In Denmark a relatively large share (about 20 %) of the round tours below 22 km (from home and back to home) is driven on a bicycle and additional 22 % is done by foot. In all 23 % of the short tour kilometres is today done by cycling and walking corresponding to 4 % of all daily kilometres. In central Copenhagen it is indeed 43 %. Still, a lot (about 60 %) of these tours are made by car and here might be a big potential for shifting from car to bicycle or walking. By a simple calculation it is found that 27 % of the actual car kilometres as driver on short round tours could be shifted to cycling and walking if families with one car and holding two or more driving licenses were cycling and walking as much as those with no car, and those with as many cars as driving licenses as cars were walking and cycling as much as those with more licenses than cars. It is a share of 4 % of all daily car kilometres. This is seen as the absolute maximum potential for mode shift. Approximately 90 % would shift to bicycles and only 10 % to walking. In practise it will never be possible to shift so much to bicycle or walking because of the extra time and physical effort needed and the need for carrying goods or people. But still a considerable amount of trips may be transferred if the necessary political effort is made. The paper analyzes the determining factors for mode choice by using a logit model on round tours shorter than 22 km. The potential for mode shift as a result of political means is further assessed. The analyses are based on 43,000 round tours during the period 1998-2003 from TU containing extensive information on travels in Denmark. Only persons with driving license and above 18 year are included in the model as they are the only who have a relevant mode choise. The logit model includes socio-economic data, car ownership and license holding of the family, mode dependent travel time, outdoor temperatures, wind speed, rainfall, topography, daylight, parking facilities, trip purpose and campaigns as independent variables. Travel time is in turn dependent on mode and distance, the time of day and the locus of the trip reflecting rush hours and congestion. The socio-economic data includes age, gender, family size, children of different age, income and position.