Prasanta K. Sahu

Simultaneous dynamic demand estimation models for major seaports in India

Abstract Prediction of cargo volumes is important for authorities such as port administrations and port operators, as cargo throughput affects port planning and operation. The forecasting results help planners and policy-makers to take decisions on issues such as port investment, port development, berth location selection, port operation, and freight rate. The success of the port operation policy depends on the accuracy of the cargo forecast. This paper presents an improved model known as dynamic regression (DR) model to forecast cargo demand simultaneously for ports within a port system; the model is applied to forecast cargo demand at major seaports in India. Past cargo flow data from years 1980–1981 to 2013–2014 at 11 major ports in India are used for estimating the proposed model. Average prediction error is found to be within 10% for most of the ports. The DR model is used to produce forecasts up to 2019–2020 for all the ports studied. The model forecast is compared with the projections of the Ministry of Shipping, Government of India. This study is intended to provide guidance to planners in taking decisions on issues related to port infrastructure development, such as construction of new terminals and improvement of access roads to ports for the major ports in India. The study will also be beneficial to shipping agencies for their investment strategies in the Indian ports sector.

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...

Classification of Indian Seaports Using Hierarchical Grouping Method

Of the various African groups employed aboard British ships in the nineteenth century, migrant labourers from the Kru coast territory were the most numerous. Kru most often worked on merchant trading vessels, but they also joined the Royal Navy. Some took part in famous voyages of exploration. Upwards of fifty Kru sailed up the Niger as part of the disastrous antislavery expedition of 1841, for example, and half of the crew of the Pleiad, which explored the same river in 1854, were Kru.1 This makes them the largest single ethnic group on the latter expedition, which was celebrated in Britain as a new dawn in British exploration of Africa. Kru sailors were also integral to the navy’s efforts to suppress the transatlantic slave trade. It has been estimated that up to a third of personnel on anti-slave-trade ships on the western coast of Africa were of African ancestry; the overwhelming majority of these were Kru migrants recruited in Sierra Leone.2

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.