Search Results (6)

Passengers often struggle to identify intended pickup locations when autonomous taxis (ATs) arrive, leading to confusion and delays. While prior external human–machine interface (eHMI) studies have focused on pedestrian crossings, few have systematically compared feedback modes and display positions for AT pickup guidance at varying distances. This study investigates the effectiveness of three eHMI feedback modes (Eye, Arrow, and Number) displayed at two positions (Body and Top) for communicating AT pickup locations. Through a controlled virtual reality experiment, we examined how these design variations impact user performance across key metrics including selection time, error rates, and decision confidence across varied parking distances. The results revealed distinct advantages for each feedback mode: Number feedback provided the fastest response times, particularly when displayed at the top position; Arrow feedback facilitated more confident decisions with lower error rates in close-range scenarios; and Eye feedback demonstrated superior performance in distant conditions by preventing severe identification errors. Body position displays consistently outperformed top-mounted ones, improving users’ understanding of the vehicle’s intended actions. These findings highlight the importance of context-aware eHMI systems that dynamically adapt to interaction distances and operational requirements. Based on our evidence, we propose practical design strategies for implementing these feedback modes in real-world AT services to optimize both system efficiency and user experience in urban mobility environments. Future work should address user learning challenges and validate these findings across diverse environmental conditions and implementation frameworks. Full article

Integrated arrival–departure and surface scheduling (IADS) is a critical research task in next-generation air traffic management that aims to harmonize the complex and interrelated processes of airspace and airport operations in the Metroplex. This paper investigates the microscopic-level collaborative optimization framework for IADS operations, i.e., the problem of coordinating aircraft scheduling on runways and taxiways. It also describes the mixed-integer linear programming (MILP) bi-layer decision support for solving this problem. In runway scheduling, a combined arrival–departure scheduling method is introduced based on our previous research, which can identify the optimal sequence of arrival and departure streams to minimize runway delays. For taxiway scheduling, the Multi-Route Airport Surface Scheduling Method (MASM) is proposed, aiming to determine the routes and taxi metering for each aircraft while minimizing the gap compared with the runway scheduling solution. Furthermore, this paper develops a feedback mechanism to further close the runway and taxiway schedule deviation. To demonstrate the universality and validity of the proposed bi-layer decision support method, two hub airports, Chengdu Shuangliu International Airport (ICAO code: ZUUU) and Chengdu Tianfu International Airport (ICAO code: ZUTF), within the Cheng-Yu Metroplex, were selected for validation. The obtained results show that the proposed method could achieve closed-loop decision making for runway scheduling and taxiway scheduling and reduce runway delay and taxi time. The key anticipated mechanisms of benefits from this research include improving the efficiency and predictability of operations on the airport surface and maintaining situational awareness and coordination between the stand and the tower. Full article

University bus services provide fixed-route public transport for students to and from large universities. They are relatively affordable and resource-efficient, but must compete against private cars and taxis which tend to be more convenient and flexible than University bus services. Many students, particularly those with lower incomes, depend on them. University administrators must understand how to improve University bus service in order to treat this group fairly. The main contribution of this paper is to examine how University bus service can help achieve social equity and sustainability goals. It investigates this by reporting the results of a survey of 303 students at Shahid Bahonar University of Kerman in Iran concerning their travel demands and University bus service utilization. Grounded theory and structural equation modeling is used to analyze the daily commute preferences of university students influenced by their characteristics, fleet condition, fares, station condition, and time factors. This study identifies various steps that the university administration can take to make University bus service more efficient and attractive, such as updating the bus fleet, improving stations, increasing security, and improving pedestrian access to stations. Survey respondents reported the highest satisfaction levels with fare levels, the security in buses, and waiting times. Women reported feeling discriminated against in their daily commutes. Poor-quality University bus service contributes to a negative feedback loop that increases automobile use and reduces university travel sustainability. Full article

The aircraft anti-skid braking system (AABS) plays an important role in aircraft taking off, taxiing, and safe landing. In addition to the disturbances from the complex runway environment, potential component faults, such as actuators faults, can also reduce the safety and reliability of AABS. To meet the increasing performance requirements of AABS under fault and disturbance conditions, a novel reconfiguration controller based on linear active disturbance rejection control combined with deep reinforcement learning was proposed in this paper. The proposed controller treated component faults, external perturbations, and measurement noise as the total disturbances. The twin delayed deep deterministic policy gradient algorithm (TD3) was introduced to realize the parameter self-adjustments of both the extended state observer and the state error feedback law. The action space, state space, reward function, and network structure for the algorithm training were properly designed, so that the total disturbances could be estimated and compensated for more accurately. The simulation results validated the environmental adaptability and robustness of the proposed reconfiguration controller. Full article

This paper took a new energy vehicle feedback system as the research object, aiming to study the energy recovery law of the new energy vehicle under braking feedback and taxiing feedback conditions. Firstly, the braking energy feedback control strategy and different forms of taxiing energy feedback were studied. Then the integration and application of braking energy recovery system were carried out on a pure electric bus and a hybrid electric bus, with each vehicle model corresponding to different integration and test schemes, which provided a guarantee for the relevant test of real vehicle environments. Finally, relevant vehicle experiments were carried out to test the impact of superposition and coordination strategies on the contribution rate of braking energy recovery under a typical Chinese city bus circle and compared the difference in vehicle energy consumption with and without taxi feedback strategy. The test results showed that the coordinated braking energy recovery control strategy can make more effective use of the maximum torque that can be fed back by the motor, and the fuel consumption of the taxiing feedback mode was lower than that of the no taxiing feedback mode under different driving conditions. Full article

The aircraft anti-skid braking system (AABS) is an essential aero electromechanical system to ensure safe take-off, landing, and taxiing of aircraft. In addition to the strong nonlinearity, strong coupling, and time-varying parameters in aircraft dynamics, the faults of actuators, sensors, and other components can also seriously affect the safety and reliability of AABS. In this paper, a reconfiguration controller-based adaptive fuzzy active-disturbance rejection control (AFADRC) is proposed for AABS to meet increased performance demands in fault-perturbed conditions as well as those concerning reliability and safety requirements. The developed controller takes component faults, external disturbance, and measurement noise as the total perturbations, which are estimated by an adaptive extended state observer (AESO). The nonlinear state error feedback (NLSEF) combined with fuzzy logic can compensate for the adverse effects and ensure that the faulty AABS maintains acceptable performance. Numerical simulations are carried out in different runway environments. The results validate the robustness and reconfiguration control capability of the proposed method, which improves AABS safety as well as braking efficiency. Full article