Zhou, X.S.[Xue-Song],
Mahmassani, H.S.,
Dynamic origin-destination demand estimation using automatic vehicle
identification data,
ITS(7), No. 1, March 2006, pp. 105-114.
IEEE DOI
0604
BibRef
Hu, S.R.,
Wang, C.M.,
Vehicle Detector Deployment Strategies for the Estimation of Network
Origin-Destination Demands Using Partial Link Traffic Counts,
ITS(9), No. 2, June 2008, pp. 288-300.
IEEE DOI
0806
BibRef
Bauer, D.,
Estimating origin-destination-matrices depending on the time of the day
from high frequent pedestrian entry and exit counts,
IET-ITS(6), No. 4, 2012, pp. 463-473.
DOI Link
1302
BibRef
Kattan, L.,
Abdulhai, B.,
Sensitivity Analysis of an Evolutionary-Based Time-Dependent
Origin/Destination Estimation Framework,
ITS(13), No. 3, September 2012, pp. 1442-1453.
IEEE DOI
1209
BibRef
Toledo, T.,
Kolechkina, T.,
Estimation of Dynamic Origin-Destination Matrices Using
Linear Assignment Matrix Approximations,
ITS(14), No. 2, 2013, pp. 618-626.
IEEE DOI
1307
dynamic traffic assignment; origin-destination (OD) matrix estimation
BibRef
Yang, G.[Gege],
Song, C.[Ci],
Shu, H.[Hua],
Zhang, J.[Jia],
Pei, T.[Tao],
Zhou, C.H.[Cheng-Hu],
Assessing Patient bypass Behavior Using Taxi Trip Origin-Destination
(OD) Data,
IJGI(5), No. 9, 2016, pp. 157.
DOI Link
1610
BibRef
Ye, P.,
Wen, D.,
Optimal Traffic Sensor Location for Origin-Destination Estimation
Using a Compressed Sensing Framework,
ITS(18), No. 7, July 2017, pp. 1857-1866.
IEEE DOI
1706
Coherence, Compressed sensing, Estimation, Minimization,
Optimization, Sparse matrices, Transportation,
Traffic sensor location, compressed sensing, traffic, flow, estimation
BibRef
Hu, X.,
Chiu, Y.C.,
Villalobos, J.A.,
Nava, E.,
A Sequential Decomposition Framework and Method for Calibrating
Dynamic Origin-Destination Demand in a Congested Network,
ITS(18), No. 10, October 2017, pp. 2790-2797.
IEEE DOI
1710
Calibration, Computational modeling, Data models, Estimation,
Intelligent transportation systems, Solid modeling,
Vehicle dynamics, O-D calibration, dynamic traffic assignment,
one-norm formulation, shockwave propagation, simulation, model, calibration
BibRef
Wang, Y.D.[Yan-Dong],
Gu, Y.Y.[Yan-Yan],
Dou, M.X.[Ming-Xuan],
Qiao, M.L.[Meng-Ling],
Using Spatial Semantics and Interactions to Identify Urban Functional
Regions,
IJGI(7), No. 4, 2018, pp. xx-yy.
DOI Link
1805
Using taxi origin/destination (O/D) flows.
BibRef
Liu, L.,
Qiu, Z.,
Li, G.,
Wang, Q.,
Ouyang, W.,
Lin, L.,
Contextualized Spatial-Temporal Network for Taxi Origin-Destination
Demand Prediction,
ITS(20), No. 10, October 2019, pp. 3875-3887.
IEEE DOI
1910
Public transportation, Task analysis, Correlation, Neural networks,
Urban areas, Context modeling, Predictive models,
spatial-temporal modeling
BibRef
Bauer, D.,
Richter, G.,
Asamer, J.,
Heilmann, B.,
Lenz, G.,
Kölbl, R.,
Quasi-Dynamic Estimation of OD Flows From Traffic Counts Without
Prior OD Matrix,
ITS(19), No. 6, June 2018, pp. 2025-2034.
IEEE DOI
1806
Entropy, Estimation, Public transportation, Road transportation,
Sensors, Urban areas, Origin-destination flow estimation,
quasi-dynamic assumption
BibRef
Wen, T.[Tao],
Cai, C.[Chen],
Gardner, L.[Lauren],
Waller, S.T.[Steven Travis],
Dixit, V.[Vinayak],
Chen, F.[Fang],
Estimation of sparse O-D matrix accounting for demand volatility,
IET-ITS(12), No. 9, November 2018, pp. 1020-1026.
