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Cited by 104Traffic Prediction and Management TechniquesTransportation Planning and OptimizationTraffic control and management

The DOI matches its Crossref record, with a small difference in title or year; 1 of the 1 references it cites were found in a registry or an index.

Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city

Yang, Jiachen, Han, Yurong, Wang, Yafang, Jiang, Bin, Lv, Zhihan, Song, Houbing·2020·Future Generation Computer Systems
DOI 10.1016/j.future.2017.12.012
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Research works printed but not linked: 45

  1. Toral, 2011, Distributed urban traffic applications based on CORBA event services, Int. J. Space-Based Situat. Comput., 1, 86, 10.1504/IJSSC.2011.039110 · DOI
  2. Fatos Xhafa, Victor Naranjo, Leonard Barolli, Makoto Takizawa, On streaming consistency of big data stream processing in heterogenous clutsers, in: International Conference on Network-Based Information Systems. 2015, pp. 476–482. · DOI
  3. Gosman, 2016, Controlling and filtering users data in intelligent transportation system, Future Gener. Comput. Syst.
  4. Raney, 2004, Iterative route planning for large-scale modular transportation simulations, Future Gener. Comput. Syst., 20, 1101, 10.1016/j.future.2003.11.001 · DOI
  5. Ni, 2016, A message efficient intersection control algorithm for intelligent transportation in smart cities, Future Gener. Comput. Syst.
  6. Hou, 2015, Traffic flow forecasting for urban work zones, IEEE Trans. Intell. Transp. Syst., 16, 1761, 10.1109/TITS.2014.2371993 · DOI · Paper page
  7. Doan, 2015, Dynamic system optimal model for multi-OD traffic networks with an advanced spatial queuing model, Transp. Res. C, 51, 41, 10.1016/j.trc.2014.10.011 · DOI
  8. Han, 2015, A robust optimization approach for dynamic traffic signal control with emission considerations, Transp. Res. C, 70, 3, 10.1016/j.trc.2015.04.001 · DOI
  9. Fang, 2012, Optimal lane reservation in transportation network, IEEE Trans. Intell. Transp. Syst., 13, 482, 10.1109/TITS.2011.2171337 · DOI
  10. Menouar, 2017, UAV-enabled intelligent transportation systems for the smart city: Applications and challenges, IEEE Commun. Mag., 55, 22, 10.1109/MCOM.2017.1600238CM · DOI
  11. Zhu, 2016, Public vehicles for future urban transportation, IEEE Trans. Intell. Transp. Syst., PP, 1
  12. Hong, 2016, Precise estimation of connections of metro passengers from smart card data, Transportation, 43, 749, 10.1007/s11116-015-9617-y · DOI
  13. Barreto, 2007, Using clustering analysis in a capacitated location-routing problem, European J. Oper. Res., 179, 968, 10.1016/j.ejor.2005.06.074 · DOI
  14. Kazakovtsev, 2014, Genetic algorithm with fast greedy heuristic for clustering and location problems, Informatica, 229
  15. Esnaf, 2009, A fuzzy clustering-based hybrid method for a multi-facility location problem, J. Intell. Manuf., 20, 259, 10.1007/s10845-008-0233-y · DOI
  16. Mittenthal, 2013, Capacitated hierarchical clustering heuristic for multi depot location-routing problems, Int. J. Logist., 16, 433, 10.1080/13675567.2013.820272 · DOI
  17. Liebig, 2016, Dynamic route planning with real-time traffic predictions, Inf. Syst.
  18. Kotikov, 2015, Optimizing transport-logistic cluster freight flows of a port megacity on the basis of GIS, Appl. Mech. Mater., 725–726, 403
  19. Kaundinya, 2013, A GIS (geographical information system)-based spatial data mining approach for optimal location and capacity planning of distributed biomass power generation facilities: A case study of Tumkur district, India, Energy, 52, 77, 10.1016/j.energy.2013.02.011 · DOI
  20. Yang, 2016, Robust visual tracking using adaptive local appearance model for smart transportation, Multimedia Tools Appl., 1
  21. Vishwanath, 2015, Personalized public transportation: A mobility model and its application to melbourne, IEEE Intell. Transp. Syst. Mag., 7, 37
  22. Melkote, 2001, An integrated model of facility location and transportation network design, Transp. Res. A, 35, 515
  23. Farahani, 2013, A review of urban transportation network design problems, European J. Oper. Res., 229, 281, 10.1016/j.ejor.2013.01.001 · DOI
  24. Zhang, 2011, Data-driven intelligent transportation systems: A survey, IEEE Trans. Intell. Transp. Syst., 12, 1624, 10.1109/TITS.2011.2158001 · DOI · Paper page
  25. Koulakezian, 2015, Robust network design for roadway networks: Unifying framework and application, IEEE Intell. Transp. Syst. Mag., 7, 34
  26. Kim, 2016, Cooperative autonomous driving: A mirror neuron inspired intention awareness and cooperative perception approach, IEEE Intell. Transp. Syst. Mag., 8, 23
  27. Yang, 2015, A novel multivariate performance optimization method based on sparse coding and hyper-predictor learning, Neural Netw., 71, 45, 10.1016/j.neunet.2015.07.011 · DOI
  28. Lu, 2015, An enhanced SPSA algorithm for the calibration of Dynamic Traffic Assignment models, Transp. Res. C, 51, 149, 10.1016/j.trc.2014.11.006 · DOI
  29. Levin, 2015, Improving the convergence of simulation-based dynamic traffic assignment methodologies, Netw. Spat. Econ., 15, 1, 10.1007/s11067-014-9242-x · DOI
  30. Levin, 2015, Intersection auctions and reservation-based control in dynamic traffic assignment, Transp. Res. Rec., 2497, 35, 10.3141/2497-04 · DOI · Paper page
  31. Lahiri, 2006, Economic indicators for the US transportation sector, Transp. Res. A, 40, 872
  32. Nagurney, 2002, Supply chain networks and electronic commerce: A theoretical perspective, Netnomics, 4, 187, 10.1023/A:1021213420802 · DOI
  33. Bonham-Carter, 1994, Geographic information systems for geoscientists-modeling with GIS, Comput. Methods Geosci., 13
  34. Miller, 2000, Gis software for measuring space-time accessibility in transportation planning and analysis, GeoInformatica, 4, 141, 10.1023/A:1009820006075 · DOI
  35. Tao Qu, Steven T. Parker, Bin Ran, Large scale intelligent transportation system traffic detector data archiving, 2015. · DOI
  36. L.I. Qiang, Carl E. Kurt, GIS-based itinerary planning system for multimodal and fixed-route transit network, in: Mid-Continent Transportation Symposium 2000, 2000.
  37. Klose, 2005, Facility location models for distribution system design, European J. Oper. Res., 162, 4, 10.1016/j.ejor.2003.10.031 · DOI
  38. Sherlock, 2002, Shortest path computation: a comparative analysis, Proc. Gisruk
  39. Current, 1990, Multiobjective analysis of facility location decisions, European J. Oper. Res., 49, 295, 10.1016/0377-2217(90)90401-V · DOI
  40. Jie Yu, Gang Len Chang, H.W. Ho, Yue Liu, Variation based online travel time prediction using clustered neural networks, in: International IEEE Conference on Intelligent Transportation Systems, 2008, pp. 85–90. · DOI · Paper page
  41. Zhi Hua Fan, Da Ke Huang, Juan Zi Li, Ke Hong Wang, Morphing cluster dynamics: time series clustering with multipartite graph, in: International Conference on Machine Learning and Cybernetics. 2004, vol.3, pp. 1749–1754. · DOI
  42. Xiaozhe Wang, A. Wirth, Liang Wang, Structure-based statistical features and multivariate time series clustering. 2007, pp. 351–360. · DOI
  43. B. Higgs, M. Abbas, A two-step segmentation algorithm for behavioral clustering of naturalistic driving styles, in: International IEEE Conference on Intelligent Transportation Systems. 2013, pp. 857–862. · DOI
  44. Celebi, 2013, A comparative study of efficient initialization methods for the k-means clustering algorithm, Expert Syst. Appl., 40, 200, 10.1016/j.eswa.2012.07.021 · DOI
  45. Hatamlou, 2012, A combined approach for clustering based on K-means and gravitational search algorithms, Swarm Evol. Comput., 6, 47, 10.1016/j.swevo.2012.02.003 · DOI

