Real-time Tracking and Analysis of T he Dynamics in Activity Scheduling and Schedule Execution

1. Real-time Tracking and Analysis of The Dynamics in Activity Scheduling and Schedule Execution By Jianyu (Jack) Zhou 8/08/06 Advisor: Reginald Golledge Committee members: Jack Loomis, Keith Clarke, and Richard Church.

2. Outline • Problem Statement • Research Assumptions • Research Hypotheses • Theoretical Relevance • Methodology • Survey Design • Data processing • Data Analysis and Modeling • Conclusion • Further Research

3. Problem Statement • Activity scheduling is a continuous process of spatial and temporal choice over time. • Activity execution represents the process that the planned schedule is converted into the sequence of implemented activities that are continuous in space and time.

4. Problem Statement (cont’) • Activity scheduling study in Transportation and Geography research context focuses on two aspects: • The temporal-spatial decision-making structure embedded in the scheduling process. • The linkage of schedule to actual activity execution. • Two existing approaches for analyzing and predicting the dynamic process of activity scheduling: • Econometric approach • Cognitive approach

5. Problem Statement (cont’) • Objectives of this research are two-fold: • Develop the systematic techniques for tracking and recording the interlaced process of real-life activity scheduling and execution. • Reveal the critical factors that affect the relations between activity schedules and their actual execution based on “revealed” in-field data. Model the relationship and quantify the effects of the factors changes on people’s activity temporal-spatial choices with respect to their schedules.

6. Research Assumptions • Obligatory vs. Discretionary Activities • Stochastic decision making • Continuous revisions of activity scheduling

7. Research Hypotheses • The congruence and deviation relations between individual activity schedules and their actual execution can be consistently described in a series of relevant factors -- socio-demographic characteristics, spatial-temporal constraints, etc. • These factors affect the schedules and their executions in different ways. Not every type of activity is thoroughly planned ahead of time. • 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.

8. Theoretical Relevance • Two existing propositions by Hayes-Roth and Hayes-Roth (1979) reflected different views and understanding about the scheduling process - Successive refinement model and Opportunistic model. • Hagerstrand’s (1970) time-space geography

9. Methodology: Survey Design -Integrated Activity Scheduling/Execution Data Collection • This research implemented a data collectionsystem for pilot study. • The system offers unique advantages for travel/activity Survey.

10. Methodology: Survey Design • Start-up form presents four modules that constitutes the main function of the system devices. • Module 1 – Personal Info and Week Schedule • It helps the survey respondents to set up personal demographic background and establish a preliminary week schedule that came up to them at the interview time.

11. Methodology: Survey Design (cont’) • Module 2 - Schedule Activities or Refine Schedules • Capture schedule-related information with Schedule-an-activity form. • The accomplished schedules are listed on the weekday tab pane with a brief description

12. Methodology: Survey Design (cont’) • Module 3- Trace Activity Implementation Module • Trace travel and activity Execution. • Capture the travel route by drawing tool when most of GPS points are invalid. • Identify the relevant schedule to the current activity and their congruence / deviation relationship

13. Methodology: Pilot Survey • A total of 20 volunteers (13 males, 7 females) recruited for one-week survey. • The ages of the survey respondents fall within the range of 20-35, with the average being 28.75. • Each survey respondent commonly uses 4 types of travel modes - at most 6 and at least 2. • Each survey respondent indicated 20.3 visited or frequently-visited locations over the survey period.

14. Methodology: Pilot Survey • The non-response rate for activities and scheduling tracking were 13.75% and 4.1% respectively. • Survey Feedbacks: • Most survey participants consider the survey questions clearly and concisely organized. • Most of the survey participants (90%) have no problem with data uploading at the end of the survey day. • Fatigue Effects: • Respondents’ activity counts and their data entry steps are highly correlated. • Average entry time for the travel/activity tracking module - 26.18 seconds per form. The average data entry steps in the module – 35.34 steps per day.

15. Methodology: Survey Results Highlight-Schedule and Activity Intensities • No strong correlation between activity intensity and the scheduling steps were revealed. • Relative scheduling intensity --measured as the ratio of schedule count against the total activity count. • Recreation and Entertainment activities-- the most actively scheduled. • Seconded by Social activities. • Household Obligation activities -- least planned before execution.

16. Methodology: Survey Results Highlight – Activity Constraints • Spatial-temporal constraints: • spatial constraints along the path are more rigid than their temporal counterparts • Coupling constraints: • Household Obligation and Work/school activities are subject to the least coupling constraints (about 65-75% completed alone), • About 75% Social activities are expected to be completed in group.

