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.

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.

Many cities around the world face the challenge of an aging population. A full understanding of the mobility behavior characteristics of the elderly is one necessary and urgent consideration as regards the current aging trend if sustainable urban development is to be fully realized. This paper presents a systematic approach to analyzing the dynamic mobility characteristics of the elderly who travel by bus using smart card big data. The characteristics include temporal distribution, travel distance, travel duration, travel frequency, and also the spatial distribution of such travelers. The findings of these mobility characteristics can directly contribute to both public transport policy making, service, and management. In this study, the analytics of the elderly are also compared with that of the average adult group so as to identify both the similarities and differences between the two groups. Beijing, a megacity, with a very high life expectancy and in which the bus is the dominant mode of public transport for the elderly, was used as the study area. The significance of this research concerns a newly developed systematic approach that is able to analyze the dynamic mobility characteristics of the elderly using smart card data.

With the recent emergence of big data, there has been significant progress in the study of big data mining and rapid developments in urban computing. With the integration of planning and management in urban areas, there is an urgent need to focus on the identification of urban functional areas (UFAs) based on big data. This paper describes the concept of communication activity intensity, which is more meaningful than the number of communication activities or the user density in identifying UFAs. The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. A case study on the region within Beijing’s 3rd Ring Road is conducted, and the results of UFA identification are qualitatively and quantitatively verified. The causes of large passenger flows on certain metro lines in Beijing are also analyzed. The highest identification accuracy is obtained for park and scenery areas, followed by residential areas and office areas. In conclusion, the proposed method offers a significant improvement over the identification accuracy of previous techniques, which verifies the reliability of the method.

In this era of population aging, it is essential to understand the spatial distribution patterns of the elderly. Based on the smart card data of the elderly, this study aims to detect the home location and examine the spatial distribution patterns of the elderly cardholders in Beijing. A framework is proposed that includes three methods. First, a rule-based approach is proposed to identify the home location of the elderly cardholders based on individual travel pattern. The result has strong correlation with the real elderly population. Second, the clustering method is adopted to group bus stops based on the elderly travel flow. The center points of clusters are utilized to construct a Voronoi diagram. Third, a quasi-gravity model is proposed to reveal the elderly mobility between regions, using the public facilities index. The model measures the elderly travel number between regions, according to public facilities index on the basis of the total number of point of interest (POI) data. Beijing is used as an example to demonstrate the applicability of the proposed methods, and the methods can be widely used for urban planning, design and management regarding the aging population.