Estimating multi-year 24 / 7 origin-destination demand using high-granular multi-source traffic data

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Untitled work

(2010) · Cited by 8,261 · see citing works

Solving Least Squares Problems

Lawson, C. L., & Hanson, R. J. (1995) · doi:10.1137/1.9781611971217 · Cited by 7,103 · see citing works

Finding the K Shortest Loopless Paths in a Network

Yen, J. Y. (1971) · Management Science · doi:10.1287/mnsc.17.11.712 · Cited by 2,642 · see citing works

Finding the k Shortest Paths

Eppstein, D. (1998) · SIAM Journal on Computing · doi:10.1137/s0097539795290477 · Cited by 1,395 · see citing works

Multimodal Learning with Deep Boltzmann Machines

Srivastava, N., & Salakhutdinov, R. (2012) · doi:10.5555/2627435.2697059 · Cited by 1,070 · see citing works

Estimating Origin-Destination Flows Using Mobile Phone Location Data

Calabrese, F., Lorenzo, G. D., Liu, L., & Ratti, C. (2011) · IEEE Pervasive Computing · doi:10.1109/mprv.2011.41 · Cited by 511 · see citing works

Development of origin–destination matrices using mobile phone call data

Iqbal, S., Choudhury, C. F., Wang, P., & González, M. C. (2014) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2014.01.002 · Cited by 508 · see citing works

Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts

Cascetta, E., Inaudi, D., & Marquis, G. (1993) · Transportation Science · doi:10.1287/trsc.27.4.363 · Cited by 371 · see citing works

Estimation of origin-destination matrices from link traffic counts on congested networks

Yang, H., Sasaki, T., Iida, Y., & Asakura, Y. (1992) · Transportation Research Part B Methodological · doi:10.1016/0191-2615(92)90008-k · Cited by 366 · see citing works

A 3D Morphable Model Learnt from 10,000 Faces

Booth, J. G., Roussos, A., Zafeiriou, S. P., Ponniah, A. J. T., & Dunaway, D. J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.598 · Cited by 352 · see citing works

Heuristic algorithms for the bilevel origin-destination matrix estimation problem

Yang, H. (1995) · Transportation Research Part B Methodological · doi:10.1016/0191-2615(95)00003-v · Cited by 286 · see citing works

Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin–Destination Flows

Ashok, K., & Ben-Akiva, M. E. (2000) · Transportation Science · doi:10.1287/trsc.34.1.21.12282 · Cited by 277 · see citing works

Travel Time Forecasting and Dynamic Origin-Destination Estimation for Freeways Based on Bluetooth Traffic Monitoring

Barceló, J., Montero, L., Marqués, L., & Carmona, C. (2010) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2175-03 · Cited by 220 · see citing works

Dynamic Origin–Destination Demand Estimation Using Automatic Vehicle Identification Data

Zhou, X. S., & Mahmassani, H. S. (2006) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2006.869629 · Cited by 205 · see citing works

A structural state space model for real-time traffic origin–destination demand estimation and prediction in a day-to-day learning framework

Zhou, X. S., & Mahmassani, H. S. (2007) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2007.02.004 · Cited by 202 · see citing works

An Efficient Algorithm for Real-Time Estimation and Prediction of Dynamic OD Tables

Bierlaire, M., & Crittin, F. (2004) · Operations Research · doi:10.1287/opre.1030.0071 · Cited by 164 · see citing works

A dynamic traffic assignment model for highly congested urban networks

Ben-Akiva, M. E., Gao, S., Wei, Z., & Yang, W. (2012) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2012.02.006 · Cited by 161 · see citing works

Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition

Chen, X., He, Z., & Wang, J. (2017) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2017.10.023 · Cited by 155 · see citing works

Origin-destination pattern estimation based on trajectory reconstruction using automatic license plate recognition data

Rao, W., Wu, Y., Xia, J., Ou, J., & Kluger, R. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.07.002 · Cited by 147 · see citing works

