Journal articles: '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 (June 28, 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 (November 17, 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, Kakali Basak, Andrea Araldo, Ravi Seshadri, Arun Akkinepally, Carlos Lima Azevedo, and Moshe Ben-Akiva. "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 (June 25, 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 (September 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, Harold Soh, Muhammad Adnan, Kakali Basak, Harish Loganathan, et al. "Microsimulation of Demand and Supply of Autonomous Mobility On Demand." Transportation Research Record: Journal of the Transportation Research Board 2564, no. 1 (January 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.

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Dia, Hussein, and Farid Javanshour. "Autonomous Shared Mobility-On-Demand: Melbourne Pilot Simulation Study." Transportation Research Procedia 22 (2017): 285–96. http://dx.doi.org/10.1016/j.trpro.2017.03.035.

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Javanshour, Farid, Hussein Dia, and Gordon Duncan. "Exploring the performance of autonomous mobility on-demand systems under demand uncertainty." Transportmetrica A: Transport Science 15, no. 2 (October 3, 2018): 698–721. http://dx.doi.org/10.1080/23249935.2018.1528485.

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Marczuk, Katarzyna A., Harold S. H. Soh, Carlos M. L. Azevedo, Der-Horng Lee, and Emilio Frazzoli. "Simulation Framework for Rebalancing of Autonomous Mobility on Demand Systems." MATEC Web of Conferences 81 (2016): 01005. http://dx.doi.org/10.1051/matecconf/20168101005.

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Prado-Díaz, Alberto, Stella Schroeder, and Claudio Cortes-Aros. "Impactos del corredor migratorio en ciudades de Perú y Chile: transformaciones urbanas durante la pandemia." Revista Urbano 25, no. 45 (May 31, 2022): 08–19. http://dx.doi.org/10.22320/07183607.2022.25.45.01.

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The eviction of migrants from Plaza Brasil, in the city of Iquique, an act that was questioned due to the violence used by public forces, revealed not just the humanitarian problem involved, but also a turning point in attempts to normalize a process where cities have been altered by the exodus of migrants. This study looks into the impacts generated by the successive stages of migration within the Venezuelan migratory flow, one characterized by the great vulnerability of these migrants. Since the start of 2020, amid a health crisis and border closures, they have entered Colombia, Ecuador, Peru, Bolivia, and Chile by land using unauthorized crossings, to find better living conditions in these destinations. Here, the approaches which connect flows and transformations as responses to reproduction factors of global society, with those that conceive mobility as a “creative force” that interacts autonomously with these structures, are discussed. To this end, mobility in the historical centers of three cities located in border transit and entry zones to each country, where the interrelation in public space has been transformed and stressed, is explored, namely Piura, in Peru, and Iquique and Antofagasta in Chile. The results show similarities in the dynamics and transformations generated. Given the vulnerable condition of migrants, there is an increase in the occupation of public space, through autonomous actions of self-management and organization, as well as local resistance, demonstrating the relevance of mobility in modern society. All-in-all, it is recommended to adopt a differentiated agenda to understand the connection between migrants and places during the mobility experience.

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Szigeti, Szilárd, Csaba Csiszár, and Dávid Földes. "Information Management of Demand-responsive Mobility Service Based on Autonomous Vehicles." Procedia Engineering 187 (2017): 483–91. http://dx.doi.org/10.1016/j.proeng.2017.04.404.

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Hogeveen, Peter, Maarten Steinbuch, Geert Verbong, and Auke Hoekstra. "Quantifying the Fleet Composition at Full Adoption of Shared Autonomous Electric Vehicles: An Agent-based Approach." Open Transportation Journal 15, no. 1 (May 17, 2021): 47–60. http://dx.doi.org/10.2174/1874447802115010047.

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Aims: Exploring the impact of full adoption of fit-for-demand shared and autonomous electric vehicles on the passenger vehicle fleet of a society. Background: Shared Eutonomous Electric Vehicles (SAEVs) are expected to have a disruptive impact on the mobility sector. Reduced cost for mobility and increased accessibility will induce new mobility demand and the vehicles that provide it will be fit-for-demand vehicles. Both these aspects have been qualitatively covered in recent research, but there have not yet been attempts to quantify fleet compositions in scenarios where passenger transport is dominated by fit-for-demand, one-person autonomous vehicles. Objective: To quantify the composition of the future vehicle fleet when all passenger vehicles are autonomous, shared and fit-for-demand and where cheap and accessible mobility has significantly increased the mobility demand. Methods: An agent-based model is developed to model detailed travel dynamics of a large population. Numerical data is used to mimic actual driving motions in the Netherlands. Next, passenger vehicle trips are changed to trips with fit-for-demand vehicles, and new mobility demand is added in the form of longer tips, more frequent trips, modal shifts from public transport, redistribution of shared vehicles, and new user groups. Two scenarios are defined for the induced mobility demand from SAEVs, one scenario with limited increased mobility demand, and one scenario with more than double the current mobility demand. Three categories of fit-for-demand vehicles are stochastically mapped to all vehicle trips based on each trip's characteristics. The vehicle categories contain two one-person vehicle types and one multi-person vehicle type. Results: The simulations show that at full adoption of SAEVs, the maximum daily number of passenger vehicles on the road increases by 60% to 180%. However, the total fleet size could shrink by up to 90% if the increase in mobility demand is limited. An 80% reduction in fleet size is possible at more than doubling the current mobility demand. Additionally, about three-quarters of the SAEVs can be small one-person vehicles. Conclusion: Full adoption of fit-for-demand SAEVs is expected to induce new mobility demand. However, the results of this research indicate that there would be 80% to 90% less vehicles required in such a situation, and the vast majority would be one-person vehicles. Such vehicles are less resource-intense and, because of their size and electric drivetrains, are significantly more energy-efficient than the average current-day vehicle. This research indicates the massive potential of SAEVs to lower both the cost and the environmental impact of the mobility sector. Quantification of these environmental benefits and reduced mobility costs are proposed for further research.

