Data Envelopment Analysis Model for Assessment of Safety and Security of Intermodal Transportation Facilities

Following September 11, 2001, numerous security policies have been created which have caused a number of unique challenges in planning for transportation networks. Transportation policy and funding to improve the transportation infrastructure has historically been addressed as individual modes not as intermodal transportation. As a consequence of this inopportune allocation, it is now apparent that the transportation modes are disconnected and have unequal levels of security and efficiency. Improved intermodal connectivity has therefore been identified as one of the main challenges to achieve a safer, secure, and productive transportation network. Tools need to be refined for collaboration and consensus building to serve as catalysts for efficient transportation solutions. In this study, a mathematical model using data envelopment analysis (DEA) was developed and investigated to assess the safety and security of intermodal transportation facilities. The model identifies the best and worst performers by assessing several safety and security-related variables. The DEA model can assess the efficiency level of safety and security of intermodal facilities and identify potential solutions for improvement. The DEA methodology presented is general in its framework and can be applied to any network of intermodal transportation systems. Availability of credible data, complemented with DEA methodology will help in management decisions making concrete safety and security decisions for intermodal transportation facilities.

Simulation of wind-driven circulation and temperature in the near-shore region of southern Lake Michigan by using a channelized model

To enhance the efficiency of a pathogen forecasting model in the beach areas of southern Lake Michigan and to reduce the computation time, the near-shore current is approximated as a channelized flow parallel to the shorelines in clockwise or anti-clock- wise direction within the accuracy tolerance range. A channelized model with a curvilinear boundary can significantly reduce the computation effort, and at the same time achieve a good agreement between the predicted and measured water surface elevations, currents, and water temperatures. The sensitivity analysis results show that the suitable channel width for the near-shore region of southern Lake Michigan should be no less than 10 kin. The modeling results of the water temperature are much less sensitive to the channel width than those of the current velocity and the water surface elevation. The modeling results also show a close correlation between the speeds of the wind and the near-shore current. The current may fully respond the wind stress with a time lag of several hours. The correlation may provide an approximate estimation of the lake circulation under some wind conditions for a practical fore- casting purpose. More complex wind-current relationships need to be described with a more sophisticated hydrodynamic model. This verified model can be used for the pathogen forecasting in the near-shore regions of southern Lake Michigan in the future.

Can We Model Driver Perceptions?An In-Situ Experiment in Real-World Conditions

It is clear that perceptions play a significant role in traveler decisions.Consequently,traveler perceptions are a corner stone in the feasibility of traveler information systems;for traveler information systems are only valuable if the drivers are incapable of accurately acquiring the provided information on their own,and if the provided information is relevant for the drivers’decision criteria.Accuracy of traveler perceptions has been repeatedly researched in public transportation,and has been found to vary according to different reasons.However,in spite of the clear significance of traveler perceptions,minimal effort has been put into modeling it.Almost all travel behavior models are based on traveler experiences,which are assumed to reflect traveler perceptions via the addition of some random error component.This works introduces an alternative approach:instead of adding an error component to represent driver perceptions,it proposes to model driver perceptions.This work is based on a real-world route choice experiment of a sample of 20 drivers who made more than 2,000 real-world route choices.Each of the drivers’experiences,perceptions,and choices were recorded,analyzed and cross examined.The paper demonstrates that:i)driver experiences are different from driver perceptions,ii)driver perceptions explain driver choices better than driver experiences,iii)it is possible to model and predict driver perceptions of travel distance,time and speed.