Modeling

Increasing Model Precision Can Reduce Accuracy

Daganzo, Carlos F.
1987

In the field of logistics, a variable that is to be predicted (e.g., cost) often varies in a nonsmooth, irregular, but known manner, with various factors (e.g., distances, quantity, and density of material to be carried, etc.). This paper identifies conditions, where given approximate input factors, a prediction of the variable is less error prone if one uses a smooth approximation to the exact function of the factors. This phenomenon, which is quite prevalent, may enhance the appeal of continuous approximation models in some instances.

Large Scale Nested Logit Models: Theory and Experience

Daganzo, C.
Kusnic, M.
1990

Implementing Vehicle Routing Models

Robusté, Francesc
Daganzo, Carlos F.
Souleyrette, Reginald R.
1990

This paper shows how idealized models can be used to obtain cost-effective, implementable solutions to large and complex logistics problems. It advocates the use of fine tuning software to translate the guidelines produced by idealized models into specific feasible solutions. The “traveling salesman” (TSP) and “vehicle routing” (VRP) problems were used to test the approach. For sufficiently large problems the proposed procedure leads to solutions that improve on those produced by either idealized models or numerical methods alone. Simulated annealing (SA) was chosen for fine tuning. This...

Simple Upper Bound Formulae for the VRP Distance

Daganzo, C.
1992

Another Look at the Nested Logit Model

Daganzo, C.
Kusnic, M.
1992

Technical Note—Two Properties of the Nested Logit Model

Daganzo, Carlos F.
Kusnic, Michael
1993

This paper presents simple formulae for the utility covariances of the nested logit (NL) model, and based on these defines a “scaled tree” that can be used as an aid for the interpretation of estimation results. The paper also shows that the full information log-likelihood function of linear-in-the-parameters NL models is concave in the utility parameters. Thus, conditional on the scaling parameters, full information maximum likelihood (FIML) searches cannot get trapped in local maxima.

Remarks on Traffic Flow Modeling and Its Applications

Daganzo, Carlos F.
Brilon, Werner
Huber, Felix
Schreckenberg, Michael
Wallentowitz, Henning
1999

This document presents some recent results and ideas from the University of California (Berkeley) traffic operations group, and at the same time discusses the role of traffic flow modeling in traffic management and control. It stresses the steps that can be taken to reduce congestion and improve traffic efficiency, and how traffic models and theories fit within this picture.

The Lagged Cell-Transmission Model

Daganzo, Carlos F.
1999

In cell-transmission models of highway traffic one partitions a highway into small sections (cells) and keeps track of the cell contents (number of vehicles) as time passes. The record is updated at closely spaced instants (clock ticks) by calculating the number of vehicles that cross the boundary separating each pair of adjoining cells during the corresponding clock interval. This paper shows that the accuracy of the cell-transmission approach is enhanced if the downstream density that is used to calculate the receiving flow(s) is read L clock intervals earlier than the current time...

The Use of Succinct Models and Data Summaries

Daganzo, Carlos F.
1999

As we do in this chapter, Blumenfeld et al. (1987) describe the advantages of simple models; the opinions expressed in this reference are based on a case study where succinct models based on data summaries proved very effective; the reference is easy to read. Newell (1973) argues that a family of related transportation and location problems can be solved approximately with an approach that ignores “details”; this paper was the “seed” for the continuum approximation method to be presented in Chapter 3.

On Planning and Design of Logistics Systems for Uncertain Environments

Daganzo, Carlos F.
Erera, Alan L.
Speranza, M. Garcia
Stähly, Paul
1999

This paper addresses some issues that arise in the planning and design of logistics systems when the environment in which they are to be operated cannot be modeled accurately with certainty. The paper describes the analytical difficulties introduced by explicitly considering uncertainty, and suggests possible modeling steps that may result in more efficient, uncertainty-friendly plans.