Online food delivery (OFD) services, known for offering varied meals at home, have gained global popularity. Meituan has recently ventured into the affordable market segment with its "Pinhaofan'' service, highlighting the imperative to delivery efficiency. To achieve this, delivery scope is regarded as one of the most effective operational tools. The delivery scope of a merchant refers to the geo-graphical area where they can serve customers. Current methods for generating delivery scopes primarily focus on optimizing a single merchant's efficiency or rely on manual delineated from the merchant's perspective, neglecting the merchant substitution effect and potentially resulting in order loss. In this paper, we propose a novel method, named Collaborative Scope, which views the delivery scope as an assortment optimization problem, considering the substitution effect between merchants from the user's perspective. We introduce the discrete choice model of econometrics and use the Enhanced Multinomial Logit Model to predict user conversion rates in the merchant list. Next, we formulate the delivery scope optimization problem of multiple merchants as a mixed integer programming problem. The city-wide solution of this problem, owing to the large-scale combinatorial optimization triggered by high-dimensional decision variables, incurs high computational complexity. To address this, we propose an approximate solution to the original problem through a first-order Taylor series approximation, which significantly reduces the computation complexity at the expense of a slight decrease in solution accuracy. Offline and online A/B test results indicate that, compared to existing methods, Collaborative Scope significantly improves delivery efficiency by reducing delivery difficulty without hurt of order volume. Notably, Collaborative Scope is currently deployed on "Pinhaofan'', serving tens of millions of online users.

The recent past has witnessed a notable surge in on-demand food delivery (OFD) services, offering delivery fulfillment within dozens of minutes after an order is placed. In OFD, pooling multiple orders for simultaneous delivery in real-time order assignment is a pivotal efficiency source, which may in turn extend delivery time. Constructing high-quality order pooling to harmonize platform efficiency with the experiences of consumers and couriers, is crucial to OFD platforms. However, the complexity and real-time nature of order assignment, making extensive calculations impractical, significantly limit the potential for order consolidation. Moreover, offline environment is frequently riddled with unknown factors, posing challenges for the platform's perceptibility and pooling decisions.

On-demand food delivery (OFD) service has gained fast-growing popularity all around the world. Order dispatching is instrumental to large-scale OFD platforms, such as Meituan, which continuously match food order requests to couriers at a scale of tens of millions each day to satisfy the needs of consumers, couriers, and merchants. However, due to high dynamism and inevitable uncertainties in the real-world environment, it is not an easy task to achieve long-term global objective optimization through continuous isolated optimization decisions at each dispatch moment. Our work proposes the concept of "courier occupancy" (CO) to precisely quantify the impact of order assignment on the courier's delivery efficiency, realizing a decomposition of long-term and macro goals into various dispatch moments and micro decision-making dimensions. Then in the prediction phase, an improved and universally applicable distribution estimation method is designed to quantify CO which is a stochastic variable and contains future information, combining Monte Carlo dropout and knowledge distillation. In the optimization phase, we use CO to model the objective function at each dispatch moment to introduce future information and extend dispatch decisions from merely who to assign the order to both when and who to assign it, significantly enhancing the long-term optimization capability of dispatching decisions and avoiding local greed. We conduct extensive offline simulations based on real dispatching data as well as online AB tests through Meituan's platform. Results show that our method consistently improves the couriers' delivery efficiency and consumers' satisfaction.

Online food delivery (OFD) services have become popular globally, serving people's daily needs. Precise area-of-interest (AOI) boundaries help OFD platforms determine customers' exact locations, which is crucial for maintaining consistency in delivery difficulty and providing a uniform customer experience within an AOI. Existing AOI generation methods primarily rely on predefined shapes or density-based clustering, which limits the quality of the contours. Recently, Meituan has treated the AOI contours as a binary semantic segmentation problem. Their approach involves a multi-step post-process to address the issues with boundary breaks caused by semantic segmentation models, leading to decreased quality and inefficiency in the learning process. In this paper, we propose a novel method for AOI contour generation called C-AOI (Contour-based Area-of-Interest). C-AOI is an instance segmentation model that focuses on generating high-quality AOI contours. Unlike the former method, which relies on pixel-by-pixel classification, C-AOI starts from the center point of the AOI and regresses the boundary. This approach results in a higher-quality boundary and is less computationally intensive. C-AOI first corrects errors on the contour using a local aggregation mechanism. Then, we propose a novel deforming module called the contour transformer, which captures the global geometry of the object. To enhance the positional relationship among vertices, we introduce a learnable cyclic positional encoding applied to the contour transformer. Finally, to improve the boundary details, we propose the Adaptive Matching Loss (AML) that eliminates over-smoothed boundaries and promotes optimized convergence pathways. Experimental results on real-world datasets collected from Meituan have demonstrated that C-AOI significantly improves the mask and boundary quality compared to Meituan's previous work. Moreover, Its inference speed is comparable to that of E2EC, a state-of-the-art real-time contour-based method. It is noteworthy that C-AOI has been deployed in the Meituan platform for producing AOIs.

