Transportation planners have traditionally focused on physical travel only, and disregarded the fact that other modes of communication may influence travel demand. However, modern telecommunications are rapidly increasing the accessibility to activities that previously only could be reached by physical transportation. This development calls for methods to analyse interactions between telecommunications and transport systems. The objective of this thesis is to accomplish a better understanding of if and how impacts of information technology could be implemented in travel demand models. An important part of this issue is to investigate what kind of data that is needed. This thesis also aims at investigating whether the Communication Survey, KOM, collected by Swedish Institute for Transport and Communications, SIKA, can be used to improve transport modelling with respect to how modern telecommunications influence travel demand. KOM is a one-day travel and communication diary survey, including information on the respondents telecommuting habits as well as socio-economic status. One problem was the small sample size in KOM, which made the analyses uncertain. Since KOM is collected on a yearly basis, it is still possible to apply similar analysis methods within a few years, using a larger data set, which might enable extended analyses. The small sample in KOM available to date is best suited for general descriptive analyses of communication patterns in Sweden. The main conclusions of the paper are therefore connected to the methods and future data collection. The thesis includes three papers. The first paper tested a model approach that assumes substitution between travel and non-travel based communication, using the KOM database. Travel demand models are in general constructed as nested logit models with frequency, mode and destination choice levels. In the paper, non-travel based modes of communication were included in the choice set of such a model. The non-travel based modes of communication considered were Internet (and e-mail), ordinary mail and telephone contacts. The model was developed for post and bank activities only, since that was the only activity for which the numbers of contacts and trips were large enough to allow model estimation. Several conclusions could be drawn. Describing the utility of the non-travel based alternatives is difficult and needs more research. The analysis is also very sensitive to how activities are defined. It is further essential that the data collection is more process oriented than traditional cross-sectional data is when analysing travel and telecommunications interactions. That is, habits of performing particular activities, including both trips and different types of contacts, must be studied. The second and third papers investigate telecommuting. As a first step to reach the goal of forecasting telecommuting, the second paper examined the characteristics of current telecommuters by use of KOM. This was mainly accomplished by estimating a telecommuting adoption model of logit type. However, only 122 employees out of 7578 actually telecommutes full days at home. These telecommuters work primarily in information- and service-based industrial sectors concerned with computers, finance or communication. The difficulties in describing the utility of the telecommunications based alternatives (representing ”no travel”) concerned also the telecommuting adoption model. Also impacts on travel from telecommuting were investigated. Comparing the average commuting distance showed that employees who exclusively telecommute full days have longer commuting distances than others, but that other telecommuters do not have longer average commuting distances. Telecommuting in general does not seem to be influenced by low accessibility to the labour market. The third paper used data collected from a working site within the company Ericsson, located in the office district of Nacka Strand in Stockholm during the autumn 2002. The telecommuting frequency was substantially higher at Ericsson than in the workforce as a whole. The propensity to adopt telecommuting was modelled as a function of socio-economic variables and access to technical equipment, work task suitability and management attitudes, as perceived by the employees. The focus was to identify tools that the company can use to promote telecommuting, and to find incentives for the company to promote telecommuting. Technical equipment, suitable work tasks and managers attitude were identified as constraints for telecommuting. The employees also perceived that they became more efficient and saved time when telecommuting.