Citation

Gao LN, Tao F, Ma PL, Wang CY, Kong W, Chen WK, Zhou T. J. Transp. Health 2022; 24: e101314.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.jth.2021.101314

PMID

unavailable

Abstract

With the development of the economy and the accumulation of social wealth, urban residents have begun to give more attention to quality of life than to material needs. Consequently, environmental factors that affect human health, such as air quality, have become a new focus when traveling. A travel scheme with relatively low pollutant exposure to travelers can not only improve their health and satisfy their goals but also benefit social stability and sustained progress. However, low spatiotemporal resolution and coarse spatial details of the distribution of PM2.5 (particles with an aerodynamic diameter of 2.5 μm or less) educe the success rate of short-distance healthy travel route planning. This paper proposes a short-distance healthy route planning approach that is based on PM2.5 retrieval with high spatiotemporal resolution and a dynamic Dijkstra algorithm. First, fine spatial resolution images, meteorological data, and socioeconomic data are used to retrieve the spatial distribution of PM2.5 concentration in hourly intervals via a back-propagation neural network (BPNN). Second, a PM2.5 concentration value is obtained for each road section, and the harm degree to the human body is calculated as the weight of each road section. Then, the healthiest route is obtained based on the Dijkstra algorithm. Finally, the route planning effectiveness is verified by comparing the PM2.5 potential dose descending rate between the healthy route and the shortest route. The results show that the coefficient of determination (R2) of the PM2.5 retrieval approach that is based on multisource data and BPNN is 0.85, which can ensure the accuracy of the PM2.5 data at the street level. On this basis, the potential dose reduction rate of the healthy route can reach up to 20%, which proves that our approach can perform well. It can effectively improve the safety of travel and alleviate the anxiety that is caused by air pollution. In addition, it provides an easy implementation strategy for software for health management.

Language: en

Keywords

BPNN; Dynamic dijkstra algorithm; Healthy route planning; PM2.5 concentration; Short-distance