skip to main content
10.1145/3632366.3632381acmconferencesArticle/Chapter ViewAbstractPublication PagesbdcatConference Proceedingsconference-collections
research-article

Real-time Route Planning to Reduce Pedestrian Pollution Exposure in Urban Settings

Published: 03 April 2024 Publication History

Abstract

PM2.5 refers to fine particulate matter less than 2.5 micrometers in diameter. PM2.5 is a common air pollutant. It is capable of entering the respiratory system, and is associated with a variety of health issues such as asthma and other diseases. Pedestrians are at risk of exposure to traffic-related PM2.5 due in part to increased numbers of vehicles in city settings and their associated exhaust fumes - a key contributor to PM2.5. In this paper, we present a framework to minimise PM2.5 exposure for pedestrians by helping them avoid areas with high PM2.5 concentration levels. Specifically we predict the concentration levels through an XGBoost model and background concentration levels from official air quality monitoring stations around Melbourne. We factor in real-time, portable, air quality monitoring devices, weather conditions and real-time traffic flow information. The coefficient of determination (R2), root mean squared error (RMSE) and the mean average error (MAE) for the XGBoost model achieves 0.71, 1.98 and 1.1 respectively. The Dijkstra algorithm is then applied to generate the minimum PM2.5 exposure of routes with alternative routes suggested trading off distance and PM2.5 exposure. Compared with the shortest route, experiments show that PM2.5 exposure can be decreased by 11 - 15% with only a marginal increase in route length.