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Schnieder, Maren, Chris Hinde, and Andrew West. "Land Efficient Mobility: Evaluation of Autonomous Last Mile Delivery Concepts in London." International Journal of Environmental Research and Public Health 19, no. 16 (August 18, 2022): 10290. http://dx.doi.org/10.3390/ijerph191610290.

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Land efficient last mile delivery concepts are key to reducing the traffic in cities and to minimising its environmental impact. This paper proposes a decision support method that evaluates the autonomous delivery concept and applies it to one year’s worth of real parcel delivery data in London. Deliveries to modular and fixed lockers with autonomous delivery vans and road-based autonomous lockers (RAL) and sidewalk autonomous delivery robots (SADRs) have been simulated. Various types of autonomous delivery van fleets, depot locations, customer modes of transport, parcel demand levels, parcel locker network densities and adjustment frequencies of modular lockers are considered. A routing and scheduling algorithm is used to optimise delivery tours and vehicle choice. The optimisation algorithm finds both the optimal number of collection and delivery points (CDPs) and the delivery concept (e.g., modular lockers, sidewalk autonomous delivery robot) depending on the customer mode chosen. The results show that modular lockers which are adjusted weekly are the best option for the current or higher parcel demand levels and road-autonomous parcel lockers (RAL-R) are the best option at the lowest parcel demand level.

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Meneses-Cime, Karina, Bilin Aksun-Guvenc, and Levent Guvenc. "Optimization of On-Demand Shared Autonomous Vehicle Deployments Utilizing Reinforcement Learning." Sensors 22, no. 21 (October 29, 2022): 8317. http://dx.doi.org/10.3390/s22218317.

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Ride-hailed shared autonomous vehicles (SAV) have emerged recently as an economically feasible way of introducing autonomous driving technologies while serving the mobility needs of under-served communities. There has also been corresponding research work on optimization of the operation of these SAVs. However, the current state-of-the-art research in this area treats very simple networks, neglecting the effect of a realistic other traffic representation, and is not useful for planning deployments of SAV service. In contrast, this paper utilizes a recent autonomous shuttle deployment site in Columbus, Ohio, as a basis for mobility studies and the optimization of SAV fleet deployment. Furthermore, this paper creates an SAV dispatcher based on reinforcement learning (RL) to minimize passenger wait time and to maximize the number of passengers served. The created taxi-dispatcher is then simulated in a realistic scenario while avoiding generalization or over-fitting to the area. It is found that an RL-aided taxi dispatcher algorithm can greatly improve the performance of a deployment of SAVs by increasing the overall number of trips completed and passengers served while decreasing the wait time for passengers.

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Medina-Tapia, Marcos, and Francesc Robusté. "Implementation of Connected and Autonomous Vehicles in Cities Could Have Neutral Effects on the Total Travel Time Costs: Modeling and Analysis for a Circular City." Sustainability 11, no. 2 (January 17, 2019): 482. http://dx.doi.org/10.3390/su11020482.

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Autonomous vehicles promise to revolutionize the automobile market, although their implementation could take several decades in which both types of cars will coexist on the streets. We formulate a model for a circular city based on continuous approximations, considering demand surfaces over the city. Numerical results from our model predict direct and indirect effects of connected and autonomous vehicles. Direct effects will be positive for our cities: (a) less street supply is needed to accommodate the traffic; (b) congestion levels decrease: travel costs may decrease by 30%. Some indirect effects will counterbalance these positive effects: (c) a decrease of 20% in the value of travel time can reduce the total cost by a third; (d) induced demand could be as high as 50%, bringing equivalent total costs in the future scenario; (e) the vehicle-kilometers traveled could also affect the future scenario; and (f) increases in city size and urban sprawl. As a conclusion, the implementation of autonomous vehicles could be neutral for the cities regarding travel time costs. City planning agencies still have to promote complementary modes such as active mobility (walking and bicycle), transit (public transportation), and shared mobility (shared autonomous vehicles and mobility as a service).

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Lu, Ying, Yanchang Liang, Zhaohao Ding, Qiuwei Wu, Tao Ding, and Wei-Jen Lee. "Deep Reinforcement Learning-Based Charging Pricing for Autonomous Mobility-on-Demand System." IEEE Transactions on Smart Grid 13, no. 2 (March 2022): 1412–26. http://dx.doi.org/10.1109/tsg.2021.3131804.

