A two-step identification method of secondary crashes (SC) is developed in this study, and the effects of traffic variables on the SC risks on freeways are captured. Crash related data were obtained from the I-880 freeway in California, United States. Combined with the speed contour map, SC were identified by the real shock wave speed index (RSWSI) and SC identification index (SCII). The random effect logit (REL) regression was applied to analyze the significantly contributing factors and their mechanism on SC. The results showed that the significantly contributing factors are different between the initial SC model and SC models (Threshold=20mile/h, 15mile/h, 10mile/h, 5mile/h), except for the variable of standard deviation of 30s vehicle speed during 5-10 minutes. In the SC models, the number of significant contributing factors increases with the increase of threshold value. The results of elasticity analysis showed that the elasticity values of the hit object crash, wet surface road, the average 30s vehicle speed during 5-10 minutes and the average 30s vehicle count during 5-10 minutes are greater than 10%.

Overtaking behavior between non-motorized vehicles is one of the main characteristics of the cycling path, and unsafe overtaking behavior has a certain negative impact on riders’ safety. However, little is known about the factors affecting riders’ overtaking decisions. This study aimed to identify the influence of road facilities, types of non-motorized vehicles, and human factors on the characteristics of overtaking behavior on bicycle lanes. DJI drone-based naturalistic riding research was explored in China and a random parameter logit regression model was estimated to model the overtaking decisions of non-motorized vehicle riders. The results showed that gender, age, professional deliverer, type of lead non-motor vehicle, type of non-motorized vehicles, and width of cycling lane influence overtaking behavior significantly. The present study provides theoretical evidence to strengthen the safety design and evaluation of cycling lane infrastructure.

A deep understanding of the underlying mechanisms of the built environment on commuting behavior along with rail transit is considered of great importance for both TOD land use and formulation of transport policies. The impact of the built environmental factors on commuting behavior has been already explored in the literature. However, the main focus has been laid on the separate effects of each factor and the interactions among these factors have been neglected. Along these lines, taking Hefei, China as the case, this work filled this gap by employing a social ecological model to systematically investigate the interactive effects of the built environment and urban rail transit on commuting behavior. From our analysis, it was demonstrated that land-use intensity was negatively correlated with car commuting, and mixed land use was positively related to metro commuting. Additionally, rail access near the workplace plays a key role in reducing car commuting than residential neighborhoods. This work revealed also some interesting findings on the association between rail transit and commuting behavior, which were significantly affected by land use intensity and mixed land-use. Our work provides valuable insights for the TOD land use to effectively reduce car commuting.

Intensive lane-changing (LC) events are one of the great causes that make freeway weaving areas become bottlenecks. This study proposes an approach using vehicle trajectory data to investigate the spatiotemporal distributions of the number of LC events, void occupancies, and throughput variations at the freeway weaving area. Firstly, all LC events are extracted from the cleaned dataset and classified into four types according to the LC vehicles’ origin–destination lanes and LC directions. Secondly, the time and space void occupancies are calculated using the kinematic theory. Thirdly, the throughput variations are identified with the oblique N-curve method. Finally, the spatial and temporal distributions of the LC events, void occupancies, and throughput variations are plotted to analyze their characteristics and relationships. The spatial distributions of different types of LC events indicate that most LC events occur at the surrounding area of the on-ramp entrance. Spatial distributions of time void occupancies show that the time void in the original lanes is quite small while that in the target lanes is much larger. Furthermore, the time void occupancies amplify downstream when considering vehicles traveling on the road. By comparing the temporal distributions of LC events, void occupancies, and throughput variations, there is a lag effect between the large value occurrences of space void occupancy and throughput reduction and that of the LC events, which can conclude a causal relationship between LC events and the occurrences of the space void occupancies and throughput reductions.

This study aims to explore the relationship between traffic flow states and crash type/severity in the scenarios of normal crashes, primary crashes, and secondary crashes using the association rules mining approach. The crash data and real‐time traffic data were collected from the I‐880 freeway for five years in California, USA. The secondary crashes were identified using a speed contour plot approach. Traffic flow states were identified by the three‐phase flow theory. The results showed that the free flow is associated with the proportion of the sideswipe normal crash, the hit object primary crash, and the injury primary crash. The synchronized flow, the wide moving jams, and the transitional state from synchronized flow to wide moving jams are associated with the proportion of the rear‐end secondary crash. The transitional state from synchronized flow to free flow is associated with the proportion of the rear‐end primary crash and the property damage only primary crash. In addition, the unsafe speed behaviour can increase the proportion of the rear‐end normal, primary, and secondary crashes. The unsafe lane change behaviour can increase the proportion of the sideswipe normal, primary, and secondary crashes. These results have the potential to reduce the secondary crash probability.

The objective of this study is to analyse the relationship between secondary crash risk and traffic flow states and explore the contributing factors of secondary crashes in different traffic flow states. Crash data and traffic data were collected on the I-880 freeway in California from 2006 to 2011. The traffic flow states are categorised by three-phase traffic theory. The Bayesian conditional logit model has been established to analyse the statistical relationship between the secondary crash probability and various traffic flow states. The results showed that free flow (F) state has the best safety performance of secondary crash and synchronized flow (S) state has the worst safety performance of secondary crashes. The traditional logistic regression model has been used to analyse the contributing factors of secondary crashes in different traffic flow states. The results indicated that the contributing factors in different traffic flow states are significantly different.