Real-world multichannel time series prediction faces growing demands for efficiency across edge and cloud environments, making channel compression a timely and essential problem. Motivated by success of Multiple-Input Multiple-Output (MIMO) methods in signal processing, we propose a predictability-aware compression–decompression framework to reduce runtime, decrease communication cost, and maintain prediction accuracy across diverse predictors. The core idea involves using a circular seasonal key matrix with orthogonality to capture underlying time series predictability during compression and to mitigate reconstruction errors during decompression by introducing more realistic data assumptions. Theoretical analyses show that the proposed framework is both time-efficient and accuracy-preserving under a large number of channels. Extensive experiments on six datasets across various predictors demonstrate that the proposed method achieves superior overall performance by jointly considering prediction accuracy and runtime, while maintaining strong compatibility with diverse predictors.

Mobile network operators (e.g., China Mobile, Verizon) are significant for providing communication services and collecting massive amounts of data. However, operators are increasingly concerned about customer data breaches involving third-party application providers (e.g., Tencent, Apple, Netflix). This concern is particularly aggravated when anonymous datasets shared with third-party providers or publicly released can be linked to user data compromised in breaches, leading to severe re-identification attacks and privacy threats. However, comprehensive methods for identifying such privacy risks on a large scale are lacking due to limited networking behavioral data. To address this, we aim to measure the re-identification privacy risk associated with sharing or releasing cellular traces amidst data breaches. Based on the analysis of key privacyimpacting features in traffic usage and base station association data, we propose a novel re-identification method, SURE, which learns similarities between cellular traces to classify if traces belong to the same user. Extensive experiments on a largescale dataset of 10,000 users over four months demonstrate SURE's superior performance, with AUC scores exceeding 0.9. Our findings reveal significant re-identification risks in data sharing/release, influenced by data scale and user attributes, corroborated by a public dataset.

The rapid growth of e-commerce has made last-mile delivery a critical service in daily life. Despite regulations mandating doorstep delivery, the pressure of penalties for delays can lead to fraudulent delivery behaviors, where couriers may report package receipt without actually deliver the package to assigned locations. Existing studies on fraud behavior detection focus on exploring user (courier) behaviors for fraud behavior detection. However, due to the inaccuracy of GPS positioning and the variability of user behavior patterns caused by dynamic environmental factors, relying solely on behavior data remains insufficient for detecting fraudulent deliveries. In this paper, we present a Multimodal Fraudulent Delivery Detection framework (MFDD), which integrates heterogeneous data from multiple agents (courier-side and user-side)-including couriers' physical behavior, digital behavior, and conversations containing customer feedback-for detecting fraudulent deliveries in the last-mile delivery. We employ attention mechanisms to extract features from each modality and use cross-modal fusion to capture complex and varied relationships between multimodal data. To further mitigate modality imbalance during training, we introduce a dynamic gradient-modulation strategy that balances learning across all modalities. We implement and evaluate MFDD on real-world, human-annotated data, achieving a 9.6% improvement in precision and a 5.8% increase in accuracy over the state-of-the-art methods. We also deploy the model in the production environment of JD Logistics, and results show that compared to existing methods, MFDD improves accuracy by 15.3%, reducing estimated annual costs by over 18.5 million CNY.

A long-standing challenge for pushing sensor-based human activity recognition (HAR) to industrial usage is the distribution shift between training data and testing data: significant variations in data distribution lead to a notable decline in performance. Recently, Large Language Models (LLMs) have demonstrated exceptional generalization capability, which provides a new opportunity to mitigate the distribution shift problem of HAR. However, since LLMs are inherently designed and trained on textual data, their potential to enhance generalization in HAR applications remains an open question. In this paper, we introduce LLM4HAR, a novel LLM-based model to improve cross-domain HAR. LLM4HAR consists of three main modules: (i) the Sensor Data Adaptation module, which aligns IMU signals with LLMs via sensor embedding(ii) the Sensor Knowledge Learning module, which injects sensor knowledge into LLMs for activity recognition, and (iii) the Efficiency Enhancement module, which employs a partial training strategy and reduces the model size by more than 10 times. Extensive evaluations show that LLM4HAR outperforms the existing methods by 13.82% in average F1 score, demonstrating the feasibility and effectiveness of transferring knowledge from pretrained LLMs to enhance HAR. Further, LLM4HAR has been adopted by JD Logistics to support downstream applications such as Courier Welfare Improvement and Map Data Generation.

