Merging behavior under varying work zone sign scenarios: A heterogeneity-based analysis

Proper merging behavior is critical for work zone safety. A thorough understanding of the factors influencing how vehicles merge under different sign scenarios is essential for developing and evaluating effective safety strategies. This study investigates merging behaviors at highway work zones under different flashing speed limit sign (FSLS) and portable changeable message sign (PCMS) scenarios. Two groups of random parameters logit models with heterogeneity in means and variances are estimated for different FSLS and PCMS configurations. Using real-world radar, thermal camera, and meteorological data, three categories of merging behavior are defined as outcome variables: risky merge, somewhat risky merge, and safe merge. Multiple traffic and environmental characteristics are included as explanatory variables. Likelihood ratio tests indicate the determinants of merging behaviors vary across traffic control scenarios, which is further validated through comparisons between out-of-sample and within-sample predictions. The results reveal that the determinants of merging behaviors vary across sign scenarios and that ignoring scenario interactions may introduce systematic prediction bias. These findings highlight the importance of accounting for scenario-specific variations when identifying precursors and forecasting merging behaviors. The results also suggest that PCMS message design should consider potential interactions with raised or flashing FSLS, as simultaneous visual stimuli may increase drivers' cognitive load. In addition, adapting traffic control strategies to environmental conditions, particularly during adverse weather or nighttime scenarios, may help promote more cautious merging behavior. Finally, given the scenario-dependent variations observed in model parameters, further research is needed to examine how the effectiveness of FSLS and PCMS evolves over time. Future studies could also explore spatiotemporal stability using larger datasets across multiple work zones.