E-bike use in urban commuting: empirical evidence from the home-work plan
DOI:
https://doi.org/10.5604/01.3001.0015.9568Keywords:
e-bike, commuting to work, sustainable mobility, environment, mobility management, micromobilityAbstract
A substantial part of the environmental issues relies on fossil fuels. This dependence is crucial in transport even though many incentives and interventions have been proposed to reduce pollutant emissions. Electric vehicles with zero emissions might represent a viable solution in urban areas. Many cities encouraged modal shift policies from cars to an e-bike or car-sharing/pooling with electric vehicle fleets. This paper reports the ongoing outputs from a pilot project, relying on a modal shift to the e-bike, promoted in the city of Messina (Southern Italy) by the Ministry of Ecological Transition. The objective is to assess, in the territorial context of Messina, the e-bike as a competitive transport mode in terms of social awareness of eco-friendly mobility solutions. The available dataset consists of about nine months of observations; data on total distance and trips have been gathered for each e-bike. It emerged how, in a typical working day, the average distance travelled is about 6.9 km, the usage rate for working days is about 81 %, and the carbon dioxide reduction is about 245 kg per person each year. During the project, information was also collected on the satisfaction with the e-bike and the quality of travel. It emerged that regular bicycle use has good repercussions on the interviewees' psycho-physical well-being, reducing the stress factor connected with urban mobility. Despite mechanical breakdowns and the lack of an infrastructure dedicated to active mobility representing a limitation, travel comfort and safety are two latent variables that are transver-sally valid within the population; about 15 % became familiar with the e-bike and made it their primary mode choice for everyday activities. In this sense, outputs represent a starting point for future policies and give back adjustments before introducing similar services to students from the university and second-grade schools.
References
Astegiano, P., Fermi, F., & Martino, A. (2019). Investigating the impact of e-bikes on modal share and greenhouse emissions: A system dynamic approach. Transportation Research Procedia, 37, 163-170. https://doi.org/10.1016/j.trpro.2018.12.179
Bebkiewicz, K., Chłopek, Z., Sar, H., Szczepański, K., & Zimakowska-Laskowska, M. (2021). Assessment of impact of vehicle traffic conditions: Urban, rural and highway, on the results of pollutant emissions inventory. Archives of Transport, 60(4), 57-69.
Bieliński, T., Dopierała, Ł., Tarkowski, M., & Ważna, A. (2020). Lessons from Implementing a Metropolitan Electric Bike Sharing System. Energies, 13(23), 6240. https://doi.org/10.3390/en13236240.
Bieliński, T., Kwapisz, A., & Ważna, A. (2021). Electric bike-sharing services mode substitution for driving, public transit, and cy-cling. Transportation Research Part D: Transport and Environment, 96, 102883. https://doi.org/10.1016/j.trd.2021.102883
Brüchert, T., Quentin, P., Baumgart, S., & Bolte, G. (2021). Barriers, Facilitating Factors, and Intersectoral Collaboration for Promoting Active Mobility for Healthy Aging-A Qualitative Study within Local Government in Ger-many. International Journal of Environmental Research and Public Health, 18(7), 3807. https://doi.org/10.3390/ijerph18073807.
Bucher, D., Buffat, R., Froemelt, A., & Raubal, M. (2019). Energy and greenhouse gas emission reduction potentials resulting from different commuter electric bicycle adoption scenarios in Switzerland. Renewable and Sustainable Energy Reviews, 114, 109298. https://doi.org/10.1016/j.rser.2019.109298
Caggiani, L., M. Ottomanelli, R. Camporeale, and M. Binetti. (2017). Spatio-Temporal Clustering and Forecasting Method for Free-Floating Bike Sharing Systems. Advances in Intelligent Systems and Computing 539:244–54. https://doi.org/10.1007/978-3-319-48944-5_23.
Caggiani, L., R. Camporeale, M. Ottomanelli, and W. Y. Szeto. (2018). A Modeling Framework for the Dynamic Management of Free-Floating Bike-Sharing Systems . Transportation Research Part C: Emerging Technologies 87:159–82. https://doi.org/10.1016/j.trc.2018.01.001.
