J. V. Lint

发表

Analysis of network-wide transit passenger flows based on principal component analysis

Ding Luo, Oded Cats, J. W. C. van Lint, 2017, 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

Investigating Potential Transit Ridership by Fusing Smartcard Data and GSM Data

O. Cats, N. V. Oort, J. V. Lint, 2017 .

Improving a Travel-Time Estimation Algorithm by Using Dual Loop Detectors

J. V. van Lint, N. J. V. Zijpp, J. V. Lint, 2003 .

Assessment of multi class kinematic wave models

C. Vuik, S. Hoogendoorn, J. V. Lint, 2012 .

Travel Time Reliability on Freeways

Huizhao Tu, J. W. C. van Lint, Henk J van Zuylen, 2004 .

Emergency evacuation of Tehran city: Simulation results

S. Hoogendoorn, J. V. Lint, Mahtab Joueiai, 2014 .

Multi-scale traffic flow theory and modelling

Serge P. Hoogendoorn, Mahtab Joueiai, J.W.C. Van Lint, 2012 .

A genetic algorithm-based method for improving quality of travel time prediction intervals

Saeid Nahavandi, Abbas Khosravi, J. W. C. van Lint, 2011 .

Monitoring and Predicting Freeway Travel Time Reliability: Using Width and Skew of Day-to-Day Travel Time Distribution

H. J. Van Zuylen, J. W. C. van Lint, H. Zuylen, 2005 .

Bayesian committee of neural networks to predict travel times with confidence intervals

H. J. Van Zuylen, J. W. C. van Lint, C. P. Ij. van Hinsbergen, 2009 .

Accurate freeway travel time prediction with state-space neural networks under missing data

H. J. Van Zuylen, Serge P. Hoogendoorn, J. W. C. van Lint, 2005 .

Prediction Intervals to Account for Uncertainties in Travel Time Prediction

Saeid Nahavandi, Abbas Khosravi, Douglas C. Creighton, 2011, IEEE Transactions on Intelligent Transportation Systems.

Incorporating the structural information into the estimation of OD matrices

S. Hoogendoorn, J. V. Lint, Tamara Djukic, 2013 .

Day-to-day and seasonal regularity of network passenger delay for metro networks pdf

Oded Cats, J. W. C. van Lint, Panchamy Krishnakumari, 2021, ArXiv.

A Robust and Efficient Method for Fusing Heterogeneous Data from Traffic Sensors on Freeways

Serge P. Hoogendoorn, J. W. C. van Lint, S. Hoogendoorn, 2010, Comput. Aided Civ. Infrastructure Eng..

Empirical Evaluation of New Robust Travel Time Estimation Algorithms

J. W. C. van Lint, J. V. van Lint, J. V. Lint, 2010 .

Automatic speed-bias correction with flow-density relationships

Serge P. Hoogendoorn, Yufei Yuan, Jos L. M. Vrancken, 2010, 2010 International Conference on Networking, Sensing and Control (ICNSC).

Network-Wide Traffic State Estimation Using Loop Detector and Floating Car Data

Serge P. Hoogendoorn, Yufei Yuan, J. W. C. van Lint, 2014, J. Intell. Transp. Syst..

Real-Time Lagrangian Traffic State Estimator for Freeways

Serge P. Hoogendoorn, Yufei Yuan, R. Eddie Wilson, 2012, IEEE Transactions on Intelligent Transportation Systems.

Freeway traffic state estimation using extended Kalman filter for first-order traffic model in Lagrangian coordinates

Serge P. Hoogendoorn, Yufei Yuan, Jos L. M. Vrancken, 2011, 2011 International Conference on Networking, Sensing and Control.

Genealogy of traffic flow models

Serge P. Hoogendoorn, Cornelis Vuik, J. W. C. van Lint, 2015, EURO J. Transp. Logist..

Fastlane: New Multiclass First-Order Traffic Flow Model

Serge P. Hoogendoorn, J. W. C. van Lint, Marco Schreuder, 2008 .

Quantifying travellers’ evaluation of waiting time uncertainty in public transport networks

O. Cats, J. V. Lint, S. Shelat, 2021 .

Calibrating Route Choice Sets for an Urban Public Transport Network using Smart Card Data

Oded Cats, Niels van Oort, J. W. C. van Lint, 2019, 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS).