UAV-assisted inspection is critical for modern power grid maintenance, enhancing efficiency and safety in remote areas. However, automatically designing UAV inspection trajectories is challenging due to the cluttered inspection environments, small inspection targets, and pervasive obstacles. We propose Auto-UIT, a novel method for generating inspection trajectories in noisy, sparse, and complex 3D point cloud. Auto-UIT has three core techniques: (1) A local structure-enhanced pylon segmentation, which accurately segments pylons, power lines, and surroundings in noisy point cloud for effective inspection target identification and trajectory planning. (2) A 3D fingerprint-based pylon type recognition that compensates for point cloud sparsity to complete missing inspection targets based on the pylon type. (3) An adaptive trajectory generation that samples positions in response to diverse pylon orientations and pervasive environmental obstacles, ensuring UAV operational safety. Our experiments on a real-world dataset across four distinct areas demonstrate that Auto-UIT outperforms existing baseline methods in all three tasks. Furthermore, a four-month deployment in a power grid inspection system—covering a 270 km2 primary mountainous area—yielded an expert first-review acceptance rate of 91.86% for the generated trajectories, and reduced design time by an average of 88.19% compared to manual methods, significantly improving inspection efficiency.

UAV-assisted inspection is critical for modern power grid maintenance, enhancing efficiency and safety in remote areas. However, automatically designing UAV inspection trajectories is challenging due to the cluttered inspection environments, small inspection targets, and pervasive obstacles. We propose a novel method for generating inspection trajectories in noisy, sparse, and complex 3D point cloud. It has three core techniques: (1) A local structure-enhanced pylon segmentation, which accurately segments pylons, power lines, and surroundings in noisy point cloud for effective inspection target identification and trajectory planning. (2) A 3D fingerprint-based pylon type recognition that compensates for point cloud sparsity to complete missing inspection targets based on the pylon type. (3) An adaptive trajectory generation that samples positions in response to diverse pylon orientations and pervasive environmental obstacles, ensuring UAV operational safety. A four-month deployment in a power grid inspection system—covering a 270 km2 primary mountainous area—yielded an expert first-review acceptance rate of 91.86% for the generated trajectories, and reduced design time by an average of 88.19% compared to manual methods, significantly improving inspection efficiency. Demo video and dataset are available at https://ljhe006.github.io/autouit/.

Human behavior sensing has been receiving growing attention from both academia and industry in recent years. We report - to the best of our knowledge - the first AI-driven nationwide human behavior sensing system called SMILE in urban last-mile delivery. SMILE detects real-time human behaviors with self-supervised sensor data pretraining and uploads detection results to cloud servers with mobile networks. During its full deployment phase at JD Logistics, SMILE is deployed on over 500,000 mobile devices carried by over 300,000 delivery couriers who travel more than 10 million KM every day in 366 Chinese cities. SMILE serves the delivery of 7 billion E-commerce orders every year for more than 500 million customers. SMILE detects walking, upstairs, downstairs, still, and driving behaviors. SMILE has been fully deployed at JD Logistics to support two real-world applications that benefit both the delivery couriers and the logistics platform: (1) workload measurement to improve couriers' welfare; (2) large-scale delivery map data generation to improve the logistics delivery efficiency.

