## Additional ProblemsEdit

- Are number of destinations always less than origins?
- Pose 5 hypotheses about factors that affect work, non-work trips? How do these factors affect accuracy, and thus normalization?
- What is the acceptable level of error?
- Describe one variable used in trip generation and how it affects the model.
- What is the basic equation for normalization?
- Which of these models (home-end, work-end) are assumed to be more accurate? Why is it important to normalize trip generation models
- What are the different trip purposes/types trip generation?
- Why is it difficult to know who is traveling when?
- What share of trips during peak afternoon peak periods are work to home (>50%, <50%?), why?
- What does ORIO abbreviate?
- What types of employees (ORIO) are more likely to travel from work to home in the evening peak
- What does the trip rate tell us about various parts of the population?
- What does the “T-statistic” value tell us about the trip rate estimation?
- Why might afternoon work to home trips be more or less than morning home to work trips? Why might the percent of trips be different?
- Define frequency.
- Why do individuals > 65 years of age make fewer work to home trips?
- Solve the following problem. You have the following trip generation model:

And you are given the following coefficients derived from a regression model.

B_1 = 0.61 B_2 = 0.15 B_3 = 0.123

If there are 600 office employees, 300 industrial employees, and 200 retail employees, how many trips are going from work to home?