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Turan, Berkay, Ramtin Pedarsani, and Mahnoosh Alizadeh. "Dynamic pricing and fleet management for electric autonomous mobility on demand systems." Transportation Research Part C: Emerging Technologies 121 (December 2020): 102829. http://dx.doi.org/10.1016/j.trc.2020.102829.

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Alexander, Serena, Asha Weinstein Agrawal, and Benjamin Clark. "Local Climate Action Planning as a Tool to Harness Greenhouse Gas Emissions Mitigation and the Equity Potential of Autonomous Vehicles and On-Demand Mobility." Transportation Research Record: Journal of the Transportation Research Board 2676, no. 3 (October 30, 2021): 521–34. http://dx.doi.org/10.1177/03611981211052541.

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This paper 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, greenhouse 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 (GPs) 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 policy and planning discussions should consider the synergies between AVs and on-demand mobility as two closely related 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 the deployment of AVs would help reduce GHG emissions from transportation. Additionally, AVs and on-demand mobility have the potential to 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. Although many municipal CAPs and GPs in California have adopted several strategies and programs relevant to AVs and on-demand mobility, several untapped opportunities exist to harness the GHG emissions reduction and social benefits potential of AVs and on-demand mobility.

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Hao, Mingyang, Yanyan Li, and Toshiyuki Yamamoto. "Public Preferences and Willingness to Pay for Shared Autonomous Vehicles Services in Nagoya, Japan." Smart Cities 2, no. 2 (June 11, 2019): 230–44. http://dx.doi.org/10.3390/smartcities2020015.

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Shared autonomous vehicle systems are anticipated to offer cleaner, safer, and cheaper mobility services when autonomous vehicles are finally implemented on the roads. The evaluation of people’s intentions regarding shared autonomous vehicle services appears to be critical prior to the promotion of this emerging mobility on demand approach. Based on a stated preference survey in Nagoya, Japan, the preference for shared autonomous vehicle services as well as willingness to pay for these services were examined among 1036 respondents in order to understand the relationship between people’s socioeconomic characteristics and their preferred shared autonomous vehicle services. For this purpose, k-modes clustering technique was selected and six clusters were obtained. Six groups with respect to different interests on shared autonomous vehicle services were clustered. The result of correlation analysis and discussion of willingness to pay on services provided insightful results for the future shared autonomous vehicle services. This study not only aids in revealing the demands of customer different clusters, but also states the prospective needs of users for stakeholders from research, policymaker and industry field, who are preparing to work on promoting shared autonomous vehicle systems, and subsequently, develops an optimum transportation mode by considering both demand and services as a whole.

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Golbabaei, Fahimeh, Tan Yigitcanlar, Alexander Paz, and Jonathan Bunker. "Understanding Autonomous Shuttle Adoption Intention: Predictive Power of Pre-Trial Perceptions and Attitudes." Sensors 22, no. 23 (November 26, 2022): 9193. http://dx.doi.org/10.3390/s22239193.

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The capability of ‘demand-responsive transport’, particularly in autonomous shared form, to better facilitate road-based mobility is considered a significant advantage because improved mobility leads to enhanced quality of life and wellbeing. A central point in implementing a demand-responsive transit system in a new area is adapting the operational concept to the respective structural and socioeconomic conditions. This requires an extensive analysis of the users’ needs. There is presently limited understanding of public perceptions and attitudes toward the adoption of autonomous demand-responsive transport. To address this gap, a theory-based conceptual framework is proposed to provide detailed empirical insights into the public’s adoption intention of ‘autonomous shuttle buses’ as a form of autonomous demand-responsive transport. South East Queensland, Australia, was selected as the testbed. In this case study, relationships between perceptions, attitudes, and usage intention were examined by employing a partial least squares structural equation modeling method. The results support the basic technology acceptance model casual relationships that correspond with previous studies. Although the direct effects of perceived relative advantages and perceived service quality on usage intention are not significant, they could still affect usage intention indirectly through the attitude factor. Conversely, perceived risks are shown to have no association with perceived usefulness but can negatively impact travelers’ attitudes and usage intention toward autonomous shuttle buses. The research findings provide implications to assist policymakers, transport planners, and engineers in their policy decisions and system plans as well as achieving higher public acknowledgment and wider uptake of autonomous demand-responsive transport technology solutions.

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König, Alexandra, Christina Wirth, and Jan Grippenkoven. "Generation Y’s Information Needs Concerning Sharing Rides in Autonomous Mobility on Demand Systems." Sustainability 13, no. 14 (July 20, 2021): 8095. http://dx.doi.org/10.3390/su13148095.

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Empirical studies show that autonomous vehicles can contribute to sustainability goals when rides are shared. However, sharing rides with strangers in shared autonomous mobility-on-demand systems (SAMODSs) might impede the adoption of these systems. The present study addresses the research question whether a comprehensive information provision about fellow passengers could increase acceptability of the shared rides in SAMODSs. A discrete choice experiment (N = 154) assessed the potential of different levels of information on fellow passengers: (1) no information, (2) name, (3) picture, (4) rating, (5) combination of name, picture and rating. The results show that the overall compensation demands for sharing a ride was a reduction of approximately 25% of the nonshared reference price. The provision of detailed information about fellow travelers proved beneficial for reducing the compensation demands of travelers while the provision of a name only resulted in higher compensation demands. A significant effect of the fellow passengers’ gender indicated that male gender information was related to a higher refusal rate than female gender information. This was particularly relevant when only names were presented. The study provides first empirical insights into the psychological factors concerning the emerging trend of shared mobility.