A coarse-grained parallel approach for seismic damage simulations of urban areas based on refined models and GPU/CPU cooperative computing

Lu, X., Han, B., Hori, M., Chen, X., & Xu, Z. (2014) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2014.01.010 · Cited by 146 · see citing works

An enhanced SPSA algorithm for the calibration of Dynamic Traffic Assignment models

Lu, L., Xu, Y., Antoniou, C., & Ben-Akiva, M. E. (2014) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2014.11.006 · Cited by 134 · see citing works

Dynamic origin–destination demand flow estimation under congested traffic conditions

Lu, C., Zhou, X. S., & Zhang, K. (2013) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2013.05.006 · Cited by 123 · see citing works

A gradient approximation approach for adjusting temporal origin–destination matrices

Cipriani, E., Florian, M., Mahut, M., & Nigro, M. (2010) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2010.05.013 · Cited by 122 · see citing works

Simultaneous Estimation of the Origin-Destination Matrices and Travel-Cost Coefficient for Congested Networks in a Stochastic User Equilibrium

Yang, H., Meng, Q., & Bell, M. G. H. (2001) · Transportation Science · doi:10.1287/trsc.35.2.107.10133 · Cited by 120 · see citing works

Sensitivity analysis of separable traffic equilibrium equilibria with application to bilevel optimization in network design

Josefsson, M., & Patriksson, M. (2006) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2005.12.004 · Cited by 110 · see citing works

Evaluating distributed word representations for capturing semantics of biomedical concepts

TH, M., Sahu, S. K., & Anand, A. (2015) · doi:10.18653/v1/w15-3820 · Cited by 92 · see citing works

Statistical inference for time varying origin–destination matrices

Hazelton, M. L. (2008) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2007.11.003 · Cited by 90 · see citing works

Dynamic Origin-Destination Demand Estimation with Multiday Link Traffic Counts for Planning Applications

Zhou, X. S., Qin, X. C., & Mahmassani, H. S. (2003) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1831-04 · Cited by 90 · see citing works

c-SPSA: Cluster-wise simultaneous perturbation stochastic approximation algorithm and its application to dynamic origin–destination matrix estimation

Tympakianaki, A., Koutsopoulos, H. N., & Jenelius, E. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.01.016 · Cited by 85 · see citing works

A model for the dynamic system optimum traffic assignment problem

Ghali, M. O., & Smith, M. (1995) · Transportation Research Part B Methodological · doi:10.1016/0191-2615(94)00024-t · Cited by 85 · see citing works

A coordinate descent method for the bi-level O-D matrix adjustment problem

Florian, M. (1995) · International Transactions in Operational Research · doi:10.1016/0969-6016(95)00001-n · Cited by 85 · see citing works

Trip matrix estimation from link traffic counts: The congested network case

Fisk, C. S. (1989) · Transportation Research Part B Methodological · doi:10.1016/0191-2615(89)90009-x · Cited by 85 · see citing works

Bayesian Demand Calibration for Dynamic Traffic Simulations

Flötteröd, G., Bierlaire, M., & Nagel, K. (2011) · Transportation Science · doi:10.1287/trsc.1100.0367 · Cited by 85 · see citing works

Incorporating Automated Vehicle Identification Data into Origin-Destination Estimation

Antoniou, C., Ben-Akiva, M. E., & Koutsopoulos, H. N. (2004) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1882-05 · Cited by 82 · see citing works

Dynamic Origin-Destination Matrix Estimation from Traffic Counts and Automated Vehicle Identification Data

van der Zijpp, N. J. (1997) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1607-13 · Cited by 76 · see citing works

Selection of a trip table which reproduces observed link flows

LeBlanc, L. J., & Farhangian, K. (1982) · Transportation Research Part B Methodological · doi:10.1016/0191-2615(82)90029-7 · Cited by 73 · see citing works

Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance

Vaze, V., Antoniou, C., Yang, W., & Ben-Akiva, M. E. (2009) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2090-01 · Cited by 72 · see citing works

