Instructor: Yi Ding
Office: ECSS 4.703
Office hours: 4:30 pm - 5:30 pm, Tuesday
Lecture: 5:30 pm - 6:45 pm, Tuesday/Thursday
Location: FN 2.102
Empowered by rich data collected from various infrastructures in our cities and machine learning techniques, our cities are becoming “smarter”. In this course, we discuss how data science and other computer science technologies are used to innovate our cities. We cover topics such as urban sensing, data-driven modeling and analytics for smart city services, data-driven decision-making, and also some speical and novel topics like environment, LLM, privacy, and computational social science. We will also use Singapore as an example to show how these technologies are adopted in a modern city. Students are expected to (i) read and present research papers from top conferences, (ii) participate in discussions of the papers, and (iii) design, implement, and present their ideas for the final class project.
Course Learning Objectives
By the end of this course, you will be able to
- Understand the basic principle underlying data science and related computer science technologies (e.g., IoT, Cyber-Physical Systems) for smart cities;
- Explain the state-of-the-art research in this area;
- Demonstrate ideas for smart cities;
- Implement ideas based on real-world data using tools including but not limited to data analytics, machine learning, statistics, data visualization, etc.
CS 1336 and (STAT 3355 or CS 4375) OR other equivalent courses.
No books are required. All the materials will be online.
Reading summary: 25%
Topic presentations: 15%
Class projects: 50% (10% for Proposal Reports, 10% for Proposal Presentation, 20% for Final Reports, 10% for Final Presentation)
Course Schedule (Tentative)
W1: Course Introduction & Guidance on Paper Reading and Presentation (08/22, 08/24)
- Smart cities study in general
- Smart cities under the framework of Cyber-Physical Systems
- IoT and CPS
W2: Data-Driven Smart Cities: Sensing (08/29, 08/31)
- W2-Thurs-Sample Ding, Yi, et al. “Nationwide Deployment and Operation of a Virtual Arrival Detection System in the Wild”, ACM SIGCOMM 2021
- W2-Thurs-1 Zhou, Pengfei, et al. “IODetector: A Generic Service for Indoor Outdoor Detection”, ACM SenSys 2012
- W2-Thurs-2 Ma, Yunfei, et al. “Drone relays for battery-free networks”,ACM SIGCOMM 2017
- W2-Thurs-3 Adib, Fadel, et al. “See Through Walls with Wi-Fi!”, ACM SIGCOMM 2013
- W2-Thurs-4 Ahmed, Fawad, et al. “CarMap: Fast 3D Feature Map Updates for Automobiles”, USENIX NSDI 2020
- W2-Thurs-5 He, Yuze, et al. “AutoMatch: Leveraging Traffic Camera to Improve Perception and Localization of Autonomous Vehicles”, ACM SenSys 2022
W3: Data-Driven Smart Cities: Prediction & Introduction on Final Project (09/05, 09/07)
- W3-Tue-Sample Zhang, Yan, et. al. ‘“Route Prediction for Instant Delivery”, ACM IMWUT 2019
- W3-Tue-1 Yu, Fudan, et. al. “Spatio-Temporal Vehicle Trajectory Recovery on Road Network Based on Traffic Camera Video Data”, ACM SIGKDD 2022
- W3-Tue-2 Geng, Xu, et. al. “Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting”, AAAI 2019
- W3-Tue-3 Jiang, Renhe, et. al. “DeepUrbanMomentum: An Online Deep-Learning System for Short-Term Urban Mobility Prediction”, AAAI 2018
- W3-Thurs-1 Hong, Ye, et. al. “How do you go where? Improving next location prediction by learning travel mode information using transformers”, ACM SIGSPATIAL 2022
- W3-Thurs-2 Qin, Zhou, et. al. “CellPred: A Behavior-aware Scheme for Cellular Data Usage Prediction”, ACM IMWUT 2020
- W3-Thurs-3 Tran, Luan, et. al. “DeepTRANS : A Deep Learning System for Public Bus Travel Time Estimation using Traffic Forecasting”, VLDB 2020
