出行机器学习相关（韩国科学家创建）

• Machine Learning / Deep Learning Contents in Mobility Industry(Transportation)
  • I collected it for the purpose of studying
  • I selected paper with at least 10 citations or latest paper
• Made by Seongyun Byeon working at SOCAR(Korea Car Sharing Company)
• Last modified date : 20.03.24

• Mobility Company List
• Tech Blog
• Presentation
• Data
• Map Matching
• Route Planning
• ETA
• Traffic Estimation and Forecasting
• Dispatching
• Dynamic Pricing
• Rebalancing Problem
• Graph
• Supply and Demand Forecasting
• Electric Vehicle
• Platform
• Scheduling Optimization
• Autonomous Vehicle
• Bike Sharing

• Aotonomous Vehicle and Mobility Acquisition/Investment/Teams-Up Network - Doowon Cha

• A Map of Mobility Service in Korea - Doowon Cha

• Landscape of Mobility Industry - Korean Autonomous Vehicle Industry

• Uber Engineering Blog
• Lyft Engineering Blog
• Grab Tech
• GO-JEK Tech
• Kakao Brain(Only Korea)
• VCNC Tech Blog(Only Korea)

• DiDi : Deep Reinforcement Learning with Applications in Transportation
• DiDi : Artificial Intelligence in Transportation

• Awesome Public Datasets(Transportation), [Github]
• Google Dataset Search, [Car Sharing], [Ride Hailing]
• Highway Tollgates Traffic Flow Prediction(KDD Cup 2017), [URL]
• Uber Movement(Uber), [URL]
• NYC Taxi DATA(NYC), [URL]
• Next Generation Simulation(Federal Highway Administration), [URL]
• GAIA Open Dataset(DiDi Chuxing) : Trajectory Data, [URL]
• BSS Dataset(Consumer Data Research Centre), [URL]
• Awesome Transportation Network Data

• Some map matching algorithms for personal navigation assistants(2000), Christopher E. White. [pdf]
• On map-matching vehicle tracking data(2005), Sotiris Brakatsoula et al. [pdf]
• Map Matching with Travel Time Constraints(2006), John Krumm et al. [pdf]
• Hidden Markov map matching through noise and sparseness(2009), Paul Newson et al. [pdf]
• Map-matching for low-sampling-rate GPS trajectories(2009), Yin Lou et al. [pdf]
• Online map-matching based on Hidden Markov model for real-time traffic sensing applications(2012), C.Y. Goh, J. Dauwels et al. [pdf]
• Large-Scale Joint Map Matching of GPS Traces(2013), Yang Li et al. [pdf]
• Map Matching with Inverse Reinforcement Learning(2013), T. Osogami et al. [pdf]

• Contraction hierarchies: Faster and simpler hierarchical routing in road networks(2008), R. Geisberger et al. [pdf]
• Customizable Route Planning in Road Networks(2013), Daniel Delling et al. [pdf]
• Route Planning in Transportation Networks(2015), Hannah Bast et al. [pdf]
• Modeling Trajectories with Recurrent Neural Networks(2017), H Wu et al. [pdf]
• Imagination-Augmented Agents for Deep Reinforcement Learning(2017), T. Weber et al. [pdf]
• Learning to navigate in cities without a map(2018), Piotr Mirowski et al. [pdf]
• A Unified Approach to Route Planning for Shared Mobility(2018), Yongxin Tong et al. [pdf]

• Estimation Time Arrival
• Traffic Estimation And Prediction Based On Real Time Floating Car Data(2008), Corrado de Fabritiis et al. [pdf]
• Travel time estimation for urban road networks using low frequency probe vehicle data(2013), Erik Jenelius et al. [pdf]
• Travel time estimation of a path using sparse trajectories(2014), Yilun Wang et al. [pdf]
• Learning to estimate the travel time(2018), Zheng Wang et al(DiDi AI Labs). [pdf]
• Multi-task Representation Learning for Travel Time Estimation(2018), Yaguang Li et al(DiDi AI Labs). [pdf]
• When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks(2018), Dong Wang et al. [pdf]

