Academic literature on the topic 'Autonomous mobility on-demand (AMoD)'
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Journal articles on the topic "Autonomous mobility on-demand (AMoD)"
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Iglesias, Ramon, Federico Rossi, Rick Zhang, and Marco Pavone. "A BCMP network approach to modeling and controlling autonomous mobility-on-demand systems." International Journal of Robotics Research 38, no. 2-3 (2018): 357–74. http://dx.doi.org/10.1177/0278364918780335.
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In this paper we present a queuing network approach to the problem of routing and rebalancing a fleet of self-driving vehicles providing on-demand mobility within a capacitated road network. We refer to such systems as autonomous mobility-on-demand (AMoD) systems. We first cast an AMoD system into a closed, multi-class Baskett–Chandy–Muntz–Palacios (BCMP) queuing network model capable of capturing the passenger arrival process, traffic, the state-of-charge of electric vehicles, and the availability of vehicles at the stations. Second, we propose a scalable method for the synthesis of routing and charging policies, with performance guarantees in the limit of large fleet sizes. Third, we explore the applicability of our theoretical results on a case study of Manhattan. Collectively, this paper provides a unifying framework for the analysis and control of AMoD systems, which provides a large set of modeling options (e.g. the inclusion of road capacities and charging constraints), and subsumes earlier Jackson and network flow models.
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Dai, Jiajie, Qianyu Zhu, Nan Jiang, and Wuyang Wang. "Rebalancing Autonomous Vehicles using Deep Reinforcement Learning." International Journal of Circuits, Systems and Signal Processing 16 (January 15, 2022): 646–52. http://dx.doi.org/10.46300/9106.2022.16.80.
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The shared autonomous mobility-on-demand (AMoD) system is a promising business model in the coming future which provides a more efficient and affordable urban travel mode. However, to maintain the efficient operation of AMoD and address the demand and supply mismatching, a good rebalancing strategy is required. This paper proposes a reinforcement learning-based rebalancing strategy to minimize passengers’ waiting in a shared AMoD system. The state is defined as the nearby supply and demand information of a vehicle. The action is defined as moving to a nearby area with eight different directions or staying idle. A 4.6 4.4 km2 region in Cambridge, Massachusetts, is used as the case study. We trained and tested the rebalancing strategy in two different demand patterns: random and first-mile. Results show the proposed method can reduce passenger’s waiting time by 7% for random demand patterns and 10% for first-mile demand patterns.
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Grahle, A., Y. W. Song, K. Brüske, B. Bender, and D. Göhlich. "AUTONOMOUS SHUTTLES FOR URBAN MOBILITY ON DEMAND APPLICATIONS – ECOSYSTEM DEPENDENT REQUIREMENT ELICITATION." Proceedings of the Design Society: DESIGN Conference 1 (May 2020): 887–96. http://dx.doi.org/10.1017/dsd.2020.100.
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AbstractFuture transport will change drastically with the introduction of automated vehicles. Here, Autonomous Mobility on Demand (AMoD) will play a major role, requiring a radical change of vehicle design, with many different conceivable concepts. This technology shift holds high potentials and high risks. Uncertainties about future usage profiles, operator and customer requirements have to be dealt with. An approach to elicit initial requirements for future vehicle concepts considering the entire ecosystem is introduced. The applicability is shown for a specific urban mobility scenario.
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Wang, Ning, and Jiahui Guo. "Modeling and Optimization of Multiaction Dynamic Dispatching Problem for Shared Autonomous Electric Vehicles." Journal of Advanced Transportation 2021 (November 15, 2021): 1–19. http://dx.doi.org/10.1155/2021/1368286.
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The fusion of electricity, automation, and sharing is forming a new Autonomous Mobility-on-Demand (AMoD) system in current urban transportation, in which the Shared Autonomous Electric Vehicles (SAEVs) are a fleet to execute delivery, parking, recharging, and repositioning tasks automatically. To model the decision-making process of AMoD system and optimize multiaction dynamic dispatching of SAEVs over a long horizon, the dispatching problem of SAEVs is modeled according to Markov Decision Process (MDP) at first. Then two optimization models from short-sighted view and farsighted view based on combinatorial optimization theory are built, respectively. The former focuses on the instant and single-step reward, while the latter aims at the accumulative and multistep return. After that, the Kuhn–Munkres algorithm is set as the baseline method to solve the first model to achieve optimal multiaction allocation instructions for SAEVs, and the combination of deep Q-learning algorithm and Kuhn–Munkres algorithm is designed to solve the second model to realize the global optimization. Finally, a toy example, a macrosimulation of 1 month, and a microsimulation of 6 hours based on actual historical operation data are conducted. Results show that (1) the Kuhn–Munkres algorithm ensures the computational effectiveness in the large-scale real-time application of the AMoD system; (2) the second optimization model considering long-term return can decrease average user waiting time and achieve a 2.78% increase in total revenue compared with the first model; (3) and integrating combinatorial optimization theory with reinforcement learning theory is a perfect package for solving the multiaction dynamic dispatching problem of SAEVs.
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Nahmias-Biran, Bat-Hen, Gabriel Dadashev, and Yedidya Levi. "Sustainable Automated Mobility-On-Demand Strategies in Dense Urban Areas: A Case Study of the Tel Aviv Metropolis in 2040." Sustainability 15, no. 22 (2023): 16037. http://dx.doi.org/10.3390/su152216037.
