An up-to-date city-scale lane-level map is an indispensable infrastructure and a key enabling technology for ensuring the safety and user experience of autonomous driving systems. In industrial scenarios, reliance on manual annotation for map updates creates a critical bottleneck. Lane-level updates require precise change information and must ensure consistency with adjacent data while adhering to strict standards. Traditional methods utilize a three-stage approach -- construction, change detection, and updating -- which often necessitates manual verification due to accuracy limitations. This results in labor-intensive processes and hampers timely updates. To address these challenges, we propose LDMapNet-U, which implements a new end-to-end paradigm for city-scale lane-level map updating. By reconceptualizing the update task as an end-to-end map generation process grounded in historical map data, we introduce a paradigm shift in map updating that simultaneously generates vectorized maps and change information. To achieve this, a Prior-Map Encoding (PME) module is introduced to effectively encode historical maps, serving as a critical reference for detecting changes. Additionally, we incorporate a novel Instance Change Prediction (ICP) module that learns to predict associations with historical maps. Consequently, LDMapNet-U simultaneously achieves vectorized map element generation and change detection. To demonstrate the superiority and effectiveness of LDMapNet-U, extensive experiments are conducted using large-scale real-world datasets. In addition, LDMapNet-U has been successfully deployed in production at Baidu Maps since April 2024, supporting lane-level map updating for over 360 cities and significantly shortening the update cycle from quarterly to weekly, thereby enhancing the timeliness and accuracy of lane-level map. The nationwide, high-frequency city-scale lane-level map has been instrumental in the development of the lane-level navigation product serving hundreds of millions of users, while also integrating into the autonomous driving systems of several leading vehicle companies.

Generating city-scale lane-level maps faces significant challenges due to the intricate urban environments, such as blurred or absent lane markings. Additionally, a standard lane-level map requires a comprehensive organization of lane groupings, encompassing lane direction, style, boundary, and topology, yet has not been thoroughly examined in prior research. These obstacles result in labor-intensive human annotation and high maintenance costs. This paper overcomes these limitations and presents an industrial-grade solution named DuMapNet that outputs standardized, vectorized map elements and their topology in an end-to-end paradigm. To this end, we propose a group-wise lane prediction (GLP) system that outputs vectorized results of lane groups by meticulously tailoring a transformer-based network. Meanwhile, to enhance generalization in challenging scenarios, such as road wear and occlusions, as well as to improve global consistency, a contextual prompts encoder (CPE) module is proposed, which leverages the predicted results of spatial neighborhoods as contextual information. Extensive experiments conducted on large-scale real-world datasets demonstrate the superiority and effectiveness of DuMapNet. Additionally, DuMapNet has already been deployed in production at Baidu Maps since June 2023, supporting lane-level map generation tasks for over 360 cities while bringing a 95% reduction in costs. This demonstrates that DuMapNet serves as a practical and cost-effective industrial solution for city-scale lane-level map generation.

Understanding urban regional characteristics is pivotal in driving critical insights for urban planning and management. We have witnessed the successful application of pre-trained Foundation Models (FMs) in generating universal representations for various downstream tasks. However, applying this principle to the geospatial domain remains challenging, primarily due to the difficulty of gathering extensive data for developing a dedicated urban foundation model. Though there have been some attempts to empower the existing FMs with urban data, most of them focus on single-modality FMs without considering the multi-modality nature of urban region understanding tasks. To address this gap, we introduce ReFound - a novel framework for Re-training a Foundation model for urban region understanding, harnessing the strengths of both language and visual FMs. In this framework, we first invent a Mixture-of-Geospatial-Expert (MoGE) Transformer, to effectively integrate the embedding of multi-source geospatial data. Building on this, ReFound is enhanced by jointly distilling knowledge from language, visual, and visual-language FMs respectively, thus augmenting its generalization capabilities. Meanwhile, we design a masked geospatial data modeling approach alongside a cross-modal spatial alignment mechanism, to enhance the spatial knowledge of ReFound derived from geospatial data. Extensive experiments conducted on six real-world datasets over three urban region understanding tasks demonstrate the superior performance of our framework.

