--- title: "Enhancing Fine-Grained Urban Flow Inference via Incremental Neural Operator" authors: - "Qiang Gao" - "Xiaolong Song" - "Li Huang" - "Goce Trajcevski" - "Fan Zhou" - "Xueqin Chen" doi: "10.24963/ijcai.2024/644" url: "https://ijcai.org/proceedings/2024/644" pdf: "https://ijcai.org/proceedings/2024/0644.pdf" venue: "Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence" year: 2024 pages: "5826-5834" track: "Main Track" keywords: - "Data Mining: DM: Applications" - "Data Mining: DM: Mining spatial and/or temporal data" - "Multidisciplinary Topics and Applications: MTA: Transportation" - "Multidisciplinary Topics and Applications: MTA: Sensor networks and smart cities" --- # Enhancing Fine-Grained Urban Flow Inference via Incremental Neural Operator Qiang Gao, Xiaolong Song, Li Huang, Goce Trajcevski, Fan Zhou, Xueqin Chen *Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence*, pages 5826-5834. DOI: [10.24963/ijcai.2024/644](https://doi.org/10.24963/ijcai.2024/644) ## Abstract Fine-grained urban flow inference (FUFI), which involves inferring fine-grained flow maps from their coarse-grained counterparts, is of tremendous interest in the realm of sustainable urban traffic services. To address the FUFI, existing solutions mainly concentrate on investigating spatial dependencies, introducing external factors, reducing excessive memory costs, etc., -- while rarely considering the catastrophic forgetting (CF) problem. Motivated by recent operator learning, we present an Urban Neural Operator solution with Incremental learning (UNOI), primarily seeking to learn grained-invariant solutions for FUFI in addition to addressing CF. Specifically, we devise an urban neural operator (UNO) in UNOI that learns mappings between approximation spaces by treating the different-grained flows as continuous functions, allowing a more flexible capture of spatial correlations. Furthermore, the phenomenon of CF behind time-related flows could hinder the capture of flow dynamics. Thus, UNOI mitigates CF concerns as well as privacy issues by placing UNO blocks in two incremental settings, i.e., flow-related and task-related. Experimental results on large-scale real-world datasets demonstrate the superiority of our proposed solution against the baselines. ## Full text ## 1 Introduction In the development of smart cities [Zheng et al., 2014], studying fine-grained urban flow is crucial for providing accurate warnings in specific traffic situations such as congestion [Zhou et al., 2020; Liang et al., 2022; Yu et al., 2023]. However, obtaining data at a spatially fine-granularity (FG) is not conducive to urban environments, as it requires deploying a (prohibitively) large number of sensing devices to cover a citywide landscape [Liang et al., 2019]. In contrast, acquiring coarse-grained (CG) data is much simpler. Hence, finegrained urban flow inference (FUFI) from CG observations has become an essential task for urban planners. In practice, solving FUFI can significantly alleviate the burden associated with deploying massive piezoelectric sensors and loop detectors on road segments. βˆ— Corresponding author (Li Huang) Typically, FUFI can be viewed as a mapping problem that transforms data from a space with low information content into a more informative space [Liang et al., 2019; Ouyang et al., 2020]. Motivated by the successful methods of image super-resolution (SR) in computer vision (CV) [Dong et al., 2015; Lim et al., 2017], the first convolutional neural network (CNN)-based FUFI framework named UrbanFM was proposed in [Liang et al., 2019], with a primary focus on addressing spatial correlations between coarse-grained and fine-grained maps as well as the complexities introduced by external factors. Grounded in the concept of UrbanFM, recent works enhanced FUFI accuracy by exploring spatial dependencies [Liang et al., 2020; Zhou et al., 2021], inferring on a large scale [Ouyang et al., 2020], and mitigating catastrophic forgetting [Yu et al., 2023]. Although these novel methods have achieved remarkable results, there are still some challenges that have not been resolved yet. In practice, urban flow maps usually exhibit intricate patterns and structures with varying complexities at different scales. Existing efforts are based on the CG map and target FG map with the view of super-resolution rules, directly learning a mapping function between them, which is naturally scale sensitive [Liang et al., 2019; Zhou et al., 2021]. Since these models lack affluent exploration and adaptation to diverse scales, this hinders the models' ability to capture nuanced spatial relationships and patterns present in urban flow maps across various scales, resulting in the learned neural network not generalizing well to different scales of maps beyond the map size of the training data and even yielding unstable training. Some recent ODE-based solutions (e.g., FODE [Zhou et al., 2020]) can partially solve the instability problem in FUFI - however, by relying on overly complex function solving, we find that they still face the gradientexploding problem. We consider that exploring the