[Paper Review] Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems

Sayyed Mohsen Vazirizade, Ayan Mukhopadhyay+4 more|arXiv (Cornell University)|Jun 15, 2021

Traffic Prediction and Management Techniques4 citations

TL;DR

This paper proposes a data-driven pipeline combining synthetic resampling, non-spatial clustering, and multi-source data fusion to predict sparse spatial-temporal traffic incidents across large geographical areas like Tennessee’s interstate network. The approach significantly reduces emergency response times—up to 10% long-term reduction—by improving short-term forecasting and enhancing classical p-median resource allocation with a balancing penalty term for geographic spread.

ABSTRACT

Principled decision making in emergency response management necessitates the use of statistical models that predict the spatial-temporal likelihood of incident occurrence. These statistical models are then used for proactive stationing which allocates first responders across the spatial area in order to reduce overall response time. Traditional methods that simply aggregate past incidents over space and time fail to make useful short-term predictions when the spatial region is large and focused on fine-grained spatial entities like interstate highway networks. This is partially due to the sparsity of incidents with respect to the area in consideration. Further, accidents are affected by several covariates, and collecting, cleaning, and managing multiple streams of data from various sources is challenging for large spatial areas. In this paper, we highlight how this problem is being solved for the state of Tennessee, a state in the USA with a total area of over 100,000 sq. km. Our pipeline, based on a combination of synthetic resampling, non-spatial clustering, and learning from data can efficiently forecast the spatial and temporal dynamics of accident occurrence, even under sparse conditions. In the paper, we describe our pipeline that uses data related to roadway geometry, weather, historical accidents, and real-time traffic congestion to aid accident forecasting. To understand how our forecasting model can affect allocation and dispatch, we improve upon a classical resource allocation approach. Experimental results show that our approach can significantly reduce response times in the field in comparison with current approaches followed by first responders.

Motivation & Objective

• To address the challenge of predicting sparse, high-impact traffic incidents across large geographical areas like entire states, where traditional aggregation methods fail due to data sparsity.
• To develop a scalable forecasting pipeline that integrates diverse data sources—roadway geometry, weather, traffic congestion, and historical incidents—into accurate short-term predictions.
• To improve emergency resource allocation by modifying the classical p-median problem with a balancing term that optimizes geographic spread and responder availability.
• To evaluate the real-world impact of forecasting models on response time reduction, moving beyond accuracy metrics to operational performance.
• To demonstrate that forecasting models with high F1 scores (not just accuracy) lead to better allocation outcomes in sparse emergency scenarios.

Proposed method

• The pipeline uses synthetic resampling to augment sparse incident data, improving model generalization in low-frequency regions.
• Non-spatial clustering groups road segments based on covariates (e.g., speed limits, terrain) to enable transfer learning across geographically distant but similar segments.
• A multi-modal learning framework fuses historical accident data, real-time traffic congestion, weather conditions, and roadway geometry to train forecasting models.
• The forecasting models are evaluated using F1 score, AUC, and precision-recall metrics to prioritize detection of rare incidents.
• The classical p-median problem is extended with a hyperparameter α that penalizes over-concentration of responders, balancing coverage and response efficiency.
• Simulations are conducted across 186 four-hour windows with 1,865 incidents to evaluate response time and coverage under varying numbers of responders and α values.

Experimental results

Research questions

• RQ1 Can synthetic resampling and non-spatial clustering improve incident prediction accuracy in large, sparsely populated geographical areas?
• RQ2 How does integrating multi-source data (weather, traffic, road geometry) enhance short-term forecasting of rare traffic incidents?
• RQ3 Does using F1 score as a model selection criterion lead to better operational outcomes than accuracy in sparse emergency forecasting?
• RQ4 To what extent does modifying the p-median problem with a geographic spread penalty reduce average response time in emergency response systems?
• RQ5 Which forecasting model (e.g., random forest, neural networks) leads to the greatest reduction in response time when used in the allocation pipeline?

Key findings

• The proposed forecasting pipeline reduced average response time by up to 10% in long-term simulations, significantly improving emergency response efficiency.
• The modified p-median model with α ∈ [0.5, 1] achieved a maximum reduction of 3 km in average distance traveled per incident by responders.
• Random forest-based forecasting models provided the highest response time reduction, followed closely by neural networks, outperforming other models in operational impact.
• Models with high F1 scores—emphasizing recall and precision—yielded better allocation outcomes than those with high accuracy alone, highlighting the importance of false negative control in sparse incidents.
• A large penalty (high α) increased response times, confirming that excessive geographic balancing harms coverage and must be carefully tuned.
• The framework demonstrated robust performance across varying numbers of available responders, showing scalability and adaptability to real-world operational constraints.