1. USING DATA TO MAKE YOUR TRUCKING OR LOGISTICS COMPANY MORE EFFICIENT AND PROFITABLE FOR THE TRAFFIC CLUB OF MEMPHIS Dan Pallme, Director4/8/2014

2. Contents  Set the Stage: Memphis, TN  Background  Literature Review  Research Objectives  Case Study Descriptions  Conclusions  Memphis – Are you prepared?

3. Set the Stage: Memphis 5 class one railroads serving one region

4. Set the Stage: Memphis 4 th largest inland water port in the USA

5. Set the Stage: Memphis Nation’s 3 rd busiest trucking corridor

6. Set the Stage: Memphis 2 modes moving time sensitive freight

7. Set the Stage: Memphis 1 university leading the way researching how to keep America’s business moving

8. Blue-Gray Spring Game – This Friday

9. Background  The efficient freight transportation planning is based on the existence of an accurate and comprehensive database  GPS data provide the ability to track singular vehicle movements and the corresponding trip characteristics  This information can reduce the number of assumptions and increase the accuracy of analysis  Therefore, GPS data can be extremely useful in freight planning and research  Research topics could include the development of freight performance measures, the evaluation of freight policies, etc.

10. 1 st Case Study : GPS Data for Developing FPMs  Freight movement is a significant aspect of transportation planning and economic success of a region  Therefore, it is important to develop Freight Performance Measures (FPMs)  With MAP-21, new incentives are in place for (DOTs) to integrate FPMs into transportation planning and operations  GPS data can be extremely useful in developing FPMs  With FPMs, agencies can have additional tools for more effective freight transportation planning and research

11. Literature Review (Freight Performance Measures)  Mallet et al., 2006  FHWA created the Freight Performance Measure Initiative in 2002, to address the lack of data on freight movements and its impact on congestion  The initiative focused on collecting travel time data for freight corridors and delay times at border crossings combined with urban congestion data  Gordon Proctor & Assoc., 2011  In 2010, the National Cooperative Freight Research Program (NCFRP) produced a report defining PMs related to freight  Analysis included the highest freight movement in the U.S. by different modes of transportation  The report focused on different FPMs such as average speeds, travel time, link delay, miles of congested roadway, travel rate cost-per-mile, driver wages, fuel cost, number of crashes, cost of crashes, etc.

12. Literature Review (Freight Performance Measures)  Southworth et al., 2011  FHWA collected and published the results of the Commodity Flow Survey taken every five years since 2007 called the Freight Analysis Framework (FAF)  Database provided detailed information on commodity tonnage by mode but did not provide enough data regarding single vehicle movements, required for many FPMs  McCormack, 2009  Collecting and processing truck GPS data to evaluate network performance  Travel time and reliability were found to be the most common performance measures  GPS Data can be very useful for public agencies. Data limitations and cost are major concerns

13. Literature Review (GPS data in FPMs development)  Figliozzi et al., 2011  Processing truck GPS data to produce performance measures  Study focused on analyzing the travel time reliability of specific corridors  Data provided by ATRI  Wang et al., 2014  Suggested a methodology to estimate link travel time using GPS data  Mapping methods were found to be more efficient compared to naïve methods

14. Research Objectives Describe case studies of utilizing GPS data in freight transportation research 1 st Case Study: Use GPS Data to Develop FPMs  Develop truck travel demand and temporal patterns on interstates and intermodal freight facilities 2 nd Case Study: Use GPS Data to evaluate a freight policy  Evaluate the impact of new HOS rules on traffic congestion 3 rd Case Study: Using real data from a Memphis West Coast Railroad  What does it mean

15. Case Study Area Case study area included: a 212 miles long segment on I-40 between Memphis and Nashville, TN major freight facilities within the borders of the greater Memphis area I-40 between Memphis and Nashville, TN Memphis Study Area

16. Dataset Characteristics  Dataset provided by American Transportation Research Institute (ATRI)  GPS database comprised of attributes such as truck routes and trip characteristics  The database included:  Truck unique ID  Truck location  Date and time of observation  Truck speed and heading  Data analyzed for a two-month period (Sep. –Oct. 2011), 3%-8% of total truck population

17. Data Display

18. Truck Travel Demand Pattern on I-40

19. Truck Trip Time Pattern on I-40

20. Truck Turn Time Distribution

21. Facilities Turn Time Prediction Model  Regression models were developed to predict turn times using truck volumes as the predictor  Variables for model development:  Y: turn-time of the facility (min)  x 1 : volume per time interval  Three time intervals (15 min, 30 min, and 60 min) were tested for each facility  A 5-fold “Hold Out” cross validation technique was applied for selecting the most representative models

22. Facility Turn-Time Models Facility Type Time Interval (min) Model (min)R2R2 Intermodal Facilities150.55 Distribution Centers600.014 Private Warehouse150.35 Public Warehouse600.0001

