1. Characterizing Vessel Travel Times with Waterway Levels in Case Studies Ivy L. Riley Jackson State University Civil and Environmental Engineering Department

2. Overview  Motivation for study  Study Objectives  Methodology  Data Sources  Automatic Identification System (AIS)  River Gage Database  Examples  Two locations  Analyze over a period of two years  Results  Study Conclusions  Future Research

3. Motivation for Study  Utilize the need of travel times to:  Improve knowledge and navigation on the inland waterway system  Better understand impacts of environmental factors on vessel performance  Voyage planning  Help bring operator awareness

4. Study Objectives  Analyze the relationship between vessel travel times and water levels by: 1.Measuring the travel times as vessels exit and enter into another Area of Interest (AOI) traveling up bound or down bound 2.Developing a methodology to study the transit time difference as water levels change 3.Analyzing over a period of two years to quantify the relationship

5. Methodology 1.Clarified sufficient available data to support a meaningful analysis 2.Selected an area of study and a time period 3.Removed outliers and develop a water level stage increment 4.Calculated average travel times and number of trips according to the water level 5.Analyzed the travel times as water levels increase or decrease

6. Step 1: Data Source 1  Automatic Identification System (AIS)  Provided by the United States Coast Guard (USCG) which improves the quality of maritime domain awareness by focusing on the improvements on: Security Navigational safety Environmental protection Search and rescue  Collects position information and other data on vessels operating in coastal waters and on inland rivers of the nation.  Operators are able to obtain information about other vessels including: Call sign Vessel name Position Course speed Size Cargo Destination

7. Step 1 continued: Data Source 2 River Gage Database  Provided by the US Army Corps of Engineers (USACE)

8. Step 2: Example 1  Lower Ohio River, between mile 583 and mile 603 near Louisville, KY  2 year period, 2012 and 2013

9. Analysis 20 mile distance between the two AOIs for both examples  The distance as a vessel exits one AOI and enters another Outlier boundary of 10 hours is applied Different increasing increments (in feet) are tested to analyze the differences in travel times Found that between increments of 2.5-5 feet the output of number of trips increases  Below 2.5 feet; the sample size (data points) increases as the number of trips decreases  Above 5 feet; the sample size decreases as the number of trips increases

10. Analysis Continued The upstream trend for both years showed a variation in the number of hours traveled compared to traveling downstream between the two directions The upstream travel times (in hours) for both time periods 2012 and 2013 are higher than the downstream as expected.  Upstream, increasing from 4.6-6 hours  Downstream, decreasing from 3-2.4 hours

11. Step 3: Results

12. Step 2: Example 2  Lower Mississippi River, between mile 343 and mile 363 near Natchez, MS  2 year period, 2012 and 2013

13. Step 3: Results

14. Study Conclusions  The AIS data provides detailed operational information aiding the matter of rising water levels.  A relationship between river stages and travel times of commercial vessels traveling upstream and downstream is analyzed.  The methodology presents a strategy to recognize the issue of fluctuating water levels on inland rivers affecting vessel travel times.  Archived AIS vessel position reports were used to estimate travel times along 20-mile portions of the Mississippi and Ohio Rivers.

15. Future Research In the future, research should be focused towards controlling for additional factors such as size of the vessels, horsepower ratings, and the amount of cargo and/or number of loaded barges being pushed. The AIS data applied in this research can also be utilized to improve knowledge on environmental conditions and how these conditions may also affect vessel navigation.

16. Thank You. Ivy L. Riley J00605393@students.jsums.edu Acknowledgements: Dr. Feng Wang and Dr. Robert W. Whalin, Jackson State University, Jackson, MS Dr. Kenneth N. Mitchell and Dr. Patricia K. DiJoseph, US Army Corps of Engineers (ERDC), Vicksburg, MS