To contain the transmission of respiratory diseases, such as COVID-19, it is vital to control the locations visited by the cases. However, not all locations pose the same risk, and quarantining all close contacts is costly. Therefore, precise identification of outbreak locations is essential for public health. Fortunately, public health data includes detailed epidemiological surveys, offering a data-driven approach. In this paper, we propose a novel epidemic amplifier detection model, namely EADetector, which extracts spatiotemporal features from candidate locations, and employs a multitask learning-based method to fuse the infected location detection task along with the epidemic location inference task to acquire potential locations. We perform extensive experiments and present a set of case studies based on the real epidemiological surveys collected in Beijing. The proposed model is deployed as a part of the epidemiological survey system in Beijing, China.

Urban growth benefits significantly from local business development. However, factors like traffic and labor shortages sometimes cause companies to operate away from their registered addresses, resulting in governance challenges. This paper introduces "LocRecognizer," a data mining method that leverages e-commerce data to pinpoint companies' real-world operational locations. Based on the principle that areas with a high concentration of company-related users likely indicate actual workplaces, LocRecognizer combines hierarchical clustering with a deep learning model for accurate detection. When tested on datasets from Beijing and Nantong, it outperformed six baselines. A practical implementation of this system has been operational in Nantong since September 2021, attesting to its effectiveness.

Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. The government highly relies on active human efforts to detect illegal parking events. However, such an approach is extremely ineffective to cover a large city since the police have to patrol over the entire city roads. The massive and high-quality sharing bike trajectories from Mobike offer us a unique opportunity to design a ubiquitous illegal parking detection approach, as most of the illegal parking events happen at curbsides and have significant impact on the bike users. The detection result can guide the patrol schedule, i.e., send the patrol policemen to the region with higher illegal parking risks, and further improve the patrol efficiency. Inspired by this idea, three main components are employed in the proposed framework: 1)trajectory pre-processing, which filters outlier GPS points, performs map-matching, and builds trajectory indexes; 2)illegal parking detection, which models the normal trajectories, extracts features from the evaluation trajectories, and utilizes a distribution test-based method to discover the illegal parking events; and 3)patrol scheduling, which leverages the detection result as reference context, and models the scheduling task as a multi-agent reinforcement learning problem to guide the patrol police. Finally, extensive experiments are presented to validate the effectiveness of illegal parking detection, as well as the improvement of patrol efficiency.

With the development of positioning technology, a large number of trajectories have been generated, which are very useful for many urban applications. However, it is challenging to manage trajectory data for its spatio-temporal dynamics and high-volume properties. Existing trajectory data management frameworks suffer from efficiency or scalability problem, and only support limited trajectory query types. This paper takes the first attempt to build a holistic distributed NoSQL trajectory storage engine, named TrajMesa, based on GeoMesa, an open-source indexing toolkit for spatio-temporal data. TrajMesa can manage a prohibitively large number of trajectories, and support plenty of query types efficiently. Specifically, we first design a novel trajectory storage schema, which reduces the storage size tremendously. We then devise a novel indexing key schema for time ranges, based on which ID temporal query can be supported efficiently. To reduce the amount of retrieved trajectory data for a spatial range query, we innovatively propose a position code to indicate the spatial location of trajectories accurately. We also propose a bunch of pruning strategies for similarity query and k-NN query in the NoSQL environment. Extensive experiments are conducted using two real datasets and one synthetic dataset, verifying the powerful query efficiency and scalability of TrajMesa.

Similarity search has recently become an integral part of many trajectory data analysis tasks. As the number of trajectories increases, we must find similar trajectories among massive trajectories, necessitating a scalable and efficient frame-work. Typically, massive trajectory data can be managed by key-value data stores. However, existing works with key-value data stores use a coarse representation to store trajectory data. Besides, they do not provide efficient query processing to search similar trajectories. Thus, this paper proposes TraSS, an efficient framework for trajectory similarity search in key-value data stores. We propose a novel spatial index, XZ*, which utilizes fine-grained index spaces with irregular shapes and sizes to represent trajectories elaborately. Further, we devise a bijective function from the index spaces of XZ* to continuous integers, which is simple but effective for query processing. To improve the efficiency of similarity search, we employ two steps to prune dissimilar trajectories: (1) global pruning. It leverages the XZ* index to prune index spaces with no trajectories similar to the query trajectory. Our global pruning can only pick out index spaces with similar sizes and shapes to the query trajectory. Compared to the state-of-the-art index, our global pruning reduces I/O overhead up to 66.4 % during query processing; (2) local filtering. It filters dissimilar trajectories in a way with low complexity. We use a few representative features extracted from a trajectory by the Douglas-Peucker algorithm to accelerate the local filtering. We implement an open-source toolkit (TraSS) on a popular key-value data store. Extensive experiments show that TraSS outperforms state-of-the-art solutions.

