Due to the advancements in real-time information communication technologies and sharing economies, rideshare services have gained significant momentum by offering dynamic and/or on-demand services. Rideshare service companies evolved from personal rideshare, where riders traveled solo or with known individuals, into pooled rideshare (PR), where riders can travel with one to multiple unknown riders. Similar to other shared economy services, pooled rideshare is beneficial as it efficiently utilizes resources, resulting in reduced energy usage, as well as reduced costs for the riders. However, previous research has demonstrated that riders have concerns about using pooled rideshare, especially regarding personal safety. A U.S. national survey with 5385 participants was used to understand human factor-related barriers and user preferences to develop a novel Pooled Rideshare Acceptance Model (PRAM). This model used a covariance-based structural equation model (CB-SEM) to identify the relationships between willingness to consider PR factors (time/cost, privacy, safety, service experience, and traffic/environment) and optimizing one’s experience of PR factors (vehicle technology/accessibility, convenience, comfort/ease of use, and passenger safety), resulting in the higher-order factor trust service. We examined the factors’ relative contribution to one’s willingness/attitude towards PR and user acceptance of PR. Privacy, safety, trust service, and convenience were statistically significant factors in the model, as were the comfort/ease of use factor and the service experience, traffic/environment, and passenger safety factors. The only two non-significant factors in the model were time/cost and vehicle technology/accessibility; it is only when a rider feels safe that individuals then consider the additional non-significant variables of time, cost, technology, and accessibility. Privacy, safety, and service experience were factors that discouraged the use of PR, whereas the convenience factor greatly encouraged the acceptance of PR. Despite the time/cost factor’s lack of significance, individual items related to time and cost were crucial when viewed within the context of convenience. This highlights that while user perceptions of privacy and safety are paramount to their attitude towards PR, once safety concerns are addressed, and services are deemed convenient, time and cost elements significantly enhance their trust in pooled rideshare services. This study provides a comprehensive understanding of user acceptance of PR services and offers actionable insights for policymakers and rideshare companies to improve their services and increase user adoption.

The rise of real-time information communication through smartphones and wireless networks enabled the growth of ridesharing services. While personal rideshare services (individuals riding alone or with acquaintances) initially dominated the market, the popularity of pooled ridesharing (individuals sharing rides with people they do not know) has grown globally. However, pooled ridesharing remains less common in the U.S., where personal vehicle usage is still the norm. Vehicle design and rideshare services may need to be tailored to user preferences to increase pooled rideshare adoption. Based on a large, national U.S. survey (N = 5385), the results of exploratory and confirmatory factor analyses suggested that four key factors influence riders’ willingness to consider pooled ridesharing: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. A binomial logistic regression was conducted to determine how the four factors influence one’s willingness to consider pooled ridesharing. The two factors that positively influence riders’ willingness to consider pooled ridesharing are vehicle technology/accessibility (B = 1.10) and convenience (B = 0.94), while lack of passenger safety (B = −0.63) and comfort/ease of use (B = −0.17) are pooled ridesharing deterrents. Understanding user-centered design and service factors are critical to increase the use of pooled ridesharing services in the future.

Rear-end crashes are one of the leading types of crashes today. The anti-lock braking system (ABS) was designed to assist drivers during emergency braking situations by preventing wheel lock up, allowing drivers to retain control of the vehicle. Not all drivers have knowledge and experience with ABS, especially the haptic brake pedal feedback produced by ABS. There studies introduce a new method to train ABS using an emergency braking task on a simulator and an interactive exercise, Pedals Emergency Stop©. The interactive exercise was designed to prompt participants to press the brake pedal in a motion that is consistent with emergency braking and experience haptic ABS feedback. Participants that depressed the brake pedal quickly to the maximum travel and held that pedal position “passed” the trial. In Study 1, participants (N = 63) completed 15 trials, where it took on average three trials to “pass” for the first time, and 85% of the participants experienced their first “pass” within the first four trials. There were no differences observed between participants with previous knowledge and experience feeling ABS versus those who did not have prior knowledge and experience with ABS. The participants in this study thought that they had enough trials, that the exercise was a practical tool, and recommended the task for new drivers and refresher training, as well as for evaluating fitness to drive. Suggested improvements included a practice followed by three tests, all with four trials each; passing criteria of three out of four trials; and modifications to the feedback presented immediately after completing each trail. Study 2 was conducted (N = 38) to investigate the proposed “passing” criteria, where 95% passed on Test 1. The results revealed that the criteria to pass was reasonable. Future research should examine how the Pedals Emergency Stop© interactive exercise generalizes to on-road driving.