On-demand food delivery (ODFD) is booming globally. ODFD services rely heavily on accurate Areas of Interest (AOIs) data to make effective operational decisions, such as service areas of restaurants. Few methods can automatically generate AOIs with contours highly matched to geographic boundaries, so manual labeling and auditing still play a vital role in data production. Most existing studies can merely generate approximate ranges which are not accurate enough for business usage. The others, which can generate AOIs fitting geographic boundaries through map matching methods, are limited by the deficiency of road network data, as geographic boundaries contain rivers, mountains, and so on. To address this issue, we propose a novel AOI generation framework using large-scale geospatial data to generate AOIs which are closer to geographic boundaries. In our framework, we firstly extract multimodal features from satellite images, road networks as well as delivery data (customer addresses, locations, etc.), and then use a semantic segmentation model to infer pixel-level points that possibly lie within the boundaries of AOIs. After that, a contour learning method and simple post-processing are applied to fit the discrete pixel-level points to reconstruct contours of AOIs in arbitrary shapes. Experiments were conducted aiming at comparing the proposed framework with six competing methods qualitatively and quantitatively. As a result, our proposed framework performs better in generating AOIs with accurate and geometry-preserving contours.

With the development of mobile Internet, online food delivery (OFD) services have become increasingly popular in our daily lives. OFD platforms rely heavily on accurate Areas-of-Interest (AOIs) information on many aspects of their operations to pinpoint customers' exact locations and to define the service areas of restaurants. Recently, OFD platforms have started to tap into the vast amount of geospatial data generated in their day-to-day business to improve the accuracy of their AOI information. Although there has been a proliferation of studies that leverage such data to detect the underlying AOIs, for example, to identify the names and spatial boundaries of the AOIs, they focus on the single-AOI detection problem, that is, they detect AOIs one at a time and ignore their spatial dependency. This would end up with inconsistent results, i.e., AOIs with overlapping spatial boundaries. To address this issue, we propose a new approach to detect multiple AOIs simultaneously and solve the multi-AOIs detection problem. In our approach, we first apply the existing single-AOI detection algorithms to generate candidate spatial boundaries for AOIs in a neighborhood, and then develop a Binary Integer Linear Programming (BILP) model to determine the best candidate spatial boundaries for these AOIs while accounting for their spatial dependency. We conduct numerical experiments using real data from Meituan, the largest OFD platform in China. Results show that our model not only produces consistent AOI boundaries, but also improves the average F1 score by 4.7%.

Road network generation from massive trajectories has mainly focused on the mining of urban arterial roads while the demand for refined road networks in communities full of low-grade roads is increasing. Trajectory GPS signals in communities tend to be very noisy and hard to label for general modeling. We propose Style-Transfer-Roadnet-Generation(STRG), a method based on un-supervised image style transfer to discover roads from trajectories in these communities. First, we convert the trajectory data into raster images. Then we train a style transferer to transform the raster images into road network style images. At last, the road networks are recovered and refined from predicted images. Experiments on the large-scale data and online deployment results show that STRG can effectively model the texture style of road networks in communities and discover new road networks from trajectories in a low-cost manner, outperforming strong baseline approaches.

On-demand food delivery service has widely served people's daily demands worldwide, e.g., customers place over 40 million online orders in Meituan food delivery platform per day in Q3 of 2021. Predicting the food preparation time (FPT) of each order accurately is very significant for the courier and customer experience over the platform. However, there are two challenges, namely incomplete label and huge uncertainty in FPT data, to make the prediction of FPT in practice. In this paper, we apply probabilistic forecasting to FPT for the first time and propose a non-parametric method based on deep learning. Apart from the data with precise label of FPT, we make full use of the lower/upper bound of orders without precise label, during feature extraction and model construction. A number of categories of meaningful features are extracted based on the detailed data analysis to produce sharp probability distribution. For probabilistic forecasting, we propose S-QL and prove its relationship with S-CRPS for interval-censored data for the first time, which serves the quantile discretization of S-CRPS and optimization for the constructed neural network model. Extensive offline experiments over the large-scale real-world dataset, and online A/B test both demonstrate the effectiveness of our proposed method.

