All Title Author
Keywords Abstract


Fear Factor: Level of Traffic Stress and GPS Assessed Cycling Routes

DOI: 10.4236/jtts.2019.91002, PP. 14-30

Keywords: Bicycling, Traffic Stress, GPS, Route Choice, LTS

Full-Text   Cite this paper   Add to My Lib

Abstract:

Background: Cycling currently comprises only 1% of transport trips in the U.S. despite benefits for air pollution, traffic congestion, and improved public health. Methods: Building upon the Level of Traffic Stress (LTS) methodology, we assessed GPS trip data from utilitarian cyclists to understand route preferences and the level of low stress cycling connection between origins and destinations. GPS data were obtained from adult transport cyclists over multiple days. All bikeable road segments in the network were assigned an LTS score. The shortest paths between each origin and destination along bikeable roadways and along low stress (LTS 1 or 2) routes were calculated. Route trajectories were mapped to the LTS network, and the LTS and distances of observed, the shortest and low stress routes were compared. LTS maps and animations were developed to highlight where low stress connections were lacking. Results: There were 1038 unique cycling trips from 87 participants included in the analysis. An exclusively low stress route did not exist for 51% of trips. Low stress routes that were possible were, on average, 74% longer than the shortest possible path and 56% longer than the observed route. Observed routes were longer and lower stress than the shortest possible route. Conclusions: Results indicate that transport cyclists traveled beyond low stress residential areas and that low stress routes with acceptable detour distances were lacking. Cyclists appeared to weigh both route distance and quality and were willing to trade maximum directness for lower stress. GPS data provide additional information to support planning decisions to increase the impact of infrastructure investments on cycling mode share.