Wireless sensing technologies have shown significant promise in various applications, but their spatial coverage is confined to the vicinity of the transmitters, limiting their applicability in broader environments. In this paper, we introduce an innovative wireless sensing approach based on the globally covered Global Navigation Satellite System (GNSS) signals. While GNSS signals have been widely used in remote sensing to monitor slow changes in the Earth’s surface, like sea level and snow depth, their ability to accurately detect highly dynamic target motions, such as human respiration, gestures, and intrusions, remains unclear. The main challenge arises from the interference brought by the large-scale satellite movement and severe GNSS signal errors. In this study, we present a novel GNSS signal enhancement system named FineSat to address these interference. Specifically, we first utilize polynomial representations to cancel satellite movement interference. Then, based on the analysis of GNSS signal errors, we propose a signal differential processing module to mitigate the errors. We implement our system on commercial devices and validate its performance in three sensing applications: respiration monitoring, gesture recognition, and intrusion detection. Results show that we achieve 0.42 bpm mean absolute error in respiration monitoring, 96.5% average accuracy in gesture recognition, and 98.6% accuracy in intrusion detection.

An efficient last-mile delivery scheme in logistics benefits customers, couriers, and the platform. In practice, the delivery scope of a delivery station is divided into multiple areas, each of which is covered by a courier. The long distances between the delivery station and areas limit the couriers' delivery efficiency given that they need to travel back and forth multiple times a day. To solve this problem, we explore an inter-trip cooperation scheme for last-mile delivery, in which couriers traveling to the delivery station and back to corresponding areas earlier can help to take others' orders back. Coordinating the courier cooperation is challenging because we need to consider the courier's status, e.g., locations, and vehicle capacity constraint simultaneously. In this work, we design an inter-trip cooperation-based last-mile delivery system, InCo, aiming to minimize the average order delivery time. InCo includes two components: i) a time-aware spanning tree algorithm to generate the cooperation result for a group of couriers; and ii) a capacity-constrained courier grouping algorithm to optimize the courier grouping result iteratively. Extensive evaluation results with real-world order data collected from one of the largest logistics companies show that InCo improves the average saved delivery time and reduces average travel time by up to 80.2% and 28.4%, respectively, compared to baseline methods. The deployment results show InCo improves the average courier working efficiency by 21.6% to the state-of-the-practice.

Illegal parking prediction is a crucial problem to help stakeholders with better urban planning and management. Existing works advance the field by capturing complex traffic correlations from spatial and temporal perspectives using deep learning models, and achieve state-of-the-art performance. However, current works do not consider the unique perspective from the illegal parking data collection process carried out by patrol officers, which can reflect a wealth of knowledge gained from each officer's on-the-ground experiences for more effective patrol. In this paper, we propose a novel behavior-aware hypergraph convolutional network named BHIPP for city-wide illegal parking prediction. To better represent the correlations of illegal parking events from patrol officers' perspective, we construct a new patrol hypergraph integrating patrol officers' experience alongsie multi-source contextual information. Additionally, we design a behavior-aware hypergraph convolutional network, which captures the complex and high-order illegal parking event correlations with officers' patrol behaviors explicitly considered. Further, we introduce a spatial-temporal illegal parking approximation module to estimate parking violations in under-patrolled regions using both historical and multi-source contextual data. Extensive experiments on real-world datasets demonstrate the superiority of our proposed BHIPP compared with a broad range of state-of-the-art baseline models across varying spatial-temporal granularities, from both regression and ranking aspects.

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 designCoMiner, an unsupervised coordinate inference framework based on textual address data, delivery event data, and courier trajectory data.CoMinerincludes 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 designCoMiner-W, a coordinates mining algorithm based on WiFi data, to further enhance the effectiveness ofCoMiner. We conduct extensive experiments on three large-scale datasets whereCoMineroutperforms the state-of-the-art methods by 20.3%. Moreover, we have designed an abnormal delivery event detection system based onCoMinerand deployed it at JD Logistics, which brings a significant reduction in abnormal delivery event rates.