System-optimal dynamic traffic assignment with and without queue spillback: Its path-based formulation and solution via approximate path marginal cost

Qian, S., Shen, W., & Zhang, H. (2012) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2012.02.008 · Cited by 71 · see citing works

New Calibration Methodology for Microscopic Traffic Simulation Using Enhanced Simultaneous Perturbation Stochastic Approximation Approach

Lee, J., & Özbay, K. M. A. (2009) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2124-23 · Cited by 71 · see citing works

Internally-consistent estimation of dynamic network origin-destination flows from intelligent transportation systems data using bi-level optimization

Tavana, H. (2001) · Texas ScholarWorks (Texas Digital Library) · Cited by 71 · see citing works

Origin-Destination Matrices Estimated with a Genetic Algorithm from Link Traffic Counts

Kim, H., Baek, S., & Lim, Y. (2001) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1771-20 · Cited by 65 · see citing works

Development and Calibration of a Large-Scale Microscopic Traffic Simulation Model

Jha, M., Gopalan, G., Garms, A., Mahanti, B. P., Toledo, T., & Ben-Akiva, M. E. (2004) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1876-13 · Cited by 64 · see citing works

New Gradient Approximation Method for Dynamic Origin–Destination Matrix Estimation on Congested Networks

Frederix, R., Viti, F., Corthout, R., & Tampère, C. M. J. (2011) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2263-03 · Cited by 63 · see citing works

W–SPSA in Practice: Approximation of Weight Matrices and Calibration of Traffic Simulation Models

Antoniou, C., Azevedo, C. L., Lu, L., Pereira, F. C., & Ben-Akiva, M. E. (2015) · Transportation research procedia · doi:10.1016/j.trpro.2015.06.013 · Cited by 62 · see citing works

Dynamic traffic demand prediction using conventional and emerging data sources

Antoniou, C., Ben-Akiva, M. E., & Koutsopoulos, H. N. (2006) · IEE Proceedings - Intelligent Transport Systems · doi:10.1049/ip-its:20055006 · Cited by 57 · see citing works

A variational inequality formulation for inferring dynamic origin–destination travel demands

Nie, Y. M., & Zhang, H. M. (2008) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2008.01.001 · Cited by 56 · see citing works

Adaptive coordinated traffic control for stochastic demand

Li, L., Huang, W., & Lo, H. K. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.01.007 · Cited by 54 · see citing works

Statistical inference of probabilistic origin-destination demand using day-to-day traffic data

Ma, W., & Qian, S. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2017.12.015 · Cited by 52 · see citing works

A new one-level convex optimization approach for estimating origin–destination demand

Shen, W., & Wynter, L. (2012) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2012.07.005 · Cited by 45 · see citing works

W-SPSA in practice: Approximation of weight matrices and calibration of traffic simulation models

Antoniou, C., Azevedo, C. L., Lu, L., Pereira, F. C., & Ben-Akiva, M. E. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.04.030 · Cited by 43 · see citing works

Time-Dependent Origin–Destination Demand Estimation

Verbas, Ö., Mahmassani, H. S., & Zhang, K. (2011) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2263-06 · Cited by 41 · see citing works

A Relaxation Approach for Estimating Origin–Destination Trip Tables

Nie, Y. M., & Zhang, H. M. (2008) · Networks and Spatial Economics · doi:10.1007/s11067-007-9059-y · Cited by 33 · see citing works

On the variance of recurrent traffic flow for statistical traffic assignment

Ma, W., & Qian, S. (2017) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2017.05.009 · Cited by 31 · see citing works

Noniterative Approach to Dynamic Traffic Origin-Destination Estimation with Parallel Evolutionary Algorithms

Kattan, L., & Abdulhai, B. (2006) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1964-22 · Cited by 28 · see citing works

Time-Dependent Origin-Destination Estimation Without Assignment Matrices

Balakrishna, R., Ben-Akiva, M. E., & Koutsopoulos, H. N. (2019) · EPFL Press eBooks · doi:10.1201/9780429093258-12 · Cited by 27 · see citing works

