Shared micromobility (e.g., shared bikes and electric scooters), as a kind of emerging urban transportation, has become more and more popular in the world. However, the blooming of shared micromobility vehicles brings some social problems to the city (e.g., overloaded vehicles on roads, and the inequity of vehicle deployment), which deviate from the city regulator's expectation of the service of the shared micromobility system. In addition, the multi-operator shared micromobility system in a city complicates the problem because of their non-cooperative self-interested pursuits. Existing regulatory frameworks of multi-operator vehicle rebalancing generally assume the intrusive control of vehicle rebalancing of all the operators, which is not practical in the real world. To address this limitation, we design REALISM, a regulatory framework for coordinated scheduling in multi-operator shared micromobility services that incorporates the city regulator's regulations in the form of assigning a score to each operator according to the city goal achievements and operators' individual contributions to achieving the city goal, measured by Shapley value. To realize the fairness-aware score assignment, we measure the fairness of assigned scores and use them as one of the components to optimize the score assignment model. To optimize the whole framework, we develop an alternating procedure to make operators and the city regulator interact with each other until convergence. We evaluate our framework based on real-world e-scooter usage data in Chicago. Our experiment results show that our method achieves a performance gain of at least 39.93% in the equity of vehicle usage and 1.82% in the average demand satisfaction of the whole city.

The development of sensing technologies has broad-ened the scope of urban dynamics research. However, existing methods primarily focus on using isolated aspects of urban data, limiting their ability to capture the complex spatial-temporal dependency among different urban dynamics and generalizability across applications. Addressing these shortcomings requires a more comprehensive model capable of integrating multifaceted data, generating generalized representations adaptable to diverse applications and scenarios. In this paper, we introduce the Multifaceted SpAtial-TempoRal ContrastivE Learning framework, i.e., MARCEL, an innovative approach designed to learn robust, universally applicable, and adaptable representations of multifaceted urban dynamics through contrastive learning. MARCEL employs pretrained preliminary representation learning modules to extract distinct spatial-temporal dependencies inherent to each urban dynamic independently. It then features a Spatial-Temporal Contrastive Learning strategy to capture unified spatial-temporal patterns, including asynchronous, conflicting, and complementary behaviors across multifaceted urban dynamics. Additionally, MARCEL integrates a Multifaceted Knowledge Transfer mechanism to capture inter-dependencies among different urban dynamics and facilitate knowledge sharing. The learned representations are highly generalizable and can be applied effectively to various downstream tasks. Extensive experiments on real-world urban datasets demonstrate that MARCEL is effective and significantly outperforms state-of-the-art baselines.

Textual description of a physical location, commonly known as an address, plays an important role in location-based services(LBS) such as on-demand delivery and navigation. However, the prevalence of abnormal addresses, those containing inaccuracies that fail to pinpoint a location, have led to significant costs. Address rewriting has emerged as a solution to rectify these abnormal addresses. Despite the critical need, existing address rewriting methods are limited, typically tailored to correct specific error types, or frequently require retraining to process new address data effectively. In this study, we introduce AddrLLM, an innovative framework for address rewriting that is built upon a retrieval augmented large language model. AddrLLM overcomes aforementioned limitations through a meticulously designed Supervised Fine-Tuning module, an Address-centric Retrieval Augmented Generation module and a Bias-free Objective Alignment module. To the best of our knowledge, this study pioneers the application of LLM-based address rewriting approach to solve the issue of abnormal addresses. Through comprehensive offline testing with real-world data on a national scale and subsequent online deployment, AddrLLM has demonstrated superior performance in integration with existing logistics system. It has significantly decreased the rate of parcel re-routing by approximately 43%, underscoring its exceptional efficacy in real-world applications.

In on-demand delivery,online orders are delivered by couriers from merchants to customers within a short time (e.g., 45 minutes). An important task is to provide an efficient order dispatching solution. Existing studies focus on scenarios with stable routing behavior using pre-determined courier-order matching before delivery while ignoring real-time dynamics during delivery. In this work, we leverage courier-courier encounter events as an opportunity to enable cooperative order dispatching (i.e., conducting order transfers among couriers during delivery) for better delivery efficiency. However, it is non-trivial to conduct encounter-aware cooperative order dispatching in real-time dynamics due to two major challenges: (i) the dynamic nature of encounters in diverse real-world scenarios, and (ii) global delivery efficiency optimization by local order transfers. To address the above challenges, we design a detection-driven cooperative dispatching framework, called DECO. Specifically, we design (i) a Received Signal Strength Indicator (RSSI) variance-based state encoder to model encounter dynamics, (ii) an encounter event selector to choose encounter scenarios, (iii) a time-constrained order mask module to filter unsuitable orders, and (iv) an encounter-aware order transfer scheduler to make detailed order transfer decisions. Extensive experiments on real-world data from two large companies (i.e., JD Logistics, Eleme) show that DECO outperforms other baselines.Real-world deployment results at JD Logistics show that DECO improves the order overdue rate by 4.8%.

