journal article·2026-02-19Open access

Decarbonizing Urban Transportation: A Case Study of Montreal

Atiya Atiya, Sepideh Khorramisarvestani, Ursula Eicker

Applied Sciences· Volume 16· Issue 4

Abstract

Urban passenger transportation contributes substantially to greenhouse gas emissions, yet the relative effectiveness of different decarbonization strategies remains difficult to assess due to inconsistent travel demand assumptions across studies. This study conducts a city-scale scenario analysis of daily passenger transportation CO2 emissions for the Island of Montréal using a reconstructed representation of weekday passenger trips. An externally generated, survey-calibrated travel demand dataset is used as a fixed baseline, enabling consistent comparison across six decarbonization scenarios spanning vehicle electrification, modal shift, active travel substitution, and ride pooling. By holding daily travel demand constant, the analysis isolates the emissions impacts attributable to each intervention rather than to changes in mobility patterns. The scenario results represent upper-bound technical mitigation potential and provide system-level insight into how different strategies affect emissions across modes, vehicle categories, and network segments. The study demonstrates the value of city-scale scenario analysis for informing urban transport decarbonization under data-scarce conditions.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Vehicle emissions and performance · Transportation and Mobility Innovations · Electric Vehicles and Infrastructure

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Concordia Institute for Information Systems Engineering (CIISE), Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC H3G 1M8, Canada

Department of Geography, Planning and Environment, Concordia University, Montreal, QC H3G 1M8, Canada

Next Generation Cities, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC H3G 1M8, Canada

Funding

Natural Sciences and Engineering Research Council of Canada

References (60)

