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Modelling and evaluating urban air mobility – an early research approach
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Transportation Research Procedia 00 (2016) 000–000 Available online at www.sciencedirect.com Transportation Research Procedia 00 (2016) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Transportation Research Procedia 41 (2019) 41–44
International Scientific Conference on Mobility and Transport International onFuture Mobility and Transport UrbanScientific Mobility –Conference Shaping the Together Urban Mobility – Shaping the Future Together mobil.TUM 2018, 13-14 June 2018, Munich, Germany mobil.TUM 2018, 13-14 June 2018, Munich, Germany
Modelling and evaluating urban air mobility – an Modelling and evaluating urban air mobility – an early research approach early research approach Raoul Rothfeld11*, Milos Balac22, and Constantinos Antoniou33 Raoul Rothfeld *, Milos Balac , and Constantinos Antoniou
1 Bauhaus Luftfahrt e.V., 82024 Taufkirchen, Germany 1 ETH Zurich, Institute for Luftfahrt Transporte.V., Planning Systems, 8093 Zurich, Switzerland Bauhaus 82024and Taufkirchen, Germany 3 ETH Zurich, Transport Planning and Systems, 8093 Zurich, Switzerland Technical2University of Institute Munich, for Chair of Transportation Systems Engineering, 80333 München, Germany 3 Technical University of Munich, Chair of Transportation Systems Engineering, 80333 München, Germany 2
© 2019 The Authors. Published by Elsevier Ltd. © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2017 The Authors. Published by Elsevier B.V. Peer-review organizing 2018. Peer-reviewunder underresponsibility responsibilityofofthethe scientificcommittee committeeofofmobil.TUM the mobil.TUM18. Peer-review under responsibility of the organizing committee of mobil.TUM 2018. Keywords: Urban Air Mobility; MATSim; Operations Simulation and Modelling; Keywords: Urban Air Mobility; MATSim; Operations Simulation and Modelling;
1. Introduction 1. Introduction Urban Air Mobility (UAM), the concept of utilising flying vehicles that are – predominantly – able to take-off and Urban Air Mobility the concept of utilising vehicles interest. that are –Though predominantly to take-off and land vertically (VTOL),(UAM), increasingly becomes of publicflying and research the idea–ofable airborne mobility land vertically (VTOL), increasingly becomes of public and research interest. Though the idea of airborne mobility using VTOL vehicles is not novel, as helicopter-based taxi services have been and are still existent in global using VTOL vehicles nottechnological novel, as helicopter-based taxi the services have been and are still existent global metropolises, it is nowisthat advances enabled development of so-called Personal Air in Vehicles metropolises, it is now that technological advances enabled the development of so-called Personal Air Vehicles (PAV). PAVs, based on advances in electrical and distributed propulsion as well as an increase in energy densities of (PAV). based on electrical andurban distributed propulsion as wellfeasible: as an increase in energy suggest densitiesthat of batteries,PAVs, are believed toadvances be able toinfinally make air travel economically “All indications batteries, are believed to be able to finally make urban air travel economically feasible: “All indications suggest that we may be on the cusp of a revolution in the aerospace and aviation industries”, state Thomas et al. (2017) from we mayBerger. be on the a revolution in the aerospace andthey aviation industries”, state Thomas et al. Roland It iscusp not aofquestion of if, but when’, in which specifically include urban air taxis, i.e.(2017) PAVs,from and Roland Berger. It is not a question of if, but when’, in which they specifically include urban air taxis, i.e. and mention the “clear acceleration in the launch of development projects for 1-4 passenger Urban Air Taxis” PAVs, (Thomson mention the “clear acceleration in the launch of development projects for 1-4 passenger Urban Air Taxis” (Thomson et al. 2017). et al. 2017). While numerous PAV development projects have been launched within the last years (c.f. Shamiyeh et al. (2017; While numerous projects haveintegration been launched within theurban last years (c.f. Shamiyeh (2017; 2018)), there is onlyPAV littledevelopment research on the potential of PAVs into transport system, i.e.etaal. Personal 2018)), there is only little research on the potential integration of PAVs into urban transport system, i.e. a Personal * Corresponding author. Tel.: +49 89 3074-8490; fax: +49 89 3074-84920. * Corresponding Tel.: +49 89 3074-8490; fax: +49 89 3074-84920. E-mail address:author. [email protected] E-mail address: [email protected] 2214-241X © 2017 The Authors. Published by Elsevier B.V. Peer-review©under the organizing committee 2214-241X 2017responsibility The Authors.of Published by Elsevier B.V. of mobil.TUM 2018. Peer-review under responsibility of the organizing committee of mobil.TUM 2018.
