# Querying and Extracting Timeline Information from Road Traffic Sensor Data
## 2 Related Work
### Traffic Management and Query System  
As vehicular ad hoc networks (VANETs) have evolved, we can collect huge volumes of traffic information from On-Board Units (OBU) and various sensors of connected vehicles. A peer-to-peer (P2P)-based vehicular network technique [30], a mobile device-to-device video distribution scheme [31], and routing protocols in the Internet of Vehicles [32] help to collect multimedia traffic data such as images of license plates and videos of traffic congestions. In addition, the efficient distributed query processing platform [33] enables users to directly interact with the wireless network of traffic sensors. However, in this work, we focus on processing traffic sensor data which are collected and provided from current intelligent transportation systems. Because of the accelerated growth of smart-phone technology, crowd-sourced data collection is now viable and can be implemented for traffic data processing domains. Google Traffic [7], Daum Map [4], Naver Map [5], and Here Map [6] use individual users' locations to track real-time traffic congestion. [PERSON] et al. [9] computed the degree of traffic congestion based on traffic information collected from SNS (social networking service) messages. Although there is an analysis process for obtaining real-time traffic information, these approaches do not provide deep insight into traffic behaviors. These systems also lack the capabilities to answer user queries (including timeline queries) about the analyzed information. Most traffic data management systems use raw traffic data collected from intelligent transportation systems [34] which integrate information, sensor, control, and communication technologies [35]. According to the study of [PERSON] et al. [36], traffic management systems use ten types of sensing technologies. One of the technologies is an inductive loop sensing device, which has contributed to this work by providing freeway traffic data for Seattle [18]. DSRC (Dedicated Short Range Communications) has been used to obtain speed information from the Busan ITS center [17]. DSRC is a wireless communication technology developed for vehicular communication. To effectively manage massive amounts of traffic data from sensors, one can use spatio-temporal query systems that have novel index structures developed by the database community [37; 38; 39; 40]. Among these index structures, a timeline index is the most suitable index for managing temporal data [40]. This index structure is designed to support queries on temporal events. However, the structure should be extended to support spatial-temporal characteristics of road traffic information. The most similar work to our QET system is a TransDec [41] system, which is a spatio-temporal query processing framework for transportation systems. TransDec offers a framework that enablesreal-time visualization, querying, and analysis of dynamic transportation. TransDec uses a Google Maps-based web application as a graphical user interface which allows users to formulate queries and provides query results. TransDec supports four types of queries: (1) monitoring queries on streaming data; (2) analysis and mining queries on historical data; (3) route planning queries; and (4) location-based queries. A monitoring query in TransDec is a continuous query which is issued once and is logically run continuously over the input streaming traffic sensor data. This query can report the speed information from highway sensor data in the map interface. To process analysis and mining queries on historical data, TransDec utilizes an online analytical query processing (OLAP) solution or wavelet-based techniques, depending on the size of datasets. The results are displayed as two-dimensional graphs on certain locations. A novel time-dependent route planning query is computed by using a time-dependent edge cost model for the networks. TransDec calculates different fastest paths from a source to a destination, depending on the time of the day. The result is displayed as a route in the Google Map-based interface.