Results for “mobility on demand” — Page 4

NASA NTRSDate not supplied

Demand Capacity Balancing at Vertiports for Initial Strategic Conflict Management of Urban Air Mobility Operations

Demand Capacity Balancing (DCB) can be applied in strategic conflict management for safe Urban Air Mobility (UAM) operations. Even when the operational tempo is low, traffic demand can locally exceed the capacity at airspace resources like vertiports. This paper proposes a DCB algorithm to manage the UAM traffic demand strategically, given the capacity at vertiports. The DCB algorithm is evaluated with traffic scenarios at Dallas/Fort Worth urban area in terms of various metrics such as demand distribution changes, pre-departure delay, and the number of simultaneous operations in the air. With the same experiment setup, more extended studies are also conducted to investigate how the UAM flight scheduling based on the DCB algorithm is affected by various conditions that can occur in a practical UAM environment, including vertiport capacity changes, a slot size parameter in capacity constraint, differences in operational policy between operators like lead time for flight plan submission and cruise flight speed, and uncertainties in actual departure and arrival times.

Urban Air Mobility↗

NASA NTRS2013

High Speed Mobility Through On-Demand Aviation

Game changing advances come about by the introduction of new technologies at a time when societal needs create the opportunity for new market solutions. A unique opportunity exists for NASA to bring about such a mobility revolution in General Aviation, extendable to other aviation markets, to maintain leadership in aviation by the United States. This report outlines the research carried out so far under NASA's leadership towards developing a new mobility choice, called Zip Aviation1,2,3. The feasibility, technology and system gaps that need to be addressed, and pathways for successful implementation have been investigated to guide future investment. The past decade indicates exciting trends in transportation technologies, which are quickly evolving. Automobiles are embracing automation to ease driver tasks as well as to completely control the vehicle with added safety (Figure 1). Electric propulsion is providing zero tail-pipe emission vehicles with dramatically lower energy and maintenance costs. These technologies have not yet been applied to aviation, yet offer compelling potential benefits across all aviation markets, and in particular to General Aviation (GA) as an early adopter market. The benefits of such an adoption are applicable in the following areas: 􀁸 Safety: The GA market experiences accident rates that are substantially higher than automobiles or commercial airlines, with 7.5 fatal accidents per 100 million vehicle miles compared to 1.3 for automobiles and.068 for airlines. Approximately 80% of these accidents are caused by some form of pilot error, with another 13% caused by single point propulsion system failure. 􀁸 Emissions: Environmental constraints are pushing for the elimination of 100Low Lead (LL) fuel used in most GA aircraft, with aviation fuel the #1 source of lead emissions into the environment. Aircraft also have no emission control systems (i.e. no catalytic converters etc.), so they are gross hydrocarbon polluters compared to automobiles. 􀁸 Community Noise: Hub and smaller GA airports are facing increasing noise restrictions, and while commercial airliners have dramatically decreased their community noise footprint over the past 30 years, GA aircraft noise has essentially remained same, and moreover, is located in closer proximity to neighborhoods and businesses. 􀁸 Operating Costs: GA operating costs have risen dramatically due to average fuel costs of over $6 per gallon, which has constrained the market over the past decade and resulted in more than 50% lower sales and 35% less yearly operations. Infusion of autonomy and electric propulsion technologies can accomplish not only a transformation of the GA market, but also provide a technology enablement bridge for both larger aircraft and the emerging civil Unmanned Aerial Systems (UAS) markets. The NASA Advanced General Aviation Transport Experiments (AGATE) project successfully used a similar approach to enable the introduction of primary composite structures and flat panel displays in the 1990s, establishing both the technology and certification standardization to permit quick adoption through partnerships with industry, academia, and the Federal Aviation Administration (FAA). Regional and airliner markets are experiencing constant pressure to achieve decreasing levels of community emissions and noise, while lowering operating costs and improving safety. But to what degree can these new technology frontiers impact aircraft safety, the environment, operations, cost, and performance? Are the benefits transformational enough to fundamentally alter aircraft competiveness and productivity to permit much greater aviation use for high speed and On-Demand Mobility (ODM)? These questions were asked in a Zip aviation system study named after the Zip Car, an emerging car-sharing business model. Zip Aviation investigates the potential to enable new emergent markets for aviation that offer "more flexibility than the existing transportation solutions." These studies indicate that autonomy and electric propulsion technology infusions offer a unique opportunity to provide breakthrough capabilities for new high speed, on-demand travel alternatives that can leapfrog the need for future expensive ground-based infrastructure investment. At the same time, such investments offer a method of laying the foundation for these technologies to be incubated for commercial aviation at lower cost, and with lower initial certification thresholds due to the relatively poor capabilities of GA aircraft to permit early adoption and private market capitalization by rapid technology accelerations, as depicted in Figure 2.