References

[1]  Oja, P., Titze, S., Bauman, A., de Geus, B., Krenn, P., Reger-Nash, B. and Kohlberger, T. (2011) Health Benefits of Cycling: A Systematic Review. Scandinavian Journal of Medicine & Science in Sports, 21, 496-509.
https://doi.org/10.1111/j.1600-0838.2011.01299.x
[2]  World Health Organization (WHO) (2018) Global Action Plan on Physical Activity 2018-2030. World Health Organization, Geneva.
[3]  Maizlish, N., Woodcock, J., Co, S., Ostro, B., Fanai, A., Imeche, C. and Fairley, D. (2013) Health Cobenefits and Transportation-Related Reductions in Greenhouse Gas Emissions in the San Francisco Bay Area. American Journal of Public Health, 103, 703-709.
https://doi.org/10.2105/AJPH.2012.300939
[4]  Giles-Corti, B., Vernez-Moudon, A., Reis, R., Turrell, G., Dannenberg, A.L., Badland, H., Foster, S., Lowe, M., Sallis, J.F., Stevenson, M. and Owen, N. (2016) City Planning and Population Health: A Global Challenge. Lancet, 388, 2912-2924.
https://doi.org/10.1016/S0140-6736(16)30066-6
[5]  Global Advocacy for Physical Activity (GAPA) the Advocacy Council of the International Society for Physical Activity and Health (ISPAH) (2011) NCD Prevention: Investments that Work for Physical Activity.
[6]  FHWA (2016) Strategic Agenda for Pedestrian and Bicycle Transportation. FHWA, Washington DC.
[7]  FHWA (2011) Summary of Travel Trends: 2009 National Household Travel Survey. FHWA, Washington DC.
[8]  City of San Diego (2015) Climate Action Plan. San Diego.
[9]  County of San Diego (2018) Climate Action Plan. San Diego.
[10]  Pucher, J., Buehler, R. and Seinen, M. (2011) Bicycling Renaissance in North America? An Update and Re-Appraisal of Cycling Trends and Policies. Transportation Research Part A: Policy and Practice, 45, 451-475.
https://doi.org/10.1016/j.tra.2011.03.001
[11]  U.S. Census Bureau (2015) 2015 American Community Survey.
https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_16_ 5YR_B08301&prodType=table
[12]  Heinen, E., Van Wee, B., Maat, K. and Ttrv, F. (2010) Commuting by Bicycle: An Overview of the Literature. Transport Reviews, 30, 59-96.
https://doi.org/10.1080/01441640903187001
[13]  Fowler, S.L., Berrigan, D. and Pollack, K.M. (2017) Perceived Barriers to Bicycling in an Urban U.S. Environment. Journal of Transport & Health, 6, 474-480.
https://doi.org/10.1016/j.jth.2017.04.003
[14]  Handy, S.L. and Xing, Y. (2011) Factors Correlated with Bicycle Commuting: A Study in Six Small U.S. Cities. International Journal of Sustainable Transportation, 5, 91-110.
https://doi.org/10.1080/15568310903514789
[15]  Winters, M., Davidson, G., Kao, D. and Teschke, K. (2011) Motivators and Deterrents of Bicycling: Comparing Influences on Decisions to Ride. Transportation (AMST), 38, 153-168.
https://doi.org/10.1007/s11116-010-9284-y
[16]  Heesch, K.C., Sahlqvist, S. and Garrard, J. (2011) Brief Original Report Cyclists’ Experiences of Harassment from Motorists: Findings from a Survey of Cyclists in Queensland, Australia. Preventive Medicine, 53, 417-420.
https://doi.org/10.1016/j.ypmed.2011.09.015
[17]  Chataway, E.S., Kaplan, S., Nielsen, T.A.S. and Prato, C.G. (2014) Safety Perceptions and Reported Behavior Related to Cycling in Mixed Traffic: A Comparison between Brisbane and Copenhagen. Transportation Research Part F: Traffic Psychology and Behaviour, 23, 32-43.
https://doi.org/10.1016/j.trf.2013.12.021
[18]  Mertens, L., Compernolle, S., Gheysen, F., Deforche, B., Brug, J., Mackenbach, J.D., Lakerveld, J., Oppert, J.-M., Feuillet, T., Glonti, K., Bárdos, H. and De Bourdeaudhuij, I. (2016) Perceived Environmental Correlates of Cycling for Transport among Adults in Five Regions of Europe. Obesity Reviews, 17, 53-61.
https://doi.org/10.1111/obr.12379
[19]  Sorton, A. and Walsh, T. (1994) Bicycle Stress Level as a Tool to Evaluate Urban and Suburban Bicycle Compatibility. Transportation Research Record 1438, Washington DC.
[20]  Davis, W.J. (1987) Bicycle Safety Evaluation. Auburn University, Chattanooga.
