Analyze Mode choice from SE Salmon and SE 51st Ave. to the Engineering building. (https://goo.gl/maps/nqSZr4s7z3Up9Q8E7 ) (25%) a. Calculate and compare mode choice probabilities for Transit, Transit boarding 2 buses, biking, walking, and Lyft/Uber (TNCs). Use METRO model coefficients and data provided (see excel file, “Data 1.” Tab). Assume mid-income household with 2 workers and 1 vehicle. Use auto model for TNCs. Compare overall mode shares, travel times, costs, and speeds for each option (door to door). b. Calculate direct and cross-elasticities for each mode and travel option. From a policy perspective, what are the variables or parameters that can significantly increase transit ridership? Discuss.
Compare base case (1) and the following scenarios: (25%) a. Transit costs $1 and free. b. TNC cost $6 (autonomous) and $2 (autonomous shared scheduled minivan). Increase TNC travel time by 30% when analyzing shared scheduled. c. Analysis: based on the results of this case study, are TNCs and new technologies a threat for traditional transit agencies? Discuss.
Hawthorne-Madison Boulevard with BRT (bus rapid transit) (20%) a. Study mode choice impacts when travel along Hawthorne-Madison (3.2 miles) when in bus travel time decreases 30% with respect to (w.r.t.) base case (1). b. In addition to decreasing bus travel times BRT implementation changes auto travel times in Hawthorne by changing its capacity. Assume BPR functions for Hawthorne and a Rev. 02/02/22 2 parallel corridor using the data provided (see excel file, “Data 3.” Tab). Find new equilibrium volumes and travel time. Apply new travel time to the travel time in the mode choice model in 3.a. c. Analysis: based on the results of this case study, what is the potential of BRT and lane reallocation to increase ridership? Discuss.
Use google maps on your smartphone to create a matrix that compares all available options in terms of cost, total distance, time, walking distance (it could be more than one), and transfers (it could be more than one) for the following modes: walking, biking, biketown, scooter (e.g. Lime (30%), Lyft (economy), and fastest transit option. Assume traveling to the EB to arrive at 11:50 am for this class (don’t be late !) . Analysis: based on the values of the matrix and your knowledge of mode choice, how does distance and Neighborhood Character affect the competitiveness of different modes? What is the best “niche” for transit (think about different type of trips, customers, etc.) ? Discuss. (20%)
Try to apply ODOT sketch model to characterize each potential origin (see excel file, “Data 4.” Tab) in terms of Neighborhood Character. Discuss pros/cons of this sketch approach to determine mode choice. You can use easy data sources like Google Street Map and Satellite views as well as Walk Score to help you with the analysis. Summarize in a Table Walk Score data (Travel time, distance, cost, and scores by mode, etc.) and compare it to Google Map data. (10%) Bonus: Use Census data* to estimate and tabulate number of workers (home and work) in a ¼ mile radius. * http://onthemap.ces.census.gov/