Click for next page ( 29

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 28
28 Truck Drayage Productivity Guide 26. What drayage or dispatching practices have you developed or learned from others that help reduce turn times, trouble tickets, or extra trips? 27. What else should we know about? (Add pages if necessary.) Thank you very much for your help with this survey. Distributed by: ___________ Terminal Webcam Data Collection Methodology A number of marine terminals provide live views of their gates via webcams. These gate cameras are set up by the terminal operators to allow drayage firms to monitor the gate conditions. They are intended as a means of managing demand for the marine terminals, assuming that drayage firms will adjust their plans based on the real-time feedback of gate congestion. In this study, gate cameras were used to assess truck queues outside the terminal gates at two busy terminals in two different geographic regions. The placement and viewing angle of the cameras allowed measure- ment of the following: The gate processing time of each truck; The time the truck spent waiting outside the gate; The time lost when a gate closed for lunch; The level of congestion at a marine gate throughout the week; and The level of gate activity during off-peak, nighttime, and pre-opening hours. The general method for all of these tasks was to manually capture a series of images and store them in Microsoft Excel for post-processing. That is, researchers copied the camera's view on a Web browser and then pasted the image into Excel. Thus, the number of images captured is a function of how fast the copy and paste task can be accomplished. Also, it is dependent on the refresh rate of the camera. Some cameras provide a live feed whereas others provide snapshots at a certain interval (e.g., 30 seconds). Table 31 provides some key statistics concerning the rate at which images were recorded at the two study terminals. Each recorded image includes a time stamp. To measure the terminal's processing time, the time at which each truck left the gate area was recorded in the corresponding column in an Excel file. The gate processing time is simply the dif- ference between the departure times of trucks in the same lane. Using this procedure, the study Table 31. Image capture rates at marine terminals. Terminal A Date 11/2/2009 11/10/2009 11/4/2009 11/5/2009 11/6/2009 Day Monday Tuesday Wednesday Thursday Friday Observation period (EST) 13:00-14:00 14:00-15:00 10:00-11:00 15:00-16:00 14:00-15:00 Number of images captured in 1 hour 309 533 428 514 540 Average rate (seconds per image) 11.65 6.75 8.41 7.00 6.67 Number of trucks processed 92 111 106 71 115 Terminal B Date 1/20/2010 1/21/2010 1/22/2010 1/25/2010 1/26/2010 Day Wednesday Thursday Friday Monday Tuesday Observation period (EST) 17:35-18:35 17:03-18:03 17:15-18:15 17:46-18:46 17:00-18:00 Number of images captured in 1 hour 116 159 163 166 153 Average rate (seconds per image) 31.03 22.64 22.09 21.69 23.53 Number of trucks processed 36 63 84 65 96

OCR for page 28
Drayage Data and Information Sources 29 team was able to obtain the truck processing times for nearly all trucks. The exceptions arose when rain made it difficult to distinguish one truck from another. Using the webcam method, one also can observe queuing time. The measurement is the differ- ence between the time the truck joined the queue and the time that it left the gate area. A prob- lem with some terminals is that, depending on the camera's position and angle, one may not be able to see the entire queue. This method was used to determine time lost due to lunchtime clo- sure of the gate. Finally, the webcam method also enables monitoring of gate congestion levels over an extended period (days/weeks). In this study, to determine how frequently and severely a gate was congested, for an entire week team members took snapshots of a marine terminal gate every hour that the ter- minal was open. The result clearly indicated the peak associated with the initial opening of the gate and higher congestion due to ship schedules. Photos also were taken of various terminals during the night to determine the level of queuing activity when the terminals were closed. Recording the images in Excel facilitated subsequent analysis, which typically involved develop- ment of a frequency distribution of the results, as well as ordinary statistical measures including means and modes for the various data sets that were collected. As illustrated in this work, the webcam method can be used effectively to obtain truck process- ing times, truck inter-arrival times, and truck queuing times, as well as early morning queuing, lunch hour queuing, and truck weekly arrival pattern. This method potentially can be used to per- form more rigorous studies such as the effect of weather on gate operations and the impact of a change in the gate infrastructure (e.g., additional lane) or gate operations (e.g., appointment sys- tem). Additionally, the webcam method provides access to a greater number of terminals that may be practically impossible to study using the traditional field-based method. Finally, although the webcam method offers many advantages, it does have a number of limita- tions. The camera lens could be blocked with water during stormy conditions. There were also cases where the camera was completely off target, possibly due to strong winds. Another reliability issue is that sometimes the camera stops working after normal duty hours. Depending on the camera's view, one may not be able to observe all lanes at some terminals or the end of a queue. The resolu- tion of the camera is typically low, which can make image analysis difficult. Marine terminals provide webcams for the use of truckers and customers. Publication of actual webcam images raises potential issues of confidentiality, ownership, and legality that should be addressed in advance. Sample Webcam Study Results At one terminal, the NCFRP Project 14 study team sampled gate processing time a different hour per day on five different days of the week (Figure 32). The result showed a median wait time of 4.3 minutes and an average wait time of 5.1 minutes. Observations of the full service portion of the gate also were taken hourly for a week. In Fig- ure 33, Congestion Level 0 means that the next arriving driver would be serviced immediately. Level 1 equates to a wait of 15 minutes or less. Level 2 equates to a wait of 1530 minutes. Level 3 equates to an average wait of more than 30 minutes. The times are based on the average wait times determined in the initial stage of the analysis. The graph shows that this terminal always has a substantial number of trucks waiting for the gate to open. For half of the week, gate queues are 15 minutes or less. The heavy pattern at the end of the week is due to the need to process a large number of export loads to meet ship departure schedules.

OCR for page 28
30 Truck Drayage Productivity Guide Figure 32. Gate waiting times from webcam study. Average Congestion Level For Each Time Slot (Sorted by Day) 3 2.5 2 CONGESTION LEVEL 1.5 1 Level 0 - an open lane exists, no wait 0.5 Level 1 - 1 to 3 trucks in line, 0-15 min. wait Level 2 - 4 to 6 trucks in line, 15-30 min. wait Level 3 - end of line not visible, 30+ min. wait 0 10:00 11:00 12:00 13:00 14:00 15:00 16:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 6:00 7:00 8:00 9:00 6:00 7:00 8:00 9:00 6:00 7:00 8:00 9:00 6:00 7:00 8:00 9:00 6:00 7:00 8:00 9:00 MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY Time Slot (Monday to Friday) Figure 33. Webcam data on gate queuing.