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Special Issue "Sensing the Future of Intelligent Transportation Systems"

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 16302

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Special Issue Editors

Dr. Michail Makridis

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Guest Editor

ETH Zurich, Institute for Transport Planning and Systems, Switzerland
Interests: Traffic Flow; Traffic Estimation; Modeling and Simulation; Intelligent Transportation Systems; Connected and Automated Vehicles; Machine Learning
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Dr. Anastasios Kouvelas

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Guest Editor

ETH Zurich, Institute for Transport Planning and Systems, Switzerland
Interests: Traffic Flow Theory; Traffic Systems Engineering; Systems and Control Theory; Operations Research; Big Data; Machine Learning

Prof. Dr. Tomer Toledo

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Guest Editor

Faculty of Civil and Environmental Engineering, Transportation Research Institute Technion - Israel Institute of Technology
Interests: Traffic Modeling and Simulation; Driving and Travel Behavior; Intelligent Transportation Systems; Transportation Network Analysis

Prof. Dr. Rui Jiang

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Guest Editor

Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing 100044, China
Interests: transportation systems; traffic flow; bicycles and pedestrians traffic; connected and automated vehicles; cellular automata
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite submissions to a Special Issue of Sensors journal entitled “Sensing the Future of Intelligent Transportation Systems.”

Technological advancements of the last few decades at the level of sensing technologies, communications, and in-vehicle technologies are expected to disrupt existing transportation systems as we know them today. Light Detection and Ranging (LiDAR), distributed acoustic fibers, virtual loops, mobile phones, vehicle communication systems, (thermal and optical) cameras, and other innovative sensors, coupled with high-speed communication technologies, such as 5G, can alter the type, quality, and amount of available data sources.

These types of different data sources are associated with individual characteristics and specifications, providing insights only on some dimensions of the transport system and with different often varying noise levels per sensor and occasion. This practically means that fusing all the above information in an optimal way, in order to provide high-level services, is among the most challenging tasks that transport researchers will face in the coming years.

A wide range of applications may use this data. Examples include:

Advanced traffic management strategies, real-time traffic estimation, prediction and control
Monitoring for safety and smooth integration of road transport with other modes, such as bicycles, pedestrians, scooters, and public transport in a centralized/distributed and optimal way
Energy-related optimal solutions for service operators and network users

The aim of this Special Issue is to bring together innovative developments in areas related to sensor-based decision-making applied to intelligent transportation systems. The topics of interest include, but are not limited to, the following:

Modeling and analysis of sensor-based transportation systems.
Networks of sensors for data acquisition and data fusion.
Traffic management on freeway and urban networks.
Smart solutions for infrastructure monitoring and optimal traffic capacity.
Applications of sensor technologies to transportation.
Methodologies for traffic estimation and control based on novel sensor-based data.
Strategies for exploiting new sensor data and systems for providing advanced services to pedestrians and cyclists.
New insights for driver and travel behaviors based on sensor-based data.
Real-time systems for driver information and guidance.
Enabling energy-efficient mobility and eco-routing.

Dr. Michail Makridis
Dr. Anastasios Kouvelas
Prof. Dr. Tomer Toledo
Prof. Dr. Rui Jiang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

Open AccessArticle

An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation

and

Sensors 2022, 22(1), 144; https://doi.org/10.3390/s22010144 - 26 Dec 2021

Cited by 4 | Viewed by 3793

Abstract

A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. The idea of using the same type of sensors along large networks is not feasible; as a result, data fusion from different sources for the same location should be performed. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex for several reasons, such as variable specifications per sensor type, different noise levels, and heterogeneous data inputs. To assess sensor accuracy and propose a fusion methodology, we organized a video measurement campaign in an urban test area in Zurich, Switzerland. The work focuses on capturing traffic conditions regarding traffic flows and travel times. The video measurements are processed (a) manually for ground truth and (b) with an algorithm for license plate recognition. Additional processing of data from established thermal imaging cameras and the Google Distance Matrix allows for evaluating the various sensors’ accuracy and robustness. Finally, we propose an estimation baseline MLR (multiple linear regression) model (5% of ground truth) that is compared to a final MLR model that fuses the 5% sample with conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology. Full article

(This article belongs to the Special Issue Sensing the Future of Intelligent Transportation Systems)

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Open AccessArticle

Towards Data-Driven Vehicle Estimation for Signalised Intersections in a Partially Connected Environment

Roozbeh Mohammadi

and

Claudio Roncoli

Sensors 2021, 21(24), 8477; https://doi.org/10.3390/s21248477 - 19 Dec 2021

Viewed by 2136

Abstract

Connected vehicles (CVs) have the potential to collect and share information that, if appropriately processed, can be employed for advanced traffic control strategies, rendering infrastructure-based sensing obsolete. However, before we reach a fully connected environment, where all vehicles are CVs, we have to deal with the challenge of incomplete data. In this paper, we develop data-driven methods for the estimation of vehicles approaching a signalised intersection, based on the availability of partial information stemming from an unknown penetration rate of CVs. In particular, we build machine learning models with the aim of capturing the nonlinear relations between the inputs (CV data) and the output (number of non-connected vehicles), which are characterised by highly complex interactions and may be affected by a large number of factors. We show that, in order to train these models, we may use data that can be easily collected with modern technologies. Moreover, we demonstrate that, if the available real data is not deemed sufficient, training can be performed using synthetic data, produced via microscopic simulations calibrated with real data, without a significant loss of performance. Numerical experiments, where the estimation methods are tested using real vehicle data simulating the presence of various penetration rates of CVs, show very good performance of the estimators, making them promising candidates for applications in the near future. Full article