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The emergence of automated mobility-on-demand (AMoD) services in urban regions has underscored crucial issues concerning the sustainable advancement of urban mobility. In particular, the impact of various AMoD implementation strategies in dense, transit-oriented cities has yet to be investigated in a generalized manner. To address this gap, we quantify the effects of AMoD on trip patterns, congestion, and energy and emissions in a dense, transit-oriented prototype city via high-fidelity simulation. We employ an activity- and agent-based framework, with specific demand and supply considerations for both single and shared AMoD rides. Our findings suggest that, in densely populated, transit-oriented cities such as the Tel Aviv metropolis, AMoD contributes to higher congestion levels and increased passenger vehicle kilometers traveled (VKT). However, when AMoD is integrated with public transit systems or introduced alongside measures to reduce household car ownership, it helps alleviate the VKT impact. Furthermore, these combined approaches effectively counter the negative impact of AMoD on public transit ridership. None of the AMoD strategies analyzed in our study reduce the congestion effects of AMoD and all strategies cannibalize active mobility in dense, transit-oriented cities compared to the base case. Nevertheless, our analysis reveals that a policy leading to decreased car ownership proves to be a more efficient measure in curbing energy consumption and greenhouse gas emissions.
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Nahmias-Biran, Bat-hen, Jimi B. Oke, Nishant Kumar, et al. "From Traditional to Automated Mobility on Demand: A Comprehensive Framework for Modeling On-Demand Services in SimMobility." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (2019): 15–29. http://dx.doi.org/10.1177/0361198119853553.
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Mobility on demand (MoD) systems have recently emerged as a promising paradigm for sustainable personal urban mobility in cities. In the context of multi-agent simulation technology, the state-of-the-art lacks a platform that captures the dynamics between decentralized driver decision-making and the centralized coordinated decision-making. This work aims to fill this gap by introducing a comprehensive framework that models various facets of MoD, namely heterogeneous MoD driver decision-making and coordinated fleet management within SimMobility, an agent- and activity-based demand model integrated with a dynamic multi-modal network assignment model. To facilitate such a study, we propose an event-based modeling framework. Behavioral models were estimated to characterize the decision-making of drivers using a GPS dataset from a major MoD fleet operator in Singapore. The proposed framework was designed to accommodate behaviors of multiple on-demand services such as traditional MoD, Lyft-like services, and automated MoD (AMoD) services which interact with traffic simulators and a multi-modal transportation network. We demonstrate the benefits of the proposed framework through a large-scale case study in Singapore comparing the fully decentralized traditional MoD with the future AMoD services in a realistic simulation setting. We found that AMoD results in a more efficient service even with increased demand. Parking strategies and fleet sizes will also have an effect on user satisfaction and network performance.
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Salazar, Mauro, Nicolas Lanzetti, Federico Rossi, Maximilian Schiffer, and Marco Pavone. "Intermodal Autonomous Mobility-on-Demand." IEEE Transactions on Intelligent Transportation Systems 21, no. 9 (2020): 3946–60. http://dx.doi.org/10.1109/tits.2019.2950720.
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Wang, Senlei, Gonçalo Homem de Almeida Correia, and Hai Xiang Lin. "Assessing the Potential of the Strategic Formation of Urban Platoons for Shared Automated Vehicle Fleets." Journal of Advanced Transportation 2022 (July 21, 2022): 1–20. http://dx.doi.org/10.1155/2022/1005979.
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This paper addresses the problem of studying the impacts of the strategic formation of platoons in automated mobility-on-demand (AMoD) systems in future cities. Forming platoons has the potential to improve traffic efficiency, resulting in reduced travel times and energy consumption. However, in the platoon formation phase, coordinating the vehicles at formation locations for forming a platoon may delay travelers. In order to assess these effects, an agent-based model has been developed to simulate an urban AMoD system in which vehicles travel between service points transporting passengers either forming or not forming platoons. A simulation study was performed on the road network of the city of The Hague, Netherlands, to assess the impact on traveling and energy usage by the strategic formation of platoons. Results show that forming platoons could save up to 9.6% of the system-wide energy consumption for the most efficient car model. However, this effect can vary significantly with the vehicle types and strategies used to form platoons. Findings suggest that, on average, forming platoons reduces the travel times for travelers even if they experience delays while waiting for a platoon to be formed. However, delays lead to longer travel times for the travelers with the platoon leaders, similar to what people experience while traveling in highly congested networks when platoon formation does not happen. Moreover, the platoon delay increases as the volume of AMoD requests decreases; in the case of an AMoD system serving only 20% of the commuter trips (by private cars in the case-study city), the average platoon delays experienced by these trips increase by 25%. We conclude that it is beneficial to form platoons to achieve energy and travel efficiency goals when the volume of AMoD requests is high.
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Azevedo, Carlos Lima, Katarzyna Marczuk, Sebastián Raveau, et al. "Microsimulation of Demand and Supply of Autonomous Mobility On Demand." Transportation Research Record: Journal of the Transportation Research Board 2564, no. 1 (2016): 21–30. http://dx.doi.org/10.3141/2564-03.
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Wen, Jian, Neema Nassir, and Jinhua Zhao. "Value of demand information in autonomous mobility-on-demand systems." Transportation Research Part A: Policy and Practice 121 (March 2019): 346–59. http://dx.doi.org/10.1016/j.tra.2019.01.018.
Full textDissertations / Theses on the topic "Autonomous mobility on-demand (AMoD)"
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Cortina, Mélanie. "Fostering synergy between public transportation and autonomous mobility on demand : the prospects of regulation." Electronic Thesis or Diss., Vaulx-en-Velin, École nationale des travaux publics de l’État, 2023. http://www.theses.fr/2023ENTP0010.