Caggiani, L., A. Colovic, and M. Ottomanelli. (2020). An Equality-Based Model for Bike-Sharing Stations Location in Bicycle-Public Transport Multimodal Mobility. Transportation Research Part A: Policy and Practice 140:251–65. https://doi.org/ 10.1016/j.tra.2020.08.015.
Cairns, S., Behrendt, F., Raffo, D., Beaumont, C., & Kiefer, C. (2017). Electrically-assisted bikes: Potential impacts on travel behaviour. Transportation Research Part A: Policy and Practice, 103, 327-342. https://doi.org/10.1016/j.tra.2017.03.007
Chamier-Gliszczyński, N. (2011). Sustainable operation of a transport system in cities. Key Engineering Materials, 486, 175-178
Chamier-Gliszczyński, N. (2012). Modeling system mobility in urban areas. Carpathian Logistics Congress, Congress Proccedings CLC2012, 501-508.
Chamier-Gliszczyński, N. Bohdal, T. (2016). Urban mobility assessment indicators in the perspective of the environment protection, Rocznik Ochrona Środowiska, 18(1), 670-681.
Chang, F., Haque, Md. M., Yasmin, S., & Huang, H. (2022). Crash injury severity analysis of E-Bike Riders: A random parameters generalized ordered probit model with heterogeneity in means. Safety Science, 146, 105545. https://doi.org/10.1016/j.ssci.2021.105545
Chen, Z., Hu, Y., Li, J., & Wu, X. (2020). Optimal Deployment of Electric Bicycle Sharing Stations: Model Formulation and Solution Technique. Networks and Spatial Economics, 20(1), 99-136. https://doi.org/10.1007/s11067-019-09469-2
Comi, A., & Savchenko, L. (2021). Last-mile delivering: Analysis of environment-friendly transport. Sustainable Cities and Society, 74, 103213. https://doi.org/10.1016/j.scs.2021.103213.
Comi, A., Persia, L., Polimeni, A., Campagna, A., & Mezzavilla, L. (2020). A methodology to design and assess scenarios within SULPs: The case of Bologna. Transportation Research Procedia, 46, 269-276. https://doi.org/10.1016/j.trpro.2020.03.190 .
Comi, A., Polimeni, A., & Nuzzolo, A. (2022). An Innovative Methodology for Micro-Mobility Network Planning. Transportation Research Procedia, 60, 20-27. https://doi.org/10.1016/j.trpro.2021.12.004.
Conway, A., Cheng, J., Kamga, C., & Wan, D. (2017). Cargo cycles for local delivery in New York City: Performance and impacts. Research in Transportation Business & Management, 24, 90-100.
de Haas, M., Kroesen, M., Chorus, C., Hoogendoorn-Lanser, S., & Hoogendoorn, S. (2021). E-bike user groups and substitution effects: Evidence from longitudinal travel data in the Netherlands. Transportation. https://doi.org/10.1007/s11116-021-10195-3.
de Kruijf, J., Ettema, D., & Dijst, M. (2019). A longitudinal evaluation of satisfaction with e-cycling in daily commuting in the Netherlands. Travel Behaviour and Society, 16, 192-200. https://doi.org/10.1016/j.tbs.2018.04.003
Di Salvo, R., Galletta, A., Belcore, O. M., & Villari, M. (2020). Modeling Users’ Performance: Predictive Analytics in an IoT Cloud Monitoring System. In A. Brogi, W. Zimmermann, & K. Kritikos (A c. Di), Service-Oriented and Cloud Computing (pgg. 149-158). Springer International Publishing. https://doi.org/10.1007/978-3-030-44769-4_12
Di Gangi M., & Polimeni A., (2022) Path choice models in stochastic assignment: implementation and comparative analysis. Front. Future Transp. - Transportation Systems Modeling. DOI: 10.3389/ffutr.2022.885967
Di Gangi M., Polimeni A., Belcore, O.M., (Forthcoming) C-Weibit discrete choice model: a path-based approach. ODS2022 conference, Florence (Italy).
Elliot, T., McLaren, S. J., & Sims, R. (2018). Potential environmental impacts of electric bicycles replacing other transport modes in Wellington, New Zealand. Sustainable Production and Consumption, 16, 227-236. https://doi.org/10.1016/j.spc.2018.08.007.
