Spatiotemporal Traffic Patterns Research Articles

Di-GraphGAN: An enhanced adversarial learning framework for accurate spatial-temporal traffic forecasting under data missing scenarios

Nowadays, various disturbances in urban transportation data acquisition/processing/storage lead to the inevitable data missing problem, which undermines the valuable traffic information and greatly threats the reliability of existing benchmark traffic prediction models. Inspired from the powerful generative learning ability of GANs, we propose an integrated spatiotemporal Data imputation Graph Attention Generative Adversarial Networks (Di-GraphGAN) for accurate and efficient spatial-temporal traffic forecasting under data missing scenarios. Specifically, we first propose a traffic data imputation module named DI-LSTM, which adopts the architecture of LSTM Network with an extra Time Damping unit to accurately estimating the missing values. Then, we facilitate Di-GraphGAN with an original developed Task-Efficient Graph Attention Networks (TE-GAT) for better graph representation learning and a Temporal Contextual Attention (TCA) mechanism to capture the dynamic spatiotemporal traffic patterns. Finally, extensive evaluations are conducted on two real-world traffic speed datasets from China, demonstrating that Di-GraphGAN achieves state-of-the-art performance in both traffic forecasting and spatiotemporal data imputation tasks.

Knowledge Expansion and Consolidation for Continual Traffic Prediction With Expanding Graphs

Accurate traffic prediction plays a vital role in intelligent transport managements and applications. However, in the vast majority of existing works, the focus is mainly on modeling spatiotemporal correlations in static traffic networks. Thus, the continuous expansion and evolution of traffic networks are ignored. In this work, we study the problem of traffic prediction with expanding road network structures under the continual learning paradigm. Considering the model prediction performance, efficiency, and data accessibility, a SpatioTemporal Knowledge Expansion and Consolidation (STKEC) framework is proposed. This framework contains an influence-based knowledge expansion strategy to help the spatiotemporal learning model integrate new spatiotemporal traffic patterns and a memory-augmented knowledge consolidation mechanism to preserve the learned spatiotemporal patterns without accessing the data in previous graphs. Extensive experiments are conducted on a large-scale dataset and verify the superior performance of STKEC in continual traffic prediction.

A deep learning approach for network-wide dynamic traffic prediction during hurricane evacuation

Proactive evacuation traffic management largely depends on real-time monitoring and prediction of traffic flow at a high spatiotemporal resolution. However, evacuation traffic prediction is challenging due to the uncertainties caused by sudden changes in projected hurricane paths and consequently populations’ evacuation behavior. Moreover, modeling spatiotemporal traffic flow patterns requires extensive data over a longer time period, whereas evacuations typically last for two to five days. In this paper, we present a novel data-driven approach for predicting evacuation traffic at a network scale. We develop a dynamic graph convolutional long short-term memory neural network (DGCN-LSTM) model to learn the network dynamics during hurricane evacuation. We first train the model for non-evacuation period traffic data and found that the model outperforms existing deep learning models for predicting non-evacuation period traffic with an RMSE value of 226.84. However, when the model is applied for predicting evacuation traffic, the RMSE value increased to 1440.99. We overcome this issue by adopting a transfer learning approach with additional features related to evacuation traffic demand such as distance from the evacuation zone, time to landfall, and other zonal level features to control the transfer of information (network dynamics) from non-evacuation periods to evacuation periods. The final transfer learned DGCN-LSTM model performs well to predict evacuation traffic flow (RMSE = 399.69). The implemented model can be applied to predict evacuation traffic over a longer forecasting horizon (up to 6-hour). It will assist transportation agencies to activate appropriate traffic management strategies to reduce delays for evacuating traffic.

