To improve existing parking models and to meet several additional requirements of practitioners, a parking analysis model at the scale of local areas is developed. The model called Pamela which stands for Parking Analysis Model for predicting Effects in Local Areas. Pamela simulates at the local level different travel and parking decisions from the moment an individual has decided to leave home for weekly or non-weekly shopping until the moment the individual has completed her/his activity, leaves the chosen parking facility and goes home. Three different choice models form the heart of Pamela: (i) a parking choice set composition model to generate the car drivers parking choice set, (ii) a combined travel choice model combining the choice of shopping destination, travel mode and parking/bicycle stall, and (iii) an adaptive parking choice model that describes car drivers’ reactions when facing a fully occupied parking facility. The models include a variety of characteristics related to shopping destination, travel mode, and parking and storage facility. In addition, the adaptive parking choice model also includes characteristics that describe the situation of the parking facility at the moment a car drivers enters a fully occupied parking facility. All included models are estimated using stated choice data collected in the town of Veldhoven and the city of Eindhoven, the Netherlands. For each part of Pamela a stated choice experiment is set up and presented to residents of Veldhoven and Eindhoven in a home sent questionnaire. The data of 1024 residents are used for the analyses. The data are analyzed using mixed logit models that include both mean (consisting of means and standard deviations) and context (only means) effects where context effects represent the difference between weekly and non-weekly shopping. Most estimation results are satisfactory indicating that the estimated models give a good representation of the respondents’ stated choice behavior. The percentage correctly predicted choices varies from almost 36 (in the case of the combined travel choice model) to more than 70 (in the case of the parking consideration set model) percent. In all cases the mixed multinomial logit model performs better than the traditional multinomial logit model. Most effects of the included model attributes are as expected. Regarding the composition of parking choice sets it appears that the characteristics parking costs and maximum parking duration influences the probability of a parking facility to be included in the car drivers’ choice set mostly. At some distance these characteristics are followed by the chance of a free space and walking distance between parking facility and nearest supermarket/department store. The effects found for the characteristics differ significantly for weekly and non-weekly shopping visits. Looking to the combined travel choice behavior, it appears that most influential characteristics are in order of influence: travel time of bicycle, parking costs, travel time bus, maximum parking duration, and supply of shops. Also in this case differences in influence are found between weekly and non-weekly shopping visits. Car drivers’ adaptive parking choice is mostly influenced by the expected waiting time, the number of parking facilities visited before entering the fully occupied parking, and the chance of getting a parking fine. Differences between weekly and non-weekly shopping visits only exist for number of parking facilities visited before and number of cars waiting for a free space. The validity of the estimated models is tested by applying the models to the town of Veghel, a comparable town to Veldhoven. Because of the available observations, only the parking choice set composition and the combined travel choice models for weekly shopping trips could be validated. Regarding the performance of the models, it appears that the consideration set model is well able to predict the composition of parking consideration sets that are observed in Veghel. On average the model predicts in approximately 67 percent the presence or non-presence correctly. The performance of combined travel choice model is low, especially at the individual level. At the aggregate level the model is able to explain 84 percent of the distribution across the choice alternatives. However, at the individual level only 9 percent of the choices were correctly predicted which is somewhat better than the null model (4 percent correctly predicted). The model mainly predicts choice combinations that include the car as travel mode. To illustrate the working of Pamela a micro-simulation is worked out using the multiagent system NetLogo. A hypothetical setting is created consisting of three shopping centers, nine parking facilities, and three bicycle stalls. The simulation includes the whole process from the generation of a traveler until the traveler’s move from the shopping center to her/his home location. Besides the estimated model parameters the simulation is complemented with additional data retrieved from empirical data (type of shopping) and the data collection (shopping duration). The simulation is used to evaluate the following three different transport policies: leveling out the parking costs for all parking facilities, setting all storage costs of bicycle stalls to ‘no charge’, and equalizing walking distance between parking facilities and nearest supermarket or department store to 150 meters. The travel decisions of 500 residents are simulated for a base situation and the three transport policies. To level out random effects, the simulation is carried out ten times and all results are averaged over these ten simulation runs. The simulation shows the changes in destination, travel mode and parking/storage choice at an overall level (daytime period from 8:00 – 20:00 hours) and at the level of time slices (every minute during the day time period). It also shows for each travel mode the changes in average and total distance traveled of all included residents during the daytime period
