Spatiotemporal Object Database Approach to Dynamic Segmentation

Dynamic segmentation is viewed as one of the most important functions of geographic information systems for transportation applications. Although the road network and associated events (e.g., pavement material, traffic volume, incidents) can be referenced to both space and time, the spatial and temporal dimensions have not been well integrated. Modeling space-varying, time-varying, and space-time-varying events in dynamic segmentation by using an object database approach that is in line with the Object Database Management Group standard is explored. A mechanism called parametric polymorphism is used to lift conventional data types to spatial, temporal, and spatiotemporal types for maintaining knowledge about events that could change spatially, temporally, and spatiotemporally along linear features. An associated object query language, DS-OQL, was designed to support the formulation of spatial, temporal, and spatiotemporal queries on the road and event information.

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Spatiotemporal Data Model and Query Language for Tracking Land Use Change

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198105190200113 ◽

2005 ◽

Vol 1902 (1) ◽

pp. 107-113 ◽

Cited By ~ 1

Author(s):

Bo Huang ◽

Christophe Claramunt

Keyword(s):

Land Use ◽

Data Model ◽

Query Language ◽

Spatiotemporal Data ◽

Spatiotemporal Information ◽

Management Group ◽

Object Databases ◽

Temporal Dimensions ◽

Spatiotemporal Queries ◽

Spatiotemporal Data Model

Management of spatiotemporal information requires a more generic and consolidated data model to facilitate applications such as tracking land use parcel changes. This paper presents such a spatiotemporal data model in the context of object databases by extending the Object Data Management Group (ODMG) standard and examines its feasibility in a land use application. This model extends the ODMG object model with a parameterized type, TimeSeries<T>, which allows the shifting of spatial types into spatiotemporal types to support the representation of a series of states (i.e., the history) of an object. An object query language (OQL), spatiotemporal OQL (STOQL), which adds spatial and temporal dimensions to ODMG's OQL, is also designed. A case study demonstrates that STOQL supports the formulation of various spatiotemporal queries pertaining to historical states of spatial objects as well as spatial changes, including spatial type substitution. The model and query language have been implemented by using an object-oriented language in a geographic information system environment.

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Optimal Trajectory Generation for Intelligent Vehicles in Complex Traffic Based on Iteration Convex Optimization

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421590400 ◽

2021 ◽

pp. 2159040

Author(s):

Xiaoyu Zhang ◽

Aihua Li

Keyword(s):

Convex Optimization ◽

Quadratic Programming ◽

Trajectory Planning ◽

Three Dimensional ◽

Intelligent Vehicles ◽

Feasible Region ◽

Planning Problem ◽

Time Varying ◽

The Road ◽

On The Road

Intelligent vehicles face considerable challenges in the complex traffic environment since they need to deal with various constraints and elements. This dissertation puts forward a novel trajectory planning framework for intelligent vehicles to generate safe and optimal driving trajectories. First, we design a spatiotemporal occupancy framework to deal with all kinds of elements in the complex driving environment based on the Frenét frame. This framework unifies various constraints on the road in the three-dimensional spatiotemporal representation and clearly describes the collision-free configuration space. Then we use the convex approximation method to construct a time-varying convex feasible region based on the above accurate temporal and spatial description. We formulate the trajectory planning problem as a standard quadratic programming formulation with collision-free and dynamics constraints. Finally, we apply the iterative convex optimization algorithm to solve the quadratic programming problem in the time-varying convex feasible region. Moreover, we design several typical experimental scenarios and have verified that the proposed method has good effectiveness and real-time.

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Risk Analysis and Route Optimization of Dangerous Goods Transportation Based on the Empirical Path Set

Journal of Advanced Transportation ◽

10.1155/2020/8838692 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Haixing Wang ◽

Qianqian Liang

Keyword(s):

Risk Analysis ◽

Path Selection ◽

Route Optimization ◽

Time Varying ◽

Road Transportation ◽

The Road ◽

Dangerous Goods ◽

Comprehensive Method ◽

On The Road ◽

Population And Environment

Based on the empirical path set generated from the track data of dangerous goods vehicles, we present a new method for the risk analysis and path optimization of dangerous goods transportation. First of all, by exploring the travel rules of dangerous goods transport vehicles hidden in the track data, combined with the path set generation algorithm, the method of determining the empirical path set of dangerous goods transport is studied. Secondly, based on the empirical path set, mainly considering the travel rules of vehicles and people on the road, as well as the distribution of population and environment-sensitive areas along the road, a dual objective path selection model is proposed to comprehensively measure the risk and cost of road transportation under time-varying conditions. On this basis, given the principle of avoiding high-risk transportation paths, a comprehensive method of integrating multiple algorithms is proposed to solve the model. Finally, taking a road network as an example, the practicability and effectiveness of the proposed method are verified. The method proposed takes both practicability and safety into account. Based on the experience path set, considering the time-varying characteristics, the decision-maker could choose the appropriate transportation path of dangerous goods according to different preferences, so as to better solve the problem of path selection for dangerous goods transportation.

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