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<p>The rapid response of police officers plays a crucial role in managing traffic incidents, as delayed responses increase the risk of secondary crashes and prolonged blockages. This delay in crash response varies spatially, posing a critical challenge to effective incident management. However, the spatial variability in police response times and its influence on response time prediction have received limited scholarly attention. Prior to model development, spatial dependence is assessed using global Moran’s I statistic, while spatial heterogeneity is explored through choropleth mapping and geographically weighted regression. Based on this evidence, an Integrated Spatial Deep Neural Network (ISDNN) is developed as a spatially explicit prediction framework to serve as an initial decision-support tool in pre-dispatch operations. The model incorporates radial basis functions and periodic kernels to embed spatial and temporal characteristics, enabling the representation of spatially varying response processes. The proposed model is evaluated using traffic crash records and police operational data from Bartlett, Tennessee, United States. Compared with several non-spatial and spatial baseline models, the ISDNN achieves lower prediction errors (RMSE = 0.43; MAE = 0.33; MAPE = 23.85%), indicating the potential benefits of accounting for spatial effects in response time prediction. Beyond temporal variations and crash intensity, spatial factors, including intersections and land use, play a key role in crash response prediction. Overall, the implications of the ISDNN model enable accurate predictions across police zones, which may potentially support targeted pre-dispatch decisions in historically delayed zones.</p>

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Accounting for Spatial Effects in Police Response Time Prediction: A Spatially Explicit Deep Learning Framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

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  • Veera Manikanda Prabhu Nagamanickam,
  • Sabyasachee Mishra

摘要

The rapid response of police officers plays a crucial role in managing traffic incidents, as delayed responses increase the risk of secondary crashes and prolonged blockages. This delay in crash response varies spatially, posing a critical challenge to effective incident management. However, the spatial variability in police response times and its influence on response time prediction have received limited scholarly attention. Prior to model development, spatial dependence is assessed using global Moran’s I statistic, while spatial heterogeneity is explored through choropleth mapping and geographically weighted regression. Based on this evidence, an Integrated Spatial Deep Neural Network (ISDNN) is developed as a spatially explicit prediction framework to serve as an initial decision-support tool in pre-dispatch operations. The model incorporates radial basis functions and periodic kernels to embed spatial and temporal characteristics, enabling the representation of spatially varying response processes. The proposed model is evaluated using traffic crash records and police operational data from Bartlett, Tennessee, United States. Compared with several non-spatial and spatial baseline models, the ISDNN achieves lower prediction errors (RMSE = 0.43; MAE = 0.33; MAPE = 23.85%), indicating the potential benefits of accounting for spatial effects in response time prediction. Beyond temporal variations and crash intensity, spatial factors, including intersections and land use, play a key role in crash response prediction. Overall, the implications of the ISDNN model enable accurate predictions across police zones, which may potentially support targeted pre-dispatch decisions in historically delayed zones.