GIS based Multi-modal Ttransportation Network

GIS based Multi-modal Ttransportation Network

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LU Feng
State Key Lab of Resources and
Environmental Information System
CAS, Beijing, china

Shen Paiwei
Resources and Environmental Information System
Institute of Geographic Sciences
And Natural Resources Research
[email protected]

ChenJie
Resources and Environmental Information System
Institute of Geographic Sciences
And Natural Resources Research
[email protected]

Abstract
In this paper, considering the intrinsic rules of connectivity in urban transportation network, an intelligent network model based on a network topology automation (NETA) framework is proposed. Adopting the trigger concept in DBMS, the authors implement the key flow and executive engines of the control system within NETA framework

For intelligent transportation systems (ITS) applications, GIS provides a useful method to manage, manipulate and analyse the spatial data related to transportation network. The conventional GIS spatial data models are far behind the need of modelling urban transportation where highly dynamic changes, non-planar topology, multi-modal networks and complicated connection rules prevail.
Intelligent data modelling is the aim of spatial data representation. For transportation networks, an intelligent spatial data model means intelligently maintaining not only the semantic relationships but also the topological relationships within and between various transportation modals which concern geometric rules, semantic relation rules and traffic rules, as well as a capacity of self-studying through rule and knowledge bases.

Topological relationships in transportation networks behave through connectivity which has two hierarchies, i.e., spatial connectivity and semantic connectivity. Spatial connectivity is the geometrical connectivity of road segments, and semantic connectivity means the defined traffic connectivity in practice. The geometrical connectivity is the fundamental and prerequisite for semantic connectivity and the semantic connectivity is the core of urban transportation network topology.

Although many researches have been conducted on geometrical network topology automation and today’s commercial GIS software have provided perfect functions of topology automation, little progress has been made at meeting the needs of semantic topology automation in transportation network. Lots of related research known include Nielsen’s traffic intersection topology automation method based on rules of expert system (1998) and the intelligent data model in ArcGIS 8.0 or higher based on valid rules. Both of them have developed some advantages in the field of GIS-T, but unfortunately the former is only applicable in planar intersection while the latter cannot deal with transportation network semantic topology because of the lack and immoderate simplicity of restrictive factors and types in valid rules.

To make the semantic topology automation, we have to look into these intrinsic rules and features in transportation networks, and make them effective in semantic topology automation. This creates a need for an intelligent data model and data processing framework.