Alonso, A., Monzon, A., & Wang, Y. (2017). Modelling land use and transport policies to measure their contribution to urban challenges: The case of Madrid. Sustainability (Switzerland), 9(3). https://doi.org/10.3390/su9030378 Anda, C., Ordonez Medina, S. A., & Fourie, P. (2018). Multi-agent urban transport simulations using OD matrices from mobile phone data. Procedia Computer Science, 130, 803–809. https://doi.org/10.1016/j.procs.2018.04.139 ArcGIS. (n.d.). Create spider diagram (desire lines)—Help | ArcGIS Desktop. Retrieved January 14, 2020, from https://desktop.arcgis.com/de/arcmap/latest/extensions/business-analyst/create-spider-diagrams-desire-lines.htm Ayed, H., Khadraoui, D., & Aggoune, R. (2015). Using MATSim to simulate carpooling and car-sharing trips. 2015 World Congress on Information Technology and Computer Applications (WCITCA), 1–5. https://doi.org/10.1109/WCITCA.2015.7367046 Babulak, E., & Ming Wang. (2009). Discrete event simulation: State of the art. International Journal of Online Engineering, 4(2), 60–63. Basaric, V., Djoric, V., Jevdjenic, A., & Jovic, J. (2015). Efficient methodology for assessment of targets and policy measures for sustainable mobility systems. International Journal of Sustainable Transportation, 9(3), 217–226. https://doi.org/10.1080/15568318.2012.756088 Bastani, S., Libman, L., & Waller, S. T. (2014). Impact of beaconing policies on traffic density estimation accuracy in traffic information systems. 15th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2014, June 19, 2014. https://doi.org/10.1109/WoWMoM.2014.6918963 Batur, rfan, & Koc, M. (2017). Travel Demand Management (TDM) case study for social behavioral change towards sustainable urban transportation in Istanbul. Cities, 69, 20–35. https://doi.org/10.1016/j.cities.2017.05.017 Bazzan, A. L. C., & Klügl, F. (Eds.). (2009). Multi-agent systems for traffic and transportation engineering. Information Science Reference. Bhatia, A., Varakantham, P., & Kumar, A. (2018). Resource constrained deep reinforcement learning [arXiv]. ArXiv, 5 pp. Bodde, D. L., & Jianan Sun. (2016). Emergent entrepreneurial networks for the transition to automated urban mobility. 2016 IEEE Transportation Electrification Conference and Expo (ITEC), 27-29 June 2016, 6 pp. https://doi.org/10.1109/ITEC.2016.7520274 Borrego, C., Cascao, P., Lopes, M., Amorim, J. H., Tavares, R., Rodrigues, V., Martins, J., Miranda, A. I., & Chrysoulakis, N. (2011). Impact of urban planning alternatives on air quality: URBAIR model application. 19th International Conference on Modelling, Monitoring and Management of Air Pollution, AIR 2011, September 19, 2011 - September 21, 2011, 147, 13–24. https://doi.org/10.2495/AIR110021 Bruemmer, R., October 4, M. G. U., & 2019. (2019, October 4). Montreal plans carbon-neutral neighbourhood at Hippodrôme site | Montreal Gazette. https://montrealgazette.com/news/local-news/montreal-eyes-carbon-neutral-neighbourhood-for-hippodrome-site Buil, R., Piera, M. A., Gusev, M., Ginters, E., & Aizstrauts, A. (2015). MAS simulation for decision making in urban policy design: Bicycle infrastructure. 17th International Conference on Harbor, Maritime and Multimodal Logistics Modelling and Simulation, HMS 2015, September 21, 2015 - September 23, 2015, 95–102. http://www.msc-les.org/proceedings/hms/2015/HMS2015_95.pdf Caiati, V., Bedogni, L., Bononi, L., Ferrero, F., Fiore, M., & Vesco, A. (2016). Estimating urban mobility with open data: A case study in Bologna. 