Introduction
As an integral important part of intelligent transport system
(ITS), Advanced Travelers Information System (ATIS) will
provide travelers with pre-trip information about travel options
as well as real-time advice on navigating through a dynamic
transportation network, where conditions may change rapidly
many times in the course of a typical day. In many countries,
P&R facilities are introduced, which facilitate changes between
private (e.g., car) and a public transport mode (e.g., train), to
alleviate congestion problems in inner city areas. Therefore, the
ability to model multi-modal trips that involves both private
and public transport modes is increasingly relevant. The
fundamental issues behind above mentioned services are how
to model properly the multimodal transport network for ATIS
and how to design the corresponding algorithms for supporting
queries of travelers. To the best of the authors knowledge, no
general multimodal transport network models (or seamless
integrated models) and algorithms are available or suitable for
large-scale ATIS applications that simultaneously consider
private and public transport modes.
The purpose of this study is to develop and test a generic
multimodal transport network model for ATIS application that
can be used for large-scale transport systems. We propose and
test a supernetwork approach where the networks for different
modalities are integrated in a single network [1]. Although time
is the only attribute in a current test application, the framework
explicitly intends to support multi-criteria evaluation of modes
and routes taking into account possible considerations such as
monetary costs, comfort, safety, reliability and emission as
well time. The model will provide the multi-modal routing
system of i-Tour – a new generation personal mobility system
that is currently under development [2]. The result of a test
experiment in Eindhoven region verified the validity and
feasibility of our model. The paper is structured as follows:
some related researches and applications will be introduced in
section two; some basic concepts will be introduced in section
three; our model and algorithms will be presented in section
four; test results will be discussed in section five; discussion
will be given in section six; the final section will summarize
the major conclusions.