2352-1465 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the mobil.TUM18. 10.1016/j.trpro.2019.09.007
42 2
Raoul Rothfeld et al. / Transportation Research Procedia 41 (2019) 41–44 Rothfeld/ Transportation Research Procedia 00 (2016) 000–000
Air Transportation System (PATS). Due to the expected high interaction between existing transport systems and PATS however, it is fundamental to understand and evaluate the effects of UAM introduction. Hence, development of a PAV model and simulation environment is required. Analysing PATS transport performance and potential modal shares are of particular importance. It is the intention to enable such analyses with the – hereinafter – presented UAM extension for the open-source multi-agent transport simulation MATSim (Horni et al. 2016). 2. Literature Review Besides MATSim, there are other potent transport modelling frameworks, such as PTV Visum and TransCAD. PTV Visum, for example, utilises the four-step modelling methodology for transport of (1) trip generation, (2) trip distribution, (3) mode choice, and (4) assignment; and, thus, is a trip-based, macroscopic model. TransCAD, on the other hand, utilises geographic information system (GIS)-based travel demand modelling to provide multiple demand modelling methodologies, including the above-mentioned four-step approach, activity models, and sketch planning methods. MATSim uses a microscopic, activity-based methodology, where individual households have their trips chained and modelled as parts of larger daily activity patterns. While each framework has its distinct advantages and disadvantages in, e.g., run-time, data requirements, and features, MATSim has been selected for UAM modelling due to it being a research-driven, open-source, and licence-free transport-modelling tool. Further, according to Balac (2017), MATSim has been the “only known attempt to simulate commercial flights using an agent-based approach, where both aircraft and passengers are modelled”. The inclusion of air travel in MATSim was done by Grether (2014) with its focus on conventional, i.e. inter-city, commercial air transport with large aircraft. An introduction of UAM, with its small-scale vehicles and intra-city operations, thus, poses novel challenges for MATSim integration. With the introduction of autonomous vehicle simulation within MATSim (Hörl 2016; Boesch & Ciari 2015), as Balac (2017) states, the foundation for the introduction of UAM in MATSim has been set. Under the assumption of on-demand operational models for UAM, the transport modelling of PAVs mirrors that of autonomous ground-based taxis. Thus, the UAM extension for MATSim is based on the works of Bischoff and Maciejewski (2016), who maintain the autonomous vehicle (AV) extension for MATSim based on Maciejewski’s (2016) Dynamic Vehicle Routing Problem (DVRP) MATSim contribution and Boesch and Ciari’s (2015; 2016) research on autonomous vehicle modelling using MATSim. 3. Methodology As mentioned, MATSim uses an agent-based demand and traffic assignment method, where each agent has plans. Plans are, as Ziemke (2016) describes, a “chain of activities (e.g. home–work–shop–home), including their locations and end times”. The plans are fulfilled using transport, by road network or public transport schedules, which have to be provided as input data together with a population definition. Within execution loop of MATSim (see Figure 1), three major recurring events make up the main functionality of MATSim: (1) the mobility simulation (mobsim), (2) scoring, and replanning. The mobility simulation executes each agent’s plan using the provided transport network – potentially causing partially high network loads, i.e. traffic congestion. During scoring, all executed plans are given a score by utility functions based, not on their planned, but actual simulated performance. Finally, agents might replan their day by switching to an alternative plan or creating new ones utilising alternative routes or modes of transport. This process is repeated until the system nears a Nash-equilibriums-like state, were – as Ziemke (2016) describes it – the further development of agents’ plan scores is “sufficiently relaxed”.
