Combined estimation of activity generation models incorporating unobserved small trips using probe person data Sep. 22nd, 2013 The University of Tokyo Sohta Itoh
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Research background Comparison between PT and PP data Combined estimation model Correcting sampling bias Conclusion
3 Person Trip survey (Paper-based) 1960s (1955 CATS, 1:967 Hiroshima) 1:980s Activity based model – disaggregate data Probe Person survey (GPS-based) 2000s (Zitto and D’este, 1995; Murakami and Wagner, 1999; Asakura and Hato, 2004; Hato et al., 2006; Stopher et al., 2011) Non-response activities Short trips and activities are often underreported (Wolf et al., 2001; Bricka and Bhat, 2006; Itsubo and Hato, 2006) Non-response bias Changes of activity patterns .. • Aging society • Inner-city problems Short activities becomes important
Timestamp Latitude Lontitude Web Trip purpose diary L  Transportation mode + personal information 41
5 ✿ Walk️Car ✿ Bike ✿ Motorcycle ✿ Bus ✿ Train
6 PT survey data Massive location data PP survey data Paper-based (Rely on respondents’ memories) GPS (Automatical but fragmentary) GPS (Automatical) + Web diary Zone-based Dot data (High-resolution) Dot-based (High-resolution) Large sample Large sample Small sample Combined Estimation using both PT and PP data
• Both data are obtained in Yokohama, Japan •Respondents are resided in Yokohama
■PT survey

■ PP survey

•In almost all of categories, the number of activities of PT data is smaller than that of PP data
* : reject the null hypothesis of no difference between the mean of PT data and that of PP data at 5% significant level 88
9 • It is assumed that PP data does not have unreported activities. •If missing activities have some characteristics in common, sampling bias affects the estimation result Detecting the factors influencing the propensities to record activities
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Latent variable about activity generation of individual i and zone n x in1：explanatory variables of individual i and zone n & jn1 : error term of individual i and zone n
Latent variable about observation of individual i and zone n x in2 : explanatory variables of individual i and zone n &in2 : error term of individual i and zone n Yin2：unobserved variable of individual i and zone n
Ф : cumulative distribution function of the standard normal distribution φ : probability density function of the standard normal distribution
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Following attributes associate with activity under-reporting at t he significant level • male ▪ stay duration ▪age 20-39 years • age 60+ 1) : The sum of space about retail stores in the zone
To correct the bias, the inverse of observation probability is considered the weight as: Win β* : the parameter estimated in the model comes from the estimation results Observation activity data (disaggregate) Corrected results activities with attributes x 13
The rate of frequency of weighted PT is similar to PP, which represents the bias of short activities is corrected
15 ■Work ■School ■Business ■Shopping ■Private ■Other ■Unknown The rate of discretionary activities is expanded by weighting. Work 240 min.
16 Comparison between PT and PP We have discussed the advantages of both new GPS-based PP surveys and conventional PT surveys Combined estimation using PT and PP data Introducing the selection model, we show several demographic attributes and activity characteristics associate if activities are missed or not and consider the selection bias Correcting the sampling bias By multiplying the inverse of probabilities of observation obtained from the selection model, the bias is appropriately assessed and corrected Thank you for your attention!
• Asakura, Y., and Hato, E. (2004) Tracking Survey for Individual Travel Behavior Using Mobile Communication Instruments, Transportation Research C, 12, 273-291. •Bricka, S., Bhat, C. (2006) Comparative Analysis of Global Positioning System-Based and Travel Survey-Based Data, Transportation Research Record No. 1972, 9-20. •Brog, W., Erl, E., Meyburg, A. H., Wermuth, M. J. (1982) Problems of Nonreported Trips in Surveys of Nonhome Activity Patterns, Transportation Research Record, Vol. 891, 1-5. •Itsubo, S., Hato, E. (2006) A Study of the Effectiveness of a Household Travel Survey Using GPS- Equipped Cell Phones and a Web Diary through a Comparative Study with a Paper Based Travel Survey, TRB Annual Meeting in Washington DC (CDROM). • Kitamura, R., Bovy, P. (1987) Analysis of Attrition Biases and Trip Reporting Errors for Panel Data, Transportation Research A, 21, 287-302. • Kitamura, R. (1990) Panel Analysis in Transportation Planning: An Overview, Transportation Research A, 24, 401-415. • Hato, E., Itsubo, S., Mitani, T. (2006) Development of MoALs (Mobile Activity Loggers Supported by GPS-Phones) for Travel Behavior Analysis, TRB Annual Meeting in Washington DC (CDROM). •Hato, E. (2006) Evaluation of Trip-Activity Pattern Variability Using Probe Person Data, TRB Annual Meeting in Washington DC (CDROM). •Hato, E. (2010) Development of Behavioral Context Addressable Loggers in the Shell for Travel- Activity Analysis, Transportation Research C, 18, 55-67. •Murakami, E., Wagner, D. P. (1999) Can Using Global Positioning System (GPS) Improve Trip Reporting?, Transportation Research C, 7, 149-165. •Rubin, D. B. (1976) Inference and Missing Data, Biometrika, 63, 581-590.