Car drivers' evaluation of parking garages

Because of growing competition between parking facilities in inner city areas, managers of parking garages are looking more carefully to the requirements of (segments of) their customers (e.g., Visser, 2000). According to Visser, the increase of operation costs requires an optimal operation of parking facilities. Parking operators have to act more professionally and focus more on the requirements and evaluation of their costumers, which concern various characteristics of parking garages

Pamela, a parking analysis model for predicting effects in local areas

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

Simulating the influence of life trajectory events on transport mode behavior in an agent-based system

this paper describes the results of a study on the impact of lifecycle or life trajectory events on activity-travel decisions. This lifecycle trajectory of individual agents can be easily incorporated in an agent-based simulation system. This paper focuses on two lifecycle events, change in residential location and change in number of household members. An Internet-based survey was designed to collect data concerning structural lifecycle events. Previous papers describe the conceptual framework underlying the model and the temporal effects of lifecycle events on mode choice. This paper focuses on predicting the occurrence of structural lifecycle events at a certain time. Structure and parameter learning are applied to build a Bayesian Belief Network based on the data

Modeling the impact of key events on long-term transport mode choice decisions : a decision network approach using event history data

This paper describes the first phase of a study of the impact of key events on long-term transport mode choice decisions. The suggested complexity of transport mode choice is modeled using a Bayesian Decision Network (BDN). An Internet-based questionnaire was designed to measure the various Conditional Probability Tables and the Conditional Utility Tables of the BDN. In total seven different key events were implemented in the questionnaire: Change in residential location, Change in household composition, Change in work location, Change in study location, Change in car availability, Change in availability of public transport pass, and Change in household income. The data of 554 respondents was used to illustrate how the tables can be constructed based on event history data

Gedrag van winkelende voetgangers in binnensteden : model ontwikkeling en empirische toetsing

No abstract

Modeling the influence of structural lifecycle events on activity-travel decisions using a structure learning algorithm

This paper describes the results of a study on the impact of lifecycle events on activity-travel choice decisions of individuals. An Internet-based survey was designed to collect data concerning structural lifecycle events. In addition, respondents answered questions about personal and household characteristics, possession and availability of transport modes and their current travel behavior. In total, 710 respondents completed the online survey. The complexity of transport mode choice is modeled using a Bayesian Belief Network. Previous papers describe the conceptual framework underlying the model and the temporal effects of lifecycle events on mode choice. This paper focuses on influences of structural life trajectory events on each other and on changes in resources that impact activity-travel decisions. We investigate the extent to which causal relations exist between these events and their direct and indirect effects on changes in transport mode availability and the possession of transit passes. A structure learning algorithm is used to build a Bayesian Belief Network of interdependencies between these events from the data

The relation between train access mode and travelers’ transport mode-choice decisions in the context of medium- and long-distance trips in the Netherlands

This paper presents the findings of a study that investigated travelers’ transport mode-choice behavior in the context of medium- and long-distance trips, with special attention given to attributes of train access modes. The goal of the paper is to provide greater insight into the contribution of both access and main travel-mode attributes to the travelers’ decision to use the car or the train for medium- and long-distance trips. Based on data collected through a stated choice experiment, a mixed logit model is estimated to identify the contributions of all included attributes. In total, 32 attributes were included in the experiment describing the main transport modes, train and car, and the access modes: bicycle, bus, and drop-off/car. Based on a fractional factorial design, the attributes and corresponding levels were combined into 81 different mode-choice situations. The stated choice experiment was included in an online questionnaire that was distributed among members of a marketing panel. Panel members were invited to choose the preferred travel-mode for a medium- and long-distance trip given a detailed description of both main and access modes. Each member evaluated nine choice situations. In total, 415 panel members completed the questionnaire. Combined, panel members evaluated 3,735 choice situations. The mixed logit model analysis shows that time and cost-related attributes significantly contribute to the attractiveness of transport modes. However, these effects differ considerably between the investigated modes. Conversely, safety-related attributes, chance of delay, and transfer time from access mode to train platform play a minor role