16.7.2.6.21 Travel Time, Evaluation

Moylan, E.K.M., Rashidi, T.H.,
Latent-Segmentation, Hazard-Based Models of Travel Time,
ITS(18), No. 8, August 2017, pp. 2174-2180.
IEEE DOI 1708
Analytical models, Context, Context modeling, Data models, Hazards, Meteorology, Probabilistic logic, Travel time variability, congestion, hazard-based analysis, latent segmentation formulation, traffic, modeling BibRef

Shi, C.Y.[Chao-Yang], Chen, B.Y.[Bi Yu], Li, Q.Q.[Qing-Quan],
Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data,
IJGI(6), No. 8, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Cebecauer, M.[Matej], Jenelius, E.[Erik], Burghout, W.[Wilco],
Integrated framework for real-time urban network travel time prediction on sparse probe data,
IET-ITS(12), No. 1, February 2018, pp. 66-74.
DOI Link 1801
BibRef

Li, W.[Wan], Cheng, D.H.[Dan-Hong], Bian, R.J.[Rui-Jie], Ishak, S.[Sherif], Osman, O.A.[Osama A.],
Accounting for travel time reliability, trip purpose and departure time choice in an agent-based dynamic toll pricing approach,
IET-ITS(12), No. 1, February 2018, pp. 58-65.
DOI Link 1801
BibRef

Jenelius, E., Koutsopoulos, H.N.,
Urban Network Travel Time Prediction Based on a Probabilistic Principal Component Analysis Model of Probe Data,
ITS(19), No. 2, February 2018, pp. 436-445.
IEEE DOI 1802
Computational modeling, Data models, Load modeling, Predictive models, Principal component analysis, probe data BibRef

Zhang, D.T.[De-Tian], Chow, C.Y.[Chi-Yin], Liu, A.[An], Zhang, X.L.[Xiang-Liang], Ding, Q.Z.[Qing-Zhu], Li, Q.[Qing],
Efficient evaluation of shortest travel-time path queries through spatial mashups,
GeoInfo(22), No. 1, January 2018, pp. 3-28.
Springer DOI 1802
BibRef

Zhu, L.[Lin], Guo, F.[Fangce], Polak, J.W.[John W.], Krishnan, R.[Rajesh],
Urban link travel time estimation using traffic states-based data fusion,
IET-ITS(12), No. 7, September 2018, pp. 651-663.
DOI Link 1808
BibRef

Tang, K., Chen, S., Liu, Z.,
Citywide Spatial-Temporal Travel Time Estimation Using Big and Sparse Trajectories,
ITS(19), No. 12, December 2018, pp. 4023-4034.
IEEE DOI 1812
Roads, Trajectory, Estimation, Tensile stress, Hidden Markov models, Correlation, Global Positioning System, Data-driven, occurrence probability BibRef

Yang, S.[Shu], Ou, J.[Jishun], Feng, Y.H.[Yi-Heng], Wang, Y.[Yuan],
Freeway travel time estimation based on the general motors model: a genetic algorithm calibration framework,
IET-ITS(13), No. 7, July 2019, pp. 1154-1163.
DOI Link 1906
BibRef

Sharmila, R.B., Velaga, N.R.[Nagendra R.], Kumar, A.[Akhilesh],
SVM-based hybrid approach for corridor-level travel-time estimation,
IET-ITS(13), No. 9, September 2019, pp. 1429-1439.
DOI Link 1908
Multiple roads and routes in a transportation corridor. Cars and buses. BibRef

Karimpour, A.[Abolfazl], Ariannezhad, A.[Amin], Wu, Y.J.[Yao-Jan],
Hybrid data-driven approach for truck travel time imputation,
IET-ITS(13), No. 10, October 2019, pp. 1518-1524.
DOI Link 1909
BibRef

van Essen, J.T., Correia, G.H.A.,
Exact Formulation and Comparison Between the User Optimum and System Optimum Solution for Routing Privately Owned Automated Vehicles,
ITS(20), No. 12, December 2019, pp. 4567-4578.
IEEE DOI 2001
Automobiles, Routing, Urban areas, Automation, Mathematical model, Autonomous vehicles, integer linear programming, value of travel time BibRef