Ermagun, A., & Stathopoulos, A. (2018). To bid or not to bid: An empirical study of the supply determinants of crowd-shipping. Transportation Research Part A: Policy and Practice, 116, 468-483. https://doi.org/10.1016/j.tra.2018.06.019.
Fukushige, T., Fitch, D. T., & Handy, S. (2021). Factors influencing dock-less E-bike-share mode substitution: Evidence from Sacramento, California. Transportation Research Part D: Transport and Environment, 99, 102990. https://doi.org/10.1016/j.trd.2021.102990.
Galatoulas, N.-F., Genikomsakis, K. N., & Ioakimidis, C. S. (2020). Spatio-Temporal Trends of E-Bike Sharing System Deployment: A Review in Europe, North America and Asia. Sustainability, 12(11), 4611. https://doi.org/10.3390/su12114611.
Gruber, J., Kihm, A., & Lenz, B. (2014). A new vehicle for urban freight? An ex-ante evaluation of electric cargo bikes in courier services. Research in Transportation Business & Management, 11, 53-62.
Haustein, S., & Møller, M. (2016). E-bike safety: Individual-level factors and incident characteristics. Journal of Transport & Health, 3(3), 386-394. https://doi.org/10.1016/j.jth.2016.07.001.
He, Y., Song, Z., Liu, Z., & Sze, N. N. (2019). Factors Influencing Electric Bike Share Ridership: Analysis of Park City, Utah. Transportation Research Record, 2673(5), 12-22.
Hertach, P., Uhr, A., Niemann, S., & Cavegn, M. (2018). Characteristics of single-vehicle crashes with e-bikes in Switzerland. Accident Analysis & Prevention, 117, 232–238. https://doi.org/10.1016/j.aap.2018.04.021.
Hu, L., Hu, X., Wang, J., Kuang, A., Hao, W., & Lin, M. (2020). Casualty risk of e-bike rider struck by passenger vehicle using China in-depth accident data. Traffic Injury Prevention, 21(4), 283-287. https://doi.org/10.1080/15389588.2020.1747614
ISFORT (2018). 15° Rapporto sulla mobilità degli italiani. Rapporto sulla Mobilità in Italia 2018 – ISFORT. Last access: 10/06/2022
ISPRA (2022 Inventario Nazionale – EMISSIONI (isprambiente.it). Last access: 01/02/2022.
ISTAT, (2022) http://dati.istat.it/Index.aspx?DataSetCode=DCIS_INDDEMOG1 Last Access.27/07/2022.
Jacyna, M., Żochowska, R., Sobota, A., Wasiak, M. (2021). Scenario analyses of exhaust emissions reduction through the introduction of electric vehicles into the city. Energies, 14(7), 2030. https://doi.org/10.3390/en14072030.
Janecki, R., Karoń, G. (2014). Concept of smart cities and economic model of electric buses implementation. Communications in Computer and Information Science, 471, 100-109. DOI: 10.1007/978-3-662-45317-9_11.
Krukowicz, T., Firląg, k., Sobota, a., Kołodziej, T., & Novačko, l. (2021). The relationship between bicycle traffic and the development of bicycle infrastructure on the example of Warsaw. Archives of transport, 60(4), 187-203. https://doi: 10.5604/01.3001.0015.6930.
Langford, B. C., Cherry, C. R., Bassett, D. R., Fitzhugh, E. C., & Dhakal, N. (2017). Comparing physical activity of pedal-assist electric bikes with walking and conventional bicycles. Journal of Transport & Health, 6, 463-473. https://doi.org/10.1016/j.jth.2017.06.002.
Lazarus, J., Pourquier, J. C., Feng, F., Hammel, H., & Shaheen, S. (2020). Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco. Journal of Transport Geography, 84, 102620.
Liu, W., Liu, H., Liu, W., & Cui, Z. (2021). Life cycle assessment of power batteries used in electric bicycles in China. Renewable and Sustainable Energy Reviews, 139, 110596. https://doi.org/10.1016/j.rser.2020.110596.
Liu, Y., Wan, X., Xu, W., Shi, L., Deng, G., & Bai, Z. (2022). An intelligent method for accident reconstruction involving car and e-bike coupling automatic simulation and multi-objective optimizations. Accident Analysis & Prevention, 164, 106476. https://doi.org/10.1016/j.aap.2021.106476.