Mesoscopic traffic simulation on CPU/GPU

Xu, Y., Tan, G., Li, X., & Song, X. (2014) · ACM/IEEE/SCS Workshop on Principles of Advanced and Distributed Simulation (PADS) · doi:10.1145/2601381.2601396 · Cited by 26 · see citing works

Estimating Time-Dependent Freeway Origin–Destination Demands with Different Data Coverage

Zhang, H. M., Nie, Y. M., & Qian, S. (2008) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2047-11 · Cited by 21 · see citing works

Computing Individual Path Marginal Cost in Networks with Queue Spillbacks

Qian, S., & Zhang, H. M. (2011) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2263-02 · Cited by 21 · see citing works

Noniterative Approach to Dynamic Traffic Origin–Destination Estimation with Parallel Evolutionary Algorithms

Kattan, L., & Abdulhai, B. (2006) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198106196400122 · Cited by 18 · see citing works

A link queue model of network traffic flowPreprint

Jin, W. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1209.2361 · Cited by 17 · see citing works

Development of A Path Flow Estimator for Inferring Steady-State and Time-Dependent Origin-Destination Trip MatricesPreprint

Chen, A., Chootinan, P., Recker, W. W., & Zhang, H. (2008) · RePEc: Research Papers in Economics · Cited by 13 · see citing works

Computational-Based Approach to Estimating Travel Demand in Large-Scale Microscopic Traffic Simulation Models

Huang, S., Sadek, A. W., & Guo, L. (2012) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000202 · Cited by 13 · see citing works

Stability Comparison of Dimensionality Reduction Techniques Attending to Data and Parameter Variations.

García-Fernández, F. J., Verleysen, M., Lee, J. A., & Díaz, I. (2013) · Consultation of the Doctoral Thesis Database (TESEO) (Ministerio de Educación, Cultura y Deporte) · Cited by 11 · see citing works

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works

A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources

Yang, S., Ma, W., Pi, X., & Qian, S. (2019) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2019.08.010 · Cited by 241 · see citing works

iLOCuS: Incentivizing Vehicle Mobility to Optimize Sensing Distribution in Crowd Sensing

Xu, S., Chen, X., Pi, X., Joe‐Wong, C., Zhang, P., & Noh, H. Y. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2915838 · Cited by 116 · see citing works

Estimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphs

Ma, W., Pi, X., & Qian, S. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102747 · Cited by 87 · see citing works

A general formulation for multi-modal dynamic traffic assignment considering multi-class vehicles, public transit and parking

Pi, X., Ma, W., & Qian, S. (2019) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2019.05.011 · Cited by 78 · see citing works

Day-to-day dynamic origin–destination flow estimation using connected vehicle trajectories and automatic vehicle identification data

Cao, Y., Tang, K., Sun, J., & Ji, Y. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2021.103241 · Cited by 56 · see citing works

A multi-task memory network with knowledge adaptation for multimodal demand forecasting

Li, C., Bai, L., Liu, W., Yao, L., & Waller, S. T. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2021.103352 · Cited by 48 · see citing works

Exploring the roles of large language models in reshaping transportation systems: A survey, framework, and roadmap

Nie, T., Sun, J., & Ma, W. (2025) · Artificial Intelligence for Transportation · doi:10.1016/j.ait.2025.100003 · Cited by 56 · see citing works

Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach

Huo, J., Liu, C., Chen, J., Meng, Q., Wang, J., & Liu, Z. (2023) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2023.103108 · Cited by 39 · see citing works

Online transportation network cyber-attack detection based on stationary sensor data

Sun, R., Luo, Q., & Chen, Y. (2023) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2023.104058 · Cited by 34 · see citing works

Urban mobility analytics: A deep spatial–temporal product neural network for traveler attributes inference

Li, C., Bai, L., Liu, W., Yao, L., & Waller, S. T. (2020) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2020.102921 · Cited by 34 · see citing works