• Traffic flow theory and control(1968), Donald R Drew, [pdf]
• Dynamic Prediction of Traffic Volume Through Kalman Filtering Theory(1984), Okutani et al. [pdf]
• Predicting time series with support vector machines(1991), Muller et al. [pdf]
• Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results(2003), Billy M et al. [pdf]
• Travel-time prediction with support vector regression(2004), Wu et al. [pdf]
• Gaussian processes for short-term traffic volume forecasting(2010), Xie et al. [pdf]
• Road Traffic Prediction with Spatio-Temporal Correlations(2011), Wanli Min et al. [pdf]
• Utilizing real-world transportation data for accurate traffic prediction(2012), Pan et al. [pdf]
• A k-nearest neighbor locally weighted regression method for short-term traffic flow forecasting(2012), Li S et al. [pdf]
• Traffic Flow Prediction With Big Data: A Deep Learning Approach(2015), Lv Y et al. [pdf]
• SMiler: A Semi-Lazy Time Series Prediction System for sensors(2015), Zhou et al. [pdf]
• Latent Space Model for Road Networks to Predict Time-Varying Traffic(2016), Deng, D et al.[pdf]
• Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting(2017), Li Y et al. [paer]
• Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction(2017), Ma X et al. [pdf]
• Diffusion Convolutional Recurrent Neural Network: Data-driven Traffic Forecasting(2018), Li Y et al. [pdf]
• Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models(2018), Ma X et al. [pdf]
• Short-Term Traffic Forecasting: Modeling and Learning Spatio-Temporal Relations in Transportation Networks Using Graph Neural Networks(2015), B Shahsavari [pdf]

• Design and Modeling of Real-time Shared-Taxi Dispatch Algorithms(2013), J Jun et al. [pdf]
• Large-Scale Order Dispatch in On-Demand Ride-Sharing Platforms: A Learning and Planning Approach(2018), Zhe Xu et al(DiDi AI Labs). [pdf]
• Order Dispatch in Price-aware Ridesharing(2018), Libin Zheng et al. [pdf]
• Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning(2019), Minne Li et al(DiDi Research). [pdf]
• Dynamic Pricing and Matching in Ride-Hailing Platforms(2018), Nikita Korolko et al(Uber Technologies). [pdf]
• DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning(2019), Abubakr Alabbasi et al. [pdf]
• Deep Reinforcement Learning for Ride-sharing Dispatching and Repositioning(2019), Zhiwei Qin et al. [pdf]
• Employee Ridesharing: Reinforcement Learning and Choice Modeling(2019), Wangcheon Yan et al. [pdf]

• Vehicle Sharing System Pricing Optimization(2013), A Waserhole. [pdf]
• Pricing in Ride-share Platforms: A Queueing-Theoretic Approach(2015), Carlos Riquelme et al. [pdf]
• Dynamic Pricing in Ridesharing Platforms(2015), [pdf], [video]
• Dynamic Pricing and Matching in Ride-Hailing Platforms(2018), Nikita Korolko et al(Uber Technologies). [pdf]
• Dynamic Pricing in Shared Mobility on Demand Service(2018), Han Qiu et al. [pdf]

• Framework for automated taxi operation: The family model(2016), Michal Kümmel, [pdf]
• The bike sharing rebalancing problem: Mathematical formulations and benchmark instances(2014), Mauro Dell [link]
• An Exact Algorithm for the Static Rebalancing Problem arising in Bicycle Sharing Systems(2015), G Erdoğan, [link]
• Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization(2016), J Liu [pdf], [video]
• A Heuristic algorithm for a single vehicle static bike sharing rebalancing problem(2016), Fabio Cruz [pdf]
• Rebalancing shared mobility-on-demand systems: A reinforcement learning approach(2017), Jian Wen et al. [pdf]
• A Dynamic Approach to Rebalancing Bike-Sharing Systems(2018), Frederico Chiariotti [pdf]
• Towards Stations-level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems(2018), Pierre Hulot [pdf]
• A Rebalancing Strategy for the Imbalance Problem in Bike-Sharing Systems(2019), Peiyu et al. [pdf]
• A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems (2018), Pan et al. [link]