Llorca, C., & Moeckel, R. (2021). Assessment of the potential of cargo bikes and electrification for last-mile parcel delivery by means of simulation of urban freight flows. European Transport Research Review, 13(1), 33. https://doi.org/10.1186/s12544-021-00491-5.
Luo, H., Kou, Z., Zhao, F., & Cai, H. (2019). Comparative life cycle assessment of station-based and dock-less bike sharing systems. Re-sources, Conservation and Recycling, 146, 180-189.
Ma, X., Jin, Y., & He, M. (2018). Measuring Bikeshare Access/Egress Transferring Distance and Catchment Area around Metro Stations from Smartcard Data. Information, 9(11), 289.
Marujo, L. G., Goes, G. V., D’Agosto, M. A., Ferreira, A. F., Winkenbach, M., & Bandeira, R. A. M. (2018). Assessing the sustainability of mobile depots: The case of urban freight distribution in Rio de Janeiro. Transportation Research Part D: Transport and Environment, 62, 256-267. https://doi.org/10.1016/j.trd.2018.02.022.
McKenzie, G. (2018). Docked vs. Dockless Bike-sharing: Contrasting Spatiotemporal Patterns. In S. Winter, A. Griffin, & M. Sester (A c. Di), 10th International Conference on Geographic Information Science (GIScience 2018) (Vol. 114, pag. 46:1-46:7). https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.46.
McQueen, M., MacArthur, J., & Cherry, C. (2020). The E-Bike Potential: Estimating regional e-bike impacts on greenhouse gas emissions. Transportation Research Part D: Transport and Environment, 87, 102482. https://doi.org/10.1016/j.trd.2020.102482.
Ministero della Transizione Ecologica (2022) https://www.mite.gov.it/sites/default/files/archivio/allegati/mobilita_sostenibile/mobilita_programma_sperimentale_istruzioni_compilazione_moduli.pdf Last Access 27/07/2022.
Musolino, G., Rindone, C., & Vitetta, A. (2022). Models for Supporting Mobility as a Service (MaaS) Design. Smart Cities, 5(1), 206–222. DOI: 10.3390/smartcities5010013
Narayanan, S., Gruber, J., Liedtke, G., & Antoniou, C. (2022). Purchase intention and actual purchase of cargo cycles: Influencing factors and policy insights. Transportation Research Part A: Policy and Practice, 155, 31–45. https://doi.org/10.1016/j.tra.2021.10.007.
Nigro, M., Castiglione, M., Maria Colasanti, F., De Vincentis, R., Valenti, G., Liberto, C., & Comi, A. (2022). Exploiting floating car data to derive the shifting potential to electric micro-mobility. Transportation Research Part A: Policy and Practice, 157, 78–93. DOI: 10.1016/j.tra.2022.01.008.
Nuzzolo, A., Persia, L., & Polimeni, A. (2018). Agent-Based Simulation of urban goods distribution: A literature review. Transportation Re-search Procedia, 30, 33–42. https://doi.org/10.1016/j.trpro.2018.09.005.
Pazdan, S. (2020). The impact of weather on bicycle risk exposure. Archives of Transport, 56, https://doi.org/10.5604/01.3001.0014.5629.
Punel, A., Ermagun, A., & Stathopoulos, A. (2018). Studying determinants of crowd-ship-ping use. Travel Behaviour and Society, 12, 30–40. https://doi.org/10.1016/j.tbs.2018.03.005.
Panwinkler, T., & Holz-Rau, C. (2021). Causes of pedelec (pedal electric cycle) single accidents and their influence on injury severity. Accident Analysis & Prevention, 154, 106082. https://doi.org/10.1016/j.aap.2021.106082.
Philips, I., Anable, J., & Chatterton, T. (2022). E-bikes and their capability to reduce car CO2 emissions. Transport Policy, 116, 11–23. https://doi.org/10.1016/j.tranpol.2021.11.019
Radzimski, A., & Dzięcielski, M. (2021). Exploring the relationship between bike-sharing and public transport in Poznań, Poland. Transportation Research Part A: Policy and Practice, 145, 189–202. https://doi.org/10.1016/j.tra.2021.01.003
Rérat, P. (2021). The rise of the e-bike: To-wards an extension of the practice of cycling? Mobilities, 16(3), 423-439. https://doi.org/10.1080/17450101.2021.1897236.
