PURPOSES : We propose a framework to evaluate the reliability of integrating homogeneous or heterogeneous mobility data to produce the various data required for greenhouse gas emission estimation. METHODS : The mobility data used in the framework were collected at a fixed time from a specific point and were based on raster data. In general, the traffic volume for all traffic measurement points over 24 h can be considered raster data. In the future, the proposed framework can be applied to specific road points or road sections, depending on the presence or absence of raster data. RESULTS : The activity data required to calculate greenhouse gas emissions were derived from the mobility data analysis. With recent developments in information, communication, and artificial intelligence technologies, mobility data collected from different sources with the same collection purpose can be integrated to increase the reliability and accuracy of previously unknown or inaccurate information. CONCLUSIONS : This study will help assess the reliability of mobility data fusion as it is collected on the road, and will ultimately lead to more accurate estimates of greenhouse gas emissions.

PURPOSES : This study aims to suggest how to utilize "standby data" of shared mobility that does not contain personal information and examine whether "standby data" can derive existing shared mobility operation analysis items similarly. METHODS : An existing Personal Mobility (PM) traffic pattern analysis was performed by identifying the user (User ID) and the user's route in a time frame. In this study, the PM traffic pattern analysis focuses on a vehicle (ID of the standby vehicle) and its standby location. We examined whether the items derived from the User ID-based traffic pattern analysis could also be derived from the standby Vehicle ID-based analysis. RESULTS : The analysis showed that all five items (traffic volume by time slot, peak time, average travel time, average travel distance, and average travel speed) of the existing User ID-based PM travel analysis result could be derived similarly using the standby Vehicle ID-based PM traffic analysis. However, the disadvantage is that the average driving distance is calculated as a straight-line distance. It seems possible to overcome this limitation by correcting the average driving distance through linkage analysis with road network data. However, it is not possible to derive the instantaneous maximum speed or acceleration/deceleration. CONCLUSIONS : In an era in which various means of transportation are being introduced, data sharing is not preferred because of legal issues.Consequently, it is difficult to understand the use of new means of transportation and formulate new policies. To address this, data sharing can be active based on standby data that is not related to personal information.

PURPOSES : This study proposes an index for analyzing mobility based on smartcard and taxi data to evaluate imbalances in public transit. METHODS : The proposed mobility index is calculated based on the difference between the mobility indexes of public transit and taxis using the variables of the in-vehicle time, waiting time, and driving ratio. For a more detailed analysis, the distances are divided into short distances, medium distances, and long distances. RESULTS : Public transit mobility indexes are generally evenly distributed, but the taxi mobility indexes are located in the largest legend. When comparing the respective mobilities of public transit and taxis, many areas with a high mobility of taxis (similar to the distribution) exist, especially in the outskirts such as Dobong-gu, Nowon-gu, Gangdong-gu, Guro-gu, Geumcheon-gu, and Eunpyeong-gu. On average, the mobility of public transit according to the distance is smaller in a short distance and higher in a long distance. CONCLUSIONS : The results demonstrate the use of the proposed index for analyzing the basic statuses of complementary indexes for evaluating public transit imbalances. In the future, more detailed results (including socioeconomic variables corresponding to the grid areas) should be studied to identify the impacts of the mobility index.

PURPOSES : Transportation researchers have contributed to try to both understand and accumulate scientifically how individuals move across time and space. Currently, data generated from household travel diary surveys have been used as the basis of travel behavior (or human mobility patterns) analysis in transportation planning and have been buoyed in a sustainability for decades. Against this backdrop, this study introduces mobile phone signaling big data that can convey more detailed human mobility information across time and space and supplement travel behavior analysis. METHODS: We propose a usable and useful dataset driven from the mobile phone signaling data for better travel behavior analysis. Several data-driven approaches have been tried to estimated and calculate variables inserted into the dataset. RESULTS: In the result, this paper discussed the promise of mobile phone based dataset to applications for inferring human mobility patterns applied to directly transport policy. CONCLUSIONS: It is our hope that this study will stimulate future studies on transportation planning and mobility analysis using mobile phone signaling data in Korea.