Show source-provided references
1. Transport: A roadblock to climate change mitigation?10.1126/science.aac8033 ↗
2. Carbon footprints of 13 000 cities10.1088/1748-9326/aac72a ↗
3. Gouldson, A., Colenbrander, S., Sudmant, A., Godfrey, N., Millward-Hopkins, J., Fang, W., and Zhao, X. (2015). Accelerating Low Carbon Development in the World’s Cities, New Climate Economy.
4. Global public health implications of traffic related air pollution: Systematic review10.1177/11786302241272403 ↗
5. An integrated modeling perspective on climate change mitigation and co-benefits in the transport sector10.1088/1748-9326/adf23f ↗
6. Toward Zero-Carbon Urban 562 Transitions with Health, Climate Resilience, and Equity Co-Benefits: Assessing Nexus 563 Linkages10.1146/annurev-environ-112621-063931 ↗
7. Transportation transformation: Is micromobility making a macro impact on sustainability?10.1177/0885412220972696 ↗
8. Exploring sustainable accessibility through multimodal networks: Assessing healthy food access in Montreal10.1016/j.urbmob.2025.100170 ↗
9. Big data analytics for mitigating carbon emissions in smart cities: Opportunities and challenges10.1080/09654313.2017.1294149 ↗
10. Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management10.1016/j.apenergy.2022.120407 ↗
11. The universal visitation law of human mobility10.1038/s41586-021-03480-9 ↗
12. Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data10.1016/j.trc.2021.103291 ↗
13. Population synthesis for urban resident modeling using deep generative models10.1007/s00521-021-06622-2 ↗
14. Agriesti, S., Roncoli, C., and Nahmias-Biran, B.-H. (2022). Assignment of a synthetic population for activity-based modeling employing publicly available data. ISPRS Int. J. Geo-Inf., 11, Correction in ISPRS Int. J. Geo-Inf. 2024, 13, 284.10.3390/ijgi13080284 ↗
15. Khachman, M., Morency, C., and Ciari, F. (2024). A novel machine learning-based spatialized population synthesis framework. Transportation.10.1007/s11116-024-10534-0 ↗
16. Rodrigue, J.P. (2025, September 02). Greenhouse Gas Emissions from Transportation. The Geography of Transport Systems. Available online: https://transportgeography.org/contents/chapter4/transportation-and-environment/greenhouse-gas-emissions-transportation/.
17. Masson-Delmotte, V., Zhai, P., Pörtner, H.O., Roberts, D., Skea, J., Shukla, P., Pirani, A., Moufouma-Okia, W., Péan, C., and Pidcock, R. (2018). Global Warming of 1.5 °C, IPCC. Available online: https://www.ipcc.ch/site/assets/uploads/sites/2/2019/06/SR15_Full_Report_Low_Res.pdf.
18. Olivier, J.G., Schure, K., and Peters, J. (2017). Trends in Global CO2 and Total Greenhouse Gas Emissions, PBL Netherlands Environmental Assessment Agency.
19. Impact of congestion on greenhouse gas emissions for road transport in Mumbai metropolitan region10.1016/j.trpro.2017.05.282 ↗
20. The evolution of city-scale GHG emissions inventory methods: A systematic review10.1016/j.eiar.2019.106316 ↗
21. Vieweg, M. (2017). Bottom-Up GHG Inventory and MRV of Measures, GIZ. Available online: https://changing-transport.org/wp-content/uploads/2017_ViewegMersman_Bottom-Up_GHG_Inventory.pdf.
22. Validation of road vehicle and traffic emission models—A review and meta-analysis10.1016/j.atmosenv.2010.05.022 ↗
23. Vehicle emission trends and spatial distribution in Shandong province, China, from 2000 to 201410.1016/j.atmosenv.2016.09.065 ↗
24. Spatial-temporal distribution of carbon emissions by daily travel and its response to urban form: A case study of Hangzhou, China10.1016/j.jclepro.2020.120797 ↗
25. U.S. Environmental Protection Agency (2025, August 12). EPA MOVES Guidance, Available online: https://www.epa.gov/moves/can-i-model-emissions-electric-fuel-cell-and-hybrid-vehicles-and-equipment-moves.
26. Carbon emission from urban passenger transportation in Beijing10.1016/j.trd.2015.10.001 ↗
27. Piras, G., Orsini, G., and Muzi, F. (2025). Energy Transition of Road Infrastructures: Analysis of the Photovoltaic Potential on the A3 Napoli–Pompei–Salerno Highway. Energies, 18.10.3390/en18123042 ↗
28. Wills, W., Meirelles, J., Baptista, V.G., Cury, G., and Cerdeira, P. (2019). A Simple Sinuosity-Based Method using GPS data to Support Mitigation Policies for Public Buses GHG Emissions. arXiv.
29. Fake it till you make it: Synthetic data for emerging carsharing programs10.1016/j.trd.2024.104067 ↗
30. Comparative environmental life cycle assessment of conventional and electric vehicles10.1111/j.1530-9290.2012.00532.x ↗
31. Electric car life cycle assessment based on real-world mileage and the electric conversion scenario10.1007/s11367-015-0934-3 ↗
