To Topic-Graph

7.552 Topics available

To Map

748 Locations available

230.548 People

PeopleLocationsStatistics
Aksjonov, AndreiEspoo
  • 9
  • 26
  • 47
  • 2022
Wei, Wei
  • 10
  • 50
  • 182
  • 2022
Boussauw, Kobe
  • 8
  • 17
  • 55
  • 2022
Correia, G Homem De Almeida RodriguezDelft
  • 74
  • 203
  • 1k
  • 2022
Liu, YongfengLinköping, Villeurbanne
  • 126
  • 541
  • 2k
  • 2022
Marconi, Edoardo
  • 4
  • 6
  • 6
  • 2022
Šarkan, Branislav
  • 6
  • 15
  • 11
  • 2022
Ruponen, PekkaEspoo
  • 8
  • 21
  • 63
  • 2022
Sorniotti, AldoGuildford
  • 71
  • 295
  • 1k
  • 2022
Orlovska, JuliaGothenburg
  • 3
  • 7
  • 13
  • 2022
Costescu, Dorinela
  • 8
  • 28
  • 13
  • 2022
Bonander, Carl
  • 3
  • 8
  • 27
  • 2022
Kim, Sunghwan
  • 2
  • 6
  • 0
  • 2022
Kluge, M.
  • 13
  • 81
  • 272
  • 2022
Van Vuuren, Detlef PeterBerlin
  • 12
  • 129
  • 1k
  • 2022
Maternini, Giulio
  • 9
  • 32
  • 69
  • 2022
Kouwenhoven, Marco
  • 11
  • 39
  • 288
  • 2022
Zwick, Thomas
  • 51
  • 167
  • 990
  • 2022
Haddad, HediLyon
  • 4
  • 12
  • 10
  • 2022
SHARMA, SANJEEV
  • 1
  • 1
  • 0
  • 2022
Rakhmangulov, Aleksandr Nelevich
  • 6
  • 46
  • 22
  • 2022
Rusca, Aura
  • 5
  • 21
  • 2
  • 2022
Osintsev, Nikita Anatolyevich
  • 6
  • 46
  • 22
  • 2022
Rosca, Mircea Augustin
  • 6
  • 25
  • 15
  • 2022
Yang, BinAalborg
  • 44
  • 247
  • 1k
  • 2022

Wei, Wei

in Cooperation with on an Cooperation-Score of 37%

Places of action

Chart of shared publication
LIU, JINZHAO - 83 publications, 1 shared
Liu, Ling - 2 publications, 2 shared
Peng, Qi-Yuan - 10 publications, 1 shared
Zhang, Biao - 17 publications, 2 shared
Han, Cheng - 1 publications, 1 shared
Mei, Shengwei - 2 publications, 1 shared
Wu, Qiuwei - 83 publications, 5 shared
Ding, Tao - 3 publications, 2 shared
Xie, Rui - 2 publications, 1 shared
Li, Canbing - 4 publications, 1 shared
Liu, Zhaoxi - 8 publications, 1 shared
Huang, Shaojun - 5 publications, 1 shared
Shahidehpour, Mohammad - 5 publications, 1 shared
Wang, Ping - 17 publications, 1 shared
Shafie-khah, Miadreza - 36 publications, 1 shared
Catalao, Joao P. S. - 63 publications, 1 shared
Wu, Danman - 1 publications, 1 shared
Hu, Longxian - 1 publications, 1 shared
Wu, Honghua - 1 publications, 1 shared
Zhao, Xubao - 1 publications, 1 shared
Cheli, Federico - 64 publications, 1 shared
Guo, Gang - 1 publications, 1 shared
Bosso, Nicola - 17 publications, 1 shared
Chang, Chongyi - 1 publications, 1 shared
Cole, Colin - 66 publications, 1 shared
Melzi, Stefano - 6 publications, 1 shared
Di Gialleonardo, Egidio - 10 publications, 1 shared
Wiersma, Pier - 1 publications, 1 shared
Shamdani, Amir - 1 publications, 1 shared
Sebes, Michel - 1 publications, 1 shared
Chollet, Hugues - 2 publications, 1 shared
Zampieri, Nicolò - 5 publications, 1 shared
Spiryagin, Maksym - 76 publications, 1 shared
Burgelman, Nico - 2 publications, 1 shared
Kaza, Guy-léon - 1 publications, 1 shared
Sakalo, Alexey - 1 publications, 1 shared
Luo, Shi Hui - 4 publications, 1 shared
Qiaowen, Bai - 1 publications, 1 shared
Zhaowei, Qu - 1 publications, 1 shared
Ningbo, Cao - 1 publications, 1 shared
Liying, Zhao - 1 publications, 1 shared
Wang, Jiandong - 225 publications, 1 shared
WANG, Li-geng - 1 publications, 1 shared
Chart of publication period
2022
2020
2019
2018
2017

document

Spatio-Temporal Autocorrelation-Based Clustering Analysis for Traffic Condition: A Case Study of Road Network in Beijing

  • LIU, JINZHAO
  • Wei, Wei
  • Liu, Ling
  • Peng, Qi-Yuan
  • Zhang, Biao
  • Han, Cheng
Abstract

Traffic congestion is an increasingly serious problem worldwide. In the last decade, many cities have paid great efforts to establish Intelligent Transportation Systems (ITS), and a large amount of spatio-temporal data from traffic monitoring system is also accumulated. However, with the devices and facilities of ITS getting completed, effectiveness of ITS practices is always restricted by traffic information fusion and exaction technique. Traffic condition-determining is a crucial issue for Advanced Traffic Management Systems, on which many researchers have done profound studies. The existing studies are mostly focused on traffic condition recognition at a certain road and time point; while in practice, it’s more meaningful how different kinds of traffic condition are correlated and distributed in space-time. Therefore, in this research we present an improved spatio-temporal Moran scatterplot (STMS), by which traffic conditions are pre-classified into four types: homogenous uncongested traffic, heterogeneous uncongested traffic, homogenous congested traffic and heterogeneous congested traffic. Then at the basis of STMS, a novel spatio-temporal clustering method combining pre-classification of traffic condition is proposed. Finally, the feasibility and effectiveness of the clustering methodology are demonstrated by case studies of Beijing. Result shows that the proposed clustering method can not only effectively reveal the relation of traffic demand to road network facilities, but also recognize the road sections where congestion originates or gets alleviated in the network, which provides foundations for traffic managers to alleviate congestion and improve urban transport services.

Topics
  • traffic congestion
  • monitoring
  • data
  • case study
  • transport demand
  • urban transportation
  • road network
  • foundation
  • traffic management
  • highway traffic control
  • traffic manager
  • traffic monitoring system
  • autocorrelation
  • advanced traffic management system