Hazardous chemicals transportation (HCT) brings significant financial, environmental, and health-related risks. It is imperative that a robust regulatory system is in place to reduce the risk of accidents occurring while such hazardous chemicals are being transported. Governments around the world use GPS sensors to monitor the raw trajectories of HCT trucks, but they have difficulty detecting the loaded trajectories, which is of utmost importance for the management of H CT processes. The loaded trajectory refers to the subtrajectory generated by tracking an HCT truck when it is loaded with hazardous chemical in an HCT process. The stay points in the raw trajectory provide some feasibility to detect the loaded trajectory as they reflect the potential loading and unloading actions of the HCT truck. However, directly using the stay points to detect the loaded trajectory usually leads to unsatisfactory results due to two chal-lenges: (1) complex staying scenarios, and (2) numerous loading and unloading locations. To tackle the challenges, we propose a LoadEd trAjectory Detectlon framework, called LEAD, to detect the loaded trajectory from the raw HCT trajectory accurately and efficiently. LEAD processes a raw trajectory into a set of candidate trajectories, encodes each candidate trajectory into a latent representation, and detects the loaded trajectory using the latent representations of candidate trajectories. Extensive experiments based on a real-world dataset from Nantong, China confirm the effectiveness of our framework. The results show that the detection accuracy of LEAD exceeds 83 % which outperforms competing baselines by over 42 %.

Delivery locations are fundamental data source for intelligent logistics, which can be used in route planning, arrival time estimation, parcel allocation, etc. Using the Geocoded way-bill location of an address as the delivery location is not sufficient, due to wrong address parsing, coarse-grained POI database, or different preferences of customers. To mitigate the insufficiency of Geocoding, some methods have been proposed, which utilize couriers' locations when waybills are confirmed to be delivered for delivery location inference. Nevertheless, these methods highly rely on the quality of couriers' annotations and fail when couriers confirm deliveries with delays. We propose to infer actual delivery locations of addresses from couriers' trajectories. This idea lies on an observation that the semantics of delivering a parcel are well captured by couriers' trajectories (e.g., a stay point would be generated when a delivery occurs), which holds even couriers confirm deliveries with delays. Specifically, we design Delivery Location Inference under Mis-Annotation (DLInfMA), which (1)generates location candidates from stay points in couriers' trajectories; (2) extracts features from both an address and its location candidates; and (3) uses an attention-based neural network model LocMatcher to predict the delivery location for each address. Experiments on two real-world datasets from JD Logistics as well as synthetic datasets demonstrate the effectiveness, robustness and scalability of DLInfMA. We also present a deployed system along with two applications based on DLInfMA.

Service time is a part of time cost in the last-mile delivery, which is the time spent on delivering parcels at a certain location. Predicting the service time is fundamental for many downstream logistics applications, e.g., route planning with time windows, courier workload balancing and delivery time prediction. Nevertheless, it is non-trivial given the complex delivery circumstances, location heterogeneity, and skewed observations in space. The existing solution trains a supervised model based on aggregated features extracted from parcels to deliver, which cannot handle above challenges well. In this paper, we propose MetaSTP, a meta-learning based neural network model to predict the service time. MetaSTP treats the service time prediction at each location as a learning task, leverages a Transformer-based representation layer to encode the complex delivery circumstances, and devises a model-based meta-learning method enhanced by location prior knowledge to reserve the uniqueness of each location and handle the imbalanced distribution issue. Experiments show MetaSTP outperforms baselines by at least 9.5% and 7.6% on two real-world datasets. Finally, an intelligent waybill assignment system based on MetaSTP is deployed and used internally in JD Logistics.

