Earlier transportation-related applications, such as urban planning, required only static spatial databases or geographic information systems (GISs). In addition, various intelligent transportation systems (ITS) often use static GIS map databases for location referencing and frequently exchange spatial information with other map databases. However, more interesting transportation applications need to consider the values of traffic parameters that vary continuously over time. Spatial database systems deal with these data sets in an inefficient way via discrete time points or intervals. Given traffic data’s multiform and multidimensional nature, more efficient traffic data archiving is needed to add a temporal dimension to GIS-based transportation management systems.
Today, video cameras are widely used for traffic monitoring and data collection. The combination of space and time is a defining feature of digital video. However, considering the large space and expensive cost in traffic video storage, traffic video data are usually saved into video segments, scenes, shots, or frames. In addition, it is very difficult to extract key spatio-temporal data, such as individual vehicle trajectories and traffic aggregate data, from the physical storage medium. Ideally, this would be done automatically, but typically this information is obtained using manual methods. Therefore, it is difficult for current video database systems to quickly scan traffic video data and find the desired transportation-related spatio-temporal query results.
There are two main ideas for storing video data. First, once the video cameras collect discrete vehicular trajectory data (some number of frames per second), the trajectory data is interpolated into continuous traffic data. The continuous traffic data is described by some functions of a temporal parameter t and the spatial parameters. Second, the continuous traffic data is stored in spatio-temporal databases.
The transportation spatio-temporal system can optimize traffic data completeness and offer high-level spatio-temporal queries of transportation data. The design and development of the transportation spatio-temporal system consists of the following four main parts
Data completeness requires that data sources in databases should cover all information (i.e., all data types and the complete information of each data type) to meet the current and future demands of various data users. Traffic stream is observed at each spatial point within some distance interval over time, not just at one spatial point.
Existing transportation software systems store discrete traffic aggregate data, such as volume, density, headway, queue length, spacing etc., in relational databases. Aggregate data incompleteness in space and time causes the insufficient performance of traffic engineering models in transportation software systems. For example, due to the lack of volume over continuous time and space, not all travelers can gain desired travel time query information from volume-based travel time estimation models in advanced traveler information systems (ATIS).
The transportation spatio-temporal system can offer complete individual vehicle trajectory and traffic aggregate data over continuous space and time. Complete traffic data sources are useful for the description of traffic flow phenomena and for the calculation of various transportation engineering models. Such spatio-temporal system can be particularly advantageous in understanding highway flow breakdown (i.e., incident detection), and dynamical traffic congestion, because a detailed picture of traffic parameters over both time and space is better than these parameters in time alone.
Besides individual vehicular and traffic aggregate parameters, the transportation spatio-temporal system can offer other traffic data over continuous time and space. These specific traffic data involve the speed difference among moving cars at an intersection at any time, the identification of cars that drive in excess of the speed limit during any period, the number of trips during any period in a city, and so on.
Discrete traffic aggregate data archives cause not only the loss of a large amount of aggregate traffic data, but also the increase of data redundancy in databases. Aggregate data are a typical data redundancy in databases, and it means that some data are stored for multiple times. Efficient data operations require data consistency and data synchronization in databases by minimizing or avoiding data redundancy.
In relational databases traffic data redundancy often causes data anomalies, data corruption, and data retrieval errors. It is difficult for existing transportation management systems to keep data synchronization between volume values and the above four traffic parameters. The frequent operations of traffic data in databases easily cause data inconsistency or anomalies and data retrieval errors.
By using new traffic data models, spatio-temporal databases just request the collection and storage of individual vehicular time, location, and instantaneous velocity. Traffic aggregate data can be retrieved from the transportation spatio-temporal system by database query designs. Therefore, the transportation spatio-temporal system provides traffic data archiving methods that can solve the above problems concerning traffic data redundancy. In addition, traffic devices for aggregate data collection could be removed from highways.
In contrast to existing static traffic data sources, the transportation spatio-temporal system offers an adjustable dynamic transportation information environment. It means that the data collection of individual vehicular trajectory would be more important than traffic aggregate data for data collection and storage for transportation applications. The integration of highway spatial data and vehicular trajectory data create the spatio-temporal logical relationships among the entire transportation motion data.
Based on the above data integration, vehicular trajectory data not only control the accuracy of all aggregate data, but also determine data synchronous operations among traffic aggregate data. This synchronicity of adjustable traffic information is an immense advantage for the analysis and verification of dynamic traffic phenomena. In addition, the dynamic information environment provides potential opportunities for the development of dynamic or multi-dimensional transportation engineering theory and the optimization of transportation software systems.
Video cameras can easily collect traffic information, but storing the raw video data generally requires a huge storage space. More importantly, it is difficult to retrieve the values of traffic parameters from video data for the calculations of transportation engineering models or the development of transportation software, not to mention traffic data operation or adjustability. The transportation spatio-temporal system is recommended to overcome the storage problem by converting traffic videos into a spatio-temporal database.
The transportation spatio-temporal system converts traffic video data into vehicular motion information in spatio-temporal databases. The transportation spatio-temporal system interpolates the vehicular trajectory data (time, location, and velocity), which are extracted from video, and integrates them with spatial road information for the storage of dynamic transportation environments. The transportation spatio-temporal system can avoid data storage and retrieval issues caused by traffic videos. Moreover, users can manage and operate multiform and multidimensional traffic data in a spatio-temporal transportation environment.