Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation

Travel time estimation is one of the core tasks for the development of intelligent transportation systems. Most previous works model the road segments or intersections separately by learning their spatio-temporal characteristics to estimate travel time. However, due to the continuous alternations of the road segments and intersections in a path, the dynamic features are supposed to be coupled and interactive. Therefore, modeling one of them limits further improvement in accuracy of estimating travel time. To address the above problems, a novel graph-based deep learning framework for travel time estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural Networks (STDGNN). Specifically, we first establish the node-wise and edge-wise graphs to respectively characterize the adjacency relations of intersections and that of road segments. In order to extract the joint spatio-temporal correlations of the intersections and road segments, we adopt the spatio-temporal dual graph learning approach that incorporates multiple spatial-temporal dual graph learning modules with multi-scale network architectures for capturing multi-level spatial-temporal information from the dual graph. Finally, we employ the multi-task learning approach to estimate the travel time of a given whole route, each road segment and intersection simultaneously. We conduct extensive experiments to evaluate our proposed model on three real-world trajectory datasets, and the experimental results show that STDGNN significantly outperforms several state-of-art baselines

A systematic literature review

Albuquerque, V., Dias, M. S., & Bacao, F. (2021). Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information, 10(2), 1-25. [62]. https://doi.org/10.3390/ijgi10020062Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.publishersversionpublishe

Spatio-temporal traffic anomaly detection for urban networks

Urban road networks are often affected by disruptions such as accidents and roadworks, giving rise to congestion and delays, which can, in turn, create a wide range of negative impacts to the economy, environment, safety and security. Accurate detection of the onset of traffic anomalies, specifically Recurrent Congestion (RC) and Nonrecurrent Congestion (NRC) in the traffic networks, is an important ITS function to facilitate proactive intervention measures to reduce the level of severity of congestion. A substantial body of literature is dedicated to models with varying levels of complexity that attempt to identify such anomalies. Given the complexity of the problem, however, very less effort is dedicated to the development of methods that attempt to detect traffic anomalies using spatio-temporal features. Driven both by the recent advances in deep learning techniques and the development of Traffic Incident Management Systems (TIMS), the aim of this research is to develop novel traffic anomaly detection models that can incorporate both spatial and temporal traffic information to detect traffic anomalies at a network level. This thesis first reviews the state of the art in traffic anomaly detection techniques, including the existing methods and emerging machine learning and deep learning methods, before identifying the gaps in the current understanding of traffic anomaly and its detection. One of the problems in terms of adapting the deep learning models to traffic anomaly detection is the translation of time series traffic data from multiple locations to the format necessary for the deep learning model to learn the spatial and temporal features effectively. To address this challenging problem and build a systematic traffic anomaly detection method at a network level, this thesis proposes a methodological framework consisting of (a) the translation layer (which is designed to translate the time series traffic data from multiple locations over the road network into a desired format with spatial and temporal features), (b) detection methods and (c) localisation. This methodological framework is subsequently tested for early RC detection and NRC detection. Three translation layers including connectivity matrix, geographical grid translation and spatial temporal translation are presented and evaluated for both RC and NRC detection. The early RC detection approach is a deep learning based method that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). The NRC detection, on the other hand, involves only the application of the CNN. The performance of the proposed approach is compared against other conventional congestion detection methods, using a comprehensive evaluation framework that includes metrics such as detection rates and false positive rates, and the sensitivity analysis of time windows as well as prediction horizons. The conventional congestion detection methods used for the comparison include Multilayer Perceptron, Random Forest and Gradient Boost Classifier, all of which are commonly used in the literature. Real-world traffic data from the City of Bath are used for the comparative analysis of RC, while traffic data in conjunction with incident data extracted from Central London are used for NRC detection. The results show that while the connectivity matrix may be capable of extracting features of a small network, the increased sparsity in the matrix in a large network reduces its effectiveness in feature learning compared to geographical grid translation. The results also indicate that the proposed deep learning method demonstrates superior detection accuracy compared to alternative methods and that it can detect recurrent congestion as early as one hour ahead with acceptable accuracy. The proposed method is capable of being implemented within a real-world ITS system making use of traffic sensor data, thereby providing a practically useful tool for road network managers to manage traffic proactively. In addition, the results demonstrate that a deep learning-based approach may improve the accuracy of incident detection and locate traffic anomalies precisely, especially in a large urban network. Finally, the framework is further tested for robustness in terms of network topology, sensor faults and missing data. The robustness analysis demonstrates that the proposed traffic anomaly detection approaches are transferable to different sizes of road networks, and that they are robust in the presence of sensor faults and missing data.Open Acces