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Grâce à son aptitude à répondre à une demande asymétrique de manière efficace et son coût d'exploitation réduit, la mobilité autonome à la demande (MAD) pourrait permettre de diminuer l'empreinte carbone du système de transport urbain tout en maintenant sa performance. Mais pour éviter l'augmentation du nombre de kilomètres parcourus, la MAD doit fonctionner en synergie avec les transports en commun (TC), plus efficaces pour dégager des économies d'échelle. Or la coopération entre MAD privée et TC n'est pas garantie comme en témoigne l'exemple des Uber. Cette thèse cherche donc à explorer la question suivante : comment réguler de manière optimale la MAD pour favoriser sa coopération avec les TC? La problématique est étudiée dans deux cas d'études distincts, un couloir de mobilité et une aire métropolitaine. Avec une approche en optimisation, nous étudions la désagrégation de la MAD en plusieurs flottes opérant sur des zones de couverture bien distinctes dans le cas du couloir, et explorons différents schémas de tarification dans le cas de l'agglomération. Sur chacun des cas, le travail répond à trois objectifs. Premièrement, il s'agit de rendre compte des bénéfices de la complétion des TC par la MAD en termes d'efficacité du système (temps de parcours, coût de trajet, individuels et cumulés), d'indicateurs environnementaux (part de la mobilité collective dans les déplacements, émissions carbone), et d'équité. Deuxièmement, nous cherchons à mettre en évidence les modalités de coopération et compétition entre MAD et TC et de décrire les motifs de mobilité correspondant. Enfin, il s'agit de proposer des politiques de régulation optimales, capables d'exploiter le plein potentiel de la MAD intermodale<br>With its ability to overcome common limitations of shared mobility, such as supply-demand imbalances, stochasticity in supply, and unaffordable rides, Autonomous Mobility on Demand (AMoD) could help face today's environmental challenges. But to prevent a too-high induced demand and increase the vehicle kilometers traveled, integrating AMoD and Public Transportation (PT) is required. However, the cooperation of AMoD and PT is not ensured, as shown by Uber's example. The main issue addressed in this thesis is how to regulate AMoD to foster cooperation with PT and achieve the benefits of intermodal AMoD. This question is tackled in two study cases: a transportation corridor and a large urban area. The work has three main objectives. First, it accounts for the benefits of a multimodal system based on the cooperation between PT and AMoD regarding efficiency, sustainability, and equity. Second, it aims at understanding the circumstances of cooperation/competition between PT and AMoD. The idea is to identify under which conditions AMoD cooperates or competes with PT and describe the associated mobility patterns. Third, we propose optimized means to realize the benefits of intermodal AMoD. It consists in optimizing the regulation strategies chosen for both case studies. In the corridor, we optimize the joint PT design and AMoD service disaggregation into fleets operating on geofenced coverage zones for the corridor. In the large city, we explore several pricing schemes affecting both travelers and autonomous vehicles
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Miller, Justin (Justin Lee). "Demand estimation and fleet management for autonomous mobility on demand systems." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113541.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 131-137).<br>Mobility On Demand (MOD) systems are creating a paradigm shift in transportation, where mobility is provided not through personally owned vehicles but rather through a fleet of shared vehicles. To maintain a high customer quality of service (QoS), MOD systems need to manage the distribution of vehicles under spatial and temporal fluctuations in customer demand. A challenge for MOD systems is developing and informing a customer demand model. A new proactive demand model is presented which correlates real-time traffic data to predict customer demand on short timescales. Traditional traffic data collection approaches use pervasive fixed sensors which are costly for system-wide coverage. To address this, new frameworks are presented for measuring real-time traffic data using MOD vehicles as mobile sensors. The frameworks are evaluated using hardware and simulation implementations of a real-world MOD system developed for MIT campus. First, a mobile sensing framework is introduced that uses camera and Lidar sensors onboard MOD shuttles to observe system-wide traffic. Through a principled approach for decoupling dependencies between observation data and vehicle motion, the framework provides traffic rate estimates comparable to those of costly fixed sensors. Second, an active sensing framework is introduced which quantifies demand uncertainty with a Bayesian model and routes mobile sensors to reduce parameter uncertainty. The active sensing framework reduces error in demand estimates over both short and long timescales when compared to baseline approaches. Given estimates of customer demand, the challenge for MOD systems is maintaining high customer QoS through fleet management. New automated fleet management planners are introduced for improving customer QoS in ride hailing, ride requesting, and ridesharing MOD operating frameworks. The planners are evaluated using data-driven simulation of the MIT MOD system. For ride hailing, to address the challenge of missed customers, a chance-constrained planner is introduced for positioning vehicles at likely customer hailing locations. The chance-constrained planner provides a significant improvement in the number of served hailing customers over a baseline exploration approach. For ride requesting, to address the challenge of high customer wait times, a predictive positioning planner is introduced to position vehicles at key locations in the MOD system based on customer demand. The predictive positioning planner provides a reduction in service times for requesting customers compared to a baseline waiting approach. For ridesharing, incorrect assumptions on customer preference for transit delays can lead to poor realized customer QoS. A ridesharing planner is introduced for assigning customers to vehicles based on a trained ratings-based QoS model. The ridesharing planner provides robust performance over a range of unknown customer preferences compared to approaches with assumed customer preferences.<br>by Justin Lee Miller.<br>Ph. D.
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Chen, Yu Xin (Yu Xin Leo). "Simulation-based design of integrated public transit and shared autonomous mobility-on-demand systems." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120641.
