The traction drive system of a high-speed train is the key subsystem of a high-speed train, which controls the driving and braking of the train through electromechanical coupling, and determines the running speed, power quality, and comfort of the train. The traction drive system is subjected to many internal and external excitation sources and frequency bandwidth, and the dynamic response and dynamic characteristics of the system are extremely complex. The results of the traction motor field vibration test showed that 100 Hz vibration frequency occurred during traction and braking of high-speed trains when the inverter output voltage frequency was close to 100 Hz, and its vibration amplitude was higher than other frequency bands. When traction power was cut-off, the 100 Hz frequency was not significant. Through simulation analysis of fatigue damage, it was found that 100 Hz DC-link voltage pulsation would aggravate the fatigue damage of the motor hanger. The line vibration test and bench test of the gearbox showed that there was a natural frequency of the gearbox at about 2500 Hz, when the meshing frequency was close to it. Thus, the resonance characteristic became significant.

Two-wheeled (TW) vehicle accidents are one of the major types of urban traffic accidents. TW cyclists who lack safety protection usually suffer more serious injuries and deaths in collisions. Developments in automotive active safety technologies are expected to reduce cyclist injuries and deaths, such as automatic emergency braking (AEB). To facilitate the development and testing of AEB technology, typical TW vehicle scenarios need to be constructed. Based on 400 cases of car-to-TW vehicle accident data from the National Automobile Accident In-Depth Investigation System (NAIS) database, we investigated the scenario elements that influence AEB robustness, such as weather, accident time, and road wetness. We obtained seven static scenarios using an improved clustering method, and we obtained specific speed and distance combinations in each scenario using a deduction method. Further, we compared the present findings to those of other scholars and the China New Car Assessment Program (C-NCAP). The kinematic states of the two were similar to that of C-NCAP, but the speed distribution was significantly different. The TW vehicle speed in the C-NCAP is set to 15 km/h or 20 km/h concerning the European test scenarios, but the TW vehicle speed in the present study was 10–60 km/h. Thus, the present findings recommended that subsequent C-NCAP test scenarios increase the category of motorcycles and the speed range of cars covering 20–70 km/h and consider the test conditions of bad weather and wet roads, to test the robustness of AEB.

The timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into account external factors such as weather and accidents is still a challenge. In this research, we propose a hybrid deep learning framework, double attention graph neural network BiLSTM (DAGNBL), that utilizes a graph neural network to represent spatial characteristics and bidirectional LSTM units to capture temporal dependencies between features. Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. It can be utilized as a useful tool for predicting traffic flow on certain stretches of road.

This paper proposed an eco-speed harmonization method at intersections. It is able to reduce carbon emissions by controlling partially connected and automated traffic and signal timing. It has the following features: (i) traffic emission reduction enhancement at various demand levels; (ii) traffic emission achievement while improving the mobility of entire traffic at intersections; (iii) enhanced traffic emission reduction with the help of a small portion of connected and automated vehicles; and (iv) potential implementations in the near feature. To validate the effectiveness, the proposed method is evaluated against a state-of-the-art strategy. Sensitivity analysis is conducted under various demand levels and market penetration rates (MPRs) of connected and automated vehicles (CAVs). The result shows that the proposed method outperforms and has the benefits of traffic emission reduction, throughput improvement, and stop frequency reduction. The proposed method demonstrates consistent performance across all demand levels and CAVs’ MPR. The proposed approach can achieve a reduction in emissions ranging from 4% to 61%, an average increase in throughput of around 14.91%, and a decrease in the stop frequency of at least 26%. This provides the foundation for future CAVs-based eco-approaching strategies.

The study aims to examine the impact of the built environment and weather conditions on travel time for bus passengers in Weinan, China. Various sources of data, including smart card data, bus GPS data, bus station data, road information data, and smart card swiping time, were integrated and analyzed. The study employed the light gradient boosting machine (LightGBM) model and SHapley Additive exPlanations (SHAP) value to assess the feature importance and nonlinear effects of different types of POI density, weather conditions, and time series on bus passengers’ travel time. The study findings indicate that several factors are associated with bus passengers’ travel time, including destination residential density, destination diversity, destination life service density, origin science and education density, origin residential density, origin diversity, humidity, visibility, boarding time between 7 and 8 a.m., and precipitation. This study also reveals nonlinear threshold effects. The study findings provide valuable insights that can be utilized to optimize the bus network and develop low-carbon-oriented land-use planning.

The necessity for an external control mechanism that optimizes daily urban trips becomes evident when considering numerous factors at play within a complex environment. This research introduces an activity-based travel personalization tool that incorporates 10 travel decision-making factors driven by the genetic algorithm. To evaluate the framework, a complex artificial scenario is created comprising six activities in a daily plan. Afterwards, the scenario is simulated for predefined user profiles, and the results of the simulation are compared based on the users’ characteristics. The simulations of the scenario successfully demonstrate the appropriate utilization of activity constraints and the efficient implementation of users’ spatiotemporal priorities. In comparison to the base case, significant time savings ranging from 31.2% to 70.2% are observed in the daily activity chains of the simulations. These results indicate that the magnitude of time savings in daily activity simulations depends on how users assign values to the travel decision-making parameters, reflecting the attitudinal differences among the predefined users in this study. This tool holds promise for advancing longitudinal travel behavior research, particularly in gaining a more profound understanding of travel patterns.

A novel model approach is proposed to estimate the spatiotemporal distribution of demand for free-floating carsharing. The proposed model is based on a Poisson regression model for right-censored data and estimates possibly time-varying demand rates of small subareas of a service region based on booking data with spatiotemporal information on pickups and dropoffs of cars. The approach allows operators to gain insights into the spatiotemporal distribution of demand for their service and to estimate the loss of demand due to unavailability of cars. Moreover, it can also be used as an input to improve the design of the service, through relocation techniques or to analyze the service with macrosimulation models. In addition, the approach is applied to a case study with real data.

Expressway transportation is an essential part of regional development. An efficient expressway system can enhance cities’ connectivity and coordinate long-distance trips between urban areas. Understanding how travel demand affects the flow of expressways is crucial to designing an efficient expressway management system. However, congested expressways are substantial obstacles to unimpeded expressway travel. Here, we explore the relationship between demand origin locations and congested expressways. We extract the time-varying OD demand matrix from empirical tollgate data collected in Shandong province, China. The incremental traffic assignment method is introduced to obtain the traffic flow of expressway road segments. It was found that congested expressways were generated due to only a few origin locations. In addition, expressway congestion during peak hours could be effectively alleviated by controlling the travel demand of these origin locations. Therefore, the proposed method can provide a novel perspective for expressway management.