The main objectives of this paper are to assess and define ways to enhance the transferability of freight trip generation (FTG) models. After the key premises that should guide the development of FTG models have been presented, the paper assesses transferability in two ways. The first is through analyses of how well representative FTG models are able to estimate the actual FTG for a number of external validation cases. The second is through FTG econometric models that assess the statistical significance of binary variables that represent specific geographic locations. In addition, the paper introduces and assesses the accuracy of a synthetic correction procedure that is intended to improve the transfer-ability and quality of the estimates provided by the FTG rates available in the literature. The results show that the models developed as part of the National Cooperative Freight Research Program's Project 25, Freight Trip Generation and Land Use, have better prediction capabilities than the models included in other compilations. In addition, the synthetic correction procedures improve transferability, and no locational effects are present in the test data.
The preferences of drivers and their willingness to pay (WTP) for connected vehicle (CV) technologies were estimated with the use of adaptive choice-based conjoint (ACBC) analysis, the newest such method available. More than 500 usable surveys were collected through an online survey. Respondents were asked to choose from variously priced CV technology bundles (e.g., collision prevention, roadway information system). The study found that the acceptance level of the CV technologies was high, given that an absolute majority of survey respondents had the highest preferences for the most comprehensive technology bundle in each attribute. However, a comparison of the average importance of each attribute, including bundle prices, implied that price would be an important constraint and would influence CV deployment rates. At the attribute level, collision prevention technology received the highest importance score (i.e., the safety benefits most appealed to drivers). The ACBC analysis seemed to mimic well the trade-offs that people would consider in their actual purchasing decisions. The difference between WTP and self-explicated prices obtained before preferences were estimated was statistically significant (i.e., participants chose bundles after they considered product attributes and prices). This finding also affirmed that the ACBC analysis was a more appropriate method than the direct questioning methods used in past studies. Finally, certain socioeconomic characteristics were positively related to WTP. Those respondents that were knowledgeable about CV technologies and showed more innovativeness had higher WTP as well.
An approach to determine a statistically reliable sample size for developing local calibration factors (LCFs) was proposed to complement the Highway Safety Manual's (HSM) sampling guidance. The HSM suggests a minimum sample size of 30 to 50 sites per facility type with at least 100 annual crashes. However, the HSM fails to provide clear guidance on how to determine a minimum sample size to ensure the statistical reliability of LCFs. The proposed approach based on the finite population correction (FPC) factor determined minimum sample sizes by considering trade-offs between the desired error levels of the estimated LCFs, confidence levels, and sample standard deviations. The sample sizes by facility types were drawn on the basis of various statistical assumptions; then they were assured by the comparisons between FPC-based samples and the HSM-based samples. LCF values estimated from the HSM-based sample sizes yielded inconsistent reliabilities depending on the facility types. In contrast, those estimated from the samples by the FPC-based approach satisfied the desired reliabilities of the LCFs for all facility types.
This paper discusses Maryland's experience in developing local calibration factors (LCFs) in the application of the Highway Safety Manual (HSM), which is the required process for adjusting predicted crashes estimated by the HSM's safety performance functions (SPFs) to local jurisdictions. The LCFs for 18 facility types were calculated with data for the period 2008 to 2010. Additional variables were gathered by alternative data collection methods. Because HSM's crash proportion was different from Maryland's, Maryland's crash proportion was used to predict crash frequency and calculate the LCFs. Maryland in general had fewer crashes than predicted crash frequency generated by the HSM's SPFs. The LCFs for 15 of 18 facility types were less than 1.0. In particular, intersection LCFs were extremely low. Because of potential issues with unreported minor and property damage only crashes, the authors recommend using the LCFs for fatal and injury crashes where available. The pairwise comparison of Maryland LCFs with the LCFs of nine case studies showed statistically significant differences between states, providing grounds for jurisdiction-specific LCF development.
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