Path representations are widely applied in smart-city tasks, such as travel time estimation, road classification, and trajectory search. Existing approaches effectively learn these representations by jointly utilizing multi-mode data, including GPS trajectories, road paths, and road networks. Although these methods achieve state-of-the-art performance, the theoretical understanding of the importance and effectiveness of key designs in path representation pre-training remains unclear. Moreover, these methods often rely on hard-constrained labeled data and manual design, resulting in limited performance gains and high annotation costs. In this paper, we proposeSimPRL, a simple yet powerful contrastive learning framework that capitalizes on unannotated data. Building on the exploration of key factors contributing to successful path representation learning, SimPRL integrates unannotated GPS trajectories and road paths directly through contrastive self-supervised learning. Specifically, we introduce a pre-training task that predicts correspondences between trajectories from different modes using a shared encoder, without requiring labeled data. We also design an auxiliary segment imputation task and a minimal trajectory sampling technique with random offsets for data augmentation. Additionally, we employ any trajectory within the mini-batch, except for the anchor itself, as the negative sample. Extensive experiments on two large-scale, real-world datasets from Chengdu and Porto demonstrate the effectiveness of SimPRL in two downstream tasks: travel time estimation and road classification. The results show that SimPRL not only surpasses state-of-the-art methods but also achieves more stable generalization and greater efficiency in terms of runtime and memory usage. Our source code is available athttps://github.com/TXLiao/SimPRL

Large-scale human mobility data contains rich spatial and temporal information for urban sensing, crowd flow modeling, and urban planning. However, it is usually difficult to access wide-coverage, long-term, and consistent-time human mobility data. Most of the publicly available datasets are actually only records of discontinuous trajectories of a very small portion of urban citizens in asynchronous time due to the limited usage of apps for location data collection or the limited number of volunteers. To address this problem and empower open urban research, this paper constructs a high-quality human mobility dataset by generating large-scale citizen trajectories based on massive cellular signaling data. Particularly, we first propose a heatmap diffusion module to generate a probability heatmap that produces plausible trajectories at both the individual and city scales. Then, we propose a masked trajectory AutoEncoder, which can generate individual trajectory embeddings from partially given or empty trajectories. Third, a flexible framework is provided to incorporate the heatmap diffusion module with the masked trajectory embeddings, demonstrating significant flexibility in handling both fully masked trajectories for city-wide analysis and partially masked trajectories for specific locations. We have conducted extensive experiments to validate the utility of the regenerated trajectories at both individual and region levels for various applications. Numerous case studies further illustrate that our model learns not only the distribution of the trajectories but also the semantics of different urban areas.

Travel time estimation (TTE) is a fundamental and challenging problem for navigation and travel planning. Though many efforts have been devoted to this task, most of the previous research has focused on extracting useful features of the routes to improve the estimation accuracy. In our opinion, the key issue of TTE is how to handle the rich spatiotemporal information underlying a route and how to model the multi-faceted factors that affect travel time. Along this line, we propose a multi-faceted route representation learning framework that divides a route into three sequences: a trajectory sequence consists of GPS coordinates to describe spatial information, an attribute sequence to encode the features of each road segment, and a semantic sequence consists of the IDs of road segments to capture the context information of routes. Then, we design a sequential learning module and transformer encoder to get the representations of three sequences for each route respectively. Finally, we fuse the multi-faceted route representations together, and provide a self-supervised learning module to improve the generalization of final representation. Experiments on two real-world datasets demonstrate that our method could provide more accurate travel time estimation than baselines, and all the multi-faceted route representations contribute to the improvement of estimation accuracy.