On-demand delivery has experienced rapid growth in recent years, revolutionizing people's lifestyles with its timeliness and convenience. The order dispatch process in on-demand delivery isconcurrent, wherein couriers continuously accept new orders and deliver them to customers within strict time constraints and dynamic demand and supply. Most of the existing order dispatch mechanisms are designed for independent dispatch or concurrent dispatch without strict deadlines, rendering them unsuitable for real-time concurrent dispatch in on-demand delivery. To address the challenge, we propose aTime-ConstrainedActor-Critic Reinforcement learning based concurrent dispatch system calledTCAC-Dispatchto reduce the overdue rate and enhance the long-term revenue. Specifically, we first design a deep matching network (DMN) with a variable action space, which integrates both states embedding (including route behaviors encoding) and actions' embedding into a long-term value for dispatching decisions. Additionally, we design a time-constrained action pruning module to ensure compliance with time constraints. Then we utilize the Actor-Critic framework to tackle the concurrent dispatch considering strict time constraints and stochastic demand-supply. To further optimize the efficiency and delivery resource utilization, we propose an extension of TCAC (i.e., TCAC+), which consists of (i) a learning-based order service time prediction module to determine whether to relax the deadline of some orders; and (ii) a multi-critic framework to optimize concurrent order dispatch with both tight deadlines and relaxed deadlines using dynamic weighting mechanism. We evaluate the TCAC-Dispatch with one-month data involved with 36.48 million orders and 42,000 couriers collected from Eleme, one of the largest on-demand delivery companies in China. Experiments are conducted on a data-driven emulator deployed on the development environment of Eleme and the results demonstrate that our method outperforms state-of-the-art baselines with various metrics in both tight deadline and mixed deadline scenarios.

For a nationwide logistics transportation system, it is critical to make the vehicle loading plans (i.e., given many packages, deciding vehicle types and numbers) at each sorting and distribution center. This task is currently completed by dispatchers at each center in many logistics companies and consumes a lot of workloads for dispatchers. Existing works formulate such an issue as a cargo loading problem and solve it by combinatorial optimization methods. However, it cannot work in some real-world nationwide applications due to the lack of accurate cargo volume information and effective model design under complicated impact factors as well as temporal correlation. In this paper, we explore a new opportunity to utilize large-scale route and human behavior data (i.e., dispatchers' decision process on planning vehicles) to generate vehicle loading plans (i.e., plans). Specifically, we collect a five-month nationwide operational dataset from JD Logistics in China and comprehensively analyze human behaviors. Based on the data-driven analytics insights, we design a Vehicle Loading Plan learning model, named VeLP, which consists of a pattern mining module and a deep temporal cross neural network, to learn the human behaviors on regular and irregular routes, respectively. Extensive experiments demonstrate the superiority of VeLP, which achieves performance improvement by 35.8% and 50% for trunk and branch routes compared with baselines, respectively. Besides, we deployed VeLP in JDL and applied it in about 400 routes, reducing the time by approximately 20% in creating plans. It saves significant human workload and improves operational efficiency for the logistics company.

We report a 30-month nationwide deployment and operation study of an indoor arrival detection system based on Bluetooth Low Energy calledVALIDin 364 Chinese cities.VALIDis 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),VALIDconsists 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 operateVALIDfrom 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.

On-demand delivery has become an increasingly popular urban service in recent years as it facilitates citizens’ daily lives significantly. In the fulfillment cycle, the order preparation time estimation is extremely important and can be used for many applications, such as improving order dispatching and fulfillment time estimation. Existing work is generally based on high-cost physical devices or large-scale labeled training data, which are not feasible in on-demand delivery services. We solve this problem based on already collected different kinds of data from the on-demand delivery platform, e.g., the courier’s reported arrival time to the merchant. Our intuition is that the couriers’ reported time implicitly reflects the order preparation time, which leads to a challenge: complicated correlations between the couriers’ reported arrival time and the order preparation time. To solve this challenge, we design an order preparation time inference framework OPTI, which first constructs a self-supervised classification task based on the couriers’ reported arrival time to infer the coarse-grained order preparation time and then exploits semi-supervised learning to transfer the coarse-grained time to fine-grained time inference. Experimental results show that OPTI can improve the accuracy of inference by 5% to 17% compared to the state-of-the-art solutions.