Trajectory data is a valuable asset for service management and spatio-temporal mining in transportation and logistics systems. However, due to equipment failure, network delay, and energy constraints, some trajectory point may be missed, which makes it difficult for trajectory-based management. Some researchers have focused on recovering sparse trajectories from road networks and historical trajectory data, but these methods are ineffective when the road network is incomplete. Recent research works have explored learning-based methods to recover trajectories in free space but lack user movement behavior modeling and efficient feature extraction on sparse long-range trajectories. Our work exploits the periodic behavior of couriers and fine-grained Area of Interest (AOI) data for sparse trajectory recovery in last-mile delivery. However, we face challenges with AOI access sequence deviations due to GPS inaccuracies and abnormal courier behaviors, as well as the complex, dynamic relationships within and between courier routes due to uncertain pick-up demands. To address these challenges, we design a graph-based multi-task learning framework, focusing on multi-scale attention fusion for end-to-end free space trajectory recovery. Our approach starts with a behavior-aware graph network that generates detailed spatial features. Following this, we propose a multi-scale attention fusion mechanism to extract intra- and inter-trajectory features. Finally, we design a multi-task learning module that predicts both coarse-grained spatial access sequences and fine-grained trajectory points. We evaluate the model with six-month data involved with more than 360,000 trajectory segments and more than 7.2 million waybills collected from one of the largest logistic companies in China. Extensive experiments on real-world datasets demonstrate that our method outperforms state-of-the-arts in multiple metrics.

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.

Urban traffic is subject to disruptions that cause extended waiting time and safety issues at signalized intersections. While numerous studies have addressed the issue of intelligent traffic systems in the context of various disturbances, traffic signal malfunction, a common real-world occurrence with significant repercussions, has received comparatively limited attention. The primary objective of this research is to mitigate the adverse effects of traffic signal malfunction, such as traffic congestion and collision, by optimizing the control of neighboring functioning signals. To achieve this goal, this paper presents a novel traffic signal control framework (MalLight), which leverages an Influence-aware State Aggregation Module (ISAM) and an Influence-aware Reward Aggregation Module (IRAM) to achieve coordinated control of surrounding traffic signals. To the best of our knowledge, this study pioneers the application of a Reinforcement Learning(RL)-based approach to address the challenges posed by traffic signal malfunction. Empirical investigations conducted on real-world datasets substantiate the superior performance of our proposed methodology over conventional and deep learning-based alternatives in the presence of signal malfunction, with reduction of throughput alleviated by as much as 48.6%.

Multi-modal transportation leverages the advantages of various transportation modes, leading to more efficient urban traveling services. Accurately predicting transfer times between different modes provides guidance for tasks such as trip planning and transportation management. Most existing transfer time prediction works rely on strong assumptions, e.g., predetermined routes, assumed speeds, and predefined downstream transportation timetables. However, these assumptions are hard to hold in practice due to internal factors like individual preferences and external factors like dynamic traffic conditions. These factors are dynamic and vary with location and time, presenting a significant challenge. To address this, we introduce an adaptive transfer time prediction framework, AdaTrans, to forecast personalized transfer times between upstream and downstream transportation modes. Firstly, an attribute learning module is designed to model the trends of internal factors. Then a spatial-temporal adaptive learning component is designed to learn dynamic external factors. Finally, an aggregation component with a capsule network is employed to fuse the influences of these factors. The extensive evaluation results in two real-world datasets demonstrate that AdaTrans effectively harnesses insights from internal and external factors, outperforming state-of-the-art methods by ~20%.

