Hyuk-Jae Roh

Statistical Investigations of Snowfall and Temperature Interaction with Passenger Car and Truck Traffic on Primary Highways in Canada

AbstractWinter weather conditions such as extremely cold temperatures, heavy snowfall, and high wind chills are common occurrences in Canada. Impacts of such adverse weather conditions on total highway traffic volume have been the subject of several research studies in the past. However, none of the past studies investigated thoroughly the impacts of severe cold and heavy snowfall on temporal and spatial variations of truck traffic on Canadian highways. Impacts of weather on route choice behavior of truck and passenger car drivers have also not been addressed in the past. This paper presents an in-depth analysis of the winter weather impacts on classified traffic volume in terms of passenger cars and trucks with considerations of highway types. This study is based on large traffic and weather data sets from weigh-in-motion sites and weather stations in Alberta, Canada. The data were collected from six sites located on two primary highways: Highway 2 and Highway 2A, in Alberta. Winter-weather traffic model...

The impact of cold and snow on weekday and weekend highway total and passenger cars traffic volumes

Presented in this paper is an investigation of the impact of cold and snow on daily traffic volumes of total traffic and passenger cars. It is based on a detailed case study of five years of Weigh-In-Motion data recorded continuously at a highway site in Alberta, Canada. Dummy-variable regression models are used to relate daily traffic volumes with snowfall and categorized cold variables. The importance of all the independent variables used in the model are established by conducting tests of statistical significance. The total traffic and passenger car volumes are influenced by both the snowfall and the cold categories. Plots of the partial effect of each independent variable on the dependent variable are generated. It is found that a daily snowfall of 10 cm may cause a 25% reduction in the daily volume of passenger cars, and temperatures below -25°C may reduce the passenger car volumes by 10% or more. It is believed that the developed traffic-weather models of this study can benefit highway agencies in developing more advanced imputation method or identifying weather adjustment factors for accurate estimation of AADT from short duration traffic counts. Language: en

Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice Modeling

Until recently, a majority of modeling tasks of transportation planning, especially in discrete choice modeling, is conducted with the help of commercial software and only concerned about the result of parameter estimates to get a policy-sensitive interpretation. This common practice prevents researchers from gaining a systematic knowledge involved in estimation mechanism. In this research, to shed a light on these limited modeling practices, a standard discrete choice model’s parameter is estimated using Quasi-Newton method, DFP, and BFGS. Two extended algorithms, called DFP-GSM and BFGS-GSM, are proposed for the first time to overcome the weakness of the Quasi-Newton method. The golden section method (GSM) incorporates a nonlinear programming technique to choose an optimal step size automatically. Partial derivatives of log-likelihood function are derived and coded using Visual Basic Application (VBA). Through extensive numerical evaluation, estimation capability of each proposed estimation algorithms is compared in terms of performance measures. The proposed algorithms show a stable estimation performance and the reasons were studied and discussed. Furthermore, useful insights educated in custom-built modeling are present.