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Model Development For National Assessment of Commercial Vehicle Parking

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5.0 SUMMARY AND CONCLUSIONS

The objective of this research was to estimate the extent and geographic distribution of truck rest parking supply and demand along the National Highway System in accordance with Section 4027 of the Transportation Equity Act for the 21^st Century. This report described the development, calibration, and application of the truck parking demand model used to estimate truck rest parking demand.

The parking demand model developed for this study estimates parking demand for a highway segment (defined by the analyst) rather than a single parking facility. The model incorporates a variety of factors known to affect the demand for truck parking, which include: traffic engineering factors (e.g., 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 and applying the model is presented.

About half of the model parameters where 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).

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 four 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 nine corridors (80 percent). The absolute errors in estimated demand at the segment, corridor, and regional level were 38, 12, and 3 percent, respectively.

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.

It should also be noted, however, that the error at the segment level does not necessarily indicate that the demand model is inaccurate when applied to highway segments, but may indicate that the lack of parking spaces on some highway segments creates unmet demand that appears in field observations as unusually high demand on nearby segments with a surplus of parking.

In conclusion, the first step in alleviating parking shortages is to identify 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, an estimate within 269 spaces of the observed parked trucks. Because at the time the observational studies were conducted, half of the 29 segments were at or exceeding capacity, the use of this model will result in conservative estimates of truck parking demand.

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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