Moore, Mark D.↗

NASA NTRSDate not supplied

Quantifying the Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗

NASA NTRSDate not supplied

Quantifying Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗

DOE OSTI2024

Community Public Mobility Using On-Demand, Low-Speed Electric Vehicles: A Case Study in Downtown St. Louis, Missouri

Legacy fixed route transit systems designed to serve commuters struggle to provide efficient and effective service for short neighborhood trips and for population groups unable to access and egress transit stops using active modes (e.g., elderly, disabled). Neighborhood on-demand transit (ODT) services using low-speed electric vehicles (LSEV) are an innovative technological solution that can help fill this gap in service (e.g., short, high-frequency trips) for diverse populations and trip types. This study evaluated user characteristics and travel behavior for a neighborhood ODT service (using LSEVs) in downtown St. Louis, Missouri using responses from a community survey (n=244), ridership data, and vehicle trajectory information. A comparative analysis between neighborhood ODT, fixed route transit, and transportation network companies (TNC) was also conducted from the perspectives of total travel time, cost, and greenhouse gas emissions. Ultimately, the goal of the analysis was to motivate and inform holistic public mobility systems where different services are optimized to meet specific community needs. Findings indicate that the neighborhood ODT was effective at reaching diverse populations (elderly (20%), lower income (27%), and households with limited access to private vehicles (34%)). ODT reduced total travel time by 32% compared to fixed route transit, produced 2.4 - 4.3 times less greenhouse gas emissions per passenger mile (compared to transit and TNCs), and was more affordable (free to users) than alternative options ($1 for transit, $10-12 for TNCs). Overall satisfaction rates were high, with 80% of respondents rating the service a 4 or 5 out of 5.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

A Convergence of Public-Private Benefits in Denver: Surveys and Analyses to Inform Urban Mobility-, Energy-, Infrastructure- and Behavior-Related Innovation: Preprint

Cities, public transit agencies, and new private ride hailing services seek to understand emerging traveler dynamics, the shifting demographics of urban travelers,and new energy-efficient mobility opportunities. This includes exploring how new infrastructure investments, public and private mobility services, and smart-phone mobility apps are reshaping behaviors, demands (e.g. mobility-on-demand services), travel experiences and energy-efficient urban travel preferences. Currently, cities and metropolitan regions are providing and experimenting with many new mobility options, technologies, and personalized information services at the intersection of urban mobility, energy, and infrastructure systems (e.g., new commuter rail). To date, technology alone has not been able to crack the nut of 'creating faster trip times, less congestion, safer streets, and cleaner air for its citizens through fewer cars on the road'. This paper focuses on this gap by offering new concepts and potential for integrated approaches. Accommodating more vehicles miles traveled in cities, without increases in person miles traveled (PMT), could be costly, generating: 1) tremendous demands for new infrastructure, land, road space, materials, and energy; 2) higher traffic fatality risks; and 3) worsening air quality. Therefore, this study focuses on reducing single occupancy vehicle use by enhancing integrated mobility, helping transit and ridehailing increase occupancy in ways that also reduce energy use, and improve quality of life for urban travelers and communities. This study focuses on a survey of urban travelers in Denver, as a representative case study for metropolitan regions experiencing rapid growth, ageing populations, increased urban sprawl, traffic-related delays, and inefficient energy use per PMT.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1985

