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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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49 records · Page 3
All sourcesNASA NTRSDOE OSTI
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 OSTI2020

Charge Management for an Inductively Charged On-Demand Battery-Electric Shuttle Service with High Penetration of Renewable Energy

This paper presents a charge management control strategy for an on-demand battery-electric shuttle van operating at the National Renewable Energy Laboratory (NREL) campus and supported by day-time inductive charging at the vehicle's waiting spot. A new control algorithm has been proposed for reducing the demand charge costs incurred from wireless charging of the on-demand shuttle. A custom controller has been developed to monitor the shuttle, wireless charger, renewable energy generation, and various loads at NREL's campus, and regulate charging behavior for demand response. The intermittent renewable generation and sporadic operation of the on-demand shuttle service contribute to a high level of uncertainty in expected campus load profile, which must be carefully managed. The control algorithm predicts energy profile to estimate the mobility needs of the vehicle and maintain uninterrupted service during operation while still minimizing peak demand. The proposed controller has been designed and optimized using a Simulink model for the entire system. Next, it has been implemented and tested in real-time on the NREL campus. Two primary vehicle-use cases, charge sustaining and charge depletion operation, are tested under different load profiles and drive cycles to assess the controller's effectiveness at reducing peak demand and therefore demand charges. The proposed controller showed robust performance under different driving scenarios with high correlation between simulated and experimental data. The results showed that proper demand response can be achieved with an average of 94% reduction of charging loads during peak demand events.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
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 OSTI2026

Community and Passenger Survey Responses for Bastrop, Texas Low-Speed Electric Vehicle Mobility Service Project

This document describes the contents of the following data products: 1. ALL-survey_results_NREL_LiveWire_04_14_2023 (Excel file with two worksheets) 2. eCab_DOE_NREL_LiveWire_04_14_2023_results_all_surveys_except_SP_csv (CSV file) 3. eCab_DOE_NREL_LiveWire_04_14_2023_SP_survey_results_only_csv (CSV file). These data products contain the results of the surveys conducted throughout the DOE-funded Electric First-/Last-Mile On-Demand Shuttle Service for Rural Communities in Central Texas project. The Excel file is the original database of all the survey results (contained in two worksheets). The CSV files are those two worksheets saved as individual CSV files.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Synopsis of NREL's Automated Mobility District (AMD) Research Program and Associated Publications

An automated mobility district (AMD) envisions a system of integrated mobility options that serves major activity centers such as campuses, central business districts, and large medical facilities. The National Renewable Energy Laboratory (NREL) has been investigating the implementation prospects for fully automated passenger transport systems that are deployed to operate within dense urban settings. This document provides a synopsis of findings revealed over the last three phases of work, which have yielded insights into the creation and management of AMDs anticipated to use automated vehicle (AV) technology over the next decade. Phase I and Phase II tracked the deployment and lessons learned from 10 early-stage demonstrations of automated shuttle deployments, and their associated insights into the challenges for automated driving systems to achieve safe operations within district-scale deployments. Phase III began in-depth investigations of critical subsystem components, as automation, electrification, and on-demand service continue to converge within initial AMD operations. These directed studies focus on elements of electrification, curbfront/station management, the role of infrastructure sensing, and overall integration of AMD safety management in central, simultaneous coordination of multiple AMD fleets. Future research in AMDs includes systems engineering methodology (more frequently referred to as "digital twins") for planning, design, testing, and ongoing operation of AMDs; location (or co-location) of management functions; and human supervision and passenger communications for safety and security in unattended vehicles. The synopsis references the foundational research products (papers and presentations) that have been published through conference proceedings, journal articles, and NREL reports.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Human Supervision of Autonomous Vehicle Fleet Operations and Associated Passenger Communications: Preprint