Online meal delivery is undergoing explosive growth, as this service is becoming increasingly popular. A meal delivery platform aims to provide excellent and stable services for customers and restaurants. However, in reality, several hundred thousand orders are canceled per day in the Meituan meal delivery platform since they are not accepted by the crowd soucing drivers. The cancellation of the orders is incredibly detrimental to the customer's repurchase rate and the reputation of the Meituan meal delivery platform. To solve this problem, a certain amount of specific funds is provided by Meituan's business managers to encourage the crowdsourcing drivers to accept more orders. To make better use of the funds, in this work, we propose a framework to deal with the multi-stage bonus allocation problem for a meal delivery platform. The objective of this framework is to maximize the number of accepted orders within a limited bonus budget. This framework consists of a semi-black-box acceptance probability model, a Lagrangian dual-based dynamic programming algorithm, and an online allocation algorithm. The semi-black-box acceptance probability model is employed to forecast the relationship between the bonus allocated to order and its acceptance probability, the Lagrangian dual-based dynamic programming algorithm aims to calculate the empirical Lagrangian multiplier for each allocation stage offline based on the historical data set, and the online allocation algorithm uses the results attained in the offline part to calculate a proper delivery bonus for each order. To verify the effectiveness and efficiency of our framework, both offline experiments on a real-world data set and online A/B tests on the Meituan meal delivery platform are conducted. Our results show that using the proposed framework, the total order cancellations can be decreased by more than 25% in reality.

Nowadays, on-demand food delivery service has become fashionable in China. The efficiency of food delivery relies heavily on accurate coordinates of destination Points of Interest (POI). However, the coordinates of the destination POIs from the existing geospatial data warehouses still have many problems that perplex couriers severely. The major problems can be concluded in two categories: 1) the deviation of POI coordinates; 2) the lack of POI coordinates. To address these problems, we propose a POI-coordinate-generating framework based on couriers' and users' behavioral data of historical waybills. In particular, we start with a combinatorial strategy to assign waybills to Areas of Interest (AOI). Second, we generate a destination POI name by processing the user address for each waybill, and all waybills are grouped by the corresponding POI name. Then, a data density image of the behavioral data is generated for each group, with the ground-truth location of the POI labeled. Finally, a U-Net is trained by using the images generated in the previous step to infer locations of the POIs. We evaluated this framework by launching experiments and case studies on large-scale datasets, and the result shows our framework can predict coordinates of POIs accurately. These predicted coordinates can be used to calibrate deviated coordinates of many POIs and complement the geospatial data warehouse.

Online food ordering and delivery service has widely served people's daily demands worldwide, e.g., it has reached a number of 34.9 million online orders per day in Q3 of 2020 in Meituan food delivery platform. For the food delivery service, accurate estimation of the driver's delivery route and time, defined as the FD-RTP task, is very significant to customer satisfaction and driver experience. In the paper, we apply deep learning to the FD-RTP task for the first time, and propose a deep network named FDNET. Different from traditional heuristic search algorithms, we predict the probability of each feasible location the driver will visit next, through mining a large amount of food delivery data. Guided by the probabilities, FDNET greatly reduces the search space in delivery route generation, and the calculation times of time prediction. As a result, various kinds of information can be fully utilized in FDNET within the limited computation time. Careful consideration of the factors having effect on the driver's behaviors and introduction of more abundant spatiotemporal information both contribute to the improvements. Offline experiments over the large-scale real-world dataset, and online A/B test demonstrate the effectiveness of our proposed FDNET.

Recently on-demand food delivery service has become very popular in China. More than 30 million orders are placed by eaters of Meituan-Dianping everyday. Delicacies are delivered to eaters in 30 minutes on average. To fully leverage the ability of our couriers and restaurants, delivery scope is proposed as an infrastructure product for on-demand food delivery area. A delivery scope based retrieval system is designed and built on our platform. In order to draw suitable delivery scopes for millions of restaurant partners, we propose a pioneering delivery scope generation framework. In our framework, a single delivery scope generation algorithm is proposed by using spatial computational techniques and data mining techniques. Moreover, a scope scoring algorithm and decision algorithm are proposed by utilizing machine learning models and combinatorial optimization techniques. Specifically, we propose a novel delivery scope sample generation method and use the scope related features to estimate order numbers and average delivery time in a period of time for each delivery scope. Then we formalize the candidate scopes selection process as a binary integer programming problem. Both branch&bound algorithm and a heuristic search algorithm are integrated in our system. Results of online experiments show that scopes generated by our new algorithm significantly outperform manual generated ones. Our algorithm brings more orders without hurt of users' experience. After deployed online, our system has saved thousands of hours for operation staff, and it is considered to be one of the most useful operation tools to balance demand of eaters and supply of restaurants and couriers.