An optimized bandwidth efficient demand assigned protocol for integrated Mobile Satellite Services

This paper describes the design of a demand assigned protocol based on bandwidth efficiency for integrated services of a Mobile Satellite System (MSS). An MSS provides data (closed-ended) and voice (open-ended) communications services for a large number of mobile users dispersed over a wide geographical area. Each mobile requests its desired service through a designated set of channels to a network management center. Either pure or slotted ALOHA random access scheme can be used to make connection requests, while data and voice communications are demand assigned. All channels have equal bandwidth and can be adaptively used for reservation requests, data connections or voice connections to maximize the bandwidth utilization. In this paper, perfect communications channels are assumed. It has been shown that, for the case considered, using the slotted ALOHA scheme for making connection requests can save about 30 percent on the total number of channels over using the pure ALHOA scheme.

Yan, T.-Y.↗

NASA NTRS2014

NASA Wearable Technology CLUSTER 2013-2014 Report

Wearable technology has the potential to revolutionize the way humans interact with one another, with information, and with the electronic systems that surround them. This change can already be seen in the dramatic increase in the availability and use of wearable health and activity monitors. These devices continuously monitor the wearer using on-‐body sensors and wireless communication. They provide feedback that can be used to improve physical health and performance. Smart watches and head mounted displays are also receiving a great deal of commercial attention, providing immediate access to information via graphical displays, as well as additional sensing features. For the purposes of the Wearable Technology CLUSTER, wearable technology is broadly defined as any electronic sensing, human interfaces, computing, or communication that is mounted on the body. Current commercially available wearable devices primarily house electronics in rigid packaging to provide protection from flexing, moisture, and other contaminants. NASA mentors are interested in this approach, but are also interested in direct integration of electronics into clothing to enable more comfortable systems. For human spaceflight, wearable technology holds a great deal of promise for significantly improving safety, efficiency, autonomy, and research capacity for the crew in space and support personnel on the ground. Specific capabilities of interest include: Continuous biomedical monitoring for research and detection of health problems. Environmental monitoring for individual exposure assessments and alarms. Activity monitoring for responsive robotics and environments. Multi-modal caution and warning using tactile, auditory, and visual alarms. Wireless, hands-free, on-demand voice communication. Mobile, on-demand access to space vehicle and robotic displays and controls. Many technical challenges must be overcome to realize these wearable technology applications. For example, to make a wearable device that is both functional and comfortable for long duration wear, developers must strive to reduce electronic mass and volume while also addressing constraints imposed by the body attachment method. Depending on the application, the device must be placed in a location that the user can see and reach, and that provides the appropriate access to air and the wearer's skin. Limited power is available from body-‐worn batteries and heat must be managed to prevent discomfort. If the clothing is to be washed, there are additional durability and washability hurdles that traditional electronics are not designed to address. Finally, each specific capability has unique technical challenges that will likely require unique solutions. In addition to the technical challenges, development of wearable devices is made more difficult by the diversity of skills required and the historic lack of collaboration across domains. Wearable technology development requires expertise in textiles engineering, apparel design, software and computer engineering, electronic design and manufacturing, human factors engineering, and application-‐specific fields such as acoustics, medical devices, and sensing. Knowledge from each of these domains must be integrated to create functional and comfortable devices. For this reason, the diversity of knowledge and experience represented in the Wearable Technology is critical to overcoming the fundamental challenges in the field.