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 (e.g., Google Maps) by uploading textual addresses to obtain corresponding coordinates. 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 for service providers. 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. There are three main components in CoMiner. (1) A POI-level clustering model by modeling customers' shopping patterns at different spatial granularities; (2) A Delivery Mobility Graph (DMG) by modeling couriers' delivery events and geographic coordinates; (3) A behavior-driven address ranking model by mining couriers' uncertain reporting behaviors to further infer coordinates on DMG. We extensively verify the performance of CoMiner with a three-phase evaluation from data-driven experiments to real-world deployment. (i) We conduct extensive experiments on three large-scale datasets where CoMiner achieves an average accuracy of 95.1%, which outperforms the state-of-the-art methods by 20.3%. (ii) We deploy CoMiner in JD Logistics, inferring coordinates for over 30 million addresses with an average accuracy of 93.3%. (iii) We utilize CoMiner for two Geocoding-based applications, i.e., parcel re-routing optimization and abnormal delivery event detection.

On-demand delivery is a new form of logistics where customers place orders through online platforms and the platform arranges couriers to deliver them within a short time. The acquisition of indoor status (i.e., arrival or departure at the merchants) of couriers plays an important role in order dispatching and route planning. The Bluetooth Low Energy (BLE) device is a promising solution for city-wide indoor status estimation due to the low hardware and deployment costs and low power consumption. However, the environment and smartphone model heterogeneities affect the status characteristics contained in the Bluetooth signal, resulting in the decline of status estimation performance. The previous methods to alleviate the heterogeneity are not suitable for city-wide scenarios with thousands of merchants and hundreds of smartphone models. In this paper, we propose Para-Pred, an indoor status estimation framework based on the graph neural network, which directly Predicts the effective indoor status estimation model Parameters for unseen scenarios. Our key idea is to utilize similarity between the influence patterns of heterogeneities on the Bluetooth signal to directly infer unseen scenarios' influence patterns. We evaluate the Para-Pred on 109,378 couriers with 672 smartphone models in 12,109 merchants from an on-demand delivery company. The evaluation results show that across environment and smartphone model heterogeneities, the accuracy and recall of our method achieve 93.62% and 95.20%, outperforming state-of-the-art solutions.

This paper presents our experience in adopting recent research results of mobile phone based indoor/outdoor detection (IODetector) to support the real world business of on-demand food delivery. The real world deployment of the adopted IODetector involves three phases spanning 20 months, during which the deployment scales from a feasibility study across a few areas of interest to a city-wide trial in Shanghai, and eventually to nationwide deployment over 367 cities in China. Iterative development has been performed throughout different deployment phases to excel the IODetector. Large scale evaluation and comparative A/B testing suggest key value of adopting indoor/outdoor detection in the real world business. We also present the lessons learned from the deployment experience including real world know-hows, practical limits and constraints, as well as discussions on design alternatives. We believe this paper provides insights to guide future efforts in translating research results to industry adoptions.

We report a 3-year city-wide study of an operational indoor sensing system based on Bluetooth Low Energy (BLE) calledaBeacon(short foralibabaBeacon).aBeaconis pilot-studied, A/B tested, deployed, and operated in Shanghai, China to infer the indoor status of Alibaba couriers, e.g., arrival and departure at the merchants participating in the Alibaba Local Services platform. In its full operation stage (2018/01-2020/04),aBeaconconsists of customized BLE devices at 12,109 merchants, interacting with 109,378 couriers to infer their status to assist the scheduling of 64 million delivery orders for 7.3 million customers with a total amount of$\$ $600 million order values. Although in an academic setting, using BLE devices to detect arrival and departure looks straightforward, it is non-trivial to design, build, deploy, and operateaBeaconfrom its conception to its retirement at city scale in a metric-based approach by considering the tradeoffs between various practical factors (e.g., cost and performance) during long-term system evolution. We report our study in two phases, i.e., an 8-month pilot study and a 28-month deployment and operation in the wild. We focus on an in-depth reporting on the five lessons learned and provide their implications in other systems with long-term operation and broad geospatial coverage, e.g., Edge Computing.

