Load Generators for Automatic Simulation of Urban Fleets

[EN] To ensure cities sustainability, we must deal with, among other challenges, traffic congestion, and its associated carbon emissions. We can approach such a problem from two perspectives: the transition to electric vehicles, which implies the need for charging station infrastructure, and the optimization of traffic flow. However, cities are complex systems, so it is helpful to test changes on them in controlled environments like the ones provided by simulators. In our work, we use SimFleet, an agent-based fleet simulator. Nevertheless, SimFleet does not provide tools for easily setting up big experiments, neither to simulate the realistic movement of its agents inside a city. Aiming to solve that, we enhanced SimFleet introducing two fully configurable generators that automatize the creation of experiments. First, the charging stations generator, which allocates a given amount of charging stations following a certain distribution, enabling to simulate how transports would charge and compare distributions. Second, the load generator, which populates the experiment with a given number of agents of a given type, introducing them dynamically in the simulation, and assigns them a movement that can be either random or based on real city data. The generators proved to be useful for comparing different distributions of charging stations as well as different agent behaviors over the same complex setup.This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government. Pasqual Martí and Jaume Jordán are funded by UPV PAID-06-18 project. Jaume Jordán is also funded by grant APOSTD/2018/010 of Generalitat Valenciana - Fondo Social Europeo.Martí Gimeno, P.; Jordán, J.; Palanca Cámara, J.; Julian Inglada, VJ. (2020). Load Generators for Automatic Simulation of Urban Fleets. Springer. 394-405. https://doi.org/10.1007/978-3-030-51999-5_33S394405Campo, C.: Directory facilitator and service discovery agent. FIPA Document Repository (2002)Dong, J., Liu, C., Lin, Z.: Charging infrastructure planning for promoting battery electric vehicles: an activity-based approach using multiday travel data. Transp. Res. Part C: Emerg. Technol. 38, 44–55 (2014)Jordán, J., Palanca, J., Del Val, E., Julian, V., Botti, V.: A multi-agent system for the dynamic emplacement of electric vehicle charging stations. Appl. Sci. 8(2), 313 (2018)Noori, H.: Realistic urban traffic simulation as vehicular Ad-hoc network (VANET) via Veins framework. In: 2012 12th Conference of Open Innovations Association (FRUCT), pp. 1–7. IEEE (2012)Palanca, J., Terrasa, A., Carrascosa, C., Julián, V.: SimFleet: a new transport fleet simulator based on MAS. In: De La Prieta, F., et al. (eds.) PAAMS 2019. CCIS, vol. 1047, pp. 257–264. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24299-2_22Skippon, S., Garwood, M.: Responses to battery electric vehicles: UK consumer attitudes and attributions of symbolic meaning following direct experience to reduce psychological distance. Transp. Res. Part D: Transp. Environ. 16(7), 525–531 (2011)del Val, E., Palanca, J., Rebollo, M.: U-tool: a urban-toolkit for enhancing city maps through citizens’ activity. In: Demazeau, Y., Ito, T., Bajo, J., Escalona, M.J. (eds.) PAAMS 2016. LNCS (LNAI), vol. 9662, pp. 243–246. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39324-7_2

Modelado de sistemas de free-floating carsharing mediante MATSim

[ES] Uno de los mayores problemas actuales de las ciudades es la alta contaminación causada por las emisiones de carbono y demás gases contaminantes de los automóviles. Esto es especialmente importante en ciudades con grandes masas de población, donde generalmente cada familia tiene al menos un vehículo privado. Una forma de aliviar estos problemas puede ser el carsharing, que consiste en un modelo de alquiler de automóviles durante un periodo corto de tiempo. Esta alternativa está llamando rápidamente la atención y ya se está aplicando en muchas ciudades del mundo. En esta memoria detallamos qué es el concepto del carsharing con las diferentes modalidades que tiene, las ventajas que nos puede aportar, y por qué es interesante su estudio y aplicación. Además, también se explica cómo obtener datos para poder realizar simulaciones multiagente utilizando MATSim y visualizarlo mediante VIA. Finalmente, se realizan las simulaciones en la ciudad de Valencia y se estudia la forma óptima de aplicar el carsharing a esta ciudad.[EN] One of the biggest problems in cities nowadays is the high pollution caused by carbon emissions and other polluting gases from automobiles. This is especially important in cities with large populations, where generally every family has at least one private vehicle. One way to alleviate these problems may be carsharing, which is a model of renting cars for a short period of time. This alternative is rapidly gaining attention and is already being implemented in many cities around the world. In this report we detail what is the concept of carsharing with the different modalities it has, the advantages it can bring us, and why it is interesting to study and apply it. In addition, we also explain how to obtain data to perform multi-agent simulations using MATSim and visualize it using VIA. Finally, we perform the simulations in the city of Valencia, and we study the optimal way to apply carsharing to this city.Zhou, P. (2021). Modelado de sistemas de free-floating carsharing mediante MATSim. Universitat Politècnica de València. http://hdl.handle.net/10251/173980TFG