Revealing representative day-types in transport networks using traffic data clustering

Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to rely on well-known general unsupervised machine-learning methods (e.g., k-means, hierarchical, spectral, DBSCAN) to select the most representative structure and number of day-types based solely on internal evaluation indices. These are easy to calculate but are limited since they only use information in the clustered dataset itself. In addition, the quality of clustering should ideally be demonstrated by external validation criteria, by expert assessment or the performance in its intended application. The main contribution of this paper is to test and compare the common practice of internal validation with external validation criteria represented by the application to short-term prediction, which also serves as a proxy for more general traffic management applications. When compared to external evaluation using short-term prediction, internal evaluation methods have a tendency to underestimate the number of representative day-types needed for the application. Additionally, the paper investigates the impact of using dimensionality reduction. By using just 0.1% of the original dataset dimensions, very similar clustering and prediction performance can be achieved, with up to 20 times lower computational costs, depending on the clustering method. K-means and agglomerative clustering may be the most scalable methods, using up to 60 times fewer computational resources for very similar prediction performance to the p-median clustering.

Modeling Melburnians—Using the Koopman operator to gain insight into crowd dynamics

Describing and forecasting city traffic is challenging, given the array of factors influencing the movement of pedestrians and vehicles. Faced with this complexity, research has focused on machine learning as a way to capture spatio-temporal traffic patterns, based on past sensor data. While the methods can accurately forecast high-dimensional observations, it is often hard to explain the models and their predictions. In many cases, only the predictive performance of an otherwise black box model is evaluated. An accurate but explainable alternative is provided by the Koopman operator, an emerging methodology for data-driven identification of high-dimensional and nonlinear dynamical systems. In this study, we showcase the method by analyzing pedestrian traffic data from Melbourne, Australia. We formulate our computations in the Extended Dynamic Mode Decomposition framework, where we approximate the Koopman operator in a function basis computed with time delay embedding and the Diffusion Map algorithm. The model captures the distinct temporal patterns of 11 traffic sensors simultaneously. Because the dynamics become linear in the operator-theoretic perspective, we can decompose the model into its spectral components. Importantly, these components facilitate interpretation and can increase scientific understanding of the underlying system and the model’s operations. For the Melbourne data, we show that the spectral components connect to the underlying state space geometry and indicate the model’s stability over a prediction horizon. Our study showcases how the Koopman operator framework offers explainable and accurate data-driven predictions in a real-world traffic system. The results can easily be transferred to other traffic systems.

Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models

Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results.

How to Identify Patterns of Citywide Dynamic Traffic at a Low Cost? An In-Depth Neural Network Approach with Digital Maps

The identification and analysis of the spatiotemporal dynamic traffic patterns in citywide road networks constitute a crucial process for complex traffic management and control. However, city-scale and synchronal traffic data pose challenges for such kind of quantification, especially during peak hours. Traditional studies rely on data from road-based detectors or multiple communication systems, which are limited in not only access but also coverage. To avoid these limitations, we introduce real-time, traffic condition digital maps as our input. The digital maps keep spatiotemporal urban traffic information in nature and are open to access. Their pixel colors represent traffic conditions on corresponding road segments. We propose a stacked convolutional autoencoder-based method to extract a low-dimension feature vector for each input. We compute and analyze the distances between vectors. The statistical results show different traffic patterns during given periods. With the actual data of Chongqing city, we compare the feature extraction performance between our proposed method and histogram. The result shows our proposed method can extract spatiotemporal features better. For the same data set, there is little difference in the number distribution of red pixels found in the statistics result of the histogram, while differences do exist in the results of our proposed method. We find the most fluctuated morning is on Friday; the most fluctuated evening is on Tuesday; and the most stable evening is on Wednesday. The distance captured by our method can represent the evolution of different traffic conditions during the morning and evening peak hours. Our proposed method provides managers with assistance to sense the dynamics of citywide traffic conditions in quantity.

Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods

This paper aims to evaluate the impacts of the economic context on traffic congestion and its consequential effects on private vehicle accessibility. We conduct a long-term analysis of spatiotemporal traffic congestion patterns in Madrid (Spain), comparing two urban realms: the 2008 economic crisis and the following post-crisis situation. We apply TomTom Speed Profiles data to assess daily variations in traffic congestion and their changes between both periods, and Twitter data to capture spatial patterns of the daily pulse of the city. Increased traffic, a by-product of economic recovery, resulted in higher congestion, particularly during peak hours. Nevertheless, these changes are spatially uneven. In the city core, an increase in congestion is relatively temporally homogeneous, while in the peripheral suburban zones, there has been only a marginal increase in travel times. On the other hand, in the urban outskirts, increased traffic congestion is more severe but visibly different between north and south. These differences have strong social connotations: over 40% of the population experienced a dramatic increase in travel times (more than 25%) during peak hours. Moreover, low-income groups are more likely to live in the more affected southern districts, suffering most the negative consequences of increased congestion.