Raoul Rothfeld et al. / Transportation Research Procedia 41 (2019) 41–44 Straubinger/ Transportation Research Procedia 00 (2016) 000–000
43 3
Figure 1. MATSim execution loop with numbered controller events (Zilske 2016)
The UAM extension of MATSim builds on the developments for autonomous vehicles with its main aspects of enabling the modelling of one-way, shared, autonomous, and on-demand vehicle usage. The major distinction between these autonomous ground-based taxis and the introduction of UAM are: (1) the requirement of VTOL infrastructure for operation instead of roads, (2) the usage and management of urban airspace, and (3) the introduction of a fleet mix, i.e. different combinations of limiting vehicle properties, such as speed, range, and maximum payload. In contrast to the modelling of conventional taxis, not all PAVs are able to fulfil any given agent’s trip, as there are great variations in current PAV projects with some having ranges of less than 50 km (Shamiyeh et al. 2017; Thomson et al. 2017). Thus, the UAM extension provides the ability to define properties of potential on-demand PAVs and VTOL infrastructure and its placement. Additionally, in order to calibrate agents' dynamic mode choice, the results of a stated preference survey, including PAV options are integrated, in accordance with Rieser and Nagel (2010). 4. Conclusion We present the documentation of the UAM extension for MATSim and its methodology for simulating the use of on-demand PAVs in urban transport environments. The UAM extension focusses on allowing variations in PAV performance and properties and its required VTOL infrastructure in order to perform sensitivity analyses on such variations and their effect on overall, system-wide transport performance. Utilising the UAM extension will provide urban transport and urban planning stakeholders with an open-source framework to evaluate potential PATS realisations. An initial prototyping use case, utilising the UAM extension, will be presented in “Initial Analysis of Personal Air Transportation Systems’ Transport Performance (Abstract submitted)”(Rothfeld et al. 2018). References Balac, M., Vetrella, A.R. & Axhausen, K.W., 2017. Towards the integration of aerial transportation in urban settings, Zürich. Bischoff, J. & Maciejewski, M., 2016. Autonomous Taxicabs in Berlin - A Spatiotemporal Analysis of Service Performance. Transportation Research Procedia, 19(June), pp.176–186. Available at: http://dx.doi.org/10.1016/j.trpro.2016.12.078. Boesch, P.M. & Ciari, F., 2015. Agent-based simulation of autonomous cars. Proceedings of the American Control Conference, 2015–July, pp.2588–2592. Boesch, P.M., Ciari, F. & Axhausen, K.W., 2016. Autonomous Vehicle Fleet Sizes Required to Serve Different Levels of Demand. Transportation Research Record: Journal of the Transportation Research Board, 2542, pp.111–119. Available at: http://trrjournalonline.trb.org/doi/10.3141/2542-13. Grether, D., 2014. Extension of a Multi-Agent Transport Simulation for Traffic Signal Control and Air Transport Systems. Hörl, S., 2016. Implementation of an autonomous taxi service in a multi-modal traffic simulation using MATSim. Department of Energy and Environment Chalmers University of Technology, (June), p.82. Horni, A., Nagel, K. & Axhausen, K.W., 2016. The Multi-Agent Transport Simulation MATSim,
Raoul Rothfeld et al. / Transportation Research Procedia 41 (2019) 41–44 Rothfeld/ Transportation Research Procedia 00 (2016) 000–000
44 4
Maciejewski, M., 2016. Dynamic Transport Services. In A. Horni, K. Nagel, & K. W. Axhausen, eds. The Multi-Agent Transport Simulation MATSim. London: Ubiquity Press, pp. 145–152. Rieser, M. & Nagel, K., 2010. Adding Transit to an Agent-Based Transportation Simulation: Concepts and Implementation. Vsp, PhD. Rothfeld, R. et al., 2018. Initial Analysis of Personal Air Transportation Systems’ Transport Performance (Abstract submitted). In 18th AIAA Aviation Technology, Integration, and Operations Conference. Atlanta, Georgia. Shamiyeh, M., Bijewitz, J. & Hornung, M., 2017. A Review of Recent Personal Air Vehicle Concepts. In Aerospace 6th CEAS Conference. Bucharest: Council of European Aerospace Societies, pp. 1–16. Shamiyeh, M., Rothfeld, R. & Hornung, M., 2018. A Performance Benchmark of Recent Personal Air Vehicle Concepts for Urban Air Mobility. In ICAS Congress. Belo Horizonte, Brasil: (Abstract submitted). Thomson, R. et al., 2017. Think:Act Aircraft Electrical Propulsion – The Next Chapter of Aviation? Ziemke, D., Nagel, K. & Moeckel, R., 2016. Towards an Agent-based, Integrated Land-use Transport Modeling System. Procedia Computer Science, 83, pp.958–963. Zilske, M., 2016. How to Write Your Own Extensions and Possibly Contribute Them to MATSim. In A. Horni, K. Nagel, & K. W. Axhausen, eds. The Multi-Agent Transport Simulation MATSim. London: Ubiquity Press, pp. 297–304.
