Public transport unreliability is usually evaluated through travel time, service quality and operational energy use, while the electricity associated with passengers’ digital behaviour during disruptions remains largely outside transport-energy assessments. This study introduces Delay-Induced Passenger Electricity Use (DPEU) as a counterfactual framework for examining whether unreliable service changes passenger-related digital electricity consumption relative to equivalent normal-service conditions. A critical review integrates evidence on transport reliability, passenger accumulation, digital behaviour, smartphone electricity demand and supporting ICT infrastructure. The framework represents passenger exposure dynamically and distinguishes additional digital activity from activity that is temporally displaced or suppressed. An order-of-magnitude real-system application is developed for GTT Line 4 in Turin using a documented frequency-based service baseline and analytical headway perturbations rather than reconstructed historical disruption events. Parameter uncertainty is propagated through 300,000 Monte Carlo combinations. Device-side DPEU has theoretical bounds of approximately −4.05 to +4.05 Wh per disruption event, while the central 95% of simulated outcomes lies between approximately −1.37 and +1.36 Wh. Sensitivity analysis identifies the counterfactual behavioural difference as the principal determinant of DPEU, particularly its direction, while behavioural intensity, device power, and passenger exposure jointly determine its absolute magnitude. The results indicate that event-level DPEU is small relative to public-transport operational energy use. More importantly, the analysis provides a transparent approach for examining whether a delay-induced electricity effect exists, estimating its possible magnitude and uncertainty, and identifying the empirical observations required for future validation and practical assessment.
- Open Access
- Article
Delay-Induced Passenger Electricity Use in Public Transport: A Critical Review and Analytical Framework
Author Information
Received: 22 May 2026 | Revised: 25 Aug 2026 | Accepted: 26 Aug 2026 | Published: 10 Sep 2026
Abstract
Graphical Abstract
Keywords
public transport reliability | urban energy systems | sustainable mobility | passenger behaviour | indirect electricity consumption | delay-induced passenger electricity use (DPEU)
References
- 1.
Anselmo, S.; Safaeianpour, A.; Moghadam, S.T.; et al. GIS-based solar radiation modelling for photovoltaic potential in cities: A sensitivity analysis for the evaluation of output variability range. Energy Rep. 2024, 12, 4656–4669. https://doi.org/10.1016/j.egyr.2024.10.031.
- 2.
Safaeianpour, A. Application of an automated procedure for Solar Radiation estimation for the City of Turin A pilot application in a portion of District 6. Master of Science in Digital Skills for Sustainable Societal Transitions, Master’s Thesis, Politecnico di Torino, Turin, Italy, 2024.
- 3.
Abdeyazdan, H.; Safaeianpour, A.; Amini, M.A. Explainable machine learning for passive design: Early-stage building energy reduction in hot-arid climates. Sustain. Energy Technol. Assess. 2025, 83, 104589. https://doi.org/10.1016/j.seta.2025.104589.
- 4.
Yan, H.; Lv, Z. A survey of sustainable development of intelligent transportation system based on urban travel demand. Sustain. Society Dev. 2024, 2, 2399. https://doi.org/10.54517/ssd.v2i1.2399.
- 5.
Khalili, S.; Rantanen, E.; Bogdanov, D.; et al. Global Transportation Demand Development with Impacts on the Energy Demand and Greenhouse Gas Emissions in a Climate-Constrained World. Energies 2019, 12, 3870. https://doi.org/10.3390/en12203870.
- 6.
Kisielewski, P.; Duda, J.; Karkula, M.; et al. Optimization of urban transport vehicle tasks for large, mixed fleet of vehicles and real-world constraints. Arch. Transp. 2024, 70, 65–78. https://doi.org/10.61089/aot2024.qnwb3h25.
- 7.
Bolanle, A.S.; Oyedele, O.M.; Mariam, I.A.; et al. Internet of Things (IoT) Solutions for smart transportation infrastructure and fleet management. Tuijin Jishu J. Propuls. Technol. 2024, 45, 1492–1509.
