Eco-driving is a multidimensional concept that includes driving behavior, route selection and all other choices or behaviors related to the vehicles’ fuel consumption (e.g., the use of quality fuel, the use of air conditioning, driving at peak hours, etc.). The scope of this paper is to present an overview of recent literature referring to eco-driving and developed models for calculating fuel consumption, as well as the most important factors affecting it. Recent literature contains a large number of models that estimate fuel consumption, based on naturalistic driving data, which are collected using smartphones and OBDs. In this work, the existing literature is critically assessed in relation to conceptual, methodological and data related aspects. The analyses result to a set of limitations and challenges that are further discussed in the framework of system wide implementations for deriving policies that increase drivers’ awareness, but also improve system performance.

Research in short-term traffic forecasting has been blooming in recent years due to its significant implications in traffic management and intelligent transportation systems. The unprecedented advancements in deep learning have provided immense opportunities to leverage traffic data sensed from various locations of the road network, yet significantly increased the models’ complexity and data and computational requirements, limiting the actionability of the models. Consequently, the meaningful representation of traffic flow data and the road network has been highlighted as a key challenge in improving the efficiency, as well as the accuracy and reliability of forecasting models. This paper provides a systematic review of literature dedicated to spatiotemporal traffic forecasting. Three main representation approaches are identified, namely the stacked vector, image/grid, and graph, and are critically analyzed and compared in relation to their efficiency, accuracy and associated modeling techniques. Based on the findings, future research directions in traffic forecasting are proposed, aiming to increase the adoption of the developed models in real-world applications.

Short-term traffic forecasting is anticipated as an always evolving research topic, boosted by the tremendous recent advances of Machine Learning and Deep Learning, as well as computational power of modern PCs. In this paper, the Dilated Recurrent Neural Networks are introduced in traffic forecasting. Their architecture promotes the deployment of long-term relations and prevents common issues of RNNs, such as exploding and vanishing gradients. The Dilated LSTM Network is exploited to perform traffic conditions forecasting using network-wide data. The data consist of GPS trajectories of ride-hailing company DiDi’s vehicles from November of 2016. After preprocessing the data and organizing them into section’s travel speed of five-minute time resolution timeseries for each one of the 498 road sections of the road network of Xi’an, China, we fed them to the Dilated LSTM Network. The model consists of four hidden layers, each of them implementing an LSTM Network with one, two and four-step dilation correspondingly. The model achieves 85% accuracy, which is improved over a classic LSTM structure, trained on the same data.