TRIP PURPOSE ESTIMATION METHOD FOR PROBE PERSON SURVEY USING SEQUENTIAL LEARNING

A machine learning framework for TCP round-trip time estimation

In this paper, we explore a novel approach to end-to-end round-trip time (RTT) estimation using a machine-learning technique known as the experts framework. In our proposal, each of several ‘experts’ guesses a fixed value. The weighted average of these guesses estimates the RTT, with the weights updated after every RTT measurement based on the difference between the estimated and actual RTT. Th...

Practical Method for the Estimation of Trip Generation and Trip Chaining

Using Probe Person Data for Travel Mode Detection

Recently GPS data is used in a lot of studies to automatically reconstruct travel patterns for trip survey. The aim is to minimize the use of questionnaire surveys and travel diaries so as to reduce their negative effects. In this paper data acquired from GPS and accelerometer embedded in smart phones is utilized to predict the mode of transportation used by the phone carrier. For prediction, S...

Forecasting Net Induced Travel Demand for Optional Trip Purpose by Trip Production Model with Accessibility

Predicting net induced demand can be helpful for better feasibility study before investigating transportation infrastructure. The net induced demand can be defined as newly generated trips, but not shifted trips, by some improvements in transportation systems. This study only focused on the net induced demand rather than any other concepts belong to the general term “induced demand”. The net in...

Hematocrit estimation using online sequential extreme learning machine.

Hematocrit is a blood test that is defined as the volume percentage of red blood cells in the whole blood. It is one of the important indicators for clinical decision making and the most effective factor in glucose measurement using handheld devices. In this paper, a method for hematocrit estimation that is based upon the transduced current curve and the neural network is presented. The salient...