- 8.
Liu, K.; Feng, T.; Yamamoto, T.; et al. Electrification pathways for public transport systems. Transp. Res. Part D Transp. Env. 2024, 126, 103997. https://doi.org/10.1016/j.trd.2023.103997.
- 9.
Kosmidis, I.; Müller-Eie, D. The synergy of bicycles and public transport: A systematic literature review. Transp. Rev. 2024, 44, 34–68. https://doi.org/10.1080/01441647.2023.2222911.
- 10.
Zhang, N.; Yang, Q. Public transport inclusion and active aging: A systematic review on elderly mobility. J. Traffic Transp. Eng. Engl. Ed. 2024, 11, 312–347. https://doi.org/10.1016/j.jtte.2024.04.001.
- 11.
Potter, S. Transport Energy and Emissions: Urban Public Transport. In Handbook of Transport and the Environment; Hensher, D.A., Button, K.J., Eds; Emerald Group Publishing Limited: Bingley, UK, 2003; pp. 247–262. https://doi.org/10.1108/9781786359513-013.
- 12.
Barrero, R.; Mierlo, J.V.; Tackoen, X. Energy savings in public transport. IEEE Veh. Technol. Mag. 2008, 3, 26–36. https://doi.org/10.1109/mvt.2008.927485.
- 13.
Malmodin, J.; Lövehagen, N.; Bergmark, P.; et al. ICT Sector Electricity Consumption and Greenhouse Gas Emissions—2020 Outcome. Telecommun. Policy 2024, 48, 102701. https://doi.org/10.1016/j.telpol.2023.102701.
- 14.
Andrae, A.; Corcoran, P. Emerging Trends in Electricity Consumption for Consumer ICT; University of Galway Research Repository: Galway, Ireland, 2013. https://doi.org/10.13025/18707.
- 15.
Watkins, K.E.; Ferris, B.; Borning, A.; et al. Where Is My Bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders. Transp. Res. Part A Policy Pract. 2011, 45, 839–848. https://doi.org/10.1016/j.tra.2011.06.010.
- 16.
Yu, D.; Yao, E.; Liu, S.; et al. The Impact of Disruption Information Strategies on the Dynamic Travel Choice Behaviour of Stranded Metro Passengers During Unplanned Service Disruptions. IET Intell. Transp. Syst. 2026, 20, e70220. https://doi.org/10.1049/itr2.70220.
- 17.
Groza, C.; Dumitru-Cristian, A.; Marcu, M.; et al. A Developer-Oriented Framework for Assessing Power Consumption in Mobile Applications: Android Energy Smells Case Study. Sensors 2024, 24, 6469. https://doi.org/10.3390/s24196469.
- 18.
Ali, H.; Khan, H.A.; Pecht, M. Evaluation of in-service smartphone battery drainage profile for video calling feature in major apps. Sci. Rep. 2023, 13, 11699. https://doi.org/10.1038/s41598-023-38859-3.
- 19.
Istrate, R.; Tulus, V.; Grass, R.N.; et al. The environmental sustainability of digital content consumption. Nat. Commun. 2024, 15, 3724. https://doi.org/10.1038/s41467-024-47621-w.
- 20.
Mytton, D.; Ashtine, M. Sources of data center energy estimates: A comprehensive review. Joule 2022, 6, 2032–2056. https://doi.org/10.1016/j.joule.2022.07.011.
- 21.
Mytton, D.; Lundén, D.; Malmodin, J. Network energy use not directly proportional to data volume: The power model approach for more reliable network energy consumption calculations. J. Ind. Ecol. 2024, 28, 966–980. https://doi.org/10.1111/jiec.13512.
- 22.
Soza-Parra, J.; Raveau, S.; Muñoz, J.C.; et al. The underlying effect of public transport reliability on users’ satisfaction. Transp. Res. Part A Policy Pract. 2019, 126, 83–93. https://doi.org/10.1016/j.tra.2019.06.004.
