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Commute mode choice and number of non-work stops during the commute are joint decisions that have interaction. If an individual chooses a vehicle for the commute, regarding restriction of that vehicle, could has some stops. On the other hand, if an individual need to has some stops, chooses a vehicle for commute regarding number of stops. In this study to consider the interaction between these decisions, we employed copula-based joint modeling framework.The data used in this study is drawn from origin-destination travels data of Shiraz-Iran conducted in 1997. The commute mode choice modeling is undertaken using a multinomial logit model and the number of non-work stops is modeled using an ordered response formulation. To capturing interactive between these decisions several copula functions have been used. Results approve that mode and number of none-work stop choices are interrelated choices by estimating commonly observed factors and dependence parameters with high statistical significance. By determining common effective factors, we can analyze the current situation in the community. also, we can use results for forecasting future travel demand and set some policies leading to promoting trip chaining.

A new approach to modeling telecommuting suitability is proposed in this paper. The approach, based on the concept of abstract job, can be employed to assess the level of suitability for telecommuting of the bundle of tasks comprising a job. By abstract job is meant a way of considering jobs on the basis of their elements and tasks, representing the general structure of the job. In this study, the basic tasks a job is composed of, pertaining to telecommuting suitability, are identified. To show the applicability of the approach, discrete choice models are calibrated, based on a sample of 245 employees in Tehran, Iran, indicating that from among the 6 tasks identified, 5 tasks are significantly associated with the level of telecommuting suitability. Copyright Springer 2006abstract job, telecommuting suitability, job-task vector, discrete choice models, stated preference,