To better guide the sustainable developing of freight transport aligning with environmental objectives it is of strategic importance to capture freight transportation characteristics more realistically. This paper characterizes freight transportation by using a complex network approach from multidimensional perspectives based on freight vehicle trips data. We first build two subnetworks from prefecture-level city-scale and county-level city-scale. Subsequently, network analysis indices based on complex network theory were applied to examine the topological structure and complexity of the freight transportation networks. Furthermore, the community detection method is introduced to reveal the networks’ clustering characteristics. The findings show that the prefecture-level city-scale network and the county-level city-scale network both have obvious small-world network characteristics, but the prefecture-level city-scale network has higher operating efficiency for goods movement. Additionally, the influence of the cross-border effect on the freight transportation network was verified. In terms of the community structure, the freight network shows distinct clustering features only at the county-level city-scale.

Understanding the factors that contribute to traffic crashes can help provide a fundamental basis to plan and develop appropriate countermeasures for road safety issues emerging in particular on two-lane rural roads. However, most of the studies have focused on urban roadways and freeway systems, and few studies have investigated the issue of heterogeneity on two-lane rural roads. The purpose of this study is to uncover the risk factors influencing crash severity on two-lane rural roads in China. A sample of 1490 traffic crashes occurring on two-lane rural roads between 2012 and 2017 was collected from the Mouding County Highway Bureau in Yunnan, China. A random-parameter ordered probit model was estimated using these data to capture underlying unobserved characteristics in personal traits, vehicle attributes, roadway conditions, environmental factors, and crash attribute. To better understand the effect of critical factors on crash severity outcome probability, an elasticity analysis was then introduced. The results show that six factors such as driver’s attribution, illegal driving behaviour, access segment, day of week, vehicle type, and crash form have a significant impact on the injury severity, and the impacts of driving behaviours, access segment, and vehicle-fixed object crashes had significant variation across observations. Besides, the correlations between critical factors and the probability of serious injury sustained in traffic crashes are identified and discussed. The local driver indicator has more positive impact on the crash severity than nonlocal driver, and nonaccess segment appears a higher probability of serious or vicious collisions. It is worth mentioning that motorcycle-involved crashes do show an obvious correlation with crash injury severity. As for crash forms, vehicle-vehicle crashes are more likely to lead to severe crash injury. Besides, high-risk driving behaviour (e.g., fatigue driving, speeding, and converse driving), weekends, and holidays are found to have significant contribution to increasing the probability of traffic crash injuries and fatalities on two-lane rural roads.

A mountainous road network with special geological and meteorological characteristics is extremely vulnerable to nonrecurring accidents, such as traffic crashes and geohazard breakdowns. Geohazard accidents significantly impact the operation of the road network. Timely and accurate prediction of how long geohazard accidents will last is of significant importance to regional traffic safety management and control schemes. However, none of the existing studies focus on the topic of predicting geohazard accident duration on regional large-scale road networks. To fill this gap, this paper proposes an approach integrated with the Kaplan–Meier (K-M) model and random survival forest (RSF) model for geohazard accident duration prediction based on text data collected from mountainous road networks in Yunnan, China. The results indicate that geohazard accidents in road networks have a strong aggregation in tectonically active, steep mountainous, and fragmented areas. Especially the time of the rainy season, and the morning peak, brings high incident occurrences. In addition, accident type, secondary accidents, impounded vehicles or personnel, morning rush hour, closed roads, and accident management level significantly affect the duration of road geohazards. The RSF model was 0.756 and 0.867 in terms of the C-index and the average area under the curve, respectively, outperforming the traditional hazard model (Cox proportional hazards regression) and other survival machine learning models (survival support vector machine). Without censored data, the mean absolute error and mean squared error of the RSF model were 11.32 and 346.99, respectively, which were higher than the machine learning models (random forest and extreme gradient boosting model).

Urban arterial travel times are inherently uncertain due to the volatile traffic flows, signal controls, bus stops and roadside parking, etc. An effective way of characterizing such uncertainty is the estimation of travel time distribution (TTD), which provides some features of distributions to assist with looking at travel time variability. However, a common method both in literature and practice prefer to estimate average arterial travel times rather than TTDs, even when multistate travel times and link correlations may exist. In this context, a pair-copula construction approach is proposed to estimate the probability distribution of urban arterial travel times from link TTDs. First, link correlations are investigated and empirical evidence reveals that adjacent link travel times present multiple relationships. Then, in each pair-copula associated with two connected links, a copula mixture model is developed for capturing multiple relationships between link travel times. Finally, the arterial TTD is derived as the product of the pair-copulas and marginal probability distributions of link travel times. The proposed approach is validated with Radio Frequency Identification Data collected from an urban arterial including four links in Nanjing, China. The results indicate that the approach can dynamically capture the positively correlated, negatively correlated, and uncorrelated relationships between link travel times. The considered competing approaches are also used to benchmark the proposed approach, and comparison results confirm the effectiveness and accuracy of the approach, especially when multistate distributions, complex correlations and limited data sample are encountered.

Coordinated development between landside transport at civil airports and aviation networks is key for determining the attractiveness and competition of tourist cities. However, only a few studies have focused on the accessibility of tourist locations around civil airports in tourist cities in China. This paper calculates the service coverage of civil airports for tourist locations in Yunnan in selected years, consisting of 2000, 2005, 2010, and 2015, by using an accessibility calculation method with the shortest path and a spatial analysis method in ArcGIS software. The results are compared with the structural characteristics of the aviation network in corresponding years. Additionally, some suggestions are given regarding transportation development and sustainable environmental development in tourist cities. The findings show that the service coverage of civil airports in Yunnan has gradually improved over time. Specifically, 83.41% of tourist locations can be reached within 1.5 h of driving from an airport, and all tourist destinations could be reached within 3 h in 2015. Among all civil airports, the airports in the hub city of Kunming and at world-renowned tourist destinations such as Dali and Lijiang displayed the highest airport service capabilities for tourist locations. Meanwhile, the aviation network of Yunnan Province is constantly improving with an increased number of airports and airlines, and it shows the centralization trend toward KMG. However, the mismatch is observed not only in tourist cities with hub airports, such as Kunming, but also in some cities located in remote areas (i.e., far from the central city). This finding reveals that in these tourist cities in Yunnan, the development of airport transport has not considered coordination between the airline network and the service coverage of civil airports for tourist locations. For the sustainable development of tourist cities, the equal importance of airport landside transport and the airline network in the planning and management of air transport should be emphasized.