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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 89-95).<br>The autonomous vehicle (AV) is poised to be one of the most disruptive technologies in the transportation industry. The advent of three major trends in transportation: automation, on-demand mobility and ride-sharing, are set to revolutionize the way we travel. The forthcoming adoption and commercialization of AVs are expected to have extensive impacts on our road networks, congestion, safety, land use, public transportation (PT) and more. Rapid advances in AV technology are convincing many that AV services will play a significant role in future transportation systems. The advancement of AVs presents both opportunities and threats to transportation. It has the potential to significantly impact traffic congestion, traffic accidents, parking and VMT (vehicle miles traveled), especially for people that are not able to drive such as children and elderly people. Motivated by the potential of autonomous vehicles, authorities around the world are preparing for this revolution in transport and deems this an important research direction that requires significant investigation. This thesis tackled and contributed to three main research questions related to the impact of autonomous vehicles on transportation systems. First, this thesis proposes a simulation-based approach to the design and evaluation of integrated autonomous vehicle and public transportation systems. We highlight the transit-orientation by respecting the social-purpose considerations of transit agencies (such as maintaining service availability and ensuring equity) and identifying the synergistic opportunities between AV and PT. Specifically, we identified that AV has a strong potential to serve first-mile connections to the PT stations and provide efficient and affordable shared mobility in low-density suburban areas that are typically inefficient to serve by conventional fixed-route PT services. The design decisions reflect the interest of multiple stakeholders in the system. Second, the interaction between travelers (demand) and operators (supply) is modeled using a system of equations that is solved as a fixed-point problem through an iterative procedure. In this, we developed demand and supply as two sub-problems. The demand will be predicted using a nested logit model to estimate the volume for different modes based on modal attributes. The supply will use a simulation platform capable of incorporating critical operational decisions on factors including fleet sizes, vehicle capacities, sharing policies, fare schemes and hailing strategies such as in-advance and on-demand requests. Using feedback between demand and supply, we enable interactions between the decisions of the service operator and those of the travelers, in order to model the choices of both parties. Finally, this thesis systematically optimizes service design variables to determine the best outcome in accordance to AV+PT stakeholder goals. Optimization objective functions can be formulated to reflect the different objectives of different stakeholders. In this paper, we specifically propose and develop a simulation-based service design method where we quantify various benefits and costs to reflect the objectives of key AV+PT stakeholders. We simulate the service with different sets of system settings and identify the highest performing set. We employ a case study of regional service contracting to showcase the ability of this method to inform AV+PT service design. We tested our approach with a case study area in a major European city on an agent-based simulation platform, amod-abm. Agent-based simulation has the advantage of capturing individual (agent) behaviors and the interactions of the various individual agents in a realistic synthetic environment where the intent is to re-create a complex phenomenon of mobility on demand service delivered by AV. Although this thesis will focus on a major European city, the general framework and methodologies proposed here can be widely applicable. The thesis concludes that the demand-supply interaction can be effective for designing and assessing the role of AV technology in our mobility systems. Moreover, simulation-based optimization can be an effective method for transit agencies to make decisions that support their overall AV related transport strategy as well as operational planning.<br>by Yu Xin Chen.<br>S.M. in Transportation
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Wen, Jian S. M. Massachusetts Institute of Technology. "Value of information in dispatching shared autonomous mobility-on-demand systems : a simulation framework." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115797.
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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 87-91).<br>The concept of shared mobility-on-demand (MoD) systems describes an innovative mode of transportation in which rides are tailored as per the immediate requests in a shared manner. Convenience of hailing, ease of transactions, and economic efficiency of crowd-sourcing the rides have made these services very attractive today. It is anticipated that autonomous vehicle (AV) technology may further improve the economics of such services by reducing the operational costs. The design and operation of such an shared autonomous mobility-on-demand (AMoD) system is therefore an important research direction that requires significant investigation. This thesis mainly addresses three issues revolving around the dispatching strategies of shared AMoD systems. First, it responds to the special dispatching need that is critical for effective AMoD operation. This includes a dynamic request-vehicle assignment heuristic and an optimal rebalancing policy. In addition, the dispatching strategies also reflect transit-oriented designs in two ways: (a) the objective function embodies the considerations of service availability and equity through the support of various hailing policies; and (b), the service facilitates first-mile connections to public transportation. Second, this thesis models the interaction between demand and supply through simulation. Using the level of service as interface, this mechanism enables feedback between operators and travelers to more closely represent the choices of both parties. A fixed-point approach is then applied to reach balance iteratively, estimating both the demand volume and the system performance at equilibrium. The results from the simulation support decision-making with regard to comprehensive system design problems such as fleet sizing, vehicle capacities and hailing policies. Third, the thesis evaluates the value of demand information through simulation experiments. To quantify the system performance gain that can be derived from the demand information, this thesis proposes to study two dimensions, level of information and value of information, and builds up the relationship between them. The numerical results help rationalize the efforts operators should spend on data collection, information inference and advanced dispatching algorithms. This thesis also implements an agent-based modeling platform, amod-abm, for simulating large-scale shared AMoD applications. Specifically, it models individual travelers and vehicles with demand-supply interaction and analyzes system performance through various metrics of indicators. This includes wait time, travel time, detour factor and service rate at the traveler's side, as well as vehicle distance traveled, load and profit at the operator's side. A case study area in London is selected to support the presentation of methodology. Results show that encouraging ride-sharing and allowing in-advance requests are powerful tools to enhance service efficiency and equity. Demand information from in-advance requests also enables the operator to plan service ahead of time, which leads to better performance and higher profit. The thesis concludes that the demand-supply interaction can be effective for defining and assessing the roles of AV technology in our future transportation systems. Combining efficient dispatching strategies and demand information management tools is also important for more affordable and efficient services.<br>by Jian Wen.<br>S.M. in Transportation
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Carreyre, Félix. "Are robotaxis worth it ? On-demand Autonomous Vehicle Mobility Services in heterogeneous Territories : A Cost Benefit Analysis." Electronic Thesis or Diss., Marne-la-vallée, ENPC, 2023. http://www.theses.fr/2023ENPC0043.