Taxi services and the carsharing alternative: a case study of Valencia city

[EN] The public's awareness of pollution in cities is growing. The decrease of carbon dioxide emissions from the use of fossil-fuel-powered cars stands out among the different viable alternatives. To this purpose, more sustainable options, such as carsharing fleets, could be used to replace private automobiles and other services such as taxis. This type of vehicle, which is usually electric, is becoming more common in cities, providing a green mobility option. In this research, we use multi-agent simulations to examine the efficiency of the current taxi fleet in Valencia. After that, we evaluate various carsharing fleet arrangements. Our findings demonstrate the possibility for a mix of the two types of fleets to meet present demand while also improving the city's sustainability.This work is partially supported by grant RTI2018-095390-B-C31 funded by MCIN/AEI/ 10.13039/501100011033 and by "ERDF A way of making Europe". Pasqual Martí is supported by grant ACIF/2021/259 funded by the "Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana". Jaume Jordán is supported by grant IJC2020-045683-I funded by MCIN/AEI/ 10.13039/501100011033 and by "European Union NextGenerationEU/PRTR". Pablo Chamoso is supported by grant CCTT3/20/SA/0002 (AIR-SCity project), funded by Institute for Business Competitiveness of Castilla y León, and the European Regional Development Fund.Martí, P.; Jordán, J.; Chamoso, P.; Julian, V. (2022). Taxi services and the carsharing alternative: a case study of valencia city. Mathematical Biosciences and Engineering. 19(7):6680-6698. https://doi.org/10.3934/mbe.202231466806698197L. Rayle, D. Dai, N. Chan, R. Cervero, S. Shaheen, Just a better taxi? a survey-based comparison of taxis, transit, and ridesourcing services in san francisco, Transp. Policy, 45 (2016), 168–178. https://doi.org/10.1016/j.tranpol.2015.10.004R. Katzev, Car sharing: a new approach to urban transportation problems, Anal. Soc. Issues Public Policy, 3 (2003), 65–86. https://doi.org/10.1111/j.1530-2415.2003.00015.xM. Namazu, H. Dowlatabadi, Vehicle ownership reduction: a comparison of one-way and two-way carsharing systems, Transp. Policy, 64 (2018), 38–50. https://doi.org/10.1016/j.tranpol.2017.11.001A. Kolleck, Does car-sharing reduce car ownership? empirical evidence from Germany, Sustainability, 13 (2021), 7384. https://doi.org/10.3390/su13137384J. Firnkorn, M. Müller, What will be the environmental effects of new free-floating car-sharing systems? the case of car2go in Ulm, Ecol. Econ., 70 (2011), 1519–1528. https://doi.org/10.1016/j.ecolecon.2011.03.014X. Dong, Y. Cai, J. Cheng, B. Hu, H. Sun, Understanding the competitive advantages of car sharing from the travel-cost perspective, Int. J. Environ. Res. Public Health, 17 (2020), 4666. https://doi.org/10.3390/ijerph17134666T. Yoon, C. R. Cherry, M. S. Ryerson, J. E. Bell, Carsharing demand estimation and fleet simulation with EV adoption, J. Cleaner Prod., 206 (2019), 1051–1058. https://doi.org/10.1016/j.jclepro.2018.09.124J. Palanca, A. Terrasa, C. Carrascosa, V. Julián, Simfleet: a new transport fleet simulator based on MAS, in International Conference on Practical Applications of Agents and Multi-Agent Systems, (2019), 257–264. https://doi.org/10.1007/978-3-030-24299-2_22P. Martí, J. Jordán, V. Julián, Carsharing in valencia: