Shuqi Xue

(1) Efficient resource utilization in urban transport necessitates the integration of passenger and freight transport systems. Current research focuses on dynamically responding to both passenger and parcel orders, typically by initially planning passenger routes and then dynamically inserting parcel requests. However, this approach overlooks the inherent flexibility in parcel delivery times compared to the stringent time constraints of passenger transport. (2) This study introduces a novel approach to enhance taxi resource utilization by proposing a shared model for people and parcel transport, designated as the SARP-LTW (Sharing a ride problem with loose time windows of parcels) model. Our model accommodates loose time windows for parcel deliveries and initially defines the parcel delivery routes for each taxi before each working day, which was prior to addressing passenger requests. Once the working day of each taxi commences, all taxis will prioritize serving the dynamic passenger travel requests, minimizing the delay for these requests, with the only requirement being to ensure that all pre-scheduled parcels can be delivered to their destinations. (3) This dual-service approach aims to optimize profits while balancing the time-sensitivity of passenger orders against the flexibility in parcel delivery. Furthermore, we improved the adaptive large neighborhood search algorithm by introducing an ant colony information update mechanism (AC-ALNS) to solve the SARP-LTW efficiently. (4) Numerical analysis of the well-known Solomon set of benchmark instances demonstrates that the SARP-LTW model outperforms the SARP model in profit rate, revenue, and revenue stability, with improvements of 48%, 46%, and 49%, respectively. Our proposed approach enables taxi companies to maximize vehicle utilization, reducing idle time and increasing revenue.

This study addresses the challenging problem of increasing passengers’ travel efficiency while lowering the infection transmission risk at metro stations during COVID-19 pandemic. To achieve this objective, we deploy Anylogic software and formulate an infection risk model. As a case study, this study focuses on a transfer metro station in Xi’an, China. Firstly, by utilizing Anylogic software, three distinct strategies are simulated: flow-control fences, travel reservation, and the collaborative use of travel reservations and flow-control fences. Secondly, the passenger density and average dwell time under these strategies are assessed while constructing an infection risk model to quantify the risk faced by passengers. Thirdly, when compared to the absence of any strategy, the results are as follows: (1) The flow-control fences strategy: implementing flow-control fences can effectively reduce the risk of passenger infection when the length of the flow-control fences is fixed at 47.5 m, but comes at the cost of a 20.15% decrease in passenger travel efficiency; however, excessively long flow-control fences will neither alleviate congestion nor reduce the infection risk. (2) The travel reservation strategy: the adoption of travel reservations, along with a fast track for reserved users, when the reservation proportion is 40%, leads to a remarkable 29.05% improvement in travel efficiency and reduces the risk of passenger infection by 67.12%. (3) The combined strategy: the combined utilization of travel reservations and flow-control fences enhances travel efficiency by 15.80% and reduces the risk of passenger infection by 56.77% when the reservation proportion is set at 30%. When the reservation proportion is between 10 and 30%, its infection risk reduction effect is better than that of the travel reservation strategy, but this is not necessarily true for their effects on travel efficiency. Finally, this study was compared to an existing study that proposed a new strategy by combining travel reservations with departure intervals, analyzing the effect of the implementation of the strategy with different departure intervals. The findings from this study have implications for developing appropriate strategies to optimize passenger flow without significantly compromising the transmission of infection risk during the pandemic.

The single-file movement experiment offered a convenient way to investigate the one-dimensional leader–follower behavior of pedestrians. This study investigated the time delays of children pedestrians in the leader–follower behavior by introducing a time-dependent delayed speed correlation. A total of 118 German students from the fifth grade (aged 11–12 years old) and the 11th grade (aged 17–18 years old) participated the single-file experiment. The characteristic delay time for each pedestrian was identified by optimising the time-dependent delayed speed correlation. The influences of the curvature of the experimental scenario, density, age, and gender on the delay time were statistically examined. The results suggested that to a large extent, the revealed characteristic delay time was a density-dependent variable, and none of the curvatures, the age and gender of the individual, and the age and gender of the leader had a significant influence on it. The findings from this study are variable resources to understand the leader–follower behavior among children pedestrians and to build related simulation models.

Over the past decade, the rapid development of e-commerce and express industries in China has resulted in huge environmental costs. Compared with manufacturing industries, the values of green innovation are less recognized in logistics industries. To promote the green practices in logistic enterprises, it is imperative to have a thorough understanding of the determinants of green innovation adoption. To this end, this paper performs an empirical investigation into the intentions to adopt green innovation from 196 Chinese express companies. The determinant variables were constructed from the perspective of technology characteristics (perceived green usefulness and perceived integration ease of use), stakeholder pressure (government, customer, and platform pressures), and social influence. Then, a 20-item scale was designed based on the literature review and expert opinions. The results revealed the significant positive effects of technology characteristics and social influence on the intentions to adopt green innovation. Meanwhile, only the platform pressure was significant with the adopting intentions among the variables from stakeholder pressure. Moreover, variables from technology characteristics were found to have meditation effects between social influence and adopting intentions. Based on the findings, theoretical and practical implications are proposed to promote the green and sustainable development of express companies in China.

Wall-following is an important means for pedestrians to navigate during evacuation under limited visibility. Empirical and experimental results regarding wall-following behaviour are scarce in the literature. How pedestrians approach a wall, how they decide on a wall-following direction, and how they address conflicts are still poorly understood. To these ends, we performed evacuation experiments in a mock room. Each participant wore a baseball cap covered with an opaque veil to create a limited visibility condition. Experiment results showed the participants stretched out their arms and attempted to search for the wall tactually in 205 of 270 cases, and in the remaining cases, the participants searched for the wall visually rather than tactually. The findings also reveal underlying behaviour pattern of pedestrians on the decision of wall-following direction. Finally, we propose a wall-following model based on the social force model. The simulation results are consistent with the experimental outcomes.

In recent years, with the acceleration of the urbanization process, the contradiction between transportation infrastructure facilities and spatial layout of urban land use is escalating. However, integrated urban planning and transportation planning provides a new way to significantly relieve traffic congestion and the phenomenon of jobs-housing separation brought by urbanization. In this study, the massive internet data of urban planning (e.g. internet-based positioning data) and traditional data resources of the traffic system (e.g. smart card data) are combined to identify the jobs-housing relationship around urban rail transit stations instead of the method of theoretical modeling. All the stations are classified into different categories on this basis. Besides, as for the connections of different regions and the spatial distribution of the urban population, the method of smart card data mining is adopted to analyze commuting characteristics and spatial distribution of origin-destination travel demand for different categories of stations. Finally, analysis of the correlation between the resident population around urban rail transit stations and commuting passenger flow is carried out by statistical methods. Corresponding results can be further applied to the integrated planning of transportation and land use.