Assessing the Importance of the Marine Chokepoint: Evidence from Tracking the Global Marine Traffic

Wang, X., Du, D., & Peng, Y. (2023) · Sustainability · doi:10.3390/su16010384 · Cited by 31 · see citing works

High-dimensional population inflow time series forecasting via an interpretable hierarchical transformer

Hu, S., & Xiong, C. (2022) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2022.103962 · Cited by 29 · see citing works

Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-Offs: A Causal Inference Approach

Liu, X., Qian, S., Teo, H., & Ma, W. (2023) · Transportation Science · doi:10.1287/trsc.2022.0195 · Cited by 27 · see citing works

A generalized Bayesian traffic model

Zhu, Z., Zhu, S., Zheng, Z., & Yang, H. (2019) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2019.09.011 · Cited by 26 · see citing works

Modeling and analyzing the traffic flow during evacuation in Hurricane Irma (2017)

Feng, K., & Lin, N. (2022) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2022.103412 · Cited by 25 · see citing works

Cost-sharing mechanism design for ride-sharing

Hu, S., Dessouky, M. M., Uhan, N. A., & Vayanos, P. (2021) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2021.06.018 · Cited by 24 · see citing works

Congestion reduction via personalized incentives

Ghafelebashi, A., Razaviyayn, M., & Dessouky, M. M. (2023) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2023.104153 · Cited by 22 · see citing works

Modeling Multimodal Curbside Usage in Dynamic Networks

Liu, J., & Qian, S. (2024) · Transportation Science · doi:10.1287/trsc.2024.0522 · Cited by 20 · see citing works

O–D matrix estimation based on data-driven network assignment

Tsanakas, N., Gundlegård, D., & Rydergren, C. (2022) · Transportmetrica B Transport Dynamics · doi:10.1080/21680566.2022.2080128 · Cited by 19 · see citing works

Estimating probability distributions of travel demand on a congested network

Yang, Y., Fan, Y., & Røyset, J. Ø. (2019) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2019.01.008 · Cited by 18 · see citing works

Clustering Analysis of the Spatio-Temporal On-Street Parking Occupancy Data: A Case Study in Hong Kong

Wu, F., & Ma, W. (2022) · Sustainability · doi:10.3390/su14137957 · Cited by 17 · see citing works

A data-parallel many-source shortest-path algorithm to accelerate macroscopic transport network assignment

Heywood, P., Maddock, S., Bradley, R., Swain, D., Wright, I., Mawson, M., Fletcher, G., Guichard, R., Himlin, R., & Richmond, P. (2019) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2019.05.020 · Cited by 14 · see citing works

A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data

Yu, H., Zhu, S., Yang, J., Guo, Y., & Tang, T. (2021) · Sensors · doi:10.3390/s21154971 · Cited by 12 · see citing works

Convolutional Long Short-Term Memory Two-Dimensional Bidirectional Graph Convolutional Network for Taxi Demand Prediction

Cao, Y., Liu, L., & Dong, Y. (2023) · Sustainability · doi:10.3390/su15107903 · Cited by 11 · see citing works

Multi-view feature engineering for day-to-day joint clustering of multiple traffic datasets

Sharma, S., Nayak, R., & Bhaskar, A. (2024) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2024.104607 · Cited by 9 · see citing works

Estimating Link Flows in Road Networks With Synthetic Trajectory Data Generation: Inverse Reinforcement Learning Approach

Zhong, M., Kim, J., & Zheng, Z. (2023) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2022.3233904 · Cited by 9 · see citing works

Designing a Novel Two-Stage Fusion Framework to Predict Short-Term Origin–Destination Flow

Li, D., Wang, W., & Zhao, D. (2023) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.teeng-7573 · Cited by 9 · see citing works

Maximin headway control of automated vehicles for system optimal dynamic traffic assignment in general networks

Du, J., & Ma, W. (2024) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2024.103628 · Cited by 9 · see citing works

Origin-Destination Demand Reconstruction Using Observed Travel Time under Congested Network