Simon, Cory↗

DOE OSTI2026

Overview of NLR Automated Mobility District Implementation Research - Phases I, II, and III. The Convergence of Automation, Electrification, and On-Demand Services: Enabling Resilient Automated Mobility Districts

A research program by the National Renewable Energy Laboratory has been investigating the implementation prospects for fully automated passenger transport systems that are deployed to operate within dense urban settings, referred to as Automated Mobility Districts (AMDs). An AMD emphasizes the deployment of automated vehicles (AV) passenger transport services within a dense urban setting and other major activity centers with intense passenger origin-destination demand patterns, such as those found in large business districts, airports, and university and medical campuses. Phase I and II surveyed 10 early deployment sites and subsequently collected and evaluated the lessons learned from these early deployment sites, with particular attention to fleet operations, impacts of service reliability, and vehicle technology evolution as the field of companies was being progressively winnowed by the challenges of full automation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Community Public Mobility Using On-Demand, Low-Speed Electric Vehicles: A Case Study in Downtown St. Louis, Missouri

Legacy fixed route transit systems designed to serve commuters struggle to provide efficient and effective service for short neighborhood trips and for population groups unable to access and egress transit stops using active modes (e.g., elderly, disabled). Neighborhood on-demand transit (ODT) services using low-speed electric vehicles (LSEV) are an innovative technological solution that can help fill this gap in service (e.g., short, high-frequency trips) for diverse populations and trip types. This study evaluated user characteristics and travel behavior for a neighborhood ODT service (using LSEVs) in downtown St. Louis, Missouri using responses from a community survey (n=244), ridership data, and vehicle trajectory information. A comparative analysis between neighborhood ODT, fixed route transit, and transportation network companies (TNC) was also conducted from the perspectives of total travel time, cost, and greenhouse gas emissions. Ultimately, the goal of the analysis was to motivate and inform holistic public mobility systems where different services are optimized to meet specific community needs. Findings indicate that the neighborhood ODT was effective at reaching diverse populations (elderly (20%), lower income (27%), and households with limited access to private vehicles (34%)). ODT reduced total travel time by 32% compared to fixed route transit, produced 2.4 - 4.3 times less greenhouse gas emissions per passenger mile (compared to transit and TNCs), and was more affordable (free to users) than alternative options ($1 for transit, $10-12 for TNCs). Overall satisfaction rates were high, with 80% of respondents rating the service a 4 or 5 out of 5.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Optimizing Traffic Signal Control to Enhance Transportation Efficiency and Maximize Pedestrian Benefits in the Road Network

Increasing urban mobility requirements demand efficient transportation system strategies for both vehicular and pedestrian movement. This study enhances the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) approach, originally tailored for vehicular traffic signal timing, to incorporate pedestrian traffic dynamics. The improved algorithm considers crucial metrics such as Eco_PI, assesses vehicle fuel consumption by factoring in stops and delays, and addresses pedestrian waiting time, crucial for system efficiency while acknowledging driver waiting time impact. Utilizing Digital Twin simulation along the MLK Smart Corridor in Chattanooga, Tennessee, the algorithm's performance is compared for various pedestrian control scenarios. To evaluate the effectiveness of DGMARL, this study compared DGMARL-enabled signal management with automated pedestrian traffic detection and an actuated signal management system (real-word baseline) with pedestrian recall, which predetermingly enforces a pedestrian phase every cycle. Findings indicate substantial improvements with DGMARL, showing a 28.29% enhancement in vehicle Eco_PI, a 60.55 % reduction in pedestrian waiting time, and a 55.74% decrease in driver stop delay, on average, compared to the baseline actuated signal timing plan.

Kumarasamy, Vijayalakshmi K [The University of Ten↗

NASA NTRS1993

Use of negotiated rulemaking in developing technical rules for low-Earth orbit mobile satellite systems

Technical innovations have converged with the exploding market demand for mobile telecommunications to create the impetus for low-earth orbit (LEO) communications satellite systems. The so-called 'Little LEO's' propose use of VHF and UHF spectrum to provide position - location and data messaging services. The so-called 'Big LEO's' propose to utilize the RDSS bands to provide voice and data services. In the United States, several applications were filed with the U.S. Federal Communications Commission (FCC) to construct and operate these mobile satellite systems. To enable the prompt introduction of such new technology services, the FCC is using innovative approaches to process the applications. Traditionally, when the FCC is faced with 'mutually exclusive' applications, e.g. a grant of one would preclude a grant of the others, it uses selection mechanisms such as comparative hearings or lotteries. In the case of the LEO systems, the FCC has sought to avoid these time-consuming approaches by using negotiated rulemakings. The FCC's objective is to enable the multiple applicants and other interested parties to agree on technical and service rules which will enable the grant of all qualified applications. With regard to the VHF/UHF systems, the Advisory Committee submitted a consensus report to the FCC. The process for the systems operating in the bands above 1 GHz involved more parties and more issues but still provided the FCC useful technical information to guide the adoption of rules for the new mobile satellite service.