\beginabstract Segmenting an urban area into regions is fundamentally important for many spatio-temporal applications. The traditional grid-based method offers a simple solution as it divides the city map into equal-sized grids, but it fails to preserve semantic information about the original urban structure. Several studies apply the road network to cut the metropolitan area into meaningful blocks. However, existing works do not achieve good scalability, and there is no public system provided so far. To address those problems, we build\mysystem, the first scalable vector-based system which generates reasonable regions using all levels of the road network. We conduct an evaluation to prove the efficiency and effectiveness of our system. We also publish our system as a Python library through Python Package Index (PyPI), and demonstrate its utility using real public datasets in this paper. The source code and useful instructions can be found on https://github.com/PaddlePaddle/PaddleSpatial/tree/main/paddlespatial/tools/genregion. \endabstract

Urban villages (UVs) refer to the underdeveloped informal settlement falling behind the rapid urbanization in a city. Since there are high levels of social inequality and social risks in these UVs, it is critical for city managers to discover all UVs for making appropriate renovation policies. Existing approaches to detecting UVs are labor-intensive or have not fully addressed the unique challenges in UV detection such as the scarcity of labeled UVs and the diverse urban patterns in different regions. To this end, we first build an urban region graph (URG) to model the urban area in a hierarchically structured way. Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG. The core idea of such a framework is to firstly pre-train a basis (or master) model over the URG, and then to adaptively derive specific (or slave) models from the basis model for different regions. The proposed framework can learn to balance the generality and specificity for UV detection in an urban area. Finally, we conduct extensive experiments in three cities to demonstrate the effectiveness of our approach.

Graph Neural Networks (GNNs) have been broadly applied in many urban applications upon formulating a city as an urban graph whose nodes are urban objects like regions or points of interest. Recently, a few enhanced GNN architectures have been developed to tackle heterophily graphs where connected nodes are dissimilar. However, urban graphs usually can be observed to possess a unique spatial heterophily property; that is, the dissimilarity of neighbors at different spatial distances can exhibit great diversity. This property has not been explored, while it often exists. To this end, in this paper, we propose a metric, named Spatial Diversity Score, to quantitatively measure the spatial heterophily and show how it can influence the performance of GNNs. Indeed, our experimental investigation clearly shows that existing heterophilic GNNs are still deficient in handling the urban graph with high spatial diversity score. This, in turn, may degrade their effectiveness in urban applications. Along this line, we propose a Spatial Heterophily Aware Graph Neural Network (SHGNN), to tackle the spatial diversity of heterophily of urban graphs. Based on the key observation that spatially close neighbors on the urban graph present a more similar mode of difference to the central node, we first design a rotation-scaling spatial aggregation module, whose core idea is to properly group the spatially close neighbors and separately process each group with less diversity inside. Then, a heterophily-sensitive spatial interaction module is designed to adaptively capture the commonality and diverse dissimilarity in different spatial groups. Extensive experiments on three real-world urban datasets demonstrate the superiority of our SHGNN over several its competitors.

The past few years have witnessed an explosive growth of user-generated POI-centric travel blogs, which can provide a comprehensive understanding of a POI for people. However, evaluating the quality of the POI-centric travel blogs and ranking the blogs is not a simple task without domain knowledge or actual travel experience on the target POI. Nevertheless, our insight is that the user search behavior related to the target POI on the online map service can partly valid the rationality of the POIs appearing in the travel blogs, which helps for travel blogs ranking. To this end, in this paper, we propose a novel end-to-end framework for travel blogs ranking, coined Matching POI and Travel Blogs with Multi-view InFormation (MOTIF). Concretely, we first construct two POI graphs as multi-view information: (1) the search-level POI graph which reflects the user behaviors on the online map service; and (2) the document-level POI graph which shows the POI co-occurrence frequency in travel blogs. Then, to better model the intrinsic correlation of the two graphs, we adopt Mutual Information Maximization to align the search-level and document-level semantic spaces. Moreover, we leverage a pair-wise ranking loss for POI-document relevance scoring. Extensive experiments on two real-world datasets demonstrate the superiority of our method.

With the increased popularity of mobile devices, Web mapping services have become an indispensable tool in our daily lives. To provide user-satisfied services, such as location searches, the point of interest (POI) database is the fundamental infrastructure, as it archives multimodal information on billions of geographic locations closely related to people's lives, such as a shop or a bank. Therefore, verifying the correctness of a large-scale POI database is vital. To achieve this goal, many industrial companies adopt volunteered geographic information (VGI) platforms that enable thousands of crowdworkers and expert mappers to verify POIs seamlessly; but to do so, they have to spend millions of dollars every year. To save the tremendous labor costs, we devised DuMapper, an automatic system for large-scale POI verification with the multimodal street-view data at Baidu Maps. This paper presents not only DuMapper I, which imitates the process of POI verification conducted by expert mappers, but also proposes DuMapper II, a highly efficient framework to accelerate POI verification by means of deep multimodal embedding and approximate nearest neighbor (ANN) search. DuMapper II takes the signboard image and the coordinates of a real-world place as input to generate a low-dimensional vector, which can be leveraged by ANN algorithms to conduct a more accurate search through billions of archived POIs in the database for verification within milliseconds. Compared with DuMapper I, experimental results demonstrate that DuMapper II can significantly increase the throughput of POI verification by 50 times. DuMapper has already been deployed in production since June 2018, which dramatically improves the productivity and efficiency of POI verification at Baidu Maps. As of December 31, 2021, it has enacted over 405 million iterations of POI verification within a 3.5-year period, representing an approximate workload of 800 high-performance expert mappers.