DOI Link
1810
Origin-destination (O-D) demand estimation.
BibRef
Huang, Y.[Yang],
Shi, K.F.[Kai-Fang],
Zong, H.M.[Hui-Ming],
Zhou, T.G.[Ting-Gang],
Shen, J.W.[Jing-Wei],
Exploring Spatial and Temporal Connection Patterns among the
Districts in Chongqing Based on Highway Passenger Flow,
RS(12), No. 1, 2019, pp. xx-yy.
DOI Link
2001
BibRef
Zhang, L.F.[Lian-Fa],
Cheng, J.Q.[Jian-Quan],
Jin, C.[Cheng],
Spatial Interaction Modeling of OD Flow Data: Comparing
Geographically Weighted Negative Binomial Regression (GWNBR) and OLS
(GWOLSR),
IJGI(8), No. 5, 2019, pp. xx-yy.
DOI Link
1906
Origin-Destination flow data.
BibRef
Ishikawa, K.[Kazuki],
Nakayama, D.[Daichi],
Estimation of Origin-Destination Flows of Passenger Cars in 1925 in
Old Tokyo City, Japan,
IJGI(8), No. 11, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Xiang, Q.L.[Qiu-Liang],
Wu, Q.Y.[Qun-Yong],
Tree-Based and Optimum Cut-Based Origin-Destination Flow Clustering,
IJGI(8), No. 11, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Tao, R.[Ran],
Gong, Z.Y.[Zhao-Ya],
Ma, Q.W.[Qi-Wei],
Thill, J.C.[Jean-Claude],
Boosting Computational Effectiveness in Big Spatial Flow Data
Analysis with Intelligent Data Reduction,
IJGI(9), No. 5, 2020, pp. xx-yy.
DOI Link
2005
spatial origin-destination (OD) flow data.
BibRef
Chu, K.,
Lam, A.Y.S.,
Li, V.O.K.,
Deep Multi-Scale Convolutional LSTM Network for Travel Demand and
Origin-Destination Predictions,
ITS(21), No. 8, August 2020, pp. 3219-3232.
IEEE DOI
2008
Deep learning, Public transportation, Correlation,
Predictive models, Data models, Travel demand prediction,
origin-destination tensor
BibRef
Guo, X.G.[Xiao-Gang],
Xu, Z.J.[Zhi-Jie],
Zhang, J.Q.[Jian-Qin],
Lu, J.[Jian],
Zhang, H.[Hao],
An OD Flow Clustering Method Based on Vector Constraints: A Case
Study for Beijing Taxi Origin-Destination Data,
IJGI(9), No. 2, 2020, pp. xx-yy.
DOI Link
2003
BibRef
Rao, W.M.[Wen-Ming],
Xia, J.X.[Jing-Xin],
Wang, C.[Chen],
Lu, Z.B.[Zhen-Bo],
Chen, Q.[Qian],
Investigating impact of the heterogeneity of trajectory data
distribution on origin-destination estimation:
A spatial statistics approach,
IET-ITS(14), No. 10, October 2020, pp. 1218-1227.
DOI Link
2009
BibRef
Gao, Q.G.[Qing-Gang],
Molloy, J.[Joseph],
Axhausen, K.W.[Kay W.],
Trip Purpose Imputation Using GPS Trajectories with Machine Learning,
IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Sobral, T.,
Galvăo, T.,
Borges, J.,
Knowledge-Assisted Visualization of Multi-Level Origin-Destination
Flows Using Ontologies,
ITS(22), No. 4, April 2021, pp. 2168-2177.
IEEE DOI
2104
Ontologies, Data visualization, Stakeholders, Semantics, Data models,
Urban areas, Tools, Ontologies, origin-destination matrices,
Semantic Web
BibRef
Yao, X.[Xin],
Gao, Y.[Yong],
Zhu, D.[Di],
Manley, E.[Ed],
Wang, J.[Jiaoe],
Liu, Y.[Yu],
Spatial Origin-Destination Flow Imputation Using Graph Convolutional
Networks,
ITS(22), No. 12, December 2021, pp. 7474-7484.
IEEE DOI
2112
Spatial databases, Gravity, Convolution,
Biological system modeling, Data models, Predictive models,
graph convolution
BibRef
Behara, K.N.S.[Krishna N. S.],
Bhaskar, A.[Ashish],
Chung, E.[Edward],
A Novel Methodology to Assimilate Sub-Path Flows in Bi-Level OD
Matrix Estimation Process,
ITS(22), No. 11, November 2021, pp. 6931-6941.