2nd IEEE International Smart Cities Conference, ISC2 2016, September 12, 2016 - September 15, 2016, IEEE Smart Cities; University of Trento. https://doi.org/10.1109/ISC2.2016.07580765 Carteni, A., & De Luca, S. (2014). Greening the transportation sector: A methodology for assessing sustainable mobility policies within a sustainable energy action plan. International Journal of Powertrains, 3(4), 354–374. https://doi.org/10.1504/IJPT.2014.066420 Categorical Data. (n.d.). Department of Statistics and Data Science, Yale University. Retrieved May 18, 2019, from http://www.stat.yale.edu/Courses/1997-98/101/catdat.htm Chrysostomou, K., Petrou, A., Aifadopoulou, G., & Morfoulaki, M. (2019). Microsimulation Modelling of the Impacts of Double-Parking Along an Urban Axis (pp. 164–171). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-030-02305-8_20 Concordia University. (2020). Fast facts. http://www.concordia.ca/content/concordia/en/about/fast-facts.html DeLorenzo, N., & Dugger, A. (n.d.). Choropleth Map. Retrieved January 14, 2020, from https://www.arcgis.com/apps/MapJournal/index.html?appid=75eff041036d40cf8e70df99641004ca Dresner, K., & Stone, P. (2004). A Protocol for Multi-Agent Traffic Control at Intersections. 10. Dresner, K., & Stone, P. (2007). Sharing the Road: Autonomous Vehicles meet Human Drivers. The 20th International Joint Conference on Artificial Intelligence, 1263–68. Dynamic Transportation Systems. (2019). QGIS Edge Bundling [Python]. Dynamic Transportation Systems. https://github.com/dts-ait/qgis-edge-bundling (Original work published 2017) Ebru, E. (2013). Pedestrian Zones. In M. Ozyavuz (Ed.), Advances in Landscape Architecture. InTech. https://doi.org/10.5772/55748 Edwards, S., Hill, G., Goodman, P., Blythe, P., Mitchell, P., & Huebner, Y. (2018). Quantifying the impact of a real world cooperative-ITS deployment across multiple cities. Transportation Research Part A: Policy and Practice, 115, 102–113. https://doi.org/10.1016/j.tra.2017.10.001 Elahi, M., Steverson, G., Dey, S., Dock, S., & Green, L. (2016). Framework for assessing effectiveness of peak hour parking restrictions: Case study from Washington, D.C. Transportation Research Record, 2554, 27–36. https://doi.org/10.3141/2554-04 Elbanhawy, E. Y., Dalton, R., & Nassar, K. (2013). Integrating space-syntax and discrete-event simulation for e-mobility analysis. 2013 Architectural Engineering National Conference: Building Solutions for Architectural Engineering, AEI 2013, April 3, 2013 - April 5, 2013, 934–945. https://doi.org/10.1061/9780784412909.091 Engineering Services - Transportation, City of Vancouver. (2006, June 3). Transportation Plan (1997 Report)—Glossary. https://web.archive.org/web/20060603041834/http://vancouver.ca/engsvcs/transport/plan/1997report/glossary.htm Engineering Village Database. (n.d.). Retrieved May 15, 2019, from https://www.elsevier.com/solutions/engineering-village Ettema, D., Arentze, T., & Timmermans, H. (2011). Social influences on household location, mobility and activity choice in integrated micro-simulation models. Transportation Research Part A: Policy and Practice, 45(4), 283–295. https://doi.org/10.1016/j.tra.2011.01.010 European Commission. (2016, September 22). Green Paper on urban mobility [Text]. Mobility and Transport. https://ec.europa.eu/transport/themes/urban/urban_mobility/green_paper_en Feng, T., & Timmermans, H. J. P. (2014). Trade-offs between mobility and equity maximization under environmental capacity constraints: A case study of an integrated multi-objective model. Transportation Research Part C: Emerging Technologies, 43, 267–279. https://doi.org/10.1016/j.trc.2014.03.012 Ferrara, A., Sacone, S., & Siri, S. (2018). Microscopic and Mesoscopic Traffic Models. In A. Ferrara, S. Sacone, & S. Siri (Eds.), Freeway