Urban traffic flow prediction, a spatial-temporal approach

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesCurrent advances in computational technologies such as machine learning combined with traffic data availability are inspiring the development and growth of intelligent transport Systems (ITS). As urban authorities strive for efficient traffic systems, traffic forecasting is a vital element for effective control and management of traffic networks. Traffic forecasting methods have progressed from traditional statistical techniques to optimized data driven methods eulogised with artificial intelligence. Today, most techniques in traffic forecasting are mainly timeseries methods that ignore the spatial impact of traffic networks in traffic flow modelling. The consideration of both spatial and temporal dimensions in traffic forecasting efforts is key to achieving inclusive traffic forecasts. This research paper presents approaches to analyse spatial temporal patterns existing in networks and goes on to use a machine learning model that integrates both spatial and temporal dependency in traffic flow prediction. The application of the model to a traffic dataset for the city of Singapore shows that we can accurately predict traffic flow up to 15 minutes in advance and also accuracy results obtained outperform other classical traffic prediction methods

Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202

Modeling of the spatiotemporal distribution patterns and transmission dynamics of dengue, for an early warning surveillance system

As doenças emergentes transmitidas por vetores representam um desafio significativo para a saúde pública global. Nos últimos tempos, os surtos de doenças como a dengue e a febre de chikungunya, aumentaram em frequência. Tal é facilitado pela globalização, pelo aumento do comércio e das viagens, e pela dispersão para novas áreas dos seus vetores invasores. Na Europa, este facto é exemplificado pela recente introdução e estabelecimento de espécies de mosquitos do género Aedes com a subsequente ocorrência de surtos de doenças como a dengue. Com a crescente disseminação da dengue em todo o mundo, a região europeia também tem vindo a registar um aumento de casos - a maioria destes relacionados com viagens. Da mesma forma, tem havido um aumento de eventos esporádicos de transmissão autóctone de dengue em áreas onde ocorre o vetor sob condições ambientais favoráveis. Assim, atualmente, a Europa enfrenta o desafio de avaliar o risco de importação de casos virémicos de dengue e a probabilidade de ocorrência de transmissão local deste vírus. Esta tese visa contribuir para a compreensão dos fatores relacionados com a importação do vírus da dengue na Europa e a sua transmissão neste território, nomeadamente na ilha da Madeira. Para tal foi implementado uma estrutura integrada de modelos computacionais da importação e transmissão da doença. A estrutura combina três submodelos: (i) um modelo explicativo de importação da doença assente em teoria de redes (ii) um modelo preditivo de aprendizagem automática e, (iii) um modelo compartimental de transmissão vetor-hospedeiro. Os modelos de teoria de redes e de aprendizagem automática foram parametrizados com recurso a dados históricos referentes a estimativas de casos importados de dengue em 21 países na Europa e índices que caracterizam parâmetros com relevância na importação da dengue: (i) tráfego de passageiros aéreos, (ii) atividade e sazonalidade da dengue, (iii) taxa de incidência, (iv) proximidade geográfica, (v) vulnerabilidade à epidemia, e, (vi) contexto económico do país de origem. O modelo compartimental de transmissão foi calibrado com parâmetros empíricos referentes ao ciclo de vida do mosquito, à transmissão viral e à variação anual de temperatura do Funchal, na ilha da Madeira. Os resultados dos modelos de teoria de redes e aprendizagem automática demonstram um maior risco de importação de casos virémicos de países com elevado tráfego de passageiros, elevadas taxas de incidência, situação económica débil e com maior proximidade geográfica em relação ao país de destino. O modelo de aprendizagem automática alcançou elevada performance preditiva, com uma pontuação AUC de 0,94. O modelo compartimental de transmissão demonstra a existência de um potencial de transmissão da dengue no Funchal nos períodos de verão e outono, com a data de chegada da pessoa infeciosa a afetar significativamente a distribuição no tempo e tamanho do pico da epidemia. Da mesma forma, a variação sazonal da temperatura afeta dramaticamente a dinâmica da epidemia, em que temperaturas iniciais mais quentes levam a surtos de maiores proporções, com o pico de casos a ocorrer mais cedo. A estrutura de modelação descrita nesta tese tem o potencial de servir como uma ferramenta integrada de vigilância de alerta precoce para a ocorrência de surtos de dengue na Europa. Este trabalho fornece orientação prática para auxiliar as autoridades de saúde pública na prevenção de surtos de dengue e na redução do risco de transmissão local, em áreas onde ocorrem os vetores. Essa estrutura, com os devidos reajustamentos, pode ser aplicada a outras doenças transmitidas por Aedes, como chikungunya e febre amarela.Emerging vector-borne diseases pose a significant global public health challenge. In recent times, outbreaks of diseases, such as dengue and chikungunya fever, have increased in frequency. This is facilitated by globalization, increase in trade and travel, and the spread of invasive vectors into new areas. In Europe, this is exemplified by the recent introduction and establishment of Aedes mosquito species and subsequent outbreaks of diseases like dengue. With the increasing spread of dengue worldwide, the European region has also experienced increase in reported cases - majority being travel related. Likewise, there has been an increase in sporadic events of autochthonous dengue transmission, in areas with established vector presence and favourable environmental conditions. Europe is currently faced with the challenge of assessing its importation risk of viraemic cases of dengue, and the probability of local transmission. This thesis aims to study the dynamics of viraemic cases importation and virus transmission of dengue fever in Europe, namely in Madeira Island. This is achieved by establishing an importation and transmission modelling framework. The framework combines three sub-models: (i) a network connectivity importation model (ii) a machine learning predictive model and, (iii) a compartmental vector-host transmission model. The network connectivity and machine learning model were both parameterized using a historical dengue importation data for 21 countries in Europe, and indices that characterize important parameters for dengue importation: (i) the air passenger traffic, (ii) dengue activity and seasonality, (iii) incidence rate, (iv) geographical proximity, (v) epidemic vulnerability, and (vi) wealth of a source country. The transmission model was calibrated using empirical parameters for the mosquito life history traits, viral transmission, and temperature seasonality of Funchal, Madeira Island. The results of the network connectivity and machine learning models demonstrate a higher importation risk of a viraemic case from source countries with high passenger traffic, high incidence rates, lower economic status, and geographical proximity to a destination country. The machine learning model achieved high predictive accuracy with an AUC score of 0.94. The transmission model demonstrates the potential for summer and autumn season transmission of dengue in Funchal, with the arrival date of the infectious person significantly affecting the distribution of the timing and peak size of the epidemic. Likewise, seasonal temperature variation dramatically affects the epidemic dynamics, with warmer starting temperatures producing large epidemics with peaks occurring more rapidly. The modelling framework described in this thesis has the potential to serve as an integrated early warning surveillance tool for dengue in Europe. This work provides practical guidance to assist public health officials in preventing outbreaks of dengue and reducing the risk of local transmission in areas with vectors presence. This framework could be applied to other Aedes-borne diseases such as chikungunya and yellow fever

