1 / 17

Topic 2 – Searching Algorithms CEE 763

Topic 2 – Searching Algorithms CEE 763. SEARCHING ALGORITHMS. Expected. Segment average. Segment average does not correspond to the highest. Expected. Segment average. Segments of different length with the highest crash. 0.0 mi. 0.1 mi. 0.2 mi. 0.3 mi. 0.4 mi. 0.5 mi. 0.6 mi.

Download Presentation

Topic 2 – Searching Algorithms CEE 763

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Topic 2 – Searching Algorithms CEE 763

  2. SEARCHING ALGORITHMS Expected Segment average Segment average does not correspond to the highest Expected Segment average Segments of different length with the highest crash

  3. 0.0 mi 0.1 mi 0.2 mi 0.3 mi 0.4 mi 0.5 mi 0.6 mi 0.67 mi PEAK SEARCHING0.1-mile window Roadway Segment Win # 1 Win # 2 0.03 mi 0.07 mi Win # 3 Win # 4 Note: Window length = 0.1 mi Win # 5 Win # 6 Win # 7

  4. Roadway Segment 0.03 mi 0.0 mi 0.1 mi 0.2 mi 0.3 mi 0.4 mi 0.5 mi 0.6 mi 0.67 mi Win # 1 0.07 mi Win # 2 Win # 3 Win # 4 Win # 5 Win # 6 PEAK SEARCHING0.2-mile window Note: Window length = 0.2 mi

  5. Roadway Segment 0.0 mi 0.1 mi 0.2 mi 0.3 mi 0.4 mi 0.5 mi 0.6 mi 0.67 mi Win # 1 Win # 2 Win # 3 Win # 4 PEAK SEARCHING0.4-mile window Note: Window length = 0.4 mi

  6. Roadway Segment 0.0 mi 0.1 mi 0.2 mi 0.3 mi 0.4 mi 0.5 mi 0.6 mi Win # 1 Win # 2 Win # 3 Win # 4 SLIDING WINDOW0.3-mile window with 0.1 increment

  7. EXAMPLE • A roadway segment is 0.47 miles long. Using a window length of 0.1 miles, the following crash data were obtained for each sub-segment. Calculate the CV for each sub-segment, and determine whether the search should continue with longer window sizes (assume the limiting CV is 0.3). Assume there is no accident from 0.27 to 0.3mi.

  8. SLIDING WINDOW0.3-mi window with 0.1-mi increment

  9. SLIDING WINDOW0.3-mi window with 0.1-mi increment

  10. Sliding Window Concepts: Bridging Three Contiguous Roadway Segments SLIDING WINDOW0.3-mi window with 0.1-mi increment

  11. SLIDING WINDOW0.3-mi window with 0.1-mi increment Sliding Window Concepts: Window Positions at the End of Contiguous Roadway Segments When Window is Moved Incrementally by 0.1 Miles

  12. SLIDING WINDOW0.3-mi window with 0.1-mi increment Sliding Window Concepts: Example of Position and Location of Sliding Windows and Subsegments

  13. SLIDING WINDOW0.3-mi window with 0.1-mi increment Sliding Window Concepts: Ranking Example limiting value: 40 acc/mi/yr

  14. EXAMPLE • A segment with 2 lanes, rural • ADT= 6000 • Limiting frequency: 10 • SPF: Intercept:-3.63 ADT coefficient: 0.53 Over dispersion Parameter: 0.5

  15. EXAMPLE Accident locations (mile) Site A: 0-0.4 mile Site C: 0.9-1 mile Non contiguous Site B: 0.4-0.9 mile Contiguous

  16. Example • What are the: Rank of Sites and ranking scores Windows with high potential for safety improvement

  17. Things to be aware of… • Process is iterative and ongoing • Resources are limited • it is important that the process concentrates on sites with the greatest potential for cost-effective treatment • Other pressures • A sudden unexpected increase in crashes at a site • Political pressure to do something at a site or • Media attention to a particular crash

More Related