Planning, design and management of multimodal transportation systems is an area in which engineers can contribute to addressing a wide variety of challenges, ranging from congestion to security to environmental impact. A transportation system includes vehicles, network infrastructure and information technology, used both for monitoring and control and to provide information to users of the system. The interactions of people with the system are especially complex and important. Faculty are currently pursuing research in automated traveler information systems, freight transportation and security concerns, transportation of hazardous materials, and air quality issues in urban transportation. Study in transportation systems engineering often includes work in related areas such as economics, operations research, systems engineering, city and regional planning, and management.
In this paper we analyze demand for cycling using a discrete choice model with latent variables and a discrete heterogeneity distribution for the taste parameters. More specifically, we use a hybrid choice model where latent variables not only enter into utility but also inform assignment to latent classes. Using a discrete choice experiment we analyze the effects of weather (temperature, rain, and snow), cycling time, slope, cycling facilities (bike lanes), and traffic on cycling decisions by members of Cornell University (in an area with cold and snowy winters and hilly topography). We show that cyclists can be separated into two segments based on a latent factor that summarizes cycling skills and experience. Specifically, cyclists with more skills and experience are less affected by adverse weather conditions. By deriving the median of the ratio of the marginal rate of substitution for the two classes, we show that rain deters cyclists with lower skills from bicycling 2.5 times more strongly than those with better cycling skills. The median effects also show that snow is almost 4 times more deterrent to the class of less experienced cyclists. We also model the effect of external restrictions (accidents, crime, mechanical problems) and physical condition as latent factors affecting cycling choices.
In this paper we use Bayes estimates of a multinomial probit model with fully flexible substitution patterns to forecast consumer response to ultra-low-emission vehicles. In this empirical application of the probit Gibbs sampler, we use stated-preference data on vehicle choice from a Germany-wide survey of potential light-duty-vehicle buyers using computer-assisted personal interviewing. We show that Bayesian estimation of a multinomial probit model with a full covariance matrix is feasible for this medium-scale problem and provides results that are very similar to maximum simulated likelihood estimates. Using the posterior distribution of the parameters of the vehicle choice model as well as the GHK simulator we derive the choice probabilities of the different alternatives. We first show that the Bayes point estimates of the market shares reproduce the observed values. Then, we define a base scenario of vehicle attributes that aims at representing an average of the current vehicle choice situation in Germany. Consumer response to qualitative changes in the base scenario is subsequently studied. In particular, we analyze the effect of increasing the network of service stations for charging electric vehicles as well as for refueling hydrogen. The result is the posterior distribution of the choice probabilities that represent adoption of the energy-efficient technologies.
Project planning and scheduling when there are both resource constraints and
uncertainty in task durations is an important and complex problem. There is a long
history of work on deterministic resource-constrained project scheduling problems,
but efforts directed at stochastic versions of that problem are fewer and more recent.
Incorporating the ability to reallocate resources among tasks to change the
characteristics of their duration probability distributions adds another important
dimension to the problem, and enables integration of project planning and scheduling.
Among the small number of previous works on this subject, there are two very
different perspectives. Golenko-Ginzburg and Gonik (1997, 1998) have created a
simulation-based approach that ?operates? the project through time and attempts to
optimize locally regarding decisions on starting specific tasks at specific times.
Turnquist and Nozick (2004) have formulated a nonlinear optimization model to plan
resource allocations and schedule decisions a priori. This has the advantage of taking
a global perspective on the project in making resource allocation decisions, but it is
not adaptive to the experience with earlier tasks when making later decisions in the
same way that the simulation approach is. Although the solution to their model
produces a ?baseline schedule? (i.e., times when tasks are planned to start), the
formulation puts much greater emphasis on resource allocation decisions.
The paper by Turnquist and Nozick (2004) describes the problem formulation
as a nonlinear optimization. For small problem instances (up to about 30 tasks), good
solutions can be found using standard nonlinear programming packages(e.g., NPSOL).
However, for larger problems, the standard packages often fail to find any solution in
a reasonable amount of computational time. One major contribution of this
dissertation is the development of a solution method that can solve larger problem
instances efficiently and reliably. In this dissertation, we recommend using the
partially augmented Lagrangian (PAL) method to solve the suggested nonlinear
optimization. The test problems considered here include projects with up to 90 tasks,
and solutions to the 90-task problems take about 2 minutes on a desktop PC.
A second contribution of this dissertation is exploration of insights that can be
gained through systematic variation of the basic parameters of the model formulation
on a given problem. These insights have both computational and managerial
implications for practical application of the model.
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