Simplification

• Traditional Methods
  • Batch Mode
    • Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature
    • Direction-preserving trajectory simplification
  • Online Mode
    • An online algorithm for segmenting time series
    • Spatiotemporal compression techniques for moving point objects
  • Semantic-based
    • Trace: Realtime compression of streaming trajectories in road networks
• Deep Learning Methods
  • Trajectory simplification with reinforcement learning
  • A Lightweight Framework for Fast Trajectory Simplification
  • Error-bounded Online Trajectory Simplification with Multi-agent Reinforcement Learning
  • Collectively simplifying trajectories in a database: A query accuracy driven approach

Recovery

• Traditional Methods
  • Kinematic interpolation of movement data
  • A comparison of two methods to create tracks of moving objects: linear weighted distance and constrained random walk
  • Interpolation of animal tracking data in a fluid environment
• Deep Learning Methods
  • Free-space based
    • Deep trajectory recovery with fine-grained calibration using kalman filter
    • Attnmove: History enhanced trajectory recovery via attentional network
    • Periodicmove: shift-aware human mobility recovery with graph neural network
    • Trajbert: Bert-based trajectory recovery with spatial-temporal refinement for implicit sparse trajectories
    • Teri: An effective framework for trajectory recovery with irregular time intervals
  • Non Free-space based
    • Mtrajrec: Map-constrained trajectory recovery via seq2seq multitask learning
    • Rntrajrec: Road network enhanced trajectory recovery with spatial-temporal transformer
    • Visiontraj: A noise-robust trajectory recovery framework based on large-scale camera network
    • Learning semantic behavior for human mobility trajectory recovery
    • Traj2traj: A road network constrained spatiotemporal interpolation model for traffic trajectory restoration
    • Patr: Periodicity-aware trajectory recovery for express system via seq2seq model
  • Road Networks
    • Learning to generate maps from trajectories
    • Multimodal deep learning for robust road attribute detection
    • Aerial images meet crowdsourced trajectories: a new approach to robust road extraction
    • Deepdualmapper: A gated fusion network for automatic map extraction using aerial images and trajectories
    • Df-drunet: A decoder fusion model for automatic road extraction leveraging remote sensing images and GPS trajectory data
    • Delvmap: Completing residential roads in maps based on couriers’ trajectories and satellite imagery

Map-Matching

• Traditional Methods
  • Road reduction filtering for GPS-gis navigation
  • A general map matching algorithm for transport telematics applications
  • Map-matching in complex urban road networks
  • Fast map matching, an algorithm integrating hidden markov model with precomputation
• Deep Learning Methods
  • Deepmm: Deep learning based map matching with data augmentation
  • Transformer-based mapmatching model with limited labeled data using transfer-learning approach
  • L2mm: learning to map matching with deep models for low-quality GPS trajectory data
  • Graphmm: Graph-based vehicular map matching by leveraging trajectory and road correlations
  • Dmm: Fast map matching for cellular data
  • Map-matching on wireless traffic sensor data with a sequence-to-sequence model
  • Fl-amm: Federated learning augmented map matching with heterogeneous cellular moving trajectories

Storage Database

• Trajectory Management Systems
  • Sharkdb: An in-memory column-oriented trajectory storage
  • Elite: an elastic infrastructure for big spatiotemporal trajectories
  • Dragoon: a hybrid and efficient big trajectory management system for offline and online analytics
  • Trajmesa: A distributed nosql-based trajectory data management system
• Vector Databases
  • Vector-based trajectory storage and query for intelligent transport system
  • Ghost: A general framework for high-performance online similarity queries over distributed trajectory streams

Index & Query

• Traditional index
  • Trajectory similarity join in spatial networks,
  • Distributed Trajectory Similarity Search
  • Distributed In-Memory Trajectory Similarity Search and Join on Road Network
  • Trass: Efficient trajectory similarity search based on key-value data stores
• Deep Learning Methods
  • Effectively Learning Spatial Indices
  • The Case for Learned Spatial Indexes
  • X-FIST: Extended Flood Index for Efficient Similarity Search in Massive Trajectory Dataset