ScienceDirect ScienceDirect
Transportation Research Procedia 00 (2016) 000–000 Available online at www.sciencedirect.com Transportation Research Procedia 00 (2016) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Transportation Research Procedia 41 (2019) 41–44
International Scientific Conference on Mobility and Transport International onFuture Mobility and Transport UrbanScientific Mobility –Conference Shaping the Together Urban Mobility – Shaping the Future Together mobil.TUM 2018, 13-14 June 2018, Munich, Germany mobil.TUM 2018, 13-14 June 2018, Munich, Germany
Modelling and evaluating urban air mobility – an Modelling and evaluating urban air mobility – an early research approach early research approach Raoul Rothfeld11*, Milos Balac22, and Constantinos Antoniou33 Raoul Rothfeld *, Milos Balac , and Constantinos Antoniou
1 Bauhaus Luftfahrt e.V., 82024 Taufkirchen, Germany 1 ETH Zurich, Institute for Luftfahrt Transporte.V., Planning Systems, 8093 Zurich, Switzerland Bauhaus 82024and Taufkirchen, Germany 3 ETH Zurich, Transport Planning and Systems, 8093 Zurich, Switzerland Technical2University of Institute Munich, for Chair of Transportation Systems Engineering, 80333 München, Germany 3 Technical University of Munich, Chair of Transportation Systems Engineering, 80333 München, Germany 2
© 2019 The Authors. Published by Elsevier Ltd. © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2017 The Authors. Published by Elsevier B.V. Peer-review organizing 2018. Peer-reviewunder underresponsibility responsibilityofofthethe scientificcommittee committeeofofmobil.TUM the mobil.TUM18. Peer-review under responsibility of the organizing committee of mobil.TUM 2018. Keywords: Urban Air Mobility; MATSim; Operations Simulation and Modelling; Keywords: Urban Air Mobility; MATSim; Operations Simulation and Modelling;
1. Introduction 1. Introduction Urban Air Mobility (UAM), the concept of utilising flying vehicles that are – predominantly – able to take-off and Urban Air Mobility the concept of utilising vehicles interest. that are –Though predominantly to take-off and land vertically (VTOL),(UAM), increasingly becomes of publicflying and research the idea–ofable airborne mobility land vertically (VTOL), increasingly becomes of public and research interest. Though the idea of airborne mobility using VTOL vehicles is not novel, as helicopter-based taxi services have been and are still existent in global using VTOL vehicles nottechnological novel, as helicopter-based taxi the services have been and are still existent global metropolises, it is nowisthat advances enabled development of so-called Personal Air in Vehicles metropolises, it is now that technological advances enabled the development of so-called Personal Air Vehicles (PAV). PAVs, based on advances in electrical and distributed propulsion as well as an increase in energy densities of (PAV). based on electrical andurban distributed propulsion as wellfeasible: as an increase in energy suggest densitiesthat of batteries,PAVs, are believed toadvances be able toinfinally make air travel economically “All indications batteries, are believed to be able to finally make urban air travel economically feasible: “All indications suggest that we may be on the cusp of a revolution in the aerospace and aviation industries”, state Thomas et al. (2017) from we mayBerger. be on the a revolution in the aerospace andthey aviation industries”, state Thomas et al. Roland It iscusp not aofquestion of if, but when’, in which specifically include urban air taxis, i.e.(2017) PAVs,from and Roland Berger. It is not a question of if, but when’, in which they specifically include urban air taxis, i.e. and mention the “clear acceleration in the launch of development projects for 1-4 passenger Urban Air Taxis” PAVs, (Thomson mention the “clear acceleration in the launch of development projects for 1-4 passenger Urban Air Taxis” (Thomson et al. 2017). et al. 2017). While numerous PAV development projects have been launched within the last years (c.f. Shamiyeh et al. (2017; While numerous projects haveintegration been launched within theurban last years (c.f. Shamiyeh (2017; 2018)), there is onlyPAV littledevelopment research on the potential of PAVs into transport system, i.e.etaal. Personal 2018)), there is only little research on the potential integration of PAVs into urban transport system, i.e. a Personal * Corresponding author. Tel.: +49 89 3074-8490; fax: +49 89 3074-84920. * Corresponding Tel.: +49 89 3074-8490; fax: +49 89 3074-84920. E-mail address:author. [email protected] E-mail address: [email protected] 2214-241X © 2017 The Authors. Published by Elsevier B.V. Peer-review©under the organizing committee 2214-241X 2017responsibility The Authors.of Published by Elsevier B.V. of mobil.TUM 2018. Peer-review under responsibility of the organizing committee of mobil.TUM 2018.