Logistics platforms provide real-time door-to-door order pickup services to enhance customer convenience. However, a high volume of unexpected order cancellations negatively impacts both customer satisfaction and logistics profitability. Identifying whether these cancellations are due to customers' decisions or couriers' behaviors is crucial for implementing targeted operational improvements. While traditional methods directly interpret customer-courier dialogues, incorporating situational context (e.g., couriers' historical performance and current workloads) helps us to accurately understand the hidden content. The main challenges lie in dynamically correlating couriers' varying behaviors with dialogue content. To tackle this challenge, we develop COCO, a cause identification framework for order cancellation in logistics, which includes: i) Multi-modal features exploration, which analyzes dialogues and couriers' behaviors (both historical and current); ii) Multi-modal features aggregation, which uses a hierarchical attention mechanism to adaptively capture the dynamic correlations within dialogues and behaviors; iii) LLM-enhanced refinement, which leverages Large Language Models to accurately process a large number of unlabeled dialogues, significantly enhancing COCO's generalization and performance. Our extensive evaluation with JD Logistics demonstrates COCO's exceptional performance, achieving an 12.2% increase in precision and a 9.1% improvement in recall over existing methods. Furthermore, after deploying COCO at JD Logistics, it has achieved an accuracy of 89.5%, further demonstrating its practical utility.

Accurate prediction of order transportation time is essential for customer satisfaction in logistics. Existing methods based on origin-destination (OD) pairs do not consider the diversity of road segments, while route-based methods may fail to account for real-time traffic conditions due to the infrequent dispatch schedules of logistics vehicles. In reality, e-commerce platforms have collaborated with multiple logistics companies for parcel delivery, providing a richer dataset that offers a more comprehensive view of real-time transportation conditions. The key insight is that data from one company can serve as internal capability detectors and data from others can act as external environment detectors. However, a significant challenge arises in inferring travel-time-correlated station pairs across different companies, especially without full disclosure of station information. To address this, we design an Adaptive cross-platform Transportation time prediction framework built upon a hypergraph structure, named AdaTrans, comprising: i) A spatial-temporal routing graph learner employs node-centric and edge-centric hyperedges to address the complex, non-pairwise correlations among stations and station pairs within and across companies; ii) A spatial-temporal graph-based transportation time predictor that utilizes multi-task learning to enhance overall transportation time prediction by leveraging the correlations between interconnected sub-tasks (i.e., dwell and travel times prediction) Extensive evaluation with real-world data collected from JD.com, a leading e-commerce platform in China, demonstrates that consolidating records from other companies reduces RMSE, MAE, and MAPE by 12.63%, 5.18%, and 16.67%, compared to state-of-the-art methods.

Individual cellular networks have been very robust to random cell tower failure due to redundant cell tower deployments. However, a large-scale clustered failure (e.g., due to fiber cut or cyber attacks) with multiple cell towers can lead to the loss of services of a cellular network. Recently, off-the-shelf smartphones can support multiple network standards, so cellular network infrastructure sharing is a promising direction to improve the service robustness under potential large-scale clustered cell tower failure. The existing work on cellular network robustness is usually limited to large-scale studies of individual networks or small-scale studies of multiple networks. In this work, we conduct the first investigation, to our knowledge, into the benefits of cross-network infrastructure sharing for enhancing robustness at a full cellular penetration rate. We design a new metric to quantify cellular network robustness with or without cross-network sharing under both random and clustered cell tower failures. We further study the impact of spatial dynamics on cellular network robustness.

The problem of traffic dynamics prediction, aiming to capture the complicated patterns of urban dynamics and forecast short-term future traffic status, is essential for managing transportation systems, reducing congestion, enhancing safety, improving commuter efficiency, and supporting urban planning and infrastructure development. Current approaches using ma-chine learning and deep neural networks have advanced traffic prediction but often focus on individual urban dynamic aspects and rely on auto-regressive methods for consecutive predictions, which can be inaccurate and computationally expensive. In this work, we propose the Spatial- Temporal Graph LAtent DIffusion ModeL (STGAIL) to address these limitations. STGAIL views geographical regions as graphs with various traffic features, capturing their interconnections. Operating in a pre-trained latent space, STGAIL uses latent diffusion processes and inno-vative spatial-temporal graph layers for accurate and efficient multi-step predictions. Fine-tuning with temporal binary masks further enhances its performance, avoiding error accumulation and reducing computational costs. Experiments on real-world datasets demonstrate STGAIL's superior accuracy and efficiency over state-of-the-art methods. We also make our code and dataset available, contributing to ongoing research in traffic dynamics prediction.

