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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

This dissertation explores factors influencing pooled rideshare (PR) adoption to provide actionable insights for transportation network companies (TNCs) and policymakers. PR allows travelers to share rides with unknown passengers, offering benefits such as cost reduction and congestion relief. However, adoption remains limited due to safety concerns, privacy issues, and trust in rideshare platforms. A national U.S. survey with 5,385 respondents examined transportation preferences and barriers to PR adoption. Exploratory and confirmatory factor analyses identified five key factors influencing PR consideration—safety, service experience, privacy, traffic/environment, and time/cost. Second factor analyses examined ways to optimize PR experiences, revealing four factors—comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. Privacy concerns, for instance, using regression analysis, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. The Pooled Rideshare Acceptance Model (PRAM), based on the Technology Acceptance Model, assessed the impact of these factors using the Structural Equation Model (SEM). Privacy, safety, trust, and convenience had a large effect (Cohen's f2 > 0.35) on PR acceptance, while multigroup analyses (PRAMMA) explored 16 demographic variables such as gender, generation, and income, emphasizing the need for tailored strategies. Based on all the statistical analysis and workshops using descriptive statistics, 95 actionable recommendations were made from the riders' perspective. Findings highlight the importance of customized services, user experience improvements, and policy interventions to enhance PR adoption. This dissertation provides a roadmap for future research and policy development, ensuring evidence-based, practical strategies to improve PR services in the U.S. and beyond.

Gangadharaiah, Rakesh↗
DOE OSTI2025

Proactive Assignment Strategy With Human Choice Models for Boosting Pooled Rideshare Service

This study analyzes various human factors considerations in estimating discounts for pooled rideshare trips. The discounts are utilized in an optimization-based rideshare assignment strategy (proactive strategy) and compared against each other, as well as a heuristic strategy attempting to replicate current real-world pooling rates. Simulations within Austin, Texas and Greenville, South Carolina, reveal the proactive strategy’s ability to increase average vehicle occupancy by 0.23 persons/mile in Austin and 0.52 persons/mile in Greenville. A significant ability to decrease trip rejections and increase profitability is also observed. Finally, the strengths of particular combinations of factors are discussed relative to their effectiveness in each region.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Opinions from Users Across the Lifespan about Fully Autonomous and Rideshare Vehicles with Associated Features

Fully autonomous vehicles have the potential to fundamentally transform the future transportation system. While previous research has examined individuals’ perceptions towards fully autonomous vehicles, a complete understanding of attitudes and opinions across the lifespan is unknown. Therefore, individuals’ awareness, acceptance, and preferences towards autonomous vehicles were obtained from 75 participants through interviews with three diverse groups of participants: 20 automotive engineering graduate students who were building an autonomous concept vehicle, 21 non-technical adults, and 34 senior citizens. The results showed that regardless of age, an individual’s readiness to ride in a fully autonomous vehicle and the vehicle’s requirements were influenced by the users’ understanding of autonomous vehicles. All of the engineering students understand what a fully autonomous vehicle is and this group was the most willing to ride especially compared to the seniors, where only half of the seniors knew what a fully autonomous vehicle is and 58.8% were not at all ready to ride one. The desire to have a manual control option or the ability to override the vehicle was common (90% of the engineering students, 95.2% of the adults, and 82.4% of the seniors), especially for individuals who reported not being ready to ride in a fully autonomous vehicle. The majority of all three groups of participants (85% of the engineering students, 81% of the adults, and 52.9% of the seniors) considered it essential that the vehicle should convey information about the vehicle’s status and intended behavior. Diagnostic information about the vehicle was desired by the engineering students (71.4%), who had a technical understanding of autonomous vehicles and current automotive related technologies. When autonomous vehicles are available, most participants anticipate preferring to use them as a rideshare service model (75% of the engineering students, 38% of the adults, and 27% of the seniors) rather than owning (5% of the engineering students, 19% of the adults, and 21% of the seniors) the autonomous vehicle themselves. Regarding the topic of sharing rides with strangers, both the automotive engineering students (90%) and the adults (52.6%) were comfortable with the idea of pooled rideshare in comparison to the seniors (29.4%). In future efforts, it will be important to include potential autonomous vehicle users of a wide age range as well as physical, cognitive, and visual abilities.