Advances in automated vehicle (AV) technology and expanded operations are rapidly emerging with Automated Mobility District (AMD) deployments in global cities. NLR's AMD research addresses critical elements of human supervision of AV fleet operations and associated passenger communications for vehicles in which no driver or safety attendant is present. Although sufficiently advanced AVs no longer have direct oversight by a driver, fleet management remains staffed with operations personnel at the operations command and control (OCC) facility. This paper examines the functionality of the OCC, drawing comparisons of how automated train control and automated people mover OCCs operate. Within an AMD, the OCC manages various vehicle types, sizes, and operational modes, including on-demand and fixed route service, to facilitate a 'network of networks' for transport within a metropolitan area. The OCC serves as oversight for multiple AV fleets assisting AVs via remote operation of vehicles, communication, and dispatching personnel to resolve problems. The OCC also coordinates system operation, geographically staging vehicles, and managing weather, police, and emergency events. Informed by traffic management center (TMC) strategies using highly integrated software and communications, OCCs facilitate seamless information flows. OCC personnel remotely assist passengers and oversee multi-party operation to ensure safety and security. Although social norms mitigate large-capacity unattended vehicle operations, social interaction in multi-party automated small vehicles has little precedent. This poses a new frontier for society and requires research to effectively understand and manage. Future research will monitor OCC implementations, passenger interfaces, and deployment scaling of initial AMD systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Commuter preferences for a first-mile/last-mile microtransit service in the United States

Transportation system models rely heavily upon value of time (VOT) estimates to predict customer behavior. Accurate VOT estimates are particularly vital for planning new services such as on-demand ride hailing or microtransit because customers’ sensitivity to wait time, walk time, and route detour time affects their likelihood of selecting these modes. If an incorrect VOT is assumed during service planning, then ridership will be depressed because of a mismatch between their preferences and how the system is designed. In this paper, we report on the measurement of VOT for microtransit, a shared first-mile/last-mile mobility service, obtained using stated preference microdata from four U.S. cities. Here, we found a median in-vehicle VOT for microtransit of $\$$18.63 (95% CI: $\$$13.39–$\$$24.46) and an access VOT of $\$$75.38 (95% CI: $\$$59.22–$\$$94.96). The former is practically equal to the VOT we found for respondents’ current modes ($\$$20.24, 95% CI: $\$$13.71–$\$$26.94). We also found that men, younger riders, the highly educated, and transit riders are more likely to be interested in microtransit. Since the disutility of time spent on microtransit is not higher than that of other modes, we believe this new service has the potential to attract riders, and particularly if the system is designed with low waiting and walking times.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Real-Time Implementation of Smart Wireless Charging of On-Demand Shuttle Service for Demand Charge Mitigation

This paper presents a smart charge management strategy for an on-demand electric shuttle operating at the National Renewable Energy Laboratory (NREL) campus and supported by an inductive charger at the vehicle's waiting spot. A new control algorithm has been proposed for mitigating the demand charges incurred from the wireless charger. It monitors the shuttle, wireless charger, renewable energy generation, and other loads and regulates charging behavior for demand charge mitigation. Within the control algorithm, an energy prediction is made to estimate the mobility needs of the vehicle and maintain uninterrupted service during operation while still minimizing peak demand. The proposed controller is designed and optimized using a Simulink model for the entire system. It is then implemented and tested in real time at the NREL campus using online cloud services. Two vehicle-use cases' charge-sustaining and charge-depletion operation' are tested under different campus power profiles and drive cycles to assess the controller's performance. In this work, the proposed controller showed a robust performance under different driving scenarios, with high correlation between simulation and experimental data. The results show that proper demand response can be achieved, with an average of 94% reduction of charging loads during peak demand events.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Ride Pingo to Transit

In this project, we developed an on-demand microtransit first- and last-mile service. To integrate the service with fixed-route transit, we developed a feature called Transit Connect that prioritized riders’ on-time arrival at the transit station over other service requirements. We first prototyped service and related algorithms in a simulated environment, and then piloted the service in the city of Kent, Washington. Our algorithm incorporates request-specific hard drop-off deadlines to ensure timely arrivals for transit transfers. In the pilot, these constraints were obtained from GTFS Realtime data to accurately determine the schedule of the transit and the location of the stations. This approach introduced the ability to accept or decline new requests based on the timing of transit connections for these new requests and connection status of onboarding customers. The pilot (called “Ride Pingo to Transit”) deployed a fleet of three 14-person vans, ran from September 2021 to March 2023, and served a total of 21,329 trips. Transit Connect was offered for drop-offs at both Kent Station and the Kent Valley hub. In total, 2,844 such trips were completed. This dataset was collected from our pilot, which includes the following: - Requests: List of all trip requests, including those that were actually served and those not materialized. - Fleet: Daily vehicle service logs. - Service details: Daily vehicle stop logs (boarding and alighting). - Trip types: First mile, last mile, or point-to-point. ![pingo to transit](pingo-to-transit.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Pooled Rideshare in the U.S.: An Exploratory Study of User Preferences

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.

