Day-to-day variability en la elección modal: estimación de un modelo Logit mixto con datos de paneles

Conocer la variabilidad del comportamiento de los individuos es crucial para comprender los patrones de viajes y para el desarrollo y la evaluación de políticas de planificación. La mayoría de las investigaciones en este tema se han en enfocado en la participación en las actividades o en la generación de viajes. Los estudios de la variabilidad en la elección modales han estudiados los efectos producidos por cambios en el sistema de transporte ofrecido. Para medir estos cambios se usan datos de paneles con pocas olas repetidas a lo largo de meses. El objectivo de este trabajo es estudiar la variabilidad intrínseca en las preferencias de los individuos entre medios. La variabilidad intrínseca se refiere a la variabilidad entre los días de una semana y entre varias semanas cuando no hay cambios en la oferta de transporte. Además, se pretende estudiar el efecto en la elección modal debido a los planes de actividades realizados y en particular repetidos a lo largo de la semana o de un par de meses. A este efecto se han estimados modelos logit mixto de elección modales usando datos de paneles recollectados en un periodo continuo de seis semanas. Los resultados muestran que la elección modal es estable entre días de la semana con la exepción del día Viernes, y que también hay una fuerte componente de hábito en la preferencia para los modos

Day-to-day variability and habit in modal choices: a mixed logit model on panel data

Understanding variability in individual behaviour is crucial for the comprehension of travel patterns and for the development and evaluation of planning policies. In the last 30 years a vast body of research has approached the issue in a variety of ways, but there are no studies on the intrinsic day-to-day variability in the individual preferences for mode choices and on the effect of habitual behaviours in absence of external changes. This requires using continuous panel data. Few papers have studied mode choice with continuous panel data but focused on the panel correlation. In this work we use a six-week travel diary survey to study the intrinsic day-to-day variability in the individual preferences for mode choices, the effect of habitual behaviour in the daily mode choices and the effect of long period plans. Mixed logit models are estimated that account for the above effects as well as for systematic and random heterogeneity over individual preferences and responses. We also account for correlation over several time periods. Our results suggest that individual tastes for time and cost are fairly stable but there is a significant systematic and random heterogeneity around these mean values and in the preferences for the different alternatives. We found that there is a strong inertia effect in mode choice that increases with (or is reinforced by) the number of time the same tour is repeated. The sequence of mode choice made is influenced by the duration of the activity and the weekly structure of the activities. Finally, models improve significantly when panel correlation is accounted for. But it seems that inertia can explain to some extent for panel effect

İstanbul sesi

Taha Toros Arşivi, Dosya Adı: İstanbul Genel Dokümanlarıİstanbul Kalkınma Ajansı (TR10/14/YEN/0033) İstanbul Development Agency (TR10/14/YEN/0033

Reducing simulation bias in mixed logit model estimation

Maximum simulated likelihood (MSL) procedure is generally adopted in discrete choice analysis to solve complex models without closed mathematical formulation. This procedure differs from the maximum likelihood simply because simulated probabilities are inserted into the Log-Likelihood (LL) function. The LL function to be maximized is the sum of the logarithm of the expected choice probabilities; since the log operation is a nonlinear transformation bias is then introduced. The simulation bias depends on the number of draws that are used in the simulation and on the sample size. Although the asymptotic properties of the MSL estimator are well known, the question is how simulation bias affects parameters estimation and therefore the main outcomes of choice models (for instance value of travel time savings and market shares). In this paper, we explicitly estimate simulation bias in the context of mixed logit models using Taylor expansion and we correct the log-likelihood objective function during the maximization process. The method is developed in the context of Monte Carlo simulation. We report significant error reduction on the final objective value but also on the optimal parameters. The method could be extended to quasi-Monte Carlo techniques as long as standard deviations are computed. Numerical costs can be neglected when using Monte Carlo draws and even when advanced strategies as the adaptive sample methodology are in use

Workshop synthesis: recent advances and new challenges in stated preference surveying methods

This paper summarizes the findings from the workshop Recent advances and new challenges in Stated Preference surveying methods. In this workshop we intended to analyze the most recent advances made in the field of survey methods, as well as to identify elements that could pose important challenges in their development. We relied on the presentation of works that raise elements of innovation. We aimed at offering a vision of procedures different from those commonly used in this field, to open a discussion on their appropriateness. The fundamental underlying question was whether these new approaches help us to better answer our research questions. This paper provides an overview of the current state of research on the topics that were selected for discussion, as well as of the opinions of participants on them. Our intention is to depict the challenges that were identified so that this field can move forwar

Classification of potential electric vehicle purchasers: A machine learning approach

13 p.Among the many approaches towards fuel economy, the adoption of electric vehicles (EV) may have the greatest impact. However, existing studies on EV adoption predict very different market evolutions, which causes a lack of solid ground for strategic decision making. New methodological tools, based on Artificial Intelligence, might offer a different perspective. This paper proposes supervised Machine Learning (ML) techniques to identify key elements in EV adoption, comparing different ML methods for the classification of potential EV purchasers. Namely, Support Vector Machines, Artificial Neural Networks, Deep Neural Networks, Gradient Boosting Models, Distributed Random Forests, and Extremely Randomized Forests are modeled utilizing data gathered on users’ inclinations towards EV. Although a Support Vector Machine with polynomial kernel slightly outperforms the other algorithms, all of them exhibit comparable predictability, implying robust findings. Further analysis provides evidence that having only partial information (e.g. only socioeconomic variables) has a significant negative impact on model performance, and that the synergy across several types of variables leads to higher accuracy. Finally, the examination of misclassified observations reveals two well-differentiated groups, unveiling the importance that the profiling of potential purchaser may have for marketing campaigns as well as for public agencies that seek to promote EV adoption

An interpretable machine learning approach to understanding the impacts of attitudinal and ridesourcing factors on electric vehicle adoption

13 p.The global electric vehicle (EV) market has been experiencing an impressive growth in recent times. Understanding consumer preferences on this cleaner, more eco-friendly mobility option could help guide public policy toward accelerating EV adoption and sustainable transportation systems. Previous studies suggest the strong influence of individual and external factors on EV adoption decisions. In this study, we apply machine learning techniques on EV stated preference survey data to predict EV adoption using attitudinal factors, ridesourcing factors (e.g., frequency of Uber/Lyft rides), as well as underlying sociodemographic and vehicle factors. To overcome machine learning models’ low interpretability, we adopt the innovative Local Interpretable Model-Agnostic Explanations (LIME) method to elaborate each factor’s contribution to the predicting outcomes. Besides what was found in previous EV preference literature, we find that the frequent usage of ridesourcing, knowledge about EVs, and awareness of environmental protection are important factors in explaining high willingness of adopting EVs

Random Effect Models to Predict Operating Speed Distribution on Rural Two-Lane Highways

This paper presents the results obtained from the estimation of free-flow speed on two-lane rural highways. The data used for the analysis were collected in Northwest Italy using video cameras and a laser speed gun. The model structure adopted separates the estimate of the central tendency of speeds from the typical deviations of individual speeds. Hence, in the model, the same set of variables can be used to determine both the mean value and the standard deviation of the speed distribution; the desired speed percentile is then calculated by considering the associated standard normal random variable (Z). Random effects (RE) were included in the model to account for the variability in time and space of the data that contain multiple measurements for the same road/section/direction and to remove any dependency between estimation errors from individual observations