• Short-Term Traffic Forecasting: Modeling and Learning Spatio-Temporal Relations in Transportation Networks Using Graph Neural Networks(2015), B Shahsavari [pdf]
• Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting(2018), Zhiyong Cui [pdf]
• Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search(2018), Z Li [pdf]
• Optimal Transport for structured data with application on graphs(2019), Titouan Vayer [pdf]

• The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms(2017), Tong et al. [pdf]
• Supply-demand Forecasting For a Ride-Hailing System(2017), Wang, Runyi. [pdf]
• Predicting Short-Term Uber Demand Using Spatio-Temporal Modeling: A New York City Case Study(2017), Sabiheh Sadat et al. [pdf]
• Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction(2016), Zhang et al. [pdf]
• Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach(2017), Jintao Ke et al. [pdf]
• Predicting citywide crowd flows using deep spatio-temporal residual networks(2017), Zhang et al. [pdf]
• Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction(2018), Yao et al. [pdf]
• Forecasting Taxi Demands with Fully Convolutional Networks and Temporal Guided Embedding(2018), Doyup Lee et al(Kakao Brain). [pdf], [blog #1], [blog #2]

• A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads(2014), Zhile Yang et al. [pdf]
• A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development(2017), F Un-Noor et al. [pdf]
• Planning of Electric Vehicle Charging Infrastructure for Urban Areas with Tight Land Supply(2018), C Guo et al. [pdf]
• Optimal Allocation Model for EV Charging Stations Coordinating Investor and User Benefits(2018), Youbo Lie et al. [pdf]

• Flexible Dynamic Task Assignment in Real Time Spatial Data(2017), Yongxin Tong et al. [pdf]
• Ride-Hailing Networks with Strategic Drivers: The Impact of Platform Control Capabilities on Performance(2018), Philipp et al. [pdf]

• Constraint Programming for Scheduling(2004), John et al. [pdf]
• Scheduling problem using genetic algorithm, simulated annealing and the effects of parameter values on GA performance(2006), A Sadegheih. [pdf]
• Scheduling part-time personnel with availability restrictions and preferences to maximize employee satisfaction(2008), S Mohan et al. [pdf]
• Genetic Algorithms For Shop Scheduling Problems : A Survey(2011), Frank Werner. [pdf]
• Scheduling part-time and mixed-skilled workers to maximize employee satisfaction(2012), M Akbari et al. [pdf]
• Optimization of Scheduling and Dispatching Cars on Demand(2014), Vu Tran. [pdf]
• Vehicle Relocation Scheduling Method for Car Sharing Service System based on Markov Chain and Genetic Algorithm (2018), Tingying Song et al. [pdf]
• Uber Driver Schedule Optimization(2018), Ivan Zhou. [blog]

• Awesome Autonomous Vehicles, [Github]
• Deep Autonomous Driving Papers, [Github]

• Bicycle-sharing system, [Wikipedia]
• Bike-sharing: History, Impacts, Models of Provision, and Future(2009), Paul DeMaio. [pdf]
• Bicycle-Sharing Schemes: Enhancing Sustainable Mobility in Urban Areas(2011), P Midgley et al. [pdf]
• Static repositioning in a bike-sharing system: models and solution approaches(2013), Tal Raviv et al. [pdf]
• Bicycle sharing systems demand(2014), I Frade et al. [pdf]
• Incentivizing Users for Balancing Bike Sharing Systems(2015), A Singla et al. [pdf]
• Mobility Modeling and Prediction in Bike-Sharing Systems(2016), Z Yang et al. [pdf]
• A Dynamic Approach to Rebalancing Bike-Sharing Systems(2018), Frederico Chiariotti [pdf]

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