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Belakaria, Syrine, Mustafa Ammous, Sameh Sorour, and Ahmed Abdel-Rahim. "Fog-Based Multi-Class Dispatching and Charging for Autonomous Electric Mobility On-Demand." IEEE Transactions on Intelligent Transportation Systems 21, no. 2 (February 2020): 762–76. http://dx.doi.org/10.1109/tits.2019.2897121.

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Paparella, F., K. Chauhan, T. Hofman, and M. Salazar. "Electric Autonomous Mobility-on-Demand: Joint Optimization of Routing and Charging Infrastructure Siting." IFAC-PapersOnLine 56, no. 2 (2023): 2526–31. http://dx.doi.org/10.1016/j.ifacol.2023.10.1302.

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Wang, Dengzhong, Tongyu Sun, Anzheng Xie, and Zhao Cheng. "Simulation Study on the Coupling Relationship between Traffic Network Model and Traffic Mobility under the Background of Autonomous Driving." Sustainability 15, no. 2 (January 13, 2023): 1535. http://dx.doi.org/10.3390/su15021535.

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Autonomous driving technology will bring revolutionary changes to the development of future cities and transportation. In order to study the impact of autonomous driving on urban transportation networks, this paper first summarizes the development status of autonomous driving technology, and then three space–traffic network coupling models are proposed based on the differences of speed and space, which are the traditional difference type, scale variation type, and slow-guided type. On this basis, a new 4 * 4 km grid city model is constructed. Based on the MATSim multi-agent simulation method, the traffic parameters of the three models are studied. The results show that under the same traffic demand, the service scale and level of the three traffic networks are significantly different. The optimal service level of the traditional differential type is 2.15 times the efficiency of the slow-guided type. Under the same demand and road network mode, the travel speed of the autonomous driving mode is 1.7–2.8 times that of the traditional mode. Under the same lane area ratio, the travel speed of traditional driving is much smaller than that of autonomous driving, which is about 2.6–3.6 times greater than the former. The research conclusion has certain reference significance for formulating urban spatial development strategies and policies under autonomous driving environments and for promoting the sustainable development of urban transportation.

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Metz, David. "Developing Policy for Urban Autonomous Vehicles: Impact on Congestion." Urban Science 2, no. 2 (April 13, 2018): 33. http://dx.doi.org/10.3390/urbansci2020033.

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An important problem for surface transport is road traffic congestion, which is ubiquitous and difficult to mitigate. Accordingly, a question for policymakers is the possible impact on congestion of autonomous vehicles. It seems likely that the main impact of vehicle automation will not be seen until driverless vehicles are sufficiently safe for use amid general traffic on urban streets. Shared use driverless vehicles could reduce the cost of taxis and a wider range of public transport vehicles could be economic. Individually owned autonomous vehicles would have the ability to travel unoccupied and may need to be regulated where this might add to congestion. It is possible that autonomous vehicles could provide mobility services at lower cost and wider scope, such that private car use in urban areas could decline and congestion reduce. City authorities should be alert to these possibilities in developing transport policy.

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Nemoto, Eliane Horschutz, Inna Morozova, Ralf Wörner, Ines Jaroudi, Guy Fournier, and Adrian Boos. "Substituting individual mobility by mobility on demand using autonomous vehicles - a sustainable assessment simulation of Berlin and Stuttgart." International Journal of Automotive Technology and Management 20, no. 4 (2020): 369. http://dx.doi.org/10.1504/ijatm.2020.10034385.

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Fournier, Guy, Adrian Boos, Ralf Wörner, Ines Jaroudi, Inna Morozova, and Eliane Horschutz Nemoto. "Substituting individual mobility by mobility on demand using autonomous vehicles - a sustainable assessment simulation of Berlin and Stuttgart." International Journal of Automotive Technology and Management 20, no. 4 (2020): 369. http://dx.doi.org/10.1504/ijatm.2020.112029.

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Belakaria, Syrine, Mustafa Ammous, Lauren Smith, Sameh Sorour, and Ahmed Abdel-Rahim. "Multi-Class Management With Sub-Class Service for Autonomous Electric Mobility On-Demand Systems." IEEE Transactions on Vehicular Technology 68, no. 7 (July 2019): 7155–59. http://dx.doi.org/10.1109/tvt.2019.2920104.

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Dolins, Sigma, Yale Z. Wong, and John D. Nelson. "The ‘Sharing Trap’: A Case Study of Societal and Stakeholder Readiness for On-Demand and Autonomous Public Transport in New South Wales, Australia." Sustainability 13, no. 17 (August 25, 2021): 9574. http://dx.doi.org/10.3390/su13179574.