2352-1465 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the mobil.TUM18. 10.1016/j.trpro.2019.09.007
42 2
Raoul Rothfeld et al. / Transportation Research Procedia 41 (2019) 41–44 Rothfeld/ Transportation Research Procedia 00 (2016) 000–000
Air Transportation System (PATS). Due to the expected high interaction between existing transport systems and PATS however, it is fundamental to understand and evaluate the effects of UAM introduction. Hence, development of a PAV model and simulation environment is required. Analysing PATS transport performance and potential modal shares are of particular importance. It is the intention to enable such analyses with the – hereinafter – presented UAM extension for the open-source multi-agent transport simulation MATSim (Horni et al. 2016). 2. Literature Review Besides MATSim, there are other potent transport modelling frameworks, such as PTV Visum and TransCAD. PTV Visum, for example, utilises the four-step modelling methodology for transport of (1) trip generation, (2) trip distribution, (3) mode choice, and (4) assignment; and, thus, is a trip-based, macroscopic model. TransCAD, on the other hand, utilises geographic information system (GIS)-based travel demand modelling to provide multiple demand modelling methodologies, including the above-mentioned four-step approach, activity models, and sketch planning methods. MATSim uses a microscopic, activity-based methodology, where individual households have their trips chained and modelled as parts of larger daily activity patterns. While each framework has its distinct advantages and disadvantages in, e.g., run-time, data requirements, and features, MATSim has been selected for UAM modelling due to it being a research-driven, open-source, and licence-free transport-modelling tool. Further, according to Balac (2017), MATSim has been the “only known attempt to simulate commercial flights using an agent-based approach, where both aircraft and passengers are modelled”. The inclusion of air travel in MATSim was done by Grether (2014) with its focus on conventional, i.e. inter-city, commercial air transport with large aircraft. An introduction of UAM, with its small-scale vehicles and intra-city operations, thus, poses novel challenges for MATSim integration. With the introduction of autonomous vehicle simulation within MATSim (Hörl 2016; Boesch & Ciari 2015), as Balac (2017) states, the foundation for the introduction of UAM in MATSim has been set. Under the assumption of on-demand operational models for UAM, the transport modelling of PAVs mirrors that of autonomous ground-based taxis. Thus, the UAM extension for MATSim is based on the works of Bischoff and Maciejewski (2016), who maintain the autonomous vehicle (AV) extension for MATSim based on Maciejewski’s (2016) Dynamic Vehicle Routing Problem (DVRP) MATSim contribution and Boesch and Ciari’s (2015; 2016) research on autonomous vehicle modelling using MATSim. 3. Methodology As mentioned, MATSim uses an agent-based demand and traffic assignment method, where each agent has plans. Plans are, as Ziemke (2016) describes, a “chain of activities (e.g. home–work–shop–home), including their locations and end times”. The plans are fulfilled using transport, by road network or public transport schedules, which have to be provided as input data together with a population definition. Within execution loop of MATSim (see Figure 1), three major recurring events make up the main functionality of MATSim: (1) the mobility simulation (mobsim), (2) scoring, and replanning. The mobility simulation executes each agent’s plan using the provided transport network – potentially causing partially high network loads, i.e. traffic congestion. During scoring, all executed plans are given a score by utility functions based, not on their planned, but actual simulated performance. Finally, agents might replan their day by switching to an alternative plan or creating new ones utilising alternative routes or modes of transport. This process is repeated until the system nears a Nash-equilibriums-like state, were – as Ziemke (2016) describes it – the further development of agents’ plan scores is “sufficiently relaxed”.