Estimated time of arrival (ETA) prediction, also known as travel time estimation, is a fundamental task for a wide range of intelligent transportation applications, such as navigation, route planning, and ride-hailing services. To accurately predict the travel time of a route, it is essential to take into account both contextual and predictive factors, such as spatial-temporal interaction, driving behavior, and traffic congestion propagation inference. The ETA prediction models previously deployed at Baidu Maps have addressed the factors of spatial-temporal interaction (ConSTGAT) and driving behavior (SSML). In this work, we believe that modeling traffic congestion propagation patterns is of great importance toward accurately performing ETA prediction, and we focus on this factor to improve ETA performance. Traffic congestion propagation pattern modeling is challenging, and it requires accounting for impact regions over time and cumulative effect of delay variations over time caused by traffic events on the road network. In this paper, we present a practical industrial-grade ETA prediction framework named DuETA. Specifically, we construct a congestion-sensitive graph based on the correlations of traffic patterns, and we develop a route-aware graph transformer to directly learn the long-distance correlations of the road segments. This design enables DuETA to capture the interactions between the road segment pairs that are spatially distant but highly correlated with traffic conditions. Extensive experiments are conducted on large-scale, real-world datasets collected from Baidu Maps. Experimental results show that ETA prediction can significantly benefit from the learned traffic congestion propagation patterns, which demonstrates the effectiveness and practical applicability of DuETA. In addition, DuETA has already been deployed in production at Baidu Maps, serving billions of requests every day. This demonstrates that DuETA is an industrial-grade and robust solution for large-scale ETA prediction services.

The task of POI attribute acquisition, which aims at completing missing attributes (e.g., POI name, address, status, phone, and open/close time) for a point of interest (POI) or updating existing attribute values of a POI, plays an essential role in enabling users to entertain location-based services using commercial map applications, such as Baidu Maps. Existing solutions have adopted street views or web documents to acquire POI attributes, which have a major limitation in applying for large-scale production due to the labor-intensive and time-consuming nature of collecting data, error accumulation in processing textual/visual data in unstructured or free format, and necessitating post-processing steps with manual efforts. In this paper, we present our efforts and findings from a 3-year longitudinal study on designing and implementing DuIVRS, which is an alternative, fully automatic, and production-proven solution for large-scale POI attribute acquisition via completely machine-directed dialogues. Specifically, DuIVRS is designed to proactively acquire POI attributes via a telephonic interactive voice response system, whose tasks are to generate machine-initiative directed dialogues, make scripted telephone calls to businesses, and interact with people who answered the phone to achieve predefined goals through multi-turn dialogues. DuIVRS has already been deployed in production at Baidu Maps since December 2018, which greatly improves productivity and reduces production cost of POI attribute acquisition. As of December 31, 2021, DuIVRS has made 140 million calls and 42 million POI attribute updates within a 3-year period, which represents an approximately 3-year workload for a high-performance team of 1,000 call center workers. This demonstrates that DuIVRS is an industrial-grade and robust solution for cost-effective, large-scale acquisition of POI attributes.

As the core foundation of web mapping, each geographic object (geo-object), such as a traffic sign, plays a vital role in navigation and intelligent driving. Determining how to obtain the latest high-precision geo-object information is a classic topic in updating road databases. Benefiting from the cost-effective attribute and availability of the positioning equipment and camera, the vision-based update pattern is becoming increasingly popular in the industry. Generally speaking, the road database update mainly includes three phases: geo-object recognition, localization, and change detection. Previous change detection strategies are mainly performed by comparing the historical road information (i.e., geo-object type and position) with the new geographic data of geo-objects collected from the street-view imagery. However, limited by the localization precision of the positioning equipment and the discriminative power of the vanilla differential-based method, the accuracy, recall, and efficiency of previous systems for geo-object change detection are greatly impaired. In addition, the artificially prescribed production standards make the geo-object position in the map data deviate from its position in the real world, as well as some geo-objects do not need to be updated (e.g., temporary speed limit), which further yields many false-positive detections and significantly increases the labor costs of existing systems. To address these challenges, we propose a novel framework called DuARUS for automatic geo-object change detection with street-view imagery. In this paper, we mainly focus on automatic geo-object localization and change detection. Specifically, for geo-object localization, we propose a two-stage, integrated localization algorithm based on image matching and monocular depth estimation. Furthermore, to achieve automatic change detection, vision-based representation learning and scene understanding strategies are introduced to build a large-scale geo-object semantic map, which can provide sufficient multimodal information support for change detection. Based on such artful modeling, we recast the complicated, labor-based change detection problem as a vanilla binary classification task, which is a robust and efficient strategy that contributes to resolving this problem. By combining these operations, we construct an industrial-grade, fully automatic production system for road database updates. Extensive experiments conducted on large-scale, real-world datasets from Baidu Maps demonstrate the superiority and effectiveness of the system. Moreover, this system has already been deployed in production at Baidu Maps since July 2020, handling 96% of automatic road database updates. DuARUS improves the annual update mileage from millions to tens of millions, and it achieves weekly updates.

