Investigation of the Transport-Health Links: An Observational Study from the UAE

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1 Investigation of the Transport-Health Links: An Observational Study from the UAE Mohamed Al Qahtani Ghassan Abu-Lebdeh, Ph.D. Dept. of Civil Engineering American University of Sharjah Sharjah, UAE Transportation, Air Quality, and Health Symposium Austin, TX Februray 18-20, 2019

2 United Arab Emirates (UAE)? 2/27/2019 2

3 American University of Sharjah? 3

4 Background UAE: Developed countries standards of living Modern infrastructure (transportation, included) High Incomes levels; 19 th World-wide High standards of living Prevalent modernity illnesses Similar to (on par with) developed counties Public/government recognition: health issues are serious 2/27/2019 4

5 This Study Elements of healthy living Health factors 5

6 Goals & Objectives: Determine the extent of contribution of transport system to public health in the UAE. Examine correlations between transportation choices/decisions and health outcomes for residents of the UAE Develop models: health vs. transport/travel choices 6

7 Methodology: Collect data (stated responses) Regular work/school commuters Qualitative descriptive models Quantitative analysis/models 7

8 Data: Socio-economic Transport/travel choices Health/exposure issues Stated responses Sample:

9 Results: 1. Qualitative: trends & relevant observations 2. Quantitative: Neural Network (NNet) models 9

10 Transportation/Travel choices 2/27/

11 Number of Responses Mode of Transport_Work or School ~ 96% ~ 4% /27/

12 Vehicle Ownership per HH 4 cars 5% 5 cars 3% 6 or more cars 8% 16% > 4 vehicles 3 cars 8% Zero (no) cars 27% 2 cars 17% 1 car 32% 12

13 Hours of Commute per Day 56% > 1 hr or more 19% > 3 hrs 3-4 hours 13% 1 2 hours 38% More than 4 hours 6% Less than 1 hour 43% 13

14 Exposure to Traffic Congestion 50% > most or all time Not at all 24% All the time 16% Occasionall y 26% Most of the time 34% 14

15 Commute_ Walking, min./day minutes 31 minutes > 1 hr 0.5% Less than 15 minutes 97% > no walking do not walk 97% 15

16 Commute _ Bicycle, min./day minutes, 0.5 Less than 15 minutes, minutes 1 hour, 0.0 More than 1 hour, % > no Cycling do not ride bicycle 99.1% 16

17 Access to Public Transport Home Work/College No 27% Access no issue Coverage is No 44% Yes 56% Yes 73% 17

18 Health Issues 2/27/

19 Doctors Visits _past 3 months 2-3 times 15% More than 3 times 54% at least once Not at all 46% 20 % > 2 times 1 time 34% Comparable to Canada, 1.9; Germany,

20 Noise a Problem at Destination No 73% Yes 27% During Commute No 73% Yes 27% No 78% Home Yes 22% 20

21 Hours of Sleep per Work/College Day 57% get enough > 9 hrs 16% Less than 4 hours 7% 7-9 hours 41% 4-6 hours 36% 2/27/

22 Health Issues_Physical ~ 70% at least one issue Diabetes 4% None of the above 31% Obesity 37% Anxiety/stress 11% Asthma 13% Cardiovascular 4% 22

23 Health Issues-Psychological: State-of-mind, a typical Work/College Day ~ 43% not OK Stressed 33% Depressed 10% Optimistic and happy 23% Neutral 34% 2/27/

24 Feeling Stressed While Commuting 72% are Not at all 28% Occasional ly 37% All the time 9% Most of the time 26% 2/27/

25 Quantitative Analysis, Modeling 25

26 Conceptually Travel/trans port choices Socioeconomic data A Model/an algorithm Health issues Physical Psychological 2/27/

27 NNet Models for this study Travel/tra nsport choices Socioeconomi c data Neural Network Health issues Physical Psychological 2/27/

28 Neural Network (NNets) Models..computing systems vaguely inspired by the biological neural networks that constitute brains[1] The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs.[2] Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. Wikipedia 28

29 Travel/trans port choices Socioeconomic data Health issues Physical Psychological 29

30 Why NNets? Fault tolerance Multiple simultaneous outpts Noisy data OK Missing data OK. 30

31 Implementation NNets? Divide data: training, test/validation Select NNet architecture & parameters Train NNet until pre set quality measures met Use Trained NNet 2/27/

32 I n p u t s O u t p u t s X1 Y1 Travel/tra nsport choices Socioeconomi c data X2 X3 X4 X5.. Y2 Y3 Y4.. Yn Health issues Physical Psychological Nn 2/27/

33 Sample results Expsre to congt. hrs commuting hrs social Stress/dpress 33

34 Min. walk Expr. Cong. Hrs. comut BMI Stress/dpress 2/27/

35 Min. walk Expr. congest. Hrs. comut At lest 1 illness Stress/dpress 2/27/

36 Hrs. commut. Expsur. congest Hrs. comut Hrs sleep Stress/dpress 36

37 Income Mode of trasnp. Freq. visit doctr Stress/dpress 2/27/

38 Summary: UAE: common (developed countries) health issues prevalent Qualitatively: transport-health connections not total evident Transport/travel choice health issues examined Preliminary (quantitative) models developed NNet-based models suited, robust 2/27/

39 39

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