Selecting Safe Walking Routes to Minimize Exposure Time in Outdoor Environments

  • José Antonio García-DíazEmail author
  • José Ángel Noguera-Arnaldos
  • Isabel María Robles-Marín
  • Francisco García-Sánchez
  • Rafael Valencia-García
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 901)


Walking is a beneficial activity both for the environment and for one’s health. However, the safety of pedestrians may be compromised due to the presence of harmful substances in the atmosphere, areas with poor light conditions, or physical barriers among other factors. In this work we propose a solution that takes into account the risk factors affecting users with the multiple hazards detected in outdoor environments. Hazards are identified by gathering data from heterogeneous sources, such as a network of air-quality monitoring stations and open-data sources. The developed software component has been attached to the AllergyLESS platform, a recommender system that provides safe routes recommendations. In addition, a field test was carried out to test the effectiveness of the system in a real environment with successful results.


Information systems Decision support Multi-objective shortest path Routing problem 


  1. 1.
    WHO: Helmets: a road safety manual for decision-makers and practitioners (2006)Google Scholar
  2. 2.
    Lee, I. M. Buchner, D. M.: The importance of walking to public health. Medicine and science in sports and exercise, (7 Suppl), S512-8 pp. 40 (2008)CrossRefGoogle Scholar
  3. 3.
    Künzli, N., Kaiser, R., Medina, S., Studnicka, M., Chanel, O., Filliger, P., Schneider, J.: Public-health impact of outdoor and traffic-related air pollution: a European assessment. The Lancet 356(9232), 795–801 (2000)CrossRefGoogle Scholar
  4. 4.
    Wood, N., Jones, J., Schmidtlein, M., Schelling, J., Frazier, T.: Pedestrian flow-path modeling to support tsunami evacuation and disaster relief planning in the US Pacific Northwest. Int. J. Disaster Risk Reduct. 18, 41–55 (2016)CrossRefGoogle Scholar
  5. 5.
    Shi, Q., Abdel-Aty, M.: Big data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Emerging Technologies 58, 380–394 (2015)CrossRefGoogle Scholar
  6. 6.
    Russo, B., Gómez, M., Macchione, F.: Pedestrian hazard criteria for flooded urban areas. Natural hazards 69(1), 251–265 (2013)CrossRefGoogle Scholar
  7. 7.
    García-Díaz, J.A., Noguera-Arnaldos, J.A., Hernández-Alcaraz, M.L., Robles-Marín, I.M., García-Sánchez, F., Valencia-García, R.: AllergyLESS. An intelligent recommender system to reduce exposition time to allergens in smart-cities. In: DCAI’18 Distributed Computing and Artificial Intelligence, pp. 61–68. Toledo, Spain (2018)Google Scholar
  8. 8.
    Wang, T., Cardone, G., Corradi, A., Torresani, L., Campbell, A.T.: WalkSafe: a pedestrian safety app for mobile phone users who walk and talk while crossing roads. In: HotMobile’12, Workshop on Mobile Computing Systems & Applications, pp. 5 (2012)Google Scholar
  9. 9.
    You, C.W., et al.: Carsafe app: alerting drowsy and distracted drivers using dual cameras on smartphones. In: AMC Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, pp. 13–26 (2013)Google Scholar
  10. 10.
    Jain, S., Borgiattino, C., Ren, Y., Gruteser, M., Chen, Y., Chiasserini, C.F.: Lookup: enabling pedestrian safety services via shoe sensing. In: AMC Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, pp. 257–271 (2015)Google Scholar
  11. 11.
    Anaya, J.J., Merdrignac, P., Shagdar, O., Nashashibi, F., Naranjo, J.E.: Vehicle to pedestrian communications for protection of vulnerable road users. In: IEEE Intelligent Vehicles Symposium Proceedings, pp. 1037–1042 (2014)Google Scholar
  12. 12.
    Temes Cordovez, R.R., Hernández Fernández de Rojas, D., Moya Fuero, A., Martí Garrido, J.: APP R-ALERGO: allergy-healthy routes in Valencia. In: Back to the Sense of the City: International Monograph Book. pp. 1095–1105 (2016)Google Scholar
  13. 13.
    Oprea, M. M: AIR_POLLUTION_Onto: an ontology for air pollution analysis and control. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, pp. 135–143. Boston (2009)Google Scholar
  14. 14.
    IARC Working Group on the Evaluation of Carcinogenic Risks to Humans: Outdoor air pollution measurement methods. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans 109, p. 9 (2016)Google Scholar
  15. 15.
    Richardson, L. Ruby, S.: RESTful web services, O’Reilly Media, Inc. (2008)Google Scholar
  16. 16.
    Wang: Coverage problems in sensor networks: a survey. ACM computing surveys, 43(4), pp. 32 (2011)CrossRefGoogle Scholar
  17. 17.
    Feige, U.: A threshold of ln n for approximating set cover. J. ACM 45(4), 634–652 (1998)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Xiao, Y., Gao, Z., Qu, Y.: Li, X: A pedestrian flow model considering the impact of local density: Voronoi diagram-based heuristics approach. Trans. Res. Part C: Emerg. Technol. 68, 566–580 (2016)CrossRefGoogle Scholar
  19. 19.
    Austerlitz, F., Dick, C.W., Dutech, C., Klein, E.K., Oddou-Muratorio, S., Smouse, P.E., Sork, V.L.: Using genetic markers to estimate the pollen dispersal curve. Mol. Ecol. 13(4), 937–954 (2004)CrossRefGoogle Scholar
  20. 20.
    Bennett, K.D., Willis, K.J.: Pollen. Tracking environmental change using lake sediments. In: Developments in Paleoenvironmental Research pp. 5–32 (2002)Google Scholar
  21. 21.
    Boulet, L.P., et al.: Comparative degree and type of sensitization to common indoor and outdoor allergens in subjects with allergic rhinitis and/or asthma. Clin. Exp. Allerg. 27(1), pp. 52–59 (1997)Google Scholar
  22. 22.
    Osyczka, A.: Multicriteria optimization for engineering design. Design Optimization, pp. 193–227 (1985)CrossRefGoogle Scholar
  23. 23.
    Ehrgott, M.: Multicriteria optimization (491). Springer Science & Business Media, Berlin (2005)Google Scholar
  24. 24.
    Spangl, W., Schneider, J., Moosmann, L., Nagl, C.: Representativeness and classification of air quality monitoring stations. Umweltbundesamt Report. (2007)Google Scholar
  25. 25.
    Veness, C.: Calculate distance and bearing between two latitude/longitude points using Haversine formula in javascript. movable type scripts (2011)Google Scholar
  26. 26.
    Luxen, D., Vetter, C.: Real-time routing with OpenStreetMap data. In: 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information System, pp. 513–516. Chicago, USA (2011)Google Scholar
  27. 27.
    Neis, P., Zipf, A. Schmitz, S.:–combining open standards and open geodata. The state of the map. In: 2nd OSM Conference, Limerik, Ireland (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information and Systems, Faculty of Computer ScienceUniversity of MurciaMurciaSpain
  2. 2.Proyectos y soluciones tecnológicos avanzadas SLP (Proasistech)MurciaSpain

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