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learning algorithms will further enhance the success for detecting activity travel
behavior data while reducing respondents' burden gradually.
In the medium term, the rapid development of positioning technologies, in
particular, the use of mobile phone and wireless communication technologies for
indoor positioning, is very likely to provide much greater positioning accuracy.
Therefore, the post-processing of detecting useful data from GPS loggers can be
much easier.
In the long term, it should be noted that logged data presents high demand for
memory capacity of GPS or GSM device because it has to record and store huge
mass of tracking data. With the help of 3G mobile communication technologies
and other high-speed facilities stored with a full set of deduction program, real-
time tracking of individuals' behavior can be theoretically an inevitable trend and
potentially realized in the future study.
Acknowledgements
This research is supported by two General Research Fund (GRF) projects of
the Hong Kong Research Grant Council (HKBU245008 and HKBU244610) and a FRG project of
Hong Kong Baptist University (FRGI/10-11/049).
References
Andre, J., Siarry, P., & Dognon, T. (2001). An improvement of the standard genetic algorithm
fighting premature convergence in continuous optimization.
Advances in Engineering Software,
32
(1), 49-60.
Bleeker, S. E., Moll, H. A., Steyerberg, E. W., Donders, A. R. T., Derksen-Lubsen, G., Grobbee,
D. E., & Moons, K. G. M. (2003). External validation is necessary in prediction research: A
clinical example.
Journal of Clinical Epidemiology, 56
(9), 826-832.
Bohte, W., & Maat, K. (2008). Deriving and validating trip destinations and modes for multi-day
GPS-based travel surveys: A large-scale application in the Netherlands. In 8th international
conference on survey methods in transport: Harmonization and data comparability, France.
Buckles, B. P., & Petry, F. E. (1992). Genetic algorithms: Introduction and applications. In IEEE
Computer Society Press, Catalog #2935, Los Alamitos, CA, USA.
Buliung, R. N. (2005).
Activity/travel behaviour research: Approaches and findings with. Identifi-
cation of research themes and emerging methods
(Working Paper Series No. 008). Hamilton:
Center for Spatial Analysis, McMaster University.
Ceylan, H., & Bell, M. G. H. (2004). Traffic signal timing optimization based on genetic algorithm
approach, including drivers' routing.
Transportation Research Part B, 38
, 329-342.
Chen, C., & Mokhtarian, P. L. (2006). Tradeoffs between time allocations to maintenance
activities/travel and discretionary activities/travel.
Transportation, 33
, 223-240.
Chen, C., Gong, H., Lawson, C., & Bialostozky, E. (2010). 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, 44
, 830-840.
Chung, E., & Shalaby, A. (2005). A trip reconstruction tool for GPS-based personal travel surveys.
Transportation Planning and Technology, 28
(5), 381-401.
Cook, T. D., & Campbell, D. T. (1979).
Quasi-experimentation: Design and analysis issues for
field settings
. Chicago: Rand McNally.
Curtis, C., & Perkins, T. (2006).
Travel behaviour: A review of recent literature
.
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