Behzad Karimi

Joint Model of Freight Mode and Shipment Size Choice

Mode and shipment size choice are among the most critical logistics decisions but are typically studied separately in freight demand studies despite their strong correlations. The study described in this paper used an innovative copula-based framework to model freight mode and shipment size choice simultaneously as a joint decision-making problem. The study used a copula-based joint multinomial logit–multinomial logit model in which both mode choice and shipment size were modeled as discrete choices in a multinomial logit structure. The proposed copula-based model was intended to capture the potential effects of observed and unobserved factors that jointly affect both choices. The data used in this study were gathered through a large-scale establishment survey conducted in 2011 in the United States. The survey provided detailed information on more than 1,840 individual shipments that were used to develop the disaggregate models. Results of the estimated model underline the importance of the interrelationship between freight mode choice and shipment size and suggest that common unobserved factors influencing mode and shipment size choices exist. The model provides a better way to capture the effects of observed and unobserved factors that affect both choices simultaneously.

Young-Old Elderly and Baby Boomers: Explanatory Analysis of Activity Duration, Time-of-Day Choice, and Planning Time Horizons

At the beginning of 2011, the first generation of more than 77 million baby boomers began to turn 65. In this study, researchers explore the situation in which those baby boomers who are currently 55 to 64 years old will replace current senior citizens, 65- to 74-year-olds. This study presents a detailed descriptive analysis of activity generation and the planning and scheduling behavior of these two age groups. Global Positioning System–based data from a prompted recall activity-travel survey (the Urban Travel Route and Activity Choice Survey) are used in this study. This highly disaggregate survey with detailed activity attributes has made it possible to distinguish the preferences and flexibilities of preretirement baby boomers (55 to 64) and senior citizens (65 to 74) with respect to their daily activities. The study focuses on a diverse set of activity categories that include the following: work, school, personal, religious, health care, services, errands, discretionary, and shopping. For these activities, activity durations, times of day, and planning time horizons were studied, compared, and analyzed for both age groups. It was revealed that the main difference between these two age groups was the difference in the participation in mandatory activities. Although the two age groups had very similar behavior in choice of activity duration, their time-of-day choice behavior was very different. In addition, both age groups executed a major part of their activities impulsively. Seniors and baby boomers planned 61.6% and 56.9%, respectively, of their activities on “less than 1 h” and “same-day” planning time horizons.

Shipping Chain Choices in Long-Distance Supply Chains

Shipping chain configuration is one of the key logistics choices that have been ignored or treated insufficiently in the current freight transportation models. This study focuses on the shipping chain configuration and the modeling of its relevant logistics choices, including number of stops and stop types. A brief descriptive analysis of the results of an online establishment survey to explore different shipping chain configurations used by decision makers (business establishments) was done. Also, a system of hierarchical database models of shipping chain choice for freight transportation was presented. A system of decision tree models was proposed to determine the shipping chain configuration of freight transportation by identifying number of stops and type of stops per chain. The results of the survey were used for analysis and model estimation. The proposed decision tree models were developed by using 80% of the observations in the data, and the remaining 20% were used to validate the models. The results of model estimation indicated that shipments’ attributes were the most significant variables in predicting the configuration of the shipping chain. The results of the validation showed that the estimated trees could predict the shipping chain configuration for the test data with an acceptable precision, which confirmed that decision tree models were powerful tools that could be used to predict shipping chain configurations of freight flows.