UMR Statistical Analysis Inland Navigation Appointment System Study Upper Mississippi River Locks Center For Transportation Studies University Of Missouri, St. Louis 15 June 2005

Phases of UMR Statistical Analysis Produce performance benchmarks with OMNI data under historical operating rules and physical conditions Create system for development and maintenance of sets of statistical models to support enhanced simulation of UMR traffic flows and lockage operations Provide comparisons of simulated system performance under alternative operating procedures against historical bench marks

Performance Bench Marks System status through the shipping season -Monthly time-weighted averages and monthly maxima for Number of vessels queued for lockage at each lock (upbound and downbound) Status of locks (percentage of time busy versus idle) Number of vessels under way in each river pool between locks (upbound and downbound)

Performance Bench Marks (Continued) Lock operations -Time spent waiting by vessels from arrival at lock to beginning of lockage -Time spent by vessels in lockage operation -Throughputs of vessels and tows Vessel transit time -From completion of current lockage to arrival for next lockage Vessel itineraries for selected periods -Generated for chosen vessels for comparison against actual vessel tracking data - validation of OMNI data

General Observations From Descriptive Statistics for 2003 Based on 104,730 records of non-recreational lockage records for the UMR system Average utilization (percentage of times locks were engaged serving vessels) peaked in summer months at values between 74% and 79% for locks 20 through 25 Maximum queues for lockage ranged between six and eight vessels with tows Vessels spent 1.5% of their time (from first recorded UMR lockage to last recorded UMR lockage in the shipping season) waiting for lockage somewhere in the UMR system and 1.7% of their time undergoing lockage somewhere in the UMR system

General Observations (Continued) While there were considerable delays in locking vessels in the congested areas of the river, the delays constitute a small percentage of the annual vessel operation times Limited potential of increasing the utilization of towboat resources by: -using alternative sequencing rules -or increasing lock capacity at the five bottleneck locks -unless volumes of river traffic increase substantially above 2003 levels

Analytical Issues Seasonal variation in numbers of tows, characteristics of tows and efficiency of lock operations Extent to which OMNI data accurately reflect vessel movements and lockage operations -Interpretation of start lockage times at various locks -Possible variation in the reporting points for vessel arrivals at locks (with alleged tendency to call in at an earlier point when there are several vessels ahead) -Occasional data entry errors or omissions -Single vessel numbers recorded for lockages of several vessels

Analytical Issues (Continued) Extent to which vessels currently reduce speed to save fuel when delays at locks are anticipated Extent to which delays are caused by impairments at locks (due to weather or other causes) Extent to which transit times are affected by other river conditions

Alternative Structures For Enhanced Simulation Model Initial version (December, 2004) -All tows except recreational vessels cycle through the entire section of the river between Lock 25 and Lock 20. -Recreational vessels are generated randomly for lockages according to seasonal patterns

Alternative Structures For Enhanced Simulation Model (Continued) Enhancement Level 1 (June 2005) -Vessels enter the system northbound at Lock 25 randomly according to seasonal patterns and southbound at Lock 20 randomly according to seasonal patterns. Alternative configurations are (1) double, (2) single, (3) single with jackknife lockage, (4) single with knockout lockage, or (5) other, including recreational -At each intermediate lock, a vessel may either (1) continue to the next lock in the same configuration, or (2) be removed from the system and handled through random generation of other vessels that appear for lockage according to seasonal patterns

Alternative Structures For Enhanced Simulation Model (Continued) Enhancement Level 2 (future implementation) -Vessels enter the system northbound at Lock 25 randomly according to seasonal patterns and southbound at Lock 20 randomly according to seasonal patterns -At each intermediate lock, a vessel may either (1) continue to the next lock in the same configuration, (2) stop for possible change in configuration and proceed in the same direction to the next lock, (3) stop for possible change in configuration and return to the same lock for lockage in the opposite direction, or (4) be removed from the system and handled through random generation of other vessels that appear for lockage according to seasonal patterns

Statistical Models To Support The Enhanced Simulation Models Logistic models for likelihoods of alternative dispositions of each vessel (transition probabilities) after completion of lockage -Give likelihoods of transition to alternative configurations and locations of next lockage Regression models for average time required to complete the lockage of a vessel Regression models for average transit times (from completion of current lockage to arrival for next lockage) -Total transit times in pools, including stop times for vessels that stop en route from current lockage to location of next lockage

Factors Considered in Statistical Models In logistical models for determining next lockage location and tow configuration -Month of year -Current tow configuration In regression models for average lockage time -Month of year -Tow configuration (double, single, jackknife or knockout) -Proportion of lockage that occurs at night (suppressed in current version of simulation model) -Whether exchange or turn-back occurs

Factors Considered in Statistical Models (Continued) In regression models for average transit time -Month of year -Changes in tow configuration and location of next lockage -Percent of journey occurring at night (suppressed for current version of simulation model) -Percent of journey during which impairment is experienced at the next lock (suppressed for current version of the simulation model)

System For Generating and Updating Statistical Models Written as SAS (Statistical Analysis System) macros that generate model equations and automatically write them to ASCII files for importation into the simulation model

System For Generating and Updating Statistical Models Macro parameters allow user to determine: -Lists of possible explanatory variables to be used in constructing models -Levels of statistical significance required for individual explanatory variables -Beginning and ending dates of data used to calibrate the models continued…

System For Generating and Updating Statistical Models (Continued) -Percentiles to be used for screening out unusual (extreme) observations when constructing regression models (e.g., Excluding observations below the 1 st percentile or above the 99 th percentile) -Whether to include recreational lockages in developing equations for lockage times -Whether to produce corresponding random arrival rates (and average inter-arrival times) and distributions of configuration types at monthly or weekly intervals for each direction at each lock

Incorporation of Information re Ambient River Conditions and Lock Impairments Seasonal distributions of times between breakdowns at locks and duration of breakdowns at locks -Incorporated into the simulation model as independent events at each lock according to seasonal data Consideration of sunrise and sunset times in determining the percentage of time that an activity occurs in daylight (or conversely, at night) -Allowed in some statistical models but yet not incorporated into the simulation model continued…

Incorporation of Information re Ambient River Conditions and Lock Impairments (Continued) Consideration of the percentage of vessel transit time that occurred during an impairment at the destination lock -Allowed in some statistical models -Not incorporated into the simulation model Water level data and flow rates did not materially enhance the statistical models and were not complete for all pools