IEEE DOI
2112
Trraffic origin-destination.
Estimation, Trajectory, Bluetooth, Matrices, Linear programming,
Australia, Mathematical model, OD matrix estimation,
Brisbane
BibRef
Chen, D.J.[De-Jun],
Wang, J.[Jing],
Xiong, C.C.[Cong-Cong],
Research on origin-destination travel demand prediction method of
inter-regional online taxi based on SpatialOD-BiConvLSTM,
IET-ITS(15), No. 12, 2021, pp. 1533-1547.
DOI Link
2112
BibRef
Heredia, C.[Cristóbal],
Moreno, S.[Sebastián],
Yushimito, W.F.[Wilfredo F.],
Characterization of Mobility Patterns With a Hierarchical Clustering
of Origin-Destination GPS Taxi Data,
ITS(23), No. 8, August 2022, pp. 12700-12710.
IEEE DOI
2208
Public transportation, Global Positioning System,
Clustering methods, Clustering algorithms, Data models,
urban mobility patterns
BibRef
Liang, Y.Y.[Yun-Yi],
Wu, Z.Z.[Zhi-Zhou],
Yang, H.C.[Hao-Chun],
Wang, Y.H.[Yin-Hai],
A Novel Framework for Road Side Unit Location Optimization for
Origin-Destination Demand Estimation,
ITS(23), No. 11, November 2022, pp. 21113-21126.
IEEE DOI
2212
Optimization, Estimation, Delays, Connected vehicles, Transportation,
Roads, Pareto optimization, Sensor location, road side unit,
origin-destination demand estimation
BibRef
Luo, Q.[Qin],
Lin, B.[Bin],
Lyu, Y.T.[Yi-Tong],
He, Y.X.[Yu-Xin],
Zhang, X.C.[Xiao-Chun],
Zhang, Z.Q.[Zhi-Qing],
Spatiotemporal path inference model for urban rail transit passengers
based on travel time data,
IET-ITS(17), No. 7, 2023, pp. 1395-1414.
DOI Link
2307
passenger detention, passenger flow assignment, path inference,
travel time, urban rail transit
BibRef
Englezou, Y.[Yiolanda],
Timotheou, S.[Stelios],
Panayiotou, C.G.[Christos G.],
Path-Based Origin-Destination Matrix Estimation Utilizing Macroscopic
Traffic Dynamics,
ITS(25), No. 8, August 2024, pp. 8819-8836.
IEEE DOI
2408
Estimation, Vehicle dynamics, Data models, Optimization, Europe, Roads,
Matrix converters, Origin-destination (OD) matrix estimation,
nonlinear optimisation
BibRef
Chen, P.[Peng],
Wang, Z.Y.[Zi-Yan],
Zhou, B.[Bin],
Yu, G.Z.[Gui-Zhen],
Dynamic Origin-Destination Flow Imputation Using Feature-Based
Transfer Learning,
ITS(25), No. 11, November 2024, pp. 17147-17159.
IEEE DOI
2411
Estimation, Roads, Data models, Feature extraction, Trajectory,
Vehicle dynamics, Noise reduction, Urban traffic,
data fusion
BibRef
Kheyrabadi, S.A.[Salman Aghidi],
Mamdoohi, A.R.[Amir Reza],
The Influence of Origin Attributes on the Destination Choice of
Discretionary Home-Based Walk Trips,
IJGI(13), No. 7, 2024, pp. 218.
DOI Link
2408
BibRef
Xu, Y.[Yi],
Han, L.Z.[Liang-Zhe],
Zhu, T.Y.[Tong-Yu],
Sun, L.L.[Lei-Lei],
Du, B.[Bowen],
Lv, W.F.[Wei-Feng],
Continuous-Time and Discrete-Time Representation Learning for
Origin-Destination Demand Prediction,
ITS(25), No. 3, March 2024, pp. 2382-2393.
IEEE DOI
2405
Representation learning, Transportation, Predictive models,
Planning, Heuristic algorithms, Fuses, Forecasting,
urban transportation systems
BibRef
Owais, M.[Mahmoud],
Deep Learning for Integrated Origin-Destination Estimation and
Traffic Sensor Location Problems,
ITS(25), No. 7, July 2024, pp. 6501-6513.
