. Would be interesting to test if theses changes are statistically significant compared to changes caused by traffic congestion.
P16 L348ff: “Secondly, the city's emergency response department should be equipped with some special vehicles and hovercraft with better water wading capabilities to ensure that emergency rescue missions are completed in areas with deep water.” This is a useful recommendation, but I would think that special vehicles such as boats etc. are pretty standard for the flood emergency response in large cities.
Citation: https://doi.org/10.5194/nhess-2021-368-RC2 -
AC2: 'Reply on RC2', qian yao, 20 Mar 2022
Thank you for your support and your comments were very helpful in improving the article. However, due to the first reviewer's comments, we decided to withdraw the manuscript and make major changes.
In the revision, we will focus on explaining the reasons why emergency response time is important to the hospitality industry. More clearly identify what the research in this paper is about. To explain clearly the types, causes, and characteristics of the chosen flood events.
We would add the following information: sources of flood maps; criteria for the impact of flooding on accessibility; and discuss in conclusion the interaction between flood events and traffic congestion and incorporate this interaction into the accessibility measurement model where possible.
Thank you for your other comments on this article; we will reflect on whether Hurricane Katrina and the Thai tsunami as examples support our views; the unit of revenue generation over $23 million we got wrong, it should be revenue generation over $230 billion; re-structuring the article; testing the statistical significance of the findings; thinking about the plausibility of emergency response.
Although this article cannot be published in the HNESS journal, your suggestions are of great significance in enhancing this article and I thank you again.
Citation: https://doi.org/10.5194/nhess-2021-368-AC2
Interactive discussion
Status: closed
-
RC1: 'Comment on nhess-2021-368', Anonymous Referee #1, 14 Jan 2022
This article proposes a methodological approach to assess how flooding can affect the accessibility of rescue services to hotels. The approach is then applied to the city centre of Shanghai, China. The topic of emergency accessibility analysis in urban areas following natural hazards is certainly a relevant one. However, I do not think that this article provides a meaningful scientific contribution to the field, as detailed below. My recommendation is that this article is not accepted for publication in NHESS.
In short, the proposed methodology is based on obtaining travel paths, and respective distances and durations, from each fire station to each hotel in a city, using a web service such as Google Maps (in this case, Gaode Map). The variables of interest are collected for different times of the day to reflect different traffic conditions. A 100-year flood extent map is then overlapped on the road network, and affected roads are marked as impassable. The web service is queried again to find alternative routes from the fire stations to the hotels, and the results between normal and flood conditions are compared. The article does not actually propose a new model or method, instead relying on results obtained from a web service whose underlying models and assumptions are not adequately described.
A core issue with the approach is that the post-event estimation of travel distances and durations for rescue services to reach hotels does not actually take into consideration the post-event traffic conditions in the city. This would require a traffic model to estimate how overall traffic would change during and/or after a flood event in a given city, which is a more complex problem - and arguably a more interesting one from a scientific viewpoint. Simply relying on real-time traffic and calculating routes that avoid certain roads assumed to be blocked by a flood is not a reliable approach, as overall traffic conditions in the flood situation - which will naturally influence the arrival time of rescue services - are not captured.
Adding to this, the case study itself is quite narrow in scope, and of very limited practical use. A large number of hotels exist in the city centre and are included in the analysis. Out of these, directly affected hotels are few, and they are considered inaccessible (note that this has nothing to do with traffic modelling, but simply results from spatially overlapping hotel locations and the flood map). Thus, the flood-related traffic results are mostly focused on the additional time that fire rescue services would take to reach hotels that are not affected by the event, due to road blockages caused by the event, but without considering overall traffic changes due to the event. The relevance of this analysis is questionable at best.
A number of other more minor issues are present throughout the article. For example, the authors state that they obtained their variables of interest from the web service “several times in January 2021”. This is too vague: the reader understands that the data was collected in January 2021, but how? Were travel routes calculated only once for each hotel/fire station, or was this done multiple times in order to get a representative sample? If so, how were those numbers combined? More information is necessary.