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Focus groups on shared, autonomous vehicles (SAVs) in New South Wales expressed “sharing anxiety”—an intense concern about the prospect of sharing their mobility journey with strangers, without a driver or authority figure present. This presents a significant barrier to the acceptance of SAVs, particularly autonomous public and on-demand transport (ODT), which is a major focus for Transport for New South Wales (TfNSW). Given this potential barrier, we interviewed (N = 13) operators, academics, and regulators with TfNSW to assess their role and abilities in overcoming sharing anxiety. However, our findings revealed a relative lack of awareness from experts in the mobility industry about the existence of sharing anxiety in users, suggesting additional barriers to adoption. We make suggestions for policy considerations for stakeholders that could mitigate sharing anxiety: promoting dynamic ridepooling products in commercial services, using tax breaks as incentivization; requiring ODT services and operators in jurisdiction to use a standardized, unified interface for users (“single-app”); shared, on-demand transport services likely need longer incubation/pilot periods in order for the sharing behavior to become culturally established. We conclude with a reflection on how COVID-19 has impacted the development of shared mobility and suggest further exploration in policy implementation.

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Kania, Malte, Vasu Dev Mukku, Karen Kastner, and Tom Assmann. "Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets." Applied Sciences 14, no. 1 (December 25, 2023): 180. http://dx.doi.org/10.3390/app14010180.

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Bike sharing systems have become a sustainable alternative to motorized private transport in urban areas. However, users often face high costs and availability issues due to the operational effort required to redistribute bicycles between stations. For addressing those issues, the AuRa (Autonomes Rad, Eng. Autonomous Bicycle) project introduces a new mobility offer in terms of an on-demand, shared-use, self-driving cargo bikes service (OSABS) that enables automated redistribution. Within the project, we develop different order management and rebalancing strategies and validate them using simulation models. One prerequisite for this is sound demand scenarios. However, due to the novelty of OSABS, there is currently no information about its utilization. Consequently, the objective of this study was to develop an approach for defining OSABS demand scenarios in a temporally and spatially disaggregated manner as an input for simulation models. Therefore, we first derived city-wide usage potentials of OSABS from a survey on mobility needs. We then spatially and temporally disaggregated the determined usage likelihood using travel demand matrices and usage patterns from a conventional bike-sharing system, respectively. Finally, we performed cluster analyses on the resulting annual demand to summarize sections of the yearly profile into representative units and thus reduce the simulation effort. As we applied this approach as a case study to the city of Magdeburg, Germany, we could show that our methodology enables the determination of reasonable OSABS demand scenarios from scratch. Furthermore, we were able to show that annual usage patterns of (conventional) bike sharing systems can be modeled by using demand data for only eight representative weeks.

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Haj Salah, Imen, Vasu Dev Mukku, Malte Kania, Tom Assmann, and Hartmut Zadek. "Implications of the Relocation Type and Frequency for Shared Autonomous Bike Service: Comparison between the Inner and Complete City Scenarios for Magdeburg as a Case Study." Sustainability 14, no. 10 (May 11, 2022): 5798. http://dx.doi.org/10.3390/su14105798.

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Finding a sustainable mobility solution for the future is one of the most competitive challenges in the logistics and transportation sector nowadays. Researchers, universities, and companies are working intensively to provide novel mobility options that can be environmentally friendly and sustainable. While autonomous car-sharing services have been introduced as a very promising solution, an innovative alternative is arising using self-driving bikes. Shared autonomous cargo bike fleets are likely to increase the livability and sustainability of the city as the use of cargo bikes in an on-demand mobility service can replace the use of cars for short-distance trips and enhance connectivity to public transportation. However, more research is still needed to develop this new concept. To address this research gap, this paper examines the on-demand shared-use autonomous bikes service (OSABS) from a fleet management perspective. In fact, such a system requires good management strategies in order to ensure its efficiency. Through an agent-based simulation of a case study in Magdeburg, we investigate various parameters that can influence the performance and the service quality of OSABS such as the rebalancing frequency and the relocation type. Tests were performed for two different operational areas: the inner city and the complete city of Magdeburg. We conclude with different management insights for an optimized functioning of the system.

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Gao, Jing, and Sen Li. "Charging autonomous electric vehicle fleet for mobility-on-demand services: Plug in or swap out?" Transportation Research Part C: Emerging Technologies 158 (January 2024): 104457. http://dx.doi.org/10.1016/j.trc.2023.104457.

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Haj Salah, Imen, Vasu Dev Mukku, Malte Kania, and Tom Assmann. "Towards Sustainable Liveable City: Management Operations of Shared Autonomous Cargo-Bike Fleets." Future Transportation 1, no. 3 (October 2, 2021): 505–32. http://dx.doi.org/10.3390/futuretransp1030027.

