Using ensembles of decision trees to predict transport mode choice decisions: Effects on predictive success and uncertainty estimates

The application of activity-based models of travel demand to planning practice has triggered interest in issues that potentially improve the accuracy and/or usefulness of model forecasts. The limited knowledge of uncertainty propagation in complex stochastic model systems has put uncertainty analysis high on the research agenda to differentiate between simulation error and policy effects. Focusing on transport mode choice, this paper draws attention to the use of model ensembles, which has hardly been explored in travel demand forecasting. Prior studies predicting transport mode choice has typically relied on a single equation, relating observed transport mode choices to a set of personal and contextual variables. The estimated single model is then assumed to apply to all individuals. This paper explores the idea of replacing a single equation/representation with an ensemble of model predictions, using the decision tree formalism. Potentially, ensembles better capture the notion that travellers may use different heuristics in their transport mode decisions. The aim of the study is to investigate whether the use of a model ensemble of different decision heuristics will reduce the error/uncertainty in predicting transport mode decisions. Results of the study, conducted in the Rotterdam region, The Netherlands, suggest that the accuracy of predicting transport mode choice is improved, albeit non-monotonically, with increasing ensemble size. Simultaneously, the uncertainty related to these predictions is decreasing. Finally, it is shown that the importance of the selected explanatory variables co-varies with ensemble size. Estimation results tend to become stable in this study with an ensemble size of approximately 20 decision trees

Car Allocation between Household Heads in Car Deficient Households: A Decision Model

This paper considers car allocation choice behaviour in car-deficient households explicitly in the context of an activity-scheduling process, focusing on work activities. A decision tree induction method is applied to derive a decision tree for the car allocation decision in automobile deficient households using a large travel-and-activity diary data set recently collected in the Netherlands. The results show a satisfactory improvement in goodness of fit of the decision tree model compared to a null model. Overall, the probability of males getting the car for work is considerably higher than that of female in many condition settings. However, activity schedule, spatial and socio-economic variables appear to have an influence as well. An analysis of impacts of condition variables on car allocation decisions reveals that socio-economic variables have only a limited impact, whereas attributes of the transportation and land-use system have a relatively big impact. The propensity of men driving a car to the work place is higher than that of women. However, the relative accessibility of the work location by bike compared to car appears to have a relatively large influence on who gets the car for work. Household income and presence of children also appear to have significant effects

Analysis of Built Environment Influence on Pedestrian route choice behavior in Dutch Design Week using GPS Data

Visitors not only have specific destinations targeting the Dutch Design Week (DDW) exhibitions distributed all over the city, but also visit the city in between exhibition activities. The mixed environment makes modeling behavior of DDW visitors more complex than shoppers and tourisms only. This research pays special attention to the influence of built environment on pedestrian route choice. The built environment includes building and transportation infrastructure. GPS tracking data and social demographic information were collected during the event. Multinomial logit model and path size logit model are used to analysis route choice behavior. The results show that some built environment factors have significant influence on route choice. Shops are more attractive for aged visitors. Females prefer shorter routes more. In big event, the alternative routes with more sharing links could increase the possibility to choose

Long-term mobility choice considering availability effects of shared and new mobility services

E-bikes, shared and new mobility services such as Mobility-as-a-Service (MaaS) are emerging as sustainable and healthy alternatives to private cars, introducing complexities in household mobility decisions and potential substitution between transportation modes and services. However, existing studies primarily examined the potential long-term adoption of these emerging mobilities separately, leaving a gap in understanding the interplay among various emerging mobilities and conventional cars. This study therefore addresses this portfolio choice incorporating a stated portfolio choice experiment encompassing pedelecs, speed pedelecs, MaaS, Shared e-Mobilities, and electric and conventional cars. Results from a random effects error component mixed logit model, based on an online survey conducted in the Netherlands, indicate significant availability effects of shared and new mobility services on personal mobility ownership decisions, and a substantial demand for pedelecs. The findings contribute to facilitating the adoption of emerging mobilities with enhanced synergy, as shared and new mobility services are gradually becoming available

Conjoint models of tourist portfolio choice: Theory and illustration

This article develops and tests a conjoint choice model of portfolio choice behavior. It shows how experimental design principles and discrete choice model specifications can be used to model the choice of travel portfolios. Design strategies, model formulations, and estimation methods are discussed and illustrated through use of Dutch tourists’ choices of destination and transportation for short city breaks as an example. Findings support the suggested methodology and indicate that the nested logit model is most appropriate in the tested application

Firm dynamic productivity and economic growth

This paper examines the dynamics of firms and their productivity growth in Kuala Lumpur during period 1990 to 2007. Using a dataset of Kuala Lumpur firms, proximity and area analysis are conduct to examine the location of firms within five transportation feature (highways, major roads, rail networks, rail stations, and terminals) and in the CBD area. The result of empirical analyses shows that most of these firms are in Financing, Insurance, Real Estate, Investment and Business Services. We also find that most of the firms are located proximate to major roads, highways and in the CBD area

A causal model relating urban form with daily travel distance through activity/travel decisions

Urban form is often assumed to influence travel distance. However, as this is no travel choice in itself, but the consequence of other decisions, this paper tests, consistent with the activity-based approach, a causal model that does not relate urban form directly with daily travel distance, but indirectly through a series of decisions. A structural equation model was developed with urban form measures for both the residential and the work environment. The model demonstrates that indirect effects can steer a total effect in another direction, indicating that the apparent effects of one variable on another can be the trade-off of opposite effects. Effects from residential density suggest that people in a dense residential environment travel a little less, although this effect is partly cancelled out by extra activities. Workplace density/mix increases total daily distances, but decreases distances by car