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Planning a Carton or Full-Case Order-Fulfillment Operation
Published in David E. Mulcahy, John P. Dieltz, Order-Fulfillment and Across-the-Dock Concepts, Design, and Operations Handbook, 2003
David E. Mulcahy, John P. Dieltz
With the MA storage vehicle method, the storage vehicle enters and exits through any storage aisles. This turning-aisle or transfer-aisle requirement results in a larger building. The facility requires fewer storage-equipment pieces, reducing the total facility investment. Some VNA ASRS storage vehicles require a transfer car (T-car) that moves the storage vehicle from one aisle to another.
Prediction of passing probability and risk evaluation along undivided road under heterogeneous traffic
Published in International Journal of Injury Control and Safety Promotion, 2023
K. M. Arun Sagar, P. N. Salini, R. Ashalatha, Binu Sara Mathew
This section presents the methodology of the study as well as the methodology adopted to collect the data is explained. For the collection of data, devices such as speed gun and video camera were used. The data was collected on a two-lane two-way section of National Highway (NH 66) passing through Trivandrum in the State of Kerala the southernmost part of India. Left hand driving is followed in India and hence the passing happens along the right lane. The road stretches selected have a level terrain, free from major intersections and road side frictions were absent. Data were collected during dry and sunny weather. The traffic data were collected for 4 hours during off-peak hours on weekdays using moving car observer method and video-graphic survey method. For the collection of data on passing, three video cameras, speed radar gun, stop watch etc were employed whereas for collecting traffic volume count, video graphic survey was adopted. In moving car method, test car was driven in such a way that it maintained the stream speed and the driver was instructed not to overtake other vehicles and to make other vehicles to overtake the test car. The passing characteristics such as speed, time to overtake etc. of vehicles were evaluated. The entire process of the passing operation was captured by using three synchronized video camera, one fixed at front facing the front, one facing the side of test car and the other at rear facing the rear of test car. Two scenarios were considered for data collection purpose as shown in Figure 1. Scenario 1 represents a successful passing maneuvering in which an passing vehicle (v1) overtakes the test car (T) in its first attempt itself. It is hereafter referred to as a pass scenario. Scenario 2 refers to an unsuccessful passing and is referred to as an abort scenario.
Modeling vehicle car-following behavior in congested traffic conditions based on different vehicle combinations
Published in Transportation Letters, 2018
Dewen Kong, George F. List, Xiucheng Guo, Dingxin Wu
This study intended to analyze the car-following features of different vehicle combinations and built the CA model. In order to minimize the impact of other factors, the data were further processed. The following criteria were executed to get suitable trajectories.All the vehicle trajectory data are classified into four groups by the lead and lag vehicle type: passenger car following a passenger car (C–C), passenger car following a truck (C–T), truck following a passenger car (T–C), and truck following a truck (T–T). In this paper, the vehicle is defined as a truck if it is longer than 10 m. Here, the item truck denotes the combination of buses and trucks.If the distance between two successive vehicles is no shorter than 76 m, it is likely that there is no interaction between the vehicles (Bham and Benekohal 2004). Therefore, the data with a space distance larger than 76 m should be filtered out.During the car-following process, vehicles should not change lanes.Every vehicle pair must interact for at least 10 s (Tian et al. 2015). If the interaction is not long enough, the follower’s acceleration and deceleration behavior cannot be observed, so there must be 100 data points or more for the car-following.A program written by MATLAB was used to find the suitable data. This study obtained 295 C–C pairs (98062 samples), 84 C–T pairs (28732 samples), 100 T–C pairs (27753 samples), and 11 T–T pairs (3995 samples). It can be seen that the number of samples is considerable for analyzing and modeling in this study.