The task of live traffic condition prediction, which aims at predicting live traffic conditions (i.e., fast, slow, and congested) based on traffic information on roads, plays a vital role in intelligent transportation systems, such as navigation, route planning, and ride-hailing services. Existing solutions have adopted aggregated trajectory data to generate traffic estimates, which inevitably suffer from GPS drift caused by cluttered urban road scenarios. In addition, the trajectory information alone is insufficient to provide evidence for sudden traffic situations and perception of street-wise elements. To alleviate these problems, in this paper, we present DuTraffic, which is a robust and production-ready solution for live traffic condition prediction by taking both trajectory data and street views into account. Specifically, the vision-based detection and segmentation modules are developed to forecast traffic flow by using street views. Then, we propose a spatial-temporal-based module, TRST-Net, to learn the latent trajectory representation. Finally, a bilinear model is introduced to mix these two representations and then predicts live traffic conditions with trajectory data and street views in a mutually complementary manner. The task is recast as a multi-task learning problem, which could benefit from the strong representation of latent space manifold modeling. Extensive experiments conducted on large-scale, real-world datasets from Baidu Maps demonstrate the superiority and effectiveness of DuTraffic. In addition, DuTraffic has already been deployed in production at Baidu Maps since December 2020, handling tens of millions of requests every day. This demonstrates that DuTraffic is a practical and robust industrial solution for live traffic condition prediction.

Pre-trained models (PTMs) have become a fundamental backbone for downstream tasks in natural language processing and computer vision. Despite initial gains that were obtained by applying generic PTMs to geo-related tasks at Baidu Maps, a clear performance plateau over time was observed. One of the main reasons for this plateau is the lack of readily available geographic knowledge in generic PTMs. To address this problem, in this paper, we present ERNIE-GeoL, which is a geography-and-language pre-trained model designed and developed for improving the geo-related tasks at Baidu Maps. ERNIE-GeoL is elaborately designed to learn a universal representation of geography-language by pre-training on large-scale data generated from a heterogeneous graph that contains abundant geographic knowledge. Extensive quantitative and qualitative experiments conducted on large-scale real-world datasets demonstrate the superiority and effectiveness of ERNIE-GeoL. ERNIE-GeoL has already been deployed in production at Baidu Maps since April 2021, which significantly benefits the performance of various downstream tasks. This demonstrates that ERNIE-GeoL can serve as a fundamental backbone for a wide range of geo-related tasks.

Mobile map apps such as the Baidu Maps app have become a ubiquitous and essential tool for users to find optimal routes and get turn-by-turn navigation services while driving. However, interacting with such apps while driving through visual-manual interaction modality inevitably causes driver distraction, due to the highly conspicuous nature of the time-sharing, multi-tasking behavior of the driver. In this paper, we present our efforts and findings of a 4-year longitudinal study on designing and implementing DuIVA, which is an intelligent voice assistant (IVA) embedded in the Baidu Maps app for hands-free, eyes-free human-to-app interaction in a fully voice-controlled manner. Specifically, DuIVA is designed to enable users to control the functionalities of Baidu Maps (e.g., navigation and location search) through voice interaction, rather than visual-manual interaction, which minimizes driver distraction and promotes safe driving by allowing the driver to keep "eyes on the road and hands on the wheel'' while interacting with the Baidu Maps app. DuIVA has already been deployed in production at Baidu Maps since November 2017, which facilitates a better interaction modality with the Baidu Maps app and improves the accessibility and usability of the app by providing users with in-app voice activation, natural language queries, and multi-round dialogue. As of December 31, 2021, over 530 million users have used DuIVA, which demonstrates that DuIVA is an industrial-grade and production-proven solution for in-app intelligent voice assistants.