Pooled ridesharing offers on-demand, one-way, cost-effective transportation for passengers traveling in similar directions via a shared vehicle ride with others they do not know. Despite its potential benefits, the adoption of pooled rideshare remains low in the United States. This exploratory study aims to evaluate potential service improvements and features that may increase users’ willingness to adopt the service. The study analyzed transportation behaviors, rideshare preferences, and willingness to adopt pooled rideshare services among 8296 U.S. participants in 2025, building on findings from a 2021 nationwide survey of 5385 U.S. participants. The study incorporated 77 actionable items developed from the results of the 2021 survey to assess whether addressing specific user-generated topics such as safety, reliability, convenience, and privacy can improve pooled rideshare use. A side-by-side comparison of the 2021 and 2025 data revealed shifts in transportation behavior, with personal rideshare usage increasing from 22% to 28%, public transportation from 21% to 27%, and pooled rideshare from 6% to 8%, while personal vehicle (79%) use remained dominant. Participants rated features such as driver verification (94%), vehicle information (93%), peak time reliability (93%), and saving time and money (92–93%) as most important for improving rideshare services. A pre-to-post analysis of willingness to use pooled rideshare utilizing the actionable items as per respondents’ preferences showed improvement: “definitely will” increased from 15.9% to 20.1% and “probably will” rose from 35.6% to 47.7%. These results suggest that well-targeted service improvements may meaningfully enhance pooled rideshare acceptance. This study offers practical guidance for Transportation Network Companies (TNCs) and policymakers aiming to improve pooled rideshare as well as potential future research opportunities.

Building on our prior research with a national survey sample of 5385 US participants, the Pooled Rideshare Acceptance Model (PRAM) was built upon two factor analyses. This exploratory study extends the PRAM framework using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) to examine how 16 demographic variables influence and interact with the acceptance of Pooled Rideshare (PR), filling a gap in understanding user segmentation and personalization. Using a national sample of 5385 US participants, this methodological approach allowed for the evaluation of how PRAM variables such as safety, privacy, service experience, and environmental impact vary across diverse groups, including gender, generation, driver’s license, rideshare experience, education level, employment status, household size, number of children, income, vehicle ownership, and typical commuting practices. Factors such as convenience, comfort, and passenger safety did not show significant differences across the moderators, suggesting their universal importance across all demographics. Furthermore, geographical differences did not significantly impact the relationships within the model, suggesting consistent relationships across different regions. The findings highlight the need to move beyond a “one size fits all” approach, demonstrating that tailored strategies may be crucial for enhancing the adoption and satisfaction of PR services among various demographic groups. The analyses provide valuable insight for policymakers and rideshare companies looking to optimize their services and increase user engagement in PR.

Occupational therapists need objective tools to evaluate and provide interventions that promote the recovery and rehabilitation of clients. Driving, a common goal for clients after an injury or illness, is a complex task that relies on visual, cognitive, and motor skills. The Functional Object Detection and Functional Object Detection (FOD)—Advanced driving simulator scenarios were developed to provide objective and repeatable driving experiences allowing clinicians to assess their clients’ forward (focal) and peripheral vision, lane keeping, and speed maintenance, as well as provide interventions. Using FOD—Advanced, clinicians can adjust variables to create various task scenarios or combinations to meet the client’s needs and facilitate recovery by providing an appropriate challenge. This study examined four driving simulator scenario combinations and age-related differences for one combination. Study 1 explored older adults’ performance using four possible combinations of FOD—Advanced. Five out of eleven variables (average target reaction time, percentage of targets detected, average brake reaction time, number of target extra presses, and average speed) were effective in distinguishing among the four combinations of FOD with a cross-validated classification rate of 72%. In Study 2, one combination was selected from Study 1 and a group of teens completed the same tasks to evaluate age-related differences. Four out of thirteen simulator variables (standard deviation of brake reaction time, number of target extra presses, average target reaction time, and standard deviation of target reaction time) maximally distinguished the older adults from the younger participants with a cross-validated classification accuracy of 78%. Implications and recommendations for clinical practice and future research are provided.