High-speed railway is a very important part of transportation industry in China, and travel choice has key effect on the development of high-speed railway. Therefore,research on travel behavior of passengers and prediction their travel choice, will offer valuable suggestion for high-speed railway running. In this paper, support vector machine (SVM) is the main method being used to predict. Supp...

This paper presents a stochastic model-based approach to freeway travel-time prediction. The approach uses the Link-Node Cell Transmission Model (LN-CTM) to model traffic and provides a probability distribution for travel time. On-ramp and mainline flow profiles are collected from loop detectors, along with their uncertainties. The probability distribution is generated using Monte 5 Carlo simul...

This paper presents a joint discrete-continuousmodel for activity-travel time allocation by employing the ordered probit model for departure time choice and the hazard model for travel time prediction. Genetic algorithm GA is employed for optimizing the parameters in the hazard model. The joint model is estimated using data collected in Beijing, 2005. With the developed model, departure and tra...

The delay costs of traffic disruptions and congestion and the value of travel time reliability are typically evaluated using single trip scheduling models, which treat the trip in isolation of previous and subsequent trips and activities. In practice, however, when activity scheduling to some extent is flexible, the impact of delay on one trip will depend on the actual and predicted travel time...

This paper examines how real-time information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods ahead (of 5-min duration). The study employed a spectral basis artificial neural network (SNN) that utilizes a sinusoidal transformation technique to increase the linear separability of the input features. Link travel tim...

Given a sequence of activities and transport modes, for which a framework has been provided in previous work, this paper evaluates the use of a Reinforcement Machine Learning technique. The technique simulates time and location allocation for these predicted sequences and enables the prediction of a more complete and consistent activity pattern. The main contributions of the paper to the curren...

Travel time is one of the most fundamental and important determinants of travel behavior. However, the travel time on which travel decision is based is subjective one, i.e., anticipated travel time. We propose a conceptual model of the formation of an anticipated travel time through information acquisition and initial driving experience. Fitting this model to day-to-day data of anticipated trav...

The value of travel time savings (VTTS) is the monetary value attached to save a determined amount of travel time. VTTS is also the most important benefit category aimed at justifying investments in transport infrastructures by public administrations. Hence VTTS played a significant role in various economic studies, both analytical and empirical (Zamparini & Reggiani, 2007). "It is difficult to...