References

[1]
Ibrahim Said Ahmad, Azuraliza Abu Bakar, Mohd Ridzwan Yaakub, and Shamsuddeen Hassan Muhammad. 2020. A survey on machine learning techniques in movie revenue prediction. SN Computer Science 1 (2020), 1--14.
[2]
AirCasting. 2023. Taking Matter into Your Own Hands:About AirBeam. https://www.habitatmap.org/airbeam
[3]
M.S. Alam, H. Perugu, and A. McNabola. 2018. A comparison of route-choice navigation across air pollution exposure, CO2 emission and traditional travel cost factors. Transportation Research Part D: Transport and Environment 65 (2018), 82--100.
[4]
Dimitra Alexiou and Stefanos Katsavounis. 2015. A multi-objective transportation routing problem. Operational Research 15 (2015), 199--211.
[5]
Mohammad Hashem Askariyeh, Madhusudhan Venugopal, Haneen Khreis, Andrew Birt, and Josias Zietsman. 2020. Near-road traffic-related air pollution: Resuspended PM2. 5 from highways and arterials. International journal of environmental research and public health 17, 8 (2020), 2851.
[6]
Shadi Ausati and Jamil Amanollahi. 2016. Assessing the accuracy of ANFIS, EEMD-GRNN, PCR, and MLR models in predicting PM2.5. Atmospheric Environment 142 (2016), 465--474.
[7]
Australian Bureau of Statistics. 2021. Regional population by age and sex. https://www.abs.gov.au/statistics/people/population/regional-population-age-and-sex/latest-release
[8]
Alexander Y. Bigazzi and Miguel A. Figliozzi. 2015. Roadway determinants of bicyclist exposure to volatile organic compounds and carbon monoxide. Transportation Research Part D: Transport and Environment 41 (2015), 13--23.
[9]
Benjamin Bowe, Yan Xie, Tingting Li, Yan Yan, Hong Xian, and Ziyad Al-Aly. 2018. The 2016 global and national burden of diabetes mellitus attributable to PM2·5 air pollution. The Lancet Planetary Health 2, 7 (2018), e301--e312.
[10]
Chate. 2005. Study of scavenging of submicron-sized aerosol particles by thunderstorm rain events. Atmospheric Environment 39, 35 (2005), 6608--6619.
[11]
Asha B. Chelani. 2019. Estimating PM2.5 concentration from satellite derived aerosol optical depth and meteorological variables using a combination model. Atmospheric Pollution Research 10, 3 (2019), 847--857.
[12]
Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 785--794.
[13]
Tao Chen, Jun He, Xiaowei Lu, Jiangfeng She, and Zhongqing Guan. 2016. Spatial and temporal variations of PM2. 5 and its relation to meteorological factors in the urban area of Nanjing, China. International journal of environmental research and public health 13, 9 (2016), 921.
[14]
Gemma Davies and J. Duncan Whyatt. 2014. A network-based approach for estimating pedestrian journey-time exposure to air pollution. Science of The Total Environment 485--486 (2014), 62--70.
[15]
Lirong Deng. 2015. Estimation of PM2.5 spatial distribution based on kriging interpolation. 1791--1794 pages.
[16]
EW Dijkstra. 1959. A Note on Two Problems in Connexion with Graphs. Numer. Math. 1 (1959), 269--271.
[17]
EPA. 2023. EPA AirWatch | Environment Protection Authority Victoria. https://www.epa.vic.gov.au/for-community/airwatch
[18]
LiNa Gao, Fei Tao, PeiLong Ma, ChenYi Wang, Wei Kong, WenKai Chen, and Tong Zhou. 2022. A short-distance healthy route planning approach. Journal of Transport & Health 24 (2022), 101314.
[19]
Google. 2023. Custom Map Tools & Products. https://mapsplatform.google.com/maps-products\/#directions
[20]
Hongdi He and H Oliver Gao. 2021. Particulate matter exposure at a densely populated urban traffic intersection and crosswalk. Environmental Pollution 268 (2021), 115931.
[21]
Tomislav Hengl, Gerard B.M. Heuvelink, and David G. Rossiter. 2007. About regression-kriging: From equations to case studies. Computers & Geosciences 33, 10 (2007), 1301--1315.
[22]
Jing Huang, Furong Deng, Shaowei Wu, and Xinbiao Guo. 2012. Comparisons of personal exposure to PM2.5 and CO by different commuting modes in Beijing, China. Science of The Total Environment 425 (2012), 52--59.
[23]
Sarah Jarjour, Michael Jerrett, Dane Westerdahl, Audrey de Nazelle, Cooper Hanning, Laura Daly, Jonah Lipsitt, and John Balmes. 2013. Cyclist route choice, traffic-related air pollution, and lung function: a scripted exposure study. Environmental Health 12 (2013), 1--12.
[24]
Michael Jerrett, Richard T Burnett, Renjun Ma, C Arden Pope III, Daniel Krewski, K Bruce Newbold, George Thurston, Yuanli Shi, Norm Finkelstein, Eugenia E Calle, et al. 2005. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology (2005), 727--736.
[25]
Patrick L. Kinney, Michael Gatari Gichuru, Nicole Volavka-Close, Nicole Ngo, Peter K. Ndiba, Anna Law, Anthony Gachanja, Samuel Mwaniki Gaita, Steven N. Chillrud, and Elliott Sclar. 2011. Traffic impacts on PM2.5 air quality in Nairobi, Kenya. Environmental Science & Policy 14, 4 (2011), 369--378.
[26]
Chayakrit Krittanawong, Yusuf Kamran Qadeer, Richard B. Hayes, Zhen Wang, Salim Virani, George D. Thurston, and Carl J. Lavie. 2023. PM2.5 and Cardiovascular Health Risks. Current Problems in Cardiology 48, 6 (2023)
[27]
Guoao Li, Wanying Su, Qi Zhong, Mingjun Hu, Jialiu He, Huanhuan Lu, Wenlei Hu, Jianjun Liu, Xue Li, Jiahu Hao, and Fen Huang. 2022. Individual PM2.5 component exposure model, elevated blood pressure and hypertension in middle-aged and older adults: A nationwide cohort study from 125 cities in China. Environmental Research 215 (2022), 114360.
[28]