Traffic Modelling and Control (pp. 113–143). Springer International Publishing. https://doi.org/10.1007/978-3-319-75961-6_5 Forbes Staff. (2017, June 6). El tope inteligente existe y fue desarrollado por mexicanos. Forbes México. https://www.forbes.com.mx/tope-inteligente-existe-fue-desarrollado-mexicanos/ Fournier, N., Chen, S., Viegas de Lima, I. H., Needell, Z., Deliali, A., Araldo, A., Prakash, A. A., Azevedo, C. L., Christofa, E., Trancik, J., & Ben-Akiva, M. (2018). Integrated simulation of activity-based demand and multi-modal dynamic supply for energy assessment. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 4-7 Nov. 2018, 2277–2282. https://doi.org/10.1109/ITSC.2018.8569541 Garfield, L. (n.d.). 13 cities that are starting to ban cars. Business Insider. Retrieved January 5, 2020, from https://www.businessinsider.com/cities-going-car-free-ban-2017-8 German Aerospace Center (DLR). (n.d.). Eclipse SUMO – Simulation of Urban MObility. DLR - Institute of Transportation Systems. Retrieved March 29, 2019, from https://www.dlr.de/ts/en/desktopdefault.aspx/tabid-9883/16931_read-41000/ German Aerospace Center (DLR). (2010). RandomTrips.py [Python]. https://github.com/eclipse/sumo German Aerospace Center (DLR). (2019a). Contributed/SUMOPy—SUMO Documentation. https://sumo.dlr.de/docs/Contributed/SUMOPy.html German Aerospace Center (DLR). (2019b). Demand/Introduction to demand modelling in SUMO - SUMO Documentation. https://sumo.dlr.de/docs/Demand/Introduction_to_demand_modelling_in_SUMO.html German Aerospace Center (DLR). (2019c). Simulation/Output/TripInfo—SUMO Documentation. https://sumo.dlr.de/docs/Simulation/Output/TripInfo.html German Aerospace Center (DLR). (2020a). Definition of Vehicles, Vehicle Types, and Routes—SUMO Documentation. https://sumo.dlr.de/docs/Definition_of_Vehicles,_Vehicle_Types,_and_Routes.html German Aerospace Center (DLR). (2020b). Simulation/Randomness—SUMO Documentation. https://sumo.dlr.de/docs/Simulation/Randomness.html German Aerospace Center (DLR). (2020c). SUMO - SUMO Documentation. https://sumo.dlr.de/docs/SUMO.html German Aerospace Center (DLR). (2020d). Tools/Trip—SUMO Documentation. https://sumo.dlr.de/docs/Tools/Trip.html#randomtripspy German Aerospace Center (DLR). (2020e). Tutorials/OSMWebWizard—SUMO Documentation. https://sumo.dlr.de/docs/Tutorials/OSMWebWizard.html Gössling, S., & Choi, A. S. (2015). Transport transitions in Copenhagen: Comparing the cost of cars and bicycles. Ecological Economics, 113, 106–113. https://doi.org/10.1016/j.ecolecon.2015.03.006 Government of Canada, S. C. (2017). The Daily — Journey to work: Key results from the 2016 Census. https://www150.statcan.gc.ca/n1/daily-quotidien/171129/dq171129c-eng.htm Grimaldo, F., Lozano, M., Barber, F., & Guerra-Hernandez, A. (2012). Towards a model for urban mobility social simulation: A perspective from J-MADeM decision-making. Progress in Artificial Intelligence, 1(2), 149–156. https://doi.org/10.1007/s13748-012-0012-z Grimaldo, F., Lozano, M., Barber, F., & Guerra-Hernandez, A. (2011). A J-MADeM agent-based social simulation to model urban mobility. Advances on Practical Applications of Agents and Multiagent Systems: 9th International Conference on Practical Applications of Agents and Multiagent Systems, 88, 1–11. https://doi.org/10.1007/978-3-642-19875-5_1 Gudwin, R. R. (2016). Urban Traffic Simulation with SUMO: A roadmap for Beginners. DCA-FEEC-UNICAMP. Han, Q., Arentze, T., Timmermans, H., Janssens, D., & Wets, G. (2009). A Multi-Agent Modeling Approach to Simulate Dynamic Activity-Travel Patterns. In A. Bazzan & F. Klügl (Eds.), Multi-Agent Systems for Traffic