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]
Route Planning
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]
PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning(2018), Guillaume Sartoretti et al. [pdf]
ETA
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 Estimation and Forecasting
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]
Dispatching
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]
Rebalancing Problem
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]
Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning(2021), Yan Jiao et al. [pdf]
Graph
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]
Supply and Demand Forecasting
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]
Electric Vehicle
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]
Platform
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]
Scheduling Optimization
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]
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