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Finding a sustainable mobility solution for the future is one of the most competitive challenges in the logistics and mobility sector at present. Policymakers, researchers, and companies are working intensively to provide novel options that are environmentally friendly and sustainable. While autonomous car-sharing services have been introduced as a very promising solution, an innovative alternative is arising: the use of self-driving bikes. Shared autonomous cargo-bike fleets are likely to increase the livability and sustainability of the city, as the use of cargo-bikes in an on-demand mobility service can replace the use of cars for short-distance trips and enhance connectivity to public transportation. However, more research is needed to develop this new concept. In this paper, we investigate different rebalancing strategies for an on-demand, shared-use, self-driving cargo-bikes service (OSABS). We simulate a case study of the system in the inner city of Magdeburg using AnyLogic. The simulation model allows us to evaluate the impact of rebalancing on service level, idle mileage, and energy consumption. We conclude that the best proactive rebalancing strategy for our case study is to relocate bikes only between neighboring regions. We also acknowledge the importance of bike relocation to improve service efficiency and reduce fleet size.

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Tarkowski, Maciej, and Krystian Puzdrakiewicz. "Connectivity Benefits of Small Zero-Emission Autonomous Ferries in Urban Mobility—Case of the Coastal City of Gdańsk (Poland)." Sustainability 13, no. 23 (November 28, 2021): 13183. http://dx.doi.org/10.3390/su132313183.

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An increase in energy-efficient transportation is one way that cities try to mitigate climate change. In coastal cities, public water transit is also undergoing transitions. A small zero-emission autonomous ferry seems to be a cutting-edge technology in this field. This study aims to decrease the knowledge gap in research on the impacts of autonomous passenger ferry development on urban mobility. In particular, the central theme regards the extent to which the new transport solution can help improve sustainable mobility patterns. This study explores the local spatial context of ferry development, land-use patterns, and transport network structure, and moderates the shift in urban mobility practices. Regarding land use patterns, the case of the coastal city of Gdańsk has been documented in secondary qualitative and quantitative data, including in a large body of policy documents, accompanying expert opinions, and scholarly literature. This study strongly emphasises that a typical short river crossing, due to autonomous vessels, can regain a competitive position, which was partially lost due to linear routes along the river. The research identified crucial benefits of autonomous ferry shipping on urban mobility by increasing public transport network connectivity, reducing travel distance, and creating modal shifts towards foot travel and bike riding. It appears as an exciting scalable solution for cities where limited or dispersed demand prevents achievement of economies of scale, therefore diminishing the necessary expenditures.

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Moreno, Ana T., Andrzej Michalski, Carlos Llorca, and Rolf Moeckel. "Shared Autonomous Vehicles Effect on Vehicle-Km Traveled and Average Trip Duration." Journal of Advanced Transportation 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/8969353.

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Intermediate modes of transport, such as shared vehicles or ride sharing, are starting to increase their market share at the expense of traditional modes of car, public transport, and taxi. In the advent of autonomous vehicles, single occupancy shared vehicles are expected to substitute at least in part private conventional vehicle trips. The objective of this paper is to estimate the impact of shared autonomous vehicles on average trip duration and vehicle-km traveled in a large metropolitan area. A stated preference online survey was designed to gather data on the willingness to use shared autonomous vehicles. Then, commute trips and home-based other trips were generated microscopically for a synthetic population in the greater Munich metropolitan area. Individuals who traveled by auto were selected to switch from a conventional vehicle to a shared autonomous vehicle subject to their willingness to use them. The effect of shared autonomous vehicles on urban mobility was assessed through traffic simulations in MATSim with a varying autonomous taxi fleet size. The results indicated that the total traveled distance increased by up to 8% after autonomous fleets were introduced. Current travel demand can still be satisfied with an acceptable waiting time when 10 conventional vehicles are replaced with 4 shared autonomous vehicles.

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Signorile, Pierdomenico, Vincenzo Larosa, and Ada Spiru. "Mobility as a service: a new model for sustainable mobility in tourism." Worldwide Hospitality and Tourism Themes 10, no. 2 (April 9, 2018): 185–200. http://dx.doi.org/10.1108/whatt-12-2017-0083.

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Purpose Developing sustainable mobility can add value to the travel and tourism experience in alpine areas and can become a challenge for destinations in terms of interests, goals, skills and values involving both public and private subjects. Mobility as a Service (MaaS) is a new model for delivering sustainable transport services that in recent experiences seem to be an alternative to the use of owned cars by allowing the personalized use of a bundle of public and private transport means. This paper aims to identify the positive aspects in the two main Alpine regions affected by tourism demand coming mainly from Lombardy by implementing a mobility model inspired by Maas in the Lombardy capital. Design/methodology/approach The tourism demand of the Autonomous Province of Trento and the Aosta Valley Region is thus analyzed using descriptive statistics on tourist flows and mobility characteristics. Findings Technology and propensity to change are the determining factors to move from traditional to innovative mobility systems. Originality/value This work, by considering the recent studies on MaaS models, limited to sustainable urban mobility models, extends the MaaS approach to the key concepts of “sustainable mobility” and “sustainable tourism” by analyzing the tourist flow, which from Lombardy invest the main alpine regions.

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Fu, Mengying, Raoul Rothfeld, and Constantinos Antoniou. "Exploring Preferences for Transportation Modes in an Urban Air Mobility Environment: Munich Case Study." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 10 (May 21, 2019): 427–42. http://dx.doi.org/10.1177/0361198119843858.