IEEE DOI
2407
Sensitivity analysis, Costs, Genetic algorithms, Reliability,
Maximum likelihood estimation, Mathematical models,
traffic data prediction
BibRef
Ye, J.[Jiexia],
Zhao, J.J.[Juan-Juan],
Zheng, F.[Furong],
Xu, C.Z.[Cheng-Zhong],
A Heterogeneous Graph Convolution Based Method for Short-Term OD Flow
Completion and Prediction in a Metro System,
ITS(25), No. 11, November 2024, pp. 15614-15627.
IEEE DOI
2411
Real-time systems, Predictive models, Correlation,
Adaptation models, Spatiotemporal phenomena, Accuracy, heterogeneous graph
BibRef
Peng, X.R.[Xiao-Ran],
Hu, R.M.[Rui-Min],
Wang, X.C.[Xiao-Chen],
Huang, N.[Nana],
Exploring changes in residents' daily activity patterns through
sequence visualization analysis,
IET-ITS(18), No. 10, 2024, pp. 1879-1894.
DOI Link
2411
behavioural sciences computing, big data, data mining,
data visualization, traveller information
BibRef
Qin, Z.L.[Zhen-Lin],
Zhang, P.F.[Peng-Fei],
Ma, Z.L.[Zhen-Liang],
DeepAGS: Deep learning with activity, geography and sequential
information in predicting an individual's next trip destination,
IET-ITS(18), No. 10, 2024, pp. 1895-1909.
DOI Link
2411
artificial intelligence, Big Data, public transport,
smart cards, transportation
BibRef
Tang, T.L.[Tian-Li],
Mao, J.N.[Jian-Nan],
Liu, R.H.[Rong-Hui],
Liu, Z.Y.[Zhi-Yuan],
Wang, Y.R.[Yi-Ran],
Huang, D.[Di],
Origin-Destination Matrix Prediction in Public Transport Networks:
Incorporating Heterogeneous Direct and Transfer Trips,
ITS(25), No. 12, December 2024, pp. 19889-19903.
IEEE DOI
2412
Predictive models, Data models, Feature extraction, Deep learning,
Wireless fidelity, Roads, Graph convolutional networks,
spatio-temporal feature
BibRef
Song, Y.C.[Yu-Chen],
Li, D.W.[Da-Wei],
Ma, Z.L.[Zhen-Liang],
Zhang, T.[Tong],
Liu, D.J.[Dong-Jie],
He, C.Q.[Chong-Qi],
Dynamic Recursive Logit Model for Vehicle Driving Route Choices and
Path Inference with Incomplete Fixed Location Sensor Data,
ITS(25), No. 12, December 2024, pp. 19929-19942.
IEEE DOI
2412
Vehicle dynamics, Radiofrequency identification,
Global Positioning System, Data models, Logistic regression,
RFID data
BibRef
Guimarăes, P.M.F.[Pedro M. F.],
Ferreira, F.[Flora],
Wojtak, W.[Weronika],
Barbosa, P.J.S.[Paulo J. S.],
Monteiro, S.[Sérgio],
Bicho, E.[Estela],
Erlhagen, W.[Wolfram],
A Dynamic Neural Field Approach for Intelligent Cockpits:
Online Learning and Prediction of Traveling Routines,
ITS(25), No. 12, December 2024, pp. 20240-20255.
IEEE DOI
2412
Vehicles, Vehicle dynamics, Predictive models, Automobiles,
Multimedia systems, Global Positioning System, Europe, Data models,
spatio-temporal prediction
BibRef
Rong, C.[Can],
Ding, J.T.[Jing-Tao],
Li, Y.[Yong],
An Interdisciplinary Survey on Origin-destination Flows Modeling:
Theory and Techniques,
Surveys(57), No. 1, October 2024, pp. xx-yy.
DOI Link Code:
WWW Link.
2501
Urban mobility, origin-destination flows, modeling, interdisciplines
BibRef
He, M.W.[Ming-Wei],
Chen, N.[Na],
He, Y.[Yueren],
Li, J.B.[Jian-Bo],
Liu, Y.[Yang],
Exploring the Activity-Travel Patterns of Multi-Purpose Commuters on
Workdays Based on Activity Chains and Time Allocation: Evidence from
Kunming, China,
IJGI(13), No. 12, 2024, pp. 446.
DOI Link
2501
BibRef
Yuan, X.M.[Xiao-Ming],
Luo, Z.Y.[Zhen-Yu],
Zhang, N.[Ning],
Guo, G.[Ge],
Wang, L.[Lin],
Li, C.L.[Chang-La],
Niyato, D.[Dusit],
Federated Transfer Learning for Privacy-Preserved Cross-City Traffic
Flow Prediction,
ITS(26), No. 4, April 2025, pp. 4418-4431.
