Deep Learning For Short Term Network Wide Road Traffic Forecasting

Traffic forecasting is a critical component of modern intelligent transportation systems for urban traffic management and control. Learning and forecasting network-scale traffic states based on spatial-temporal traffic data is particularly challenging for classical statistical and machine learning models due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. The existence of missing values in traffic data makes this task even harder. With the rise of deep learning, this work attempts to answer: how to design proper deep learning models to deal with complicated network-wide traffic data and extract comprehensive features to enhance prediction performance, and how to evaluate and apply existing deep learning-based traffic prediction models to further facilitate future research? To address those key challenges in short-term road traffic forecasting problems, this work develops deep learning models and applications to: 1) extract comprehensive features from complex spatial-temporal data to enhance prediction performance, 2) address the missing value issue in traffic forecasting tasks, and 3) deal with multi-source data, evaluate existing deep learning-based traffic forecasting models, share model results as benchmarks, and apply those models into practice. This work makes both original methodological and practical contributions to short-term network-wide traffic forecasting research. The traffic feature learning can categorized as learning traffic data as spatial-temporal matrices and learning the traffic network as a graph. Stacked bidirectional recurrent neural network is proposed to capture bidirectional temporal dependencies in traffic data. To learn localized features from the topological structure of the road network, two deep learning frameworks incorporating graph convolution and graph wavelet operations, respectively, are proposed to learn the interactions between roadway segments and predict their traffic states. To deal with missing values in traffic forecasting tasks, an imputation unit is incorporated into the recurrent neural network to increase prediction performance. Further, to fill in missing values in the graph-based traffic network, a graph Markov network is proposed, which can infer missing traffic states step by step along with the prediction process. In summary, the proposed graph-based models not only achieve superior forecasting performance but also increase the interpretability of the interaction between road segments during the forecasting process. From the practical perspective, to further facilitate future research, an open-source data and model sharing platform for evaluating existing traffic forecasting models as benchmarks is established. Additionally, a traffic performance measurement platform is presented which has the capability of taking the proposed network-wide traffic prediction models into practice.

The aim of the conference will be to bring together the majority of leading expert scientists, thought leaders and forward looking professionals from all domains of Intelligent Transportation Systems, to share ongoing research achievements, to exchange views and knowledge and to contribute to the advances in the field The main theme of the conference will be ITS within connected, automated and electric multimodal mobility systems and services

Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems.

The economy of a country or region relies vigorously on an efficient and dependable transportation system to provide accessibility and promote the safe and efficient movement of individuals and merchandise. In fact, the transportation framework has been identified by (Nicholson and Du 1997) as the most significant lifesaver in case of natural disasters, for example, earth shudders, floods, hurricanes, and others. Rebuilding of different life savers (for example water supply, electrical power system, sewer system, communication, and numerous others) depends emphatically on the capacity to ship individuals and equipment to harmed destinations. The real travel requests and street limit do differ over time, in this manner, adding to the vulnerability of travel times. With the expanded estimation of time, great loss is incurred by the drivers because of the unexpected schedule (either early or late) delay. A stable transportation system would give a serious edge in the worldwide economy. Therefore, the significance of the reliability of a transportation system cannot be overemphasized. Anticipating the traffic stream is an unpredictable procedure that is influenced by a few parameters, for example, traffic designs, information accumulation, applied zones, and so forth the rightness of traffic stream expectation can acquire preferred position to the smart traffic the executives, it can help in improving rush hour gridlock productivity and diminishing traffic blockage. Fundamentally, stream forecast targets is assessed the absolute number of vehicles given a particular district and a period interim. According to Boris S. [6] and Wei Shenet al. [69], the real-time speed of traffic flow is available to everyone thorough GPS. The traffic data providers use machine learning to predict speed for each road segment. Forecasting the real-time traffic knowledge is really helpful for traveler, it gives the potential of choosing better routes and helps in managing the transportation system.

This highly informative and carefully presented book covers the most recent advances as well as comprehensive reviews addressing novel and state-of-the-art topics from active researchers in innovative advanced materials and hybrid materials, concerning not only their synthesis, preparation, and characterization but especially focusing on the applications of such materials with outstanding performance.

Smart cities operate under more resource-efficient management and economy than ordinary cities. As such, advanced business models have emerged around smart cities, which led to the creation of smart enterprises and organizations that depend on advanced technologies. This book includes 21 selected and peer-reviewed articles contributed in the wide spectrum of artificial intelligence applications to smart cities. Chapters refer to the following areas of interest: vehicular traffic prediction, social big data analysis, smart city management, driving and routing, localization, safety, health, and life quality.

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