Section 4.4 concerning “Disaster Management Strategy” is essentially a filler subsection without a specific connection with the rest of the article, and contains many with unreferenced generalities regarding flood, e.g. “the city's emergency response department should be equipped with some special vehicles and hovercraft with better water wading capabilities to ensure that emergency rescue missions are completed”; “tourism enterprises located in areas of high flood risk should improve their own capacity to prevent flooding and drainage. This can be done by building waterproof walls and drains, constructing steel gates, installing flood-proof glass and customising their own temporary defences”; and others.
The article, although understandable, is not particularly well-written, and the level of English is below-average, with many typos and sentences that are difficult to follow.
In view of this, I suggest that the authors critically rethink their methodological approach and case study application, as well as significantly improve the quality of the text, before considering a resubmission of this article.
Citation: https://doi.org/10.5194/nhess-2021-368-RC1 -
AC1: 'Reply on RC1', qian yao, 20 Mar 2022
Thank you for your suggestion and we decided to withdraw the manuscript and make major changes.
A core issue with the approach is that the post-event estimation of travel distances and durations for rescue services to reach hotels does not take into consideration the post-event traffic conditions in the city. However, this study relies only on real-time traffic and calculations to avoid routes that are assumed to be blocked by flooding to measure accessibility, which is not a reliable method. This is because the overall traffic conditions under flooding conditions - which naturally affect the arrival time of rescue services - are not captured.
To address this issue, we will redesign the methodology, select a more general population, and try to collect real-life emergency response times in Shanghai when faced with flooding.
As for your comment about English proficiency, we will check the English language first when submitting papers in the future to improve the quality of the text. Although this article cannot be published in the HNESS journal, your suggestion is of great importance to the improvement of this paper, and I thank you again.
Citation: https://doi.org/10.5194/nhess-2021-368-AC1
-
AC1: 'Reply on RC1', qian yao, 20 Mar 2022
-
RC2: 'Review on nhess-2021-368', Anonymous Referee #2, 08 Feb 2022
The Emergency Accessibility Analysis based on Traffic Big Data and Flood Scenario Simulation in the context of Shanghai Hotel industry
By Qian Yao, Jiangyang Lin, Yong Shi, Zhihao Chen, and Qingwei Wang
Recommendation: Major revision
Summary:
The study performs an analysis of the accessibility of fire and rescue services (FRS) for hotels in Shanghai in case of a flood event. The authors combine flood hazard maps of a 1 in 100 year flood event with a commercial Big data based routing service to estimate travel times between FRS locations and affected hotels. The model considers in their travel time estimates the inaccessibility for FRS vehicles in flooded areas as well as delays due to traffic at different times during the day. The analysis for Shanghai hotels shows that the main delays from FRS come from traffic congestions even in absence of a flood event. Flood events cause additional delays, which become larger during times when there is more traffic congestion. The paper discusses the results and some of the limitations of the study and outlines future research questions that follow from this analysis.
General comments:
The study is interesting and covers a relevant topic. The methodology using a commercial Big data based routing service is clever and produces interesting results. My two main points of criticism are the unclear framing and research question of the study as well as the lack of some essential information, which makes it difficult to fully follow the argument the authors are making. I would also recommend restructuring the paper to introduce the Shanghai case study earlier in the manuscript. The paper would also benefit from a thorough English copy-editing.
Framing and Research Question
The paper addresses an interesting and relevant research question, namely “how long does it take an FRS team to arrive at a hotel during a flood in Shanghai?”. However, the way the authors frame this question in the introduction and throughout the text makes it difficult to understand the goal of their analysis. Some key questions that should be addressed in the manuscript:
- “Why is it important to know the emergency response time for a hotel?” The authors provide some general statements on avoiding damages, general decline in tourism etc. in the introduction, but I would argue that this information is most relevant for a hotel’s own emergency planning and when they can expect emergency responders to arrive. I am not sure if this can be really framed as an economic question as it is unclear to me how this information would be able to a) reduce direct damages as in most cases FRS would be called when e.g. water has already entered a building b) to reduce indirect damages such as higher cancellation rates of hotel bookings/a general loss of attractivity of a tourist destination after a flood event.
