Analysis of Activity Conflict Resolution Strategies

This paper attempts to model the process of activity scheduling conflict resolution with actual scheduling process data. The resolution of activity scheduling conflicts is a critical component of rule-based activity scheduling models. Many current scheduling models use an assumed priority for each activity type to estimate how activity conflicts will be resolved, but research has shown that these activity type-based priority assumptions often do not hold in actuality. Therefore, the conflict resolution data captured in the scheduling process survey were used to estimate and evaluate a number of conflict resolution models, including a decision tree model and two discrete choice models. Both the conflict resolution decision tree model and the discrete choice models showed a promising ability to predict the resolution strategies chosen almost entirely on the basis of the attributes of the activities in conflict and characteristics of the surrounding schedule. These models present a useful advance in increasing the realism and accuracy of rule-based activity scheduling models.

[1]  Matthew J. Roorda,et al.  Strategies for Resolving Activity Scheduling Conflicts: An Empirical Analysis , 2005 .

[2]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[3]  Matthew J. Roorda,et al.  Operationalising Household Activity Scheduling Models: Addressing Assumptions and the Use of New Sources of Behavioural Data , 2005 .

[4]  G. V. Kass.  An Exploratory Technique for Investigating Large Quantities of Categorical Data , 1980 .

[5]  Matthew J. Roorda,et al.  Design and Assessment of the Toronto Area Computerized Household Activity Scheduling Survey , 2004 .

[6]  Matthew J. Roorda,et al.  Prototype Model of Household Activity-Travel Scheduling , 2003 .

[7]  Eric J. Miller,et al.  Strategies for Resolving Activity Scheduling Conflicts , 2005 .

[8]  David Biggs,et al.  A method of choosing multiway partitions for classification and decision trees , 1991 .

[9]  T. Gärling,et al.  Computational-Process Modelling of Household Travel Decisions Using a Geographical Information System , 1994 .

[10]  Abolfazl Mohammadian,et al.  Modeling activity scheduling time horizon: Duration of time between planning and execution of pre-planned activities , 2006 .

[11]  Harry Timmermans,et al.  Changing the timing of activities in resolving Scheduling Conflicts , 2006 .

[12]  Matthew J. Roorda,et al.  Computerized Household Activity-Scheduling Survey for Toronto, Canada, Area: Design and Assessment , 2004 .

[13]  Sean T. Doherty,et al.  Should we abandon activity type analysis , 2003 .

[14]  Tommy Gärling,et al.  COMPUTATIONAL PROCESS MODELING OF HOUSEHOLD TRAVEL DECISIONS USING A GEOGRAPHICAL INFORMATION SYSTEM , 2005 .

[15]  Ta Theo Arentze,et al.  Experiences with developing ALBATROSS: a learning-based transportation oriented simulation system , 1998 .

[16]  Sean T. Doherty,et al.  2005 Fred Burggraf Award, Planning and Environment: How Far in Advance Are Activities Planned?: Measurement Challenges and Analysis , 2005 .

[17]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[18]  Sean T. Doherty.  Should we abandon activity type analysis? Redefining activities by their salient attributes , 2006 .

[19]  J. Polak,et al.  Empirical Analysis of Factors Affecting the Resolution of Activity-Scheduling Conflicts , 2005 .

[20]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[21]  Sean T. Doherty,et al.  Analysis of Factors Affecting the Frequency and Type of Activity Schedule Modification , 2005 .

[22]  T. Arentze,et al.  Modeling Individuals’ Activity-Travel Rescheduling Heuristics: Theory and Numerical Experiments , 2002 .

[23]  Abolfazl Mohammadian,et al.  The validity of using activity type to structure tour-based scheduling models , 2007 .

[24]  Tommy Gärling,et al.  Computational-Process Modelling of Household Activity Scheduling , 1993 .