Similarity Measurement

• Traditional Methods
  • Efficient retrieval of similar time sequences under time warping
  • Discovering Similar Multidimensional Trajectories
  • Robust and Fast Similarity Search for Moving Object Trajectories
  • Computing discrete fr ́ echet distance
  • The computational geometry of comparing shapes
• Deep Learning Methods
  • 
  • Free-Space
    • Supervised Learning-based
      • Computing Trajectory Similarity in Linear Time: A Generic Seed-guided Neural Netric learning approach
      • Trajectory Similarity Learning with Auxiliary Supervision and Optimal Matching
      • Embeddingbased Similarity Computation for Massive Vehicle Trajectory Data
      • TMN: Trajectory Matching Networks for Predicting Similarity
      • T3S: Effective Representation Learning for Trajectory Similarity Computation
      • TrajGAT: A Graph-based Long-term Dependency Modeling Approach for Trajectory Similarity Computation
    • Self-Supervised Learning-based
      • Deep Representation Learning for Trajectory Similarity Computation
      • Towards Robust Trajectory Similarity Computation: Representation-based Spatiotemporal Similarity Quantification
      • Similar Trajectory Search with Spatio-temporal Deep Representation Learning
      • Representation Learning with Multi-level Attention for Activity Trajectory Similarity Computation,
      • Efficient Trajectory Similarity Computation with Contrastive Learning
      • Trajectory Similarity Learning with Dual-Feature Attention
      • Self-supervised contrastive representation learning for large-scale trajectories
      • CSTRM: Contrastive Self-Supervised Trajectory Representation Model for Trajectory Similarity Computation
      • On Accurate Computation of Trajectory Similarity via Single Image Superresolution
      • Effective and Efficient Sports Play Retrieval with Deep Representation Learning
  • Road Network
    • Supervised Learning-based
      • A Graphbased Approach for Trajectory Similarity Computation in Spatial Networks
      • GRLSTM: Trajectory Similarity Computation with Graph-based Residual LSTM
      • Spatiotemporal Trajectory Similarity Learning in Road Networks
      • Spatial Structure-Aware Road Network Embedding via Graph Contrastive Learning
    • Self-Supervised Learning-based
      • Trembr: Exploring Road Networks for Trajectory Representation Learning
      • Lightpath: Lightweight and scalable path representation learning

Cluster Analysis

• Traditional Methods
  • A review of moving object trajectory clustering algorithms
• Deep Learning Methods
  • 
  • Multi-Stage
    • Trajectory clustering via deep representation learning
    • Trip2vec: a deep embedding approach for clustering and profiling taxi trip purposes
  • End-to-End
    • Deep trajectory clustering with autoencoders
    • Detect: Deep trajectory clustering for mobility-behavior analysis
    • E2dtc: An end to end deep trajectory clustering framework via self-training

• Traditional Methods
  • A descriptive framework for temporal data visualizations based on generalized space-time cubes
  • A survey of urban visual analytics: Advances and future directions
• Deep Learning Methods
  • Group Visualization
    • A visual analytics system for exploring, monitoring, and forecasting road traffic congestion
    • Visual abstraction of large scale geospatial origin-destination movement data
  • Individual Visualization
    • Deep learning-assisted comparative analysis of animal trajectories with deephl
    • Visualization of driving behavior based on hidden feature extraction by using deep learning
    • Deep learning detection of anomalous patterns from bus trajectories for traffic insight analysis

• Recovery & Enhancement
  • Spatio-temporal storytelling? leveraging generative models for semantic trajectory analysis
  • An exploratory assessment of llm’s potential toward flight trajectory reconstruction analysis