[21]  Broach, J., Dill, J. and Gliebe, J. (2012) Where Do Cyclists Ride? A Route Choice Model Developed with Revealed Preference GPS Data. Transportation Research Part A: Policy and Practice, 46, 1730-1740.
https://doi.org/10.1016/j.tra.2012.07.005
[22]  Buehler, R. and Dill, J. (2015) Bikeway Networks: A Review of Effects on Cycling. Transport Reviews, 36, 9-27.
https://doi.org/10.1080/01441647.2015.1069908
[23]  Vedel, S.E., Jacobsen, J.B. and Skov-Petersen, H. (2017) Bicyclists’ Preferences for Route Characteristics and Crowding in Copenhagen—A Choice Experiment Study of Commuters. Transportation Research Part A: Policy and Practice, 100, 53-64.
https://doi.org/10.1016/j.tra.2017.04.006
[24]  Harvey, F., Krizek, K.J. and Collins, R. (2008) Using GPS Data to Assess Bicycle Commuter Route C Hoice. Transportation Research Board 87th Annual Meeting, Washington DC, 13-17 January 2008.
[25]  Broach, J., Dill, J. and Gliebe, J. (2012) Where Do Cyclists Ride? A Route Choice Model Developed with Revealed Preference GPS Data. Transportation Research Part A: Policy and Practice, 46, 1730-1740.
https://doi.org/10.1016/j.tra.2012.07.005
[26]  Menghini, G., Carrasco, N., Schüssler, N. and Axhausen, K.W. (2010) Route Choice of Cyclists in Zurich. Transportation Research Part A: Policy and Practice, 44, 754-765.
https://doi.org/10.1016/j.tra.2010.07.008
[27]  Casello, J., Nour, A., Rewa, K. and Hill, J. (2011) Analysis of Stated-Preference and GPS Data for Bicycle Travel Forecasting. Transportation Research Board 90th Annual Meeting Location, Washington DC, 23-27 January 2011.
[28]  Casello, J. and Usyukov, V. (2014) Modeling Cyclists’ Route Choice Based on GPS Data. Transportation Research Record Journal of the Transportation Research Board, 2430, 155-161.
https://doi.org/10.3141/2430-16
[29]  Beatriz Pereira Segadilha, A. and da Penha Sanches, S. (2014) Analysis of Bicycle Commuter Routes Using GPSs and GIS. Procedia—Social and Behavioral Sciences, 162, 198-207.
https://doi.org/10.1016/j.sbspro.2014.12.200
[30]  Hood, J., Sall, E. and Charlton, B. (2011) A GPS-Based Bicycle Route Choice Model for San Francisco, California. Transportation Letters. The International Journal of Transportation Research, 3, 63-75.
https://doi.org/10.3328/TL.2011.03.01.63-75
[31]  Geller, R. (2006) Four Types of Cyclists. Portland Bureau of Transportation, Portland.
[32]  Dill, J. and McNeil, N. (2016) Revisiting the Four Types of Cyclists: Findings from a National Survey. Transportation Research Record Journal of the Transportation Research Board, 2587, 17.
https://doi.org/10.3141/2587-11
[33]  Hunt, J.D., Abraham, A.J.E., Abraham, J.E., Hunt, J.D. and Abraham, A.E.J.E. (2007) Influences on Bicycle Use. Transportation (AMST), 34, 453-470.
https://doi.org/10.1007/s11116-006-9109-1
[34]  O’Connor, J.P. and Brown, T.D. (2010) Riding with the Sharks: Serious Leisure Cyclist’s Perceptions of Sharing the Road with Motorists. Journal of Science and Medicine in Sport, 13, 53-58.
https://doi.org/10.1016/j.jsams.2008.11.003
[35]  Mekuria, M.C., Furth, P.G. and Nixon, H. (2012) Low-Stress Bicycling and Network Connectivity. Mineta Transportation Institute, San Jose.
[36]  Mekuria, M., Appleyard, B. and Nixon, H. (2017) Improving Livability Using Green and Active Modes: A Traffic Stress Level Analysis of Transit, Bicycle, and Pedestrian Access and Mobility. Mineta Transportation Institute, San Jose.
[37]  Alta Planning + Design (2018) Level of Traffic Stress—What It Means for Building Better Bike Networks.
https://blog.altaplanning.com/level-of-traffic-stress-what-it-means-for-building-better-bike- networks-c4af9800b4ee
[38]  PeopleForBikes (2018) Bike Network Analysis.
https://bna.peopleforbikes.org/#/methodology
[39]  Dill, McNeil and Nathan (2016) TRR 2587. Transportation Research Record Journal of the Transportation Research Board, 2587, 90-99.