Enhancing diverse human decision-making processes in an urban environment is a critical issue across various applications, including ride-sharing vehicle dispatching, public transportation management, and autonomous driving. Offline reinforcement learning (RL) is a promising approach to learn and optimize human urban strategies (or policies) from pre-collected human-generated spatial-temporal urban data. However, standard offline RL faces two significant challenges: (1) data scarcity and data heterogeneity, and (2) distributional shift. In this paper, we introduce MODA - a Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing approach. MODA addresses the challenges of data scarcity and heterogeneity in a multi-task urban setting through Contrastive Data Sharing among tasks. This technique involves extracting latent representations of human behaviors by contrasting positive and negative data pairs. It then shares data presenting similar representations with the target task, facilitating data augmentation for each task. Moreover, MODA develops a novel model-based multi-task offline RL algorithm. This algorithm constructs a robust Markov Decision Process (MDP) by integrating a dynamics model with a Generative Adversarial Network (GAN). Once the robust MDP is established, any online RL or planning algorithm can be applied. Extensive experiments conducted in a real-world multi-task urban setting validate the effectiveness of MODA. The results demonstrate that MODA exhibits significant improvements compared to state-of-the-art baselines, showcasing its capability in advancing urban decision-making processes. We also made our code available to the research community.

Empowered by the widespread adoption of Internet of Things (IoT) devices and smartphones, last-mile delivery services have evolved to accommodate both delivery and pickup tasks. An essential challenge in last-mile delivery is efficiently planning routes for couriers to handle pre-scheduled delivery requests as well as stochastic pickup requests. Existing work approaches this problem by either adjusting routes on the fly when new requests arise or preplanning routes based on predicted future pickup requests. However, these methods either compromise the optimality of planned routes or heavily rely on the accuracy of predictions. In this work, we take conformal prediction as an opportunity to address the issue of prediction uncertainty. We design ROPU, a novel courier route planning framework for logistics systems that incorporates conformal prediction into reinforcement learning. Our work advances the existing work from two aspects: (i) Pickup request prediction utilizes spatial-temporal conformal prediction to capture historical pickup request patterns, providing a unified spatial-temporal conformal interval with high confidence (ii) A spatial-temporal attention network assesses location importance from various perspectives and enables the actor to perceive time and integrate the spatial-temporal conformal interval. We implement and evaluate ROPU on one of the largest logistics platforms. Extensive experiment results demonstrate that our method outperforms other state-of-the-art methods with improvements of at least 30.49% in the pickup overdue rate, 25.00% in the delivery overdue rate, and 5.49% in the traveling distance metric.

Shared electric micromobility (e.g., shared electric bikes and electric scooters), as an emerging way of urban transportation, has been increasingly popular in recent years. However, managing thousands of micromobility vehicles in a city, such as rebalancing and charging vehicles to meet spatial-temporally varied demand, is challenging. Existing management frameworks generally consider demand as the number of requests without the energy consumption of these requests, which can lead to less effective management. To address this limitation, we design RECOMMEND, a rebalancing and charging framework for shared electric micromobility vehicles with energy-informed demand to improve the system revenue. Specifically, we first re-define the demand from the perspective of energy consumption and predict the future energy-informed demand based on the state-of-the-art spatial-temporal prediction method. Then we fuse the predicted energy-informed demand into different components of a rebalancing and charging framework based on reinforcement learning. We evaluate the RECOMMEND system with 2-month real-world electric micromobility system operation data. Experimental results show that our method can be easily integrated into a general RL framework and outperform state-of-the-art baselines by at least 26.89% in terms of net revenue.

Logistics audience expansion, the process for logistics companies to find potential long-term customers, is one of the most important tasks for business growth. However, existing methods for conventional audience expansion fall short due to two significant challenges, the intricate interplay of multiple complex factors in the logistics scenario and the emphasis on long-term logistics service usage instead of one-time promotions. To address the above limitations, we design LOGAE-TKG, a logistics audience expansion method based on a temporal knowledge graph, which consists of three components: (i) a temporal logistics knowledge graph pre-trained model to model the effect of multiple complex factors and build a solid logistics knowledge base for contracting and usage prediction; (ii) an intention learning model with data augmentation-based comparison to capture the contracting intention; (iii) a future pattern discovery model to uncover post-contract patterns. We evaluate and deploy our method on the JingDong e-commerce platform. Extensive offline experiment results and real-world deployment results demonstrate the effectiveness of our method.