learning of scale-invariant mappings with rich structures and patterns within spatial domains from a more flexible space rather than directly from the data space itself is worthy of investigation. In addition, a FUFI system usually needs to handle sequentially arriving datasets/tasks. Prior studies primarily strive to either build an isolation model - which, essentially, will retrain the whole model with a newly acquired FUFI dataset; or rely on a fine-tuned approach, - which is to continually update the parameters of the model trained on the previous dataset. However, recursively retraining the model for each newly coming dataset leads to a lack of insights from the previously learned knowledge, i.e., catastrophic forgetting (CF) [Kirkpatrick et al., 2017]. In fine-tuning manners, one could initially preserve old knowledge from the learned model, which can further boost the learning of the new task. However, due to the back-propagation impact, the old knowledge would also be forgotten with the constant process of fine-tuning. [Yu et al., 2023] follows the rule of continual learning and builds a replay buffer to maintain the old knowledge behind the retained samples from the old datasets, providing a new perspective that the old knowledge can enhance the learning of newly arrived tasks/datasets. Nevertheless, we argue that such a manner could be impractical in various realworld FUFI contexts. For instance, due to privacy concerns, we could not have the right to access the previous datasets after model training, failing to bind/transfer old knowledge to new tasks. Hence, how to tackle the privacy concern in addition to CF becomes another urgent issue in the FUFI problem. To remedy the above concerns, this study presents a novel Urban Neural Operator solution with Incremental learning (UNOI). Specifically, we draw inspiration from the discretization-invariant capabilities of recent neural operators (NO) and their successful application in super-resolution tasks [Wei and Zhang, 2023], and propose an Urban Neural Operator (UNO) that extends NO to capture grainedinvariant features from the original CG flow map as a supplementary for the input of the urban flow inference module. Unlike the original Neural Operator (NO) methods, UNO focuses on learning mappings between two approximation spaces of continuous functions. In addition, with the CF issue in mind, we operate our UNO in an incremental learning manner with two incremental settings, primarily seeking to transfer old knowledge to the new task/dataset. Specifically, to consider the time-dependent relationship between neighboring temporal flows, we propose a flow-related incremental enhancement method for individual task learning, aiming at mitigating the CF problem in a single task. To address the concerns related to data privacy when handling a series of FUFI tasks/datasets, we propose a task-related incremental enhancement method for sequential task learning. In this procedure, we do not need to build additional replay buffers to enforce the model to remember the old knowledge. Instead, our solution is a data-free paradigm, which treats the earlier trained model as the old knowledge carrier rather than retaining connections to real-world old samples. Our main contributions are summarized as follows: - We devise an urban neural operator (UNO) to generate grained-invariant solutions for addressing the FUFI task by following the rule of recent operator learning, yielding a more reliable and effective manner of flow inference. - We extend UNO to UNOI with two incremental enhancement methods to address the catastrophic forgetting problem as well as privacy concerns. To the best of our knowledge, this is the first attempt to alleviate the CF issue with a data-free view in handling a series of FUFI tasks. - We conduct extensive experiments on four real-world datasets/tasks to demonstrate that UNOI not only outperforms the baselines but also offers a more date-free manner to mitigate the CF problem in sequential task learning. ## 2 Preliminaries and Problem Statement We first introduce the technical foundation of neural operators (NO), followed by the specific problem that UNO aims to address, and end with the task statement of FUFI. Neural Operators. Neural operators (NO) are designed to learn mappings between infinite-dimensional function spaces based on a finite set of observed input-output pairs, which are widely employed to generate discretization-invariant solutions for a family of partial differential equations (PDEs) [Li et al., 2020c; Li et al., 2020b; Li et al., 2020a; Kovachki et al., 2023]. Suppose we have: (1) R π‘‘π‘Ž -valued input functions π‘Ž ∈ A defined on a bounded domain π·π‘Ž βŠ‚ R π‘‘π‘Ž ; and (2) output functions 𝑒 ∈ U which are R 𝑑𝑒 -valued and defined on a bounded domain 𝐷𝑒 βŠ‚ R 𝑑𝑒 . Building on the concept of the Green's Function [Greenberg, 2015], the general formula for the NO framework can be described as follows: L : R π‘‘π‘Ž ↦ β†’ R π‘‘β„Ž0 in Eq. (1) represents the Lifting mapping, which aims at mapping the input function to its first hidden representation: {π‘Ž : 𝐷 β†’ R π‘‘π‘Ž ↦ β†’ β„Ž0 : 𝐷 β†’ R π‘‘β„Ž0 }. Typically, π‘‘β„Ž0 is greater than π‘‘π‘Ž, indicating that this operation serves as a channel expansion step. The Iterative Kernel Integration in Eq. (2) involves a local linear operator π‘Šπ‘™ ∈ R π‘‘β„Žπ‘™+1 Γ—π‘‘β„Žπ‘™ and a Kernel Integral operator (K𝑙 (β„Žπ‘™))(π‘₯) = βˆ«π·π‘™ π‘˜ (𝑙 ) πœ™ (π‘₯, 𝑦)β„Žπ‘™ (𝑦)𝑑𝑦, used to map each hidden representation to the next one: {β„Žπ‘™ : 𝐷𝑙 β†’ R π‘‘β„Žπ‘™ ↦ β†’ β„Žπ‘™+1 : 𝐷𝑙+1 β†’ R π‘‘β„Žπ‘™+1 }, while maintaining spatial continuum. 