On-demand delivery has become an increasingly popular urban service in recent years as it facilitates citizens' daily lives significantly. Different from traditional logistics services, e.g., FedEx and UPS, on-demand delivery is expected to be completed within a relatively short time, e.g., 30 minutes to 1 hour. In the fulfillment cycle, the order preparation time estimation is extremely important, which can be used for many applications such as improving order dispatching and fulfillment time estimation. Existing work is generally based on high-cost physical devices or large-scale labeled training data, which are not feasible in on-demand delivery services. We solve this problem based on already collected different kinds of data from the on-demand delivery platform, e.g., the courier's reported arrival time to the merchant. Our intuition is that couriers' reported time implicitly reflects the order preparation time, which leads to a challenge: complicated correlations between the couriers' reported arrival time and the order preparation time. To solve this challenge, we design an order preparation time inference framework OPTI, which first constructs a self-supervised classification task based on the couriers' reported arrival time to infer the coarse-grained order preparation time, and then exploits semi-supervised learning to transfer the coarse-grained time to fine-grained time inference. We implement and evaluate OPTI in the [anonymous] platform, which is one of the largest on-demand delivery platforms in the world. Experimental results show that OPTI can improve the accuracy of inference by 5% to 17% compared to the state-of-the-art solutions.

Instant delivery has developed rapidly in recent years and significantly changed the lifestyle of people due to its timeliness and convenience. In instant delivery, the order dispatch process is concurrent. Couriers take new orders continuously and deliver multiple orders in a delivery trip (i.e., a batch). The delivery time of orders in a batch is often overlapped and interlinked with each other. The pickup and delivery sequence of the existing orders in a batch changes dynamically due to time constraints and real-time overdue possibility (i.e., the rate of deliveries that are not finished in promised time). Most of existing order dispatch mechanisms are designed for independent order dispatch or concurrent delivery without strict time constraints, hence are incapable of handling real-time concurrent dispatch with strict time constraints in on-demand instant delivery. To address the challenge, we propose a Time-Constrained Actor- Critic Reinforcement learning based concurrent dispatch system called TCAC-Dispatch to enhance the long-term overall revenue and reduce the overdue rate. Specifically, we design a deep matching network (DMN) with a variable action space, which integrates the state embedding (including route behaviors encoding) and actions embedding features into a long-term matching value. Then the Actor-Critic model tackles the concurrent order dispatch problem considering strict time constraints and stochastic demand-supply in instant delivery. An estimated time-based action pruning module is designed to ensure time constraints guarantee and accelerate the training as well as dispatching processes. We evaluate the TCAC-Dispatch with one-month data involved with 36.48 million orders and 42,000 couriers collected from one of the largest instant delivery companies in China, i.e., Eleme. Empirical experiments are conducted on a data-driven emulator deployed on the development environment of Eleme and results show that our method achieves 22% of the increase in total revenue and reduces the overdue rate by 21.6%.

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.

Instant delivery is an important urban service in recent years because of the increasing demand. An important issue for delivery platforms is to keep updating the status of couriers especially the real-time locations, which is challenging when they are in an indoor environment. We argue the previous indoor localization techniques cannot be applied in the instant delivery scenario because they require extra deployed infrastructures and extensive labor work. In this work, we perform the couriers' indoor localization transparently in a predictive manner without extra actions of couriers by existing data from the platform including order progress reports and couriers' trajectories. Specifically, we present TransLoc to predict couriers' indoor locations by addressing two challenges including uncertain reporting behaviors and uncertain indoor mobility behaviors. Our key idea lies in two insights (i) couriers' behaviors are consistent in indoor/outdoor environments; (ii) localization, as a spatial inference problem, could be converted to a temporal inference problem. We evaluate TransLoc on 565 couriers from an instant delivery company, which improves baselines by at most 72%, and achieves a competitive result compared to a label-extensive approach. As a case study, we apply TransLoc to optimize the order dispatching strategy, which reduces the delivery time by 24%.