Traditional traffic counting location (TCL) problem is to determine the number and locations of counting stations that would best cover the network for the purpose of estimating origin-destination (O-D) trip tables. It is well noted that the quality of the estimated O-D trip table depends on the estimation methods, an appropriate set of links with traffic counts, and the quality of the traffic ...

Multiple-station shared-use vehicle systems allow users to travel between different activity centers and are well suited for resort communities, recreational areas, as well as university and corporate campuses. In this type of shared-use vehicle system, trips are more likely to be oneway each time, differing from other shared-use vehicle system models such as neighborhood carsharing and station...

This paper presents a model supporting intelligent interactions of tourists with other tourists and locals and the tourism information of a particular Destination before, during and after the trip. The approach tries to bridge the “Community Gap” which is the lack of interactions among tourists and between tourists and locals at a particular Destination. Community interactions are very importan...

Information has been noted to be the key to the formation of destination image. Destination marketers are aware of this fact and they aggressively try to influence the tourist’s decisions by altering the image of their destinations through information provision. During their trip planning, tourists usually engage themselves with active information search. Despite motives being the crux of trave...

Transportation origin-destination analysis is investigated through the use of Poisson mixtures by introducing covariate-based models which incorporate different transport modelling phases and also allow for direct probabilistic inference on link traffic based on Bayesian predictions. Emphasis is placed on the Poisson-inverse Gaussian model as an alternative to the commonly used Poisson-gamma an...

The study analyses the searches for digital tourist information that different types of tourists, with characteristics and connectivity preferences, make at destinations on main topics they may require, allowing to know which sources use, but also what moment in situation. was based an online survey asked users about made destination during their last trip. results show tourists increasingly se...

This paper studies the applicability of kalman filtering approaches for network wide traveler Origin Destination estimation from link traffic volumes. The paper evaluates the modeling assumptions of the Kalman filters and examines the implications of such assumptions. 1. Dynamic Origin Destination Tables Dynamic Origin-Destination (O-D) trip tables represent a time varying set of traffic demand...

This paper describes the problem of locating terminals using E.164 numbers, and the problem of selecting a suitable gateway for calls from an IP telephony network to the public switched telephone network (PSTN). Generally, these are the problems of mapping the name of a destination into an address, and to find the best route to the destination in a combined IP and PSTN network. Number portabili...

Trip distribution is the second important stage in the 4-step travel demand forecasting. The purpose of the trip distribution forecasting is to estimates the trip linkages or interactions between traffic zones for trip makers. The problem of trip distribution is of non-linear nature and Neural Networks (NN) are well suited for addressing the non-linear problems. This fact supports the use of ar...