- 23.
Soza-Parra, J.; Raveau, S.; Muñoz, J.C. Travel preferences of public transport users under uneven headways. Transp. Res. Part A Policy Pract. 2021, 147, 61–75. https://doi.org/10.1016/j.tra.2021.02.012.
- 24.
Fan, Y.; Guthrie, A.; Levinson, D. Waiting time perceptions at transit stops and stations: Effects of basic amenities, gender, and security. Transp. Res. Part A Policy Pract. 2016, 88, 251–264. https://doi.org/10.1016/j.tra.2016.04.012.
- 25.
Liu, L.; Miller, H.J. Does real-time transit information reduce waiting time? An empirical analysis. Transp. Res. Part A Policy Pract. 2020, 141, 167–179. https://doi.org/10.1016/j.tra.2020.09.014.
- 26.
Mishalani, R.G.; Lee, S.; McCord, M.R. Evaluating Real-Time Bus Arrival Information Systems. Transp. Res. Rec. 2000, 1731, 81–87. https://doi.org/10.3141/1731-10.
- 27.
Mishalani, R.G.; McCord, M.M.; Wirtz, J. Passenger wait time perceptions at bus stops: Empirical results and impact on evaluating real-time bus arrival information. J. Public Transp. 2006, 9, 89–106. https://doi.org/10.5038/2375-0901.9.2.5.
- 28.
Wang, P.C.; Hsu, Y.T.; Hsu, C.W. Analysis of waiting time perception of bus passengers provided with mobile service. Transp. Res. Part A Policy Pract. 2021, 145, 319–336. https://doi.org/10.1016/j.tra.2021.01.011.
- 29.
Julsrud, T.E.; Denstadli, J.M. Smartphones, travel time-use, and attitudes to public transport services. Insights from an explorative study of urban dwellers in two Norwegian cities. Int. J. Sustain. Transp. 2017, 11, 602–610. https://doi.org/10.1080/15568318.2017.1292373.
- 30.
Ohmori, N.; Harata, N. How different are activities while commuting by train? A case in Tokyo. Tijdschr. Voor Econ. En Soc. Geogr. 2008, 99, 547–561. https://doi.org/10.1111/j.1467-9663.2008.00491.x.
- 31.
Wardman, M.; Chintakayala, P.; Heywood, C. The valuation and demand impacts of the worthwhile use of travel time with specific reference to the digital revolution and endogeneity. Transportation 2020, 47, 1515–1540. https://doi.org/10.1007/s11116-019-10059-x.
- 32.
Timmermans, H.; Van der Waerden, P. Synchronicity of activity engagement and travel in time and space: Descriptors and correlates of field observations. Transp. Res. Rec. J. Transp. Res. Board 2008, 2054, 1–9. https://doi.org/10.3141/2054-01.
- 33.
Zhang, L.; Tiwana, B.; Qian, Z.; et al. Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In Proceedings of the CODES/ISSS ’10: Eighth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, Scottsdale, AZ, USA, 24–29 October 2010. https://doi.org/10.1145/1878961.1878982.
- 34.
Duan, L.T.; Guo, B.; Shen, Y.; et al. Energy analysis and prediction for applications on smartphones. J. Syst. Archit. 2013, 59, 1375–1382. https://doi.org/10.1016/j.sysarc.2013.08.011.
- 35.
Spachos, P.; James, M.; Gregori, S. Power tradeoffs in mobile video transmission for smartphones. Comput. Commun. 2018, 118, 163–170. https://doi.org/10.1016/j.comcom.2017.10.017.
- 36.
Kim, Y.G.; Kim, M.; Kim, J.M.; et al. A novel GPU power model for accurate smartphone power breakdown. ETRI J. 2015, 37, 157–164. https://doi.org/10.4218/etrij.14.0113.1411.
- 37.
