Fatigue driving is a leading cause of traffic fatalities and injuries in China, especially among heavy truck drivers. The present study tried to examine which and how factors within the human-vehicle-roadway-environment system contribute to the occurrence of crashes involving fatigued truck drivers. To reduce such risk on the road, a total of 9168 crashes which occurred in Jiangxi and Shaanxi between 2003 and 2014 were selected to measure the effects of potential factors on fatigue related truck crashes using a case control study. Pearson Chi-square test was used to determine the relationship between crash risk and independent factors, and a stepwise logistic regression model was developed to determine the significant risk factors. According to the data analysis results, driver’s gender, age, driving experience, and overspeeding behavior, vehicle’s commercial status, overloading conditions and brake performance, road’s type, slippery pavement and existence of sharp curve and long steep grade, and time of day, season, weather and visibility conditions, etc. were identified to be significantly associated with fatigue related truck crashes on Jiangxi and Shaanxi highways. Moreover, it is found that (a) in Jiangxi, an employed truck driver has a higher risk of crash involving multi-vehicles or a passenger car at bridge locations, and (b) in Shaanxi, the adult, tunnel location, summer and winter days prohibit statistically significant association with the occurrence of multi-vehicle and single-vehicle run-off-road/rollover crashes. Young employed male truck drivers with less experience are at high risk, especially while driving across sharp curves, down long steep grades, over bridge or through tunnels, during the midnight period, on rainy, snowy or foggy days in rural areas. All these help recommend potential policy initiatives as well as effective safety promotion strategies at the public health scale for professional truck drivers.

Transfer stations are important nodes in the metro network, and it is of great significance to study the coordinated organization scheme between passenger demand and facility configuration, in order to improve the transportation efficiency. Taking the Dongfangzhimen station of the Suzhou Metro as the research object, this paper starts by analyzing the configuration of service-oriented facilities, and then dissects the spatiotemporal distribution characteristics and individual behavior characteristics of passengers via data processing, where a meticulous analysis has been carried out based on the automatic fare collection data and the field investigation data. On this basis, we have discussed the performance and bottlenecks under the current facility configuration and flow organization scheme, constructed the simulation scene of a representative peak hour in holidays using AnyLogic, and put forward a volume-based path organization scheme considering the coordination with flow demand, as well as a plan of multi-stage crowd management. As evidenced by the results, the improved scheme exhibits a higher adaptability to passenger flow and a greater balance among facility utilization, where the maximum queue length, the average time consumption and the average standing density have been reduced by 31%, 14% and 6%, respectively. The proposed methods of data analysis, flow organization and strategy decision are reliable and applicable to the management of metro transfer stations.

Faced with the contradiction between the increasing traffic volume and the aging road infrastructures in the rural area, this paper aims to propose feasible strategies to enhance the level of service and safety, by a case study of the rural area in the north Jintan district. In order to figure out current issues related to rural roads, a carefully designed investigation has been conducted, and the results of the two-week investigation include roads’ basic information, traffic signs and protective facilities, surrounding landscape, and etc. Based on the field driving tests, specific problems including signs category, signs installation and facility maintenance have been fully analyzed. Meanwhile, the problem of roadnet connectivity has also been pointed out through the theory of complex network, and results show that the average node clustering coefficient and shortest path length perform worse than the demonstration plot of other rural districts. For the sake of rural traffic safety and management efficiency, both quantified and qualified strategies have been put forward. The quantified strategies include the regular inspection indicators, the safety sight distance at T-type crossings, as well as the risk severity of sections and the crossings. The qualified strategies involve the management of trucks and roadworks, the setting of signalized intersections, and the timely updates of traffic signs and facilities. Finally, an intelligent management system framework has been established for rural road traffic, with highly interconnected modules of data acquisition, risk identification and information publishing.

Public transit has been continuously developing because of advocacy for low-carbon living, and concerns about its safety have gained prominence. The various factors that constitute the bus operating environment are extremely complex. Although existing research on operational security is crucial, previous studies often fail to fully represent this complexity. In this study, a novel method was proposed to identify the risk factors for bus operations based on a Bayesian network. Our research was based on monitoring data from the public transit system. First, the Tabu Search algorithm was applied to identify the optimal structure of the Bayesian network with the Bayesian Information Criterion. Second, the network parameters were calculated using bus monitoring data based on Bayesian Parameter Estimation. Finally, reasoning was conducted through prediction and diagnosis in the network. Additionally, the most probable explanation of bus operation spatial risk was identified. The results indicated that factors such as speed, traffic volume, isolation measures, intersections, bus stops, and lanes had a significant effect on the spatial risk of bus operation. In conclusion, the study findings can help avert dangers and support decision-making for the operation and management of public transit in metropolitan areas to enhance daily public transit safety.

This paper presents an approach to estimate the delay propagation in a high-speed railway (HSR) network under accident conditions. Considering the complexity and dynamics of HSR accidents, different levels of delay are discussed using clustering analysis, and the delay propagation is analyzed both forward and backward using different mathematical formulations. The forward delay propagation is described by a fitting distribution between arrival delay and forward resistance of accidents at different levels. The backward-delay-propagating mechanism is exploited by a combined model of horizontal propagation and vertical propagation under corresponding constraints of minimum headway and emergency measures. To integrate the forward- and the backward-propagating models, a global algorithmic framework is then designed with special consideration of routes separation. Finally, an applied case analysis of regional rail network in the Yangtze River Delta verifies the feasibility of the delay propagation algorithm. Through a validation test to accidents with detailed records, results suggest that the proposed algorithm can conservatively estimate the delay distribution with an accuracy greater than 75%.

The abnormal braking of wagons is a challenging safety problem for operators at railway marshalling yard. This paper develops an acoustic-based technology to detect the unreleased braking of wagons during the uncoupling operation. Experiments have been conducted to collect the acoustic waves of wagons abnormal braking, as well as the background sounds like train whistling and wheel vibration. Before data collection, a wayside recording system and an experimental train composed of 5 different wagons have been prepared in the marshalling yard. The recognition algorithm consists of the fast Fourier transform (FFT), feature extraction, template matching and support vector machine (SVM) classification. Based on the sample data of different acoustic waves, the FFT is firstly performed to obtain the frequency spectrum from original time-domain signals. Then the major spectrum features of different sounds are carefully extracted for SVM training through a newly-devised algorithm, where the features include the spectrum center, spectrum flux, energy peak and corresponding frequency. During the SVM training, classifiers are designed under one-against-one strategy to guarantee the recognition accuracy. Given a test data, at most 3 SVM classifiers will be activated according to the decision matrix of template matching. Meanwhile, rules have been made to regulate the classification result considering different activation cases. Finally, a case study of all 12 sound categories has been performed to illustrate the application of proposed algorithm. Results show that the acoustic-based recognition algorithm is indeed reliable to identify wagons unreleased braking, with the global warning accuracy over 98%.