Exploring bikesharing travel time and trip chain by gender and day of the week

Transportation Research Part C: Emerging Technologies

Volumn 58, Issue , 2015, Pages 251-264

  Zhao, Jinbao ^a,b   Wang, Jian ^c   Deng, Wei ^a  

^a SOUTHEAST UNIVERSITY   (China)

^b The University of North Carolina at Chapel Hill   (United States)

^c PURDUE UNIVERSITY   (United States)

Author keywords

Big data analytics; Bikesharing; Gendered cycling behaviors; Travel time; Trip chain; Visual analytics

Indexed keywords

ADVANCED ANALYTICS; BIG DATA; DATA ANALYTICS; LARGE DATASET; RAILROAD STATIONS; SMART CARDS; VISUALIZATION;

ANALYTIC TECHNIQUE; BIKESHARING; CYCLING BEHAVIOR; DEMAND AND SUPPLY; GREEN TRANSPORTATIONS; RESIDENTIAL AREAS; TRIP CHAINS; VISUAL ANALYTICS;

TRAVEL TIME;

CYCLE TRANSPORT; GENDER; TRANSPORTATION SYSTEM; TRAVEL BEHAVIOR; TRAVEL TIME;

CHINA;

EID: 84940453030     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2015.01.030     Document Type: Article

References (43)

