Xinwei Ma

Frequent light-to-moderate rainfall may influence metro demand differently across rainfall stages and station ridership rhythms. Using automatic fare collection data from 80 Hangzhou Metro stations and station-matched hourly meteorological observations, this study examines weekday entry-ridership associations with rainfall onset, continuation, and the first post-rain hour. Three station ridership-rhythm types were identified from non-rain weekday entry–exit profiles: destination-oriented, balanced bidirectional, and origin-oriented. A Bayesian hierarchical negative binomial model estimated stage-specific associations while accounting for station-hour baseline ridership, hourly temperature, peak periods, and station- and date-level heterogeneity. During off-peak periods, rainfall onset and continuation were associated with 4.95% and 6.82% reductions in expected entry ridership at balanced bidirectional stations. Continuation associations were more negative in the primary model, but this difference was substantially attenuated after accounting for cumulative precipitation and elapsed episode duration. Origin-oriented stations generally showed more negative onset and continuation associations, while negative first-post-rain associations were observed across all three station types. These findings demonstrate that routine rainfall responses vary across both event stages and station ridership rhythms, supporting more stage-specific and station-sensitive passenger-flow monitoring.

People’s attitudinal shifts toward an epidemic at different stages of the epidemic affect their travel behavior. Non-commuting travel behavior is more variable than commuting, as non-commuters have more travel options. However, few studies explored the changes in non-commuting travel and its influencing factors across different stages of sudden and localized COVID-19 outbreaks. Using survey data collected in Nanjing, China, where there was a sudden and localized outbreak of COVID-19, this research adopted the random parameter ordered logit model with heterogeneity in means and variances (HMV) to explore the factors influencing non-commuting travel in the early, middle, and late epidemic stages. The model results revealed that considering the HMV would improve the model fitness. In addition, the temporal stability of factors was investigated via a likelihood ratio test, which confirms traveler behavioral differences across different stages. The results showed that “e-bike ownership” and “the number of PCR (polymerase chain reaction) tests” is positively correlated with the number of non-commuting trips over three epidemic stages. The variables “people who live together have a red health code,”“mask replacement frequency,” and “risk-free areas” are significant in the early-stage and middle-stage models. The variables “people who live together have a green health code all the time” only become significant in the late-stage model. Research findings contribute to the understanding of non-commuting behaviors and targeted management needs during local outbreaks, and can help the government address travel issues under future major health events.

Commuting behaviors have been changed by the COVID-19 pandemic. To investigate the impacts at different stages of sudden and localized COVID-19 outbreak, this paper carries out an online survey to obtain data, targeting the residents in Nanjing China, where there had been COVID-19 outbreaks and proposes a sequential analysis method to calculate the complexity of commuting behavior changes. The Tobit model is used to explore the factors that influence the complexity of commuting behavior changes. Results show that commuters using public transportation drop significantly when sudden outbreaks occur, with 43.5% of them switching to private cars or working from home. The number of residents working from home increases by 14 times. While an outbreak gradually subsides, commuting modes tend to recover, but does not immediately return to the state before the outbreak. Regression model results indicate that commuters aged 40–60 tend to maintain their commuting habits, while younger workers are more flexible on their commuting options. Middle-income commuters, or those living in low-risk areas or near a subway within 800 m prefer to change commuting modes, opting for what they perceive to be safer ways to commute. For commuters living in medium- or high-risk areas and those who are living with people who have non-green health codes, they tend to adjust their commuting modes in real time based on the color change in the health codes and the risk level of the areas they live. The research findings contribute to our understanding of commuting behaviors and targeted management needs during local outbreaks, and can help the government formulate a comprehensive and more effective pandemic prevention policy.

When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this problem, we propose several long-term factors affecting the passenger flow of rail transit in this paper. We also create a visual analysis of these factors using ArcGIS and construct a long-term passenger flow prediction model for rail transit based on a class neural network using an SPSS Modeler. After optimizing relevant parameters, the prediction accuracy reaches 94.6%. We compare the results with other models and find that the neural network model has a good performance in predicting long-term rail transit passenger flow. Finally, the factors affecting passenger flow are ranked in terms of importance. It is found that among these factors, bicycles available for connection have the biggest influence on the passenger flow of rail stations.

Multistate information networks (MINs) are the basis of many modern communication networks, and the reliability evaluation of MINs not only contributes to the structural design of networks but also facilitates the development of modern communication technologies. However, the evaluation of the reliability of MINs in studies so far always requires massive calculations. To improve the calculation efficiency of MIN reliability evaluation, in this article, a new algorithm with lower time complexity than traditional algorithms is proposed. The comparative experiments show that the proposed algorithm outperforms the existing MIN reliability algorithms, and the running time of the proposed algorithm is at least 31.96% less than that of the node-based binary-addition tree algorithm.

Owing to the shareability and spatial-temporal imbalance of free-floating bike-sharing (FFBS), the users may fail to pick up the bike at the desired location (i.e., the demand is truncated). Consequently, the demand partially migrates to nearby areas or is lost. Thus, the observed demand recorded in the system is not the underlying real demand. To address this issue, a framework for Demand Recovery considering demand Truncation, Migration, and spatial Correlation (DRTMC) was proposed. In detail, the real demand recovery is first modeled as a maximum-a-posteriori (MAP) problem. Then, the prior term and conditional term in MAP are formulated to jointly consider the demand truncation, migration, and spatial correlation. Finally, a tailored simulated annealing approach is applied to estimate the real demand. We then present the results using simulation data for validation and real-world data for empirical analysis. The validation results indicate that the demand recovered by the DRTMC model is much closer to the real demand value than the observed demand, and it performs better than all the benchmarks. For the case study, the FFBS data from Shanghai City in a prosperous area is chosen. It shows the distribution of observed demand is significantly different from that of real demand, emphasizing the importance of real demand recovery. The proposed DRTMC model enables researchers to capture the underlying real demand for FFBS and develop more effective rebalance strategies to improve FFBS service levels.