Raoul Rothfeld et al. / Transportation Research Procedia 41 (2019) 41–44 Straubinger/ Transportation Research Procedia 00 (2016) 000–000
43 3
Figure 1. MATSim execution loop with numbered controller events (Zilske 2016)
The UAM extension of MATSim builds on the developments for autonomous vehicles with its main aspects of enabling the modelling of one-way, shared, autonomous, and on-demand vehicle usage. The major distinction between these autonomous ground-based taxis and the introduction of UAM are: (1) the requirement of VTOL infrastructure for operation instead of roads, (2) the usage and management of urban airspace, and (3) the introduction of a fleet mix, i.e. different combinations of limiting vehicle properties, such as speed, range, and maximum payload. In contrast to the modelling of conventional taxis, not all PAVs are able to fulfil any given agent’s trip, as there are great variations in current PAV projects with some having ranges of less than 50 km (Shamiyeh et al. 2017; Thomson et al. 2017). Thus, the UAM extension provides the ability to define properties of potential on-demand PAVs and VTOL infrastructure and its placement. Additionally, in order to calibrate agents' dynamic mode choice, the results of a stated preference survey, including PAV options are integrated, in accordance with Rieser and Nagel (2010). 4. Conclusion We present the documentation of the UAM extension for MATSim and its methodology for simulating the use of on-demand PAVs in urban transport environments. The UAM extension focusses on allowing variations in PAV performance and properties and its required VTOL infrastructure in order to perform sensitivity analyses on such variations and their effect on overall, system-wide transport performance. Utilising the UAM extension will provide urban transport and urban planning stakeholders with an open-source framework to evaluate potential PATS realisations. An initial prototyping use case, utilising the UAM extension, will be presented in “Initial Analysis of Personal Air Transportation Systems’ Transport Performance (Abstract submitted)”(Rothfeld et al. 2018). References Balac, M., Vetrella, A.R. & Axhausen, K.W., 2017. Towards the integration of aerial transportation in urban settings, Zürich. Bischoff, J. & Maciejewski, M., 2016. Autonomous Taxicabs in Berlin - A Spatiotemporal Analysis of Service Performance. Transportation Research Procedia, 19(June), pp.176–186. Available at: http://dx.doi.org/10.1016/j.trpro.2016.12.078. Boesch, P.M. & Ciari, F., 2015. Agent-based simulation of autonomous cars. Proceedings of the American Control Conference, 2015–July, pp.2588–2592. Boesch, P.M., Ciari, F. & Axhausen, K.W., 2016. Autonomous Vehicle Fleet Sizes Required to Serve Different Levels of Demand. Transportation Research Record: Journal of the Transportation Research Board, 2542, pp.111–119. Available at: http://trrjournalonline.trb.org/doi/10.3141/2542-13. Grether, D., 2014. Extension of a Multi-Agent Transport Simulation for Traffic Signal Control and Air Transport Systems. Hörl, S., 2016. Implementation of an autonomous taxi service in a multi-modal traffic simulation using MATSim. Department of Energy and Environment Chalmers University of Technology, (June), p.82. Horni, A., Nagel, K. & Axhausen, K.W., 2016. The Multi-Agent Transport Simulation MATSim,
Raoul Rothfeld et al. / Transportation Research Procedia 41 (2019) 41–44 Rothfeld/ Transportation Research Procedia 00 (2016) 000–000
44 4
Maciejewski, M., 2016. Dynamic Transport Services. In A. Horni, K. Nagel, & K. W. Axhausen, eds. The Multi-Agent Transport Simulation MATSim. London: Ubiquity Press, pp. 145–152. Rieser, M. & Nagel, K., 2010. Adding Transit to an Agent-Based Transportation Simulation: Concepts and Implementation. Vsp, PhD. Rothfeld, R. et al., 2018. Initial Analysis of Personal Air Transportation Systems’ Transport Performance (Abstract submitted). In 18th AIAA Aviation Technology, Integration, and Operations Conference. Atlanta, Georgia. Shamiyeh, M., Bijewitz, J. & Hornung, M., 2017. A Review of Recent Personal Air Vehicle Concepts. In Aerospace 6th CEAS Conference. Bucharest: Council of European Aerospace Societies, pp. 1–16. Shamiyeh, M., Rothfeld, R. & Hornung, M., 2018. A Performance Benchmark of Recent Personal Air Vehicle Concepts for Urban Air Mobility. In ICAS Congress. Belo Horizonte, Brasil: (Abstract submitted). Thomson, R. et al., 2017. Think:Act Aircraft Electrical Propulsion – The Next Chapter of Aviation? Ziemke, D., Nagel, K. & Moeckel, R., 2016. Towards an Agent-based, Integrated Land-use Transport Modeling System. Procedia Computer Science, 83, pp.958–963. Zilske, M., 2016. How to Write Your Own Extensions and Possibly Contribute Them to MATSim. In A. Horni, K. Nagel, & K. W. Axhausen, eds. The Multi-Agent Transport Simulation MATSim. London: Ubiquity Press, pp. 297–304.