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

İstanbul sesi

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

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

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

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

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

How the design of Complete Streets affects mode choice: Understanding the behavioral responses to the level of traffic stress

Following a federal policy statement in 2010 supporting bicycle and pedestrian accommodation in federal-aid transportation projects, many cities across the US have implemented Complete Streets principles and invested in developing better-planned infrastructure that can be safely accessed by a diversity of modes of transportation by all types of users, in a mix of land uses. However, most of the travel demand forecasting models and planning tools used in practice are not sensitive to changes in demand for non-motorized modes such as walking and cycling in response to road infrastructure improvements. Hence, there is a need for models and tools that are capable of evaluating impacts of infrastructure changes that include Complete Streets implementations on the travel behavior, and estimate shifts in mode choices from motorized to non-motorized modes. This paper proposes a specific data collection plan, a multi-modal choice model, and strategies to update traditional trip-based transportation models to forecast rates of non-motorized trips for evaluating Complete Streets plans at a higher level. Concretely, we estimate elasticities to Level of Traffic Stress, which defines the comfort or discomfort experienced by walkers and bikers, segmented by income levels and trip purposes. We then use them to compute the new non-motorized mode shares that would be achieved by improving CS attributes leading to lower levels of traffic stress. The proposed modeling framework has been successfully applied to the Maryland Statewide Transportation Model, producing reliable non-motorized trip rates, and can be extended to other methodological frameworks used by public agenciesThis research was sponsored by the Maryland Department of Transportation State Highway Administration (Project No: MD-21- SHA/UM/5-25, Erdogan et al., 2021), and the Urban Mobility & Equity Center (UMEC), based at Morgan State Universit

Ectopic Fat Depots and Cardiometabolic Burden: A Possible Dangerous Liaison in Women Planning Assisted Reproduction

Objective: We evaluated cardiometabolic burden in women planning assisted reproduction in order to identify subgroups at higher risk of pregnancy complications and cardiovascular disease. Materials and methods: In this cross-sectional study we investigated 60 infertile women with BMI≥25 kg/m(2) referred to the Center for Assisted Reproduction. All women underwent metabolic, anthropometric parameters and ultrasound evaluation of ectopic fat depots. Results: All women had waist ≥80 cm. We found that 93.3% of women had pathological subcutaneous, 58.3% visceral and 80% para-perirenal fat; all women had fatty liver. Visceral fat and severity of steatosis were significantly related to the presence of metabolic syndrome (OR =5.7; p=0.03).A significant negative correlation between low HDL-c and para-perirenal fat (p<0.0001), a significant positive correlation with fasting plasma glucose and para-perirenal fat (p=0.001) were found. We observed a significant positive correlation between visceral fat and hs-CRP (p=0.002), HOMA-IR (p=0.04) and triglycerides (p=0.002), a significant negative correlation with HDL-c (p=0.05). Conclusion: This study by highlighting a clinically “dangerous liaison” between ectopic fat depots and metabolic/inflammatory markers, might permit to identify women with a worse metabolic phenotype and encourage lifestyle changes for improving their general and reproductive health together