(This article belongs to the Special Issue Sensing the Future of Intelligent Transportation Systems)

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Open AccessArticle

Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure

Ioan Stan

Vasile Suciu

and

Rodica Potolea

Sensors 2021, 21(15), 5074; https://doi.org/10.3390/s21155074 - 27 Jul 2021

Cited by 2 | Viewed by 1659

Abstract

Traffic congestion experience in urban areas has negative impact on our daily lives by consuming our time and resources. Intelligent Transportation Systems can provide the necessary infrastructure to mitigate such challenges. In this paper, we propose a novel and scalable solution to model, store and control traffic data based on range query data structures (K-ary Interval Tree and K-ary Entry Point Tree) which allows data representation and handling in a way that better predicts and avoids traffic congestion in urban areas. Our experiments, validation scenarios, performance measurements and solution assessment were done on Brooklyn, New York traffic congestion simulation scenario and shown the validity, reliability, performance and scalability of the proposed solution in terms of time spent in traffic, run-time and memory usage. The experiments on the proposed data structures simulated up to 10,000 vehicles having microseconds time to access traffic information and below 1.5 s for congestion free route generation in complex scenarios. To the best of our knowledge, this is the first scalable approach that can be used to predict urban traffic and avoid congestion through range query data structure traffic modelling. Full article

(This article belongs to the Special Issue Sensing the Future of Intelligent Transportation Systems)

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Open AccessArticle

A Real-Time Collision Avoidance Framework of MASS Based on B-Spline and Optimal Decoupling Control

and

Sensors 2021, 21(14), 4911; https://doi.org/10.3390/s21144911 - 19 Jul 2021

Cited by 11 | Viewed by 2118

Abstract

Real-time collision-avoidance navigation of autonomous ships is required by many application scenarios, such as carriage of goods by sea, search, and rescue. The collision avoidance algorithm is the core of autonomous navigation for Maritime autonomous surface ships (MASS). In order to realize real-time and free-collision under the condition of multi-ship encounter in an uncertain environment, a real-time collision avoidance framework is proposed using B-spline and optimal decoupling control. This framework takes advantage to handle the uncertain environment with limited sensing MASS which plans dynamically feasible, highly reliable, and safe feasible collision avoidance. First, owing to the collision risk assessment, a B-spline-based collision avoidance trajectory search (BCATS) algorithm is proposed to generate free-collision trajectories effectively. Second, a waypoint-based collision avoidance trajectory optimization is proposed with the path-speed decoupling control. Two benefits, a reduction of control cost and an improvement in the smoothness of the collision avoidance trajectory, are delivered. Finally, we conducted an experiment using the Electronic Chart System (ECS). The results reveal the robustness and real-time collision avoidance trajectory planned by the proposed collision avoidance system. Full article

(This article belongs to the Special Issue Sensing the Future of Intelligent Transportation Systems)

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Open AccessArticle

Exploring Equity in Public Transportation Planning Using Smart Card Data

Kiarash Ghasemlou

Murat Ergun

and

Nima Dadashzadeh

Sensors 2021, 21(9), 3039; https://doi.org/10.3390/s21093039 - 26 Apr 2021

Cited by 2 | Viewed by 2708

Abstract

Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments. Full article

(This article belongs to the Special Issue Sensing the Future of Intelligent Transportation Systems)

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Open AccessArticle

INTEGRATION Large-Scale Modeling Framework of Direct Cellular Vehicle-to-All (C-V2X) Applications

and

Sensors 2021, 21(6), 2127; https://doi.org/10.3390/s21062127 - 18 Mar 2021

Cited by 9 | Viewed by 2457

Abstract

The transportation system has evolved into a complex cyber-physical system with the introduction of wireless communication and the emergence of connected travelers and connected automated vehicles. Such applications create an urgent need to develop high-fidelity transportation modeling tools that capture the mutual interaction of the communication and transportation systems. This paper addresses this need by developing a high-fidelity, large-scale dynamic and integrated traffic and direct cellullar vehicle-to-vehicle and vehicle-to-infrastructure (collectively known as V2X) modeling tool. The unique contributions of this work are (1) we developed a scalable implementation of the analytical communication model that captures packet movement at the millisecond level; (2) we coupled the communication and traffic simulation models in real-time to develop a fully integrated dynamic connected vehicle modeling tool; and (3) we developed scalable approaches that adjust the frequency of model coupling depending on the number of concurrent vehicles in the network. The proposed scalable modeling framework is demonstrated by running on the Los Angeles downtown network considering the morning peak hour traffic demand (145,000 vehicles), running faster than real-time on a regular personal computer (1.5 h to run 1.86 h of simulation time). Spatiotemporal estimates of packet delivery ratios for downtown Los Angeles are presented. This novel modeling framework provides a breakthrough in the development of urgently needed tools for large-scale testing of direct (C-V2X) enabled applications. Full article

(This article belongs to the Special Issue Sensing the Future of Intelligent Transportation Systems)

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