# A Study on Analyzing Travel Characteristics of Micro Electric Vehicles by Using GPS Data
## 2 Literature Review
### Research on Travel Data Analysis Using GPS Data  
Extraction of trip information and trip chain characteristics from GPS data requires a comprehensive examination of the GPS and spatiotemporal and land use data collected from the user's trip. The chief characteristics of trips are the trip duration, length, mode, number of stops, and waiting time. With the help of this information, the trip purpose and trip chain data can be estimated. To gain deeper insights into commuters' travel behavior, researchers often follow a trip chaining conceptual model; however, there are only limited studies available on deriving trip chain data [15]. To analyze users' travel behavior, crucial data such as the mode and purpose of travel, which is not explicitly obtained from the GPS data, must be determined. Hence, most of the existing literature proposes a framework or algorithm to extract the required information. [PERSON]'s research team proposed a heuristic set of rules for determining the purpose of travel using GPS data based on the average, maximum, and minimum speed, along with the spatial characteristics of the trips. Information such as location, time, speed of movement, and direction of travel are collected every second to ensure result accuracy. The authors considered a trip as having ended when there was a stop lasting 120 s or more. Considering this, along with the location information, OD matrices were formed [16]. [PERSON] and their research team conducted a study to observe the trip characteristics and usage patterns of round-trip carsharing based on GPS data [17]. [PERSON] and their co-researchers established personalized service recommendations for users. Existing services had not considered diverse users' demands for more and better travel, so the researchers developed a personalized service-trajectory correlation model to recommend the most appropriate services to users. GPS data and traffic information system data were used in this research to identify the points that users visited using dwell time at each point, and clustering radii were used to classify points by each spot [18]. [PERSON] and their co-workers conducted research about point of interest (POI) recommendations. They proposed a new methodology for POI recommendation. The models' performances were evaluated through experiments, and the proposed model was more flexible in incorporating contextual factors than other state-of-the-art methods [19]. [PERSON] and co-researchers presented a methodology to discover points of interest that are not expected by analysts. The researchers used a method considering background geographic information to overcome the drawback of existing methods. The method used in this paper was an alternative to the algorithm Stops and Moves of Trajectories (SMoT). SMoT searches for intersections in trajectories and finds geographic objects or facilities related to the intersections. However, the method in this paper is a speed-based spatiotemporal clustering approach, so interesting points or places including unknown facilities can be detected, unlike in previous methodologies [20]. [PERSON] and their co-workers developed an improved DBSCAN algorithm to extract stop locations from trajectory data. There are many methodologies to analyze trajectories and POI, and the DBSCAN algorithm was used frequently. Extracting stops from trajectory data is called'stop detection,' and they added a'move ability' to the existing DBSCAN algorithm. The performance of stop detection improved when using the upgraded DBSCAN algorithm [21]. [PERSON] and their colleagues examined the advancements in developing systems for collecting travel behavior data using GPS, PHS, and GIS technologies. The authors further evaluated the effectiveness of the data gathered by these systems and their applicability to travel behavior surveys. The study presented an extensive overview of previous studies that have utilized GPS and PHS technologies to collect data on travel behavior.