Machine Learning and the Smart City
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This document discusses using machine learning and data analytics to model urban systems and inform urban planning. It describes two case studies: (1) using travel data and machine learning to classify commuter types, and (2) using land use, amenities, and transit data to model ridership and evaluate planning scenarios. The goal is to develop iterative modeling frameworks that integrate observations, reconstruct data, and test scenarios to help optimize urban policy and infrastructure decisions.
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Machine Learning and the Smart City
- 1. Humans + Machines: Using artificial intelligence to power your people February 18 - 19, 2016 | Penthouse, TwentyFour Seven McKinley Machine Learning and the Smart City Erika Fille T. Legara, Ph.D. | @eflegara www.erikalegara.net Scientist,Complex Systems Institute of High Performance Computing
- 2. • Complexity science • Data analytics maturity • Modeling framework • Machine learning methods in urban planning – Commuter behaviour research – Land-use and transport planning research – Integrated transport model Outline
- 3. COMPLEXITY SCIENCE looking into the SCIENCE of CITIES, Computational Social Science, Computational Biology & Ageing, and Complex Networks. http://www.a-star.edu.sg/ihpc/Research/Computing-Science-CS/Complex-Systems-Group-CxSy-Group/Overview.aspx
- 4. Bus Arrivals Waiting time ½ x headway2 = the area of each triangle time headway headway EWT SWT AWT An interactive visual, demand modelling, and decision-support tool.!
- 5. An interactive visual, demand modelling, and decision-support tool.! Bus Arrivals Waiting time ½ x headway2 = the area of each triangle time headway headway EWT SWT AWT Modeling and Simulations of the Rapid Transit System Reliability Analysis of Bus Arrivals Lightless intersection control numerical simulations Land-Use & Transport Modeling Crowd Modeling and Simulations Characterizing Public Transport Commuters Resilience of Commuter Encounter Networks Aging, Biology & Computing: Healthspan Identification of Regulators in a Human Gene Network Urban Morphology Dynamical Model of Twitter Activity Profiles Diffusion & Cascading Failures on Multiplex Networks
- 6. Evolution & Adaptation Artificial NN Evolutionary computation Genetic algorithms AI / Artificial life Evo-Devo Machine learningEvolutionary robotics Networks SNA Motifs Graph Theory Small-world CentralityCommunity Detection Robustness & Vulnerability Adaptive networks SF networks Nonlinear Dynamics ODE Iterative maps Stability analysisAttraction Phase space ChaosPopulation dynamics Time series analysis Collective Behavior Collective intelligence Social dynamics Herd mentality Phase transition Synchronization Ant colony optimization Particle swarm optimization ABM Self-organized criticality Game Theory Prisoner’s Dilemma Irrational behavior Bounded rationality Evolutionary game theory Cooperation vs competition Pattern Formation Percolation Reaction-diffusion CA Spatial ecology Partial DE Systems Theory Feedbacks Information theory Entropy Computation theory Autopoiesis Cybernetics COMPLEXITY SCIENCE Adapted from: Hiroki Sayama
- 7. Data Science
- 8. The Framework Observations Reconstruct Observations Scenario Modeling + An IterativeProcessAdapted from: A. Vespignani and FuturICT
- 9. “Make the best fake metropolis.”
- 10. • Where should the next residential area be? • Where should we build the next train station? • What should be the path of the new train line? • Is the color-coding scheme effective? • What are the effects of U-turns along highways? • When is road-widening effective, when is it not? Urban Planning
- 11. Implementing policies based on intuition alone can be expensive, time consuming, and sometimes catastrophic.
- 12. The Era of Big Data
- 13. The Framework Observations Reconstruct Observations Scenario Modeling + An IterativeProcessAdapted from: A. Vespignani and FuturICT
- 15. Which typesofcommutersaretraveling? Case Study1 • EF Legara and C Monterola, "Identifying Passenger Type from Travel Routine," 2015 Conference on Complex Systems, Phoenix, Arizona, USA, September 2015. • Inferring Passenger Type from Commuter Travel Matrices, EF Legara and C Monterola, submitted 2016.
- 16. • Quantify “natural tendencies” of commuters • Understand the structure of the commuting public • Different urban-related policies affect different kinds of commuters • Awareness of which commuter types are traveling, ads, service announcements, and surveys, among others, can be made more targeted spatiotemporally Motivation
- 17. • 14-weeks travel data • Randomly sampled anonymized ID’s • 10 million journeys • Three Passenger Types: • Adult • Student • Senior citizen Dataset Smart Fare Card Tap In Tap Out
- 18. Morning Peak Hour Evening Peak Hour # Commuters Travelling Hour of Day Travel Demand Distribution Observations Reconstruct Observations Scenario Modeling + An IterativeProcess EF Legara and C Monterola, "Identifying Passenger Type from Travel Routine," 2015 Conference on Complex Systems, Phoenix, Arizona, USA, September 2015. EF Legara and C Monterola, arXiv:1509.01199 [physics.soc-ph].