1. Beecham R., Wood J. Exploring gendered cycling behaviours within a large-scale behavioural data-set. Transport. Plann. Technol. 2014, 37(1):83-97.
2. Bonham J., Wilson A. Bicycling and the life course: the start-stop-start experiences of women cycling. Int. J. Sustain. Transport. 2012, 6(4):195-213.
3. Bricka S.G., Sen S., Paleti R., Bhat C.R. An analysis of the factors influencing differences in survey-reported and GPS-recorded trips. Transport. Res. Part C 2012, 21:67-88.
4. Cervero R., Kockelman K. Travel demand and the 3Ds: density, diversity, and design. Transport. Res. Part D 1997, 2:199-219.
5. Currie G., Delbosc A. Exploring the trip chaining behaviour of public transport users in Melbourne. Transp. Policy 2011, 18(1):204-210.
6. Dill J. Evaluating a new urbanist neighborhood. Berkeley Plann. J. 2006, 19(1):59-78.
7. Emond C.R., Tang W., Handy S.L. Explaining gender difference in bicycling behavior. Transport. Res. Rec. 2009, 2125:16-25.
8. Eyler A.A., Baker E., Cromer L., King A.C., Brownson R.C., et al. Physical activity and minority women: a qualitative study. Health Educ. Behav. 1998, 25(5):640-652.
9. Field A. Discovering Statistics Using SPSS 2009, Sage, London. third ed.
10. Galland S., et al. Multi-agent simulation of individual mobility behavior in carpooling. Transport. Res. Part C 2014, 45:83-98.
11. Golob T.F. A simultaneous model of household activity participation and trip chain generation. Transport. Res. Part B 2000, 34(5):355-376.
12. Golob T.F., Hensher D.A. The trip chaining activity of Sydney residents: a cross-section assessment by age group with a focus on seniors. J. Transp. Geogr. 2007, 15(4):298-312.
13. Hensher D.A., Reyes A.J. Trip chaining as a barrier to the propensity to use public transport. Transportation 2000, 27(4):341-361.
14. Hou Y., et al. An integrated traffic-driving simulation framework: design, implementation, and validation. Transport. Res. Part C 2014, 45:138-153.
15. Jensen P., Rouquier J.-B., Ovtracht N., Robardet C. Characterizing the Speed and paths of shared bicycles in Lyon. Transport. Res. Part D 2010, 15:522-524.
16. Kitamura R., Yamamoto T., Susilo Y.O., Axhausen K.W. How routine is a routine? An analysis of day-to-day variability in prism vertex location. Transport. Res. Part A 2006, 40(3):259-279.
17. Krygsman S., Arentze T., Timmermans H. Capturing tour mode and activity choice interdependencies: a co-evolutionary logit modeling approach. Transport. Res. Part A 2007, 41:913-933.
18. Lathia N., Ahmed S., Capra L. Measuring the impact of opening the London shared bicycle scheme to casual users. Transport. Res. Part C 2012, 22:88-102.
19. Lee Y., Hickman M., Washington S. Household type and structure, time-use pattern, and trip-chaining behavior. Transport. Res. Part A 2007, 41(10):1004-1020.
20. Ma X., Wu Y.J., Wang Y., Chen F., Liu J. Mining smart card data for transit riders' travel patterns. Transport. Res. Part C 2013, 36:1-12.
21. McGuckin N., Murakami E. Examining trip-chaining behavior: comparison of travel by men and women. Transport. Res. Rec. 1999, 1963:79-85.
22. McGuckin N., Zmud J.P., Nakamoto Y. Trip-chaining trends in the United States: understanding travel behavior for policy making. Transport. Res. Rec. 2005, 1917:199-204.
23. Morency C., Trépanier M., Agard B. Measuring transit use variability with smart-card data. Transp. Policy 2007, 14(3):193-203.
24. Munizaga M.A., Palma C. Estimation of a disaggregate multimodal public transport origin-destination matrix from passive smartcard data from Santiago, Chile. Transport. Res. Part C 2012, 24:9-18.
25. O'Brien O., Cheshire J., Batty M. Mining bicycle sharing data for generating insights into sustainable transport systems. J. Transp. Geogr. 2014, 34:262-273.
26. Parkes S.D., Marsden G., Shaheen S.A., Cohen A.P. Understanding the diffusion of public bikesharing systems: evidence from Europe and North America. J. Transp. Geogr. 2013, 31:94-103.
27. Primerano F., Taylor M.A.P., Pitaksringkarn L., Tisato P. Defining and understanding trip chaining behavior. Transportation 2008, 35(1):55-72.
28. Pucher J. Renaissance of public transport in the United States?. Transport. Quart. 2002, 56(1):33-49.
29. Pucher J., Buehler R., Seinen M. Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies. Transport. Res. Part A 2011, 45(6):451-475.
30. Rodriguez D.A., Joo J. The relationship between non-motorized mode choice and the local physical environment. Transport. Res. Part D 2004, 9:151-173.
31. Shaheen S.A., Guzman S., Zhang H. Bikesharing in Europe, the Americas, and Asia: past, present, and future. Transport. Res. Rec. 2010, 2143:159-167.
32. Shaheen S.A., Zhang H., Martin E., Guzman S. Hangzhou public bicycle: Understanding early adoption and behavioral response to bikesharing in Hangzhou, China. Transport. Res. Rec. 2011, 2247:33-41.
33. Shaheen S.A., Martin E., Cohen A. Public bikesharing and modal shift behavior: a comparative study of early bikesharing systems in North America. Int. J. Transport. 2013, 1(1):35-53.
34. Shen L., Stopher P.R. A process for trip purpose imputation from Global Positioning System data. Transport. Res. Part C 2013, 36:261-267.
35. Strathman J.G., Dueker K.J., Davis J.S. Effects of household structure and selected travel characteristics on trip chaining. Transportation 1994, 21:23-45.
36. Timmermans H.J.P., van der Waerden P., Alves M.J., Polak J., Ellis S., Harvey A.S., Kurose S., Zandee R. Spatial context and the complexity of daily travel patterns: an international comparison. J. Transp. Geogr. 2003, 11:37-46.
37. Vanek O., Jakob M., Hrstka O., Pechoucek M. Agent-based model of maritime traffic in piracy-affected waters. Transport. Res. Part C 2013, 36:157-176.
38. Wen C., Koppelman F.S. A conceptual and methodological framework for the generation of activity-travel patterns. Transportation 2000, 27:5-23.
39. Wood J., Slingsby A., Dykes J. Visualizing the dynamics of London's bicycle hire scheme. Cartographica 2011, 46:239-251.
40. Zeng W., Chen P., Nakamura H., Iryo-Asano M. Application of social force model to pedestrian behavior analysis at signalized cross walk. Transport. Res. Part C 2014, 40:143-159.
41. Zhang M., Wang L. The impacts of mass transit on land development in China: the case of Beijing. Res. Transport. Econ. 2013, 40:124-133.
42. Zhang H., Shaheen S.A., Chen K. Bicycle evolution in China: from the 1990s to present. Int. J. Sustain. Transport. 2014, 8(5):317-335.
43. Zhao J., Deng W., Song Y. Ridership and effectiveness of bikesharing: the effects of urban features and system characteristics on daily use and turnover rate of public bikes in China. Transp. Policy 2014, 35:253-264.