Transportation Network Companies (TNCs)↗
DOE OSTI2022

Optimizing first- and last-mile public transit services leveraging transportation network companies (TNC)

First-mile last-mile (FMLM) mobility services that connect riders to public transit can lead to improved transit accessibility and network efficiency if such services are convenient and reliable. However, many current FMLM services are inefficient and costly because they are inflexible (e.g., fixed supply of shuttles) and do not leverage collected data for optimized decision making. At the same time, new forms of shared mobility can provide added flexibility and real-time analytics to FMLM systems when carefully integrated. This study evaluates performance and cost implications of public/private coordination between transit shuttles and transportation network companies (TNC) in the FMLM context. A real-time operations model was developed to simulate daily operations for an existing FMLM system using real-world demand data. Three supply strategies were tested with varying levels of flexibility: (1) Status Quo (two 23-passenger on-demand shuttles), (2) Hybrid (one 23-passenger on-demand shuttle + TNC), and (3) TNC Only (exclusively use TNC services). Results indicated that the added flexibility of the Hybrid service design (using shuttles and TNCs) improved service performance (a 7.7% improvement), reduced daily operating costs (– 6.0%), and improved service reliability (95th percentile travel times decreased by up to 40% during peak periods). In addition, the Hybrid service design was more robust to variations in demand. Here, the Hybrid service was significantly cheaper to operate (– 31.6%) at reduced demand levels (50% of normal), and improved service performance (a 10.2% improvement) when demand levels were increased (150% of normal). These findings emphasize the importance of flexibility in FMLM service designs, especially when demand is sparse and variable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Platform for Integrated Land use And Transportation Experiments and Simulation (PILATES) v1.0

PILATES allows for flexibly and at-scale coupling of multiple models to allow for multi-scale and multi-resolution simulation of regional-scale transport networks. In particular, it couples the MATSim-derived transportation modeling framework for Behavior, Energy, Autonomy and Mobility (BEAM) with other models operating at different time scales. Rather than tightly coupling supply and demand models using shared agents and memory within the same software process, PILATES orchestrates different model runs in a containerized framework. This structure requires passing information from the demand models to BEAM in the format of a synthetic population and agent plans, and from BEAM to the demand models in terms or origin/destination tables (also known as "skims"). This allows it to take advantage of the behavioral sophistication of existing activity-based models as well as the reinforcement learning structure of MATSim replanning and adopted by BEAM, in a way that requires minimal changes to existing models. It also takes advantage of the computational performance of BEAM, which allows for simulations with millions of agents to complete in reasonable time as well as allowing for detailed mechanistic simulation of the operation of on-demand modes.

Needell, Zachary↗
DOE OSTI2024

Deploying Mobility-On-Demand for All by Optimizing Paratransit Services

While on-demand ride-sharing services have become popular in recent years, traditional on-demand transit services cannot be used by everyone, e.g., people who use wheelchairs. Paratransit services, operated by public transit agencies, are a critical infrastructure that offers door-to-door transportation assistance for individuals who face challenges in using standard transit routes. However, with declining ridership and mounting financial pressure, public transit agencies in the USA struggle to operate existing services. We collaborate with a public transit agency from the southern USA, highlight the specific nuances of paratransit optimization, and present a vehicle routing problem formulation for optimizing paratransit. We validate our approach using real-world data from the transit agency, present results from an actual pilot deployment of the proposed approach in the city, and show how the proposed approach comprehensively outperforms existing approaches used by the transit agency. To the best of our knowledge, this work presents one of the first examples of using open-source algorithmic approaches for paratransit optimization.

Pavia, Sophie↗