Fitting distributions of travel-time in vehicle traffic is an important application of spatio-temporal data mining. While regression methods to forecast the expected travel-time are standard approaches of travel-time prediction, we need to estimate distributions of the travel-time when using stateof-the-art risk-sensitive route recommendation systems. The authors introduce a novel nonparametric...

Travel time information is used as input or auxiliary data for tasks such dynamic navigation, infrastructure planning, congestion control, and accident detection. Various data-driven Time Prediction (TTP) methods have been proposed in recent years. One of the most challenging TTP developing selecting appropriate prediction algorithm. The existing studies that empirically compare different model...

Providing accurate and real-time bus travel time information is crucial for both passengers public transportation managers. However, in the traditional prediction model, due to lack of consideration influence different drivers’ driving styles on time, result not ideal. In historical data all drivers entire line are usually used training prediction. Due great differences individual styles, eigen...

It is Friday afternoon. You have a blind date at San Francisco at 8:00pm. Since you don’t want to give a bad first impression, you plan to leave early enough not to be late for the appointment. However, the traffic on 101North on every Friday is real heavy and unpredictable. Although it takes only 50 minutes to get there on other weekdays, it might take from 1.5 hours to 3 hours. So, when shoul...

This article explores the development of previous models to determine hubs in a competitive environment. In this paper, by comparing parameters of the ticket price, travel time and the service quality of hub airports, airline hubs are divided into six categories. The degree of importance of travel time and travel cost are determined by a multivariate Lagrange interpolation method, which can pla...