𝜎 denotes the activation function. P in Eq. (3) denotes a point-wise function for Projecting the final hidden representation to the output function: {β„ŽπΏ : 𝐷 β€² β†’R π‘‘β„ŽπΏ ↦ β†’ 𝑒 : 𝐷 β€² β†’R 𝑑𝑒 }. Problem Statement. A coarse-grained flow map 𝑿 with size 𝐻 Γ— π‘Š can be defined as a vector-valued function 𝑣 in Hilbert space H with bounded domain π·π»Γ—π‘Š β†’ R+. To obtain the fine-grained flow map X, UNO aims to learn an urban neural operator GΘ between two H-spaces associated with coarse-grained (𝐷𝐼×𝐽 ) and its arbitrary fine-grained (𝐷NIΓ—NJ ) domains, respectively: where 𝑁 ∈ Z+ is upscaling factor. We can access the cell-wise function values of 𝑣 at the coordinates (cells) {π‘₯𝑖} π»Γ—π‘Š 𝑖=1 βŠ‚ 𝐷, where π‘₯𝑖 ∈ 𝑿. Typically, we can regard cell flow π‘₯𝑖 as "snapped" at the central point of a cell in a gridbased map [Hershberger, 2013]. By doing so, we transfer the infinite-dimensional function mapping problem to learn the mapping between two approximation spaces with finitedimension and continuous functions: < l a t e x i s h 1 \_ b 6 4 = " D j 9 J X y q Q L F V o K Y w > A B H c N S 8 E 3 U r / 0 m k M 2 p + z g u G Z I f d W 7 v 5 C P T n R O < l a t e x i s h 1 \_ b 6 4 = " n Q X 9 L / q 3 d Y f g F T V m 5 D Z 8 E > A B H c J O z + I j R N U k C G 7 M p w o v P S W K 2 r u y 0 < l a t e x i s h 1 \_ b 6 4 = " f J H R A F Q B v X K 9 8 W p P U > c V D L T g E O z + I j C k 0 S M Z / u w o 3 q n G 7 m Y N r 2 d y 5 < l a t e x i s h 1 \_ b 6 4 = " R 2 3 8 K m Z q 9 z 0 n + 5 g Y X V > A B / H c D L S N F W r w J U I d y k o M C P T O 7 u G v E Q p j f < l a t e x i s h 1 \_ b 6 4 = " u 9 B I q Y P S v d 3 8 A U c r z > 7 X V N E J W O K o k j w G C / 2 M m 0 Z R L + n p H T Q F 5 g f y D < l a t e x i s h 1 \_ b 6 4 = " H I F k z K C + G O m Y 9 Z f > A B X c V N S 8 E 3 U r q / o L 0 M 2 Q T 5 J w v p d y 7 R W D g u n j P < l a t e x i s h 1 \_ b 6 4 = " z J Y C m A 8 Q f v w H B N O 0 3 + > n c V S E U r q / j W y p 5 I 9 F L D Z u g d 2 X 7 T R o k P G M K < l a t e x i s h 1 \_ b 6 4 = " w S T Z c B P o y Q 0 U C V z L m M > A 7 n N 8 E J 3 r q / 9 k I F 2 + O D H G u j Y W f p v 5 d K R g X < l a t e x i s h 1 \_ b 6 4 = " g P D K R m 3 y G X C o T 7 S q r U j / n Y > A E H c V L N F J 2 v 0 W 5 I d w M k + p B O u Z z Q 8 9 f < l a t e x i s h 1 \_ b 6 4 = " D k d + v N r X Y G 3 7 Q J w > A C H c V L S g B E y / K I R U W 0 2 T P Z M z m 5 8 o q O u F 9 n p j f < l a t e x i s h 1 \_ b 6 4 = " Y N O T 7 + o L k U g D I v d 8 B y > A C 3 c V S F J r P n H q p R Q K u M 2 G Z m 5 f E / z 9 W w 0 j X < l a t e x i s h 1 \_ b 6 4 = " G d Y C S n u U V M H T f L B 8 w > A + c D N F 2 p r o j 0 E m J Z O y g k W 9 I R 3 K / q z P X 7 Q v 5 Figure 1: Overview of UNO. ![Image](https://ijcai.org/proceedings/2024/fulltext/644/image_000000_e2be3533ee1c12f71f53b12c0377871611ea10ce50beef83ad9fa4fccdd175ab.png) where x 𝑗 ∈ X and the vector-valued functions of CG and FG spaces are 𝑣 ∈ H(𝐷𝐼×𝐽) and 𝑒 ∈ H(𝐷NIΓ—NJ), respectively. Task Statement. In traditional approaches to solving the FUFI problem [Liang et al., 2020; Zhou et al., 2020; Liang et al., 2021], the learning model commonly relies on complete urban flow data. For example, we could obtain years of urban flows and feed them into the learning system all at once. As claimed in [Yu et al., 2023], such a paradigm belongs to offline learning. In contrast, we focus on solving the FUFI problem in an incremental learning manner, assuming the previously used flow data is not available due to privacy concerns. Formally, given a sequence of chronological urban flow datasets, we learn new (e.g., year 𝑑) knowledge incrementally with the help of old (e.g., year (𝑑 - 1)) knowledge, without having to revisit the actual previous urban flows, where handling each year's flow maps can be treated as an individual task (e.g., task 𝑑). ## 3 Urban Neural Operator As Fig.1 shows, while keeping the neural operator in mind, our UNO adopts an encoder-processor-decoder architecture. The encoder (i.e., Lifting Encoder) corresponds to the Lifting mapping; the processor (i.e., Contextual Processor) encompasses Interactive Kernel Integration and additional Environmental Extractor; and the decoder (i.e., Spatial Constraint Decoder) is responsible for simply getting us back to the space of the output function - all of which we detail next. ## 3.1 Lifting Encoder (LE) In contrast to the traditional neural operators from PDEs that employ a simple function such as a multi-layer perception (MLP) to lift the input scale, our devised LE Eπœ“ in UNO is responsible for lifting the input space into a higher-dimensional latent space, which helps the model capture structural interactions and basis functions from the coarse-grained flow maps, enhancing the capacity to handle complexly implicit interactions behind the urban flow volumes. For LE architecture, we employ two CNN layers at the head and tail of Eπœ“, sequentially connected by several residual blocks. Each residual block comprises two CNN layers connected by an activation layer, and the input features are added to the final output. In this manner, increasing layers rather than feature channels not only enhances model performance but also reduces memory cost(cf. [Lim et al., 2017]). Recall Eq.