This study investigates the spatiotemporal characteristics and travel patterns of micro-electric vehicles (micro-EVs) by analyzing real-world trip data collected over three years from shared micro-EV services operating in three regions of South Korea. Individual trips were extracted from GPS-based trajectory data, and a network-based detour ratio was introduced to capture non-linear trip characteristics. In addition, a hierarchical clustering analysis was applied to identify heterogeneous micro-EV trip patterns. The results show that micro-EVs are predominantly used for short-distance urban trips, while a smaller but behaviorally distinct subset of trips demonstrates their capacity to support medium-distance travel under specific functional contexts. The clustering analysis identified six distinct trip pattern groups, ranging from dominant short-distance routine travel to less frequent patterns associated with adverse weather conditions and extreme detouring behavior. Overall, the findings suggest that micro-EVs function as a complementary urban mobility mode, primarily supporting localized travel while selectively accommodating extended-range and specialized trips. From a sustainability perspective, these findings highlight the role of micro-EVs as energy-efficient, low-emission alternatives to conventional passenger vehicles for short- and medium-distance urban trips. By empirically identifying heterogeneous and long-tailed micro-EV travel patterns, this study provides practical insights for sustainable urban mobility design and environmentally responsible transportation policies.

Under right-hand traffic conditions, Pedestrians significantly affect vehicle right-turn capacity, as right-turn vehicle movements typically interact with opposing pedestrian flows. Despite substantial efforts over several decades to account for this effect in right-turn capacity models, current models may lack scalability when addressing multiple possible scenarios. In this study, a general model of the effect of pedestrians on right-turn capacity is developed by theoretically deriving the unblocked time for vehicles during the green phase. The proposed model considers variables such as pedestrian volumes and signal timings, as in several existing models, but also incorporates additional factors, including: 1) the length of the conflict zone, 2) the length and width of the crosswalk, 3) pedestrian types and their respective speeds, and 4) the types of right-turning vehicle. The model is validated under realistic conditions that take into account, for instance: 1) changes in right-turn policies, 2) unbalanced pedestrian volumes by direction, 3) mixed pedestrian traffic, and 4) mixed traffic conditions, along with signal timing and geometric variables as addressed in previous research, through comparisons with a microsimulation. Additionally, comprehensive evaluations are performed by comparing field measurements. Given its ability to account for a wider range of variables, the proposed model is more adaptable to diverse contexts than existing models. Furthermore, these characteristics make the model applicable in future autonomous vehicle environments.

Delay is a critical measure when evaluating level of service under interrupted flow conditions and is widely used to assess operational improvements and optimize traffic signals. However, existing delay models often fail to capture the effects of arrival interruptions, which are prevalent at closely spaced intersections, and are typically limited to specific facility types. To address this limitation, this paper proposes a generalized analytical delay model that explicitly accounts for the effects of arrival interruptions caused by upstream signals on delays at downstream signals. The model consists of two components that maintain consistency between delay terms while reflecting both the duration and timing of arrival interruptions. The model was evaluated against the Highway Capacity Manual (HCM) method at internal approaches of a typical diamond interchange and closely spaced arterial intersections. While the HCM model produced reasonable estimates under good progression conditions, it tended to overestimate delays under poor progression. This overestimation was attributed to double-counting of the effects of arrival interruptions; the variation in arrival rates was reflected through the progression factor, while capacity was simultaneously reduced. In contrast, the proposed model avoids this type of redundancy, demonstrating improved accuracy. Results show that it provided more reliable delay estimates across both facility types and various offset scenarios, with errors ranging from −4.6 to 1.9 s compared with VISSIM simulations or field measurements. The model is expected to support more robust delay estimations and informed decision-making under diverse traffic conditions and network configurations.