- 19. Travel Demand Travel demand curve for different types vary. Adults – two peaks “working hours” Children – one peak “half-day classes” Seniors – plateau-like “unstructured schedules ” Morning Peak Hour Evening Peak Hour #"Commuters""Travelling" Hour"of"Day" # Commuters Travelling Hour of Day EF Legara and C Monterola, "Identifying Passenger Type from Travel Routine," 2015 Conference on Complex Systems, Phoenix, Arizona, USA, September 2015. EF Legara and C Monterola, arXiv:1509.01199 [physics.soc-ph].
- 20. #"Commuters""Travelling" Hour"of"Day" Travel ModeTravel Demand Travel demand curve for different types vary. Ratio of bus to RTS usage is more pronounced for Senior Citizens. Hint to the features to include in the classification model. Travel Demand and Mode of Transport EF Legara and C Monterola, "Identifying Passenger Type from Travel Routine," 2015 Conference on Complex Systems, Phoenix, Arizona, USA, September 2015. EF Legara and C Monterola, arXiv:1509.01199 [physics.soc-ph].
- 21. m-slice (1min) h-slice (1hr) 1 2 3 4 4 AM 12 Midnight12 Noon … 6 PM 0 6051 … 0 602 h = 3 h = 4 … 0 6042 … 0 17 h = 15 h = 16 5953 15 16 Δρ = 9 Δρ = 18 Δρ = 17 Δρ = 6 17 18 19 20 14weeks WeekdaysSaturdaysSundays 42weeks 20 hours Hypothetical Journeys EF Legara and C Monterola, "Identifying Passenger Type from Travel Routine," 2015 Conference on Complex Systems, Phoenix, Arizona, USA, September 2015. EF Legara and C Monterola, arXiv:1509.01199 [physics.soc-ph], 2015.
- 22. Eigentravel Matrix (“travel DNA”) 1 Matrix :: 840 Features EF Legara and C Monterola, "Identifying Passenger Type from Travel Routine," 2015 Conference on Complex Systems, Phoenix, Arizona, USA, September 2015. EF Legara and C Monterola, arXiv:1509.01199 [physics.soc-ph].
- 23. GBM1 DRF1 SVM Train the ML models Output Input Adult Eigentravel Matrix (“travel DNA”) EF Legara and C Monterola, "Identifying Passenger Type from Travel Routine," 2015 Conference on Complex Systems, Phoenix, Arizona, USA, September 2015. EF Legara and C Monterola, arXiv:1509.01199 [physics.soc-ph].
- 24. GBM1 DRF1 SVM Train the ML models Output Input Child Eigentravel Matrix (“travel DNA”) EF Legara and C Monterola, "Identifying Passenger Type from Travel Routine," 2015 Conference on Complex Systems, Phoenix, Arizona, USA, September 2015. EF Legara and C Monterola, arXiv:1509.01199 [physics.soc-ph].
- 25. GBM1 DRF1 SVM Train the ML models Output Input Senior Eigentravel Matrix (“travel DNA”) EF Legara and C Monterola, "Identifying Passenger Type from Travel Routine," 2015 Conference on Complex Systems, Phoenix, Arizona, USA, September 2015. EF Legara and C Monterola, arXiv:1509.01199 [physics.soc-ph].
- 26. Eigentravel Matrices GBM DRF SVM 50%-50% Standard accuracy: 41% , which 25% better than proportional chance criteria 1 Matrix :: 840 Features SCORE 76% 72% 64% Models Trained! EF Legara and C Monterola, "Identifying Passenger Type from Travel Routine," 2015 Conference on Complex Systems, Phoenix, Arizona, USA, September 2015. EF Legara and C Monterola, arXiv:1509.01199 [physics.soc-ph].
- 27. Feature Importance weekdays weekdays Take mean across hours PEAK HOUR PEAK HOUR Top predictor variables are outside peak hours. v3 v8 v11 v12 EF Legara and C Monterola, "Identifying Passenger Type from Travel Routine," 2015 Conference on Complex Systems, Phoenix, Arizona, USA, September 2015. EF Legara and C Monterola, arXiv:1509.01199 [physics.soc-ph].