(1) - now we use Eπœ“ to handle each input flow map 𝑿 as the lifting operation in NO, which can be briefly defined as follows: We will feed the output of Eq.(6) into Contextual Processor. ## 3.2 Contextual Processor (CP) CP mainly contains two layers: the Spatial Kernel Integral attempts to capture structural interactions from a spatial perspective, and the Environmental Extractor strives to incorporate multiple factual bases that could affect flow dynamics. Spatial Kernel Integral. Recent works discussed various possible forms for implementing the kernel integral operator, such as graph neural networks [Li et al., 2020c; Li et al., 2020b], Fourier transformations [Li et al., 2020a], attentionbased [Kovachki et al., 2021; Cao, 2021; Li et al., 2022], etc. In contrast, we employ linear Galerkin-type attention without Softmax [Cao, 2021], which has an approximation capacity comparable to a Petrov-Galerkin projection [Reddy, 2013] under a Hilbertian setup [Cao, 2021] and is also effective in capturing structural interactions in the spatial domain [Li et al., 2022]. In Galerkin-type attention, the latent representation is interpreted column-wise, where each column represents a basis, in contrast to conventional row-wise attention. Specifically, it can be formulated as: where β„Žπ‘™ ∈ R (𝐼𝐽)Γ—π‘‘β„Ž and β„Ž0 = β„Ž0(𝑿). Similar to standard Transformer [Vaswani et al., 2017], 𝑄, 𝐾 and 𝑉 are the latent representations calculated by multiplication with three trainable projection matrices π‘Šπ‘„, π‘ŠπΎ and π‘Šπ‘‰ ∈ R π‘‘β„ŽΓ—π‘‘β„Ž , respectively, i.e., 𝑄 = π‘‹π‘Šπ‘„, 𝐾 = π‘‹π‘ŠπΎ and 𝑉 = π‘‹π‘Šπ‘‰ ∈ R (𝐼𝐽)Γ—π‘‘β„Ž . And LN(Β·) represents the layer normalization. More importantly, we use the results of 𝐴𝑑𝑑 (β„Žπ‘™) to replace π‘˜ (𝑙 ) πœ™ (π‘₯, 𝑦)β„Žπ‘™ (𝑦)𝑑𝑦 in Eq. (2). In UNO, 𝑿𝑙 = {β„Žπ‘™ (π‘₯𝑖)} π‘‘β„Ž 𝑖=1 , each item β„Žπ‘™ (π‘₯𝑖) is column-wise component. Moreover, we introduce a nonlinear feedforward neural network (FFN) with residual concatenation inside to enrich the bases in 𝑄, 𝐾, and 𝑉 (cf. [Cao, 2021]). Hence, the iterative kernel integral specified in Eq. (2) can be rewritten as: where οΏ½ 𝑣 𝑗 , π‘˜π‘š οΏ½ = (𝐾 𝑇 𝑉)π‘šπ‘— . The iterative kernel integration helps capture the relationship between the input and output, and progressively approximates the target function at different scales. Thus, the output β„ŽπΏ({x 𝑗 } 𝐼×𝐽 𝑗=1 ) is approximately mapped to the target FG space H(𝐷NIΓ—NJ). Since the input β„Ž0 ({π‘₯𝑖 } 𝐼 ×𝐽 𝑖=1 ) only possesses 𝐼 Γ— 𝐽 points, it approximates the mapping to 𝐼 ×𝐽 points in the FG space, which is less than the exact number NI Γ—NJ of points in the FG space. More specifically, considering the spatial constraint between CG and FG maps, each point in the CG space corresponds to 𝑁 Γ— 𝑁 subregions of the approximate space of FG. Therefore, we can approximate each point in β„ŽπΏ({x𝑗 } 𝐼×𝐽 𝑗=1 ) to the value of the central point of each subregion. We use a map injection operation ( 𝑓 ) on the output β„ŽπΏ({x𝑗 } 𝐼×𝐽 𝑗=1 ) based on the points' coordinates {xπ‘šπ‘›} NI,NJ 𝑛=1,π‘š=1 in the FG map to reconstruct the value around x𝑗 , 𝑗 = 1, Β· Β· Β· , 𝐼 Γ— 𝐽: Environmental Extractor. Our Environmental Extractor strives to augment the learned features by incorporating multiple factual grounds, which include three phases (P1-P3): (P1) Static Facts Encoding: With the same settings as in UrbanFM [Liang et al., 2019], we utilize several distinct embedding layers to embed continuous (e.g., temperature, wind speed, etc.) and categorical features (e.g., weather, the day of the week, etc.) into low-dimensional vectors. Specifically, for continuous features, we directly concatenate their values to form the continuous external vector 𝑒con. For categorical features, we utilize separate embedding layers and then concatenate those embeddings to construct the categorical vector 𝑒cat. The final external factor is given by 𝑒 = 𝑒con βŠ• 𝑒cat. In the end, we employ dense layers to transform it into the same function space as Λ†β„ŽπΏ(x), denoted as 𝑒(x). (P2) Spatial Relation Augmentation: We first concatenate Λ†β„ŽπΏ(x) and 𝑒(x) and then adopt two distinct blocks for local and global features, respectively: local relation block (LRB) and global relation block (GRB). In LRB, we initially slice the input into a set of disjoint sub-spaces with size 𝑁 Γ— 𝑁, leading to 𝐼 Γ— 𝐽 sub-regions in total. Each sub-space is then fed to a learning backbone with two CNN layers followed by an independent component layer [Chen