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Urban Air Mobility (UAM) is a recent mobility concept with the potential to reduce travel time and change travel patterns. When evaluating the introduction of UAM, understanding the potential users’ choice behavior regarding current available urban transportation modes and autonomous transportation services is essential to demand estimation. This preliminary research intends to gain insight into the travel behavior impacts of autonomous transportation modes, especially UAM, by deriving measures for transportation service attributes and identifying characteristics of potential users who might adopt autonomous transportation services, particularly the services of UAM. Thus, a stated preference questionnaire was designed and distributed in Munich metropolitan region. A main mode choice multinomial logit model and several sub-models, based on market segmentation, were estimated regarding four transportation alternatives: private car, public transportation, autonomous taxi, and autonomous flying taxi. The results indicate that travel time, travel cost, and safety may be critical determinants in autonomous transportation mode adoption. The potential consumers may be willing to pay more for using autonomous transportation modes, especially the service of UAM. Among different market segments, younger individuals, as well as older individuals with high household income, are more likely to adopt UAM. In addition, during the market entry stage, potential travelers may favor UAM particularly for performing non-commuting trips.

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Mo, Baichuan, Qing Yi Wang, Joanna Moody, Yu Shen, and Jinhua Zhao. "Impacts of subjective evaluations and inertia from existing travel modes on adoption of autonomous mobility-on-demand." Transportation Research Part C: Emerging Technologies 130 (September 2021): 103281. http://dx.doi.org/10.1016/j.trc.2021.103281.

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Rossi, Federico, Ramon Iglesias, Mahnoosh Alizadeh, and Marco Pavone. "On the Interaction Between Autonomous Mobility-on-Demand Systems and the Power Network: Models and Coordination Algorithms." IEEE Transactions on Control of Network Systems 7, no. 1 (March 2020): 384–97. http://dx.doi.org/10.1109/tcns.2019.2923384.

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König, Alexandra, and Jan Grippenkoven. "Travellers’ willingness to share rides in autonomous mobility on demand systems depending on travel distance and detour." Travel Behaviour and Society 21 (October 2020): 188–202. http://dx.doi.org/10.1016/j.tbs.2020.06.010.

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Beirigo, Breno A., Rudy R. Negenborn, Javier Alonso-Mora, and Frederik Schulte. "A business class for autonomous mobility-on-demand: Modeling service quality contracts in dynamic ridesharing systems." Transportation Research Part C: Emerging Technologies 136 (March 2022): 103520. http://dx.doi.org/10.1016/j.trc.2021.103520.

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Zhu, Yi, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen, and Pengjun Zheng. "Exploring the Impact of Charging Behavior on Transportation System in the Era of SAEVs: Balancing Current Request with Charging Station Availability." Systems 12, no. 2 (February 17, 2024): 61. http://dx.doi.org/10.3390/systems12020061.

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Shared autonomous electric vehicles (SAEVs) can offer safer, more efficient, and more environmentally friendly real-time mobility services with advanced autonomous driving technologies. In this study, a multi-agent-based simulation model considering SAEVs’ vehicle range and charging behavior is proposed. Based on real-world datasets from the Luohu District in Shenzhen, China, various scenarios with different fleet sizes, charging rates, and vehicle ranges are established to evaluate the impact of these parameters on parking demand, charging demand, vehicle miles traveled (VMT), and response time in the era of SAEVs. The results show there would be much more charging demand than parking demand. Moreover, a larger fleet size and longer vehicle range would lead to more parking demand, more charging demand, and more VMT while increasing the charging rate can dramatically reduce the charging demand and VMT. Average response time can be reduced by increasing the fleet size or the charging rate, and a larger vehicle range leads to longer response time due to the longer time spent recharging. It is worth noting that the VMT generated from relocating from the previous request destination to the origin of the upcoming request accounts for nearly 90% of the total VMT, which should be addressed properly with appropriate scheduling. A charging policy considering current requests and the availability of charging stations was proposed and verified in terms of reducing the response time by 2.5% to 18.9%.

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He, Yinying, and Csaba Csiszár. "Model for Crowdsourced Parcel Delivery Embedded into Mobility as a Service Based on Autonomous Electric Vehicles." Energies 14, no. 11 (May 24, 2021): 3042. http://dx.doi.org/10.3390/en14113042.

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Faced promising development of autonomous vehicles, the concept Mobility as a Service embraces AVs. The autonomous delivery has also been arising and tested for last-mile solution. Accordingly, passenger transportation and delivery service are to be jointly coordinated in the future MaaS. The research niche is summarized as how to embed crowdsourced parcel delivery into MaaS based on AVs. Electricity powered vehicles are to be applied considering energy consumption and air emission. Research questions are identified as what this novel service is and how it works, especially focusing on task coordination and the related information management. The system engineering process-oriented approaches, matching theory, and numerical method have been applied. The service concept has been elaborated, which contains information system architecture model and functional model. The matching condition of this service is summarized. As the embedded delivery can partially share the demand of traditional delivery service, the calculation scenarios of energy savings and emission decrease per parcel delivery are presented. The results facilitate service planning and development.

