Jiangling Wu

Epidemic hazards disturb mobility in a way that is both physical and behavioural. They reduce service supply, change perceived safety, and force travellers to decide whether a trip is necessary, safe, or replaceable. This review maps how risk perception, coping appraisal and structural constraints shaped mobility response and recovery during and after COVID-19. The literature search was completed on 21 June 2026. Searches in OpenAlex and PubMed, followed by a targeted update, identified 1263 records; 1145 unique records were screened and 70 studies were included. The evidence shows that perceived infection risk discouraged crowded and enclosed modes, especially public and shared transport. Yet risk perception alone did not explain behaviour. Travellers also needed feasible alternatives, trust in protective measures, money, time, information and social permission to adapt. Longitudinal and observed data were particularly useful for distinguishing temporary disruption from later recalibration, though these sources also carry their own limitations (e.g., sampling bias, algorithmic artefacts) and should not be treated as bias-free evidence.

Purpose: The existing finite element software mainly uses solid and shell elements of the preset crack to simulate the bending effect of the crack structure but not the beam element. This paper proposes a new element based on fiber beam element, which can be used to perform bending analysis of cracked structures. Design/methodology/approach: In the simplified computational model based on the Euler-Bernoulli theory, the Dirac-delta function is introduced into the beam element’s curvature function to represent the crack. The shape function of the cracked beam element is obtained by combining the displacement field function obtained by the integral curvature function with the finite element theory. Then, using the principle of virtual displacement, the element stiffness matrix is obtained by the Strain matrix that differential shape function. The crack section’s deformation and resisting force is calculated utilizing the properties of the d function. Findings: Two numerical examples of the element are presented in order to substantiate the proposed element. They cover both the numerical model of linear and nonlinear static bending analysis. The analysis result was compared with the corresponding analytical solution and numerical solution, thus proving the quality of the proposed cracked fiber beam element. Originality/value: The cracked fiber beam element is proposed and implemented in the finite element program framework.

This paper studies the parking demand characteristics of large commercial areas in the city’s central regions. The study uses non-parametric and semi-parametric analysis methods in survival analysis to explore if and how weather conditions, parking tariffs, and temporal factors (weekdays, weekends, and short holidays) impact the parking duration. The parking data of a large commercial supermarket in Zhengzhou was collected over one month. Single-factor analysis based on the Product-Limit (PL) approach suggests that the cumulative survival and relative risk curves of parking duration exhibit slight variations across different temporal categories and weather conditions. Based on Cox semi-parametric multi-factor analysis results, the parking duration is significantly influenced by weekdays (regression coefficient = 0.068, hazard ratio = 1.071, P < 0.001), weekends (regression coefficient = 0.042, hazard ratio = 1.043,P < 0.001), moderate rain (regression coefficient = -0.089, hazard ratio = 0.914, P < 0.001), and heavy rain (regression coefficient = 0.030, hazard ratio = 1.030,P = 0.034 < 0.05). The results have indicated that within the study area, compared to short holidays, the parking duration on weekdays and weekends is shorter, with the probability of vehicles ending their parking increased by 7.1% and 4.3%, respectively. Under different weather conditions, compared to sunny days, parking duration is longer during moderate rain, with the probability of vehicles departing decreased by 8.6%, whereas during heavy rain, parking duration is shorter, with the probability of vehicles departing increased by 3%. Notably, the parking tariffs demonstrated no statistically significant impact. These findings suggest that temporal variations and rainfall patterns should inform dynamic parking management strategies, including weather-responsive pricing adjustments and spatial capacity optimization during peak periods.

The latent variables of perceived efficacy and perceived risk are introduced to enhance the theory of planned behavior in investigating the influencing factors of intercity travelers' mode choice behavior. A structural equation model is constructed to represent the relationship between five latent variables, attitude, subjective norms, perceived behavioral control, perceived efficacy and perceived risk, and intercity travel mode choice intentions. This model analyzes the correlation between latent variables in four intercity travel modes of high-speed rail/bullet trains, ordinary trains, civil aviation and private cars. The results indicate that in the four intercity travel modes, the latent variables explain a total variance of 72%, 77%, 69% and 84% in the intention to choose intercity travel modes, respectively. The intention to choose intercity travel modes is significantly impacted by the varying degrees of travelers' attitudes, subjective norms, and perceived behavioral control. The latent variable of "perceived risk" has a significant negative impact on the intention to choose all four intercity travel modes, while "perceived efficacy" has a significant positive impact on "perceived behavioral control" across the four intercity travel modes. The research findings can provide a basis for optimizing the structure of intercity travel modes and for formulating policies for managing intercity transportation demand.