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La décennie 2010 a été marquée par un intérêt pour les véhicules automatisés (VA). Le nouveau marché ouvert par les promesses d'une sécurité et d'un confort améliorés a incité les constructeurs automobiles et de nouveaux venus à investir dans le développement des VA. La littérature scientifique s'est également intéressée à ce sujet et a produit des analyses pour étudier l'impact attendu de ce nouveau mode de transport. Ces travaux se sont principalement concentrés sur la capacité opérationnelle des services de VA à la demande pour remplacer les services aujourd’hui assurés par les voitures conventionnelles. Cette thèse propose une évaluation socioéconomique des services de VA à la demande dans différents territoires en répondant aux questions de recherche suivantes : Quelles sont les performances des services basés sur les VA en tenant compte des perspectives des principales parties prenantes (c'est-à-dire les usagers, les opérateurs et les autorités publiques) ? Certains services de VA sont-ils plus pertinents que d'autres ? Quel modèle opérationnel est le plus adapté à déployer dans chaque type de territoire (urbain, périurbain et rural) ?Une méta-analyse est réalisée pour appréhender l’état de l’art et ses principaux résultats. Ensuite, un cadre d'analyse coûts-bénéfices (ACB) adapté à l'évaluation des services de VA est développé. La méthodologie est appliquée à trois cas d’étude (urbain, périurbain et rural), à Berlin, à la Communauté d'agglomération de Paris-Saclay et à Dourdan (sud de Paris). Le modèle de simulation de mobilité multi-agents MATSim est utilisé pour déterminer la fréquentation des services de VA et fournir les entrées nécessaires pour l’ACB. Les contributions de cette thèse sont à la fois méthodologiques et empiriques. La contribution méthodologique réside dans le développement d'un cadre d’ACB et d'un ensemble de valeurs de référence pour l’évaluation socioéconomiques des services de VA. En ce qui concerne la contribution empirique, la méta-analyse montre que les services de VA à la demande augmenteraient les distances parcourues, mais pourraient permettre de réduire de moitié la taille de la flotte requise. Nos études de cas corroborent et étendent ces résultats par une évaluation plus complète. A Berlin, l'introduction des VA entraînerait une augmentation de la congestion, entraînant une diminution du bien-être des usagers. Cela suggère que l'automatisation dans les environnements urbains devrait d'abord se concentrer sur les modes de transport plus lourds, tels que les lignes de bus. À Saclay, un scénario intermodal (train–VA) a permis d'augmenter l'occupation moyenne des véhicules et d'améliorer l'accessibilité pour certains utilisateurs, au détriment d'une fréquentation plus faible pour les lignes de bus. Les VA à la demande pourraient remplacer les lignes de bus à faible occupation selon les territoires. Compte tenu des coûts d'infrastructure qui pourraient être importants, les VA pourraient opérer un schéma de desserte de point-à-point basé sur les stations de bus pour limiter la longueur du réseau à équiper. Dans l'étude de cas rural de Dourdan, les VA souffrent d'une faible occupation, en particulier lorsque le service ne propose pas de covoiturage. Cette faible performance économique des services de VA dans les territoires ruraux suggère qu'ils ne devraient être introduits que pour des besoins spécifiques - tels que fournir une solution pour des populations spécifiques. Cette étude de cas souligne l'importance des coûts d'infrastructure, dont l’importance relative croît dans les territoires de faible densité. Ces travaux contribuent à une meilleure compréhension des impacts potentiels des services de VA à la demande. Ils fournissent les premières recommandations sur la manière d'introduire les VA en fonction du contexte territorial afin de limiter les externalités indésirables et de réduire les coûts financiers potentiellement importants, en particulier en ce qui concerne l'infrastructure<br>The 2010’ has seen a spectacular interest for autonomous vehicles (AV). The new market opened by the promises of improved safety and comfort pushed car makers and newcomers, such as tech companies, to invest in the development of autonomous vehicles. The scientific literature has also taken interest in the topic and produced analysis to investigate the expected impact of this emerging mode of transportation. These works have mostly focused on the operational ability of on-demand autonomous vehicles services to replace services which are actually operated by conventional car, including the private use of the car. This thesis aims to provide an economic evaluation of on-demand autonomous vehicles services in different territories to complete the existing scientific literature.This thesis aims to answer the following research questions: What are the performances of AV-based services by considering the perspectives of the main stakeholders (i.e., users, operators and public authorities)? Are there more relevant AV-based services than others? Which is the most suitable operating model of AV-based services to deploy in each type of territory (i.e., urban, peri-urban and rural)?First, a bibliometric analysis and a meta-analysis is carried out to understand the focus of the literature and its main results. Then, a cost-benefit analysis framework suited to the evaluation of autonomous vehicles services is developed. The methodology is applied to three case studies, in Berlin (urban setting), the Communauté d’agglomération de Paris-Saclay (periurban setting) and Dourdan (a rural territory south of Paris). Each time, the MATSim agent-based mobility model is used to forecast the expected use of the AV services considered, and provide the necessary inputs for the cost-benefit analysis.The contributions of this thesis are both methodological and empirical. The methodological contribution lies in the development of a CBA framework and a parametrical set of reference values for autonomous vehicle services. Regarding the empirical contribution, the meta-analysis shows that on-demand AV services would increase travelled distances in most cases, but could allow to cut down the required fleet size by half. Our case studies corroborate and extend these results with a more comprehensive evaluation. In the Berlin case study, introducing AVs would lead to an increase in congestion, leading to a decrease in the consumer surplus and a negative net present value. This suggests that automation in urban settings should firstly focus on heavier modes of transportation, such as bus lines or rail lines. The Paris Saclay case study led to more promising results thanks to an intermodal (train – AV) scenario which allowed to significantly increase the average occupancy per vehicle and increase accessibility for many users, at the cost of lower patronage for the competing bus lines. On-demand AVs could replace low occupancy bus lines depending on territories. Considering the potentially substantial infrastructure costs, AVs could be implemented under a Stop-Based routing to limit the network length to equip. In the Dourdan rural case study, on-demand AVs suffer from low occupancy, especially when the service does not offer ridesharing (with a significant share of deadheading). This low economic performance of AV services in rural territories suggests that these should only be introduced for specific purposes – such as providing a mobility solution for specific populations – or in combination with other mobility policies. This case study highlights the importance of infrastructure costs, which are even more prevalent for low demand densities.This work contributes to better understanding the potential impacts of on-demand AVs. It provides first recommendations on how to introduce AVs depending on the territorial context in order to mitigate the undesirable externalities and limit the possibly large financial costs, especially regarding the infrastructure
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Zhou, Fan. "The impacts of car-sharing and shared autonomous vehicles on urban mobility: Towards a sustainable future." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/121497/1/Fan_Zhou_Thesis.pdf.
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This dissertation presents a big-picture view for policymakers and related stakeholders regarding the future development of car-sharing services. Car sharing has the potential to significantly disrupt the personal mobility market, particularly on the dawn of self-driving cars. Thus, understanding car-sharing service's market penetration and implications are urgently needed. Studies in this thesis aim to forecast the penetration of car-sharing, to investigate if car-sharing influence consumers' vehicle ownership decision, and to explore the impacts of car-sharing on households' mode choice decision.
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ARCORACI, ANDREA. "Sistemi Interattivi a supporto dei Veicoli Autonomi. User Experience all'interno del concept di mobilità Pop.Up Next." Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2915201.
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Houshmand, Arian. "Eco-routing and scheduling of Connected and Autonomous Vehicles." Thesis, 2020. https://hdl.handle.net/2144/41035.
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Connected and Autonomous Vehicles (CAVs) benefit from both connectivity between vehicles and city infrastructures and automation of vehicles. In this respect, CAVs can improve safety and reduce traffic congestion and environmental impacts of daily commutes through making collaborative decisions. This dissertation studies how to reduce the energy consumption of vehicles and traffic congestion by making high-level routing decisions of CAVs.
The first half of this dissertation considers the problem of eco-routing (finding the energy-optimal route) for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. Several algorithms are proposed that can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and an optimal power-train control strategy over this route. The results show significant energy savings for PHEVs with a near real-time execution time for the algorithms.
The second half of this dissertation tackles the problem of routing for fleets of CAVs in the presence of mixed traffic (coexistence of regular vehicles and CAVs). In this setting, all CAVs belong to the same fleet and can be routed using a centralized controller. The routing objective is to minimize a given overall fleet traveling cost (travel time or energy consumption). It is assumed that regular vehicles (non-CAVs) choose their routing decisions selfishly to minimize their traveling time. A framework is proposed that deals with the routing interaction between CAVs and regular uncontrolled vehicles under different penetration rates (fractions) of CAVs. The results suggest collaborative routing decisions of CAVs improve not only the cost of CAVs but also that of the non-CAVs.
This framework is further extended to consider congestion-aware route-planning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility under mixed traffic conditions. A network flow model is devised to optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. The results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows, while the combination of AMoD with walking or micromobility options can significantly improve the overall system performance.
Book chapters on the topic "Autonomous mobility on-demand (AMoD)"
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Pavone, Marco. "Autonomous Mobility-on-Demand Systems for Future Urban Mobility." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_19.
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Beiker, Sven. "Implementation of an Automated Mobility-on-Demand System." In Autonomous Driving. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48847-8_14.
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Pavone, Marco. "Autonomous Mobility-on-Demand Systems for Future Urban Mobility." In Autonomes Fahren. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45854-9_19.
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Javanshour, Farid, Hussein Dia, and Gordon Duncan. "Exploring System Characteristics of Autonomous Mobility On-Demand Systems Under Varying Travel Demand Patterns." In Intelligent Transport Systems for Everyone’s Mobility. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7434-0_17.
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Shen, Wen, and Cristina Lopes. "Managing Autonomous Mobility on Demand Systems for Better Passenger Experience." In PRIMA 2015: Principles and Practice of Multi-Agent Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25524-8_2.
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Yamin, Nuzhat, Syrine Belakaria, Sameh Sorour, and Mohamed Hefeida. "A Hierarchical Management Framework for Autonomous Electric Mobility-on-Demand Services." In Connected and Autonomous Vehicles in Smart Cities. CRC Press, 2020. http://dx.doi.org/10.1201/9780429329401-2.
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Kim, Ho-Yeon, Hyeon-Mun Jeong, and Han-Lim Choi. "Consensus-Based Auction Approaches for Autonomous Urban Air Mobility on Demand Systems." In Lecture Notes in Mechanical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4803-8_37.