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Garus, Ada, Borja Alonso, Maria Aloso Raposo, Biagio Ciuffo, and Luigi dell’Olio. "Impact of New Mobility Solutions on Travel Behaviour and Its Incorporation into Travel Demand Models." Journal of Advanced Transportation 2022 (July 18, 2022): 1–24. http://dx.doi.org/10.1155/2022/7293909.

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Advancement in the fields of electrification, automation, and digitalisation and emerging social trends are fuelling the transformation of road transport resulting in the introduction of various innovative mobility solutions. Yet the reaction of people to many of the new solutions is still vastly unknown. This creates an unprecedented quandary for transport planners who are requested to design future transport systems and create the related investment plans without fully validated models to base the assessment upon. As some evidence on citizens’ behaviour concerning new mobility solutions starts to be progressively made available, first attempts to update the existing models begin to emerge. Nevertheless, a lot more is needed as some of the transpiring mobility solutions have not yet reached the market, making the corresponding behaviour changes imponderable. In this context, the main purpose of this paper is to provide a review on how travel behaviour changes linked to the deployment of new mobility solutions have been considered in travel demand models. The new mobility solutions studied include carsharing, dynamic ridesharing, micromobility sharing services, and personal and shared autonomous vehicles. An overview and comparison of relevant studies implementing activity or trip-based demand models and other methodologies are presented. The analysis shows that the results of the different studies heavily depend on the extent to which behavioural changes are considered. The results of the review thus point to the need for holistic demand models that carefully mimic the urban reality with everything it has to offer and account for the importance of individual traits in the decision-making processes. Such models need an in-depth understanding of the microscopic mechanisms leading to the travel behaviour shifts linked to the most innovative mobility solutions. To achieve this level of detail, mobility living labs and their real-life experiments and experience with citizens, which are flourishing in Europe, are suggested to play a crucial role in the years to come.

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Sheppard, Colin J. R., Gordon S. Bauer, Brian F. Gerke, Jeffery B. Greenblatt, Alan T. Jenn, and Anand R. Gopal. "Joint Optimization Scheme for the Planning and Operations of Shared Autonomous Electric Vehicle Fleets Serving Mobility on Demand." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (April 13, 2019): 579–97. http://dx.doi.org/10.1177/0361198119838270.

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As the transportation sector undergoes three major transformations—electrification, shared/on-demand mobility, and automation—there are new challenges to analyzing the impacts of these trends on both the transportation system and the power sector. Most models that analyze the requirements of fleets of shared autonomous electric vehicles (SAEVs) operate at the scale of an urban region, or smaller. A quadratically constrained, quadratic programming problem is formulated, designed to model the requirements of SAEVs at a national scale. The size of the SAEV fleet, the necessary charging infrastructure, the fleet charging schedule, and the dispatch required to serve demand for trips in a region are treated as decision variables. By minimizing both the amortized cost of the fleet and chargers as well as the operational costs of charging, it is possible to explore the coupled interactions between system design and operation. To apply the model at a national scale, key complications about fleet operations are simplified; but a detailed agent-based regional simulation model to parameterize those simplifications is leveraged. Preliminary results are presented, finding that all mobility in the United States (U.S.) currently served by 276 million personally owned vehicles could be served by 12.5 million SAEVs at a cost of $ 0.27/vehicle-mile or $ 0.18/passenger-mile. The energy requirements for this fleet would be 1142 GWh/day (8.5% of 2017 U.S. electricity demand) and the peak charging load 76.7 GW (11% of U.S. power peak). Several model sensitivities are explored, and it is found that sharing is a key factor in the analysis.

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Engesser, Valeska, Evy Rombaut, Lieselot Vanhaverbeke, and Philippe Lebeau. "Autonomous Delivery Solutions for Last-Mile Logistics Operations: A Literature Review and Research Agenda." Sustainability 15, no. 3 (February 3, 2023): 2774. http://dx.doi.org/10.3390/su15032774.

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The implementation of autonomous delivery solutions in last-mile logistics operations is considered promising. Autonomous delivery solutions can help in tackling urban challenges related to last-mile logistics operations. Urbanization creates higher mobility and transportation demand, which contributes to increased congestion levels, traffic, air pollution, and accident rates. Moreover, mega-trends, such as e-commerce, demand that logistics companies react to increased customer expectations in terms of delivery time and service. Concerning service, electrified autonomous delivery solutions have the potential to operate 24/7 and can help to overcome driver shortages. This paper conducts a systematic literature review. Based on the literature set, a snowballing procedure was applied. Complementary gray literature was included. This work discusses different autonomous delivery solutions such as Autonomous Delivery Robots (ADRs), Unmanned Aerial Vehicles (UAVs), two- or multi-tiered systems, and the concept of passenger and freight integration. The work presents advantages and disadvantages, enabling the comparison of solutions. Furthermore, a research agenda is provided, from which practical-managerial and theoretical implications can be derived. The research agenda can help researchers, manufacturers, businesses, and governmental institutions to prepare for the arrival and subsequent implementation of autonomous delivery services. Various implications related to energy demand, legislation, implementation strategy, training, and risk and safety are presented. The outcome of this work calls for collaboration among various stakeholders, encourages mutual learning, and hints at the importance of national and international development projects.

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Journal articles on the topic 'Autonomous mobility on-demand (AMoD)'

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