Sun, C. M., Chang, Y., Luan, X., Tu, Q., & Tang, W. (2020) · Networks and Spatial Economics · doi:10.1007/s11067-020-09496-4 · Cited by 8 · see citing works

Data-driven network loading

Tsanakas, N., Ekström, J., Gundlegård, D., Olstam, J. J., & Rydergren, C. (2020) · Transportmetrica B Transport Dynamics · doi:10.1080/21680566.2020.1847213 · Cited by 7 · see citing works

Path-Based Origin-Destination Matrix Estimation Utilizing Macroscopic Traffic Dynamics

Englezou, Y., Timotheou, S., & Panayiotou, C. G. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3370473 · Cited by 6 · see citing works

Spatial and Temporal Analyses for Estimation of Origin-Destination Demands by Time of Day Over Year

Shen, L., Shao, H., Wu, T., & Lam, W. H. K. (2019) · IEEE Access · doi:10.1109/access.2019.2909524 · Cited by 6 · see citing works

Estimating link flow through link speed with sparse flow data sampling

Qiu, J., Fu, S., Ou, J., Tang, K., Qu, X., Liang, S., Wang, X., & Ran, B. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13323 · Cited by 5 · see citing works

Incentive Systems for Fleets of New Mobility Services

Ghafelebashi, A., Razaviyayn, M., & Dessouky, M. M. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3511473 · Cited by 5 · see citing works

Unsupervised Knowledge Adaptation for Passenger Demand ForecastingPreprint

Li, C., Bai, L., Liu, W., Yao, L., & Waller, S. T. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4053578 · Cited by 5 · see citing works

Assessing and mitigating carbon emission exposure in dynamic multimodal transport networks

Wu, Y., Hu, X., Wang, Y., & Jiang, Y. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106852 · Cited by 3 · see citing works

Understanding Origin-Destination Ride Demand with Interpretable and Scalable Nonnegative Tensor Decomposition

Li, X., Sun, R., Sharpnack, J., & Fan, Y. (2023) · Transportation Science · doi:10.1287/trsc.2022.0101 · Cited by 3 · see citing works

Iterative physics-enhanced residual learning for context-aware traffic assignment under biased inputs

Shi, Y., Long, K., Lam, W. H. K., Li, X., & Ma, W. (2025) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2025.105498 · Cited by 2 · see citing works

CQDFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation

Li, G., Wu, J., He, Y., & Li, D. (2023) · Applied Sciences · doi:10.3390/app132011257 · Cited by 2 · see citing works

Traffic Origin–Destination Flow Prediction Considering Individual Travel Frequency: A Classification-Based Approach

Huang, S., Zhang, C., Zhao, J., & Han, Y. (2024) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.teeng-8704 · Cited by 2 · see citing works

Fault-Adaptive Traffic Demand Estimation Using Network Flow Dynamics

Englezou, Y., Timotheou, S., & Panayiotou, C. G. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3551247 · Cited by 2 · see citing works

The Effect of Dynamic AI Pricing on Tipping Behavior: Empirical Evidence From UberPreprint

Liu, X., Zhang, L., Ma, Y., Ma, W., & Teo, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5080000 · Cited by 2 · see citing works

Impacts of vehicle electrification on large-scale transportation and charging infrastructure: A dynamic network modeling approach

Zou, Q., Qian, S., Detwiler, D. T., & Chhajer, R. (2025) · Case Studies on Transport Policy · doi:10.1016/j.cstp.2025.101401 · Cited by 1 · see citing works

Modeling instantaneous queuing effects in the traffic assignment problem with consideration of demand fluctuations in the modeling period

Shi, Y., Lam, W. H. K., Fu, H., Ho, H. W., Tam, M. L., & Ma, W. (2025) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2025.103248 · Cited by 1 · see citing works

Origin–Destination Demand Estimation Models

Lam, W. H. K., Shao, H., Cao, S., & Yang, H. (2021) · Elsevier eBooks · doi:10.1016/b978-0-08-102671-7.10376-8 · Cited by 1 · see citing works