The impact of traffic dynamics on macroscopic fundamental diagram

Serge P. Hoogendoorn, Victor L. Knoop, J. W. C. van Lint, 2013 .

Data requirements for traffic control on a macroscopic level

Serge P. Hoogendoorn, Victor L. Knoop, J. W. C. van Lint, 2011 .

Route Advice Based on Subnetwork Accumulations - Control based on the macroscopic fundamental diagram

Serge P. Hoogendoorn, Victor L. Knoop, J. W. C. van Lint, 2011 .

Big data in transportation and traffic engineering

Eleni I. Vlahogianni, J. W. C. van Lint, Byungkyu Park, 2015 .

Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles

V. Knoop, J. V. Lint, S. Calvert, 2023, ArXiv.

Understanding Network Traffic States using Transfer Learning

Oded Cats, J. W. C. van Lint, Alan Perotti, 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

Online Learning Solutions for Freeway Travel Time Prediction

J. W. C. van Lint, J. V. Lint, J.W.C. van Lint, 2008, IEEE Transactions on Intelligent Transportation Systems.

Two distinct ways of using kalman filters to predict urban arterial travel time

Ke Zhang, Hao Liu, Henk J. van Zuylen, 2006, 2006 IEEE Intelligent Transportation Systems Conference.

Generic solutions for consistency problems in multi-scale traffic flow models - Analysis and preliminary results

Serge P. Hoogendoorn, J. W. C. van Lint, Mahtab Joueiai, 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

Stated choices and simulated experiences: Differences in the value of travel time and reliability

Michiel C.J. Bliemer, Stephane Hess, J.W.C. van Lint, 2021, Transportation Research Part C: Emerging Technologies.

Methodology for efficient real time OD demand estimation on large scale networks

Serge P. Hoogendoorn, J. W. C. van Lint, Tamara Djukic, 2014 .

Efficient real time OD matrix estimation based on Principal Component Analysis

Gunnar Flötteröd, Serge P. Hoogendoorn, J. W. C. van Lint, 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

A generic multi-scale framework for microscopic traffic simulation part II – Anticipation Reliance as compensation mechanism for potential task overload

J. W. C. van Lint, Simeon C. Calvert, W. J. Schakel, 2020, Transportation Research Part B: Methodological.

A generic multi-level framework for microscopic traffic simulation—Theory and an example case in modelling driver distraction

J. W. C. van Lint, S. C. Calvert, J. V. Lint, 2018, Transportation Research Part B: Methodological.

Will Automated Vehicles Negatively Impact Traffic Flow

J. W. C. van Lint, S. C. Calvert, Wouter J. Schakel, 2017 .

Exploring the Effects of Perception Errors and Anticipation Strategies on Traffic Accidents - A Simulation Study

Meng Wang, Alexander Verbraeck, Hans van Lint, 2017, AHFE.

Neural Network Committee to Predict Travel Times: Comparison of Bayesian Evidence Approach to the Use of a Validation Set

Henk J. van Zuylen, J. W. C. van Lint, Chris P. IJ. van Hinsbergen, 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

Bayesian Combination of Travel Time Prediction Models

J. W. C. van Lint, C. P. I. J. van Hinsbergen, J. V. van Lint, 2008 .

Advanced Traffic Monitoring (ATMO) for Sustainable Traffic Management Experiences and Results of 5 Years of Collaborative Research in the Netherlands

Arjan J van Binsbergen, J. W. C. van Lint, A J Valkenberg, 2009 .

Short Term Traffic Prediction Models

J. W. C. van Lint, F M Sanders, C. P. I. J. van Hinsbergen, 2007 .

Dual EKF State and Parameter Estimation in Multi-Class First-Order Traffic Flow Models

Andreas Hegyi, Serge P. Hoogendoorn, J. W. C. van Lint, 2008 .

Two fast implementations of the Adaptive Smoothing Method used in highway traffic state estimation

Serge P. Hoogendoorn, Martin Treiber, J. W. C. van Lint, 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

Introduction to the Special Issue on Emergent Cooperative Technologies in Intelligent Transportation Systems

Mashrur Chowdhury, Miguel Ángel Sotelo, Urbano Nunes, 2012, IEEE Trans. Intell. Transp. Syst..