Parcel lockers have continuously growing in popularity as an alternative mode for last-mile delivery services due to their capability of effectively alleviating the risk of a delivery failure, increasing the possibility of delivery consolidation, and reducing the number of drop-off sites. However, poorly located of parcel lockers be less efficient. When determining the parcel locker location, inadequate consideration of uncertain demands can potentially increase the risk of unsatisfied demands. To remedy this issue, a robust optimization model is proposed in this paper with consideration of the demand uncertainties, including the large and small parcels to be received and sent. Not only can the collection locations be optimally determined, but so can the number of large and small parcel lockers for each location at the same time under various robust levels. Meanwhile, the sites whose demands are covered by one of the collection locations are also determined by the constraints of acceptable walking distance. A series of numerical experiments has been performed to evaluate the proposed model, with the main focus being on the solution robustness. Since the set of non-linear constraints are transformed into the linear counterparts, the robust solution can be obtained by the existing solvers within a reasonable time with moderate computing power. The experimental results also provide useful guidance for the practical application of the method, as slightly more conservative decision making can secure the solution robustness with only a marginal increase in costs.

Nowadays, a large percentage of people use smartphones frequently. The mobile phone signaling data contains various attributes that can be used to infer when and where the user is. Compared with other big data sources (e.g., social media and GPS data) for the human movement, mobile phone signaling data demonstrate the advantages of a high coverage of population, strong temporal continuity, and low cost of collection. Taking advantage of such mobile phone signaling data, this work aims to identify tourists and locals from a large volume of mobile phone signaling data in a tourism city and analyze their spatiotemporal patterns to better promote tourism service and alleviate possible disturbance to local residents. In this paper, we present a framework to differentiate these two types of people by the following procedure: first, the hidden behavior characteristics of users are extracted from mobile phone signaling data; and then, the K-means clustering method is adopted to identify tourists and locals. With the identification of both tourists and local residents, we analyze the distribution and interaction characteristics of tourists and locals in an urban area. An experimental study is conducted in a famous tourism city, Xiamen, China. The results indicate that the proposed method can successfully identify the most popular scenic spots and major transportation corridors for tourists. The feature extraction, identification, and spatiotemporal analysis presented in this paper are of great significance for analyzing the urban tourism demand, managing the urban space, and mining the tourist behavior.

The movable unit equipped with a set of lockers has been recently developed as a new mode to improve the efficiency of the last mile delivery. Locating a set of movable parcel locker units appropriately is a fundamental factor to promote the merits of movable parcel lockers. However, the difficulty in determining where to locate movable parcel locker units arises from the stochastic characteristics of demands. Therefore, we propose a robust optimization approach to determine the number of movable parcel locker units and their locations simultaneously with the aim to minimize the operating cost under stochastic demands and mobility restrictions. To reduce the complexity of the optimization model, the non-linear constraints have been transformed into the linear counterparts, resulting in an integer linear programming (ILP) model that can be solved by commercially available mathematical programming solvers. The results from the numerical examples indicate the proposed approach can obtain high robustness with a small extra cost within reasonable time. In addition, it is found that if each unit is equipped with more lockers, fewer movable parcel locker units are required to accommodate the demands with less operating cost, as the demand points can be clustered into a few intensive self-pickup sites.

The influence of the built environment on dockless bike-sharing (DBS) trips connecting to urban metro stations has always been a significant problem for planners. However, the evidence for correlations between microscale built-environment factors and DBS-metro transfer trips remains inconclusive. To address this, a framework, augmented by big data, is formulated to analyze the correlation of built environment with DBS–metro transfer trips from the macroscopic and microscopic views, considering Beijing as a case study. The trip density and cycling speed are calculated based on 11,120,676 pieces of DBS data and then used to represent the characteristic of DBS-metro transfer trips in a multiple linear regression model. Furthermore, a novel method is proposed to determine the built-environment sampling area around a station by its corresponding DBS travel distances. Accordingly, 6 microscale built-environment factors are extracted from street-view images using deep learning and integrated into the analysis model, together with 14 macroscale built-environment factors and 8 potential influencing factors of socioeconomic attributes and metro station attributes. The results reveal the significant positive influence of greenery and presence of barriers on trip density and cycling speed. Additionally, presence of streetlights is found to be negatively correlated with both trip density and cycling speed. Presence of signals is also found to have an influence on DBS-metro transfer trips, but it only negatively impacts trip density.

This study proposes a model for the quantitative evaluation of the life-cycle carbon footprints of large sporting events and the uncertainties related to them. The model was used to analyze the case of a mega sporting event in Beijing, China. First, the quantitative model for the evaluation of the carbon footprints of mega sporting events includes a preparation stage, a holding stage, and an end stage. These stages consider the energy and resources used for construction, operation, transportation, catering, and accommodation. Second, this study proposes a prediction model using model-based and simulation-based methods to address the difficulty of obtaining traffic activity. Third, a semi-quantitative method that combines a data quality indicator and stochastic simulation is adopted for the uncertainty analysis of mega sporting events. Finally, a case study is used to indicate that the preparation stage of a mega sporting event accounts for the highest CO₂ emissions at 92.1%, followed by 7.5% in the holding stage and 0.4% in the end stage. The total life-cycle CO₂ emissions of a sustainable scenario of a mega sporting event in Beijing amount to 205,080.3 t CO_2e, and the per capita CO₂ emissions during the event’s holding stage amount to 0.26 t CO_2e/person. The uncertainty in the input parameters is 0.0617, indicating that the uncertainty of the model is low, and the reliability of the results is high.