W4: Data-Driven Smart Cities: Decision-Making (09/12, 09/14)
- W4-Tue-Sample Hitchhiking
- W4-Tue-1 Rizzo, Stefano, et. al. “Time Critic Policy Gradient Methods for Traffic Signal Control in Complex and Congested Scenarios”, ACM SIGKDD 2019
- W4-Tue-2 Ji, Shenggong, et. al. “A Deep Reinforcement Learning-Enabled Dynamic Redeployment System for Mobile Ambulances”, ACM IMWUT 2019
- W4-Tue-3 Delarue, Arthur, et. al. “Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing”, NeurIPS 2020
- W4-Thurs-1 Guo, Baoshen, et. al. “Concurrent Order Dispatch for Instant Delivery with Time-Constrained Actor-Critic Reinforcement Learning”, IEEE RTSS 2021
- W4-Thurs-2 Wang, Guang, et. al. “bCharge: Data-Driven Real-Time Charging Scheduling for Large-Scale Electric Bus Fleets”, IEEE RTSS 2018
- W4-Thurs-3 Wei, Yu, et. al. “City Metro Network Expansion with Reinforcement Learning”, ACM SIGKDD 2020
W5: Proposal Presentation (9/19, 9/21)
W6: Fundamental Topic: Localization and Navigation (09/26, 09/28)
- W6-Tue-1 Ni, Jiazhi, et. al. “Experience: Pushing Indoor Localization from Laboratory to the Wild”, ACM MobiCom 2022
- W6-Tue-2 Wang, Mei, et. al. “MAVL: Multiresolution Analysis of Voice Localization”, USENIX NSDI 2021
- W6-Tue-3 Lu, Chris, et. al. “Simultaneous Localization and Mapping with Power Network Electromagnetic Field”, ACM MobiCom 2018
- W6-Thurs-1 Liu, Xiaochen, et. al. “TAR - Enabling Fine-Grained Targeted Advertising in Retail Stores”, ACM MobiSys 2018
- W6-Thurs-2 Liu, Song, et. al. “SmartLight: Light-weight 3D Indoor Localization Using a Single LED Lamp”, ACM SenSys 2017
- W6-Thurs-3 Cao, Yifeng, et. al. “ITrackU: Tracking a Pen-like Instrument via UWB-IMU Fusion”, ACM MobiSys 2021
W7: Fundamental Topic: Transportation (10/03, 10/05)
- W7-Tue-1 Sonntag, Jonas, et. al. “Predicting Parking Availability from Mobile Payment Transactions with Positive Unlabeled Learning.” AAAI 2021
- W7-Tue-2 Ruan, Sijie, et. al. “Learning to Generate Maps from Trajectories”, AAAI 2020
- W7-Tue-3 Liao, Bingbing, et. al. “Deep Sequence Learning with Auxiliary Information for Traffic Prediction”, ACM SIGKDD 2018
- W7-Thurs-1 Hulot, Pierre, et. al. “Towards Station-Level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems”, ACM SIGKDD 2018
- W7-Thurs-2 Chen, Liyue, et. al. “A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management”, ACM SIGKDD 2023
- W7-Thurs-3 He, Suining, et. al. “Spatio-Temporal Capsule-based Reinforcement Learning for Mobility-on-Demand Network Coordination”, ACM WWW 2019
W8: Fundamental Topic: Privacy and Security (10/10, 10/12)
- W8-Tue-1 Saleheen, Nazir, et. al. “WristPrint: Characterizing User Re-identification Risks fromWrist-worn Accelerometry Data” ACM CCS 2021
- W8-Tue-2 Gursoy, Mehmet, et. al. “Utility-Aware Synthesis of Differentially Private and Attack-Resilient Location Traces”, ACM CCS 2018
- W8-Tue-3 Jin, Haojian, et. al. “Exploring the Needs of Users for Supporting Privacy-Protective Behaviors in Smart Homes”, ACM CHI 2022
- W8-Thur-1 Fang, Zhihan, et. al. “PrivateBus: Privacy Identification and Protection in Large-Scale BusWiFi Systems”, ACM IMWUT 2020
- W8-Thur-2 Gao, Di, et. al. “A Nationwide Census on WiFi Security Threats: Prevalence, Riskiness, and the Economics”, ACM MobiCom 2021
- W8-Thur-3 Nguyen, Kien, et. al. “Spatial Privacy Pricing: The Interplay between Privacy, Utility and Price in Geo-Marketplaces”, ACM SIGSPATIAL 2020
W9: Special Topic: Climate and Environment (10/17, 10/19)
- W9-Tue-1 Kikstra, Jarmo, et al. “Climate mitigation scenarios with persistent COVID-19-related energy demand changes.” Nature Energy 2021.
