Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver’s fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes.

Weather is an important contributing factor for bus operations. It is essential to develop a weather-responsive bus scheduling system to improve the level of bus service. Bus travel time is a significant factor to be considered while planning bus schedules. For example, if the next day’s bus travel time can be predicted according to the weather forecast and historical bus global positioning system (GPS) data, then it will be significant for adjusting bus scheduling in a timely manner. Harbin is the northernmost provincial capital of China with a population of about 4.5 million people. The modal share of bus transport is 40%, and the bus fleet includes 5,000 GPS-equipped buses. The minimum temperature in winter can reach $-38°\mathrm{C}$ . Harbin is considered as the study area for our research because of the relative dominance of public transit usage, long winters, and cold weather. In this study, the bus operation data of Harbin in the winter of 2011 and 2012 were analyzed from a statistical perspective. The travel time series of successive buses was found to be autocorrelated, i.e., the travel times of previous two buses affect the travel time of a third bus. Thus, a fitting model for the next day’s bus travel time was proposed based on the historical GPS data and cumulative snowfall level in the weather forecast. The model was evaluated by taking Bus Lines 18, 68, and 69 in Harbin as examples. The mean absolute percentage error of each bus line was less than 9%. Taking Bus Line 18 as an example, it was found that the bus travel time increased by 0.483 min if the cumulative snowfall level increased by 1.

Saturation flow rate (SFR) is a fundamental parameter to the level of service evaluation, lane capacity calculation, and signal timing plan optimization at signalized intersections. It is affected by a variety of factors including weather conditions, lane width, and the type of the driver. How to accurately estimate the SFR remains one of the most important tasks in traffic engineering. Existing studies generally rely on the field measurement method which requires a large number of people collecting data at the intersection. As a result, the method incurs a high economic cost and cannot adapt to the dynamic change of SFR. In recent years, video detectors have been widely installed at intersections which are capable of recording the time each vehicle passes the stop line, the number plate of each vehicle, and the vehicle type. This paper therefore aims to propose an automatic estimation method for the SFR based on video detector data in order to overcome the limitation of the field measurement method. A prerequisite for estimating the SFR is to recognize the saturation headway. We consider the actual vehicle headway as time series and build an auxiliary regression equation whose parameters are estimated through the ordinary least squares method. We employ the Dickey-Fuller test to verify whether the headways in the time series are saturation headways. An iterative method using quantiles is proposed to filter out abnormal data. The SFR is finally calculated using the average value of saturation headways. To demonstrate the proposed method, we conduct a case study using data from an intersection with three entrance lanes in Qujing city, Yunnan Province, China. The overall estimation process is displayed and the impacts of quantile selection and data duration on the estimation accuracy are analyzed.

Bus holding strategy is an effective way for alleviating inevitable disruptions along a bus route, including decisions on the control point locations and slack duration. However, at present, the locations of the control points and corresponding slack times are largely determined by engineering experience and therefore lack theoretical optimization. The aim of this study is to develop a model which can jointly optimize the locations of the control points and corresponding slack times under a given number of control points. As locations of the upstream control points will influence the downstream operation states, this optimization problem is modeled as a multi-stage decision process. To evaluate the stability and efficiency of a bus system, we calculate the punctuality rate and travel time at each stage by taking into account the uncertainty along the route and propose a reasonable stage-utilization cost combing these two factors. A numerical case study based on a real bus route in Harbin of China is carried out to demonstrate our approach. The impacts of the weighting factor and slack times on the optimal control point plan are also discussed.

Since bus inter-stop travel times (BISTTs) are significant components of the route travel time, providing accurate BISTTs can effectively improve the route travel time prediction accuracy. In this paper we propose a novel prediction model for BISTTs, which takes five factors as input variables: stop distance, historical inter-stop travel times, number of intersections, intersection traffic volumes, and intersection signal timing schemes. The Lagrange Multiplier (LM) test is conducted to check autocorrelation in the model residuals. Cases of two bus routes in Harbin have been studied based on field data. Mean average errors for both bus routes are smaller than 8%, indicating the high prediction accuracy in predicting BISTTs. Sensitivity analysis is conducted and results show that excluding any one of those five factors would cause the failure of the model in the LM test, indicating that the presence of all factors is required to maintain the validity of this model.