1,721,326 research outputs found
Localizzazione automatica per le flotte di mezzi pubblici nei piani della mobilità: analisi tecnologica ed applicazioni sperimentali
No full textParametri non convenzionali per quantificare la percezione della qualità nel trasporto pubblico
No full textDiscrete choice estimator properties for finite population and simulation sample sizes
No full textEconometric models based on simulations are used extensively in transportation. Simulation methods provide only an approximation of the objective function and produce estimators that suffer from bias and loss in efficiency. Two types of bias are known to exist in simulation-based estimators: simulation bias, as a result of the nonlinear transformation in the log likelihood (LL) function, and optimization bias, caused by the maximization operator, which depends on the variance of the simulated LL with respect to the random draws and the population sample. In this paper, the properties of the estimators are studied with resampling techniques in various simulation configurations. In the experiments, optimization bias dominates simulation bias, and in the presence of panel data the use of some randomized quasi-Monte Carlo techniques aiming at reducing simulation variance only marginally affects the estimated parameters for a given sample size. Results also confirm that the population resampling, though numerically costly, is a simple and effective procedure to deliver a better understanding of parameter properties
Model system to evaluate impacts of vehicle purchase tax and fuel tax on household greenhouse gas emissions
No full textThis paper proposes a model system to forecast household-level greenhouse gas emissions (GHGEs) from private transportation and evaluate the effects of car-related taxation schemes on vehicle emissions. The system contains four submodels that specifically capture households' vehicle and vintage, quantity, usage, and GHGE rates (GHGERs) by vehicle type. The vehicle GHGERs are calculated with the Motor Vehicle Emission Simulator 2014, which is authorized by the Environmental Protection Agency. The whole model system was applied to the Washington, D.C., metropolitan area. The 2009 National Household Travel Survey was employed with supplementary data from Consumer Reports, American Fact Finder, and 2009 state motor vehicle registrations. The study proposed two tax schemes, vehicle purchase tax and fuel tax, and predicted their effects on reductions in vehicle GHGEs. The average annual GHGE per vehicle was 5.86 tons of carbon dioxide-equivalent gas without the proposed taxes. After two taxation policies were implemented, the results showed the following: (a) the impacts on reducing GHGEs from fuel taxes were higher than those from purchase taxes, (b) purchase taxes reduced GHGEs mainly by decreasing the number of cars of households with more vehicles, and (c) fuel taxes successfully reduced GHGEs by decreasing the use of cars by households with fewer vehicles. The model system can be extended to other zones, counties, states, and nations
A latent class choice based model system for railway optimal pricing and seat allocation
No full textIn this paper, discrete choice methods in the form of multinomial logit and latent class models are proposed to explain ticket purchase timing of passenger railway. The choice model and demand functions are incorporated into a revenue optimization problem which jointly considers pricing and seat allocation. The framework provides insightful policy implications in term of fare and capacity distribution derived from actual passenger behavior. It shows that accepting short-haul demand provides greater revenue than long-haul demand using the same capacity. Revenue improvement ranges from 16.24% to 24.96% in multinomial logit models and from 13.82% to 21.39% in latent class models respectively. © 2013 Elsevier Ltd
Does ridesourcing impact driving decisions: A survey weighted regression analysis
No full textThe initial public offerings (IPOs) of Uber and Lyft in 2019 marked a milestone for the decade-old ridesourcing. As we start to embrace ridesourcing in our daily life, we also rearrange our daily travel amongst different modes of transportation. As the fundamental decisions in travel behavior, car ownership and car travel should be re-examined in the advent of shared mobility. In this paper, we applied a vehicle choice model that factors in ridesourcing frequency to understand the decisions about (1) how many cars an individual would declare as the primary driver of, and (2) the annual vehicle miles traveled (VMT) for all cars he or she drive. We used a subsample of the latest 2017 National Household Travel Survey (NHTS) data that focus on the Capital region (Washington, D.C. – Maryland – Virginia) as our study area. We applied a weighted regression analysis following the NHTS survey design and derived population-representative results on both decisions. In addition, we calculated the driving cost for each household vehicle based on the latest fuel economy data and incorporated driving cost into the car travel model. The results suggest that ridesourcing is associated with a smaller chance of an individual being the primary driver of a car. However, the elasticity indicates that ridesourcing usage has a small impact on the number of primarily driven cars. Furthermore, ridesourcing has no significant impact on the annual VMT, either. Driving cost, on the other hand, still plays the key role in determining driving distances
Space-time dynamics: A modeling approach for commuting departure time on linked datasets
No full textCommuters' departure time related decisions are important in time geography. Analytic tools have been proposed to capture the inherent choice determinants both in time and space. Although the dynamic aspects of the problem have been identified, most of the existing studies are based on static models. In this paper, a dynamic modeling framework is proposed to explore the relationship between commuters' departure time choices and the evolution of en route traffic. A data linkage method is developed to create an integrated dataset that enables the observation of commuters' reaction to changes in travel time and traffic conditions over time. A regional household travel survey is linked to travel information obtained from the Google Maps application program interface (API), creating a synthetic longitudinal dataset. Two decision rules are applied to model commuters' response to the evolution of traffic. The results indicate that travel time, distance to work location, flexibility in working schedule, expected arrival time, and commuters' sociodemographic influence departure time choices. It is also found that accounting for dynamics improves model fit and out-of-sample predictions. Both the dynamic model and the proposed data linkage method contribute to the understanding of human activities in space and time and can be used to enhance transportation demand analysis and urban policy studies
A modal mixed logit choice model on panel data: accounting for systematic and random heterogeneity in preferences and tastes
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