Short-term prediction of speed contour map and travel time by matching similar spatiotemporal traffic patterns

Short-term prediction of speed contour map and travel time by matching similar spatiotemporal traffic patterns

Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model

Spatiotemporal traffic data, which represent multidimensional time series on considering different spatial locations, are ubiquitous in real-world transportation systems. However, the inevitable missing data problem makes data-driven intelligent transportation systems suffer from an incorrect response. Therefore, imputing missing values is of great importance but challenging as it is not easy to capture spatiotemporal traffic patterns, including explicit and latent features. In this study, we propose an augmented tensor factorization model by incorporating generic forms of domain knowledge from transportation systems. Specifically, we present a fully Bayesian framework for automatically learning parameters of this model using variational Bayes (VB). Relying on the publicly available urban traffic speed data set collected in Guangzhou, China, experiments on two types of missing data scenarios (i.e., random and non-random) demonstrate that the proposed Bayesian augmented tensor factorization (BATF) model achieves best imputation accuracies and outperforms the state-of-the-art baselines (e.g., Bayesian tensor factorization models). Besides, we discover interpretable patterns from the experimentally learned global parameter, biases, and latent factors that indeed conform to the dynamic of traffic states.

Revealing heterogeneous spatiotemporal traffic flow patterns of urban road network via tensor decomposition-based clustering approach

Understanding the complex heterogeneity of traffic flow for the road network is of great importance to relieving traffic congestion and designing traffic control strategies. This study proposes a 3-stage pattern extraction approach to discover the heterogeneous spatiotemporal traffic flow patterns from the enormous observations: The non-negative tensor decomposition technique is utilized to extract the features of traffic flow in the within-one-day time, day-to-day and spatial segment modes; The spectral clustering is performed in the low-dimensional feature space, and the Gaussian similarity matrix is modified by the spatial proximity coefficient to integrate the spatial compactness into the clustering algorithm; The large-scale road network is partitioned into the independent and compact subnetworks with similar traffic congestion levels, and the macroscopic traffic flow relationships are fitted for each pattern. Research results indicate that the statistical distribution of traffic flow for each pattern is diverse; the heterogeneity of traffic patterns is verified by the low-scatter diagrams of macroscopic traffic flow relation for each subnetwork; the congested subnetworks are detected for both the peak hours and non-peak hours; the structures of subnetworks are highly concordant with the urban functional regions, varying with the different periods.

Identifying spatiotemporal traffic patterns in large-scale urban road networks using a modified nonnegative matrix factorization algorithm

The identification and analysis of spatiotemporal traffic patterns in road networks constitute a crucial process for sophisticated traffic management and control. Traditional methods based on mathematical equations and statistical models can hardly be applicable to large-scale urban road networks, where traffic states exhibit high degrees of dynamics and complexity. Recently, advances in data collection and processing have provided new opportunities to effectively understand spatiotemporal traffic patterns in large-scale road networks using data-driven methods. However, limited efforts have been exerted to explore the essential structure of the networks when conducting a spatiotemporal analysis of traffic characteristics. To this end, this study proposes a modified nonnegative matrix factorization algorithm that processes high-dimensional traffic data and provides an improved representation of the global traffic state. After matrix factorization, cluster analysis is conducted based on the obtained low-dimensional representative matrices, which contain different traffic patterns and serve as the basis for exploring the temporal dynamics and spatial structure of network congestion. The applicability and effectiveness of the proposed approach are examined in a road network of Beijing, China. Results show that the methods exhibit considerable potential for identifying and interpreting the spatiotemporal traffic patterns over the entire network and provide a systematic and efficient approach for analyzing the network-level traffic state.