Rindone, C. (2022). Sustainable Mobility as a Service: Supply Analysis and Test Cases. Information, 13(7), 351. https://doi.org/10.3390/info13070351.
Russo, F., & Comi, A. (2010). A classification of city logistics measures and connected impacts. Procedia - Social and Behavioral Sciences, 2(3), 6355-6365. https://doi.org/10.1016/j.sbspro.2010.04.044
Russo, F. (2022). Sustainable Mobility as a Service: Dynamic Models for Agenda 2030 Policies. Information, 13(8), 355. https://doi.org/10.3390/info13080355
Sheth, M., Butrina, P., Goodchild, A., & McCormack, E. (2019). Measuring delivery route cost trade-offs between electric-assist cargo bicycles and delivery trucks in dense urban areas. European Transport Research Review, 11(1), 11. https://doi.org/10.1186/s12544-019-0349-5.
Siman-Tov, M., Radomislensky, I., Peleg, K., Bahouth, H., Becker, A., Jeroukhimov, I., Karawani, I., Kessel, B., Klein, Y., Lin, G., Merin, O., Bala, M., Mnouskin, Y., Rivkind, A., Shaked, G., Sivak, G., Soffer, D., Stein, M., & Weiss, M. (2018). A look at electric bike casualties: Do they differ from the mechanical bicycle? Journal of Transport & Health, 11, 176-182. https://doi.org/10.1016/j.jth.2018.10.013.
SUMP (2013). Guidelines. Developing and Implementing a Sustainable Urban Mobility Plan; European Commission: Brussels, Belgium.
Sun, Q., Feng, T., Kemperman, A., & Spahn, A. (2020). Modal shift implications of e-bike use in the Netherlands: Moving towards sustainability? Transportation Research Part D: Transport and Environment, 78, 102202. https://doi.org/10.1016/j.trd.2019.102202.
Taefi, T. T., Kreutzfeldt, J., Held, T., & Fink, A. (2015). Strategies to Increase the Profitability of Electric Vehicles in Urban Freight Transport. In W. Leal Filho & R. Kotter (Eds.), E-Mobility in Europe: Trends and Good Practice (pagg. 367-388). Springer International Publishing. https://doi.org/10.1007/978-3-319-13194-8_20.
Vitetta, A. (2022). Sustainable Mobility as a Service: Framework and Transport System Models. Information, 13(7), 346. https://doi.org/10.3390/info13070346.
Wang, C., Xu, C., Xia, J., & Qian, Z. (2018). The effects of safety knowledge and psychological factors on self-reported risky driving behaviors including group violations for e-bike riders in China. Transportation Research Part F: Traffic Psychology and Behaviour, 56, 344–353. https://doi.org/10.1016/j.trf.2018.05.004.
Winslott Hiselius, L., & Svensson, Å. (2017). E-bike use in Sweden – CO2 effects due to modal change and municipal promotion strategies. Journal of Cleaner Production, 141, 818-824. https://doi.org/10.1016/j.jcle-pro.2016.09.141.
World Health Organization, 2018. Global action plan on physical activity 2018-2030, avail-able on: https://apps.who.int/iris/bitstream/handle/10665/272722/9789241514187-eng.pdf?sequence=1&isAllowed=y.
Zhao, D., Ong, G. P., Wang, W., & Hu, X. J. (2019). Effect of built environment on shared bicycle reallocation: A case study on Nanjing, China. Transportation Research Part A: Policy and Practice, 128, 73-88.
Zochowska, R. (2012). Dynamic approach to the Origin-destination matrix estimation in dense street networks. Archives of Transport, 24(3), 389-413. https://doi.org/10.2478/v10174-012- 0025-1.
Żochowska, R., Jacyna, M., Kłos, M.J., Soczówka, P. (2021). A GIS-based method of the assessment of spatial integration of bike-sharing stations. Sustainability, 13(7), 3894. DOI: 10.3390/su13073894.
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