Robert L Smith Jr

DEVELOPMENT OF A STATEWIDE TRUCK TRIP FORECASTING MODEL BASED ON COMMODITY FLOWS AND INPUT-OUTPUT COEFFICIENTS

This research attempts to improve the modeling of statewide truck travel demand models by using commodity flow data from the U.S. Census Bureau, a private freight database (TRANSEARCH), and input-output (I-O) coefficients. The standard urban transportation planning modeling process was applied at the state level to estimate heavy truck trips. Economic-based I-O software was used to derive the I-O direct matrix and the I-O direct coefficients at the state level for developing the trip attraction rates for 28 manufacturing sectors. The Commodity Flow Survey from the U.S. Census Bureau together with a private database developed for Wisconsin were used to develop the trip production rates. Transportation planning software (TRANPLAN) was used to distribute and assign truck trips generated at the zonal level. The selected-link function in TRANPLAN was used to adjust the initial productions and attractions in order to generate link volumes that match the actual ground counts for 40 selected links. The model only required two iterations of the selected link analysis in order to produce an acceptable match with the ground counts, compared with three iterations for two prior similar models. The rapid convergence provides clear evidence that the disaggregate trip generation models give better initial estimates of trip productions and attractions than was possible with the prior studies. A “back forecast” of 15 years to the year 1977 was found to be reasonable both in terms of the percent root mean square error by volume group and the performance measures for five screen lines.

DEVELOPMENT OF PARKING CHOICE MODELS FOR SPECIAL EVENTS

Research was conducted to evaluate parking lot choice in the context of special events. Three major factors—driving time, parking cost, and walking time—are considered as the principal components of utility associated with the choice of a parking lot. The lot choice model is derived by using the logit function, while the joint parking lot destination choice and assignment model is formulated from the concepts of user equilibrium traffic assignment and entropy maximization for trip distribution. Parking lot choices by University of Wisconsin men’s basketball game attendees at the Kohl Center in Madison are used as a case study for model calibration and application. The parameter calibration was conducted both for all lots and for public and private lots separately. Parking lot choice was also analyzed by direction of approach. Two distinct assignment scenarios were evaluated by using the user equilibrium assignment methodology. The impact of the system optimal and minimum-cost assignments on total network costs was evaluated. The parking lot choice model was applied to estimate the impact of opening a new private parking lot to season permit parking.

DEVELOPMENT OF A STATEWIDE TRUCK-TRAVEL DEMAND MODEL WITH LIMITED ORIGIN-DESTINATION SURVEY DATA

A simple statewide truck-travel demand model for Wisconsin was developed using only readily available data, including a small amount of data from origin-destination travel surveys (O-D) and fairly extensive truck-classification count data. A simple trip-rate trip-generation model based on zonal population was used to estimate the initial internal trip productions and attractions. Despite the small amount of O-D survey data that were available, gravity-model friction-factor curves were developed for three trip types. The calibrated gravity models produced trip tables that were good matches for the O-D survey data. The calibrated gravity models were then used as the modeling framework for the adjustment of zonal productions and attractions by selected-link-based (SELINK) analysis. SELINK adjustment factors were computed for all zones with trips on one or more of the selected links. The adjustments were made so that the ratio of the actual volume for the selected link (ground count) to the total assigned volume approached 1 for all selected links. The SELINK analysis produced a calibrated model after only three iterations when either 16 or 32 selected links were used statewide for the calibration. The overall accuracy of the calibrated model was measured by using classification count data for 154 locations around the state. The model provided a good fit based on a wide variety of measures, including percent root-mean-square error and comparisons for screenlines, functional class, areas, and vehicle kilometers traveled.

Using Commodity Flow Survey Data to Develop a Truck Travel-Demand Model for Wisconsin

Four similar truck travel-demand (TTD) models were developed for Wisconsin using 1993 Commodity Flow Survey (CFS) origin–destination (O-D) data and a limited number of truck classification count data. First, statewide zonal-level trip tables were developed using the CFS database. Then gravity models for four trip types were calibrated to match the trip-length frequency distributions of the CFS O-D trip tables. Finally, zonal trip productions and attractions were adjusted using an iterative procedure to match assigned traffic volumes with truck classification count data for 40 selected links. The four alternative TTD models differed only in the method used to assign external trips to the external stations. All of the models provided reasonable levels of goodness-of-fit to the 40 selected calibration links, as well as 104 additional count locations across the state. The primary limitation of the four models was that they substantially underestimated the truck trips that crossed the state boundary. The CFS data focused on longer-distance transport of major commodities; therefore, the local truck trips that used the external stations were underrepresented in the model. Simple regression models for estimating trip productions and attractions were developed using zonal population and employment as the independent variables. These improved models were possible because the CFS data made available more detailed initial estimates of trip productions and attractions.

