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Special Issue "Feature Papers in Future Transportation"

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A special issue of Future Transportation (ISSN 2673-7590).

Deadline for manuscript submissions: 30 September 2023 | Viewed by 5504

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Special Issue Editors

Prof. Dr. Ouri E. Wolfson

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Guest Editor

Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA
Interests: intelligent transportation; smart city; mobile/pervasive computing; big data; distributed systems and connectomics

Dr. Shunde Yin

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Guest Editor

Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: clean energy recovery; underground storage of energy and CO₂; waste utilization; coupled thermal, hydraulic, mechanical and chemical phenomena

Special Issue Information

Dear Colleagues,

As Editor-in-Chief of Future Transportation, I am delighted to announce the call for papers for the upcoming Special Issue Feature Papers in Future Transportation. This Special Issue of the journal Future Transportation will consider high-quality articles from all the subfields of Transportation. The goal is to present cutting-edge research that will define the current scope of the discipline and possible paths forward.

We welcome submissions from the Editorial Board Members and outstanding scholars invited by the Editorial Board and the Editorial Office.

All of the accepted papers in this Special Issue will be published free of charge in open access. You are welcome to send short proposals for submissions of feature papers to our Editorial Office ([email protected]) or to contact Prof. Dr. Ouri E. Wolfson, Future Transportation Editor-in-Chief ([email protected]) to discuss and/or to comment.

We believe that we can make a great contribution to the academic community and our discipline by presenting our own research together in a single volume. Submissions will first be evaluated by academic editors. Subsequently, selected papers will be thoroughly and rigorously peer reviewed. The entire issue will be published in book format after the call for papers has closed and the collection has been carefully vetted.

Prof. Dr. Ouri E. Wolfson
Dr. Shunde Yin
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. Future Transportation is an international peer-reviewed open access quarterly 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 1000 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 (5 papers)

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Research

Open AccessArticle

Impact of Climate Change on the Performance of Permafrost Highway Subgrade Reinforced by Concrete Piles

and

Future Transp. 2023, 3(3), 996-1006; https://doi.org/10.3390/futuretransp3030055 - 03 Aug 2023

Viewed by 452

Abstract

Climate change has a detrimental impact on permafrost soil in cold regions, resulting in the thawing of permafrost and causing instability and security issues in infrastructure, as well as settlement problems in pavement engineering. To address these challenges, concrete pipe pile foundations have emerged as a viable solution for reinforcing the subgrade and mitigating settlement in isolated permafrost areas. However, the effectiveness of these foundations depends greatly on the mechanical properties of the interface between the permafrost soil and the pipe, which are strongly influenced by varying thawing conditions. While previous studies have primarily focused on the interface under frozen conditions, this paper specifically investigates the interface under thawing conditions. In this study, direct shear tests were conducted to examine the damage characteristics and shear mechanical properties of the soil-pile interface with a water content of 26% at temperatures of −3 °C, −2 °C, −1 °C, −0.5 °C, and 8 °C. The influence of different degrees of melting on the stress–strain characteristics of the soil-pile interface was also analyzed. The findings reveal that as the temperature increases, the shear strength of the interface decreases. The shear stress-displacement curve of the soil-pile interface in the thawing state exhibits a strain-softening trend and can be divided into three stages: the pre-peak shear stress growth stage, the post-peak shear stress steep drop stage, and the post-peak shear stress reconstruction stage. In contrast, the stress curve in the thawed state demonstrates a strain-hardening trend. The study further highlights that violent phase changes in the ice crystal structure have a significant impact on the peak freezing strength and residual freezing strength at the soil-pile interface, with these strengths decreasing as the temperature rises. Additionally, the cohesion and internal friction angle at the soil-pile interface decrease with increasing temperature. It can be concluded that the mechanical strength of the soil-pile interface, crucial for subgrade reinforcement in permafrost areas within transportation engineering, is greatly influenced by temperature-induced changes in the ice crystal structure. Full article

(This article belongs to the Special Issue Feature Papers in Future Transportation)

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Open AccessArticle

Develop and Validate a Survey to Assess Adult’s Perspectives on Autonomous Ridesharing and Ridehailing Services

Justin Mason

and

Sherrilene Classen

Future Transp. 2023, 3(2), 726-738; https://doi.org/10.3390/futuretransp3020042 - 01 Jun 2023

Viewed by 736

Abstract

Autonomous vehicles (AVs) have generated excitement for the future of transportation. Public transit agencies and companies (i.e., Uber) have begun developing shared autonomous transportation services. Most AV surveys focus on public opinion of perceived benefits and concerns of AVs but are not directly tied to field implementation of AVs. Experience and exposure to new technology affect adults’ perceptions and level of technology acceptance. As such, the Autonomous RideShare Services Survey (ARSSS) was developed to assess adults’ perceptions of AVs before and after being exposed to AVs. Face validity and content validity were established via focus groups and subject-matter experts (CVI = 0.95). Adults in the U.S. (N = 553) completed the ARSSS, and a subsample (N = 100) completed the survey again after two weeks. An exploratory and confirmatory factor analysis demonstrated that the ARSSS consists of three factors that can be used to reliably quantify users’ perceptions of AVs: (a) Intention to Use, Trust, and Safety (r = 0.85, p < 0.001, ICC = 0.99); (b) Potential Benefits (r = 0.70, p < 0.001, ICC = 0.97); and (c) Accessibility (r = 0.78, p < 0.001, ICC = 0.96) of AVs. These are key factors in predicting intention to use and acceptance of AVs. Results from the ARSSS may inform the acceptance among users of these AV technologies. Full article