People often refer to a place of interest (POI) by an alias. In ecommerce scenarios, the POI alias problem affects the quality of the delivery address of online orders, bringing substantial challenges to intelligent logistics systems and market decision-making. Labeling the aliases of POIs involves heavy human labor, which is inefficient and expensive. Inspired by the observation that the users' GPS locations are highly related to their delivery address, we propose a ubiquitous alias discovery framework. Firstly, for each POI name in delivery addresses, the location data of its associated users, namely Mobility Profile are extracted. Then, we identify the alias relationship by modeling the similarity of mobility profiles. Comprehensive experiments on the large-scale location data and delivery address data from JD logistics validate the effectiveness.

The rapid development of positioning technology produces an extremely large volume of spatio-temporal data with various geometry types such as point, line string, polygon, or a mixed combination of them. As one of the most basic but time-consuming operations, k nearest neighbors join (kNN join) has attracted much attention. However, most existing works for kNN join either ignore temporal information or consider point data only.

With the prevalence of positioning techniques, a prodigious number of spatio-temporal data is generated constantly. To effectively support sophisticated urban applications, e.g., location-based services, based on spatio-temporal data, it is desirable for an efficient, scalable, update-enabled, and easy-to-use spatio-temporal data management system.This paper presents JUST, i.e., JD Urban Spatio-Temporal data engine, which can efficiently manage big spatio-temporal data in a convenient way. JUST incorporates the distributed NoSQL data store, i.e., Apache HBase, as the underlying storage, GeoMesa as the spatio-temporal data indexing tool, and Apache Spark as the execution engine. We creatively design two indexing techniques, i.e., Z2T and XZ2T, which accelerates spatio-temporal queries tremendously. Furthermore, we introduce a compression mechanism, which not only greatly reduces the storage cost, but also improves the query efficiency. To make JUST easy-to-use, we design and implement a complete SQL engine, with which all operations can be performed through a SQL-like query language, i.e., JustQL. JUST also supports inherently new data insertions and historical data updates without index reconstruction. JUST is deployed as a PaaS in JD with multi-users support. Many applications have been developed based on the SDKs provided by JUST. Extensive experiments are carried out with six state-of-the-art distributed spatio-temporal data management systems based on two real datasets and one synthetic dataset. The results show that JUST has a competitive query performance and is much more scalable than them.

The rapid development of e-commerce requires efficient and reliable logistics services. Nowadays, couriers are still the main solution to address the "last mile" problem in logistics. They are usually required to record the accurate delivery time of each parcel manually, which provides vital information for applications like delivery insurances, delivery performance evaluations, and customer available time discovery. Couriers' trajectories generated by their PDAs provide a chance to infer the delivery time automatically to ease the burdens on the couriers. However, directly using the nearest stay point to infer the delivery time is under satisfactory due to two challenges: 1) inaccurate delivery locations, and 2) various stay scenarios. To this end, we propose Delivery Time Inference (DTInf), to automatically infer the delivery time of waybills based on couriers' trajectories. Our solution is composed of three steps: 1) Data Pre-processing, which detects stay points from trajectories, and separates stay points and waybills by delivery trips, 2) Delivery Location Correction, which infers true delivery locations of waybills by mining historical deliveries, and 3) Delivery Event-based Matching, which selects the best-matched stay point for waybills in the same delivery location to infer the delivery time. Extensive experiments and case studies based on large scale real-world waybill and trajectory data from JD Logistics confirm the effectiveness of our approach. Finally, we introduce a system based on DTInf, which is deployed and used internally in JD Logistics.

With the advances of web-of-things, human mobility, e.g., GPS trajectories of vehicles, sharing bikes, and mobile devices, reflects people’s travel patterns and preferences, which are especially crucial for urban applications such as urban planning and business location selection. However, collecting a large set of human mobility data is not easy because of the privacy and commercial concerns, as well as the high cost to deploy sensors and a long time to collect the data, especially in newly developed cities. Realizing this, in this paper, based on the intuition that the human mobility is driven by the mobility intentions reflected by the origin and destination (or OD) features, as well as the preference to select the path between them, we investigate the problem to generate mobility data for a new target city, by transferring knowledge from mobility data and multi-source data of the source cities. Our framework contains three main stages: 1) mobility intention transfer, which learns a latent unified mobility intention distribution across the source cities, and transfers the model of the distribution to the target city; 2) OD generation, which generates the OD pairs in the target city based on the transferred mobility intention model, and 3) path generation, which generates the paths for each OD pair, based on a utility model learned from the real trajectory data in the source cities. Also, a demo of our trajectory generator is publicly available online for two city regions. Extensive experiment results over four regions in China validate the effectiveness of the proposed solution. Besides, an on-field case study is presented in a newly developed region, i.e., Xiongan, China. With the generated trajectories in the new city, many trajectory mining techniques can be applied.

Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task to promote the cycling life style, as well-planned bike lanes can reduce traffic congestions and safety risks. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider one or more key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization. In this paper, we propose a data-driven approach to develop bike lane construction plans based on the large-scale real world bike trajectory data collected from Mobike, a station-less bike sharing system. We enforce these constraints to formulate our problem and introduce a flexible objective function to tune the benefit between coverage of users and the length of their trajectories. We prove the NP-hardness of the problem and propose greedy-based heuristics to address it. To improve the efficiency of the bike lane planning system for the urban planner, we propose a novel trajectory indexing structure and deploy the system based on a parallel computing framework (Storm) to improve the system's efficiency. Finally, extensive experiments and case studies are provided to demonstrate the system efficiency and effectiveness.

Bike-sharing systems become more and more popular in the urban transportation system, because of their convenience in recent years. However, due to the high daily usage and lack of effective maintenance, the number of bikes in good condition decreases significantly, and vast piles of broken bikes appear in many big cities. As a result, it is more difficult for regular users to get a working bike, which causes problems both economically and environmentally. Therefore, building an effective broken bike prediction and recycling model becomes a crucial task to promote cycling behavior. In this paper, we propose a predictive model to detect the broken bikes and recommend an optimal recycling program based on the large scale real-world sharing bike data. We incorporate the realistic constraints to formulate our problem and introduce a flexible objective function to tune the trade-off between the broken probability and recycled numbers of the bikes. Finally, we provide extensive experimental results and case studies to demonstrate the effectiveness of our approach.

Trajectory data preprocessing is to convert raw GPS logs into organized trajectories, which is a common, necessary but tedious task in many urban applications. This paper proposes CloudTP, a cloud-based flexible trajectory data preprocessing framework, to provide an efficient online service, easing the burdens of urban application builders. The proposed system is designed and implemented based on the cloud storage and parallel computing framework (i.e. Spark). Its features consist of 1) noise filtering, 2) trajectory segmentation, 3) map matching, and 4) index building. CloudTP is useful for both normal users and advanced users. By simply uploading trajectory datasets and setting corresponding parameters, normal users can get organized trajectories, statistics and visualizations on the cloud, while advanced users can also customize their own algorithms in any preprocessing module. Finally, usage scenarios are demonstrated to show the capability and flexibility of CloudTP.

Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. Traditional approaches to detect illegal vehicle parking events rely highly on active human efforts, e.g., police patrols or surveillance cameras. However, these approaches are extremely ineffective to cover a large city. The massive and high quality sharing bike trajectories from Mobike offer us with a unique opportunity to design a ubiquitous illegal parking detection system, as most of the illegal parking events happen at curbsides and have significant impact on the bike users. Two main components are employed to mine the trajectories in our system: 1)~trajectory pre-processing, which filters outlier GPS points, performs map-matching and builds indexes for bike trajectories; and 2)~illegal parking detection, which models the normal trajectories, extracts features from the evaluation trajectories and utilizes a distribution test-based method to discover the illegal parking events. The system is deployed on the cloud internally used by Mobike. Finally, extensive experiments and many insightful case studies based on the massive trajectories in Beijing are presented.

Cycling as a green transportation mode has been promoted by many governments all over the world. As a result, constructing effective bike lanes has become a crucial task for governments promoting the cycling life style, as well-planned bike paths can reduce traffic congestion and decrease safety risks for both cyclists and motor vehicle drivers. Unfortunately, existing trajectory mining approaches for bike lane planning do not consider key realistic government constraints: 1) budget limitations, 2) construction convenience, and 3) bike lane utilization.