- W9-Tue-2 Dang, Tuan, et. al. “IoTree: A Battery-free Wearable System with Biocompatible Sensors for Continuous Tree Health Monitoring”, ACM MobiCom 2022
- W9-Tue-3 Ding, Jian, et. al. “Towards Low Cost Soil Sensing Using Wi-Fi”, ACM MobiCom 2019
- W9-Thurs-1 Zheng, Yu, et. al. “U-Air: when urban air quality inference meets big data”, ACM SIGKDD 2013
- W9-Thurs-2 Ding, Daizong, et. al. “Modeling Extreme Events in Time Series Prediction”, ACM SIGKDD 2019
- W9-Thurs-3 Kumar, Peeyush, et. al. “Micro-climate Prediction - Multi Scale Encoder-decoder based Deep Learning Framework”, ACM SIGKDD 2021
W10: Special Topic: LLM for Smart Cities (10/24, 10/26)
- W10-Tue-1 Horikomi, Taizo, et. al. “Generating Individual Trajectories Using GPT-2 Trained from Scratch on Encoded Spatiotemporal Data”, arXiv, 2023
- W10-Tue-2 Huang, Jizhou, et. al. “ERNIE-GeoL: A Geography-and-Language Pre-trained Model and its Applications in Baidu Maps”, ACM SIGKDD 2022
- W10-Tue-3 Lin, Yan, et. al. “Pre-training Context and Time Aware Location Embeddings from Spatial-Temporal Trajectories for User Next Location Prediction”, AAAI 2021
- W10-Thurs-1 Liang, Yuxuan, et. al. “TrajFormer: Efficient Trajectory Classification with Transformers” ACM CIKM 2022
- W10-Thurs-2 Jo, Eunkyung, et. al. “Understanding the Benefits and Challenges of Deploying Conversational AI Leveraging Large Language Models for Public Health Intervention”, ACM CHI 2023
- W10-Thurs-3 Nguyen, Tuan-Phong, et. al. “Extracting Cultural Commonsense Knowledge at Scale”, ACM WWW 2023
W11: Special Topic: Human System Synergy (10/31, 11/02)
- W11-Thurs-1 Seetharaman, Bhavani, et al. “Delivery Work and the Experience of Social Isolation.” ACM CSCW 2021.
- W11-Tue-2 Kinder, Eliscia, et al. “Gig platforms, tensions, alliances and ecosystems: An actor-network perspective.” ACM CSCW 2019.
- W11-Tue-3 Matsubara, Masaki, et al. “ Task Assignment Strategies for Crowd Worker Ability Improvement” ACM CSCW 2021.
- W11-Thurs-1 Tschandl, Philipp, el. al. “Human–computer collaboration for skin cancer recognition” Nature Medicine 2020
- W11-Thurs-2 Chai, Chengliang, et. al. “Human-in-the-loop Outlier Detection”, ACM SIGMOD 2020
- W11-Thurs-3 Lin, Chuan-en, et al. “ARchitect: Building Interactive Virtual Experiences from Physical Affordances by Bringing Human-in-the-Loop”, ACM CHI
W12: Special Topic: Computational Social Science (11/07, 11/09)
- W12-Tue-Sample Moro, Esteban, et al. “Mobility patterns are associated with experienced income segregation in large US cities.” Nature Communications 2021.
- W12-Tue-1 Gonzalez, Marta, et al. “Understanding individual human mobility patterns.” Nature 2008.
- W12-Tue-2 Song, Chaoming, et al. “Limits of Predictability in Human Mobility.” Nature 2010.