et al., 2019]. Regarding GRB, we directly feed the input to the backbone, which has the same structure as the extraction backbone in LRB but with different kernel sizes in the CNN layers. Consequently, we concatenate the resulting two feature maps 𝐻 = {𝐻local, 𝐻global}. (P3) Temporal Dynamic Convolution: In order to capture the influence of the temporal factor on the urban flow distribution, we introduce a time-related block consisting of 𝐾 independent CNN layers, where 𝐾 is the number of time spans in a day. Subsequently, we feed 𝐻 into the CNN layer corresponding to its specific time span. ## 3.3 Spatial Constraint Decoder After we obtain the feature map from the approximate finegrained space, we directly feed the output of the CP to an 𝑁 2 - Normalization layer, used to obey the spatial constraint (cf. UrbanFM [Liang et al., 2019]). Hence, the training objective of UNO is the commonly used mean squared error (MSE) between the inferred flow map οΏ½X and the ground truth X: ## 4 UNOwith Incremental Enhancement Due to the inherent CF issue in tackling a series of FUFI tasks, we introduce two incremental enhancements that allow UNO to handle time-dependent flows more flexibly in individual task training and sequential task learning, respectively. ## 4.1 Individual Task Training in Incremental Given a task 𝑑 (𝑑 β‰₯ 1), we have access to current urban flow data D𝑑 , which is also ordered over time, e.g., D𝑑 = {𝑿 𝑑 1 , 𝑿 𝑑 2 , Β· Β· Β· }. Usually, existing solutions randomly select a batch of flow maps for model optimization, denoted as: where 𝐼𝑑 ∈ D𝑑 is a batch of selected urban flow pairs. However, they ignore the time-dependent relationship between these flow maps. For instance, the current flow at 𝜏 is naturally related to the previous 𝜏 - 1, yielding the CF issue throughout current urban flows. To address this, we introduce a flow-related incremental enhancement method for each individual task. Given a batch of randomly selected flow pairs I𝑑 , we further select a portion of I𝑑 (denoted as I 𝜏 𝑑 ) with a sampling rate πœ‚, where 𝜏 reports each input-target pair's timestamp. In the optimization, we feed coarse-grained flow maps at 𝜏 - 1 to the model and treat their predictions as the ground truth of I 𝜏 𝑑 at 𝜏, aiming at transferring the old knowledge in I 𝜏-1 𝑑 to I 𝜏 𝑑 . Notably, we use I β€² 𝜏 𝑑 instead of I 𝜏 𝑑 for clarity. Hence, the above Eq.(12) can be rewritten as follows: In implementation, we set πœ‚ = 0.25 to avoid excessive interference with new knowledge acquisition. ## 4.2 Sequential Task Learning in Incremental After we have trained the task 𝑑 - 1 and obtained an optimal model Θ βˆ— 𝑑-1 , we strive to use Θ βˆ— 𝑑-1 to handle the new urban flow dataset, i.e., D𝑑 . Motivated by recent continual learning paradigms [Lopez-Paz and Ranzato, 2017; Buzzega et al., 2020], a pioneering work [Yu et al., 2023] attempts to build a memory buffer that selectively preserves some of the old samples from the previous datasets to maintain the old knowledge for new task learning. However, we consider the settings where any previous datasets (i.e., D<𝑑 ) are currently not available due to privacy concerns, which renders the current solutions inapplicable. Fortunately, recent zero-shot learning solutions [Nayak et al., 2019; Gao et al., 2023] have shown that we can acquire old knowledge from previously trained models rather than retaining connections to old samples. However, they still rely on previous targets to help reconstruct old samples, which is impractical for FUFI. The reason for this is that the target (i.e., the FG flowmap) already implies coarse-grained flows of different granularities. It is impractical to preserve diverse fine-grained flow maps due to information leakage and memory costs. To this end, we propose a task-related incremental enhancement method for sequential task learning. Motivated by recent pseudo-labeling resolutions [Troisemaine et al., 2023], we feed the coarse-grained flow maps in the task 𝑑 to Θ βˆ— 𝑑 -1 . After that, we obtain pseudo-fine-grained flow maps, which can be treated as weak signals reflecting past perceptions. For any flow map 𝑿𝑑 , we have: Table 1: Performance comparison on four TaxiBJ datasets when using the single-task protocol. | | task-1 - MSE | task-1 - MAE | task-1 - MAPE | task-2 - MSE | task-2 - MAE | task-2 - MAPE | task-3 - MSE | task-3 - MAE | task-3 - MAPE | task-4 - MSE | task-4 - MAE | task-4 - MAPE | |----------|----------------|----------------|-----------------|----------------|----------------|-----------------|----------------|----------------|-----------------|----------------|----------------|-----------------| | SRCNN | 18.464 | 2.491 | 0.714 | 21.270 | 2.681 | 0.689 | 23.184 | 2.829 | 0.727 | 14.730 | 2.289 | 0.665 | | ESPCN | 17.690 | 2.497 | 0.732 | 20.875 | 2.727 | 0.732 | 22.505 | 2.862 | 0.773 | 13.898 | 2.228 | 0.711 | | VDSR | 17.297 | 2.213 | 0.467 | 21.031 | 2.498 | 0.486 | 22.372 | 2.548 | 0.461 | 13.351 | 1.978 | 0.411 | | DeepSD | 17.272 | 2.368 | 0.614 | 20.738 | 2.612 | 0.621 | 22.014 | 2.739 | 0.682 | 15.031 | 2.297 | 0.652 | | SRResNet | 17.338 | 2.457 | 0.713 | 20.466 | 2.660 | 0.688 | 21.996 | 2.775 | 0.717 | 13.446 | 2.189 | 0.637 | | UrbanFM | 16.372 | 2.066 | 0.335 | 19.548 | 2.284 | 0.328 | 21.243 | 2.398 | 0.336 | 12.744 | 1.850 | 0.311 | | DeepLGR | 17.125 | 2.103 | 0.339 | 21.217 | 2.386 | 0.350 | 23.563 | 2.497 | 0.351 | 13.390 | 1.916 | 0.345 | | FODE | 16.473 | 2.142 | 0.403 | 19.884 | 2.377 | 0.395 | 21.425 | 2.490 | 0.417 | 12.840 | 1.947 | 0.396 | | UrbanODE | 16.342 | 2.135 | 0.406 | 19.648 | 2.357 | 0.394 | 21.177 | 2.460 | 0.408 | 12.668 | 1.929 | 0.391 | | UrbanPy | 16.082 | 2.026 | 0.329 | 19.025 | 2.232 | 0.318 | 20.810 | 2.333 | 0.313 | 12.336 | 1.810 | 0.304 | | CUFAR | 14.991 | 1.952 | 0.306 | 18.259 | 2.186 | 0.301 | 19.309 | 2.243 | 0.289 | 11.681 | 1.758 | 0.288 | | UNO | 14.691 | 1.927 | 0.297 | 17.722 | 2.148 | 0.290 | 19.072 | 2.217 | 0.279 | 11.514 | 1.736 | 0.276 | | UNO+ | 14.464 | 1.900 | 0.285 | 17.756 | 2.133 | 0.283 | 18.854 | 2.194 | 0.274 | 11.564 | 1.732 | 0.272 | Table 2: Performance comparison between fine-tune and incremental protocols. | | | task-2 - MSE | task-2 - MAE | task-2 - MAPE | task-3 - MSE | task-3 - MAE | task-3 - MAPE | task-4 - MSE | task-4 - MAE | task-4 - MAPE | |-----------|----------------------|----------------|----------------|-----------------|-----------------------------|----------------|-----------------|----------------|----------------|-----------------| | fine-tune | UrbanFM | 19.162 20.571 | 2.257 2.336 | 0.322 0.334 | 20.499 21.845 20.511 20.275 | 2.341 2.427 | 0.325 0.345 | 12.285 12.820 | 1.814 1.858 | 0.314 0.318 | | fine-tune | DeepLGR | | | | | | | | | | | fine-tune | FODE | 19.251 | 2.323 | 0.379 | | 2.410 | 0.387 | 12.414 | 1.895 | 0.369 | | fine-tune | UrbanODE | 19.070 | 2.302 | 0.371 | | 2.375 | 0.372 | 12.182 | 1.862 | 0.361 | | fine-tune | UrbanPy | 18.822 | 2.208 | 0.317 | 20.117 | 2.293 | 0.314 | 12.088 | 1.800 | 0.307 | | fine-tune | CUFAR | 17.746 | 2.151 | 0.293 | 18.915 | 2.219 | 0.287 | 11.486 | 1.745 | 0.290 | | fine-tune | UNO | 17.407 | 2.121 | 0.287 | 18.794 | 2.199 | 0.280 | 11.258 | 1.716 | 0.275 | | | UrbanFM | 18.477 | 2.215 | 0.312 | 19.809 | 2.290 | 0.314 | 11.919 | 1.778 | 0.302 | | | DeepLGR | 19.202 | 2.292 | 0.342 | 19.892 | 2.331 | 0.330 | 11.977 | 1.819 | 0.314 | | | FODE | 18.799 | 2.297 | 0.370 | 20.012 | 2.369 | 0.373 | 11.997 | 1.852 | 0.359 | | | incremental UrbanODE | 18.735 | 2.289 | 0.374 | 19.779 | 2.340 | 0.361 | 11.924 | 1.836 | 0.352 | | | UrbanPy | 18.286 | 2.193 | 0.311 | 19.503 | 2.264 | 0.314 | 11.958 | 1.787 | 0.304 | | | CUFAR | 17.616 | 2.141 | 0.292 | 18.840 | 2.213 | 0.285 | 11.420 | 1.735 | 0.283 | | | CUFI | 17.665 | 2.125 | 0.282 | 19.002 | 2.203 | 0.279 | 11.414 | 1.717 | 0.278 | | | UNOI | 17.276 | 2.112 | 0.282 | 18.600 | 2.188 | 0.276 | 11.191 | 1.712 | 0.274 | Hence, our objective for the current task 𝑑 can be denoted as: Herein, we omit 𝑑 on either 𝑿 or X for simplicity. In practice, we randomly select a small portion (πœ‚ = 0.25) of CG flow maps in D𝑑 to bypass the training instability. Specifically, we record the convergence of actual flow pairs in implementation and sample again if the model performs poorly. ## 5 Experiments Datasets. Following [Yu et al., 2023], we conduct experiments on four real-world taxi traffic datasets collected continuously for four years (from 2013 to 2016) in Beijing city. Baselines. We compare our UNOI with the baselines which include five image super-resolution methods: SRCNN [Dong et al., 2015], ESPCN [Shi et al., 2016], VDSR [Kim et al., 2016], DeepSD [Vandal et al., 2017], and SRResNet [Ledig et al., 2017]; and six FUFI baselines: UrbanFM [Liang et al., 2019], DeepLGR [Liang et al., 2020], FODE [Zhou et al., 2020], UrbanODE [Zhou et al., 2021], UrbanPy [Ouyang et al., 2020], and CUFAR [Yu et al., 2023]. Training Protocols. In this study, we also use four training protocols: Single-task learns each task in isolation; Fine-tune and continual learn a new task without the help of previous task(s); Joint learns previous tasks and new tasks together. Evaluation Metrics. We employ three widely used FUFI metrics: mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Implementation. Methods are implemented using PyTorch, accelerated by one NVIDIA GeForce-RTX-4090. The Adam optimizer is employed with an initial learning rate of 1𝑒 -4 and training epoch set to 60. To prevent overfitting, we apply the learning rate decay trick and set the dropout rate to 0.3. The batch size is set to 16. For reproducibility, our source codes are available at https://github.com/Longsuni/UNO.git. ## 5.1 Model Performance UNO Performance. Table 1 reports the inference performance of all methods on four FUFI tasks using the singletask protocol, where the best result is marked in bold while the second best is underlined. We also conducted a variant of UNO, namely UNO+, which uses the flow-related incremental enhancement method to facilitate the learning of every single task but does not break the rule of the single-task protocol. We find that UNO consistently outperforms all of the baselines, including the super-resolution methods, demonstrating the effectiveness of our