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Efficient order assignment in last-mile delivery benefits customers, couriers, and the platform. State-of-the-practice order assignment is based on the static delivery area partition, which cannot adapt well to the dynamic order quantity and destination distributions on different days. State-of-the-art methods focus on balancing order amounts or the payoff among couriers dynamically, neglecting the courier's workload in delivering orders. This paper explores the courier's heterogeneous behaviors for delivering orders to different destinations to measure the courier's workload and then achieve more efficient order assignments under the fair workload constraint. We design a workload-constrained order assignment system, called WORD, to reduce the cost of the last-mile delivery, i.e., the couriers’ total travel distance and overdue order rate. Specifically, the heterogeneous behaviors for delivering orders are first utilized for workload calculation. Then a two-stage order assignment framework is designed, including a sort-based initialization algorithm for initializing the assignment under the fair workload constraint and a coalition-game-based improvement algorithm for improving the assignment. Extensive evaluation results with real-world logistics data from one of the largest logistics companies in China show that WORD reduces the cost of the order assignment by up to 51.9% under the fair workload constraint compared to the state-of-the-art methods.

Geocoding, associating textual addresses with corresponding GPS coordinates, is vital for many location-based services (e.g., logistics, ridesharing, and social networks). One of the most common Geocoding solutions is using commercial map services such as Google Maps. However, this is typically not practical for some location-based service providers due to real-world challenges like commercial competition and high costs (recurring fees). In this paper, we design a new cost-effective Geocoding framework to automatically infer the geographic coordinates from textual addresses. To achieve this, we take the E-Commerce logistics service as a concrete scenario and design CoMiner, an unsupervised coordinate inference framework based on textual address data, delivery event data, and courier trajectory data. CoMiner includes three main components, (1) A POI-level clustering model, (2) A Delivery Mobility Graph (DMG), and (3) A behavior-driven address ranking model. Furthermore, we design CoMiner-W, a coordinates mining algorithm based on WiFi data, to further enhance the effectiveness of CoMiner. We conduct extensive experiments on three large-scale datasets where CoMiner outperforms the state-of-the-art methods by 20.3%. Moreover, we have designed an abnormal delivery event detection system based on CoMiner and deployed it at JD Logistics, which brings a significant reduction in abnormal delivery event rates.

A major goal of current international urban development and construction is to lower carbon emissions. Synergies exist between the preservation of historic cities and the development of low-carbon cities; however, the relationship between preservation of historic cities and carbon emissions remains unclear. This study focuses on the cities in the “Yellow-Canal-Yangtze” watershed community, which includes the Yellow River Basin, Yangtze River Basin, and the Grand Canal region, and aims to verify the differences in CO2emissions (CE) between historic and non-historic cities through multiple comparisons after ANOVA. Additionally, Ridge Regression was used to determine the impact and dimensions of the relevant variables associated with the preservation of historic cities on CE. Combined with national development goals, this study predicts changes in the CE of historic city preservation under different scenarios. This study found significant differences between historic and non-historic cities, as the total CE of historic cities is 2.42 times higher than that of non-historic cities. The dimension with the largest difference was the CE related to transportation, with aviation emissions of historic cities reaching 15.22 times higher than that of non-historic cities. Although the preservation of historic cities has led to an overall increase in the CE, there is significant potential for CE reduction in land use, transportation infrastructure, and tourism development related to historic city preservation. In the future, historic cities can contribute to China’s carbon neutrality and the United Nation’s development goals through the creation of larger historical preservation areas, more convenient public transportation, and more vibrant tourism.

With the rapid development of sharing economy, we have access to massive sharing systems such as Uber, Airbnb, and bike sharing nowadays. The sharing economy, at its core, is to achieve efficient use of resources. However, the actual usage of shared resources is still unclear to us. Little measurement or analysis, if any, has been conducted to investigate the resource usage patterns with the large-scale data collected from these sharing systems. In this paper, we first analyze the shared bike usage patterns in three typical bike sharing systems based on 140-month multi-event data. From our data-driven analysis, we found that the most used 20% of shared bikes account for 45% of total usage, while the least used 20% of bikes account for less than 1% of usage. To efficiently utilize shared bikes, we propose a usage balancing design called eShare+ to improve the bike sharing systems by considering both the quality of service and bike maintenance, which includes three key components: (i) a statistical model based on archived data to infer historical usage; (ii) an entropy and contextual LSTM-based prediction model with both real-time and archived data to infer future usage; (iii) a model-driven optimal calibration engine for bike selection to dynamically balance usage. We develop an ID swapping-based evaluation methodology to measure the efficiency of eShare+ with data from three large-scale bike sharing systems including 84,000 bikes and 3,300 service stations. Our results show that eShare+ not only fully utilizes shared bikes with efficient maintenance but also improves the quality of service. In addition, eShare+ also has the potential to be applicable to different fleet sizes.

