From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government

Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data

Traffic forecasting has recently attracted increasing interest due to the popularity of online navigation services, ridesharing and smart city projects. Owing to the non-stationary nature of road traffic, forecasting accuracy is fundamentally limited by the lack of contextual information. To address this issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network (H-STGCN), which is able to "deduce" future travel time by exploiting the data of upcoming traffic volume. Specifically, we propose an algorithm to acquire the upcoming traffic volume from an online navigation engine. Taking advantage of the piecewise-linear flow-density relationship, a novel transformer structure converts the upcoming volume into its equivalent in travel time. We combine this signal with the commonly-utilized travel-time signal, and then apply graph convolution to capture the spatial dependency. Particularly, we construct a compound adjacency matrix which reflects the innate traffic proximity. We conduct extensive experiments on real-world datasets. The results show that H-STGCN remarkably outperforms state-of-the-art methods in various metrics, especially for the prediction of non-recurring congestion

SOME EMPIRICAL RELATIONS BETWEEN TRAVEL SPEED, TRAFFIC VOLUME AND TRAFFIC COMPOSITION IN URBAN ARTERIALS

The effects of traffic mix (the percentage of cars, trucks, buses and so on) are of particular interest in the speed-volume relationship in urban signalized arterials under various geometric and control characteristics. The paper presents some empirical observations on the relation between travel speed, traffic volume and traffic composition in urban signalized arterials. A methodology based on emerging self-organizing structures of neural networks to identify regions in the speed-volume relationship with respect to traffic composition and Bayesian networks to evaluate the effect of different types of motorized vehicles on prevailing traffic conditions is proposed. Results based on data from a large urban network indicate that the variability in traffic conditions can be described by eight regions in speed-volume relationship with respect to traffic composition. Further evaluation of the effect of motorized vehicles in each region separately indicates that the effect of traffic composition decreases with the onset of congestion. Moreover, taxis and motorcycles are the primary affecting parameter of the form of the speed-volume relationship in urban arterials

Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods

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

A Survey on Market-Inspired Intersection Control Methods for Connected Vehicles

Recent advances in wireless communication technology allow for the cooperative coordination of vehicles and infrastructures under vehicle-to-everything (V2X) communication protocols. V2X communication protocols are shaping a new reality for intersection control, allowing for individual driver preferences to be taken into account, such as heterogeneity in terms of value of time and willingness to pay to reduce individual delay. In this context, different economic instruments have been proposed for intersection control under connected vehicles, including various types of auctions, direct payments, and credit schemes. This study offers a comprehensive review on market-inspired control approaches under connected vehicles for both signalized and unsignalized intersections, focusing on auction-based and direct transaction schemes among drivers as incentive mechanisms for aligning user and system objectives. A structured way of analyzing the existing literature is introduced, underlining relevant methodological and practical issues. The key aspects of market-based control schemes are outlined, including priority allocation, types of agents employed, incentive compatibility, solution approaches, and validation. Emerging challenges for the implementation of such approaches and future research directions are then identified and discussed.ISSN:1941-1197ISSN:1939-139

Unmanned Aerial Aircraft Systems for transportation engineering: Current practice and future challenges

Acquiring and processing video streams from static cameras has been proposed as one of the most efficient tools for visualizing and gathering traffic information. With the latest advances in technology and visual media, combined with the increased needs in dealing with congestion more effectively and directly, the use of Unmanned Aerial Aircraft Systems (UAS) has emerged in the field of traffic engineering. In this paper, we review studies and applications that incorporate UAS in transportation research and practice with the aim to set the grounds from the proper understanding and implementation of UAS related surveillance systems in transportation and traffic engineering. The studies reviewed are categorized in different transportation engineering areas. Additional significant applications from other research fields are also referenced to identify other promising applications. Finally, issues and emerging challenges in both a conceptual and methodological level are revealed and discussed