Suchergebnisse

While collecting data for estimating discrete-choice models, researchers often encounter missing information in observations. In addition, endogeneity can occur whenever the error term is not independent of the observed variables. Both problems result in inconsistent estimators of the model parameters. The problems of missing information and endogeneity may occur in the same variable in the data, if, e.g., partially missing price information is correlated with another omitted variable. Extant approaches to correct for endogeneity in discrete choice models, such as the control function method, do not address the problem when the error term is correlated with a variable having missing information. Likewise, approaches to address missing information, such as the multiple imputation method, cannot handle endogeneity problems. To address this challenge, we propose a novel hybrid algorithm by combining the methods of multiple imputation and the control function. We validate the algorithm in a Monte-Carlo experiment and apply it to real data of heavy commercial vehicle parking from Singapore. In this case study, we were able to substantially correct for price endogeneity in the presence of missing price information. ; Ministry of National Development, Singapore National Research Foundation, Prime Minister's Office L2 NIC L2 NICTDF1-2016-1 Urban Redevelopment Authority of Singapore Land Transport Authority of Singapore Housing and Development Board of Singapore Ministry of Railways, Government of India Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 1191104 ANID PIA/BASAL AFB180003

The on-going globalisation and the increasing demand for flexibility in modern businesses have made transport, together with business logistics, a major functional domain. Transport growth is essentially for economic growth but is not without negative impacts. External effects such as pollution, congestion, accidents and damage to infrastructure generate considerable social costs that impose a heavy burden on society. This title addresses the need to develop new freight transport models and scientific tools to provide sound solutions that consider the wide range of internal and external impacts. The international contributions push forward frontiers in freight transport modelling and analysis.

Travel demand forecasting models play an important role in guiding policy, planning, and design of transportation systems. There is no shortage of literature critiquing the accuracy of model forecasts (see, for example, Pickrell, 1989; Wachs, 1990; Pickrell, 1992; Flyvbjerg, Skamris Holm, and Buhl 2005; Richmond, 2005; Flyvbjerg, 2007; Bain, 2009; Parthasarathi and Levinson, 2010; Welde and Odeck, 2011; Hartgen, 2013; Nicolaisen and Driscoll, 2014; Schmitt, 2016; Odeck and Welde, 2017, and Voulgaris, 2019), not to mention several high-profile lawsuits (Saulwick 2014, Stacey 2015, Rubin 2018). Many researchers and practitioners feel more can be done to advance rigorous travel analysis methods for the public good (see, e.g., zephyrtransport.org). Motivated by these critiques, a two-day, NSF-funded workshop was held at UC Berkeley in the Spring of 2017 to engage in a fundamental review of the state of the art in travel demand modeling, to discuss the future of the field, and to propose new directions and processes for advancing the science.Travel demand forecasting is an inherently practical enterprise. While academics drive the fundamental research, the users of travel demand models and forecasts are typically government agencies and transport operators that use the models to inform long-range investment, funding, and planning decisions. Private firms play a key role in assisting the agencies in both development and application of the models, and, more recently, high-tech firms have entered the development fray. While all of these actors have important roles in advancing the science of the field, in this report we focus our attention primarily on the academic side of the enterprise, consistent with the orientation of the funding agency (NSF), and in order to make the task manageable. That said, other sectors are represented in various parts of this report as they interface with academics or play particularly central roles in our proposals for advancing the science.