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106 C h a p t e r 9 This research demonstrates an advanced method for identifying, classifying, evaluating, and mitigating truck bottlenecks based on utilizing truck probe data. This method allows for evalu- ating truck bottlenecks for prioritizing investment decisions. The method differs from the use of travel demand models in three key ways in terms of the types of results generated: (1) truck probe data allows for identification of a much broader set of bottleneck locations (e.g., truck bottlenecks based on crashes and weather); (2) truck probe data allows for analysis of actual bottleneck locations as opposed to derived bottlenecks; and (3) truck probe data only allows for analysis in a base year as opposed to travel demand models which can also be used to estimate future bottlenecks. The key conclusions from this Guidebook are as follows: ⢠A uniform classification structure is described that can provide consistency to bottleneck definitions used in future analyses. ⢠Truck probe speed data can be used in conjunction with other data sources (e.g., crash data, weather data, volume data) to identify the causes of bottlenecks. In practice, there are typi- cally project-level quantitative and qualitative evaluations needed to identify truck bottleneck cause. ⢠The methodology presented in this Guidebook can be used to demonstrate the benefit of bottleneck improvements to truckers, policy decision makers, and the general public. This is particularly true for bottlenecks based on operational restrictions (i.e., geometric or height restrictions or truck bans). ⢠There are a number of practices in the literature related to facility-based mobility analysis that include a truck component (e.g., ranking roadway sections by truck delay per mile). These practices generally integrate speed and volume data sources, and these practices are scalable from roadway sections to longer sections to urban area or statewide analyses. ⢠Truck probe data is a relatively new data source. However, it is already among one of the most accurate data sets typically available to freight planners. Calculating delay from the probe data is equally reliant on accurate truck count data. Attention must be paid to ensure that truck count data is accurate in order to ensure that truck bottleneck analyses are useful for planning purposes. ⢠Truck probe data provide a valuable window into actual truck reliability performance. This provides an extra dimension to standard bottleneck analysis which typically pivots off total delay estimates. ⢠Engaging truck stakeholders remains a critical part of the truck bottleneck analysis method- ology. In particular, stakeholders can confirm locations of bottlenecks, assist in determin- ing why truck bottlenecks are occurring, and provide a sense of which mitigation efforts to consider for truck bottlenecks. Conclusions