The task of road extraction has aroused remarkable attention due to its critical role in facilitating urban development and up-to-date map maintenance, which has widespread applications such as navigation and autonomous driving. Existing solutions either rely on a single source of data for road graph extraction or simply fuse the multimodal information in a sub-optimal way. In this paper, we present an automatic road extraction solution named DuARE, which is designed to exploit the multimodal knowledge for underlying road extraction in a fully automatic manner. Specifically, we collect a large-scale real-world dataset for paired aerial image and trajectory data, covering over 33,000 km2 in more than 80 cities. First, road extraction is performed on the abundant spatial-temporal trajectory data adaptively based on the density distribution. Then, a coarse-to-fine road graph learner from aerial images is proposed to take advantage of the local and global context. Finally, our cross-check-based fusion approach keeps the optimal state of each modality while revisiting the original trajectory map with the guidance of aerial predictions to further improve the performance. Extensive experiments conducted on large-scale real-world datasets demonstrate the superiority and effectiveness of DuARE. In addition, DuARE has been deployed in production at Baidu Maps since June 2021 and keeps updating the road network by 100,000 km per month. This confirms that DuARE is a practical and industrial-grade solution for large-scale cost-effective road extraction from multimodal data.

Providing timely accessibility reminders (such as closed and relocated) of a point-of-interest (POI) plays a vital role in improving user satisfaction of finding places and making visiting decisions. However, it is difficult to keep the POI database in sync with the real-world counterparts due to the dynamic nature of business changes and innovations. To alleviate this problem, we formulate and present a practical solution that jointly extracts POI mentions and identifies their coupled accessibility labels from unstructured text (hereafter referred to as joint POI and accessibility extraction). We approach this task as a sequence tagging problem, where the goal is to produce (POI name, accessibility label) pairs from unstructured text. This task is challenging because of two main issues: (1) POI names are often newly-coined words so as to successfully register new entities or brands and (2) there may exist multiple pairs in the text, which necessitates dealing with one-to-many or many-to-one mapping to make each POI coupled with its matching accessibility label. To this end, we propose a Geographic-Enhanced and Dependency-guIded sequence Tagging (GEDIT) model to concurrently address the two challenges. First, to alleviate challenge #1, we develop a geographic-enhanced pre-trained model to learn the text representations, which is able to significantly relieve the problem of newly-coined words. Second, to mitigate challenge #2, we apply a relational graph convolutional network to learn the tree node representations from the parsed dependency tree, which enables us to establish a correlation between a POI and its accessibility label. Finally, we construct a neural sequence tagging model by integrating and feeding the previously pre-learned representations into a CRF layer. Extensive experiments conducted on a real-world dataset demonstrate the superiority and effectiveness of GEDIT. In addition, it has already been deployed in production at Baidu Maps, and it successfully keeps processing hundreds of thousands of Web documents every week. Statistics show that the proposed solution can save significant human effort and labor costs to deal with the same amount of documents, which confirms that it is a practical way for POI accessibility maintenance.

Next point-of-interest (POI) recommendation is a hot research field where a recent emerging scenario, next POI to search recommendation, has been deployed in many online map services such as Baidu Maps. One of the key issues in this scenario is providing satisfactory recommendation services for cold-start cities with a limited number of user-POI interactions, which requires transferring the knowledge hidden in rich data from many other cities to these cold-start cities. Existing literature either does not consider the city-transfer issue or cannot simultaneously tackle the data sparsity and pattern diversity issues among various users in multiple cities. To address these issues, we explore city-transfer next POI to search recommendation that transfers the knowledge from multiple cities with rich data to cold-start cities with scarce data. We propose a novel Curriculum Hardness Aware Meta-Learning (CHAML) framework, which incorporates hard sample mining and curriculum learning into a meta-learning paradigm. Concretely, the CHAML framework considers both city-level and user-level hardness to enhance the conditional sampling during meta training, and uses an easy-to-hard curriculum for the city-sampling pool to help the meta-learner converge to a better state. Extensive experiments on two real-world map search datasets from Baidu Maps demonstrate the superiority of CHAML framework.

Point Of Interest Auto-Completion (abbr. as POI-AC) is one of the featured functions for the search engine at Baidu Maps. It can dynamically suggest a list of POI candidates within milliseconds as a user enters each character (e.g., English, Chinese, or Pinyin character) into the search box. Ideally, a user may need to provide only one character and immediately obtain the desired POI at the top of the POI list suggested by POI-AC. In this way, the user's keystrokes can be dramatically saved, which significantly reduces the time and effort of typing, especially on mobile devices that have limited space for display and user interfaces. Despite using a user's profile and input prefixes for personalized POI suggestions, however, the state-of-the-art approach, i.e., P^3AC, still has a long way to go so as to generate not only personalized but, more importantly, time- and geography-aware suggestions. In this paper, we find that 17.9% of users tend to look for diverse POIs at different times or locations using the same prefix. This insight drives us to establish an end-to-end spatial-temporal POI-AC (abbr. as ST-PAC) module to replace P3AC at Baidu Maps. To alleviate the problem of the long-tail distribution of time- and location-specific data on POI-AC, we further propose a meta-learned ST-PAC (abbr. as MST-PAC) updated by an efficient MapReduce algorithm. MST-PAC can significantly overcome the "long-tail" issue and rapidly adapt to the cold-start POI-AC tasks with fewer examples. We sample several benchmark datasets from the large-scale search logs at Baidu Maps to assess the offline performance of MST-PAC in line with multiple metrics, including Mean Reciprocal Rank (MRR), Success Rate (SR) and normalized Discounted Cumulative Gain (nDCG). The consistent improvements on these metrics give us more confidence to launch this meta-learned POI-AC module online. As a result, the critical indicator on user satisfaction online, i.e., the average number of keystrokes in a POI-AC session, significantly decreases as well. For now, MST-PAC has already been deployed in production at Baidu Maps, handling billions of POI-AC requests every day. It confirms that MST-PAC is a practical and robust industrial solution for large-scale POI Search.