Location Forecasting

• Deep Learning Methods
  • Next Location Prediction
    • Deepmove: Predicting human mobility with attentional recurrent networks
    • Predicting human mobility via variational attention
    • Deeptransport: Prediction and simulation of human mobility and transportation mode at a citywide level
    • Location prediction over sparse user mobility traces using rnns
    • Mobtcast: Leveraging auxiliary trajectory forecasting for human mobility prediction
    • Artificial neural networks applied to taxi destination prediction
    • A bilstm-cnn model for predicting users’ next locations based on geotagged social media
    • A neural network approach to jointly modeling social networks and mobile trajectories
    • Context-aware deep model for joint mobility and time prediction
    • Serm: A recurrent model for next location prediction in semantic trajectories
    • Predicting the next location: A recurrent model with spatial and temporal contexts
    • Hst-lstm: A hierarchical spatial-temporal long-short term memory network for location prediction
    • Recurrent marked temporal point processes: Embedding event history to vector
    • Pre-training context and time aware location embeddings from spatial-temporal trajectories for user next location prediction
    • Mcn4rec: Multi-level collaborative neural network for next location recommendation
  • Next POI Recommendation
    • A survey on deep learning based point-of-interest (poi) recommendations
    • Point-of-interest recommender systems based on location-based social networks: a survey from an experimental perspective
  • Unfinished Route Prediction
    • A survey on service route and time prediction in instant delivery: Taxonomy, progress, and prospects
    • Package pick-up route prediction via modeling couriers’ spatial-temporal behaviors
    • Graph2route: A dynamic spatial-temporal graph neural network for pick-up and delivery route prediction

Traffic Forecasting

• Traditional Methods
  • Space–time modeling of traffic flow
  • Vector autoregressive models: specification, estimation, inference, and forecasting
  • Urban flow prediction from spatiotemporal data using machine learning: A survey
  • Recent trends in crowd analysis: A review,” Machine Learning with Applications
• Deep Learning Methods
  • Spatio-Temporal Grid
    • Deep spatio-temporal residual networks for citywide crowd flows prediction
    • Deep multi-view spatial-temporal network for taxi demand prediction
    • Spatio-temporal recurrent convolutional networks for citywide short-term crowd flows prediction
    • Periodic-crn: A convolutional recurrent model for crowd density prediction with recurring periodic patterns
    • Deepcrowd: A deep model for large-scale citywide crowd density and flow prediction
    • Urbanfm: Inferring fine-grained urban flows
    • Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction
    • Deepurbanevent: A system for predicting citywide crowd dynamics at big events
    • Promptst: Prompt-enhanced spatio-temporal multi-attribute prediction
    • Multiattention 3d residual neural network for origin-destination crowd flow prediction
    • When transfer learning meets crosscity urban flow prediction: spatio-temporal adaptation matters
  • Spatio-Temporal Graph
    • Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks
    • Temporal multiview graph convolutional networks for citywide traffic volume inference
    • Mdtp: A multisource deep traffic prediction framework over spatio-temporal trajectory data

Travel Recommendation

• Traditional Methods
  • A survey of route recommendations: Methods, applications, and opportunities
• Deep Learning Methods
  • Hybrid Type
    • Learning effective road network representation with hierarchical graph neural networks
  • Sequential-based
    • Ldferr: A fuel-efficient route recommendation approach for long-distance driving based on historical trajectories
    • Progrpgan: Progressive gan for route planning
    • Personalized path recommendation with specified way-points based on trajectory representations
    • Personalized long distance fuel-efficient route recommendation through historical trajectories mining
    • Query2trip: Dual-debiased learning for neural trip recommendation
  • Graph-based
    • Dual-grained human mobility learning for location-aware trip recommendation with spatial–temporal graph knowledge fusion
    • Learning improvement heuristics for solving routing problems
    • Learning to effectively estimate the travel time for fastest route recommendation
  • Multi-modal Type
    • Walking down a different path: route recommendation based on visual and facility based diversity
    • Multi-modal transportation recommendation with unified route representation learning
  • Reinforcement Learning
    • Spatio-temporal feature fusion for dynamic taxi route recommendation via deep reinforcement learning
    • Evacuation route recommendation using auto-encoder and markov decision process

Friend Recommendation

• Traditional Methods
  • Recommendations in location-based social networks: a survey
  • Where online friends meet: Social communities in location-based networks
  • Friend recommendation for location-based mobile social networks
  • Geo-friends recommendation in gps-based cyber-physical social network
• Deep Learning Methods
  • Revisiting user mobility and social relationships in lbsns: a hypergraph embedding approach
  • Lbsn2vec++: Heterogeneous hypergraph embedding for location-based social networks
  • Social link inference via multi view matching network from spatiotemporal trajectories
  • Trajectory-based social circle inference
  • Graph structure learning on user mobility data for social relationship inference
  • Friend recommendation in location based social networks via deep pairwise learning