Dynamic congested traffic states of lattice hydrodynamic model with an on-ramp and its immediate upstream off-ramp

This paper investigates the features of congested traffic flow near the combination of off-ramp and on-ramp. Firstly, the stochastic off-ramp and on-ramp are designed. Then, a two-lane lattice hydrodynamic traffic flow model coupled with a combination bottleneck is proposed to reproduce the empirical phenomena. In the simulation, the asymmetric-lane change rules were adopted, and many congested traffic flow patterns were observed near the combination bottlenecks, such as homogeneous synchronized traffic (HST), moving localized cluster (MLC), triggered stop-and-go traffic (TSG), oscillating congested traffic (OCT), pinned localized cluster (PLC), and homogeneous congested traffic (HCT). The obtained simulation results suggest that the proposed model is good and can produce the observed congestion spatiotemporal traffic patterns well.

Adding temporal information to direct-demand models: Hourly estimation of bicycle and pedestrian traffic in Blacksburg, VA

Cycling and walking are environmentally-friendly transport modes, providing alternatives to automobility. However, exposure to hazards (e.g., crashes) may influence the choice to walk or cycle for risk-averse populations, minimizing non-motorized travel as an alternative to driving. Most models to estimate non-motorized traffic volumes (and subsequently hazard exposure) are based on specific time periods (e.g., peak-hour) or long-term averages (e.g., Annual Average Daily Traffic), which do not allow for estimating hazard exposure by time of day. We calculated Annual Average Hourly Traffic estimates of bicycles and pedestrians from a comprehensive traffic monitoring campaign in a small university town (Blacksburg, VA) to develop hourly direct-demand models that account for both spatial (e.g., land use, transportation) and temporal (i.e., time of day) factors. We developed two types of models: (1) hour-specific models (i.e., one model for each hour of the day) and (2) a single spatiotemporal model that directly incorporates temporal variables. Our model results were reasonable (adj-R2 for the hour-specific [spatiotemporal] bicycle model: ∼0.47 [0.49]; pedestrian model: ∼0.69 [0.72]). We found correlation among non-motorized traffic, land use (e.g., population density), and transportation (e.g., on-street facility) variables. Temporal variables had a similar magnitude of correlation as the spatial variables. We produced spatial estimates that vary by time of day to illustrate spatiotemporal traffic patterns for the entire network. Our temporally-resolved models could be used to assess exposure to hazards (e.g. air pollution, crashes) or locate safety-related infrastructure (e.g., striping, lights) based on targeted time periods (e.g., peak-hour, nighttime) that temporally averaged estimates cannot.

Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation.

Probe data-driven travel time forecasting for urban expressways by matching similar spatiotemporal traffic patterns

Travel time is an effective measure of roadway traffic conditions. The provision of accurate travel time information enables travelers to make smart decisions about departure time, route choice and congestion avoidance. Based on a vast amount of probe vehicle data, this study proposes a simple but efficient pattern-matching method for travel time forecasting. Unlike previous approaches that directly employ travel time as the input variable, the proposed approach resorts to matching large-scale spatiotemporal traffic patterns for multi-step travel time forecasting. Specifically, the Gray-Level Co-occurrence Matrix (GLCM) is first employed to extract spatiotemporal traffic features. The Normalized Squared Differences (NSD) between the GLCMs of current and historical datasets serve as a basis for distance measurements of similar traffic patterns. Then, a screening process with a time constraint window is implemented for the selection of the best-matched candidates. Finally, future travel times are forecasted as a negative exponential weighted combination of each candidate’s experienced travel time for a given departure. The proposed approach is tested on Ring 2, which is a 32km urban expressway in Beijing, China. The intermediate procedures of the methodology are visualized by providing an in-depth quantitative analysis on the speed pattern matching and examples of matched speed contour plots. The prediction results confirm the desirable performance of the proposed approach and its robustness and effectiveness in various traffic conditions.