analysing an alternative to taxi fleets, in Practical Applications of Agents and Multi-Agent Systems, Springer, (2021), 270–282. https://doi.org/10.1007/978-3-030-85710-3_23M. E. Gregori, J. P. Cámara, G. A. Bada, A jabber-based multi-agent system platform, in Proceedings of the Fifth International Joint Conference on Autonomous Aagents and Multiagent Systems, (2006), 1282–1284. https://doi.org/10.1145/1160633.1160866P. Martí, J. Jordán, J. Palanca, V. Julian, Free-floating carsharing in SimFleet, in International Conference on Intelligent Data Engineering and Automated Learning, Springer, (2020), 221–232. https://doi.org/10.1007/978-3-030-62362-3_20P. Martí, J. Jordán, J. Palanca, V. Julian, Load generators for automatic simulation of urban fleets, in International Conference on Practical Applications of Agents and Multi-Agent Systems, Springer, (2020), 394–405. https://doi.org/10.1007/978-3-030-51999-5_33N. Firdausiyah, E. Taniguchi, A. G. Qureshi, Modeling city logistics using adaptive dynamic programming based multi-agent simulation, Transp. Res. Part E: Logist. Transp. Rev., 125 (2019), 74–96. https://doi.org/10.1016/j.tre.2019.02.011C. Standing, F. Jie, T. Le, S. Standing, S. Biermann, Analysis of the use and perception of shared mobility: a case study in western Australia, Sustainability, 13 (2021), 8766. https://doi.org/10.3390/su13168766H. Qin, E. Su, Y. Wang, J. Li, Branch-and-price-and-cut for the electric vehicle relocation problem in one-way carsharing systems, Omega, 109 (2022), 102609. https://doi.org/10.1016/j.omega.2022.102609H. Habekotté, Optimizing Carsharing Policies for a New Generation-A Quest on How to Upscale Carsharing as Part of Sustainable Mobility Systems in Dutch Urban Regions, PhD thesis, University of Groningen, 2021.A. Ciociola, D. Markudova, L. Vassio, D. Giordano, M. Mellia, M. Meo, Impact of charging infrastructure and policies on electric car sharing systems, in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE, (2020), 1–6. https://doi.org/10.1109/ITSC45102.2020.9294282J. Schlüter, A. Bossert, P. Rössy, M. Kersting, Impact assessment of autonomous demand responsive transport as a link between urban and rural areas, Res. Trans. Bus. Manage., 39 (2021), 100613. https://doi.org/10.1016/j.rtbm.2020.100613F. Javanshour, H. Dia, G. Duncan, R. Abduljabbar, S. Liyanage, Performance evaluation of station-based autonomous on-demand car-sharing systems, IEEE Trans. Intell. Transp. Syst., 2021 (2021), 1–12. https://doi.org/10.1109/TITS.2021.3071869P. Martí, J. Jordán, J. Palanca, V. Julian, Charging stations and mobility data generators for agent-based simulations, Neurocomputing, 484 (2022), 196–210. https://doi.org/10.1016/j.neucom.2021.06.098D. I. Grozev, D. E. Topchu, D. I. Miteva, Assessment of CO2 emissions released from the taxi vehicle fleet in Ruse, in Proceedings of the 2nd Virtual Multidisciplinary Conference, (2014), 484–487.J. Jordán, P. Martí, J. Palanca, V. Julian, V. Botti, Interurban electric vehicle charging stations through genetic algorithms, in International Conference on Hybrid Artificial Intelligence Systems, Springer, (2021), 101–112. https://doi.org/10.1007/978-3-030-86271-8_9J. Jordán, J. Palanca, E. del Val, V. Julian, V. Botti, Localization of charging stations for electric vehicles using genetic algorithms, Neurocomputing, 452 (2021), 416–423. https://doi.org/10.1016/j.neucom.2019.11.12