23. 2 nd Case Study: GPS Data for Evaluating Policies  276,000 large trucks were involved in highway crashes during 2010 in the U.S. (NHTSA, 2012)  The Federal Motor Carrier Safety Administration (FMCSA), proposed new HOS rules to improve safety standards  HOS rules define the allowable driving and working hours and the required rest periods  New regulation created significant controversy regarding the potential effects on truck operations and congestion  GPS data can be used to evaluate the impact of new HOS rules on traffic congestion

24. Hours-of Service (HOS) Regulation “Formerly, when a driver finished work between 1 a.m. and 5 a.m. on Saturday, he could go back to work Sunday night. Now he can’t start until 5 a.m. Monday” Source: http://www.truckinginfo.com/channel/fleet-management/article/story/2013/08/the-effects-of-the-new-hours-of-service.aspxhttp://www.truckinginfo.com/channel/fleet-management/article/story/2013/08/the-effects-of-the-new-hours-of-service.aspx

25. Literature Review (HOS Rules Development)  Interstate Commerce Commission (ICC), 1936 (Source: FMCSA, 2000)  First attempt of regulating HOS rules  Maximum 15 on-duty hours per day (12 working hours + 3 hour rest periods)  Maximum 60 on-duty hours per week or 70 hours per 8 continuous days  Interstate Commerce Commission (ICC), 1962 (Source: FMCSA, 2000)  24-hour restrictions no longer exist  8-hour off duty recovery period  Maximum allowable number of 10 hours continuous driving  Federal Motor Carrier Safety Administration (FMCSA), 2003 (Source: FMCSA, 2011)  Maximum allowable working hours were set to 14 per 24 hours  10-hour off-duty recovery period  Maximum of 11 hours continuous driving  Drivers could start a new weekly working period after 34 continuous hours of rest

26. Literature Review (Impact of new HOS Rules)  Blanchard, 2012  High level of rejection of new regulation among truck drivers  Increased congestion during peak hours  Potential increase in crash rates due to increased interaction with passenger vehicles  SCDigest Editorial Staff, 2013  Many believe that the impact on productivity will be minor, about 1.5%, due to the limited number of long-haul drivers which the restart period is expected to have the greatest effect  American Shipper, 2013  Need for additional drivers due to new rules  Driver shortage could result in higher operation costs  Potential late deliveries

27. Impact of HOS Rules  Methodology focused on tracking the impact of new rules on congestion after identifying the change in Level of Service (LOS) The HCM methodology (HCM, 2000) for LOS estimation was modified to utilize GPS data

28. Impact of New Rules on Congestion LOS was calculated for a 4.5 miles long highway segment on I-40 at Exit 201A

29. Formulas – Estimating Flow Rate

30. Formulas – Heavy Vehicle Adjustment Factor

31. Formulas – Adjusted per hour

32. 3rd Case Study: Applying Real-World Data  Class 1 Western Railroad  Large portion of traffic that moves to the Nashville area  Data: 6 am – 9 am on Mondays and Fridays

33. * Extreme weather event: snow & ice Holiday week taking out of the data

34. Results  10% more activity due at the gates on Monday over Friday  23% more activity in volumes on Monday over Friday  Was this due to HOS?

35. Conclusions on Research GPS data can be extremely useful in freight research. Three case studies of utilizing GPS data were presented  In the first case study, truck travel demand and temporal patterns on interstates and intermodal freight facilities were developed  The second case study focused on evaluating the impact of a freight policy (new HOS rules) on congestion  Future research Combined GPS data with commodity and trip time information to develop a comprehensive description of freight movements by trucks in TN Comparison analysis of data before HOS took place and now Comparison analysis of gate activity broken down by in-bound and out- bound

36. State of Tennessee Philosophy Article in the Memphis Business Journal yesterday. http://www.bizjournals.com/memphis/blog/2014/04/tennessee-transportation-funding-reflects-federal.html?ana=e_du_pub&s=article_du&ed=2014-04-07  Highlights Gov. Bill Haslam and TDOT unveiled 3 year $1.5 Billion budget 59 transportation projects 41 Counties 14 Statewide Program TDOT will adopt a “pay-as-you-go” strategy Tennessee is only one of four states whose transportation systems carry no debt

37. Future Construction around Memphis and Shelby County – That will impact travel patterns  I-40 / I-240 Interchange - $100 million http://www.tdot.state.tn.us/i40-240memphis/ Now through Summer, 2017  I-55 / Crump Boulevard Interchange Improvement - $35.7 million http://www.tdot.state.tn.us/i55/ Buying right of way Timeframe: 3 years (estimated completion 2020)  Lamar Corridor $637.9 million http://www.tdot.state.tn.us/documents/LamarAvenueCorridor_June2011.pdf Buying right of way Timeframe: 8 years: 2023 at the earliest  Southern Gateway Project $1 Billion http://www.southerngatewayproject.com Where? Timeframe: EIS should be completed next year

38. Recommendations  Be prepared  Use data!  Collaboration  Off peak  Pricing considerations  Potential operational changes?  What great infrastructure we will have when it is done!!!!

39. http://www.memphis.edu/ifti Dan Pallme depallme@memphis.edu

40. Closing Thought 