Multi-headed self-attention mechanism-based Transformer model for predicting bus travel times across multiple bus routes using heterogeneous datasets

Bus transit is a crucial component of transportation networks, especially in urban areas. Bus agencies must enhance the quality of their real-time bus travel information service to serve their passengers better and attract more travelers. Various models have recently been developed for estimating bus travel times to increase the quality of real-time information service. However, most are concentrated on smaller road networks due to their generally subpar performance in densely populated urban regions on a vast network and failure to produce good results with long-range dependencies. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database and the vehicle probe data. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. This study developed a multi-headed self-attention mechanism-based Univariate Transformer neural network to predict the mean vehicle travel times for different hours of the day for multiple stations across multiple routes. In addition, we developed Multivariate GRU and LSTM neural network models for our research to compare the prediction accuracy and comprehend the robustness of the Transformer model. To validate the Transformer Model's performance more in comparison to the GRU and LSTM models, we employed the Historical Average Model and XGBoost model as benchmark models. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. Only the historical average bus travel time was used as the input parameter for the Transformer model. Other features, including spatial and temporal information, volatility measures (e.g., the standard deviation and variance of travel time), dwell time, expected travel time, jam factors, hours of a day, etc., were captured from our dataset. These parameters were employed to develop the Multivariate GRU and LSTM models. The model's performance was evaluated based on a performance metric called Mean Absolute Percentage Error (MAPE). The results showed that the Transformer model outperformed other models for one-hour ahead prediction having minimum and mean MAPE values of 4.32 percent and 8.29 percent, respectively. We also investigated that the Transformer model performed the best during different traffic conditions (e.g., peak and off-peak hours). Furthermore, we also displayed the model computation time for the prediction; XGBoost was found to be the quickest, with a prediction time of 6.28 seconds, while the Transformer model had a prediction time of 7.42 seconds. The study's findings demonstrate that the Transformer model showed its applicability for real-time travel time prediction and guaranteed the high quality of the predictions produced by the model in the context of a complicated extensive transportation network in high-density urban areas and capturing long-range dependencies.Includes bibliographical references

Machine Learning Approaches for Traffic Flow Forecasting

Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions. The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data. In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used. The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning. The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time

A deep learning approach to real-time short-term traffic speed prediction with spatial-temporal features

In the realm of Intelligent Transportation Systems (ITS), accurate traffic speed prediction plays an important role in traffic control and management. The study on the prediction of traffic speed has attracted considerable attention from many researchers in this field in the past three decades. In recent years, deep learning-based methods have demonstrated their competitiveness to the time series analysis which is an essential part of traffic prediction. These methods can efficiently capture the complex spatial dependency on road networks and non-linear traffic conditions. We have adopted the convolutional neural network-based deep learning approach to traffic speed prediction in our setting, based on its capability of handling multi-dimensional data efficiently. In practice,the traffic data may not be recorded with a regular interval, due to many factors, like power failure, transmission errors,etc.,that could have an impact on the data collection. Given that some part of our dataset contains a large amount of missing values, we study the effectiveness of a multi-view approach to imputing the missing values so that various prediction models can apply. Experimental results showed that the performance of the traffic speed prediction model improved significantly after imputing the missing values with a multi-view approach, where the missing ratio is up to 50%