High-Dimensional Population Flow Time Series Forecasting Via an Interpretable Hierarchical TransformerPreprint

Hu, S., & Xiong, C. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4049754 · Cited by 1 · see citing works

A Unified Demand-Diversion Prediction Approach to Real-Time Group Route Guidance

Xu, T., & Hao, Y. (2020) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-020-2091-x

Estimating Regional Travel Demand from Intraregional Urban Demand Models

Tiwari, R., Kini, B. A., & Bhavathrathan, B. K. (2022) · Lecture notes in civil engineering · doi:10.1007/978-981-19-3494-0_9

Improving Efficiency of Dynamic Travel Demand Estimation Using a Hierarchical Temporal Resolution Method

Chen, B., Yamaguchi, S., Mitomi, T., & Segawa, E. (2024) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii58957.2024.10417257

Knowledge adaptation with model sharing for passenger demand forecasting

Li, C., Bai, L., Yao, L., Waller, S. T., & Liu, W. (2025) · Transportmetrica A Transport Science · doi:10.1080/23249935.2025.2499862

A data fusion framework to infer multi-modal time-dependent origin-destination travel demand matricesPreprint

Vo, K., Ham, S. W., Mishra, S., & Bansal, P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5250605

Systematic inference of online urban travel demand: decomposition, observability, and error correction

Ou, J., Nie, Q., Karimpour, A., Yang, H., Liu, Q., Xia, J., & Lu, J. (2025) · Transportmetrica A Transport Science · doi:10.1080/23249935.2025.2531185

Understanding the traffic pattern impacts of COVID-19 lockdown orders

Yu, H., Zhang, S., Suen, S., Dessouky, M. M., & Ordóñez, F. (2025) · Computers & Industrial Engineering · doi:10.1016/j.cie.2025.111628

Sensitivity analysis of computational graph-based dynamic multi-modal O–D estimation under data and behavioral uncertainties

Ma, X., Nardone, D., Morchio, M., Finan, J., Sannino, P., Pozza, J., Neykov, L., & Qian, S. (2026) · Artificial Intelligence for Transportation · doi:10.1016/j.ait.2026.100063

Network-wide spatiotemporal speed and flow estimation under recurrent and non-recurrent traffic conditions: A physics-informed computational graph framework

Liu, S., Lam, W. H. K., Fu, H., Liao, Q., Wu, J., & Ma, W. (2026) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2026.105832

Recovering large-scale Urban traffic flow data from sparse observations via analytical optimization

Fu, S., Shi, H., Liang, S., Ma, W., & Ran, B. (2026) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1016/j.cacaie.2026.100180

Estimating probabilistic dynamic origin-destination demands using multi-day traffic data on computational graphsPreprint

Ma, W., & Qian, S. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.09229 · Cited by 1 · see citing works

DYNAMIC ORIGIN-DESTINATION DEMAND ESTIMATION AND PREDICTION FOR OFF-LINE AND ON-LINE DYNAMIC TRAFFIC ASSIGNMENT OPERATION

Zhou, X. S. (2004) · University Libraries (University of Maryland) · Cited by 25 · see citing works

A Utility-based Dynamic Demand Estimation Model that Explicitly Accounts for Activity Scheduling and Duration.

Cantelmo, G., Viti, F., Cipriani, E., & Nigro, M. (2017) · Transportation research procedia · doi:10.1016/j.trpro.2017.05.025 · Cited by 15 · see citing works

Dynamic Origin-Destination Demand Estimation with Multiday Link Traffic Counts for Planning Applications

Zhou, X. S., Qin, X. C., & Mahmassani, H. S. (2003) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/1831-04 · Cited by 90 · see citing works

Spatial and Temporal Analyses for Estimation of Origin-Destination Demands by Time of Day Over Year

Shen, L., Shao, H., Wu, T., & Lam, W. H. K. (2019) · IEEE Access · doi:10.1109/access.2019.2909524 · Cited by 6 · see citing works