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Metadata

Authors
Yang, J., Han, Y., Wang, Y., Jiang, B., Lv, Z., & Song, H.
Affiliations
  • Tianjin University (CN)
  • Qingdao University (CN)
  • Embry-Riddle Aeronautical University Daytona Beach Florida Campus (US)
Corresponding author
Lv, Zhihan
ORCID
Venue
Future Generation Computer Systems
Publisher
Elsevier BV
Published
2020-07
Volume
108
Pages
976-986
ISSN
0167-739X (ISSN-L)
DOI
10.1016/j.future.2017.12.012
OpenAlex
W2778613407
Cited by
104
References
1
Citations by year
2026: 3 · 2025: 15 · 2024: 10 · 2023: 12 · 2022: 16 · 2021: 22 · 2020: 17
FWCI
6.01
Type
journal article
Language
en
Open access
closed
Licence
Locations
Future Generation Computer Systems (publishedVersion)
Funders
  • Natural Science Foundation of Tianjin City (16JCYBJC16000)
  • Natural Science Foundation of Shandong Province (ZR2017QF015)
  • West Virginia Higher Education Policy Commission
  • National Natural Science Foundation of China (61471260)
Topics
  • Social Sciences › Transportation › Traffic Prediction and Management Techniques
  • Social Sciences › Transportation › Transportation Planning and Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
Keywords
dynamic traffic assignment, Internet of Things data, deep belief network, K-means clustering, smart cities, traffic congestion, big data stream processing, high-performance computing

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