• Traditional Methods
  • A survey and comparison of trajectory classification methods
  • Traclass: trajectory classification using hierarchical region-based and trajectory-based clustering
  • Integrating cross-scale analysis in the spatial and temporal domains for classification of behavioral movement
  • Learning transportation mode from raw gps data for geographic applications on the web
  • Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects

Travel Mode Identification

• Deep Learning Methods
  • Estimator: An effective and scalable framework for transportation mode classification over trajectories
  • Trajectorynet: An embedded gps trajectory representation for point-based classification using recurrent neural networks
  • Spatio-temporal gru for trajectory classification
  • Modeling trajectories with neural ordinary differential equations
  • Traclets: Harnessing the power of computer vision for trajectory classification
  • Trajformer: Efficient trajectory classification with transformers
  • Semi-supervised deep learning approach for transportation mode identification using gps trajectory data
  • Distributional and spatial-temporal robust representation learning for transportation activity recognition
  • End-to-end trajectory transportation mode classification using bi-lstm recurrent neural network
  • Trajectory-as-a-sequence: A novel travel mode identification framework
  • A multi-scale attributes attention model for transport mode identification
  • Graph based embedding learning of trajectory data for transportation mode recognition by fusing sequence and dependency relations

Trajectory-User Linking

• Deep Learning Methods
  • Identifying human mobility via trajectory embeddings
  • Trajectory-user linking via variational autoencoder
  • Trajectory-user linking with attentive recurrent network
  • Mutual distillation learning network for trajectory-user linking
  • Adversarial mobility learning for human trajectory classification
  • Self-supervised human mobility learning for next location prediction and trajectory classification

Other Perspectives

• Deep Learning Methods
  • Semi-Supervised Learning
    • Semi-supervised deep learning approach for transportation mode identification using gps trajectory data
    • Semi-supervised deep ensemble learning for travel mode identification
    • Semi-supervised federated learning for travel mode identification from gps trajectories
  • Unsupervised Learning
    • Unsupervised deep learning for gps based transportation mode identification
  • Limited Data Scenario
    • Improving transportation mode identification with limited gps trajectories
    • A framework of travel mode identification fusing deep learning and map-matching algorithm
  • Unlabeled Data Scenario
    • S2tul: A semisupervised framework for trajectory-user linking
  • Cross-Platform Scenarios
    • Dplink: User identity linkage via deep neural network from heterogeneous mobility data
    • Egomuil: Enhancing spatio-temporal user identity linkage in location-based social networks with ego-mo hypergraph
  • Different Data Type Scenarios
    • Trajectory-user linking via hierarchical spatio-temporal attention networks

Traditional Methods

• • Historical data based real time prediction of vehicle arrival time
  • A simple baseline for travel time estimation using large-scale trip data
  • HTTP: A new framework for bus travel time prediction based on historical trajectories
  • Route travel time estimation using low-frequency floating car data

Deep Learning Methods

• Trajectory-based

  • When will you arrive? estimating travel time based on deep neural networks
  • Traffic speed prediction and congestion source exploration: A deep learning method
  • Deeptravel: a neural network based travel time estimation model with auxiliary supervision
  • Multi-task representation learning for travel time estimation
  • Context-aware road travel time estimation by coupled tensor decomposition based on trajectory data
  • MTLM: a multi-task learning model for travel time estimation
  • TTPNet: A neural network for travel time prediction based on tensor decomposition and graph embedding

• Road-based

  • Learning to estimate the travel time
  • CoDriver ETA: Combine driver information in estimated time of arrival by driving style learning auxiliary task
  • DeepIST: Deep image-based spatiotemporal network for travel time estimation
  • CompactETA: A fast inference system for travel time prediction
  • Road network metric learning for estimated time of arrival
  • Interpreting trajectories from multiple views: A hierarchical selfattention network for estimating the time of arrival
  • Route travel time estimation on a road network revisited: Heterogeneity, proximity, periodicity and dynamicity
  • Graph
    • ConstGAT: Contextual spatial-temporal graph attention network for travel time estimation at baidu maps
    • HetETA: Heterogeneous information network embedding for estimating time of arrival
    • Dual graph convolution architecture search for travel time estimation