Donghai Liang, Rachel Golan, Jennifer L. Moutinho, Howard H. Chang, Roby Greenwald, Stefanie E. Sarnat, Armistead G. Russell, and Jeremy A. Sarnat. 2018. Errors associated with the use of roadside monitoring in the estimation of acute traffic pollutant-related health effects. Environmental Research 165 (2018), 210--219.
[29]
Xinwei Liu, Muchuan Qin, Yue He, Xiwei Mi, and Chengqing Yu. 2021. A new multi-data-driven spatiotemporal PM2.5 forecasting model based on an ensemble graph reinforcement learning convolutional network. Atmospheric Pollution Research 12, 10 (2021), 101197.
[30]
Ji Luo, Kanok Boriboonsomsin, and Matthew Barth. 2018. Reducing pedestrians' inhalation of traffic-related air pollution through route choices: Case study in California suburb. Journal of Transport & Health 10 (2018), 111--123.
[31]
Jinghui Ma, Zhongqi Yu, Yuanhao Qu, Jianming Xu, Yu Cao, et al. 2020. Application of the XGBoost machine learning method in PM2. 5 prediction: A case study of Shanghai. Aerosol and Air Quality Research 20, 1 (2020), 128--138.
[32]
Sachit Mahajan, YuSiou Tang, DongYi Wu, TzuChieh Tsai, and LingJyh Chen. 2019. CAR: The Clean Air Routing Algorithm for Path Navigation With Minimal PM2.5 Exposure on the Move. IEEE Access 7 (2019), 147373--147382.
[33]
Elaine M Murtagh, Jacqueline L Mair, Elroy Aguiar, Catrine Tudor-Locke, and Marie H Murphy. 2021. Outdoor walking speeds of apparently healthy adults: A systematic review and meta-analysis. Sports Medicine 51 (2021), 125--141.
[34]
Timothy D. Nelin, Allan M. Joseph, Matthew W. Gorr, and Loren E. Wold. 2012. Direct and indirect effects of particulate matter on the cardiovascular system. Toxicology Letters 208, 3 (2012), 293--299.
[35]
Halûk Özkaynak, Lisa K Baxter, Kathie L Dionisio, and Janet Burke. 2013. Air pollution exposure prediction approaches used in air pollution epidemiology studies. Journal of exposure science & environmental epidemiology 23, 6 (2013), 566--572.
[36]
Jian Peng, Haisheng Han, Yong Yi, Huimin Huang, and Le Xie. 2022. Machine learning and deep learning modeling and simulation for predicting PM2.5 concentrations. Chemosphere 308 (2022), 136353.
[37]
Luke Plonsky and Hessameddin Ghanbar. 2018. Multiple regression in L2 research: A methodological synthesis and guide to interpreting R2 values. The Modern Language Journal 102, 4 (2018), 713--731.
[38]
Carla A Ramos, Humbert T Wolterbeek, and Susana M Almeida. 2016. Air pollutant exposure and inhaled dose during urban commuting: a comparison between cycling and motorized modes. Air Quality, Atmosphere & Health 9 (2016), 867--879.
[39]
Gulshan Sharma and James Goodwin. 2006. Effect of aging on respiratory system physiology and immunology. Clinical interventions in aging 1, 3 (2006), 253--260.
[40]
Admir Créso Targino, Mark David Gibson, Patricia Krecl, Marcos Vinicius Costa Rodrigues, Maurício Moreira dos Santos, and Marcelo de Paula Corrêa. 2016. Hotspots of black carbon and PM2.5 in an urban area and relationships to traffic characteristics. Environmental Pollution 218 (2016), 475--486.
[41]
B Vamshi and Raja Vara Prasad. 2018. Dynamic route planning framework for minimal air pollution exposure in urban road transportation systems. In 2018 IEEE 4th World Forum on Internet of Things (WF-IoT). IEEE, 540--545.
[42]
Wang, Kim Dirks, Matthias Ehrgott, Jon Pearce, and Alan Cheung. 2018. Supporting healthy route choice for commuter cyclists: The trade-off between travel time and pollutant dose. Operations Research for Health Care 19 (2018), 156--164.
[43]
Wang, Yizheng Wu, Zhenyu Li, Kai Liao, Chao Li, and Guohua Song. 2022. Route planning for active travel considering air pollution exposure. Transportation Research Part D: Transport and Environment 103 (2022), 103176.
[44]
Beibei Wang, Zongshuang Wang, Yongjie Wei, Feifei Wang, and Xiaoli Duan. 2015. 2 - Inhalation Rates. In Highlights of the Chinese Exposure Factors Handbook (Adults), Xiaoli Duan, Xiuge Zhao, Beibei Wang, Yiting Chen, and Suzhen Cao (Eds.). Academic Press, 15--21.
[45]
TzuTsung Wong and PoYang Yeh. 2019. Reliable accuracy estimates from k-fold cross validation. IEEE Transactions on Knowledge and Data Engineering 32, 8 (2019), 1586--1594.
[46]
TzongGang Wu, Yan-Da Chen, BangHua Chen, Kouji H. Harada, Kiyoung Lee, Furong Deng, Mark J. Rood, ChuChih Chen, CongThanh Tran, KuoLiong Chien, TzaiHung Wen, and ChangFu Wu. 2022. Identifying low-PM2.5 exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities. Environmental Pollution 294 (2022), 118597.
[47]
Zhao Xin, Sun Yue, Zhao Chuanfeng, and Jiang Huifei. 2020. Impact of precipitation with different intensity on PM2. 5 over typical regions of China. Atmosphere 11, 9 (2020), 906.
[48]
ChenXi Zhao, YunQi Wang, YuJie Wang, HuiLan Zhang, and BingQing Zhao. 2014. Temporal and spatial distribution of PM2. 5 and PM10 pollution status and the correlation of particulate matters and meteorological factors during winter and spring in Beijing. Huan jing ke xue= Huanjing kexue 35, 2 (2014), 418--427.
[49]
Liu Zhen, Shen Luming, Yan Chengyu, Du Jianshuang, Li Yang, and Zhao Hui. 2020. Analysis of the Influence of Precipitation and Wind on PM2. 5 and PM10 in the Atmosphere. Advances in Meteorology 2020 (2020), 1--13.
[50]
Bin Zou, Yanqing Luo, Neng Wan, Zhong Zheng, Troy Sternberg, and Yilan Liao. 2015. Performance comparison of LUR and OK in PM2. 5 concentration mapping: A multidimensional perspective. Scientific reports 5, 1 (2015), 1--7.

