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.

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...

Renewable hydrogen is emerging as the key to a sustainable energy transition with multiple applications and uses. In the field of transport, in addition to fuel cell vehicles, it is necessary to develop an extensive network of hydrogen refueling stations (hereafter HRSs). The characteristics and properties of hydrogen make ensuring the safe operation of these facilities a crucial element for their successful deployment and implementation. This paper shows the outcomes of an analysis of hydrogen incidents and accidents considering their potential application to HRSs. For this purpose, the HIAD 2.0 was reviewed and a total of 224 events that could be repeated in any of the major industrial processes related to hydrogen refueling stations were analyzed. This analysis was carried out using a mixed methodology of quantitative and qualitative techniques, considering the following hydrogen value chain: production, storage, delivery and industrial use. The results provide general information segmented by event frequency, damage classes and failure typology. The analysis shows the main processes of the value chain allow the identification of key aspects for the safety management of refueling facilities.

In the context of global suburbanization and population aging, a low-speed, automated vehicle (LSAV) system provides essential mobility services in suburban residential areas. Although extensive studies on shared autonomous vehicle (SAV) services have been conducted, quantitative investigations on the operation of suburban LSAV systems are limited. Based on a demonstration pilot project of an autonomous vehicle called “Slocal Automated Driving”, we investigated the performance of an SAV system considering several scenarios in Kozoji Newtown, a suburban commuter town in Japan. The agent-based simulation results revealed that 40 LSAVs can satisfy the demands of 2263 daily trips with an average wait time of 15 min. However, in the case of a high-speed scenario, the same fleet size improved the level of service (LOS) by reducing the average wait time to two and a half minutes and halving the in-vehicle time. By contrast, the wait time in terms of the average and 95th percentile of the no-sharing ride scenario drastically deteriorated to an unacceptable level. Based on the fluctuations of hourly share rates, wait times, and the number of vacant vehicles, we determined that preparing for the potential fleet insufficiency periods from 7:00–13:00 and 15:00–18:00 can improve the LOS.