and Transportation Engineering (pp. 36–56). IGI Global. https://doi.org/10.4018/978-1-60566-226-8.ch002 Henriquez, G. (2017, February 20). Canada’s worst cities for spending hours and hours in traffic. Global News. https://globalnews.ca/news/3261815/canada-worst-traffic/ Hill, J. (2018, July 2). Simulation: The bedrock of AI. Simudyne. https://medium.com/simudyne/simulation-the-bedrock-of-ai-12153eaf7971 Hofer, C., Jager, G., & Fullsack, M. (2018). Large scale simulation of CO2emissions caused by urban car traffic: An agent-based network approach. Journal of Cleaner Production, 183, 1–10. https://doi.org/10.1016/j.jclepro.2018.02.113 Holtz, Y. (n.d.). Chord diagram. From Data to Viz. Retrieved January 27, 2020, from www.data-to-viz.com/caveat/chord.html Hörl, S., Ruch, C., Becker, F., Frazzoli, E., & Axhausen, K. W. (2019). Fleet operational policies for automated mobility: A simulation assessment for Zurich. Transportation Research Part C: Emerging Technologies, 102, 20–31. https://doi.org/10.1016/j.trc.2019.02.020 Houli, D., Zhiheng, L., & Yi, Z. (2010). Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular ad hoc Network. EURASIP Journal on Advances in Signal Processing, 724035 (7 pp.). https://doi.org/10.1155/2010/724035 Isaac Olson/CBC News. (2019a, March 11). Montreal will reduce speed limits to make streets safer for pedestrians | CBC News. CBC. https://www.cbc.ca/news/canada/montreal/vision-zero-reduce-speed-limits-montreal-1.5051449 Isaac Olson/CBC News. (2019b, July 22). Montreal becoming more pedestrian friendly—One car-free zone at a time. CBC. https://www.cbc.ca/news/canada/montreal/pedestrian-zones-montreal-c%C3%B4te-des-neiges-notre-dame-de-gr%C3%A2ce-1.5216210 i-SUSTAIN. (2008). The Commuter Toolkit. www.i-sustain.com Jlassi, S., Tamayo, S., & Gaudron, A. (2018). Simulation Applied to Urban Logistics: A State of the Art. Jones, M. N., Frutiger, J., Ince, N. G., & Sin, G. (2019). The Monte Carlo driven and machine learning enhanced process simulator. Computers & Chemical Engineering, 125, 324–338. https://doi.org/10.1016/j.compchemeng.2019.03.016 Kanaroglou, P., Mercado, R., Maoh, H., Paez, A., Scott, D. M., & Newbold, B. (2008). Simulation framework for analysis of elderly mobility policies. Transportation Research Record, 2078, 62–71. https://doi.org/10.3141/2078-09 Khan, S., Maoh, H., Lee, C., & Anderson, W. (2016). Toward sustainable urban mobility: Investigating nonwork travel behavior in a sprawled Canadian city. International Journal of Sustainable Transportation, 10(4), 321–331. https://doi.org/10.1080/15568318.2014.928838 Kristensen, T., & Ezeora, N. J. (2017). Simulation of intelligent traffic control for autonomous vehicles. 2017 IEEE International Conference on Information and Automation (ICIA), 18-20 July 2017, 459–465. https://doi.org/10.1109/ICInfA.2017.8078952 Kroese, D. P., Brereton, T., Taimre, T., & Botev, Z. I. (2014). Why the Monte Carlo method is so important today. Wiley Interdisciplinary Reviews: Computational Statistics, 6(6), 386–392. https://doi.org/10.1002/wics.1314 Kwang Ming Lion, Kae Hsiang Kwong, & Weng Kin Lai. (2018). Smart speed bump detection and estimation with kinect. 2018 4th International Conference on Control, Automation and Robotics (ICCAR), 20-23 April 2018, 465–469. https://doi.org/10.1109/ICCAR.2018.8384721 L’Association des marchands de la rue Crescent. (2018). Crescent. Crescentmontreal.Com. https://www.crescentmontreal.com Lazarus, J., Ugirumurera, J., Hinardi, S., Zhao, M., Shyu, F., Yexin Wang, Shuai Yao, & Bayen, A. M. (2018). A Decision Support System for Evaluating the Impacts of Routing Applications on Urban Mobility. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 4-7 Nov. 2018, 513–518. https://doi.org/10.1109/ITSC.2018.8569622 Levy, J. I., Buonocore, J. J., & von Stackelberg, K. (2010). Evaluation of the public health impacts of traffic congestion: A health risk assessment. Environmental Health, 9, 65. https://doi.org/10.1186/1476-069X-9-65 Li, W., & Kamargianni, M. (2018). Providing quantified evidence to policy makers for promoting bike-sharing in heavily air-polluted cities: A mode choice model and policy simulation for Taiyuan-China. Transportation Research Part A: Policy and Practice, 111, 277–291. https://doi.org/10.1016/j.tra.2018.01.019 Lucas Torres, O., & Awasthi, A. (2019). Transit Policy Simulation: Towards a Sustainable Urban Mobility. In Sustainable City Logistics Planning: Methods and Applications (Vol. 1). Nova Science Publishers. https://novapublishers.com/shop/sustainable-city-logistics-planning-methods-and-applications-volume-1/ Maggioni, F., Perboli, G., & Tadei, R. (2014). The Multi-path Traveling Salesman Problem with Stochastic Travel Costs: Building Realistic Instances for City Logistics Applications. 17th Meeting of the EURO Working Group on Transportation, EWGT2014, 2-4 July 2014, Sevilla, Spain, 3, 528–536. https://doi.org/10.1016/j.trpro.2014.10.001 Marczuk, K. A., Soh, H. S. H., Azevedo, C. M. L., Lee, D.-H., & Frazzoli, E. (2016). Simulation framework for rebalancing of autonomous mobility on demand systems. 2016 5th International Conference on Transportation and Traffic Engineering (ICTTE 2016), 6-10 July 2016, 81, 01005 (6 pp.). https://doi.org/10.1051/matecconf/20168101005 McKay, R. B. (2000). Consequential Utilitarianism: Addressing Ethical Deficiencies in the Municipal Landfill Siting Process. Journal of Business Ethics, 26(4), 289–306. https://doi.org/10.1023/A:1006345600415 Menezes, E., Maia, A. G., & de Carvalho, C. S. (2017). Effectiveness of low-carbon development strategies: Evaluation of policy scenarios for the urban transport sector in a Brazilian megacity. Technological Forecasting and Social Change, 114, 226–241. https://doi.org/10.1016/j.techfore.2016.08.016 MIT SDEP. (1997). What is System Dynamics? http://web.mit.edu/sysdyn/sd-intro/ Mocanu, T. (2018). The travel demand impacts of fare-free regional public transport in Germany. https://elib.dlr.de/120469/ Musso, A., & Corazza, M. V. (2006). Improving Urban mobility management case study of Rome. Management and Public Policy 2006, 52–59. https://doi.org/10.3141/1956-07 NextGIS. (2019). OSMInfo [Python]. NextGIS. https://github.com/nextgis/osminfo (Original work published 2015) Niazi, M., & Hussain, A. (2011). Agent-based computing from multi-agent systems to agent-based models: A visual survey. Scientometrics, 89(2), 479. https://doi.org/10.1007/s11192-011-0468-9 Occelli, S., & Staricco, L. (2009). Learning about urban mobility: Experiences with a multiagent-system model. Environment and Planning B: Planning and Design, 36(5), 772–786. https://doi.org/10.1068/b34145t OpenStreetMap Wiki. (2020, January 3). Key:highway. https://wiki.openstreetmap.org/wiki/Key:highway Pageaud, S., Deslandres, V., Lehoux, V., & Hassas, S. (2018). Co-construction of adaptive public policies using smartgov. 29th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2017, November 6, 2017 - November 8, 2017, 2017-November, 1328–1335. https://doi.org/10.1109/ICTAI.2017.00199 Pavone, M., Smith, S. L., Frazzoli, E., & Rus, D. (2012). Load Balancing for Mobility-on-demand Systems. 