Origin-Destination demand prediction is a fundamental and important task in the urban transportation system. It is more challenging and complex than region demand prediction since it needs to predict traffic demand for each pair of regions rather than a single region, which means$N^{2}$time series need to be predicted given$N$stations. Most existing works are mainly proposed for the region (or station) demand prediction. On the other hand, previous Origin-Destination demand prediction methods only follow a single discrete-time setting while the input data are continuous-time OD orders, which means these methods have not sufficiently leveraged the rich information for demand prediction. To solve these challenges, we propose a novel framework consisting of both continuous-time and discrete-time representation learning modules for Origin-Destination demand prediction. Firstly, we construct memorable representations of all nodes and design a continuous-time learning module to update the nodes’ representations once a time-stamped OD transaction is observed. Then, a discrete-time representation learning module is proposed to generate discrete-time messages containing information across a fixed time interval from a macro perspective. Next, a co-updater module is designed to fuse messages from both continuous-time and discrete-time channels into node memory. Last, a graph attention module is developed, which generates final node embeddings using updated node memory and predicts the forthcoming OD demand matrix based on them. The experimental results on real-world datasets show that our method leads to significant and consistent improvements compared to other methods.

Numerous spatial-temporal learning methods have been proposed for crowd flow modeling, which is an important problem in Intelligent Transportation Systems (ITS). However, most of the existing methods were designed to use data in one specific form to solve one particular task of crowd flow modeling and the shared patterns among different tasks have been largely ignored. In this paper, we investigate how to learn generic node representations that can simultaneously support various downstream tasks of crowd flow modeling. Along this line, we develop a continuous-time dynamic graph representation learning method based onBootstrapping forCrowdFlow modeling (BootCF). Our approach follows a training procedure with two phases. In the pre-training phase, the continuous-time dynamic encoder converts edges with timestamps into messages to update the representations of the related traffic nodes. Inspired by the recent progress of contrastive learning, a bootstrapping framework for continuous-time dynamic graphs is designed to calculate pre-training loss and update the model in a self-supervised way, and thus enabling the node representation learning to be task-agnostic. Moreover, a context-aware data augmentation on continuous-time dynamic graphs is proposed to generate the augmented view of input data. Once the general node representations are obtained, the second phase can learn an effective model for any downstream task. Experiments on two real-world datasets show that our approach can achieve significant performance gain on four downstream tasks, which demonstrates that the proposed method has the powerful generalization capability for learning task-agnostic node representations.

Many deep spatio-temporal learning methods have been proposed for crowd flow modeling in recent years. However, most of them focus on designing a spatial and temporal convolution mechanism to aggregate information from nearby nodes and historical observations for a pre-defined prediction task. Different from the existing research, this paper aims to provide a generic and dynamic representation learning method for crowd flow modeling. The main idea of our method is to maintain a continuous-time representation for each node, and update the representations of all nodes continuously according to the streaming observed data. Along this line, a particular encoder-decoder architecture is proposed, where the encoder converts the newly happened transactions into a timestamped message, and then the representations of related nodes are updated according to the generated message. The role of the decoder is to guide the representation learning process by reconstructing the observed transactions based on the most recent node representations. Moreover, a number of virtual nodes are added to discover macro-level spatial patterns and also share the representations among spatially-interacted stations. Experiments have been conducted on two real-world datasets for four popular prediction tasks in crowd flow modeling. The result demonstrates that our method could achieve better prediction performance for all the tasks than baseline methods.

Recent years have witnessed a rapid growth of applying deep spatiotemporal methods in traffic forecasting. However, the prediction of origin-destination (OD) demands is still a challenging problem since the number of OD pairs is usually quadratic to the number of stations. In this case, most of the existing spatiotemporal methods fail to handle spatial relations on such a large scale. To address this problem, this paper provides a dynamic graph representation learning framework for OD demands prediction. In particular, a hierarchical memory updater is first proposed to maintain a time-aware representation for each node, and the representations are updated according to the most recently observed OD trips in continuous-time and multiple discrete-time ways. Second, a spatiotemporal propagation mechanism is provided to aggregate representations of neighbor nodes along a random spatiotemporal route which treats origin and destination as two different semantic entities. Last, an objective function is designed to derive the future OD demands according to the most recent node representations, and also to tackle the data sparsity problem in OD prediction. Extensive experiments have been conducted on two real-world datasets, and the experimental results demonstrate the superiority of the proposed method. The code and data are available at https://github.com/Rising0321/HMOD.

