Do Larger Sample Sizes Increase the Reliability of Traffic Incident Duration Models? A Case Study of East Tennessee Incidents

Abstract

Incident duration models are often developed to assist incident management and traveler information dissemination. With recent advances in data collection and management, enormous achieved incident data are now available for incident model development. However, a large volume of data may present challenges to practitioners, such as data processing and computation. Besides, data that span multiple years may have inconsistency issues because of the data collection environments and procedures. A practical question may arise in the incident modeling community—Is that much data really necessary (“all-in”) to build models? If not, then how many data are necessary? To answer these questions, this study aims to investigate the relationship between the data sample sizes and the reliability of incident duration analysis models. This study proposed and demonstrated a sample size determination framework through a case study using data of over 47,000 incidents. This study estimated handfuls of hazard-based duration models with varying sample sizes. The relationships between sample size and model performance, along with estimate outcomes (i.e., coefficients and significance levels), were examined and visualized. The results showed that the variation of estimated coefficients decreases as the sample size increases, and becomes stabilized when the sample size reaches a critical threshold value. This critical threshold value may be the recommended sample size. The case study suggested a sample size of 6,500 to be enough for a reliable incident duration model. The critical value may vary significantly with different data and model specifications. More implications are discussed in the paper.

Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data

“…The case study suggested a sample size of 6500 to be sufficient for a reliable incident duration model. However, this conclusion is based on the hazard-based duration model, and whether the data-driven machine learning algorithm is applicable remains to be studied [ 6 ].…”

Traffic accident duration prediction using text mining and ensemble learning on expressways

“…Mohammed et al 1 summarizes an analysis of factors affecting the duration of traffic accidents at different stages, data sources, and prediction methods. Zhang et al 2 confirms that a larger sample size improves the reliability of the traffic accident duration model. Wali et al 3 examined several factors incident durations from more than 45,000 events in Virginia in 2015, including detection source, incident type, road type, and temporal factors.…”

Evaluating the Impact of Freeway Service Patrol on Incident Clearance Times: A Spatial Transferability Test

“…Li and Walton [18] used event-driven simulation model in estimating the benefit-cost ratio of Kentucky FSP program, known as Safety Assistance for Freeway Emergencies (SAFE) Patrol in low-traffic areas, and shown that eventdriven simulation model is better than analytical model in terms of faster simulating speed, larger road network, and longer period of simulation. Zhang et al [19] assessed the relationship between incident data sample sizes and the reliability of incident duration analysis models.…”