Using GPS-derived speed patterns for recognition of transport modes in adults

International Journal of Health Geographics

Roel Vermeulen³  Hans Kromhout¹  Johan Beekhuizen¹  Anke Huss² 

[1] Institute for Risk Assessment Sciences, Utrecht University, PO Box 80178, Utrecht 3508 TD, The Netherlands;Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland;Julius Centre for Public Health Sciences and Primary Care, University Medical Centre, Utrecht, The Netherlands

关键词: Discriminant analysis;    Walk;    Bike;    Motorized;    Active transport;    Physical activity;   

Others  :  1231708
DOI  :  10.1186/1476-072X-13-40

 received in 2014-08-13, accepted in 2014-10-02,  发布年份 2014

【 摘 要 】

Background

Identification of active or sedentary modes of transport is of relevance for studies assessing physical activity or addressing exposure assessment. We assessed in a proof-of-principle study if speed as logged by GPSs could be used to identify modes of transport (walking, bicycling, and motorized transport: car, bus or train).

Methods

12 persons commuting to work walking, bicycling or with motorized transport carried GPSs for two commutes and recorded their mode of transport. We evaluated seven speed metrics: mean, 95^th percentile of speed, standard deviation of the mean, rate-of-change, standardized-rate-of-change, acceleration and deceleration. We assessed which speed metric would best identify the transport mode using discriminant analyses. We applied cross validation and calculated agreement (Cohen’s Kappa) between actual and derived modes of transport.

Results

Mode of transport was reliably classified whenever a person used a mode of transport for longer than one minute. Best results were observed when using the 95^th percentile of speed, acceleration and deceleration (kappa 0.73). When we combined all motorized traffic into one category, kappa increased to 0.95.

Conclusions

GPS-measured speed enable the identification of modes of transport. Given the current low costs of GPS devices and the built-in capacity of GPS tracking in most smartphones, the use of such devices in large epidemiological studies may facilitate the assessment of physical activity related to transport modes, or improve exposure assessment using automated travel mode detection.

【 授权许可】

   
2014 Huss et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Buehler R, Pucher J, Merom D, Bauman A: Active travel in Germany and the US: contributions of daily walking and cycling to physical activity. Am J Prev Med 2011, 41(3):241-250.
• [2]Hamer M, Chida Y: Active commuting and cardiovascular risk: a meta-analytic review. Prev Med 2008, 46(1):9-13.
• [3]Dirks K, Sharma P, Salmond J, Costello S: Personal Exposure to Air Pollution for Various Modes of Transport in Auckland, New Zealand. Open Atmos Sci J 2012, 6(1):84-92.
• [4]Zuurbier M, Hoek G, Oldenwening M, Lenters V, Meliefste K, van den Hazel P, Brunekreef B: Commuters’ exposure to particulate matter air pollution is affected by mode of transport, fuel type, and route. Environ Health Perspect 2010, 118(6):783.
• [5]Pleis JR, Ward BW, Lucas JW: Summary health statistics for U.S. adults: National Health Interview Survey, 2009. Vital Health Stat 2010, 249(249):1-207.
• [6]Laverty AA, Mindell JS, Webb EA, Millett C: Active Travel to Work and Cardiovascular Risk Factors in the United Kingdom. Am J Prev Med 2013, 45(3):282-288.
• [7]Petrunoff NA, Xu H, Rissel C, Wen LM, van der Ploeg HP: Measuring Workplace Travel Behaviour: Validity and Reliability of Survey Questions. J Environ Public Health 2013, 6. Article ID 423035, http://dx.doi.org/10.1155/2013/423035 webcite
• [8]Pober DM, Staudenmayer J, Raphael C, Freedson PS: Development of novel techniques to classify physical activity mode using accelerometers. Med Sci Sports Exerc 2006, 38(9):1626.
• [9]Troped PJ, Oliveira MS, Matthews CE, Cromley EK, Melly SJ, Craig BA: Prediction of activity mode with global positioning system and accelerometer data. Med Sci Sports Exerc 2008, 40(5):972.
• [10]Wu J, Jiang C, Houston D, Baker D, Delfino R: Automated time activity classification based on global positioning system (GPS) tracking data. Environ Health 2011, 10:101. BioMed Central Full Text
• [11]Krenn PJ, Titze S, Oja P, Jones A, Ogilvie D: Use of global positioning systems to study physical activity and the environment: a systematic review. Am J Prev Med 2011, 41(5):508-515.
• [12]Bohte W, Maat K: Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands. Transportation Research Part C 2009, 17(3):285-297.
• [13]Gong H, Chen C, Bialostozky E, Lawson CT: A GPS/GIS method for travel mode detection in New York City. Comput, Environ Urban Syst 2012, 36(2):131-139.
• [14]Beekhuizen J, Kromhout H, Huss A, Vermeulen R: Performance of GPS-devices for environmental exposure assessment. J Expo Sci Environ Epidemiol 2013, 23:498-505.
• [15]Foliart DE, Iriye RN, Tarr KJ, Silva JM, Kavet R, Ebi KL: Alternative magnetic field exposure metrics: relationship to TWA, appliance use, and demographic characteristics of children in a leukemia survival study. Bioelectromagnetics 2001, 22(8):574-580.
• [16]Fleiss JL, Levin B, Paik MC: The measurement of interrater agreement. In Statistical methods for rates and proportions. 3rd edition. Edited by Fleiss JL, Levin B, Paik MC. New York: Wiley; 2003:598-626.
• [17]Landis JR, Koch GG: The measurement of observer agreement for categorical data. Biometrics 1977, 33(1):159-174.
• [18]Terrier P, Schutz Y: Variability of gait patterns during unconstrained walking assessed by satellite positioning (GPS). Eur J Appl Physiol 2003, 90(5–6):554-561.
• [19]Bohannon RW, Williams A: Normal walking speed: a descriptive meta-analysis. Physiotherapy 2011 Sep, 97(3):182-189.
• [20]Chen C, Gong H, Lawson C, Bialostozky E: Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study. Transportation Research Part A: Policy and Practice 2010, 44(10):830-840.
• [21]Stopher P, Clifford E, Zhang J, FitzGerald C: Deducing mode and purpose from GPS data. In Working paper ITLS-WP-08-06. University of Sydney, Australia: Institute of Transport and Logistics Studies; 2008.
• [22]Hands B, Larkin D: Physical activity measurement methods for young children: a comparative study. Meas Phys Educ Exerc Sci 2006, 10(3):203-214.
• [23]Oja P, Titze S, Bauman A, De Geus B, Krenn P, Reger-Nash B, Kohlberger T: Health benefits of cycling: a systematic review. Scand J Med Sci Sports 2011, 21(4):496-509.
• [24]Bonomi A, Plasqui G, Goris A, Westerterp K: Aspects of activity behavior as a determinant of the physical activity level. Scand J Med Sci Sports 2012, 22(1):139-145.
• [25]Maddison R, Mhurchu CN: Global positioning system: a new opportunity in physical activity measurement. Int J Behav Nutr Phys Act 2009, 6(1):73. BioMed Central Full Text
• [26]Lee C, Lee M, Han D: Energy-efficient Location Logging for Mobile Device. Proceedings of the Applications and the Internet (SAINT), 2010 10th IEEE/IPSJ International Symposium 2010, 84-90.