Prokhorchuk, A., Dauwels, J., Jaillet, P.,
Estimating Travel Time Distributions by Bayesian Network Inference,
ITS(21), No. 5, May 2020, pp. 1867-1876.
IEEE DOI 2005
Global Positioning System, Estimation, Bayes methods, Public transportation, Trajectory, Data models, Bayesian network inference BibRef

Zhu, Y., He, Z., Sun, W.,
Network-Wide Link Travel Time Inference Using Trip-Based Data From Automatic Vehicle Identification Detectors,
ITS(21), No. 6, June 2020, pp. 2485-2495.
IEEE DOI 2006
Detectors, Estimation, Resource management, Probabilistic logic, Real-time systems, Task analysis, Stochastic processes, travel time allocation BibRef

Gundlegård, D.[David], Karlsson, J.M.[Johan M.],
Integrated tracking and route classification for travel time estimation based on cellular network signalling data,
IET-ITS(14), No. 9, September 2020, pp. 1087-1096.
DOI Link 2008
BibRef

Contreras, S., Kachroo, P.,
The Viscosity Solution for Hamilton Jacobi Travel Time Dynamics,
ITS(21), No. 11, November 2020, pp. 4715-4724.
IEEE DOI 2011
Mathematical model, Viscosity, Vehicle dynamics, Jacobian matrices, Intelligent transportation systems, Entropy, viscosity BibRef

Chiou, J.M., Liou, H.T., Chen, W.H.,
Modeling Time-Varying Variability and Reliability of Freeway Travel Time Using Functional Principal Component Analysis,
ITS(22), No. 1, January 2021, pp. 257-266.
IEEE DOI 2012
Reliability, Density functional theory, Logic gates, Traffic control, Analytical models, Kernel, Estimation, travel time reliability BibRef

Wang, Q., Xu, C., Zhang, W., Li, J.,
GraphTTE: Travel Time Estimation Based on Attention-Spatiotemporal Graphs,
SPLetters(28), 2021, pp. 239-243.
IEEE DOI 2102
Roads, Trajectory, Estimation, Network topology, Indexes, Spatiotemporal phenomena, Convolution, Attention mechanism, travel time estimation BibRef

Li, Z.H.[Zi-Hao], Chen, H.[Hui], Yan, W.T.[Wen-Tao],
Exploring Spatial Distribution of Urban Park Service Areas in Shanghai Based on Travel Time Estimation: A Method Combining Multi-Source Data,
IJGI(10), No. 9, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Abioye, O.F.[Olumide F.], Dulebenets, M.A.[Maxim A.], Kavoosi, M.[Masoud], Pasha, J.[Junayed], Theophilus, O.[Oluwatosin],
Vessel Schedule Recovery in Liner Shipping: Modeling Alternative Recovery Options,
ITS(22), No. 10, October 2021, pp. 6420-6434.
IEEE DOI 2110
Schedules, Companies, Uncertainty, Containers, Delays, Mathematical model, Reliability, Liner shipping, vessel scheduling, profit loss BibRef

Kwak, S.[Semin], Geroliminis, N.[Nikolas],
Travel Time Prediction for Congested Freeways With a Dynamic Linear Model,
ITS(22), No. 12, December 2021, pp. 7667-7677.
IEEE DOI 2112
Predictive models, Traffic control, Intelligent transportation systems, Data models, minimum mean square error BibRef

Nian-Z, G.Y.[Guang-Yue], Sun, J.[Jian], Huang, J.Y.[Jian-Yun],
Exploring the Effects of Urban Built Environment on Road Travel Speed Variability with a Spatial Panel Data Model,
IJGI(10), No. 12, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Both, A.[Alan], Gunn, L.[Lucy], Higgs, C.[Carl], Davern, M.[Melanie], Jafari, A.[Afshin], Boulange, C.[Claire], Giles-Corti, B.[Billie],
Achieving 'Active' 30 Minute Cities: How Feasible Is It to Reach Work within 30 Minutes Using Active Transport Modes?,
IJGI(11), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Zhang, J.F.[Jun-Feng], Peng, Z.[Zihan], Yang, C.[Chunwei], Wang, B.[Bin],
Data-driven flight time prediction for arrival aircraft within the terminal area,
IET-ITS(16), No. 2, 2022, pp. 263-275.
DOI Link 2201
BibRef