2011 Robotics: Science and Systems (RSS), 27 June-1 July 2011, vol.7, 249–256. Perboli, G., Ferrero, F., Musso, S., & Vesco, A. (2018). Business models and tariff simulation in car-sharing services. Transportation Research Part A: Policy and Practice, 115, 32–48. https://doi.org/10.1016/j.tra.2017.09.011 Perboli, G., & Rosano, M. (2019). Parcel delivery in urban areas: Opportunities and threats for the mix of traditional and green business models. Transportation Research Part C: Emerging Technologies, 99, 19–36. https://doi.org/10.1016/j.trc.2019.01.006 Perronnet, F., Abbas-Turki, A., El-Moudni, A., Buisson, J., & Zeo, R. (2013). Cooperative Vehicle-Actuator System: A sequence-based optimal solution algorithm as tool for evaluating policies. 2013 International Conference on Advanced Logistics and Transport, ICALT 2013, May 29, 2013 - May 31, 2013, 19–24. https://doi.org/10.1109/ICAdLT.2013.6568428 QGIS. (2020). https://www.qgis.org/en/site/ Ramos, A., & de Abreu e Silva, J. (2019). New Indicators in the Performance Analysis of a Public Transport Interchange Using Microsimulation Tools—The Colégio Militar Case Study (pp. 123–130). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-030-02305-8_15 Rivenburgh, N., & Chase, Patricia. (2019). Envisioning Better Cities. Samaranayake, S., Spieser, K., Guntha, H., & Frazzoli, E. (2018). Ridepooling with trip-chaining in a shared-vehicle mobility-on-demand system. 20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017, October 16, 2017 - October 19, 2017, 2018-March, 1–7. https://doi.org/10.1109/ITSC.2017.8317603 Sarker, A., Li, Z., Kolodzey, W., & Shen, H. (2017). Opportunistic Energy Sharing between Power Grid and Electric Vehicles: A Game Theory-Based Pricing Policy. 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017, June 5, 2017 - June 8, 2017, 0, 1197–1207. https://doi.org/10.1109/ICDCS.2017.219 Sayyadi, R., & Awasthi, A. (2017). A system dynamics based simulation model to evaluate regulatory policies for sustainable transportation planning. International Journal of Modelling and Simulation, 37(1), 25–35. https://doi.org/10.1080/02286203.2016.1219806 Sayyadi, R., & Awasthi, A. (2018). An integrated approach based on system dynamics and ANP for evaluating sustainable transportation policies. International Journal of Systems Science: Operations & Logistics, 1–10. https://doi.org/10.1080/23302674.2018.1554168 Schneider, A. (n.d.). Reverse Geocoding. Retrieved January 23, 2020, from http://geotag.sourceforge.net/ReverseGeocoding/ Schumann, B. (2018, May 7). Time to marry simulation models and machine learning. Benjamin Schumann Consulting. https://www.benjamin-schumann.com/blog/2018/5/7/time-to-marry-simulation-models-and-machine-learning Takama, T. (2009). Adaptation and Congestion in a Multi-Agent System to Analyse Empirical Traffic Problems: Concepts and a Case Study of the Road User Charging Scheme at the Upper Derwent. In Ana Bazzan & Franziska Klügl (Eds.), Multi-Agent Systems for Traffic and Transportation Engineering (pp. 1–35). IGI Global. https://doi.org/10.4018/978-1-60566-226-8.ch001 TRIP Lab. (2019). Itinerum. https://itinerum.ca/ Tsiropoulos, A., Papagiannakis, A., & Latinopoulos, D. (2019). Development of an aggregate indicator for evaluating sustainable urban mobility in the City of Xanthi, Greece. 4th Conference on Sustainable Urban Mobility, CSUM 2018, May 24, 2018 - May 25, 2018, 879, 35–43. https://doi.org/10.1007/978-3-030-02305-8_5 UC San Diego. (n.d.). Writing a Literature Review. Retrieved May 20, 2019, from https://psychology.ucsd.edu/undergraduate-program/undergraduate-resources/academic-writing-resources/writing-research-papers/writing-lit-review.html United Nations. (2018, May 16). 