Identifying regional driving risks is important for real-world applications such as driving safety warning applications, public safety management, and insurance company premium pricing. Previous approaches are either based on traffic accident reports or vehicular sensor data. They either fail to identify potential risks, such as near-miss collisions, which would need other important measurements (e.g., hard break, acceleration, etc.), or fail to generalize to cities without vehicular sensor data, severely limiting their practicality. In this work, we address these two challenges and successfully identify regional driving risks in a target city without vehicular sensor data via cross-city transfer learning. Specifically, we design a novel framework RiskTrans by optimizing both the predictor and the relationship between cities to achieve transfer learning. We advance the existing works from two aspects: (i) we achieve it in a transductive manner without accessing labeled data in the target cities; (ii) we identify and address the problem of negative transfer in cross-city transfer learning, a prominent issue that is often (surprisingly) neglected in previous works. Finally, we conduct extensive experiments based on data collected from 175 thousand vehicles in six cities. The results show RiskTrans outperforms baselines by at least 50.2% and reduces negative transfer by 49.4%.

Traffic accident prediction is a crucial problem for public safety, emergency treatment, and urban management. Existing works leverage extensive data collected from city infrastructures to achieve encouraging performance based on various machine learning techniques but cannot achieve a good performance in situations with limited data (i.e., data scarcity). Recent developments in transfer learning bring a new opportunity to solve the data scarcity problem. In this paper, we design a novel cross-city transfer learning framework named CARPG for predicting traffic accidents in data-scarce cities. We address the unique challenge of predicting traffic accidents caused by its two fundamental characteristics, i.e., spatial heterogeneity and inherent rareness, which result in the biased performance of the state-of-the-art transfer learning methods. Specifically, we build cross-city region connections by jointly learning the spatial region representations for both source and target cities with an inter-city global graph knowledge transfer process. Further, we design an efficient attention-based parameter-generating mechanism to learn region-specific traffic accident patterns, while controlling the total number of parameters. Built upon that, we ensure that only relevant patterns are transferred to each target region during the knowledge transfer process and further to be fine-tuned. We conduct extensive experiments on three real-world datasets, and the evaluation results demonstrate the superiority of our framework compared with state-of-the-art baseline models.

Last-mile delivery from delivery stations to customers’ places is now mainly finished by dedicated couriers. In practice, each courier generally collects orders destined for one delivery area at the delivery station and delivers orders to customers. However, the long distance between the delivery station and the delivery area due to practical reasons, e.g., expensive delivery station rental fee in the downtown area, increases the delivery courier’s travel time and decreases the efficiency of the state-of-the-practice last-mile delivery scheme. In this paper, we solve the problem with relay transportation, where a relay courier collects orders at the delivery station and sends them to delivery couriers, and delivery couriers focus on the order delivery at corresponding delivery areas. We design a real-time relay courier scheduling system called REDE to minimize the average relay order delivery time (ARODT) considering the relay and delivery couriers’ mobility and the order destination distribution. First, a heterogeneous task aware route prediction algorithm is proposed to characterize the delivery courier’s mobility. Then a distance-aware greedy algorithm and an ARODT-constrained exchange algorithm are designed to generate the relay route, which is updated with real-time order pickup requests. Extensive evaluation results with real-world logistics data from 100 delivery stations in 38 cities show that REDE reduces ARODT by up to 8.4% compared to baseline methods. The online A/B tests show that compared to the state-of-the-practice method, REDE improves the delivery courier’s working efficiency and the daily number of pickup orders by 20.13% and 4.51%, respectively.

Last-mile logistics is the final step of the delivery process from a transit station to customers. In last-mile logistics systems, a city is divided into many delivery areas for couriers to finish the parcel transition tasks. In recent years, last-mile logistics faces huge challenges in system efficiency and customer experience due to highly dynamic logistics service demand across different delivery areas. How to design a proper mechanism to improve the system efficiency and customer experience has become an important task. In this paper, we formulate the delivery area assignment problem and propose a Group-based Cooperative Reinforcement Learning (GCRL) framework to optimize the last-mile logistics system. Firstly, we design a multi-level attention mechanism to construct an optimal courier team that provides cooperative pick-up and delivery services. Secondly, A graph generator and graph-based strategy are proposed to represent the decision dependency and coordinate the dependent behaviors among couriers, respectively. Finally, we design a simultaneous training mechanism to maximize the discounted return and guide the delivery area for each courier. Being formulated in a multi-agent way, GCRL focuses on the cooperation among couriers while considering the system context and couriers’ preferences. Experiments on real-world data show that GCRL achieves an average of 12% improvements compared with state-of-the-art models.

