Dynamic estimation system for fire station service areas based on travel time data

Liu, Dingli; Xu, Zhisheng; Yan, Long; Fan, Chuangang

doi:10.1016/j.firesaf.2020.103238

Cited by 17 publications

(22 citation statements)

References 29 publications

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“…In the field of spatial analysis, geospatial interpolation and regression tools have been used with a focus on the spatial autocorrelation of urban spatial data [19][20][21][22]. In addition, a study [23] was conducted to analyze the spatiotemporal accessibility of emergency services.…”

Section: Travel Time Prediction Of Emergency Servicesmentioning

confidence: 99%

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Prediction of Travel Time of Fire Service using Kriging and Weighted-sum Technique

2022

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The accessibility of city fire services is an important indicator for evaluating fire services and optimizing fire resource allocation. For firefighting, rescue, and first-aid activities, it is recommended that the time from fire vehicles leaving the garage to arrive at the scene is less than 5 minutes. Therefore, the travel time of fire services is one of the main concerns for many researchers. This study assumes that changing the urban environment affects the travel time of fire services. Therefore, in this study, weights were applied over the years to predict the travel time of fire service by using the kriging technique. As a result of the case study, temporal factors (elapsed year, term of travel time, and time spent) did not significantly affect travel time prediction accuracy using the kriging technique. As observed in previous studies, it is confirmed that the prediction accuracy is high because it is less affected by traffic-related factors at short travel distances. The results of this study contribute to the development of spatial analysis techniques to improve the accuracy of travel-time prediction.

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Section: Travel Time Prediction Of Emergency Servicesmentioning

confidence: 99%

“…Deterministic or Stochastic Travel Time [7][8][9][10][11] Parametric or Nonparametric Methods [12][13][14][15][16][17][18] Geospatial Analysis [19][20][21][22][23] Table 1. Previous Studies on the Travel Time of Emergency Services…”

Section: Approach Previous Studiesmentioning

confidence: 99%

Prediction of Travel Time of Fire Service using Kriging and Weighted-sum Technique

2022

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“…Figure 3 shows the traffic properties of the study area. It should be noted that in previous research (e.g., Berman, Hajizadeh, Krass, & Rahimi-Vahed, 2018;Haghani & Yang, 2007;Huang & Pan, 2007;Kiran et al, 2020;Liu, Xu, Wang, & Fan, 2020;Liu, Xu, Wang, Zhou, & Fan, 2021;Liu, Xu, Yan, & Fan, 2020;Liu, Xu, Yan, & Wang, 2020;Xia, Li, Chen, & Yu, 2019) it was assumed that the travel time of fire trucks follows the general travel time of links in the street network. In this study, however, links were classified into congested (edges with speed up to 30 km/h; Treiber & Kesting, 2013) and uncongested (edges with speed greater than 30 km/h) groups as the travel time of fire trucks might differ from that of normal vehicles in these groups of Different parameter values can be considered for implementation of the proposed model.…”

Section: Study Area and Data Collectionmentioning

confidence: 99%

Analyzing accessibility to fire stations: A floating catchment area model for stochastic transportation networks with travel time correlation

Sahebgharani

Haghshenas

2021

Transactions in GIS

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Improving the spatial accessibility of fire stations is a key factor in minimizing property loss and reducing injuries and deaths. Most existing studies neglect the effect of travel time correlation and uncertainty on the level of accessibility in non-normally distributed networks. In this article, a twostep floating catchment area (2SFCA) model is introduced to measure accessibility in correlated non-normal uncertain transportation networks. The model extends the conventional 2SFCA measures by considering the catchment area of supply and demand locations in stochastic contexts. The proposed measure is applied to a study area to investigate the impacts of travel time correlation, travel time uncertainty, and non-normal distributions on the accessibility value. It is further used to analyze access inequality between different areas of the case study and to identify segregated places in terms of accessibility to the fire stations. In addition, the conventional 2SFCA measure is executed to provide an evaluation of the outputs of the proposed model. Results highlight significant difference between the introduced model and the other existing measure. They also show inequality of accessibility in the study area and the way that the model parameters affect the accessibility level. Furthermore, results offer several methodological implications for accessibility analysis in time-dependent networks.

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“…Several scientific publications have dealt with this topic, and several of them are summarised in the following paragraphs. Notable is a publication from the city of Changsha (China) [8,9] that is addressing the arrival times of firefighting units within the city. The premise in this study is that at least one fire tanker and one fire ladder truck should arrive at a given location within 240 s of a fire being detected.…”

Section: The Current Approachmentioning

confidence: 99%

Comprehensive Analysis of Housing Estate Infrastructure in Relation to the Passability of Firefighting Equipment

et al. 2021

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The article focuses on the assessment and evaluation of the passability in densely populated parts of cities with multi-storey housing estates, in terms of the operation of the integrated rescue system (IRS) in the Czech Republic. The aim of the research is to minimize the arrival times to conduct the intervention as efficiently as possible. The presented problem is caused by unsystematic development of housing estates and the emergence of secondary problems in the form of inability to reach the place of intervention by the larger IRS vehicles. The vision presented in this document presents a systematic approach to improve the serviceability of individual blocks of flats. The main aim is to ensure passability, even for the largest equipment, such as fire engine ladders. Detailed mapping of the selected sites by drones, construction of their digital model, and subsequent virtual verification of the passability by specific vehicle models on identified access roads was performed. The results obtained by this procedure can then be implemented in the navigation of the fire safety forces and facilitate their arrival at the site of intervention. At the end, specific ways are presented in which the whole system can be modified to be able to intuitively change and choose individual access routes in real time, based on the current situation in the area.

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Dynamic estimation system for fire station service areas based on travel time data

Cited by 17 publications

References 29 publications

Prediction of Travel Time of Fire Service using Kriging and Weighted-sum Technique

Prediction of Travel Time of Fire Service using Kriging and Weighted-sum Technique

Analyzing accessibility to fire stations: A floating catchment area model for stochastic transportation networks with travel time correlation

Comprehensive Analysis of Housing Estate Infrastructure in Relation to the Passability of Firefighting Equipment

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