Taylor, Leslie A.↗

DOE OSTI2020

Understanding the Charging Flexibility of Shared Automated Electric Vehicle Fleets

The combined anticipated trends of vehicle sharing (ride-hailing), automated control, and powertrain electrification are poised to disrupt the current paradigm of predominately owner-driven gasoline vehicles with low levels of utilization. Shared, automated, electric vehicle (SAEV) fleets offer the potential for lower cost and emissions and have garnered significant interest among the research community. While promising, unmanaged operation of these fleets may lead to unintended negative consequences. One potentially unintended consequence is a high quantity of SAEVs charging during peak demand hours on the electric grid, potentially increasing the required generation capacity. This research explores the flexibility associated with charging loads demanded by SAEV fleets in response to servicing personal mobility travel demands. Travel demand is synthesized in four major United States metropolitan areas: Detroit, MI; Austin, TX; Washington, DC; and Miami, FL. In each of these four cities, SAEV simulations are performed using local projected electricity prices from the Regional Energy Deployment System (ReEDS) for a handful of supply side scenarios. A coordinated charging model is introduced that seeks to reduce fleet charging costs in response to time-varying electricity prices and increasing shares of personal trip demand served (between 1% and 25% of all metro trips served by the SAEV fleet). Simulation results are presented across various scenarios assuming fleetwide coordination to minimize charging energy costs while constrained by offering adequate mobility service to fleet customers. The results indicate that the SAEV charging load is highly flexible; energy costs were shown to reduce between 13% and 46% across a wide range of simulated scenarios. In addition, these savings were realized without detrimentally impacting the fleet’s ability to service trips.

autonomous vehicle↗

NASA NTRS1993

Interworking and integration of the Inmarsat Standard-M with Pan-European GSM system

The market demand on mobile telephone communications has been increasing since the introduction of the cellular mobile telephone systems about twelve years ago. In Europe, projections indicate a demand of about 17 million subscribers for a fully deployed Pan European system. The Pan-European GSM system is to harmonize the growth of the terrestrial mobile system. Studies conducted by the European Space Agency (ESA) indicate that even with 60-70 percent of the area being covered by the GSM, a significant traffic demand (voice and data) will still exist for areas not covered by the terrestrial systems. This demand could be satisfied by a land mobile satellite system. The satellite system is therefore seen in a complementary role rather than in competition with the terrestrial system in an integrated telecommunications network. One possible scenario may be that initially the satellite system provides services to the rural areas together with areas still not covered by the GSM system. This service area is then gradually diminished as the terrestrial system expands until an optimum point is reached where the systems co-exist optimally. The objective of this paper is to investigate the possibility of integration of a space based system, in this case Inmarsat Standard-M, with the GSM (Global System for Mobile communications). One very important advantage of incorporating GSM services in Standard-M is that it will be feasible to extend the GSM services economically worldwide, not only to land mobiles but also to aeronautical and maritime mobiles.

Tafazolli, R.↗

NASA NTRSDate not supplied

Demand-Capacity Balancing Algorithms for Urban Air Mobility Operations

This paper proposes new Demand/Capacity Balancing (DCB) algorithms that resolve imbalances at enroute waypoints, such as crossing, merging, and UAM corridor entry or exit waypoints, in addition to vertiports; we refer to this algorithm as DCB-Waypoint, still using pre-departure delay as the sole resolution mechanism. Like DCB-Vertiport, DCB-Waypoint takes one flight at a time and resolves imbalances one waypoint at a time, starting from the origin vertiport followed by the sequence of constrained waypoints and, finally, the destination vertiport. The next advancement assigns airborne delays, in addition to pre-departure delays at vertiports. This algorithm, called DCB-Airborne-Delays, uses information on flight speeds to ensure that the assigned delays are feasible, meaning that variations in flight speeds are within feasible aircraft speed ranges. The proposed algorithms are being implemented in a new fast-time simulation tool developed especially for simulating UAM operations. The full paper will provide additional details of the new DCB algorithms that have been developed, as well as the results from simulations using these algorithms. Finally, the DCB algorithms will be compared against one another to obtain insights and recommendations for the future development of more advanced DCB algorithms.