32. Net emission reductions from electric cars and heat pumps in 59 world regions over time10.1038/s41893-020-0488-7 ↗
33. Matching Synthetic Populations with Personas: A Test Application for Urban Mobility10.1017/pds.2022.182 ↗
34. Saval, A., Minh, D.P., Chapuis, K., Tranouez, P., Caron, C., Daudé, É., and Taillandier, P. (2023). Dealing with mixed and non-normative traffic. An agent-based simulation with the GAMA platform. PLoS ONE, 18.10.1371/journal.pone.0281658 ↗
35. Open Source Routing Machine Como Alternativa ao Google Maps: Estudo Exploratório Sobre as Potencialidades do OSRM Para Cálculos de Distâncias Entre Municípios
36. OpenTripPlanner for R10.21105/joss.01926 ↗
37. Predicting transportation carbon emission with urban big data10.1109/TSUSC.2017.2728805 ↗
38. A high temporal-spatial vehicle emission inventory based on detailed hourly traffic data in a medium-sized city of China10.1016/j.envpol.2018.01.068 ↗
39. Electric vehicles for greenhouse gas reduction in China: A cost-effectiveness analysis10.1016/j.trd.2017.07.025 ↗
40. The climate change mitigation effects of daily active travel in cities10.1016/j.trd.2021.102764 ↗
41. The potential of ride-pooling in VKT reduction and its environmental implications10.1016/j.trd.2021.103155 ↗
42. Farnood, A., Eicker, U., Cucuzzella, C., Gopakumar, G., and Khorramisarvestani, S. (2025). Sustainability Assessment Framework for Urban Transportation Combining System Dynamics Modeling and GIS; A TOD and Parking Policy Approach. Smart Cities, 8.10.3390/smartcities8040107 ↗
43. Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebvre, N., and Nagel, K. (2009). MATSim-T: Architecture and simulation times. Multi-Agent Systems for Traffic and Transportation Engineering, IGI Global Scientific Publishing.10.4018/978-1-60566-226-8.ch003 ↗
44. Gali, E., Eidenbenz, S., Mniszewski, S., Cuellar, L., and Teuscher, C. (2008). ActivitySim: Large-Scale Agent Based Activity Generation for Infrastructure Simulation, Los Alamos National Laboratory (LANL). Technical Report.
45. Lu, Y., Adnan, M., Basak, K., Pereira, F.C., Carrion, C., Saber, V.H., Loganathan, H., and Ben-Akiva, M.E. (2015, January 11–15). Simmobility mid-term simulator: A state of the art integrated agent based demand and supply model. Proceedings of the 94th Annual Meeting of the Transportation Research Board, Washington, DC, USA.
46. Ben-Akiva, M., Bierlaire, M., Koutsopoulos, H., and Mishalani, R. (1998, January 13). DynaMIT: A simulation-based system for traffic prediction. Proceedings of the DACCORD Short Term Forecasting Workshop, Delft, The Netherlands.
47. Mobility-based carbon footprint analysis of urban transportation modes using synthetic travel data
48. Quantification of fossil fuel CO2 emissions on the building/street scale for a large US city10.1021/es3011282 ↗
49. Laffont, P., Waygood, E.O.D., and Patterson, Z. (2024). How Many Electric Vehicles Are Needed to Reach CO2 Emissions Goals? A Case Study from Montreal, Canada. Environ. Res. Lett., 29.
50. Autorité Régionale de Transport Métropolitain (ARTM) (2020). Faits Saillants de L’enquête Origine–Destination 2018: Région Métropolitaine de Montreal, ARTM. Available online: https://www.artm.quebec/wp-content/uploads/2020/01/CA_Faits-saillants_EOD_COMPLET_WEB_14012020_R002.pdf.
51. Société de Transport de Montreal (2019). Rapport Annuel 2018—Faits Saillants et Indicateurs de Performance, Société de Transport de Montreal. Technical Report.
52. TomTom (2024). Montreal Traffic Report, TomTom.
53. Mapzen/Valhalla Project (2025, August 06). Valhalla Routing Engine Documentation. Available online: https://valhalla.readthedocs.io/.
54. Bergman, L. (2024, April 09). Using Timestamped GPS Data for Travel Time Estimation and Route Optimization in an Open-Source Routing Engine. Available online: https://www.uppsatser.se/uppsats/f206bf7769/.
55. Cervero, R. (2004). Transit-Oriented Development in the United States: Experiences, Challenges, and Prospects, Transportation Research Board. Number 102 in TCRP Report.
56. Transforming our cities: Best practices towards clean air and active transportation10.1007/s40572-019-0228-1 ↗
57. Stewart, G., and Al-Khassaweneh, M. (2022). An implementation of the HDBSCAN* clustering algorithm. Appl. Sci., 12.10.3390/app12052405 ↗
58. Munguía Mondragón, J.C., Rendón Lara, E., Alejo Eleuterio, R., Granda Gutirrez, E.E., and Del Razo López, F. (2023). Density-based clustering to deal with highly imbalanced data in multi-class problems. Mathematics, 11.10.3390/math11184008 ↗
59. Rousseeuw, P.J., and Kaufman, L. (2009). Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons.
60. On clustering validation techniques10.1023/A:1012801612483 ↗