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@ARTICLE{Xu:1024401,
      author       = {Xu, Qiancheng and Yuan, Zhilu and Guo, Renzhong and He,
                      Biao and Chraibi, Mohcine},
      title        = {{A}nalysis and modeling of detours in pedestrian
                      operational navigation},
      journal      = {Transportation research / Part C},
      volume       = {162},
      issn         = {0968-090X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2024-02144},
      pages        = {104584},
      year         = {2024},
      abstract     = {Traditional operational navigation models for pedestrian
                      dynamics demonstrate limitations in reproducing the circle
                      antipode experiment, an artificially designed
                      multi-directional flow scenario. In the experiment,
                      pedestrians take detours to avoid the congestion caused by
                      others taking straight paths. Although the pedestrian detour
                      action is commonly incorporated into the route choice model,
                      it frequently goes unaddressed in the operational navigation
                      model, resulting in a disparity between simulation outcomes
                      and empirical observations. To reveal the mechanism
                      underlying pedestrian detours in the circle antipode
                      experiment, this study employed the K-means clustering
                      method rather than using a threshold approach to categorize
                      the experimental participants into groups taking direct or
                      detour routes. Following this, a heuristic function is
                      formulated to determine the desired direction of agents in a
                      collision-free velocity model, reflecting the trade-off
                      between shorter routes and faster speeds. The parameter of
                      the proposed function is calibrated using the proportion of
                      agents choosing detours, where the route types of agents are
                      identified by a classifier based on the random forest.
                      Compared to two traditional models that do not consider
                      detours, the proposed model can more realistically reproduce
                      the trajectory distributions, the travel time, the route
                      length, and the time series of relevant variables in the
                      circle antipode experiment. The study offers insights into
                      employing machine learning methodologies for analyzing
                      pedestrian flow, validating pedestrian dynamics models, and
                      providing an accurate simulation tool for designing
                      transportation facilities and crowd management at large
                      events.},
      cin          = {IAS-7},
      ddc          = {380},
      cid          = {I:(DE-Juel1)IAS-7-20180321},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001218999200001},
      doi          = {10.1016/j.trc.2024.104584},
      url          = {https://juser.fz-juelich.de/record/1024401},
}