The popularity of e-commerce has promoted the rapid development of the logistics industry in recent years. As an important step in logistics, last-mile delivery from delivery stations to customers' addresses is now mainly finished by couriers, which requires accurate workload assessment based on actual efforts. However, the state-of-the-practice assessment methods neglect a vital factor that orders with the same customer's address (i.e., Homogeneous orders) can be delivered in a group (i.e., in a single trip) or separately (i.e., in multiple trips). It would cause unfair assessment among couriers if following the same rule. Thus, grouping homogeneous order accurately in the workload assessment is significant for achieving fair courier's workload assessment. To this end, we design, implement, and deploy a nationwide homogeneous order grouping system called FHOG for improving the accuracy of homogeneous order grouping in last-mile delivery for fair courier's workload assessment. FHOG utilizes the courier's reporting behavior for order inspection, collection, and delivery to identify homogeneous orders in the delivery station simultaneously for homogeneous order grouping. Compared with the state-of-the-practice method, our evaluation shows FHOG can effectively reduce order amounts with the higher and lower assessed courier's workload. We further deploy FHOG online in 8336 delivery stations to provide homogeneous order grouping service for more than 120 thousand couriers and 12 million daily orders. The results of the two surveys show that the couriers' acceptance rate is improved by 67% with FHOG after the promotion.

An address, a textual description of a physical location, plays an important role in location-based services such as on-demand delivery and e-commerce. However, abnormal addresses (i.e., an address without detailed information representing a spatial location) have led to significant costs. In real-world settings like e-commerce, abnormal address detection is not trivial because it needs to be completed in real-time to support massive online queries. In this study, we design FastAddr, a fast abnormal address detection framework, which detects abnormal addresses among millions of addresses in a short time. By investigating and modeling the hierarchical structure of address data, we first design a novel contrastive address augmentation approach to generate training data via learning the entity transition probability matrix. We further design a lightweight multi-head attention model for learning compact address representation by modeling the address characteristics. We conduct a comprehensive three-phase evaluation. (i) We evaluate FastAddr on a real-world dataset and it yields the average F1 of 85.7% in 0.058 milliseconds, which outperforms the state-of-the-art models by 47.4% with similar detection time. (ii) An offline A/B test shows that FastAddr outperforms the previous deployed model significantly. (iii) We also conduct an online A/B test to compare FastAddr with the deployed model, which shows an improvement of F1 by more than 20%. Moreover, a real-world case study demonstrates both the efficiency and effectiveness of FastAddr.

The emergence of Online-to-Offline (O2O) stores based on delivery platforms (e.g., Uber Eats, DoorDash, and Eleme) provides great convenience to people's lives. In the O2O model, one of the essential problems for merchants is to select a suitable store site, i.e., store site recommendation problem. We argue that the existing works for the traditional brick-and-mortar stores cannot address this problem due to two unique factors in the O2O model including (i) dynamic supply caused by courier capacity and dispatching strategies and (ii) various customer demands caused by delivery distance and customer preferences. To incorporate these new factors, we design$O^{2}$SiteRec, a store site recommendation method under the O2O model via multi-graph attention networks, which consists of (i) a courier capacity model based on a multi-semantic relation graph attention network to capture courier capacity; (ii) a heterogeneous multi-graph based recommendation model, where the courier capacity, customer preferences, and context features are fused. We evaluate our method based on one-month real-world data consisting of 39,465 stores and 23.6 million orders from one of the largest O2O platforms in China. Experimental results demonstrate that our method outperforms state-of-the-art baselines in various metrics.

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.

Given widely adopted vehicle tracking technologies, usage-based insurance has been a rising market over the past few years. With potential discounts from insurance companies, customers voluntarily install sensing devices in their vehicles for insurance companies, which are utilized to analyze their historical driving patterns to derive the risks of future driving. However, it is challenging to characterize and predict driving patterns, especially for new users with limited data. To address this issue, we propose and evaluate a system called MoCha to accurately characterize driving patterns for usage-based insurance. The key question we aim to explore with MoCha is whether we can fully explore long-term driving patterns of new users with only limited historical data of themselves by leveraging abundant data of other users and contextual information. To answer this question, we design (i) a multi-level driving pattern modeling component to capture the spatial-temporal dependency on both individual and group level, and (ii) a multi-task learning method to utilize underlying relations of driving metrics and predict multiple driving metrics simultaneously. We implement and evaluate MoCha with real-world on-board diagnostics data from a large insurance company with more than 340,000 vehicles. Further, we validate the usefulness of MoCha by predicting driving risks based on real-world claim data in a Chinese city, Shenzhen.