Accurate estimation of traffic emissions and analysis of spatio-temporal distribution on urban roads play a crucial role in the development of low-carbon transportation system. Traditionally, a region’s emission characteristics have been studied using numerous emission models with GPS-based spatio-temporal data. Due to the heavy data processing needs of GPS-based data, emission characteristics for a large region have been studied by dividing the region into a limited number of smaller areas or units. Additionally, GPS data are based on a few vehicles in the traffic which does not fully reflect road conditions. This paper proposed an approach that can be used to study and calculate the spatio-temporal emission pattern of a region at a roadway section level by using Baidu’s online traffic data and COPERT model. The proposed method can be used to estimate road-level emission patterns while avoiding the impact of redundant data in large datasets, making the dataset more reliable, applicable, and scalable. The proposed approach has been demonstrated through a study of spatio-temporal emission patterns in the Qiaoxi district within city of Shijiazhuang, China. Online data crawling technology was used to obtain data on urban road traffic speed and driving distance. The linear reference technology was used to construct a two-layer road network model to conduct the coupling and matching of traffic data with the road network data. The COPERT model was implemented to calculate the average traffic emissions on each road in the road network, and a traffic emission intensity index was proposed to quantify the CO, VOC, NO_x and CO₂ emissions on urban roads in the study area. The analysis results show that the traffic emission intensity of the expressway, trunk road, secondary road, and branch road is high during the morning peak (7 AM-9 AM) and evening peak (5 PM—7 PM). The sections with higher traffic emission intensity are mainly concentrated on the main roads and secondary roads such as Jiefang South Street, Shitong Road and Xinhua Road. Nearly one-third of 2^nd Ring and 3^rd Ring roads also have relatively high emission intensity. The research results provide new ideas for estimating traffic emissions in urban road networks and analyzing the spatio-temporal distribution of traffic emissions. The research results can also provide a decision-making basis for traffic management departments to formulate energy-saving and emission-reduction measures and promote the development of urban green and low-carbon transportation.

This paper explores public bicycle choice behavior for urban travelers and potential travelers who may use public bicycle systems. Factors influencing public bicycle choice behavior and current public bicycle usage in China were analyzed. Then primary factors of public bicycle choice behavior among those using different travel modes were studied using the binary logistic model. Influential factors were divided into internal and external factors. High prediction accuracy of the proposed model shows that BLM can predict the public bicycle choice behavior of urban residents. Results show that travel distance, convenience of transferring to public transit, and cycling safety are the three main factors that determine whether private car owners would choose the public bicycles. For walkers, walking distance and red light waiting time are the main factors. For users of public transit, travel distance, walking distance to public transit transfer, and automobile exhaust are the three main influential factors.

Survival analysis is used to analyze site data to explore vehicles’ mandatory lane changing behaviors and related influential factors. The traffic data on the mandatory lane changing durations of vehicles were collected by an unmanned aerial vehicle (UAV) in a freeway maintenance construction area. A multiplicative hazard model of mandatory lane change was established using the semi-parametric method of survival analysis. Meanwhile, the lane change data has been analyzed by Cox regression analysis. The results show that about three quarters of vehicles’ MLC duration is less than 10 sec. There is no significant evidence showing that different vehicle types have an effect on the duration of mandatory lane changing. The cumulative survival rate of MLC duration the off-peak period has been found to be significantly lower than that in the peak period and transition period. As it turns out, the cumulative survival rate of vehicles’ MLC duration in the transition period is the highest.

The two-step left-turn control model of ring-intersection (RI) has been widely applied to many cities in China. It is still unclear which control model is more suitable when the RI must be reformed due to being unsatisfied with the rapid development of traffic demand. This paper presents a method based on the characteristic of traffic flow in RI considering the efficient performance of the vehicles. Next, a more accurate capacity calculation formula of the left-turn lane is proposed using the stop line method. Then, this method is effectively tested in a typical RI, in the Hunan Province utilizing VISSIM platform.