Tang, X.M.[Xin-Min], Ji, X.Q.[Xiao-Qi], Liu, J.[Jinan],
Predicting aircraft taxiing estimated time of arrival by cluster analysis,
IET-ITS(16), No. 2, 2022, pp. 252-262.
DOI Link 2201
BibRef

Yildirim, U.M.[U. Mahir], Çatay, B.[Bülent],
An Enhanced Network-Consistent Travel Speed Generation Scheme on Time-Dependent Shortest Path and Routing Problems,
ITS(23), No. 2, February 2022, pp. 873-884.
IEEE DOI 2202
Roads, Sensors, Routing, Urban areas, Benchmark testing, Global Positioning System, Routing, time-dependent travel times, minimum cost path BibRef

Feng, L.[Liang], Huang, Y.X.[Yu-Xiao], Tsang, I.W.[Ivor W.], Gupta, A.[Abhishek], Tang, K.[Ke], Tan, K.C.[Kay Chen], Ong, Y.S.[Yew-Soon],
Towards Faster Vehicle Routing by Transferring Knowledge From Customer Representation,
ITS(23), No. 2, February 2022, pp. 952-965.
IEEE DOI 2202
Vehicle routing, Routing, Knowledge transfer, Search problems, Optimization methods, Computer science, Vehicle routing, population-based search BibRef

Dabiri, A.[Azita], Hegyi, A.[Andreas], Hoogendoorn, S.[Serge],
Optimized Speed Trajectories for Cyclists, Based on Personal Preferences and Traffic Light Information: A Stochastic Dynamic Programming Approach,
ITS(23), No. 2, February 2022, pp. 777-793.
IEEE DOI 2202
Timing, Urban areas, Dynamic programming, Trajectory, Fuels, Heuristic algorithms, Acceleration, Speed advice, cycling, stochastic dynamic programming BibRef

Tang, K.[Keshuang], Chen, S.[Siqu], Cao, Y.[Yumin], Li, X.S.[Xiao-Song], Zang, D.[Di], Sun, J.[Jian], Ji, Y.[Yangbeibei],
Short-Term Travel Speed Prediction for Urban Expressways: Hybrid Convolutional Neural Network Models,
ITS(23), No. 3, March 2022, pp. 1829-1840.
IEEE DOI 2203
Predictive models, Data models, Feature extraction, Neural networks, Spatiotemporal phenomena, Detectors, hybrid models BibRef

Fang, H.[Hao], Chen, C.H.[Chi-Hua], Chen, D.W.[De-Wang], Hwang, F.J.[Feng-Jang],
Neuron-Network-Based Mixture Probability Model for Passenger Walking Time Distribution Estimation,
IEICE(E105-D), No. 5, May 2022, pp. 1112-1115.
WWW Link. 2205
BibRef

Sun, Y.[Yiwen], Fu, K.[Kun], Wang, Z.[Zheng], Zhou, D.H.[Dong-Hua], Wu, K.[Kailun], Ye, J.P.[Jie-Ping], Zhang, C.S.[Chang-Shui],
CoDriver ETA: Combine Driver Information in Estimated Time of Arrival by Driving Style Learning Auxiliary Task,
ITS(23), No. 5, May 2022, pp. 4037-4048.
IEEE DOI 2205
Vehicles, Task analysis, Trajectory, Data models, Predictive models, Deep learning, Regression tree analysis, transfer knowledge BibRef

Batista, S.F.A.[Sérgio F. A.], Ingole, D.[Deepak], Leclercq, L.[Ludovic], Menéndez, M.[Mónica],
The Role of Trip Lengths Calibration in Model-Based Perimeter Control Strategies,
ITS(23), No. 6, June 2022, pp. 5176-5186.
IEEE DOI 2206
Urban areas, Predictive models, Vehicles, Optimal control, Calibration, Uncertainty, Vehicle dynamics, Dynamic trip lengths, multi-regional networks BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Transit Routing, Evaluation .

Last update:Jun 27, 2022 at 12:58:02