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

Social demographics imputation based on similarity in multi-dimensional activity-travel pattern:A two-step approach

In response to the absence of demographics in increasingly emerging big data sets, we propose a novel method for inferring the missing demographic information based on similarity in people’s daily multi-dimensional activity-travel patterns as well as the characteristics of the area they move about. Instead of using isolated activity-travel attributes to infer social demographic features, our proposed method first calculates the similarity of people’s multidimensional daily activities and travels as well as characteristics of their visiting locations, between those for whom the social demographics are to be imputed (target) and those with known demographics (base) using a polynomial function. The weights of the function are determined using the permutation feature importance method, and then dynamic time warping is used to align the multidimensional activity sequences of the base and target sample and measure their similarities. For each person in the target database, a matched list is created consisting of those with the most similar activity-travel sequences in the base sample. A support vector machine is then trained using the base sample as input to impute the demographics of the target sample. The proposed model is trained using a national travel survey and validated by applying it to a GPS dataset. The results show that the proposed method outperforms existing methods in predicting four selected demographics: gender, age, education level, and work status, with an accuracy range between 91% and 94% for the national dataset and 88% to 91% for the GPS data. This study highlights the importance of considering the multidimensional and sequential nature of peoples’ daily activity-travel patterns in the imputation of demographic features

Hybrid choice models : principles and recent progress incorporating social influence and nonlinear utility functions

AbstractHybrid choice models have been developed as an extension of discrete choice models, particularly multinomial logit models, in an attempt to include attitudinal variables. The quintessence of hybrid choice models is that a model of attitude formation is estimated and the estimated attitudes are added to the commonly used set of attributes in discrete choice models: attributes of the choice alternatives and socio-demographic variables. The most commonly applied model is based on linear specifications, both for the attitude model and the utility function. In this review paper, we discuss the principles underlying the hybrid choice model, summarize the specifications used in previous applications of the model and then continue discussing recent progress that added social influence to the model specification and replaced the linear specification of the utility function with a nonlinear function

Effects of life events and attitudes on vehicle transactions: A dynamic Bayesian network approach

Individual and household life events are interdependent and influence mobility-related decisions at different levels over time. This paper developed an integrated dynamic model to capture the interdependences among life events, with a special focus on vehicle transactions. Particular attention was paid to the inclusion of vehicles’ characteristics such as the age, fuel type, and size of cars, which are pertinent to emission forecast. A dynamic Bayesian network (DBN), containing individual and household characteristics and latent attitudes toward car ownership and use alongside life events, was employed to study the interdependences. The temporal relationships among life events and lead-lag effects were also captured in the DBN. The longitudinal survey data “the Netherlands Mobility Panel (MPN)” from 2013 to 2018 was used to train and test the DBN. The analysis results confirm the dynamic interdependences between vehicle transactions and other life events and reveal noticeable associations between attitudes and purchase decisions. It is found that several life events (e.g., “Birth of a baby”, “Marital status change”) have concurrent or varied lag-effects on vehicle transaction decisions. The validation indicates that the proposed DBN approach has a high predictive accuracy of vehicle transaction decisions and other life events

Spatial heterogeneity in the nonlinear impact of built environment on commuting time of active users: A gradient boosting regression tree approach

Many studies provided evidence regarding the influence of built environment (BE) on commuting time. However, few studies have considered the spatial heterogeneity of such impacts. Using data from Nanjing, China, this study employs two-step clustering and gradient boosted regression trees (GBRT) to segment the neighborhoods into different types and investigate the effects of BE characteristics on the commuting time of active users. The results show a strong effect of BE characteristics on commuting time, involving active modes. The importance of BE characteristics varies among neighborhood types. For active commuters in the internal region of Nanjing, commuting time is affected mostly by the land use mix at the work end. The lowest impact of BE in internal regions is associated with metro station density. For active commuters in external region of the city, the relative importance of intersection density at the home end is the largest (as high as 5.76%). Moreover, other significant differences are found in the associations between BE characteristics and active commuting time in the two regions.</p

Three Tales about Limits to Smart Cities Solutions

This editorial is the introduction to a special issue on smart cities. The concept of a smart city is not well-defined, yet expectations among urban planners and decision-makers are high. This special issue contains three papers that discuss three different manifestations of smart cities and the success—or lack of it—of the solutions discussed. The papers highlight some limitations of the concept of smart cities, but at the same time also pinpoint some potentially beneficial solutions

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

Uncertainty in forecasts of complex rule-based systems of travel demand: Comparative analysis of the Albatross/Feathers model system

peer reviewedThis paper documents the results of a comparative analysis of model uncertainty of the Albatross/Feathers model system for respectively the Rotterdam region, The Netherlands and Antwerp region, Belgium. The assessment concerned the calculation of the coefficient of variation for the daily distance travelled per person. The calculations are performed both at the aggregated level and the disaggregated level (e.g. disaggregation by certain socio-demographics). Results indicate that model uncertainty differs by socio-demographic groups. Results of a regression analysis also indicate that in both regions uncertainty in daily distance travelled per person is strongly correlated with the inverse square root of the relevant socio-demographic population and the complexity of the classification, measured in terms of the number of possible classes