Gangadharaiah, Rakesh↗
DOE OSTI2026

Improving Experience Beyond the Ride: Joint Assignment and Repositioning for Rideshare

In this study, a novel joint assignment and repositioning strategy is designed, implemented, and tested in a simulated traffic environment. The strategy addresses limitations of previously constructed rideshare repositioning algorithms by integrating the consideration of rider pooling into the optimization. By considering pooling, the strategy can minimize excess vehicle movement and increase riders' use of pooled services over a zonal-flow-based repositioning strategy and a fleet operating with no repositioning at all. The new strategy can increase pooling by up to 2.3% compared to no repositioning in a large region, and up to 0.1% in a small region, without compromising overall fleet revenue. Additionally, compared to a baseline, flow-based repositioning strategy, the new strategy can reduce empty vehicle travel by up to 30% in the larger region, and 2.1% in the smaller simulated area.

Paul, Joseph↗
DOE OSTI2021

Real time operation of high-capacity electric vehicle ridesharing fleets

We study the feasibility of using electric vehicles in online, high-capacity ridepooling systems. Prior work has shown that online algorithms perform well for centrally-controlled, high-capacity ridepool systems. First, we propose a mixed integer linear program to expand past algorithms on ridepooling to electric vehicle fleets with the objective of scheduling vehicle charging to maintain sufficient fleet sizes at various times of day. Then we show a faster, scalable algorithm with similar performance that is practical for full-scale systems. Furthermore, our contributions reinforce the importance of having knowledge and estimates of future demand even when operating in the online setting.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Integrating Human Factors in Dynamic Rideshare Assignment: Willingness-To-Pay for Delay

Dynamic ride-sharing (DRS) has been projected to be a key solution to lowering system-wide congestion. Despite recent developmental progress, demand studies for DRS suggest low levels of willingness for travelers to use such services. The disconnect between DRS system designs and user preferences limits the application impacts of DRS in the real world. Therefore, this paper aims to design a new DRS trip/vehicle assignment strategy by considering the user preferences of choices under different levels of service. In this study, an agent-based simulation approach is used to model a fleet of shared vehicles that allows DRS. An optimization model is developed to match riders to vehicles while accounting for traveler delay and delay acceptance. Travelers are also dynamically issued predictive discounts, catered to their expected willingness to pay, to incentivize them to accept longer trip delays. Results show that the proposed approach can improve system efficiency by increasing average vehicle occupancy by up to 1.0 persons/trip and DRS acceptance by up to 38.9% depending on fleet size. Additionally, congestion is eased through the decrease of empty vehicle miles traveled by up to 7.1%.

Paul, Joseph↗
DOE OSTI2025

User Acceptance of Shared Autonomous Vehicles

A dissertation is proposed to explore user acceptance of shared autonomous vehicles (SAVs). SAVs are facing limited user acceptance. To systematically tackle the user acceptance barriers of SAVs, the main problem can be disintegrated into two sub-problems of user acceptance of autonomous vehicles (AVs) and ridesharing. The comfort of the ride experience in AVs is a determinant of user acceptance. Understanding the influential factors and developing methodologies to quantify human comfort in AVs are essential to facilitating future research to improve human comfort in AVs. The current pooled rideshare (PR) service closely resembles the anticipated future of SAVs. Understanding why users prefer or refuse to use PR at the current stage prepares SAVs for broader acceptance in the future. Until now, a series of peer-reviewed publications have been published to achieve the technical goals. Two simulator-based user studies were conducted to instrument the research on human comfort in AVs. Statistical analysis was performed to identify the crucial vehicular behavioral factors of human comfort in AVs. The influential factors of human comfort in AVs and methodologies to quantify and detect human comfort in AVs were investigated. Two survey-based studies were deployed to facilitate the investigation of user acceptance of rideshare services. The influential factors of users' willingness to consider PR were explored and identified, and the choice behaviors in ridesharing services were comprehensively modeled and analyzed. The proposed research answers a series of fundamental questions regarding the user acceptance of SAVs. For the branch of user acceptance of AVs, the research generated guidelines for improving passenger comfort in AVs by identifying a series of autonomous driving factors of passenger comfort. The research also provides fundamental tools to estimate human comfort levels for future research and in-AV applications. For the branch of user acceptance of PR, the research provided user acceptance-aware vehicle, service, and policy design insights that can promote the usage of PR.

