The identification of activity locations in continuous GPS trajectories is an essential preliminary step in obtaining person trip data and for activity-based transportation demand forecasting. In this research, a two-step methodology for identifying activity stop locations is proposed. In the first step, an improved density-based spatial clustering of applications with noise (DBSCAN) algorithm identifies stop points and moving points; then in the second step, the support vector machines (SVMs) method distinguishes activity stops from non-activity stops among the identified stop points. A time sequence constraint and a direction change constraint are applied as improvements to DBSCAN (yielding an improved algorithm known as C-DBSCAN). Then three major features are extracted for use in the SVMs method: stop duration, mean distance to the centroid of a cluster of points at a stop location, and the shorter of distances from current location to home and to the workplace. The proposed methodology was tested using GPS data collected from mobile phones in the Nagoya area of Japan. The C-DBSCAN algorithm achieves an accuracy of 90 % in identifying stop points in the first step, while the SVMs method is 96 % accurate in distinguishing the locations of activity stops from non-activity stops in the second step. Compared to other variants of DBSCAN used to identify activity locations from GPS trajectories, this two-step method is generally superior.

The increasingly wide usage of smart infrastructure and location-aware terminals has helped increase the availability of trajectory data with rich spatiotemporal information. The development of data mining and analysis methods has allowed researchers to use these trajectory datasets to identify urban reality (e.g., citizens’ collective behavior) in order to solve urban problems in transportation, environment, public security, etc. However, existing studies in this field have been relatively isolated, and an integrated and comprehensive review is lacking the problems that have been tackled, methods that have been tested, and services that have been generated from existing research. In this paper, we first discuss the relationships among the prevailing trajectory mining methods and then, classify the applications of trajectory data into three major groups: social dynamics, traffic dynamics, and operational dynamics. Finally, we briefly discuss the services that can be developed from studies in this field. Practical implications are also delivered for participants in trajectory data mining. With a focus on relevance and association, our review is aimed at inspiring researchers to identify gaps among tested methods and guiding data analysts and planners to select the most suitable methods for specific problems.

Comprehensive stated choice (SC) experiments were conducted in Jakarta, Indonesia, and Nagoya, Japan, where proposals for congestion charges have been introduced and remain under consideration as a way to reduce acute car dependence, particularly in Jakarta. Causal paths among psychological determinants and their strengths are measured and analyzed along with proposal acceptability from a cross-country perspective based on the similar context of the SC experiments. The findings from analysis with a multiple-sample, multiple-indicator, multiple-cause (MS-MIMIC) model show that a number of psychological determinants provide an explanation for the acceptability of the proposed scheme in both cities. Psychological motivations, including awareness of the city’s environment and awareness of the problem of cars in society, appear to be the most important direct determinants leading to recognition of the effects of a congestion charging scheme and they are indirect determinants of policy acceptance in bot...

Most of the recent automated driving systems assume the accurate functioning of localization. Unanticipated errors cause localization failures and result in failures in automated driving. An exact localization failure detection is necessary to ensure safety in automated driving; however, detection of the localization failures is challenging because sensor measurement is assumed to be independent of each other in the localization process. Owing to the assumption, the entire relation of the sensor measurement is ignored. Consequently, it is difficult to recognize the misalignment between the sensor measurement and the map when partial sensor measurement overlaps with the map. This paper proposes a method for the detection of localization failures using Markov random fields with fully connected latent variables. The full connection enables to take the entire relation into account and contributes to the exact misalignment recognition. Additionally, this paper presents localization failure probability calculation and efficient distance field representation methods. We evaluate the proposed method using two types of datasets. The first dataset is the SemanticKITTI dataset, whereby four methods are compared with the proposed method. The comparison results reveal that the proposed method achieves the most accurate failure detection. The second dataset is created based on log data acquired from the demonstrations that we conducted in Japanese public roads. The dataset includes several localization failure scenes. We apply the failure detection methods to the dataset and confirm that the proposed method achieves exact and immediate failure detection.

This study examines the benefits of considering topography in the implementation of public transport systems and improving mobility in a hillside district in Yokohama, Japan. It studies the relationship between the improvement and the actual use of the service. A multinomial logit model that incorporates topography is developed to describe mode choice. Based on this model, utility-based accessibilities, which include topographical impact, are calculated, and the improvement resulting from the new service is analysed. The correlation between the improvement and the user’s log is also examined to verify their relationship and the impact of topographical factors, which are compared with and without the new service. The mode choice model revealed that topography had a negative impact on walking and bus egress trips, with a 1-m increase in topography corresponded to a 9.54-m walk. The utility-based accessibility approach accurately illustrated the impact of topography. The improvement of accessibility positively correlated with service use, which is further enhanced when considering the topography. These results demonstrate the impact of implementing the new service, highlight the importance of considering topography in hillside cities, and underscore the significance of the utility-based accessibility approach as a relevant methodology.