urban air mobility, demand-capacity balancing↗

NASA NTRSDate not supplied

Demand-Capacity Balancing Algorithms for Urban Air Mobility Operations

This paper proposes new Demand/Capacity Balancing (DCB) algorithms that resolve imbalances at enroute waypoints, such as crossing, merging, and UAM corridor entry or exit waypoints, in addition to vertiports; we refer to this algorithm as DCB-Waypoint, still using pre-departure delay as the sole resolution mechanism. Like DCB-Vertiport, DCB-Waypoint takes one flight at a time and resolves imbalances one waypoint at a time, starting from the origin vertiport followed by the sequence of constrained waypoints and, finally, the destination vertiport. The next advancement assigns airborne delays, in addition to pre-departure delays at vertiports. This algorithm, called DCB-Airborne-Delays, uses information on flight speeds to ensure that the assigned delays are feasible, meaning that variations in flight speeds are within feasible aircraft speed ranges. The proposed algorithms are being implemented in a new fast-time simulation tool developed especially for simulating UAM operations. The full paper will provide additional details of the new DCB algorithms that have been developed, as well as the results from simulations using these algorithms. Finally, the DCB algorithms will be compared against one another to obtain insights and recommendations for the future development of more advanced DCB algorithms.

urban air mobility↗

DOE OSTI2021

A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem

Mobility on demand (MoD) systems show great promise in realizing flexible and efficient urban transportation. However, significant technical challenges arise from operational decision making associated with MoD vehicle dispatch and fleet rebalancing. For this reason, operators tend to employ simplified algorithms that have been demonstrated to work well in a particular setting. To help bridge the gap between novel and existing methods, we propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) that can leverage an existing dispatch method to minimize system cost. In particular, by treating dispatch as part of the environment dynamics, a centralized agent can learn to intermittently direct the dispatcher to reposition free vehicles and mitigate against fleet imbalance. We formulate RL state and action spaces as distributions over a grid partitioning of the operating area, making the framework scalable and avoiding the complexities associated with multiagent RL. Numerical experiments, using real-world trip and network data, demonstrate that RL reduces waiting time by 28% to 38% for the same-day evaluation, 17% to 44% for cross-day evaluation, and 22% to 25% for cross-season evaluation compared with no rebalancing scenarios. This approach has several distinct advantages over baseline methods including: improved system cost; high degree of adaptability to the selected dispatch method; and the ability to perform scale-invariant transfer learning between problem instances with similar vehicle and request distributions.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

The Highly Integrated Vehicle Ecosystem (HIVE): A Platform for Managing the Operations of On-Demand Vehicle Fleets

This paper introduces the Highly Integrated Vehicle Ecosystem (HIVE), a transportation modeling tool developed by within the Center for Integrated Mobility Sciences (CIMS) group at the National Renewable Energy Laboratory (NREL). HIVE is an agent-based supply/demand model for Mobility on Demand (MoD) which mixes agent-based modeling and centralized dispatch for automated and human-driven fleets and ride hail passengers. Research questions using HIVE span multiple categories, including intelligent fleet planning (assessing fleet, battery, and infrastructure investment decisions), intelligent fleet control (charge management, vehicle dispatching) and strategic business model decision-making (depot-based full-time drivers versus gig-based drivers, human-driven versus automated). The components of the HIVE model are explained and then HIVE is demonstrated in a case study using demand data from the New York City Taxi & Limousine Commission data set.

33 ADVANCED PROPULSION SYSTEMS↗