novel operator. In particular, traditional standard super-resolution methods perform poorly due in part to domain conflicts. Additionally, UNO+ performs the best on most of the metrics compared to UNO, indicating that our flow-related incremental enhancement can boost the knowledge transfer from the previouw to the new flow map. UNOI Performance. Now we turn to evaluate the performance of our incremental enhancement in sequential task learning. We choose fine-tune and incremental protocols for each FUFI solution, respectively. We note that all methods are initially trained on Task-1. And then each model is finetuned or incrementally leaned on new coming tasks. We have the following observations. (1) As shown in the top of Table 2, our UNO without any incremental enhancement still performs the best compared to the baseline FUFI solutions when we use the same fine-tune protocol. Compared to the methods that follow the single-task protocol (cf. Table 1), we observe that UNO, with the fine-tune protocol, allows for the maintenance of old knowledge to some extent. (2) As shown in the bottom of Table 2, we enhance traditional solutions with the same sample replay method proposed in [Yu et al., 2023]. As CUFAR performs the best among the baselines, we build a variant of CUFAR by replacing the replay module of CUFAR with our incremental enhancements, namely CUFI. We can find it achieves a competitive performance against the CUFAR. Meanwhile, we do not allow reuse of old samples from earlier datasets due to privacy concerns. Such competitive performance also demonstrates the superiority of our data-free solution. (3) UNOI performs the best against other incremental learning methods, indicating it can well boost the learning of newly coming tasks even if we do not allow to access any old samples from the previous tasks. In addition, the outperforming results of UNOI against UNO with single-task protocol indicate that our incremental enhancement can promote knowledge transfer from old datasets to the new task. Figure 2: The performance comparison on addressing the CF issue. ![Image](https://ijcai.org/proceedings/2024/fulltext/644/image_000001_12fbb000e85718b22ea428dfbc611115d1e95aeaf6a063df2365ce1d50efffe6.png) CF issue. As we claimed before, due to the catastrophic forgetting (CF) concern, the existing solutions could lack the ability to retain the old knowledge from past datasets or tasks. To validate the performance of UNOI in addressing the CF issue, we visualize validation performance (i.e., MSE loss) in the training processes of UNOI by using fine-tune and continual protocols, respectively. As shown in Fig. 2, as the number of new tasks increases, we clearly discover that the loss gap between these two protocols widens, especially on the last task. Consequently, we can conclude that the fine-tune protocol faces a serious CF problem, which in turn suggests that our UNOI is able to alleviate the CF problem effectively. ## 5.2 Model Analysis Ablation Study. We first investigate the impact of each proposed module in our proposed UNOI. We correspondingly yield three variants of UNOI, including: w/o NO removes the neural operator (i.e., Spatial Kernel Integral) in UNO block; w/o SE removes the Static Fact Encoding block; w/o TE removes the Temporal Dynamic Convolution block. As shown in Fig. 3, we can find that: (1) removing any components does degrade the model performance. More importantly, removing the neural operator in UNOI brings the largest losses, which indicates it contributes the most. (2) Compared to the UNO with single-task protocol (denoted as 'single-task') and the UNO with fine-tune protocol (denoted as 'fine-tune'), UNOI performs the best, which also indicates that UNOI successfully transfers the old knowledge from past tasks. Figure 3: An ablation study with MSE performance. ![Image](https://ijcai.org/proceedings/2024/fulltext/644/image_000002_416d3912480e31ff5f736960a0463798c84780841d9dbc5f3ea123e50350caec.png) Convergence Analysis. To evaluate the performance of our UNO module, we compare it with five popular FUFI solutions under the single-task protocol. As shown in Fig. 4, UNO converges the fastest and achieves the lowest validation losses on all tasks. In particular, traditional ODE-based methods produce drastic oscillations during model convergence and even multiple gradient explosions. In contrast, ours performs more stable due to the priority of invariant and smooth functional approximation. Compared with CUFAR, we can observe that our method converges significantly faster than it, suggesting the efficiency of our proposed UNO block. Figure 4: Convergence Analysis of UNO. ![Image](https://ijcai.org/proceedings/2024/fulltext/644/image_000003_e52372d55540def335a70f9cb699c29c189d4904005e1392c227fdfca3fd5927.png) Joint Protocol. Now we turn to compare our UNOI with a naive context. That is, we use the joint protocol to handle all of the FUFI tasks sequentially while maintaining the used datasets. Joint protocol is a commonly used paradigm that attempts to train all tasks simultaneously, usually yielding more promising results. Fig. 5 shows