Su, Haotian↗
DOE OSTI2023

Mobility-On-Demand Transportation: A System for Microtransit and Paratransit Operations

New rideshare and shared-mobility services have transformed urban mobility in recent years. Therefore, transit agencies are looking for ways to adapt to this rapidly changing environment. In this space, ridepooling has the potential to improve efficiency and reduce costs by allowing users to share rides in high-capacity vehicles and vans. Most transit agencies already operate various ridepooling services including microtransit and paratransit. However, the objectives and constraints for implementing these services vary greatly between agencies. This brings multiple challenges. First, off-the-shelf ridepooling formulations must be adapted for real-world conditions and constraints. Second, the lack of modular and reusable software makes it hard to implement and evaluate new ridepooling algorithms and approaches in real-world settings. Therefore, we propose an on-demand transportation scheduling software for microtransit and paratransit services. This software is aimed at transit agencies looking to incorporate state-of-the-art rideshare and ridepooling algorithms in their everyday operations. We provide management software for dispatchers and mobile applications for drivers and users. Lastly, we discuss the challenges in adapting state-of-the-art methods to real-world operations.

Wilbur, Michael↗
DOE OSTI2022

Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) for Transportation Hubs (Final Report)

This report summarizes the work performed under the award number EE0008524. The project develops the Multi-modal Energy-optimal Trip Scheduling in Real-time (METS-R) platform as the next-generation transportation solution based on autonomous electric vehicles (AEV) serving passenger trips from and to urban transportation hubs, to substantially reduce transportation energy consumption. Extensive data collection and analyses were first conducted to understand the demand patterns and energy consumption of hub-based on-road trips. Then, a data-driven framework that consists of an analytical module and a simulation module was proposed. For the analytical module, five planning + operation tools were developed to support the planning and energy-efficient operations of urban AEV services: the charging station planning that robotically allocates charging supplies based on the stationary charging demand distribution; the transit planning and demand adaptive scheduling model that efficiently generates\ candidate transit routes from hubs to other places and dynamically adjusts the transit time table to fit the current demand; the online energy-efficient routing that learns the energy-optimal paths from observations of link-level energy consumption in real-time; the hub-based ridesharing that matches trip requests together with account for the uncertainty of future trip demand and vehicle supply; and finally, the integrated demand prediction and anomaly detection pipeline that leverages the flight/train time table and support other planning/operation tools. To demonstrate the performance of these tools, a scalable high-performance agent-based simulator was built. We divided the urban space into multiple service zones where each zone was considered as an agent for passenger generation and vehicle charging. Two types of AEV agents were coded to model two types of mobility services: AEV taxi and AEV transit. For the AEV taxi, the team implemented the functions of pickup/drop-off passengers, energy-efficient routing, ridesharing, fleet rebalancing, and recharging. For the AEV bus, the team implemented the functions of demand-adaptive route scheduling, passenger boarding, and recharging. A high-performance computing framework was introduced to receive various profiling information (such as link energy updates, vehicle speed) from the simulator instances and communicate the operational commands back to the instances. The numerical experiments show that each of the proposed operational algorithms can reduce energy consumption and improve system efficiency. Furthermore, there exists the need to collectively consider multiple planning + operational strategies as multiple strategies can influence each other in terms of performance impacts. Recommendations for future work related to AEV planning and simulation are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

SmartTransit.AI: A Dynamic Paratransit and Microtransit Application

New rideshare and shared mobility services have transformed urban mobility in recent years. Such services have the potential to improve efficiency and reduce costs by allowing users to share rides in high-capacity vehicles and vans. Most transit agencies already operate various ridepooling services, including microtransit and paratransit. However, the objectives and constraints for implementing these services vary greatly between agencies and can be challenging. First, off-the-shelf ridepooling formulations must be adapted for real-world conditions and constraints. Second, the lack of modular and reusable software makes it hard to implement and evaluate new ridepooling algorithms and approaches in real-world settings. We demonstrate a modular on-demand public transportation scheduling software for microtransit and paratransit services. The software is aimed at transit agencies looking to incorporate state-of-the-art rideshare and ridepooling algorithms in their everyday operations. We provide management software for dispatchers and mobile applications for drivers and users and conclude with results from the demonstration in Chattanooga, TN.

Pavia, Sophie↗
DOE OSTI2022

A shared-mobility-based framework for evacuation planning and operations under forecast uncertainty

To meet evacuation needs from carless populations who need personalized assistance to evacuate safely, in this article we propose a ridesharing-based evacuation program that recruits volunteer drivers before a disaster strikes, and then matches volunteer drivers with evacuees once demand is realized. Here we optimize resource planning and evacuation operations under uncertain spatiotemporal demand, and construct a two-stage stochastic mixed-integer program to ensure high demand fulfillment rates. We consider three formulations to improve the number of evacuees served, by minimizing an expected penalty cost, imposing a probabilistic constraint, and enforcing a constraint on the conditional value at risk of the total number of unserved evacuees, respectively. We discuss the benefits and disadvantages of the different risk measures used in the three formulations, given certain carless population sizes and the variety of evacuation modes available. We also develop a heuristic approach to provide quick, dynamic and conservative solutions. We demonstrate the performance of our approaches using five different networks of varying sizes based on regions of Charleston County, South Carolina, an area that experienced a mandatory evacuation order during Hurricane Florence, and utilize real demographic data and hourly traffic count data to estimate the demand distribution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Parking Strategies and Outcomes for Shared Autonomous Vehicle Fleet Operations

