Login | Register

Decision making for urban mobility: a macro, meso and micro analysis

Title:

Decision making for urban mobility: a macro, meso and micro analysis

Lucas Torres, Omar de Jesus (2020) Decision making for urban mobility: a macro, meso and micro analysis. Masters thesis, Concordia University.

[thumbnail of LucasTorres_MASc_S2020.pdf]
Preview
Text (application/pdf)
LucasTorres_MASc_S2020.pdf - Accepted Version
Available under License Spectrum Terms of Access.
8MB

Abstract

Urban congestion is a challenge that cities commonly suffer across the globe. Traffic congestion and longer commutes are linked with poor cardiovascular and metabolic health, along with decreased energy and increased stress among the users. This is further translated into productivity and economic loss, an increase in health service expenses and a general decrease in the quality of social wellbeing.
To improve this condition, the municipality administration has the role of implementing solutions to strategically address urban mobility. However, this is a complex task to achieve and normally involves limited resources, which make real-world deployments have a great inherited risk. Thus, decision-making is a task that has to be carefully addressed by different factors and scales.
This thesis approaches multiple tools for analytics on urban mobility using skills in SQL, R and Python, and open-source software such as QGIS for spatial analysis and SUMO (Simulation of Urban Mobility) for microsimulation. The methodology includes the analysis of urban mobility in Montreal from different levels of analysis.
At the macro level, the MTL Trajet dataset provides insight of mobility behaviour of participants through their trip coordinates. Using geometry datasets of quarter and boroughs of Montreal, the analysis is framed and processed via SQL and QGIS. Data visualization is presented in Choropleth maps, Flow maps and Chord diagrams using origin and destination of trips. Supporting processing task such as reverse geocoding to join attributes between datasets are used. The macro analysis helps to identify a primary area of analysis seeking the most transited region. The quarter of René-Lévesque in/and the borough of Ville-Marie are the most accessed areas in this study.
In the meso level, street network information from OpenStreetMap allows making relations among the elements of the area, such as universities and their proximity to pedestrian zones. Resulting maps aid decision-making from a meso perspective, choosing the area of Concordia University as a suitable space for microfocus.
At the micro-level, four areas of opportunity interpreted as transit policy-testing were identified. A custom micro-network and synthetic demand for this area were used to simulate the impacts of these scenarios. The measures tested to improve urban mobility in the area are the restriction of street lanes for specific vehicle types and the inclusion of pedestrian areas. Experimentations with different levels of user modal share and shift are presented.
Results of macro, meso and micro analyses are included to provide recommendations for the administration of the city of Montreal. The inclusion of multiple restrained lanes for buses and high-occupancy vehicles around Concordia University and a pedestrian zone will allow to save time to road users, as long as single-passenger vehicle shifts towards public transit and shared–vehicles.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Lucas Torres, Omar de Jesus
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Quality Systems Engineering
Date:6 February 2020
Thesis Supervisor(s):Awasthi, Anjali
Keywords:urban mobility, decision-making, demand modelling, simulation, transit-policy
ID Code:986456
Deposited By: Omar Lucas Torres
Deposited On:23 Jun 2021 15:50
Last Modified:24 Jun 2021 01:02
Related URLs:
Additional Information:Book chapter publication related to this thesis. 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/

References:

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
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Research related to the current document (at the CORE website)
- Research related to the current document (at the CORE website)
Back to top Back to top