68% of the world population projected to live in urban areas by 2050, says UN. UN DESA | United Nations Department of Economic and Social Affairs. https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html UTC to EST Converter. (2020). Savvy Time. https://savvytime.com/converter/utc-to-est/oct-5-2017/10-30pm Vallati, M., Magazzeni, D., Schutter, B. D., Chrpa, L., & McCluskey, T. L. (2016). Efficient macroscopic urban traffic models for reducing congestion: A PDDL+ planning approach. 30th AAAI Conference on Artificial Intelligence, AAAI 2016, February 12, 2016 - February 17, 2016, 3188–3194. Vance, C., & Hedel, R. (2007). The impact of urban form on automobile travel: Disentangling causation from correlation. TRB 2007 Trnasportation Research Board. The Built Environmenr and Travel Behaviour: Making the Connection, 34, 575–588. https://doi.org/10.1007/s11116-007-9128-6 VDA. (2015). Youth without cars? Verband Der Automobilindustrie e. V., 8. Ville de Montréal. (n.d.). MTL Trajet. Retrieved January 8, 2020, from https://ville.montreal.qc.ca/mtltrajet/en/ Ville de Montréal/Service de l’habitation. (2019). Quartiers de référence en habitation [Data file]. https://www.donneesquebec.ca/recherche/fr/dataset/vmtl-quartiers Ville de Montréal/Service de l’urbanisme et de la mobilité. (2017). Déplacements MTL Trajet [Data file]. http://donnees.ville.montreal.qc.ca/dataset/mtl-trajet Ville de Montréal/Service des infrastructures du réseau routier. (2019). Limite administrative de l’agglomération de Montréal (Arrondissements et Villes liées) [Data file]. http://donnees.ville.montreal.qc.ca/dataset/polygones-arrondissements Ville de Montréal/Son-Thu Le. (2020). Feux de circulation – feux pour piétons [Data file]. http://donnees.ville.montreal.qc.ca/dataset/feux-pietons Volkov, M., Aslam, J., & Rus, D. (2012). Markov-based redistribution policy model for future urban mobility networks. 2012 15th International IEEE Conference on Intelligent Transportation Systems, ITSC 2012, September 16, 2012 - September 19, 2012, 1906–1911. https://doi.org/10.1109/ITSC.2012.6338848 Wagner, P. (2019, July). Personal interview [Personal communication]. Wang, Q., & Taylor, J. E. (2016a). Data-driven simulation of urban human mobility constrained by natural disasters. 2016 Winter Simulation Conference (WSC), 11-14 Dec. 2016, 3357–3364. https://doi.org/10.1109/WSC.2016.7822366 Wang, Q., & Taylor, J. E. (2016b). Diffusion and Simulation of Human Mobility Using Online Network Data to Examine Mobility Constraints. Construction Research Congress 2016: Old and New Construction Technologies Converge in Historic San Juan, CRC 2016, May 31, 2016 - June 2, 2016, 1497–1506. https://doi.org/10.1061/9780784479827.150 Weilong Song, Guangming Xiong, & Huiyan Chen. (2016). Intention-aware autonomous driving decision-making in an uncontrolled intersection. Mathematical Problems in Engineering, 2016, 1025349 (15 pp.). https://doi.org/10.1155/2016/1025349 Yao, E., & Morikawa, T. (2015). A study of an integrated intercity travel demand model. https://doi.org/10.1016/j.tra.2004.12.003 Zambom Santana, E. F., Kanashiro, L., Bogado Tomasiello, D., Kon, F., & Giannotti, M. (2018). Analyzing urban mobility carbon footprint with large-scale, agent-based simulation. 7th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2018, March 16, 2018 - March 18, 2018, 2018-March, 143–150. Zhou, B., Schwarting, W., Rus, D., & Alonso-Mora, J. (2018). Joint Multi-Policy Behavior Estimation and Receding-Horizon Trajectory Planning for Automated Urban Driving. 2018 IEEE International Conference on Robotics and Automation (ICRA), 21-25 May 2018, 7 pp. https://doi.org/10.1109/ICRA.2018.8461138