Travel time estimation (TTE) is one of the most critical modules at Baidu Maps, which plays a vital role in intelligent transportation services such as route planning and navigation. During the driving en route, the navigation system of Baidu Maps can provide real-time estimations on when a user will arrive at the destination. It automatically recalculates and updates the remaining travel time from the driver's current position to the destination (hereafter referred to as remaining route) every few minutes. The previously deployed TTE model at Baidu Maps, i.e., ConSTGAT, takes the remaining route as well as the current time as input and provides the corresponding estimated time of arrival. However, it ignores the route that has been already traveled from the origin to the driver's current position (hereafter referred to as traveled route), which could contribute to improving the accuracy of time estimation. In this work, we believe that the traveled route conveys valuable evidence that could facilitate the modeling of driving preference and take that into consideration for the task of en route travel time estimation (ER-TTE). This task is non-trivial because it requires adapting fast to a user's driving preference using a few observed behaviors in the traveled route. To this end, we frame ER-TTE as a few-shot learning problem and consider the observed behaviors in the traveled route as training examples while the future behaviors in the remaining route as test examples. To tackle the few-shot learning problem, we propose a novel model-based meta-learning approach, called SSML, to learn the meta-knowledge so as to fast adapt to a user's driving preference and improve the time estimation of the remaining route. SSML leverages the technique of self-supervised learning, which is equivalent to generating a significant number of synthetic learning tasks, to further improve the performance. Extensive offline tests conducted on large-scale real-world datasets collected from Baidu Maps demonstrate the superiority of SSML. The online tests before deploying in production were successfully performed, which confirms the practical applicability of SSML.

The increasing interest in international travel has raised the demand of retrieving point of interests (POIs) in multiple languages. This is even superior to find local venues such as restaurants and scenic spots in unfamiliar languages when traveling abroad. Multilingual POI retrieval, enabling users to find desired POIs in a demanded language using queries in numerous languages, has become an indispensable feature of today's global map applications such as Baidu Maps. This task is non-trivial because of two key challenges: (1) visiting sparsity and (2) multilingual query-POI matching. To this end, we propose a Heterogeneous Graph Attention Matching Network (HGAMN) to concurrently address both challenges. Specifically, we construct a heterogeneous graph that contains two types of nodes: POI node and query node using the search logs of Baidu Maps. First, to alleviate challenge #1, we construct edges between different POI nodes to link the low-frequency POIs with the high-frequency ones, which enables the transfer of knowledge from the latter to the former. Second, to mitigate challenge #2, we construct edges between POI and query nodes based on the co-occurrences between queries and POIs, where queries in different languages and formulations can be aggregated for individual POIs. Moreover, we develop an attention-based network to jointly learn node representations of the heterogeneous graph and further design a cross-attention module to fuse the representations of both types of nodes for query-POI relevance scoring. In this way, the relevance ranking between multilingual queries and POIs with different popularity can be better handled. Extensive experiments conducted on large-scale real-world datasets from Baidu Maps demonstrate the superiority and effectiveness of HGAMN. In addition, HGAMN has already been deployed in production at Baidu Maps, and it successfully keeps serving hundreds of millions of requests every day. Compared with the previously deployed model, HGAMN achieves significant performance improvement, which confirms that HGAMN is a practical and robust solution for large-scale real-world multilingual POI retrieval service.

