The challenge of cultural heritage visitation: A problem demanding new approaches

Tangible cultural heritage includes assets dating across millennia characterising many of the world’s major cityscapes and landscapes. In many cases massive and spectacular architecture has been re-purposed for mass-visitation from a burgeoning tourist economy creating wealth and employment across transportation, hospitality and heritage sectors. Historic building visitation is increasing at 6-7% annually, and however promising in the short term, such a trend is ultimately unsustainable. Limited space, and queueing and close packing of visitors diminishes the quality of the visitor experience and degrades the built environment via pressure of numbers and reduces personal safety both for visitors and employees. However, the problem is much more complex and must also address the challenge of visitor experience for different users, financial, legal and operational constraints and a profoundly changing visitor-demographic which clearly identifies that past practice will not be suitable for operating such heritage sites in the future. Added to such known unknowns are the unknown unknowns concerning the uncertainties of future travel, economic demands placed on visitor attractions, and political and security uncertainty. Rationalising possible solutions to managing visitors in the 21st and subsequent centuries demands the description and parameterisation of a complex-sociotechnical system such as SEIPS 2.0. This must first identify the known domains, and seek approaches, data and innovative future research to inform policy sufficient to persuade authorities locally, nationally and internationally that the cost of doing nothing is too high a price to pay for the legacy which so many of us enjoy.

[1]  Xiao Wang,et al.  Pedestrian Attribute Recognition: A Survey , 2019, Pattern Recognit..

[2]  Yun Fu,et al.  Human Action Recognition and Prediction: A Survey , 2018, International Journal of Computer Vision.

[3]  Uday Pratap Singh,et al.  Vision-Based Gait Recognition: A Survey , 2018, IEEE Access.

[4]  Kathrin Maria Gerling,et al.  Virtual reality crowd simulation: effects of agent density on user experience and behaviour , 2018, Virtual Reality.

[5]  Gianpaolo Francesco Trotta,et al.  Computer vision and deep learning techniques for pedestrian detection and tracking: A survey , 2018, Neurocomputing.

[6]  Ren-Yong Guo,et al.  Potential-based dynamic pedestrian flow assignment , 2018, Transportation Research Part C: Emerging Technologies.

[7]  Iasonas Kokkinos,et al.  DensePose: Dense Human Pose Estimation in the Wild , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Barbara Caputo,et al.  Looking beyond appearances: Synthetic training data for deep CNNs in re-identification , 2017, Comput. Vis. Image Underst..

[9]  John P. Isaacs,et al.  The Effect of Person Order on Egress Time: A Simulation Model of Evacuation From a Neolithic Visitor Attraction , 2017, Hum. Factors.

[10]  Nicu Sebe,et al.  Abnormal event detection in videos using generative adversarial nets , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[11]  So-Yeon Yoon,et al.  Shopping Behavioral Intentions Contributed by Store Layout and Perceived Crowding: An Exploratory Study Using Computer Walk–Through Simulation , 2016 .

[12]  Alexandre Nicolas,et al.  Pedestrian flows through a narrow doorway: Effect of individual behaviours on the global flow and microscopic dynamics , 2016 .

[13]  Nitin Pundir,et al.  Pedestrian Flow Characteristics Studies: A Review , 2015 .

[14]  Panagiotis Petridis,et al.  Learning cultural heritage by serious games , 2014 .

[15]  Lorenza Manenti,et al.  An Innovative Scenario for Pedestrian Data Collection: The Observation of an Admission Test at the University of Milano-Bicocca , 2014 .

[16]  P. Carayon,et al.  SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients , 2013, Ergonomics.

[17]  R. Sturm,et al.  Morbid obesity rates continue to rise rapidly in the United States , 2013, International Journal of Obesity.

[18]  Jacob A. Benfield,et al.  Visitor Self-Report Behavior Mapping as a Tool for Recording Exhibition Circulation , 2012 .

[19]  B. Swinburn,et al.  The global obesity pandemic: shaped by global drivers and local environments , 2011, The Lancet.

[20]  Lorenza Manenti,et al.  Towards an Agent-Based Proxemic Model for Pedestrian and Group Dynamic , 2010, WOA.

[21]  B. Bai,et al.  Affect, Travel Motivation, and Travel Intention: a Senior Market , 2009 .

[22]  Ameya Shendarkar,et al.  Crowd simulation for emergency response using BDI agents based on immersive virtual reality , 2008, Simul. Model. Pract. Theory.

[23]  Dirk Helbing,et al.  From Crowd Dynamics to Crowd Safety: a Video-Based Analysis , 2008, Adv. Complex Syst..

[24]  Norman I. Badler,et al.  Being a part of the crowd: towards validating VR crowds using presence , 2008, AAMAS.

[25]  Xiaonan Xue,et al.  Self-reported difficulty in climbing up or down stairs in nondisabled elderly. , 2008, Archives of physical medicine and rehabilitation.

[26]  Mohammad D. Al-Tahat,et al.  Modelling of Public Building Evacuation Processes , 2007 .

[27]  J. Bongaarts United Nations Department of Economic and Social Affairs, Population Division World Mortality Report 2005 , 2006 .

[28]  J. Griffiths In the local , 2006 .

[29]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[30]  S. Simon,et al.  Biomechanical gait analysis in obese men. , 1991, Archives of physical medicine and rehabilitation.