Guide for Identifying, Classifying, Evaluating, and Mitigating Truck Freight Bottlenecks (2017)

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20 This chapter provides an overview of the primary steps involved in conducting the truck bottleneck analysis. As described in the prior chapter, the speed data focus throughout these methods is truck probe speed data because they are the most cost-effective and widely available data source for most transportation agencies. Traditional travel demand models can also be used to conduct truck bottleneck analysis, but these models are limited to analyzing recurring travel speed-based bottlenecks only. Congestion locations are important to both trucks and cars. However, the patterns (time peri- ods) and significance (number of vehicles delayed) of congestion occurring at specific locations may be very different for trucks than for cars. That is, at some locations, the majority of delayed vehicles may be cars, whereas at other locations, perhaps due to roadway limitations related to a truck’s size or performance, the delayed vehicles may be mostly trucks. Consequently, truck con- gestion must be analyzed both in conjunction with congestion for cars and separately, as effec- tive departmental decision making requires an understanding of the differences. For example, an agency might prioritize projects that reduce delay if it knows that a disproportionate amount of that delay is experienced by high-value truck movements. Many of the commonly analyzed commuter bottlenecks (and commuter bottleneck indexes) focus on the morning and afternoon commute peak periods where total vehicle volumes are highest. Truck percentages during these periods of the day can vary, but typically these do not represent the highest truck percentage periods. On the other hand, bottlenecks during the mid- dle of the day may disproportionately impact trucks more than other periods as the truck per- centages during the middle of the day tend to be higher than during commute periods. This is particularly relevant for urban bottlenecks caused by crashes during off-peak periods and rural bottlenecks caused by crashes. The methodology is scalable by geography, so it can be applied to point locations, individual corridors, or statewide road networks. The scalable methodology for identifying, ranking, and mitigating travel speed-related bottlenecks, introduced earlier, consists of the six steps as shown previously in Figure S-1. The scalability of the analysis also allows for allocation of benefits to local, state, regional, and national stakeholders which can inform the investment setting pro- cesses of similarly scaled transportation agencies. This scaling also allows for information to be extracted regarding the need for private savings relative to public costs. Several of these steps can be performed simultaneously in terms of computer processing, but are discussed separately in this Guidebook (e.g., computing delay by road segment and then sorting those segments by the amount of delay computed). These same procedures—and thus the same basic software tools—are also used to identify, quantify, and rank bottlenecks for total traffic volume. The majority, but not all, of the performance metrics appropriate for truck bottlenecks also are applicable for bottlenecks related to total volume. The primary difference is C h a p t e r 2 Overview of Truck Bottleneck Analysis Steps

Overview of truck Bottleneck analysis Steps 21 that because the travel patterns of trucks are different than those of cars, bottlenecks that are most important for trucks may not be the most important ones for total volume. The same methods are usable to rank segments by truck volume if reliable vehicle classification data are available. The suggested approach described here is scalable in several ways. First, it allows the agency performing the analyses to use its available data resources regardless of the source or size of those resources. In addition, the same analytical approach works whether the analysis is performed for an entire state highway network, a regional network, or even a specific city. Second, even within an agency, the suggested approach can be applied to a single road, multiple roads within a geo- graphic corridor, an entire region, to all roads in the state, or to all roads in a multistate region. The suggested approach also accounts for the fact that not all of the data sources identified in this report will be available to all agen- cies. It is not possible to perform analyses for which no data exist, but other analyses that do not rely on those missing data can still be performed. The suggested approach is specifically designed to allow agencies to extend their analysis capabilities as new data resources become available. Finally, the suggested approach starts with an automated process that identifies and ranks the “most significant” bottlenecks within the study area. Detailed analyses are then performed on only the most important, highest-priority bottleneck locations. This approach has been suc- cessfully used by states for many of their performance management systems. For example, state DOT pavement management systems routinely describe the condition of a state’s roadways and produce both aggregated summaries of the entire state’s system pavement performance and an initial list of priority locations in need of repair and rehabilitation. Depending on the available budget, a limited number of these locations are examined in detail to produce “actual” design documents for those pavement repair and rehabilitation projects. The more money that is avail- able for pavement maintenance, the more projects that are designed at this detailed level. At this detailed level, analysis is needed early because a good design engineer can optimize a design far better, and with far more specific inputs, than is possible at the statewide level. The approach proposed here is the same. An automated process identifies the list of bottle- neck locations, their relative rank in terms selected by the agency, and the probable causes of their performance deficiencies. From this list, the agency can then select the projects that most effectively fit into the agency’s mission and budget for further analysis of detailed bottleneck mitigation. These selected projects then receive additional, detailed design attention, allowing the agency to select the most appropriate approach to bottleneck mitigation given the many fac- tors that apply to any project. The number of these designs and the resulting projects chosen for funding will scale to the resources available within that agency. The suggested approach is specifically designed to allow agencies to extend their analysis capabilities as new data resources become available.