Reducing Individual Heat Stress through Path Planning

Heat stress is a serious risk, which affects groups such as the elderly or patients with chronic diseases in particular, and is especially pronounced in cities. The ageing of society, progressive urbanization and climate change are increasing the risk of people being affected by heat stress. One way to reduce the risk is to adapt everyday behaviour. To encourage and support such a change of behaviour, we propose a two-step approach. The first step is a route planner for pedestrians which can find a route with minimal heat exposure. The second step is a tool that helps the user to select the time of day with minimal heat exposure to venture outside. The route planner is then used to calculate the heat stress and present the optimal route at that point in time. We evaluate our approach for the city of Karlsruhe. Our results show that the combined approach, as well as its individual steps, can reduce heat exposure and therefore the heat stress for typical daily tasks in a European city.

[1]  Ariel Orda,et al.  Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length , 1990, JACM.

[2]  G. Laschewski,et al.  The perceived temperature – a versatile index for the assessment of the human thermal environment. Part A: scientific basics , 2011, International Journal of Biometeorology.

[3]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[4]  Robert L. Smith,et al.  Fastest Paths in Time-dependent Networks for Intelligent Vehicle-Highway Systems Application , 1993, J. Intell. Transp. Syst..

[5]  Hassan A. Karimi,et al.  Health-optimal routing in pedestrian navigation services , 2012, HealthGIS '12.

[6]  Lela Prashad,et al.  Urban Heat Island , 2014, Encyclopedia of Remote Sensing.

[7]  D. Anderson,et al.  Algorithms for minimization without derivatives , 1974 .

[8]  P. Fanger Assessment of man's thermal comfort in practice , 1973, British journal of industrial medicine.

[9]  G. Jendritzky,et al.  The Perceived Temperature : The Method of the Deutscher Wetterdienst for the Assessment of Cold Stress and Heat Load for the Human Body , 2000 .

[10]  David Hasenfratz,et al.  Enabling Large-Scale Urban Air Quality Monitoring with Mobile Sensor Nodes , 2015 .

[11]  Urban climate change-related effects on extreme heat events in Rostock, Germany , 2016, Urban Ecosystems.

[12]  S. Davis,et al.  Thermoregulation in multiple sclerosis. , 2010, Journal of applied physiology.

[13]  Sonja Peterson,et al.  Costs of climate change: The effects of rising temperatures on health and productivity , 2008 .

[14]  Rupa Basu,et al.  High ambient temperature and mortality: a review of epidemiologic studies from 2001 to 2008 , 2009, Environmental health : a global access science source.

[15]  Dorothea Wagner,et al.  Algorithmics of Large and Complex Networks - Design, Analysis, and Simulation [DFG priority program 1126] , 2009, Algorithmics of Large and Complex Networks.

[16]  R. Steadman The Assessment of Sultriness. Part I: A Temperature-Humidity Index Based on Human Physiology and Clothing Science , 1979 .

[17]  Yoshito Tobe,et al.  A framework for pedestrian comfort navigation using multi-modal environmental sensors , 2013, Pervasive Mob. Comput..

[18]  R. Stull,et al.  Meteorology for Scientists and Engineers , 1999 .

[19]  T. Oke The energetic basis of the urban heat island , 1982 .

[20]  Peter Sanders,et al.  Engineering Route Planning Algorithms , 2009, Algorithmics of Large and Complex Networks.

[21]  R. G. Steadman,et al.  The Assessment of Sultriness. Part II: Effects of Wind, Extra Radiation and Barometric Pressure on Apparent Temperature , 1979 .

[22]  George Havenith,et al.  The Universal Thermal Climate Index UTCI in operational use , 2010 .