IEEE DOI
2504
Data models, Urban areas, Predictive models, Transfer learning,
Training, Adaptation models, Accuracy, Roads, Data privacy,
temporal convolutional network
BibRef
Li, Y.[Yige],
Jiang, Y.[Ying],
Duan, J.[Jin],
Quantifying Administrative and Functional Border Effects on Commuting
and Non-Commuting Flows: A Case Study of the Shanghai-Suzhou-Jiaxing
Area,
IJGI(14), No. 3, 2025, pp. 133.
DOI Link
2503
BibRef
Yuan, Z.[Zehao],
Chen, X.Y.[Xuan-Yan],
Chen, B.[Biyu],
Luo, Y.[Yubo],
Zhang, Y.[Yu],
Teng, W.X.[Wen-Xin],
Zhang, C.[Chao],
Generating Large-Scale Origin-Destination Matrix via Progressive
Growing Generative Adversarial Networks Model,
IJGI(14), No. 4, 2025, pp. 172.
DOI Link
2505
BibRef
Shan, Z.Y.[Zhen-Yu],
Yang, F.[Fei],
Shi, X.Z.[Xing-Zi],
Cui, Y.P.[Ya-Ping],
Hybrid Learning Model of Global-Local Graph Attention Network and
XGBoost for Inferring Origin-Destination Flows,
IJGI(14), No. 5, 2025, pp. 182.
DOI Link
2505
BibRef
Zhang, C.[Cheng],
Chen, X.[Xin],
Zhao, J.[Jing],
Jiang, Z.[Zehao],
Chung, E.[Edward],
Estimating Bus Passenger Origin-Destination Flow via Passenger
Reidentification Using Video Images,
ITS(26), No. 6, June 2025, pp. 8353-8367.
IEEE DOI
2506
Estimation, Accuracy, Data mining, Cameras, Smart cards,
Wireless fidelity, Feature extraction, Visualization,
video images
BibRef
Lun, M.[Maoqi],
Wang, P.X.[Pei-Xiao],
Wu, S.[Sheng],
Zhang, H.[Hengcai],
Cheng, S.[Shifen],
Lu, F.[Feng],
Predicting the Next Location of Urban Individuals via a
Representation-Enhanced Multi-View Learning Network,
IJGI(14), No. 8, 2025, pp. 302.
DOI Link
2509
BibRef
Li, C.J.[Chuan-Jia],
Chen, Y.[Yong],
Xu, H.[Haoge],
Chen, X.[Xiqun],
Lee, D.H.[Der-Horng],
Multi-View Hypergraph-Based Ride-Sourcing Origin-Destination Demand
Prediction,
ITS(26), No. 9, September 2025, pp. 13217-13231.
IEEE DOI
2510
Predictive models, Accuracy, Feature extraction, Correlation,
Real-time systems, Adaptation models, Pricing, Logic gates, motif
BibRef
Wu, X.S.[Xue-Song],
Pan, T.[Tianlu],
He, Z.C.[Zhao-Cheng],
Physics-Informed Mobility Perception Networks for Origin-Destination
Flow Prediction,
ITS(26), No. 10, October 2025, pp. 15134-15149.
IEEE DOI Code:
WWW Link.
2511
Predictive models, Urban areas, Neural networks,
Mathematical models, Gravity, Deep learning, Correlation,
diffusion process
BibRef
Eftekhar, Z.[Zahra],
Behrouzi, S.[Saman],
Krishnakumari, P.[Panchamy],
Pel, A.[Adam],
van Lint, H.[Hans],
The Role of Spatial Features and Adjacency in Data-Driven Short-Term
Prediction of Trip Production:
An Exploratory Study in The Netherlands,
ITS(26), No. 11, November 2025, pp. 19582-19604.
IEEE DOI
2511
Predictive models, Production, Long short term memory, Accuracy,
Hidden Markov models, Correlation, Computational modeling, residual analysis
BibRef
Lee, M.,
Nam, H.,
Jun, C.,
Origin-destination-based Public Transport Service Gap,
GeoDisast18(283-290).
DOI Link
1901
BibRef
Morimura, T.[Tetsuro],
Kato, S.[Sei],
Statistical Origin-destination generation with multiple sources,
ICPR12(3443-3446).
WWW Link.
1302
BibRef
Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Traffic Simulation, Simulator, Traffic .