Personal Background  Name: Jianyu (Jack) Zhou  Undergraduate: Geography, Beijing University, P.R. China ( ) Economic geography.  Master’s: Geography, UCSB ( ). An analysis of variability of travel behavior within one-week period based on GPS collected data. (funded by UCTC).  PH.D dissertation: Geography, UCSB (2000-now). Tracking and Analysis of Dynamics in Activity Scheduling and Schedule Execution. (funded by UCTC).  After graduation: Academic

Empirical Tracking and Analysis of Dynamics in Activity Scheduling and Schedule Execution By Jianyu(Jack) Zhou & Dr. Reginald Golledge Department of Geography University of California, Santa Barbara UCTC status meeting 09/24/2003

Outline 1.Problem Statement 2.Research Assumptions 3.Research Hypotheses 4.Integrated Activity Scheduling/Execution Data Collection 5.Survey design 6.Data Analysis and Model Construction 7.Current Progress

Problem Statement  Activity scheduling/execution study in Transportation and Geography research context focuses on two aspects: 1. The temporal-spatial decision-making structure embedded in the scheduling process. 2. The linkage of schedule to actual activity execution. The later aspect has been ignored and constrained by data capture means from being fully explored in real-life situation

Problem Statement (cont’)  Objective of this research is two-fold: Develop the systematic techniques for tracking and recording the process of real-life activity scheduling and execution within one unified framework. Analyze and model the congruence and deviation relations between individual activity schedules and the associated execution based on the in- field near real-time data collected.  The main questions to be answered: The set of factors that potentially affects the congruence and deviation relations between activity schedules and associated execution. The relative sensitivity of activity execution with respect to activity schedules under the influence of the set of associated factors (socio- demographic characteristics, spatial-temporal constraints, etc.). The effectiveness and efficiency of real-time mobile data collection means for capturing data regarding activity scheduling and execution process.

Research Assumptions  Mandatory activities serving as the skeletons for the activity schedules. Other flexible activities and their derived trips are organized around the skeleton constraints.  In general, most travel/activity-related decision making occurs stochastically over time. (e.g. en-route destination change).  Scheduling of activities are continually revised through time according to situation change. People may add, delete or reorder the activities that list in their schedule in a recursive way.

Research Hypotheses  The congruence and deviation relations between individual activity schedules and their actual execution can be consistently described in a series of associated factors -- socio-demographic characteristics, spatial- temporal constraints, etc.  Mobile real-time system constitutes a powerful tool to capture the asynchronous activity decision-making and execution process with the least time and location constraints.

Integrated Activity Scheduling/Execution Data Collection  This research implemented a test data collection system that features: Near real-time data capturing and correction in-field. Decentralized data collection at terminal side and centralized data collection monitoring at server side. Multi-modal input interface that facilitates the capturing of scheduling/execution information.  Advantages using the system for travel/activity survey: Minimize the time lag between data collection and data correction phases by interleaving them in near real-time. Make travel/activity data collection procedure less restricted to time and location constraints.

Integrated Activity Scheduling/Execution Data Collection Data will be collected via a real-time system. Pocket PC Wireless WAN card and GPS receiver

Survey Design

Data Analysis and Model Construction  The data collection methodology will be examined from two aspects: The average time that people may spend on the scheduling task each day and the variation of scheduling- time length over a one-week period to check the data input efficiency. Plot the activity and trip frequency variation across the one-week period to check if significant report fatigue effects are involved in the survey.

Data Analysis and Model Construction (cont’)  Statistics to be analyzed and presented visually for deepening our understanding of activity scheduling behavior and its relation to activity execution: The average number of scheduling decisions The fulfillment of different activity attributes at the time of scheduling. Activity pre-plan time scale variation. The relations between en-route rescheduling with the timing when the activity and derived travel are performed. The percentage of preplanned activities (and unplanned activities) in each activity category.

Data Analysis and Model Construction (cont’) Nested Logit Model Random utility concept: assume people’s evaluation of the alternatives is based on the utility functions with the attributes of these alternatives as independent variables. The greater the utility value indicates the greater chance of the corresponding alternative is selected and observed in reality. Model the relationship between activity scheduling and execution as a choice process with two nested levels: 1) Determine if the schedule is executed as specified (on schedule, deleted, postponed). 2) Model the temporal execution status, activity duration choice and locational choice (under the “on-schedule” branch)

Data analysis and Model construction (cont’)  Six categories of factors for model construction: Spatial-temporal factors: scheduled activity duration, distance of activity site to home, distance of previous activity site to current activity site, distance of current activity site to next activity site, scheduled time for the current activity, delay of previous activity, elongation of previous activity. Other physical factors: weather, traffic condition, absence of the necessary conditions to perform the activity. Activity-related factors: travel mode, activity type, if the next activity planned. Social factors: number of accompanying persons in the activity, service availability. Cognitive factors: awareness of travel accident ahead at the activity execution time, awareness of co-participant’s withdraw from the activity, activity priority, forgot the scheduled activity. Socio-demographic factors: sex, age, marital status, number of children, driver’s license, employment.

Current Progress 1. Nearly complete with the real-time data collection system. 2. Next Step: a) Data collection. b) Model construction.