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Model Development For National Assessment of Commercial Vehicle Parking

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EXECUTIVE SUMMARY

This report summarizes activities and research that were undertaken as part of a study mandated by the Transportation Equity Act for the 21^st Century (TEA-21). Section 4027 of TEA-21 specifies:

The Secretary shall conduct a study to determine the location and quantity of parking facilities at commercial truck stops and travel plazas and public rest areas that could be used by motor carriers to comply with Federal hours-of-service rules.The study shall include an inventory of current facilities serving the National Highway System, analyze where shortages exist or are projected to exist, and propose a plan to reduce the shortages.

This study involved the conduct of the following four tasks:

• Plan, organize, and provide logistical support for a meeting to bring together key national stakeholder groups as a kick-off to the study.
• Estimate the extent and geographic distribution of truck rest parking supply, demand, and shortages (current and projected) along the National Highway System (NHS) using existing national and State inventories and studies.
• Determine how commercial vehicle drivers plan for and address their parking needs for both short-duration and Federal hours-of-service rest; how drivers select when, where, and at what facility they park; and how and why drivers decide to use public versus private parking facilities.
• Provide technical support to public-private partnerships in various States in carrying out their initiatives and preparing their plans of action.

This report addresses the second of these tasks by describing the development, calibration, and application of the truck parking demand model used to estimate truck rest parking demand.This parking demand model estimates parking demand for a highway segment rather than a single parking facility. The model incorporates a variety of factors known to affect the demand for truck parking: traffic engineering factors (e.g., annual average daily traffic (AADT), travel time, peak-hour factors), truck driver behaviors (e.g., time spent loading/unloading, time spent at home, time spent resting at shipper/receiver), and Federal hours-of-service regulations (e.g., a maximum of 70 hours on duty in eight days).A step-by-step method for selecting analysis segments is presented along with data requirements, parameter values, and a sample model application.

About half of the model parameters were derived from survey responses from over 2,000 drivers across the United States.The other half of the model parameters were calibrated using overnight field observations of parked trucks in eight States:Arkansas, Georgia, Idaho, Mississippi, Missouri, Pennsylvania, Tennessee, and Virginia.Observational studies were performed on 29 segments of highway in these eight States representing four regions and ten corridors.By comparing model estimates to the field counts of parked trucks, two model parameters were calibrated:the long-haul peak parking factor (PPF_LH) and the short-haul to long-haul ratio (P_SH/P_LH) ratio.

The parking demand estimates produced by the model are highly variable at the segment level. For example, the model estimates are within ± 10 percent of the observed parked trucks for only 4 of the 29 segments (14 percent), ± 20 percent for 11 of the 29 segments (38 percent), and ± 30 percent for 20 of the 29 segments (69 percent). At the corridor level, on the other hand, the model is much more accurate; model estimates are within ± 8 percent of the observed parked trucks for six of the ten corridors (60 percent) and ± 20 percent for eight of the ten corridors (80 percent).

The variance at the segment level can be attributed to several factors. One factor is that the model does not take into account the geographic distribution of available truck parking spaces. Although the amount of available parking does not affect the actual demand, the geographic distribution of the supply will affect where the demand is met.Therefore, when field counts were compared to model estimates, it is not surprising that, in some cases, the estimates for one segment were too low, while the estimates for the next segment were too high.Additional research into how to add a factor to the model that represents the distribution of supply would make the model more accurate at the segment level and more useful for local planning.In addition, the use of only two short-haul to long-haul ratios (i.e., .36/.64 for urban segments and .07/.93 for rural segments) may not adequately reflect the variations across regions and corridors. To better understand the variability in the short-haul to long-haul ratio, origin-destination surveys could be conducted in a variety of locations that represent a range of distances from metropolitan areas.

Nevertheless, the first step in alleviating parking shortages is to identify the locations where problems are likely to exist, and the demand model is a good tool for achieving this goal. Overall, the model produces acceptable estimates of parking space demand, with an error of only -2 percent for the 29 segments where parked truck counts were conducted, which is within 269 spaces of the observed parked trucks.

In conclusion, one of the most powerful features of the truck parking demand model is its ability to estimate future demand so that long-range plans can be formulated.States could use this model to identify locations with possible parking shortages, then, based on local knowledge and field observations, refine the model to better reflect local conditions.The refined model could then be used to make projections of parking demand for long-range planning purposes.

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