- W12-Tue-3 Jia, Jayson, et al. “Population flow drives spatio-temporal distribution of COVID-19 in China.” Nature 2020.
- W12-Tue-4 Alessandretti, Laura, et al. “The scales of human mobility.” Nature 2020.
- W12-Tue-5 Chang, Serina, et al. “Mobility network models of COVID-19 explain inequities and inform reopening.” Nature 2021.
- W12-Thurs-Sample TBD
- W12-Thurs-2 Hunter, Ruth, et al. “Effect of COVID-19 response policies on walking behavior in US cities.” Nature Communications 2021.
- W12-Thurs-4 Asensio, Omar, et al. “Impacts of micromobility on car displacement with evidence from a natural experiment and geofencing policy.” Nature Energy 2022
- W12-Thurs-5 Yabe, Takahiro, et al. “Behavioral changes during the pandemic worsened income diversity of urban encounters.” Nature Communications 2023.
W13: Smart City in the Real World: Singapore (11/14, 11/16)
- W13-Tue-Sample Cao, Chu, et al. “Walkway Discovery from Large Scale Crowdsensing.” ACM/IEEE IPSN 2018
- W13-Tue-1 Jiang, Shan, et al. “Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore” ACM UbiComp 2015
- W13-Tue-2 Poonawala, Hasan, et al. “Singapore in Motion: Insights on Public Transport Service Level Through Farecard and Mobile Data Analytics.” ACM SIGKDD 2016
- W13-Tue-3 Tachet, R., et al. “Scaling Law of Urban Ride Sharing.” Scientific Report 2017
- W13-Tue-4 Lau, Billy, et. al. “Sensor Fusion for Public Space Utilization Monitoring in a Smart City”, IEEE IoT Journal, 2018
- W13-Tue-5 Kandappu, Thivya, et al. “A Feasibility Study on Crowdsourcing to Monitor Municipal Resources in Smart Cities.” ACM WWW 2018
- W13-Thurs-Sample Li, Yi, et al. “Urban Region Representation Learning with OpenStreetMap Building Footprints” ACM SIGKDD 2019
- W13-Thurs-1 Meegahapola, Lakmal, et al. “BuSCOPE : Fusing Individual & Aggregated Mobility Behavior for “Live” Smart City Services.” ACM MobiSys 2019
- W13-Thurs-2 Dong, Lei, et al. “Predicting neighborhoods’ socioeconomic attributes using restaurant data.” PNAS 2019
- W13-Thurs-3 Sahoo, Doyen, et al. “FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging” ACM SIGKDD 2019
- W13-Thurs-4 Lu, Yu, et. al. “TourSense: A Framework for Tourist Identification and Analytics Using Transport Data”, IEEE TKDE, 2019
- W13-Thurs-5 Hou, Yuting, et al. “Exploring built environment correlates of older adults’ walking travel from lifelogging images” Transportation Research Part D 2021
W14 (No Classes): Fall Break and Thanksgiving (11/21, 11/23)
W15: Emerging Technologies and Applications (11/28, 11/30)
- W15-Tue-1 Li, Danyang, et. al. “Motion Inspires Notion: Self-supervised Visual-LiDAR Fusion for Environment Depth Estimation”, ACM MobiSys 2022
- W15-Tue-2 Li, Yuanjie, et. al. “A Networking Perspective on Starlink’s Self-Driving LEO Mega-Constellation”, ACM MobiCom 2023
- W15-Tue-3 Shi, Shuyao, et. al. “VIPS: Real-Time Perception Fusion for Infrastructure-Assisted Autonomous Driving”, ACM MobiCom 2022
- W15-Thurs-1 Yuan, Xinjie, et. al. “Understanding 5G Performance for Real-world Services: a Content Provider’s Perspective”, ACM SIGCOMM 2022
- W15-Thurs-2 Guo, Baoshen, et. al. “Towards Equitable Assignment: Data-Driven Delivery Zone Partition at Last-mile Logistics”, ACM SIGKDD 2023
- W15-Thurs-3 Sharp, Jonathan, et. al. “Authentication for Drone Delivery Through a Novel Way of Using Face Biometrics”, ACM MobiCom 2022