Sequence-alignment methods which have been recently introduced in time-use research analyse the cross-sectional as well as the sequential information embedded in space–time activity-diary data. As these methods have been developed to trace the evolutionary history of random mutations of the elements in biological sequences, they are not appropriate to measure the distances by which activities change their sequential order in activity diaries. In this paper we report on the development of a position-sensitive sequence-alignment method for activity analysis. First, the problem is specified, and the key concepts to analyse the problem are addressed. Then, the principles of the new method are introduced. The method is illustrated with data on activity patterns collected in the Netherlands. The paper concludes with a critical discussion of the basic assumptions of the method and avenues of future research are identified.
Get full access to this article
View all access options for this article.
References
1.
ArentzeTBorgersATimmermansH J P, 1993, “A model of multi-purpose shopping trip behavior”Papers in Regional Science7223–256.
2.
ArentzeT AHofmanFVan MourikHTimmermansH J P, 2000, “ACBATROSS: A Multi-agent rule-based model of activity pattern decisions”, paper presented at the 79th Annual Meeting of the Transportation Research Board, Washington, DC, January; to appear in Transportation Research Record.
3.
BradleyD WBradleyR A, 1983, “Application of sequence comparison to the study of bird songs”, in Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison Eds SankoffDKruskalJ B (Addison-Wesley, Reading, MA) pp 189–209.
4.
BurnettPHansonS, 1982, “The analysis of travel as an example of complex human behavior in spatially-constrained situations: Definition and measurement issues”Transportation Research16A87–102.
5.
GowerJ C, 1971, “A general coefficient of similarity and some of its properties”Biometrics27857–871.
6.
GusfieldD, 1997, “The importance of (sub)sequence comparison in molecular biology”, in Algorithms on Strings, Trees, and Sequences: Computer Sciences and Computational BiologyGusfieldD (Cambridge University Press, Cambridge) pp 212–246.
7.
HatcherS GMahmassaniH S, 1992, “Daily variability of route and trip scheduling decisions for the evening commute”Transportation Research Record number 1357, 72–81.
8.
JohC HArentzeT ATimmermansH J P, 2001, “Pattern recognition in complex activity-travel patterns: A Comparison of Euclidean distance, signal processing theoretical, and multidimensional sequence alignment methods”, paper presented at the 80th Annual Meeting of the Transportation Research Board, Washington, DC, January; to appear in Transportation Research Record.
9.
KitamuraR, 1984, “Incorporating trip chaining into analysis of destination choice”Transportation Research18B67–81.
10.
KitamuraRPasE ILulaC VLawtonT KBensonP E, 1996, “The sequenced activity mobility simulator (SAMS): An integrated approach to modeling transportation, land use and air quality”Transportation23261–291.
11.
KoppelmanF SPasE I, 1985, “Travel-activity behavior in time and space: Methods for representation and analysis”, in Measuring the Unmeasurable Eds NijkampPLeitnerHWrigleyN (Nijhoff, Dordrecth) pp 587–627.
12.
KruskalJ B, 1983, “An overview of sequence comparison”, in Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison Eds SankoffDKruskalJ B (Addison-Wesley, Reading, MA) pp 1–44.
13.
McClureM AVasiT KFitchW M, 1994, “Comparative analysis of multiple protein-sequence alignment methods”Molecular Biology and Evolution11571–592.
14.
McNallyM G, 1997, “An activity-based micro-simulation model for travel demand forecasting”, in Activity-based Approaches to Travel Analysis Eds EttemaDTimmermansH J P (Pergamon Press, Oxford) pp 37–54.
15.
MeyersE W, 1995, “Seeing conserved signals: Using algorithms to detect similarities between biosequences”, in Calculating the Secrets of Life Eds LanderE SWatermanM S (National Academy Press, Washington, DC) pp 56–89.
16.
ReckerW WMcNallyM GRootG S, 1985, “Travel/activity analysis: Pattern recognition, classification and interpretation”Transportation Research A19(4) 279–296.
17.
RussellM JMooreR KTomlinsonM J, 1986, “Dynamic programming and statistical modelling in automatic speech recognition”Journal of the Operational Research Society37(1) 21–30.
18.
ThompsonJ DHigginsD GGibsonT J, 1994, “CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice”Nucleic Acids Research22(22) 4673–4680.
19.
TimmermansH J P, 1996, “A stated choice model of sequential mode and destination choice behavior for shopping trips”Environment and Planning A28173–184.
20.
WatermanM S, 1995Introduction to Computational Biology: Maps, Sequences and Genomes (Chapman and Hall, London).
21.
WilsonW C, 1998, “Activity pattern analysis by means of sequence-alignment methods”Environment and Planning A301017–1038.