[40]  Winters, M., Teschke, K., Grant, M., Setton, E.M., Brauer, M., Winters, M., Teschke, K. and Brauer, M. (2010) How Far Out of the Way Will We Travel? Built Environment Influences on Route Selection for Bicycle and Car Travel. Transportation Research Record Journal of the Transportation Research Board, 2190, 1-10.
https://doi.org/10.3141/2190-01
[41]  Dill, J. and McNeil, N. (2013) Four Types of Cyclists? Transportation Research Record Journal of the Transportation Research Board, 2387, 129-138.
https://doi.org/10.3141/2387-15
[42]  Furth, P.G., Mekuria, M.C. and Nixon, H. (2016) Network Connectivity for Low-Stress Bicycling. Transportation Research Record Journal of the Transportation Research Board, 2587, 41-49.
https://doi.org/10.3141/2587-06
[43]  SANDAG (2010) San Diego Regional Bike Plan 2010.
https://www.google.com/search?q=San+Diego+Regional+Bike+Plan+2010&rlz=1C1CHZL_enUS 739US739&oq=San+Diego+Regional+Bike+Plan+2010&aqs=chrome..69i57j69i64.2236j0j7& sourceid=chrome&ie=UTF-8
[44]  Kerr, J., Marshall, S., Godbole, S. and Chen, J. (2013) Using the SenseCam to Improve Classifications of Sedentary Behavior in Free-Living Settings. American Journal of Preventive Medicine, 44, 290-296.
https://doi.org/10.1016/j.amepre.2012.11.004
[45]  Haislip, L. (2011) An Examination of Utilitarian Bicycle Trip Route Choice Preference in San Diego. San Diego State University, San Diego.
[46]  Evenson, K.R., Neelon, B., Ball, S.C., Vaughn, A. and Ward, D.S. (2008) Validity and Reliability of a School Travel Survey. Journal of Physical Activity and Health, 5, S1-15.
https://doi.org/10.1123/jpah.5.s1.s1
[47]  Petrunoff, N.A., Xu, H., Rissel, C., Wen, L.M. and van der Ploeg, H.P. (2013) Measuring Workplace Travel Behaviour: Validity and Reliability of Survey Questions. Journal of Environmental and Public Health, 2013, Article ID: 423035.
https://doi.org/10.1155/2013/423035
[48]  Schipperijn, J., Kerr, J., Duncan, S., Madsen, T., Klinker, C.D. and Troelsen, J. (2014) Dynamic Accuracy of GPS Receivers for Use in Health Research: A Novel Method to Assess GPS Accuracy in Real-World Settings. Frontiers in Public Health, 2, 21.
https://doi.org/10.3389/fpubh.2014.00021
[49]  Carlson, A., Jankowska, M.M., Meseck, K., Godbole, S., Natarajan, L., Raab, F., Demchak, B., Patrick, K. and Kerr, J. (2015) Validity of PALMS GPS Scoring of Active and Passive Travel Compared with SenseCam. Medicine & Science in Sports & Exercise, 47, 662-667.
https://doi.org/10.1249/MSS.0000000000000446
[50]  UCSD-PALMS-Project-Home.
http://ucsd-palms-project.wikispaces.com/
[51]  Montgomery County Bicycle Master Plan (2018) Appendix D Level of Traffic Stress Methodology.
http://www.mcatlas.org/bikestress/
[52]  Ryan, C. (2015) Pedestrian and Bicycle Performance Measure Evaluation. Prepared for City of San Diego, San Diego.
[53]  People for Bikes (2018) Bike Network Analysis Methodology.
https://bna.peopleforbikes.org/#/methodology
[54]  Hart, A. (2001) Mann-Whitney Test Is Not Just a Test of Medians: Differences in Spread Can Be Important. BMJ, 323, 391-393. https://doi.org/10.1136/bmj.323.7309.391
[55]  Mann, H.B. and Whitney, D.R. (1947) On a Test of Whether One of Two Random Variables Is Stochastically Larger than the Other. Annals of Mathematical Statistics, 18, 50-60.
https://doi.org/10.1214/aoms/1177730491
[56]  Kruskal, W.H. and Wallis, W.A. (1952) Use of Ranks in One-Criterion Variance Analysis. Journal of the American Statistical Association, 47, 583-621.
https://doi.org/10.1080/01621459.1952.10483441
[57]  Dill, J. and Gliebe, J. (2008) Understanding and Measuring Bicycling Behavior: A Focus on Travel Time and Route Choice Final Report. Oregon Transportation Research and Education Consortium (OTREC), Portland.
https://doi.org/10.15760/trec.151
[58]  Krenn, P., Oja, P. and Titze, S. (2014) Route Choices of Transport Bicyclists: A Comparison of Actually Used and Shortest Routes. International Journal of Behavioral Nutrition and Physical Activity, 11, 31.