Parking spots are a premium commodity, especially in dense downtown settings, so this study examines the service impacts of shared autonomous vehicles (SAVs) parking in legal on- or off-street locations when idle across Travis County in Austin, Texas. Here, using an agent-based activity-based travel demand model with dynamic traffic simulation, two restricted-parking strategies for SAVs were simulated. SAVs either found the nearest available parking spot or the lowest-cost spot (via a tradeoff of parking fees and distance-based costs). Two comparisons were conducted to analyze the impacts of these strategies. First, two restricted parking strategies were compared, where SAVs park without competition with private human-driven vehicles (HVs) for parking locations. Second, a more realistic analysis compared two SAV parking strategies with a scenario where SAVs remain idle in place. Private HVs in all scenarios and strategies of this comparison park at the closest designated location unless they opt for private parking. Using a supply of 8,400 aggregated parking locations in Austin, this study simulated fleet performance under different trip demands, with SAV fares of $\$0.62$ per kilometer ($\$1$ per mile) plus a $\$1$ fixed pickup fee with dynamic ridesharing permitted. Parking costs were negligible in both SAV parking search strategies applied to the Austin network because of the region’s provision of mostly free parking. Requiring SAVs to park on designated on- and off-street parking locations and parking lots (restricted parking) also increased parking costs for HV drivers by up to 22% since SAVs occupied some free parking spaces, especially in the least-cost parking search strategy.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Shared and Ownership Mobility Technologies in the US: Data Availability and Usage Trends

This report supports the vision for a more sustainable transportation future by summarizing and analyzing the latest data on new mobility technologies, including ridesharing, shared and privately owned bikes, e-bikes, and scooters that have emerged over the past two decades. Having access to accurate and current data that is representative of new mobility systems and individual usage of these systems across different parts of the country is critical for researchers, city and regional planning professionals, and current and potential industry technology developers to better understand and forecast usage trends both nationwide as well as across different existing and potential future markets across the country. Building on the previous study published in 2022, this report incorporates the latest available market and usage data on new mobility technologies and compares usage by Chicago and New York City demographic characteristics. Moreover, this report includes recent developments and insights on privately owned micromobility technologies. Our analysis found that more downtown areas in Chicago show high per capita usage for all three modes than in the previous study, likely due to the full launch of shared e-scooter systems citywide in 2022. Notably, the majority of high shared mobility usage is concentrated in high-income, densely populated downtown areas in Chicago, which also have good public transit access. In contrast, TNC and bikeshare usage hotspots in central Manhattan are more widely distributed, though also appear to be shaped by the geography of the public transit system. Analysis of privately owned micromobility shows that the greatest energy savings occurred when e-bikes replaced single-occupancy vehicle (SOV) trips (i.e., gasoline-powered cars driven alone). Based on the literature review and analysis results, we also make recommendations for supporting the development of both shared and privately owned micromobility programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A system of shared autonomous vehicles for Chicago: Understanding the effects of geofencing the service

With autonomous vehicles (AVs) still in the testing phase, researchers and planners must resort to simulation techniques to explore possible futures regarding shared and automated mobility. An agent-based discrete-event transport simulator, POLARIS, is used in this study to simulate travel in the 20-county Chicago region with a shared AV (SAV) mobility option. Using this framework, the effect of an SAV fleet on system performance when constrained to serve within geofences is studied under four distinct scenarios: service restricted to the city, to the city plus suburban core, to the core plus exurban areas, and to the entire region — along with the choice of dynamic ridesharing (DRS) versus solo travel in an SAV. Results indicate that service areas need a balanced mix of trip generators and attractors, and an SAV fleet’s empty VMT (eVMT) can be noticeably reduced through suitable geofencing and DRS. Geofences can also help lower response times, reduce systemwide VMT across all modes, and ensure uniform access to SAVs. DRS is most useful in lowering VMT and %eVMT that arises from sprawled land development, but with insufficient demand to share rides, savings from the use of geofences is higher. Geofences targeting neighborhoods with high trip density bring about low response times and %eVMT, but fleet sizes in these regions need to be designed for uniformly low response times throughout a large region, as opposed to maximizing vehicle use in a 24-hour day.

33 ADVANCED PROPULSION SYSTEMS↗