Majbah Uddin

Freight Traffic Assignment Methodology for Large-Scale Road–Rail Intermodal Networks

A methodology is proposed for freight traffic assignment in large-scale road–rail intermodal networks. To obtain the user–equilibrium freight flows, a path-based assignment algorithm (gradient projection) was proposed. The developed methodology was tested on the U.S. intermodal network by using the 2007 freight demand for truck, rail, and road–rail intermodal from the Freight Analysis Framework, Version 3 (FAF3). The results indicate that the proposed methodologys projected flow pattern is similar to the FAF3 assignment. The proposed methodology can be used by transportation planners and decision makers to forecast freight flows and to evaluate strategic network expansion options.

Truck-involved crashes injury severity analysis for different lighting conditions on rural and urban roadways

This paper investigates factors affecting injury severity of crashes involving trucks for different lighting conditions on rural and urban roadways. It uses 2009-2013 Ohio crash data from the Highway Safety Information System. The explanatory factors include the occupant, vehicle, collision, roadway, temporal and environmental characteristics. Six separate mixed logit models were developed considering three lighting conditions (daylight, dark, and dark-lighted) on two area types (rural and urban). A series of log-likelihood ratio tests were conducted to validate that these six separate models by lighting conditions and area types are warranted. The model results suggest major differences in both the combination and the magnitude of impact of variables included in each model. Some variables were significant only in one lighting condition but not in other conditions. Similarly, some variables were found to be significant in one area type but not in other area type. These differences show that the different lighting conditions and area types do in fact have different contributing effects on injury severity in truck-involved crashes, further highlighting the importance of examining crashes based on lighting conditions on rural and urban roadways. Age and gender of occupant (who is the most severely injured in a crash), truck types, AADT, speed, and weather condition were found to be factors that have significantly different levels of impact on injury severity in truck-involved crashes.

Routing Model for Multicommodity Freight in an Intermodal Network Under Disruptions

This paper presents a mathematical model for the routing of multicommodity freight in an intermodal network under disruptions. A stochastic mixed-integer program was formulated to minimize not only operational costs of various modes and transfer costs at terminals but also penalty costs associated with unsatisfied demands. The sample average approximation algorithm was used to solve this challenging problem. The developed model was then applied to two networks—a hypothetical 15-node network and an actual intermodal network in the Gulf Coast, Southeastern, and Mid-Atlantic regions of the United States—to demonstrate its applicability with explicit consideration of disruptions at links, nodes, and terminals. The model results indicated that during disruptions, goods in the study region should be shipped by road–rail intermodal network because of the built-in redundancy of the freight transport network. Additionally, the routes generated by the model were found to be more robust than those typically used by freight carriers.

Operating room turnover time autocorrelation while using mobile applications

We would like to thank Drs. Dexter and Epstein for their well thought-out comments to our recent article about the use of an Android app to record the OR turnover times. In their letter, they raised several important questions and asked for clarifications which we now provide. At the time we performed our experiments, Greenville Memorial Hospital did not have sensor data to track OR turnover times. This is still the case as of today. The response to the inquiry related to turnover time autocorrelation is provided in the following.

Assessing operating room turnover time via the use of mobile application

Background Improving operating room (OR) utilization is crucial to hospitals. This study examines the effectiveness of a mobile application co-developed with hospital staff to track OR turnover time (TOT). Methods An Android-based app, named ORTimer, was used by staff in two OR units (GI-Lab and D-Core) of Greenville Memorial Hospital (GMH) in South Carolina. The staff used the app to record milestones and note delay reasons (if applicable). A total of 1,782 turnover observations from the GI-Lab and 694 turnover observations from the D-Core were collected for the study. Using data collected from the app and additional information from GMHs electronic medical record system, a two-sample proportionality test was conducted to test the hypothesis that the use of the app improved OR turnover performance (i.e., the TOT is equal to or less than the allotted time). Results The result of the hypothesis test indicates that a higher percentage of observations in the GI-Lab and D-Core met their turnover target time when the ORTimer app was used. Additionally, multiple regression analysis was used to identify significant factors that contribute to prolonged OR TOT and to estimate their impacts. Conclusions The app serves as both a visual management tool as well as a TOT data collection tool. By identifying barriers to the on-time completion of the turnaround, the app allows for continuous improvement of the turnover process.

Evaluation of Google’s Voice Recognition and Sentence Classification for Health Care Applications

Abstract This study examined the use of voice recognition technology in perioperative services (Periop) to enable Periop staff to record workflow milestones using mobile technology. The use of mobile technology to improve patient flow and quality of care could be facilitated if such voice recognition technology could be made robust. The goal of this experiment was to allow the Periop staff to provide care without being interrupted with data entry and querying tasks. However, the results are generalizable to other situations where an engineering manager attempts to improve communication performance using mobile technology. This study enhanced Google’s voice recognition capability by using post-processing classifiers (i.e., bag-of-sentences, support vector machine, and maximum entropy). The experiments investigated three factors (original phrasing, reduced phrasing, and personalized phrasing) at three levels (zero training repetition, 5 training repetitions, and 10 training repetitions). Results indicated that personal phrasing yielded the highest correctness and that training the device to recognize an individual’s voice improved correctness as well. Although simplistic, the bag-of-sentences classifier significantly improved voice recognition correctness. The classification efficiency of the maximum entropy and support vector machine algorithms was found to be nearly identical. These results suggest that engineering managers could significantly enhance Google’s voice recognition technology by using post-processing techniques, which would facilitate its use in health care and other applications.