Improved Traffic Analysis Methodology for Lane Closure on Two-Lane Highway Work ZoneAuthor/Presenter: Haque, Mm Shakiul
Lane closures on two-lane highways allow a single lane to be used for both directions of traffic alternatively and this negatively impact operational performance. The sixth edition of the Highway Capacity Manual (HCM-6) modeled this situation using microsimulation and field data. However, there are no guidelines on how the HCM-6 work can be replicated in other areas. Furthermore, there is no guideline for modeling a signal control plan that can optimize operation for various work zone scenarios. This dissertation proposes a comprehensive methodology that can model work zones involving one lane closures on two-lane highways. It includes an automatic calibration methodology focused on identifying microsimulation parameters that produce distribution of performance measures statistically similar to empirical observations. The efficacy of the model was tested using empirical data in Nebraska. The proposed model was also validated using empirical data. This dissertation develops the Nebraska University signal timing method (NU_STM) applicable for different work zone scenarios. Furthermore, efficacy of existing methods such as HCM-6 and Webster are analyzed and compared with NU_STM. It is found that NU_STM reduces delays by up to 78%, and decreases its variability up to 74%, and results in better operations than other signal timing methods examined. Lastly, the impact of connected and automated vehicles (CAVs) on work zones is modeled and evaluated using a new methodology. In general, it is found that 100% market penetration of CAV can reduce delay by 84% and shorten queue length by 76%. CAV also reduces the variability of these performance measures. This dissertation provides a new and comprehensive model for lane closure on two-lane work zones. It specifically addresses the shortcomings of existing models and is designed so future transportation technologies, such as CAV, can be readily incorporated. The methodology developed in this dissertation can be used by academics, engineers, and traffic agencies to analyze and predict traffic conditions outside of the scope provided by the current state of practice.