- “What scenarios are considered in the analysis” From reading the manuscript I was not able to fully understand whether the analysis focusses on a case where the FRS route to a hotel is restricted because of a flood but the hotel is not directly affected by the flood (i.e. the hotel has a non-flood related emergency) or a case where the hotel itself is flooded and the access to the hotel is restricted.
- What type of flood events are considered? Figure 1 mentions a 100-y pluvial flood, but most of the maps in the other figures look like the authors use flood maps for a river flood. The choice of flood event has important implications as river floods often have a slower onset that pluvial or surface water floods, which means different emergency planning approaches for FRS. River floods of large rivers often have warning lead times of several days which would mean that FRS would be able to plan ahead while pluvial floods often have very short to no warning lead times. The authors should clarify what type of flood event they are considering and why.
Missing information
The manuscript lacks a few critical information, which should be added to better understand the analysis.
- Chapter 3.1. very briefly describes the flood maps that have been used and while the authors refer to other publications for details, they should at least provide information on the type of flood considered (see previous comment) and the inputs they have used such as the drainage network, rainfall data etc.
- The authors should also provide more detailed information about how the flood maps are considered by the route planning algorithm. Is there any cut-off value for the flood depth that would consider a road as inaccessible or is any flooded street excluded from the route calculation?
- Chapter 4.1. explains how the authors have identified hotels that are located in a flood hazard area. However, it is not described how this information is used in the response time analysis. Is there a distinction in the analysis between hotels that are expected to be flooded in an event and hotels that are only affected by flood-related delays of the FRS?
- In the discussion several limitations of the study are discussed. One aspect that seems to be missing is the interaction between flood events and traffic congestion. Flooding in one area would directly cause more traffic congestion in another area because the same amount of vehicles have less roads they can access. It seems the model the authors use, cannot account for that. Including this interaction is very challenging and I would not expect the authors to include that in their model but would be good if they could discuss this limitation in their study.
Other comments:
P1 L28: The numbers from Hurricane Katrina and the tsunami in Thailand are really interesting and impressive, but I am wondering how that links to your analysis, as none of these damages could have been prevented from FRS arriving faster.
P10 L22: “The statistics show that Shanghai had 361,405,100 of domestic tourists, 8,972,300 inbound tourists and generated over 23 million yuan in 2019.“ Are these number correct? That would mean each tourist has spent less than 0.07 Yuan (0.01 USD) in 2019.
Chapter 4.1: Most of the information should be provided earlier in the manuscript as they provide important context to the motivation of the study.
Figure 7: Please provide access labels with the units that are used here.
P15 L333ff: The changes in arrival time due to flooding are quite small compared to the changes caused by traffic congestion. Would be interesting to test if theses changes are statistically significant compared to changes caused by traffic congestion.
P16 L348ff: “Secondly, the city's emergency response department should be equipped with some special vehicles and hovercraft with better water wading capabilities to ensure that emergency rescue missions are completed in areas with deep water.” This is a useful recommendation, but I would think that special vehicles such as boats etc. are pretty standard for the flood emergency response in large cities.
Citation: https://doi.org/10.5194/nhess-2021-368-RC2 -
AC2: 'Reply on RC2', qian yao, 20 Mar 2022
Thank you for your support and your comments were very helpful in improving the article. However, due to the first reviewer's comments, we decided to withdraw the manuscript and make major changes.
In the revision, we will focus on explaining the reasons why emergency response time is important to the hospitality industry. More clearly identify what the research in this paper is about. To explain clearly the types, causes, and characteristics of the chosen flood events.
We would add the following information: sources of flood maps; criteria for the impact of flooding on accessibility; and discuss in conclusion the interaction between flood events and traffic congestion and incorporate this interaction into the accessibility measurement model where possible.
Thank you for your other comments on this article; we will reflect on whether Hurricane Katrina and the Thai tsunami as examples support our views; the unit of revenue generation over $23 million we got wrong, it should be revenue generation over $230 billion; re-structuring the article; testing the statistical significance of the findings; thinking about the plausibility of emergency response.
Although this article cannot be published in the HNESS journal, your suggestions are of great significance in enhancing this article and I thank you again.
Citation: https://doi.org/10.5194/nhess-2021-368-AC2
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