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.

The inconsistencies of passenger flow volume between stations’ peak hours and cities’ peak hours have emerged as a phenomenon in various cities worldwide. Passenger flow forecasting at planning stages can only predict passenger flow volume in city peak hours and for the whole day. For some stations, the highest flow does not occur in the city peak hours, and station scale design is often too small. This study locates the formation mechanism of station peak in which the temporal distribution of the station is the superposition of different temporal distributions of the purpose determined by land-use attributes. Data from 63 stations in Xi’an, China, were then used to present an enlargement coefficient which can change the boarding and alighting volume in city peak hours to a station’s own peak hours. This was done by analyzing the inconsistencies of passenger flow volume between the station’s peak hours and the city’s peak hours. Morning peak deviation coefficient (PDC) and evening PDC were selected as datasets, and stations were classified accordingly. Statistics of land usage for every type of station showed that when the stations were surrounded by developed land, the relationship between the PDC and the commuter travel land proportion was to some extent orderly. More than 90.00% of stations with a proportion of commuter travel land that was more than 0.50 had PDCs under 1.10. All stations with a proportion of commuter travel land that was less than 0.50 had morning PDCs over 1.10. Finally, data from 52 stations in Chongqing, China were used to verify the findings, with the results in Chongqing predominantly corresponding to those in Xi’an.

The ridership of a metro station during a city’s peak hour is not always the same as that during the station’s own peak hour. To investigate this inconsistency, this study introduces the peak deviation coefficient to describe this phenomenon. Data from 88 metro stations in Xi’an, China, are used to analyze the peak deviation coefficient based on the geographically weighted regression model. The results demonstrate that when the land around a metro station is mainly land for work, primary and middle schools, and residences, its station’s peak hour is consistent with the city’s peak hour. Additionally, the station’s peak hour is more likely to deviate from the city’s peak hour for suburban stations. There are two ridership options when designing stations, namely the extra peak hour ridership during a city’s peak hour and that during a station’s peak hour, and the larger of the two is used to design metro stations. The mixed land use ratio must be considered in urban land use planning, because although non-commuting land can mitigate the traffic pressure of a city’s peak hour, it may cause the deviation of the station’s peak hours from that of the city.