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Di Maria, Andrea, Andrea Araldo, Giovanni Morana, and Antonella Di Stefano. "AMoDSim: An Efficient and Modular Simulation Framework for Autonomous Mobility on Demand." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05081-8_12.
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Tussyadiah, Iis P., Florian J. Zach, and Jianxi Wang. "Attitudes Toward Autonomous on Demand Mobility System: The Case of Self-Driving Taxi." In Information and Communication Technologies in Tourism 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51168-9_54.
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Iglesias, Ramon, Federico Rossi, Rick Zhang, and Marco Pavone. "A BCMP Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems." In Springer Proceedings in Advanced Robotics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43089-4_53.
Full textConference papers on the topic "Autonomous mobility on-demand (AMoD)"
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T. Alotaibi, Ebtehal, and Michael Herrmann. "Max-Policy Sharing for Multi-Agent Reinforcement Learning in Autonomous Mobility on Demand." In 12th International Conference on Digital Image Processing and Vision. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131333.
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Autonomous-Mobility-on-Demand (AMoD) systems can revolutionize urban transportation by providing mobility as a service without car ownership. However, optimizing the performance of AMoD systems presents a challenge due to competing objectives of reducing customer wait times and increasing system utilization while minimizing empty miles. To address this challenge, this study compares the performance of max-policy sharing agents and independent learners in an AMoD system using reinforcement learning. The results demonstrate the advantages of the max-policy sharing approach in improving Quality of Service (QoS) indicators such as completed orders, empty miles, lost customers due to competition, and out-of-charge events. The study identifies the importance of striking a balance between competition and cooperation among individual autonomous vehicles and tuning the frequency of policy sharing to avoid suboptimal policies. The findings suggest that the max-policy sharing approach has the potential to accelerate learning in multi-agent reinforcement learning systems, particularly under conditions of low exploration.
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Ang, Marcelo H. "Achieving mobility on demand using autonomous vehicles." In 2015 6th International Conference on Power Electronics Systems and Applications (PESA) - Advancement in Electric Transportation - Automotive, Vessel & Aircraft. IEEE, 2015. http://dx.doi.org/10.1109/pesa.2015.7398961.
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Andersen, Hans, You Hong Eng, Wei Kang Leong, et al. "Autonomous personal mobility scooter for multi-class mobility-on-demand service." In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2016. http://dx.doi.org/10.1109/itsc.2016.7795795.
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Dandl, Florian, Klaus Bogenberger, and Hani S. Mahmassani. "Autonomous Mobility-on-Demand Real-Time Gaming Framework." In 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, 2019. http://dx.doi.org/10.1109/mtits.2019.8883286.
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Distler, Verena, Carine Lallemand, and Thierry Bellet. "Acceptability and Acceptance of Autonomous Mobility on Demand." In CHI '18: CHI Conference on Human Factors in Computing Systems. ACM, 2018. http://dx.doi.org/10.1145/3173574.3174186.
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Zhang, Rick, Federico Rossi, and Marco Pavone. "Model predictive control of autonomous mobility-on-demand systems." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487272.
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Tsao, Matthew, Ramon Iglesias, and Marco Pavone. "Stochastic Model Predictive Control for Autonomous Mobility on Demand." In 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. http://dx.doi.org/10.1109/itsc.2018.8569459.
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Turan, Berkay, Nathaniel Tucker, and Mahnoosh Alizadeh. "Smart Charging Benefits in Autonomous Mobility on Demand Systems." In 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8917278.
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Pendleton, Scott Drew, Hans Andersen, Xiaotong Shen, et al. "Multi-class autonomous vehicles for mobility-on-demand service." In 2016 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2016. http://dx.doi.org/10.1109/sii.2016.7843999.
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Sun, Yimeng, Yuan Huang, and Zhaohao Ding. "Revenue Uncertainty Analysis for Autonomous Mobility-on-Demand System." In 2022 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia). IEEE, 2022. http://dx.doi.org/10.1109/icpsasia55496.2022.9949865.
Full textReports on the topic "Autonomous mobility on-demand (AMoD)"
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Alexander, Serena, Asha Weinstein Agrawal, and Benjamin Y. Clark. Local Climate Action Planning as a Tool to Harness the Greenhouse Gas Emissions Mitigation and Equity Potential of Autonomous Vehicles and On-Demand Mobility. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2020.1818.
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This report focuses on how cities can use climate action plans (CAPs) to ensure that on-demand mobility and autonomous vehicles (AVs) help reduce, rather than increase, green-house gas (GHG) emissions and inequitable impacts from the transportation system. We employed a three-pronged research strategy involving: (1) an analysis of the current literature on on-demand mobility and AVs; (2) a systematic content analysis of 23 CAPs and general plans developed by municipalities in California; and (3) a comparison of findings from the literature and content analysis of plans to identify opportunities for GHG emissions reduction and mobility equity. Findings indicate that maximizing the environmental and social benefits of AVs and on-demand mobility requires proactive and progressive planning; yet, most cities are lagging behind in this area. Although municipal CAPs and general plans in California have adopted a few strategies and programs relevant to AVs and on-demand mobility, many untapped opportunities exist to harness the GHG emissions reduction and social benefits potential of AVs and on-demand mobility. Policy and planning discussions should consider the synergies between AVs and on-demand mobility as two emerging mobility trends, as well as the key factors (e.g., vehicle electrification, fuel efficiency, use and ownership, access and distribution, etc.) that determine whether deployment of AVs would help reduce GHG emissions from transportation. Additionally, AVs and on-demand mobility can potentially contribute to a more equitable transportation system by improving independence and quality of life for individuals with disabilities and the elderly, enhancing access to transit, and helping alleviate the geographic gap in public transportation services.