Cited By

View all
  • (2024)Machine learning-based prediction of hazards fine PM2.5 concentrations: a case study of Delhi, IndiaDiscover Geoscience10.1007/s44288-024-00043-z2:1Online publication date: 18-Jul-2024

Index Terms

  1. Real-time Route Planning to Reduce Pedestrian Pollution Exposure in Urban Settings
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Information & Contributors

          Information

          Published In

          cover image ACM Conferences
          BDCAT '23: Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies
          December 2023
          187 pages
          ISBN:9798400704734
          DOI:10.1145/3632366
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Sponsors

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 03 April 2024

          Permissions

          Request permissions for this article.

          Check for updates

          Author Tags

          1. PM2.5
          2. health route planning
          3. pollution exposure
          4. XGBoost

          Qualifiers

          • Research-article

          Conference

          BDCAT '23
          Sponsor:

          Acceptance Rates

          Overall Acceptance Rate 27 of 93 submissions, 29%

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • Downloads (Last 12 months)60
          • Downloads (Last 6 weeks)4
          Reflects downloads up to 01 Mar 2025

          Other Metrics

          Citations

          Cited By

          View all
          • (2024)Machine learning-based prediction of hazards fine PM2.5 concentrations: a case study of Delhi, IndiaDiscover Geoscience10.1007/s44288-024-00043-z2:1Online publication date: 18-Jul-2024

          View Options

          Login options

          View options

          PDF

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          Figures

          Tables

          Media

          Share

          Share

          Share this Publication link

          Share on social media