Traffic demand forecasting by deep neural networks has attracted widespread interest in both academia and industry society. Among them, the pairwise Origin-Destination (OD) demand prediction is a valuable but challenging problem due to several factors: (i) the large number of possible OD pairs, (ii) implicitness of spatial dependence, and (iii) complexity of traffic states. To address the above issues, this paper proposes a Continuous-time and Multi-level dynamic graph representation learning method for Origin-Destination demand prediction (CMOD). Firstly, a continuous-time dynamic graph representation learning framework is constructed, which maintains a dynamic state vector for each traffic node (metro stations or taxi zones). The state vectors keep historical transaction information and are continuously updated according to the most recently happened transactions. Secondly, a multi-level structure learning module is proposed to model the spatial dependency of station-level nodes. It can not only exploit relations between nodes adaptively from data, but also share messages and representations via cluster-level and area-level virtual nodes. Lastly, a cross-level fusion module is designed to integrate multi-level memories and generate comprehensive node representations for the final prediction. Extensive experiments are conducted on two real-world datasets from Beijing Subway and New York Taxi, and the results demonstrate the superiority of our model against the state-of-the-art approaches.

Travel time estimation of a given path is a crucial task of Intelligent Transportation Systems (ITS). Accurate travel time estimation can benefit multiple downstream applications such as route planning, real-time navigation, and urban construction. However, it is a challenging problem since the travel time is largely affected by multiple complicated factors including spatial factors, temporal factors and external factors, and obtaining informative representations of a given path is not trivial. Most previous works solved this problem in either Euclidean space or non-Euclidean space, which was unilateral to represent the actual traveling path and led to relatively poor performance. To address this, this paper proposes a multi-semantic path representation method to exploit information in Euclidean space and non-Euclidean space simultaneously. First, since the path is composed of several segments, we generate semantic representations of segments in non-Euclidean space by taking both the time information and the historical co-occurrence into consideration. Second, as the path could be equally represented as several travelled intersections, semantic representations of intersection sequences are also extracted to improve the capability of the method by considering information in Euclidean space. Meanwhile, semantic representations from properties, including the length and the type of segments, are also incorporated into the model. Finally, a sequence learning component is added on the top to aggregate the information along the entire path and provides the final estimation. Extensive experiments were conducted on two real-world taxi trajectories datasets, and the experimental results demonstrate the superiority of the proposed method.

Dynamic Graph Neural Networks (DGNNs) have become one of the most promising methods for traffic speed forecasting. However, when adapting DGNNs for traffic speed forecasting, existing approaches are usually built on a static adjacency matrix (no matter predefined or self-learned) to learn spatial relationships among different road segments, even if the impact of two road segments can be changeable dynamically during a day. Moreover, the future traffic speed cannot only be related with the current traffic speed, but also be affected by other factors such as traffic volumes. To this end, in this paper, we aim to explore these dynamic and multi-faceted spatio-temporal characteristics inherent in traffic data for further unleashing the power of DGNNs for better traffic speed forecasting. Specifically, we design a dynamic graph construction method to learn the time-specific spatial dependencies of road segments. Then, a dynamic graph convolution module is proposed to aggregate hidden states of neighbor nodes to focal nodes by message passing on the dynamic adjacency matrices. Moreover, a multi-faceted fusion module is provided to incorporate the auxiliary hidden states learned from traffic volumes with the primary hidden states learned from traffic speeds. Finally, experimental results on real-world data demonstrate that our method can not only achieve the state-of-the-art prediction performances, but also obtain the explicit and interpretable dynamic spatial relationships of road segments.