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Alexander, Serena, Asha Weinstein Agrawal, and Benjamin Y. Clark. Local Climate Action Planning as a Tool to Harness the Greenhouse Gas Emissions Mitigation and Equity Potential of Autonomous Vehicles and On-Demand Mobility. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2020.1818.
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This report focuses on how cities can use climate action plans (CAPs) to ensure that on-demand mobility and autonomous vehicles (AVs) help reduce, rather than increase, green-house gas (GHG) emissions and inequitable impacts from the transportation system. We employed a three-pronged research strategy involving: (1) an analysis of the current literature on on-demand mobility and AVs; (2) a systematic content analysis of 23 CAPs and general plans developed by municipalities in California; and (3) a comparison of findings from the literature and content analysis of plans to identify opportunities for GHG emissions reduction and mobility equity. Findings indicate that maximizing the environmental and social benefits of AVs and on-demand mobility requires proactive and progressive planning; yet, most cities are lagging behind in this area. Although municipal CAPs and general plans in California have adopted a few strategies and programs relevant to AVs and on-demand mobility, many untapped opportunities exist to harness the GHG emissions reduction and social benefits potential of AVs and on-demand mobility. Policy and planning discussions should consider the synergies between AVs and on-demand mobility as two emerging mobility trends, as well as the key factors (e.g., vehicle electrification, fuel efficiency, use and ownership, access and distribution, etc.) that determine whether deployment of AVs would help reduce GHG emissions from transportation. Additionally, AVs and on-demand mobility can potentially contribute to a more equitable transportation system by improving independence and quality of life for individuals with disabilities and the elderly, enhancing access to transit, and helping alleviate the geographic gap in public transportation services.
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Doo, Johnny. Unsettled Issues Concerning eVTOL for Rapid-response, On-demand Firefighting. SAE International, 2021. http://dx.doi.org/10.4271/epr2021017.
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Recent advancements of electric vertical take-off and landing (eVTOL) aircraft have generated significant interest within and beyond the traditional aviation industry, and many novel applications have been identified and are in development. One promising application for these innovative systems is in firefighting, with eVTOL aircraft complementing current firefighting capabilities to help save lives and reduce fire-induced damages. With increased global occurrences and scales of wildfires—not to mention the issues firefighters face during urban and rural firefighting operations daily—eVTOL technology could offer timely, on-demand, and potentially cost-effective aerial mobility capabilities to counter these challenges. Early detection and suppression of wildfires could prevent many fires from becoming large-scale disasters. eVTOL aircraft may not have the capacity of larger aerial assets for firefighting, but targeted suppression, potentially in swarm operations, could be valuable. Most importantly, on-demand aerial extraction of firefighters can be a crucial benefit during wildfire control operations. Aerial firefighter dispatch from local fire stations or vertiports can result in more effective operations, and targeted aerial fire suppression and civilian extraction from high-rise buildings could enhance capabilities significantly. There are some challenges that need to be addressed before the identified capabilities and benefits are realized at scale, including the development of firefighting-specific eVTOL vehicles; sense and avoid capabilities in complex, smoke-inhibited environments; autonomous and remote operating capabilities; charging system compatibility and availability; operator and controller training; dynamic airspace management; and vehicle/fleet logistics and support. Acceptance from both the first-responder community and the general public is also critical for the successful implementation of these new capabilities. The purpose of this report is to identify the benefits and challenges of implementation, as well as some of the potential solutions. Based on the rapid development progress of eVTOL aircraft and infrastructures with proactive community engagement, it is envisioned that these challenges can be addressed soon. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.
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Doo, Johnny. The Use of eVTOL Aircraft for First Responder, Police, and Medical Transport Applications. SAE International, 2023. http://dx.doi.org/10.4271/epr2023020.
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<div class="section abstract"><div class="htmlview paragraph">Advancements in electric vertical takeoff and landing (eVTOL) aircraft have generated significant interest within and beyond the traditional aviation industry. One particularly promising application involves on-demand, rapid-response use cases to broaden first responders, police, and medical transport mission capabilities. With the dynamic and varying public service operations, eVTOL aircraft can offer potentially cost-effective aerial mobility components to the overall solution, including significant lifesaving benefits.</div><div class="htmlview paragraph"><b>Multi-agent Collaborative Perception for Autonomous Driving: Unsettled Aspects</b> discusses the challenges need to be addressed before identified capabilities and benefits can be realized at scale: <ul class="list disc"><li class="list-item"><div class="htmlview paragraph">Mission-specific eVTOL vehicle development </div></li><li class="list-item"><div class="htmlview paragraph">Operator- and patient-specific accommodations</div></li><li class="list-item"><div class="htmlview paragraph">Detect-and-avoid capabilities in complex and challenging operating environments</div></li><li class="list-item"><div class="htmlview paragraph">Autonomous and artificial intelligence-enhanced mission capabilities</div></li><li class="list-item"><div class="htmlview paragraph">Home-base charging systems for battery power platforms</div></li><li class="list-item"><div class="htmlview paragraph">Simplified operator and support training</div></li><li class="list-item"><div class="htmlview paragraph"> Vehicle/fleet maintenance and support</div></li><li class="list-item"><div class="htmlview paragraph">Acceptance and participation from stakeholder services, local and state-level leadership, field operators, and support team members</div></li></ul></div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>