The criticality of sustainable development to control the unprecedented consequences of climate change is clear. A vital element in launching sustainability projects is financing, especially for projects by small and medium enterprises. The first and crucial step to offering financing services for sustainable development is to identify and evaluate promising projects. The current practice to accomplish this step heavily depends on subject-matter expertise and professional networks. The current practice also involves extensive manual document reviews and subjective decisions. Therefore, existing methods are time-consuming, inefficient, and not scalable. This study proposes an automated system to identify potential sustainability projects for financing services using Artificial Intelligence (AI). The proposed method uses web crawlers and text mining solutions, including Natural Language Processing (NLP), to search the Internet, analyze text data, evaluate the information quantitatively, and identify potential sustainability projects for financing services. The proposed method was implemented and empirically assessed. The results indicate that the AI-enhanced system is able to identify and prioritize potential sustainability projects with 87% accuracy. The outcomes of this study will help financial experts and decision-makers take advantage of the information available on the Internet efficiently to improve the existing methods for identifying potential projects for financing services.

Motivated by rapid urbanization and increasing natural hazards, this study aims to develop a data-driven method for detecting urban traffic anomalies during extreme events. Past experiences have shown that abnormal traffic patterns caused by extreme events can disrupt traffic in a large portion of the road network. Timely and reliable traffic monitoring for detection of such anomalies is crucial for congestion mitigation and successful emergency operation plans. An effective traffic monitoring system should detect disruptions at both network and local levels. However, the existing methods are not capable of addressing this need. This study proposes a temporal self-expressive network monitoring method to achieve this purpose. The proposed method first utilizes a temporal self-expressive model to uncover the dynamic interdependencies between local zones of the traffic network. Next, a statistical monitoring method detects network-wide anomalies based on regular traffic interdependencies. Finally, the method identifies the zones most affected by the anomalous event. We applied the proposed method to the road network of Manhattan in New York City to evaluate its performance during Hurricane Sandy. The outcomes confirmed that the temporal self-expressive model, augmented with statistical monitoring tools, could accurately detect anomalous traffic at both network and zone levels.

Imputing missing data is a critical task in data-driven intelligent transportation systems. During recent decades there has been a considerable investment in developing various types of sensors and smart systems, including stationary devices (e.g., loop detectors) and floating vehicles equipped with global positioning system (GPS) trackers to collect large-scale traffic data. However, collected data may not include observations from all road segments in a traffic network for different reasons, including sensor failure, transmission error, and because GPS-equipped vehicles may not always travel through all road segments. The first step toward developing real-time traffic monitoring and disruption prediction models is to estimate missing values through a systematic data imputation process. Many of the existing data imputation methods are based on matrix completion techniques that utilize the inherent spatiotemporal characteristics of traffic data. However, these methods may not fully capture the clustered structure of the data. This paper addresses this issue by developing a novel data imputation method using PARATUCK2 decomposition. The proposed method captures both spatial and temporal information of traffic data and constructs a low-dimensional and clustered representation of traffic patterns. The identified spatiotemporal clusters are used to recover network traffic profiles and estimate missing values. The proposed method is implemented using traffic data in the road network of Manhattan in New York City. The performance of the proposed method is evaluated in comparison with two state-of-the-art benchmark methods. The outcomes indicate that the proposed method outperforms the existing state-of-the-art imputation methods in complex and large-scale traffic networks.

Rapidly identifying anomalous events on freeways can significantly improve emergency response time and reduce congestion. However, the unpredictable nature of these incidents, along with the limitations of current monitoring systems, makes accurate and timely detection challenging. In this study, we propose a Cycle-Consistent Bidirectional Graph Generative Adversarial Network (CCB-GraphGAN) model to enhance lane-level traffic anomaly detection on freeways. By treating freeway traffic information as graph-structured data, our model captures the joint distribution of normal traffic patterns and their underlying latent representations through an adversarial process. It also incorporates cycle-consistency constraints to effectively reconstruct both normal traffic data and its latent features during training. During the inference phase, we employ an autoencoder-based anomaly detection approach to identify lane-level traffic anomalies. We experimentally evaluate the performance of our method through comparative and case study analyses using the recently released Freeway Traffic Anomalous Event Detection (FT-AED) dataset. The results indicate that our method outperforms all baseline models in detecting freeway anomalous events early. Notably, it achieves a lower percentage of missed crashes compared to baseline models. With a false positive rate (FPR) of only 1%, our method reduces detection delay by identifying crashes an average of 5 minutes earlier than their official report time. Additionally, an analysis of real-world crash events further confirms our method’s effectiveness in accurately identifying freeway anomalies at an early stage.

Abnormal traffic patterns caused by extreme events can significantly disrupt traffic flow across large regions of urban road networks. Timely and reliable detection of such disruptions is crucial for effective traffic management. However, existing anomaly detection methods cannot simultaneously detect disruptions at both network and local levels, and they typically struggle with incomplete traffic data, which is a common issue during extreme events. To address these limitations, we propose a novel Self-Imputing Deep Multitask Sequence (SI-DMSeq) model for dual-level traffic monitoring and disruption detection. The model simultaneously performs two distinct tasks: 1) reconstructing sequences of traffic data, which enables anomaly detection through an autoencoder-based approach, and 2) making one-step-ahead predictions, which serves to impute missing data for the subsequent time step. To support robust training despite data gaps, we incorporate an iterative imputation strategy during the training phase. During inference, the reconstruction task is used to detect traffic disruptions at both network and local levels, while the prediction task provides reliable imputation of missing values under typical traffic conditions. We evaluate the SI-DMSeq model using historical traffic data from the Manhattan road network in New York City, focusing on disruptions caused by Hurricane Sandy in 2012 and the Christmas blizzard in 2010. The results demonstrate that the proposed method accurately identifies disruptions at both the network and local levels and efficiently imputes missing traffic data.

Abnormal traffic patterns caused by extreme events have the potential to disrupt traffic flow on large regions of urban road networks. Timely and reliable detection of such disruptions is crucial for effective traffic management. However, existing methods for detecting extreme traffic events are unable to simultaneously identify disruptions at both network and local levels. Moreover, most existing methods cannot handle incomplete traffic data, which can be an essential challenge during extreme events. In this study, we address these limitations by creating a novel method for traffic anomaly detection using a multitask sequence-to-sequence learning process. Specifically, we propose a Self-Imputing Deep Multitask Sequence (SI-DMSeq) model that can simultaneously reconstruct sequential traffic data and provide one-step-ahead predictions. This model also integrates an unsupervised data imputation strategy to enable training with incomplete data. During inference, the proposed model adopts an autoencoder-based anomaly detection approach to identify large-scale disruptions at both network and local levels. Additionally, it utilizes one-step-ahead predictions to reliably impute missing data under typical traffic conditions. To evaluate the performance of our proposed method, we applied it to the Manhattan road network in New York City using historical traffic data. The results indicated that the proposed method accurately detects disruptions caused by Hurricane Sandy in 2012 at both network and local levels and efficiently imputes missing traffic data under typical conditions.