https://doi.org/10.1186/1479-5868-11-31
[59]  Howard, C. and Burns, E. (2001) Cycling to Work in Phoenix: Route Choice, Travel Behavior, and Commuter Characteristics. Transportation Research Record Journal of the Transportation Research Board, 1773, 39-46.
https://doi.org/10.3141/1773-05
[60]  Skov-Petersen, H., Barkow, B., Lundhede, T. and Jacobsen, J.B. (2018) How Do Cyclists Make Their Way?—A GPS-Based Revealed Preference Study in Copenhagen. International Journal of Geographical Information Science, 32, 1469-1484.
https://doi.org/10.1080/13658816.2018.1436713
[61]  Ryan, S., Appleyard, B., Schroeder, C. and Prescott, A. (2014) Estimating Daily Bicycle Volumes Using Manual Short Duration and Automated Continuous Counts. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington DC, 12-16 January 2014.
[62]  Blanc, B. and Figliozzi, M. (2016) Modeling the Impacts of Facility Type, Trip Characteristics, and Trip Stressors on Cyclists’ Comfort Levels Utilizing Crowdsourced Data. Transportation Research Record Journal of the Transportation Research Board, 2587, 100-108.
https://doi.org/10.3141/2587-12
[63]  Krenn, P.J., Oja, P. and Titze, S. (2014) Route Choices of Transport Bicyclists: A Comparison of Actually Used and Shortest Routes. International Journal of Behavioral Nutrition and Physical Activity, 11, 1-7.
https://doi.org/10.1186/1479-5868-11-31
[64]  Krenn, P.J., Oja, P., Titze, S. and Krenn, P.J. (2015) Development of a Bikeability Index to Assess the Bicycle-Friendliness of Urban Environments. The Open Civil Engineering Journal, 5, 451-451.
https://doi.org/10.4236/ojce.2015.54045
[65]  Winters, M., Brauer, M., Setton, E.M., Teschke, K., Winters, M., Brauer, M., Setton, E.M. and Teschke, K. (2013) Mapping Bikeability: A Spatial Tool to Support Sustainable Travel. Environment and Planning B: Urban Analytics and City Science, 40, 865-883.
https://doi.org/10.1068/b38185
[66]  Nielsen, T.A.S., Olafsson, A.S., Carstensen, T.A. and Skov-Petersen, H. (2013) Environmental Correlates of Cycling: Evaluating Urban Form and Location Effects Based on Danish Micro-Data. Transportation Research Part D: Transport and Environment, 22, 40-44.
https://doi.org/10.1016/j.trd.2013.02.017
[67]  Wang, H., Vogt, R. and Palm, M. (2015) Geospatial Analysis of Bicycle Network “Level of Traffic Stress”, Bicycle Mode Choice Behaviour and Bicycle Crashes for Risk Factor Identification. Pacific Northwest Transportation Consortium (PacTrans), Seattle, 46.
[68]  Monsere, C., Dill, J., McNeil, N., Clifton, K., Foster, N. and Goddard, T. (2014) Lessons from the Green Lanes: Evaluating Protected Bike Lanes in the U.S. National Institute for Transportation and Communities, NITC-RR-58, 1-179.
[69]  Romanillos, G., Zaltz Austwick, M., Ettema, D. and De Kruijf, J. (2016) Big Data and Cycling. Transport Reviews, 36, 114-133.
https://doi.org/10.1080/01441647.2015.1084067
[70]  Strava End of Year Insights (2016)
https://www.dropbox.com/sh/bd53sbohy86rlbf/AACI6dnxvf769RZM6Fc_H4Ata?dl=0&
preview=Strava+End+of+Year+Insights.pdf
[71]  Crist, K., Bolling, K., Schipperijn, J., Hurst, S., Takemoto, M., Sallis, J.F., Badland, H. and Kerr, J. (2017) Collaboration between Physical Activity Researchers and Transport Planners: A Qualitative Study of Attitudes to Data Driven Approaches. Journal of Transport & Health, 8, 157-168.
https://doi.org/10.1016/j.jth.2017.11.142
[72]  Aldred, R., Elliott, B., Woodcock, J. and Goodman, A. (2017) Cycling Provision Separated from Motor Traffic: A Systematic Review Exploring Whether Stated Preferences Vary by Gender and Age. Transport Reviews, 37, 29-55.
https://doi.org/10.1080/01441647.2016.1200156
[73]  Kent, M. and Karner, A. (2018) Prioritizing Low-Stress and Equitable Bicycle Networks Using Neighborhood-Based Accessibility Measures. International Journal of Sustainable Transportation, 1-11.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

微信:OALib Journal