Several recent works have studied mobile charging under the “one-to-many” charging pattern where a single charger can charge multiple devices simultaneously. However, most of them focus on path planning and charging time allocation, but overlook the underlying dependence of the charging efficiency on initial deployment positions of chargers. This paper studies the problem of Placing directional wIreless chargers with Limited mObiliTy (PILOT), that is, maximize the overall charging utility for a set of static rechargeable devices on a 2D plane by determining deployment positions, stop positions and orientations, and portions of time for all deployed chargers that can move in a limited area after their deployment. To the best of our knowledge, we are the first to study placement of mobile directional chargers under the “one-to-many” pattern. To address PILOT, we propose a $(\frac{1}{2}-\epsilon)$(12-ε)-approximation algorithm. First, we present a method to approximate nonlinear charging power of chargers, and further propose an approach to construct Maximal Covered Set uniform subareas to reduce the infinite continuous search space for stop positions and orientations to a finite discrete one. Second, we present geometrical techniques to further reduce the infinite solution space for candidate deployment positions to a finite one without performance loss, and transform PILOT to a mixed integer nonlinear programming problem. Finally, we propose a linear programming based greedy algorithm to address it. Simulation and experimental results show that our algorithm outperforms six comparison algorithms by $19.74 \% \sim 500.01 \%$19.74%∼500.01%.

We report a 30-month nationwide deployment and operation study of an indoor arrival detection system based on Bluetooth Low Energy called VALID in 364 Chinese cities. VALID is pilot-studied, deployed, and operated in the wild to infer real-time indoor arrival status of couriers, and improve their status reporting behavior based on the detection. During its full nationwide operation (2018/12-2021/01), VALID consists of virtual devices at 3 million shops and restaurants, where 530,859 of them are in multi-story malls and markets to infer and influence 1 million couriers’ behavior, and assist the scheduling of 3.9 billion orders for 186 million customers. Although indoor arrival detection is straightforward in controlled environments, the scale of our platform makes the cost prohibitively high. In this work, we explore to use merchants’ smartphones under their consent as a virtual infrastructure to design, build, deploy, and operate VALID from in-lab conception to nationwide operation in three phases for 30 months. We consider metrics including system evolution, reliability, utility, participation, energy, privacy, monetary benefits, along with couriers’ behavior changes. We share three lessons and their implications for similar wireless sensing or communication systems with large geospatial operations.

Crowdsourcing is a newly emerging service platform and business model in the Internet. Analysis and description about urban emergency events, e.g., fires, storms and traffic jams are of great importance to protect the security of humans. Recently, social media feeds are rapidly emerging as a novel platform for providing and dissemination of information that is often geographic. In this paper, in order to describe the timeline of real-time urban emergency events, the new web mining task timeline description (TD) is proposed. Firstly, the related information of an urban emergency event is extracted from Weibo messages. Secondly, the valid message including the semantic or spatial information is detected in this step. Thirdly, detected valid messages are used to build the TD. Case studies on real datasets show the proposed model has good performance and high effectiveness in the analysis and description of urban emergency events.

As the web enters Big Data age, users and search engines may find it more and more difficult to effectively use and manage such big data. This paper proposes a method based on user tasks to perceive user intent, the meaningful user intent can be separated, and the elusiveness of user intent can be solved. One more thing, the method based on user task can predict the user behaviour to some extent. The main work of this paper is to propose such a concept space to fit the above features and the method to build the concept space on a large text database. We propose a new search pattern termed as ExNa, which can be incorporated into search engines to support more efficient search with better results. We conduct experimental studies upon a prototype search engine (KNOWLE), so as to evaluate the validity and performance of our proposed ExNa mechanism.

The increasing need of video based applications issues the importance of parsing and organizing the content in videos. However, the accurate understanding and managing video contents at the semantic level is still insufficient. The semantic gap between low level features and high level semantics cannot be bridged by manual or semi-automatic methods. In this paper, a semantic based model named video structural description (VSD) for representing and organizing the content in videos is proposed. Video structural description aims at parsing video content into the text information, which uses spatiotemporal segmentation, feature selection, object recognition, and semantic web technology. The proposed model uses the predefined ontologies including concepts and their semantic relations to represent the contents in videos. The defined ontologies can be used to retrieve and organize videos unambiguously. In addition, besides the defined ontologies, the semantic relations between the videos are mined. The video resources are linked and organized by their related semantic relations.

Relatedness measurement between multimedia such as images and videos plays an important role in computer vision, which is a base for many multimedia related applications including clustering, searching, recommendation, and annotation. Recently, with the explosion of social media, users can upload media data and annotate content with descriptive tags. In this paper, the authors aim at measuring the semantic relatedness of Flickr images. Firstly, information theory based functions are used to measure the semantic relatedness of tags. Secondly, the integration of tags pair based on bipartite graph is proposed to remove the noise and redundancy. The data sets including 1000 images from Flickr are used to evaluate the proposed method. Two data mining tasks including clustering and searching are performed by the proposed method, which shows the effectiveness and robust of the proposed method.