(This article belongs to the Special Issue Feature Papers in Future Transportation)

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Open AccessArticle

Analysis of the Influence of Variable Meteorological Conditions on the Performance of the EV Battery and on the Driving Range

Carlos Armenta-Déu

and

Baptiste Giorgi

Future Transp. 2023, 3(2), 626-642; https://doi.org/10.3390/futuretransp3020037 - 12 May 2023

Viewed by 539

Abstract

The influence of variable weather conditions on the performance of the battery that powers electric vehicles (EV) was studied and analyzed. This paper also deals with the effects that changes in the performance of the battery have on the driving range of the vehicle. An algorithm to evaluate the influence of temperature on the behavior of the battery and on the real driving range of electric vehicles was developed. Our theoretical approach was assessed in experimental tests run under operating conditions that reproduce real situations. A correction factor was obtained to match theoretical and experimental values with an accuracy higher than 98%. A linear relation between driving range and ambient temperature was observed from a simulation process, with a high regression coefficient. The relation shows that the driving range increases with ambient temperature. The ratio of the estimated driving range from the simulation process and the standard value for a reference temperature of 25 °C was obtained. The ratio shows that the global driving range can be increased by up to 29% in high temperatures associated with the summer season, while for very low temperatures, near −30 °C, the global driving range is reduced by 20%. The comparative analysis of the driving range for different temperatures shows that there is a reduction of about 18% for the low range of ambient temperatures, between −15 °C and 5 °C, while for medium temperatures, between 5 °C and 25 °C, the reduction in the driving range is only 4.6%. Finally, tests demonstrated that with a reduction in high temperatures from 25 °C to 35 °C, the driving range only reduced by about 0.4%. For higher temperatures, around 50 °C, the longest driving distance can be achieved, with a higher accuracy. Full article

(This article belongs to the Special Issue Feature Papers in Future Transportation)

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Open AccessArticle

A Secure Traffic Police Remote Sensing Approach via a Deep Learning-Based Low-Altitude Vehicle Speed Detector through UAVs in Smart Cites: Algorithm, Implementation and Evaluation

Ata Jahangir Moshayedi

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Future Transp. 2023, 3(1), 189-209; https://doi.org/10.3390/futuretransp3010012 - 03 Feb 2023

Cited by 6 | Viewed by 1452

Abstract

Nowadays, the unmanned aerial vehicle (UAV) has a wide application in transportation. For instance, by leveraging it, we are able to perform accurate and real-time vehicle speed detection in an IoT-based smart city. Although numerous vehicle speed estimation methods exist, most of them lack real-time detection in different situations and scenarios. To fill the gap, this paper introduces a novel low-altitude vehicle speed detector system using UAVs for remote sensing applications of smart cities, forging to increase traffic safety and security. To this aim, (1) we have found the best possible Raspberry PI’s field of view (FOV) camera in indoor and outdoor scenarios by changing its height and degree. Then, (2) Mobile Net-SSD deep learning model parameters have been embedded in the PI4B processor of a physical car at different speeds. Finally, we implemented it in a real environment at the JXUST university intersection by changing the height (0.7 to 3 m) and the camera angle on the UAV. Specifically, this paper proposed an intelligent speed control system without the presence of real police that has been implemented on the edge node with the configuration of a PI4B and an Intel Neural Computing 2, along with the PI camera, which is armed with a Mobile Net-SSD deep learning model for the smart detection of vehicles and their speeds. The main purpose of this article is to propose the use of drones as a tool to detect the speeds of vehicles, especially in areas where it is not easy to access or install a fixed camera, in the context of future smart city traffic management and control. The experimental results have proven the superior performance of the proposed low-altitude UAV system rather than current studies for detecting and estimating the vehicles’ speeds in highly dynamic situations and different speeds. As the results showed, our solution is highly effective on crowded roads, such as junctions near schools, hospitals, and with unsteady vehicles from the speed level point of view. Full article

(This article belongs to the Special Issue Feature Papers in Future Transportation)

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Open AccessArticle

Calibration of the Microsimulation Traffic Model Using Different Neural Network Applications

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Future Transp. 2023, 3(1), 150-168; https://doi.org/10.3390/futuretransp3010010 - 02 Feb 2023

Viewed by 1425

Abstract

The efficacy of the application of traffic models depends on a successful process of model calibration. Microsimulation models have a significant number of input parameters that can be optimized in the calibration process. This paper presents the optimization of input parameters that are difficult to measure or unmeasurable in real traffic conditions and includes parameters of the driver’s behavior and parameters of Wiedemann’s psychophysical car-following model. Using neural networks, models were generated for predicting travel time and queue parameters and were used in the model calibration procedure. This paper presents the results of a comparison of five different applications of neural networks in calibrating the microsimulation model. The VISSIM microsimulation traffic model was selected for calibration and field measurements were carried out on two roundabouts in a local urban transport network. The applicability of neural networks in the process of calibrating the microsimulation models was confirmed by comparison of the modelled and measured data of traffic indicators in real traffic conditions. Methods of calibration were validated with two sets of new measured data at the same intersection where the calibration of the model was carried out. The third validation was made at the intersection in a different location. The selection of the optimal calibration methodology is based on the model accuracy between the simulated and measured data of traveling time, as well as queue parameters. The microsimulation model provides access to the raw data of observed traffic parameters for each vehicle in the simulation. The dataset of the calibrated model simulation results of all travel times of the selected traffic flow was compared with the dataset of the measured field data to determine whether the data are statistically significantly different or not. Full article

(This article belongs to the Special Issue Feature Papers in Future Transportation)

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