- 28. v3" v8" v11" v12" Predictor Hour of Day Isolated Curve Remarks v3 0600 hours C-curve Children dominate travel demand Travel demand highest and narrowest v8 1100 hours S-curve Working adults in offices Children/students in classes Elderlies travelling v11 v12 1400 hours 1500 hours A-curve Working adults in offices Elderlies travelling Children/students travelling home Results
- 29. • Characterized passengers: Adults, Senior, Children • People are predictable (to some extent) • Established method to construct distinct commuter matrices • Travel start time • Travel duration • Mode of transport • Built ML models from 840 features and estimated variables importances – GBM (76%), DRF (72%), and SVM (64%) • Weekday travel features are better predictorsthan weekends. Case Summary
- 30. Howdoesland-use designaffecttraveldemand? Case Study2 Interpreting land-use and amenities in public transit ridership: implications in urban planning, N. Hu, E.F. Legara, K.K. Lee and C. Monterola, submitted 2015.
- 31. • Evaluate the impact of urban entities (land-use and amenities) to ridership. • What are the specific infrastructure or amenity types to build to improve mobility of citizens? • Develop a decision-support tool to assess impacts of land-use configurations on ridership; evaluate “what-if” scenarios) Motivation
- 32. • 1 week travel data (anonymised) • Tap-in and tap-out Datasets Smart Fare Card Tap In Tap Out
- 34. Datasets Land Use Plan Source: http://www.mnd.gov.sg/LandUsePlan/theme/default/image/hme_our_land_use_plan.jpg Source: http://100pp.com.sg/images/LAnd%20Use%20Plan%20to%20Support%20Singapore.jpg Source: A High Quality Living Environment for All Singaporeans: Land Use Plan to Support Singapore’s Future Population, January 2013
- 35. Land Use Plan (LUP) Greeneries Amenities LUP + Greeneries Land Use and Amenities N. Hu, E.F. Legara, K.K. Lee, and C. Monterola, " Interpreting land-use and amenities in public transit ridership: implications in urban planning," submitted 2015.
- 37. Transport Points & Amenities
- 38. Feature Selection Residential Business Industrial Greenery Sustenance Education Transit Finance Healthcare Entertainment Commercial Other Water Other Which land-use feature ultimately dictates the number of tap-ins and tap-outs within a station? N. Hu, E.F. Legara, K.K. Lee, and C. Monterola, " Interpreting land-use and amenities in public transit ridership: implications in urban planning," submitted 2015.
- 39. Use the surrounding urban entities to estimate travel demand (# of tap-ins and # of tap-outs). Prediction N. Hu, E.F. Legara, K.K. Lee, and C. Monterola, " Interpreting land-use and amenities in public transit ridership: implications in urban planning," submitted 2015.
- 40. Scenario Modeling “Conceptual Plan” (2030) Hypothetical Amenity Increase N. Hu, E.F. Legara, K.K. Lee, and C. Monterola, " Interpreting land-use and amenities in public transit ridership: implications in urban planning," submitted 2015.
- 41. Scenario Modeling: Results Amenity Increase N. Hu, E.F. Legara, K.K. Lee, and C. Monterola, " Interpreting land-use and amenities in public transit ridership: implications in urban planning," submitted 2015.
- 42. The BigPicture
- 44. Scenario Modeling: Mathematical + ABM + ML • EF Legara, KK Lee, GG Hung, and C Monteorla, "Mechanism-based model of a mass rapid transit system: A perspective," Int. J. Mod. Phys. Conf. Ser. 36, 1560011, 2015. • N Othman, EF Legara, V Selvam, and C Monterola, "A Data-Driven Agent-Based Model of Congestion and Scaling Dynamics of Rapid Transit Systems," J of Computational Science (2015). • EF Legara, C Monterola, KK Lee, GG Hung, "Critical capacity, travel time delays and travel time distribution of rapid mass transit systems," Physica A 406, pp. 100-106 (2014).
- 45. Summary
- 46. Summary Observations Reconstruct Observations Scenario Modeling + An IterativeProcessAdapted from: A. Vespignani and FuturICT
- 47. “Essentially, all models are wrong, but some are useful." “We are not in the business of predicting the EXACT futures.“ -EFL
- 48. Humans + Machines: Using artificial intelligence to power your people February 18 - 19, 2016 | Penthouse, TwentyFour Seven McKinley Machine Learning and the Smart City Erika Fille T. Legara, Ph.D. | @eflegara www.erikalegara.net Scientist,Complex Systems Institute of High Performance Computing