Effectiveness of open-graded base layer on subgrade moisture regime and overall pavement performance

Rockcap (open-graded crushed aggregate with a nominal maximum size of 2.5 in.) base layer is commonly used in the construction of asphalt pavements in Idaho. The effectiveness of the rockcap layer on the subgrade moisture regime and the overall pavement performance were evaluated. Two pavement sites were selected in northern and southern regions of the state. At each site, two pavement sections that are identical and adjacent to each other were instrumented by temperature, frost, and moisture sensors using time domain reflectometry (TDR) probes. One was constructed by a well-graded 3/4-in. aggregate base, whereas the other was constructed by the rockcap base. Data collected over approximately 4 years included moisture, temperature, frost condition, climatic information, groundwater level, and deflections using a falling weight deflectometer for structural support evaluation. These data were analyzed to assess effects of the rockcap layer on the variation of moisture in the subgrade and on the overall structural capacity of the pavement system. Results showed that the rockcap layer helped reduce the subgrade moisture content in pavements with daylight ditch drains, and it showed an increase in subgrade moisture for sections where the rockcap was confined by native soil and did not continue to side drains. However, performance analysis showed that sections with rockcap layer were always stronger than sections with aggregate base, even when the subgrade moisture content under rockcap was greater. The predicted rutting life for pavement sections with rockcap base layers was approximately five times greater than for sections with aggregate base.

Development of a Carrier Selection Method for Intrastate Compliance Review in Wisconsin

A method was developed for ranking intrastate carriers in Wisconsin to enhance the selection of carriers for Wisconsin’s intrastate compliance (comprehensive safety) review program. The federal SafeStat methodology for identifying interstate motor carriers that have poor safety records cannot be used in Wisconsin because of the lack of carrier size data. Missouri’s intrastate SafeStat methodology, which does not require carrier size data, was used as the starting point in developing Wisconsin’s methodology. The methodology involves merging a ranking of carriers based on historical inspection data (the total out-of-service rate) with a ranking based on total crashes. The validation based on crashes per carrier for a subsequent period showed a similar pattern for the before and after crash rates by percentile group. The overall level of crashes for the after period, however, was substantially lower because of the statistical phenomenon of regression to the mean (RTM). The methodology is also limited by the lack of exposure data for the carrier crash data. To account for RTM, an empirical Bayesian method for estimating RTM was proposed and applied to a before-and-after analysis of intrastate carrier crashes in Wisconsin. The method provided estimates of the after-period crash rates that were much better than the naive assumption of no change in the crash rate. An improved fit for the RTM estimates is likely if more homogeneous groups of carriers were created on the basis of grouping by carrier size.

Development of Intrastate Inspection Selection System for Wisconsin

Roadside safety inspections have been an important component of federally supported programs to improve motor carrier safety. The inspection selection system (ISS) and its successor, ISS-2, were designed to help roadside inspectors target the vehicles of Interstate carriers with the worst past safety performance. ISS was used as a starting point for developing an intrastate ISS for Wisconsin. Logistics regression was used to identify the relevant independent variables. The logit model estimates the probability that a vehicle will be found to have an out-of-service (OOS) safety violation. An alternative to the logit model, a simple direct-estimation technique that uses the total OOS rate for a motor carrier, was found to produce the highest success rates in predicting inspections that result in an OOS violation. The model can be implemented as a field inspection tool and as a means to generate a list of intrastate carriers ranked by their historical OOS violation record.

RESULTS FROM MINNESOTA/WISCONSIN AUTOMATIC OUT-OF-SERVICE VERIFICATION OPERATIONAL TEST

A project was designed to enhance the ability of inspectors at fixed safety and weight stations (scales) to identify “out-of-service” (OOS) commercial vehicles and drivers by using advanced video-based license plate scanners linked to database software on a personal computer, the MOOSE system. In Wisconsin the results of safety inspections at the scales are stored in real time on a central computer database. This includes commercial vehicles and drivers placed OOS because of major safety violations. The primary goals were to increase the effectiveness of OOS enforcement efforts, establish a bistate enforcement program, and identify future applications. The technology was tested on a corridor involving three scales in Wisconsin and one in Minnesota on westbound I-90/I-94. The MOOSE system did identify a large number of OOS vehicles and drivers, but upon reinspection, almost no current OOS violations were found. The MOOSE system was successfully implemented at the Minnesota scale, but, as in Wisconsin, very few current OOS violations were identified. Because the Minnesota scale operates 24 hr/day, drivers coming from Wisconsin who are still OOS will probably use bypass routes. The greatest potential benefit from the MOOSE system is likely to be from linking the license plates to a new system that provides safety rating scores. Inspectors could then select vehicles for inspection that have a higher probability of being OOS or having other safety violations.