Matches in SemOpenAlex for { <https://semopenalex.org/work/W4225007575> ?p ?o ?g. }
- W4225007575 endingPage "16" @default.
- W4225007575 startingPage "1" @default.
- W4225007575 abstract "Timely and precise traffic state estimation of urban roads is significant for urban traffic management and operation. However, most of the advanced studies focus on building complex deep learning structures to learn the spatiotemporal feature of the urban traffic flow, ignoring improving the efficiency of the traffic state estimation. Considering the benefit of the tensor decomposition, we present a novel urban traffic state estimation based on dynamic tensor and Bayesian probabilistic decomposition. Firstly, the real-time traffic speed data are organized in the form of a dynamic tensor which contains the spatiotemporal characteristics of the traffic state. Then, a dynamic tensor Bayesian probabilistic decomposition (DTBPD) approach is built by decomposing the dynamic tensor into the outer product of several vectors. Afterward, the Gibbs sampling method is introduced to calibrate the parameters of the DTBPD models. Finally, the real-world traffic speeds data extracted from online car-hailing trajectories are employed to validate the model performance. Experimental results indicate that the proposed model can greatly reduce computational time while maintaining relatively high accuracy. Meanwhile, the DTBPD model outperforms the state-of-the-art models in terms of both single-step-ahead and multistep-ahead traffic state estimation." @default.
- W4225007575 created "2022-04-29" @default.
- W4225007575 creator A5021572102 @default.
- W4225007575 creator A5038985624 @default.
- W4225007575 creator A5055587617 @default.
- W4225007575 creator A5060119548 @default.
- W4225007575 creator A5060394098 @default.
- W4225007575 creator A5078950392 @default.
- W4225007575 creator A5086326013 @default.
- W4225007575 date "2022-04-26" @default.
- W4225007575 modified "2023-10-17" @default.
- W4225007575 title "Urban Traffic State Estimation with Online Car-Hailing Data: A Dynamic Tensor-Based Bayesian Probabilistic Decomposition Approach" @default.
- W4225007575 cites W1500188831 @default.
- W4225007575 cites W1568416770 @default.
- W4225007575 cites W1579224301 @default.
- W4225007575 cites W1798398164 @default.
- W4225007575 cites W1814521481 @default.
- W4225007575 cites W1973943669 @default.
- W4225007575 cites W1991401171 @default.
- W4225007575 cites W2004353783 @default.
- W4225007575 cites W2040297119 @default.
- W4225007575 cites W2065964232 @default.
- W4225007575 cites W2083238230 @default.
- W4225007575 cites W2085040216 @default.
- W4225007575 cites W2135009431 @default.
- W4225007575 cites W2168332608 @default.
- W4225007575 cites W2179076887 @default.
- W4225007575 cites W2475436493 @default.
- W4225007575 cites W2556487516 @default.
- W4225007575 cites W2579495707 @default.
- W4225007575 cites W2761646508 @default.
- W4225007575 cites W2775717462 @default.
- W4225007575 cites W2791202102 @default.
- W4225007575 cites W2793820729 @default.
- W4225007575 cites W2801200389 @default.
- W4225007575 cites W2809617701 @default.
- W4225007575 cites W2897837027 @default.
- W4225007575 cites W2901504064 @default.
- W4225007575 cites W2902048196 @default.
- W4225007575 cites W2903871660 @default.
- W4225007575 cites W2914619357 @default.
- W4225007575 cites W2916752133 @default.
- W4225007575 cites W2920868908 @default.
- W4225007575 cites W2945622688 @default.
- W4225007575 cites W2948455542 @default.
- W4225007575 cites W2960749694 @default.
- W4225007575 cites W2962917186 @default.
- W4225007575 cites W2973546771 @default.
- W4225007575 cites W2984626942 @default.
- W4225007575 cites W2988815247 @default.
- W4225007575 cites W2995105673 @default.
- W4225007575 cites W3003461214 @default.
- W4225007575 cites W3004584189 @default.
- W4225007575 cites W3016179334 @default.
- W4225007575 cites W3026446390 @default.
- W4225007575 cites W3047365466 @default.
- W4225007575 cites W3048519985 @default.
- W4225007575 cites W3088452585 @default.
- W4225007575 cites W3094471572 @default.
- W4225007575 cites W3112643690 @default.
- W4225007575 cites W3123060870 @default.
- W4225007575 cites W3129211868 @default.
- W4225007575 cites W3138998887 @default.
- W4225007575 cites W3193812480 @default.
- W4225007575 cites W3197364647 @default.
- W4225007575 cites W3210415687 @default.
- W4225007575 cites W3217682896 @default.
- W4225007575 doi "https://doi.org/10.1155/2022/1793060" @default.
- W4225007575 hasPublicationYear "2022" @default.
- W4225007575 type Work @default.
- W4225007575 citedByCount "0" @default.
- W4225007575 crossrefType "journal-article" @default.
- W4225007575 hasAuthorship W4225007575A5021572102 @default.
- W4225007575 hasAuthorship W4225007575A5038985624 @default.
- W4225007575 hasAuthorship W4225007575A5055587617 @default.
- W4225007575 hasAuthorship W4225007575A5060119548 @default.
- W4225007575 hasAuthorship W4225007575A5060394098 @default.
- W4225007575 hasAuthorship W4225007575A5078950392 @default.
- W4225007575 hasAuthorship W4225007575A5086326013 @default.
- W4225007575 hasBestOaLocation W42250075751 @default.
- W4225007575 hasConcept C107673813 @default.
- W4225007575 hasConcept C11413529 @default.
- W4225007575 hasConcept C119857082 @default.
- W4225007575 hasConcept C124101348 @default.
- W4225007575 hasConcept C124681953 @default.
- W4225007575 hasConcept C154945302 @default.
- W4225007575 hasConcept C155281189 @default.
- W4225007575 hasConcept C158424031 @default.
- W4225007575 hasConcept C18903297 @default.
- W4225007575 hasConcept C202444582 @default.
- W4225007575 hasConcept C207512268 @default.
- W4225007575 hasConcept C2986737658 @default.
- W4225007575 hasConcept C33923547 @default.
- W4225007575 hasConcept C38652104 @default.
- W4225007575 hasConcept C41008148 @default.
- W4225007575 hasConcept C42704193 @default.
- W4225007575 hasConcept C48103436 @default.
- W4225007575 hasConcept C49937458 @default.