Pasek, A.; Vaughan, H.; Starosielski, N. The world wide web of carbon: Toward a relational footprinting of information and communications technology’s climate impacts. Big Data Society 2023, 10, 20539517231158994. https://doi.org/10.1177/20539517231158994.
- 38.
International Telecommunication Union. Recommendation ITU-T L.1410 (11/2024)—Methodology for Environmental Life Cycle Assessments of Information and Communication Technology Goods, Networks and Services; ITU Publications: Geneva, Switzerland, 2024.
- 39.
Bieser, J.C.T.; Hintemann, R.; Hilty, L.M.; et al. A review of assessments of the greenhouse gas footprint and abatement potential of information and communication technology. Environ. Impact Assess. Rev. 2023, 99, 107033. https://doi.org/10.1016/j.eiar.2022.107033.
- 40.
Liu, X.; Schmöcker, J.D.; Zhao, J.; et al. How to make service better? A review on developing service-oriented public transit systems. Transp. Rev. 2025, 45, 672–695.
- 41.
van Ardenne, M.T.; Cebecauer, M.; Cats, O.; et al. Personalised passenger information systems in public transport: A review and a 5-level personalisation taxonomy. Transp. Rev. 2025, 45, 1016–1047. https://doi.org/10.1080/01441647.2025.2537203.
- 42.
Drabicki, A.; Cats, O.; Kucharski, R. Has the COVID-19 pandemic affected travellers’ willingness to wait with real-time crowding information? Travel Behav. Society 2025, 38, 100895. https://doi.org/10.1016/j.tbs.2024.100895.
- 43.
Grant, M.J.; Booth, A. A typology of reviews: An analysis of 14 review types and associated methodologies. Health Inf. Libr. J. 2009, 26, 91–108.
- 44.
GTT—Gruppo Torinese Trasporti. Feed GTFS Trasporti GTT. 2020. Available online: https://aperto.comune.torino.it/dataset/feed-gtfs-trasporti-gtt (accessed on 8 August 2026).
- 45.
GTT—Gruppo Torinese Trasporti. Feed GTFS Real-Time Trasporti GTT. Available online: https://aperto.comune.torino.it/dataset/feed-gtfs-real-time-trasporti-gtt (accessed on 8 August 2026).
- 46.
GTT—Gruppo Torinese Trasporti. Rendicontazione di Sostenibilità 2025. 2026 Available online: https://aperto.comune.torino.it/dataset/feed-gtfs-real-time-trasporti-gtt (accessed on 8 August 2026).
- 47.
Usher, W.; Barnes, T.; Moksnes, N.; et al. Global sensitivity analysis to enhance the transparency and rigour of energy system optimisation modelling. Open Res. Eur. 2023, 3, 30. https://doi.org/10.12688/openreseurope.15461.1.
- 48.
GTT—Gruppo Torinese Trasporti. L’intervallo Indicato è Quello dei Tratti in Commune. 2026. Available online: https://www.gtt.to.it/cms/risorse/urbana/intervalli_sito.pdf (accessed on 15 August 2026).
- 49.
Caiazza, C.; Luconi, V.; Vecchio, A. Energy consumption of smartphones and IoT devices when using different versions of the HTTP protocol. Pervasive Mob. Comput. 2024, 97, 101871. https://doi.org/10.1016/j.pmcj.2023.101871.
- 50.
Henriquez-Jara, B.; Arriagada, J.; Tirachini, A. Impact of real-time information on passenger satisfaction across varying public transport quality levels in 13 Chilean cities. Transp. Res. Part A Policy Pr. 2025, 200, 104622. https://doi.org/10.1016/j.tra.2025.104622.
- 51.
Lopez, P.A.; Behrisch, M.; Bieker-Walz, L.; et al. Microscopic Traffic Simulation using SUMO. In Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4–7 November 2018; pp. 2575–2582. https://doi.org/10.1109/itsc.2018.8569938.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Suite 4002 Level 4, 447 Collins Street, Melbourne, Victoria 3000, Australia
General Inquiries: info@sciltp.com