As the recent COVID-19 outbreak rapidly expands all over the world, various containment measures have been carried out to fight against the COVID-19 pandemic. In Mainland China, the containment measures consist of three types, i.e., Wuhan travel ban, intra-city quarantine and isolation, and intercity travel restriction. In order to carry out the measures, local economy and information acquisition play an important role. In this paper, we investigate the correlation of local economy and the information acquisition on the execution of containment measures to fight against the COVID-19 pandemic in Mainland China. First, we use a parsimonious model, i.e., SIR-X model to estimate the parameters, which represent the execution of intra-city quarantine and isolation in major cities of Mainland China. In order to understand the execution of intra-city quarantine and isolation, we analyze the correlation between the representative parameters including local economy, mobility, and information acquisition. To this end, we collect the data of Gross Domestic Product (GDP), the inflows from Wuhan and outflows, and the COVID-19 related search frequency from a widely-used Web mapping service, i.e., Baidu Maps, and Web search engine, i.e., Baidu Search Engine, in Mainland China. Based on the analysis, we confirm the strong correlation between the local economy and the execution of information acquisition in major cities of Mainland China. We further evidence that, although the cities with high GDP per capita attract more inflows from Wuhan, people are more likely to conduct the quarantine measure and to reduce travelling to other cities. Finally, the correlation analysis using search data shows that well-informed individuals are likely to carry out containment measures.

The task of travel time estimation (TTE), which estimates the travel time for a given route and departure time, plays an important role in intelligent transportation systems such as navigation, route planning, and ride-hailing services. This task is challenging because of many essential aspects, such as traffic prediction and contextual information. First, the accuracy of traffic prediction is strongly correlated with the traffic speed of the road segments in a route. Existing work mainly adopts spatial-temporal graph neural networks to improve the accuracy of traffic prediction, where spatial and temporal information is used separately. However, one drawback is that the spatial and temporal correlations are not fully exploited to obtain better accuracy. Second, contextual information of a route, i.e., the connections of adjacent road segments in the route, is an essential factor that impacts the driving speed. Previous work mainly uses sequential encoding models to address this issue. However, it is difficult to scale up sequential models to large-scale real-world services. In this paper, we propose an end-to-end neural framework named ConSTGAT, which integrates traffic prediction and contextual information to address these two problems. Specifically, we first propose a spatial-temporal graph neural network that adopts a novel graph attention mechanism, which is designed to fully exploit the joint relations of spatial and temporal information. Then, in order to efficiently take advantage of the contextual information, we design a computationally efficient model that applies convolutions over local windows to capture a route's contextual information and further employs multi-task learning to improve the performance. In this way, the travel time of each road segment can be computed in parallel and in advance. Extensive experiments conducted on large-scale real-world datasets demonstrate the superiority of ConSTGAT. In addition, ConSTGAT has already been deployed in production at Baidu Maps, and it successfully keeps serving tens of billions of requests every day. This confirms that ConSTGAT is a practical and robust solution for large-scale real-world TTE services.

Point of interest auto-completion (POI-AC) is a featured function in the search engine of many Web mapping services. This function keeps suggesting a dynamic list of POIs as a user types each character, and it can dramatically save the effort of typing, which is quite useful on mobile devices. Existing approaches on POI-AC for industrial use mainly adopt various learning to rank (LTR) models with handcrafted features and even historically clicked POIs are taken into account for personalization. However, these prior arts tend to reach performance bottlenecks as both heuristic features and search history of users cannot directly model personal input habits. In this paper, we present an end-to-end neural-based framework for POI-AC, which has been recently deployed in the search engine of Baidu Maps, one of the largest Web mapping applications with hundreds of millions monthly active users worldwide. In order to establish connections among users, their personal input habits, and correspondingly interested POIs, the proposed framework (abbr. P3AC) is composed of three components, i.e., a multi-layer Bi-LSTM network to adapt to personalized prefixes, a CNN-based network to model multi-sourced information on POIs, and a triplet ranking loss function to optimize both personalized prefix embeddings and distributed representations of POIs. We first use large-scale real-world search logs of Baidu Maps to assess the performance of P3AC offline measured by multiple metrics, including Mean Reciprocal Rank (MRR), Success Rate (SR), and normalized Discounted Cumulative Gain (nDCG). Extensive experimental results demonstrate that it can achieve substantial improvements. Then we decide to launch it online and observe that some other critical indicators on user satisfaction, such as the average number of keystrokes and the average typing speed at keystrokes in a POI-AC session, which significantly decrease as well. In addition, we have released both the source codes of P3AC and the experimental data to the public for reproducibility tests.