• Other Perspectives

  • En route
    • SSML: Self-supervised meta-learner for en route travel time estimation at baidu maps
    • MetaER-TTE: An Adaptive Meta-learning Model for En Route Travel Time Estimation
  • Uncertainty
    • Privacy-preserving travel time prediction with uncertainty using GPS trace data
    • Cross-area travel time uncertainty estimation from trajectory data: a federated learning approach
  • Classification-based
    • CatETA: A categorical approximate approach for estimating time of arrival
    • Uncertainty-aware probabilistic travel time prediction for on-demand ride-hailing at Didi
  • Travel time distribution
    • Citywide estimation of travel time distributions with bayesian deep graph learning
    • Travel time distribution estimation by learning representations over temporal attributed graphs

• Traditional Methods
  • Trajectory outlier detection: A Partition-and-Detect Framework

Offline Detection

• Deep Learning Methods
  • Anomalous Trajectory Detection using Recurrent Neural Network
  • Coupled igmmgans with applications to anomaly detection in human mobility data
  • TripSafe: Retrieving Safety-related Abnormal Trips in Real-time with Trajectory Data
  • Open anomalous trajectory recognition via probabilistic metric learning

Online Detection

• Deep Learning Methods
  • A Fast Trajectory Outlier Detection Approach via Driving Behavior Modeling
  • Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning
  • Online Anomalous Trajectory Detection with Deep Generative Sequence Modeling
  • DeepTEA: Effective and Efficient Online Time-dependent Trajectory Outlier Detection

Macro-dynamic

• Traditional Methods
  • A deep gravity model for mobility flows generation
  • Unraveling the origin of exponential law in intra-urban human mobility Deep Learning Methods
  • Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks
  • Spatiotemporal scenario generation of traffic flow based on LSTM-GAN
  • TrafficGAN: Network-scale deep traffic prediction with generative adversarial nets
  • Traffic flow imputation using parallel data and generative adversarial networks
  • GANs based density distribution privacypreservation on mobility data
  • Deep multi-view spatial-temporal network for taxi demand prediction

Micro-dynamic

• Deep Learning Methods
  • Grids-based
    • A nonparametric generative model for human trajectories
    • Wherenext: a location predictor on trajectory pattern mining
    • Deeptransport: Prediction and simulation of human mobility and transportation mode at a citywide level
    • Deepmove: Predicting human mobility with attentional recurrent networks
    • How do you go where? improving next location prediction by learning travel mode information using transformers
    • Simulating continuous-time human mobility trajectories
    • COLA: Crosscity mobility transformer for human trajectory simulation
    • Activity trajectory generation via modeling spatiotemporal dynamics
    • Learning to simulate daily activities via modeling dynamic human needs
    • Where would I go next? large language models as human mobility predictors
    • Exploring large language models for human mobility prediction under public events
    • TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning
  • GPS-based
    • Large scale GPS trajectory generation using map based on two stage GAN
    • Generating mobility trajectories with retained data utility
    • Difftraj: Generating GPS trajectory with diffusion probabilistic model

• Forecasting
  • Where would i go next? large language models as human mobility predictors
  • Exploring large language models for human mobility prediction under public events
  • Prompt mining for language-based human mobility forecasting
  • Urbangpt: Spatio-temporal large language models
• Generation
  • Large language models as urban residents: An llm agent framework for personal mobility generation
  • Mobilitygpt: Enhanced human mobility modeling with a gpt model
• Identification
  • Are you being tracked? discover the power of zero-shot trajectory tracing with llms!

Here, we plan to host a Google Cloud Drive to collect all trajectory-related datasets for the convenience of researchers. (Coming soon🚀)

• SUMO
• SafeGraph
• Cblab
• PyTrack
• PyMove
• TransBigData
• Traja
• MovingPandas
• Scikit-mobility
• Tracktable
• Yupi

• Uber
• DiDi
• Google Map
• Baidu Map
• Cainiao