17. Methodology: Data Processing – A general three-step map matching algorithm • Three-step map matching • Data Preprocessing—Cluster Reduction and Density Leverage • Multiple-Hypothesis Matching with Rank Aggregation • Dempster Belief Test for Travel Off-Road/Noise Discernment • On average the map matching algorithm reaches a matching accuracy of 95.74% Map Matching Accuracy by Travel Modes

18. Methodology: Data Analysis – Travel optimization Diff of actual travel route and the shortest distance/time route by activity types Diff of actual travel route and the shortest distance/time route by travel modes

19. Methodology: Data Analysis – Route Choice Analysis • Binary Logistic analysis is used to analyze the traveler’s route choice preference between the shortest time path and the shortest distance path. • Route distance in miles. • Travel time in minutes. • Number of street links (extracted from the GIS base map). • Number of intersections encountered during the travel. • Off-road Ratio. • Gender of the traveler. • Travel Mode. • Male travelers tend to choose travel path that is relatively time-optimized compared to female travelers. • As the travel distance on a route increases, travelers will shift their routing aim toward time-optimization rather than distance optimization. P = exp(U)/(1+ exp (U) )

20. Methodology: Data Analysis - Schedule Horizon Analysis • Shopping activities and Services and Errands activities--short schedule horizon; • Work and School activities and Household Obligation--most distant schedule horizon. • Activities with short duration are less likely to be planned out early. • Activities with longer durations tend to be associated with more distant schedule horizons.

21. Methodology: Data Analysis - Missing Value Analysis (Missing/Mismatch Percentage) • Activity locations were planned out well in recorded schedules. • End time tends to suffer the greatest degree of uncertainty. • Start time and end time have the lowest mismatch percentage.

22. Methodology: Data Analysis -Missing Value Analysis (cont’) n = 211 • Activity locations is prioritized over other schedule elements . • Schedules with a short schedule horizon tends to have an undetermined start or end time ( by row 3 and 7). • Schedules with a long schedule horizon are associated with the undetermined activity date (row 5 and 9).

23. Methodology: Data Analysis – Nested Logistic Modeling • Estimation of a nested logistic model is used to study the potential deviation of the respondents’ schedule execution from their stated intention – the schedule • Assume that schedule execution is an integrated decision-making process that conforms to a model in a decision tree form. • Schedule Execution Choice Evaluation: • The utility function-- the weighted linear addition of three vectors of attributes. • Factors that affects activity participation choice (Aap), activity start time choice (Aas) and combinations of activity participation and start time choice (Aaps).

24. Methodology: Data Analysis – Nested Logistic Modeling (cont’) • Only “Total work/school time duration” variable is statistically significant (5% level) in the two-level model. • Continue to model the three discrete levels of activity start time choices under a MLM framework. • “Travel duration”, “Travel distance”, “The ratio of Off-road travel”, “Work/School activity type” and “End time missing” are the significant factors that affect the activity start time choices at the significance level of 0.05. • MLM model results offer sensitivity quantification: • Given the same status of the other variables, for each 1 mile increase of travel distance, the odds of activity start on time decrease by 1- exp (0.12 * 1) = 12.7% and odds of activity start early decrease by 1- exp (0.2 * 1) = 22.1 %

25. Conclusion • Innovative Data collection methodology: • Conceptualized and implemented a real-time system tool that facilitates the study of the dynamic linkages between the activity scheduling and execution process. • Small-scale pilot study by this research showed that the methodology was successful in achieving our goals without incurring significant survey fatigue effects. • In-depth analysis of the routing behavior, scheduling pattern of various activity categories and the inter-relationship between scheduling and correlated activity execution: • Using a nested logistic modeling approach, the research was able to identify the single factor that dominates the activity participation and start time choice decision making. • The further one-level multinomial logistic modeling efforts identified five factors that affect the activity start time choices at a significant level. The modeling results offer us the quantitative measures for effects of the factor changes on activity start time choices.

26. Further Research • Improve current data collection system • Reduce the load of the survey task. • Provide effective survey guidance. • Enhance the multi-task mode of the survey program. • Compare instrument bias and survey burden brought by the system with traditional activity/travel data collection methods • Further modeling efforts • Artificial neural network (ANN) provides us a method to learn and approximate the relationship with a discrete-valued function in the form of a network of interconnected neurons. • Decision tree modeling to infers the hierarchical decision structure from the empirical data by induction with no prior model-structure assumption made.