The constrained outbreak of COVID-19 in Mainland China has recently been regarded as a successful example of fighting this highly contagious virus. Both the short period (in about three months) of transmission and the sub-exponential increase of confirmed cases in Mainland China have proved that the Chinese authorities took effective epidemic prevention measures, such as case isolation, travel restrictions, closing recreational venues, and banning public gatherings. These measures can, of course, effectively control the spread of the COVID-19 pandemic. Meanwhile, they may dramatically change the human mobility patterns, such as the daily transportation-related behaviors of the public. To better understand the impact of COVID-19 on transportation-related behaviors and to provide more targeted anti-epidemic measures, we use the huge amount of human mobility data collected from Baidu Maps, a widely-used Web mapping service in China, to look into the detail reaction of the people there during the pandemic. To be specific, we conduct data-driven analysis on transportation-related behaviors during the pandemic from the perspectives of 1) means of transportation, 2) type of visited venues, 3) check-in time of venues, 4) preference on "origin-destination'' distance, and 5) "origin-transportation-destination'' patterns. For each topic, we also give our specific insights and policy-making suggestions. Given that the COVID-19 pandemic is still spreading in more than 200 overseas countries, infecting millions of people worldwide, the insights and suggestions provided here may help fight COVID-19.

Entity recommendation, providing users with an improved search experience by proactively recommending related entities to a given query, has become an indispensable feature of today’s Web search engine. Existing studies typically only consider the query issued at the current timestep while ignoring the in-session user search behavior (short-term search history) or historical user search behavior across all sessions (long-term search history) when generating entity recommendations. As a consequence, they may fail to recommend entities of interest relevant to a user’s actual information need. In this work, we believe that both short-term and long-term search history convey valuable evidence that could help understand the user’s search intent behind a query, and take both of them into consideration for entity recommendation. Furthermore, there has been little work on exploring whether the use of other companion tasks in Web search such as document ranking as auxiliary tasks could improve the performance of entity recommendation. To this end, we propose a multi-task learning framework with deep neural networks (DNNs) to jointly learn and optimize two companion tasks in Web search engines: entity recommendation and document ranking, which can be easily trained in an end-to-end manner. Specifically, we regard document ranking as an auxiliary task to improve the main task of entity recommendation, where the representations of queries, sessions, and users are shared across all tasks and optimized by the multi-task objective during training. We evaluate our approach using large-scale, real-world search logs of a widely-used commercial Web search engine. We also performed extensive ablation experiments over a number of facets of the proposed multi-task DNN model to figure out their relative importance. The experimental results show that both short-term and long-term search history can bring significant improvements in recommendation effectiveness, and the combination of both outperforms using either of them individually. In addition, the experiments show that the performance of both entity recommendation and document ranking can be significantly improved, which demonstrates the effectiveness of using multi-task learning to jointly optimize the two companion tasks in Web search.

The increasing number of vehicles makes it difficult to find parking spaces, especially for the citizens living in metropolises. Therefore, many people prefer parking at curbsides for convenience’s sake regardless of whether they are authorized or not for parking. It tends to cause a severe problem of traffic jams. In order to address the issue, urban planners are eager to mitigate the stress of the traffic by discovering more curbside parking spaces. The technique of image processing supports a modern solution that can only cover a small range of roads with the aid of CCTV surveillance systems. In this paper, we propose to leverage large-scale vehicle trajectories in Baidu Maps to help detect more popular curbside parking spaces, ensuring broad coverage of roads. However, the main challenge we encountered is that only a small proportion of curbside parking spaces are authorized/labeled. To tackle this challenge, we distill several effective features and develop an approach based on a weak-labeled active learning framework. The framework can utilize very few annotated training samples to label the rest unlabeled samples iteratively. In each round, our approach can adopt several pre-trained classifiers that are ensembled to find more positive or negative samples. We use two real-world datasets to measure the performance of several modern methods of curbside parking space detection. Extensive experimental results demonstrate that our method achieves significant improvements in Fl-score over the other approaches on the two datasets.

The value assessment of private properties is an attractive but challenging task which is widely concerned by a majority of people around the world. A prolonged topic among us is "how much is my house worth?". To answer this question, most experienced agencies would like to price a property given the factors of its attributes as well as the demographics and the public facilities around it. However, no one knows the exact prices of these factors, especially the values of public facilities which may help assess private properties. In this paper, we introduce our newly launched project "Monopoly" (named after a classic board game) in which we propose a distributed approach for revaluing private properties by learning to price public facilities (such as hospitals, schools, and metros) with the large-scale urban data we have accumulated via Baidu Maps. To be specific, our method organizes many points of interest (POIs) into an undirected weighted graph and formulates multiple factors including the virtual prices of surrounding public facilities as adaptive variables to parallelly estimate the housing prices we know. Then the prices of both public facilities and private properties can be iteratively updated according to the loss of prediction until convergence. We have conducted extensive experiments with the large-scale urban data of several metropolises in China. Results show that our approach outperforms several mainstream methods with significant margins. Further insights from more in-depth discussions demonstrate that the "Monopoly" is an innovative application in the interdisciplinary field of business intelligence and urban computing, and it will be beneficial to tens of millions of our users for investments and to the governments for urban planning as well as taxation.