Mathematical characterization of spatiotemporal congested traffic patterns: mixed speed data analysis in the greater Toronto and Hamilton area, Canada

ABSTRACTThis paper formulates a comprehensive methodology for analyzing, quantifying and identifying congestion characteristics based on speed distribution. Utilizing vehicle speed data, a mathematical approach is applied, in order to characterize roadway segments, in terms of travel reliability, congestion severity and duration. We argue that the Gaussian mixture model (GMM) and its parameter combination is the appropriate tool if we are to obtain quantitative congestion measures and rank roadway performance. A significant contribution of our approach is that it is based on assumptions regarding mixed components as well as speed distribution and can be applied to large databases. We test our framework on the greater Toronto and Hamilton area in Ontario, Canada, and conclude that congestion quantification through the application of the GMM can be successfully accomplished. Results indicate that speed patterns differ significantly between counties as well as days of the week.

A space-time visualization analysis method for taxi operation in Beijing

Traffic phenomena are associated with a complex dynamic behavior of spatiotemporal traffic patterns. It is possible to understand the features of real traffic by a spatiotemporal analysis approach for real measured traffic data. In this paper, a space-time visualization analysis method is designed and carried out for taxi spatiotemporal dataset from Beijing floating car data acquisition system and Beijing GIS data. Through this method, large-amount taxi GPS data is processed and the spatial–temporal trajectory is analyzed. And then taxi daily operation time, operating range and residence location of individual driver, vehicle operation and rest periods and other indicators are calculated, which are important characteristic parameters of Beijing Taxi Operations. In sum, continuous, comprehensive, and dynamic analysis information for monitoring taxi operation status can be acquired through the method. These information can provide the decision basis for city taxi operation management, and help to improve the city taxi operation management level.

Estimating Traffic and Anomaly Maps via Network Tomography

Mapping origin-destination (OD) network traffic is pivotal for network management and proactive security tasks. However, lack of sufficient flow-level measurements as well as potential anomalies pose major challenges towards this goal. Leveraging the spatiotemporal correlation of nominal traffic, and the sparse nature of anomalies, this paper brings forth a novel framework to map out nominal and anomalous traffic, which treats jointly important network monitoring tasks including traffic estimation, anomaly detection, and traffic interpolation. To this end, a convex program is first formulated with nuclear and $\ell _{1}$ -norm regularization to effect sparsity and low rank for the nominal and anomalous traffic with only the link counts and a small subset of OD-flow counts. Analysis and simulations confirm that the proposed estimator can exactly recover sufficiently low-dimensional nominal traffic and sporadic anomalies so long as the routing paths are sufficiently “spread-out” across the network, and an adequate amount of flow counts are randomly sampled. The results offer valuable insights about data acquisition strategies and network scenaria giving rise to accurate traffic estimation. For practical networks where the aforementioned conditions are possibly violated, the inherent spatiotemporal traffic patterns are taken into account by adopting a Bayesian approach along with a bilinear characterization of the nuclear and $\ell _{1}$ norms. The resultant nonconvex program involves quadratic regularizers with correlation matrices, learned systematically from (cyclo)stationary historical data. Alternating-minimization based algorithms with provable convergence are also developed to procure the estimates. Insightful tests with synthetic and real Internet data corroborate the effectiveness of the novel schemes.

Towards automated road information framework a case study of Tanzania

Abstract Traffic congestion problem has been noticed to have a serious impact on the economy of the country in terms of time wastage, energy consumption costs, human loss and environmental effects. Different strategies have been used so far all over the world as shown in the literature review. Intelligent Transportation System (ITS) is a multi-technology approach that can help to handle the issues and create a complete congestion reduction framework. This paper presents a case study for implementing automated road management system using networks in Tanzania, where three cities highly affected by traffic congestion have been studied. Study results show that these cities have not yet implemented new technologies in road traffic management; instead the traffic is controlled using traffic police officers and traffic lights only. The traffic lights use an old technology that cannot manage traffic in relation to the real-time situations. This study proposes components for a framework, which will assist automation in road traffic management. From the review of various existing ITS of which Advanced Traffic Management System (ATMS) and Advanced Travel Information System (ATIS) are subsystems, we have identified the possibility to integrate the two sub-systems within the framework. Three-phase traffic theory has been referred, FOTO and ASDA models are applied to the automatic recognition and tracking of congested spatiotemporal traffic patterns on roads.