the performance comparison regarding model efficiency and MSE performance. We can find that as more tasks arrive, the time cost of model convergence under the joint protocol increases significantly. Moreover, we also observe that the model under the joint protocol underperforms ours. We consider that the possible reason is that reusing all old samples from previous datasets may raise noise interference. Moreover, not all past knowledge is useful due to the impact of uncertainties such as improvements in urban infrastructure, i.e., the presence of concept drift. Figure 5: Comparison between joint and continual protocols. ![Image](https://ijcai.org/proceedings/2024/fulltext/644/image_000004_5c872525329a363c07471b532c72bb6835d4853b60ca77479775f048dd131bdc.png) ![Image](https://ijcai.org/proceedings/2024/fulltext/644/image_000005_35b5c6f8590aed8ea8d893941dd75cbf7b018eaf2a573d3e6060294e55625de3.png) ![Image](https://ijcai.org/proceedings/2024/fulltext/644/image_000006_319c259a62a58678493c9fc02f7efa959da7ce32e633a88339badcb6b4ead43c.png) Figure 6: A case study on error visualization. ![Image](https://ijcai.org/proceedings/2024/fulltext/644/image_000007_4641063f18166368a2badaf8fd765f21b704237575b7b419ee2600df38df5a5a.png) Figure 7: A case study on flow inference. ![Image](https://ijcai.org/proceedings/2024/fulltext/644/image_000008_5780f378fb8ec5d8401bb6d35c209c9543137543dca9dd0431af7d65aa741555.png) Case Study. In the end, we conduct two case studies to uncover UNOI performance from an interpretability perspective. First, we visualize the inference errors of three models on a case flow map regarding MSE. As shown in Fig. 6, the brighter the pixels, the larger the errors. We can find that our UNOI has much fewer brighter pixels than CUFAR while UNO, which has no incremental enhancements, performs moderately well. Next, we provide another case study by visualizing a coarse-grained flow map during inference. As shown in Fig. 7, we can find our UNOI can infer the urban flow well from a micro perspective. We especially observe that it performs better in lower-scale flow spaces when inferring coarse-grained and fine-grained flow details. ## 6 RelatedWork The FUFI problem was first formulated in [Liang et al., 2019], motivating the mapping functions between a CG and FG flow maps. Existing works addressing FUFI aim to tackle the following challenges: (1) Exploring spatial correlations and introducing external factors: UrbanFM [Liang et al., 2019] proposed the spatial constraint concept and first embedded external features as a supplementary for FUFI. DeepLGR [Liang et al., 2020] enhances the understanding of flow dynamics by revisiting CNN approaches, emphasizing the learning of both global spatial dependencies and local feature representations. FODE [Zhou et al., 2020] and UrbanODE [Zhou et al., 2021] efficiently estimate spatial correlations from a dynamic systems perspective by introducing neural ODEs to overcome the numerically unstable gradient computation problem. (2) Inference on large-scale map: UrbanPy [Ouyang et al., 2020] extends UrbanFM to provide outstanding effectiveness and efficiency for large-scale upsampling via introducing a cascading paradigm. (3) Improving generalization ability of the model. The latest state-ofthe-art work CUFAR [Yu et al., 2023], not only proposes an effective way to extract local and global features but also introduces continual learning with an adaptive knowledge replay strategy to address the catastrophic forgetting [Kirkpatrick et al., 2017] phenomenon across different tasks. However, the existing works directly learn a scale-sensitive mapping function between fixed data space, limiting generalization across diverse scales. In addition, while CUFAR introduces a continual learning-based approach to address the catastrophic forgetting (CF) problem via introducing the replay buffer mechanism, such a method may be impractical in real-world FUFI contexts. Privacy concerns often restrict access to previous datasets after model training, preventing the transfer of old knowledge to new tasks. In contrast, UNO not only explores a scale-invariant mapping from the function space rather than the data space, but also tackles privacyrelated concerns in conjunction with the CF problem. ## 7 Conclusion We presented UNOI, a novel urban neural operator solution with incremental learning, for addressing the FUFI problem. Unlike traditional fine-grained urban flow inference approaches, UNOI provides two-granularity views to transfer the knowledge learned from the past to the newly acquired one in terms of handling a series of FUFI tasks/datasets. Specifically, we devise an urban neural operator (UNO), which takes advantage of recent operator learning and learns mappings between approximation spaces by treating the different-grained flows as continuous functions. To address the CF problem under accessibility constraints for past data (e.g., due to privacy concerns), we devise two incremental enhancements to handle individual task training and sequential task learning, respectively. 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