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.

Autonomous vehicles (AV) are being widely tested around the world. California is one of the jurisdictions permitting extensive AV testing. Field testing resulted in approximately 0.02 AV crashes per 1,000 AV miles traveled, as well as incidents leading to driver disengagement of the AV systems. Factors related to human error, system failure, surrounding vehicles, and roadway features could cause an AV-involved crash to occur or result in pre-crash disengagement. This study focuses on AV crashes in which the AV was operating in active AV mode or shortly after the test operator had disengaged the AV to resume conventional control. AV crash data were extracted for the years 2017 to 2021 from California’s AV crash database maintained by the California Department of Motor Vehicles (DMV) to account for the rapid pace of AV development and focus on current causes of incidents. This paper utilized multiple statistical approaches to quantitatively investigate AV crashes and disengagement events. Investigation of latent manifest using exploratory factor analysis (EFA) was conducted and ordinal logit models and decision trees were employed in this study. EFA clarified latent variables that could identify an AV crash, in which operator involvement, incorrect maneuver decision, crash severity, and environmental conditions were the manifests obtained from the analysis. Results from the logistic regression and the decision trees showed that collision type, AV movement type, and other vehicle movement type are significant factors contributing to AV crashes.

Connected vehicle (CV) technology allows developing next-generation transit signal priority (TSP) that is based on cooperation between signals (change signal timing) and buses (alter speed). In this study, a CV-based TSP that was previously developed and evaluated through a computer simulation was tested in the field. The field experiment at the Smart Road testbed at the Virginia Tech Transportation Institute (Blacksburg, Virginia) validated the feasibility of CV-based TSP and evaluated its performance. The proposed TSP algorithm properly delivered TSP green times to buses with a 100% success rate. In addition, it reduced delays—between 32 and 75%—for a bus traveling approximately $72.4\mathrm{km}/\mathrm{h}$ ($45\mathrm{mi}/\mathrm{h}$ ) and a traffic signal with a 90-s cycle length with 30-s green time. Moreover, the field experiment showed that regular and differential global positioning system (GPS) devices demonstrate no statistically significant difference in performance. This finding could facilitate large-scale implementation of the TSP logic based on connected vehicle technology (TSPCV) because the regular GPS devices are readily available and much cheaper than differential GPS devices.

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.