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Sustainable Transportation for the Future: Automated Vehicles and Big Data on Traffic Operations

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 9694

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Special Issue Editor

Dr. Xiaobing Li

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Guest Editor

Center for Urban Transportation Research, University of South Florida, Tampa, FL 33620, USA
Interests: big data analytics; traffic operations and safety; CAV

Special Issue Information

Dear Colleagues,

I am glad to share the exciting news that this Special Issue in Sustainability offers another platform to publish articles and exchange ideas for transportation professionals. This Special Issue is intended to seek applications of automated vehicles in real-world scenarios, and answer questions about how those applications can potentially improve future transportation system performance in terms of mobility, safety, sustainability, and environmental benefits. With the penetration of automated vehicles on roads in the near future, traffic operations will be dramatically different from current traffic patterns. With advanced data collection techniques at hand, how can the policymakers be well informed where to invest their asset by analyzing that massive amount of data? Additionally, people nowadays have a lot of discussions about sustainable transportation. Thinking ahead 10, 30, or 50 years from now, what will the transportation world look like? Will surface transportation still be the dominant travel mode? Recently, unmanned aerial vehicles (UAVs) have been extensively studied to explore the possibilities to shift part of the travel demand from surface transportation to air transportation for short to medium-distance trips. Questions regarding the impact of UAVs on surface congestion reduction still remain a challenge for real-world applications. With those in mind, I would like to invite potential authors to think about those possibilities and provide evidence that with those advancements in vehicles and technologies, future transportation will be better than today across different aspects of transportation. Thus, this Special Issue is proposed to encourage authors:

To explore the potential or real-world applications of automated vehicles in different scenarios in transportation;
To apply advanced big data analytical methods to examine the performance under new traffic environments with automated vehicles (mixed traffic in surface transportation); and
To investigate the new applications of UAVs under different city environments.

This Special Issue will be an additional supplement to the current literature regarding automated vehicles and their applications. It is intended to cover various topics on mobility, safety, sustainability, and the environment. Future transportation is built upon those new ideas and applications. The new, bright future of transportation is waiting for us, and we are striving to pave the road for what is to come.

Dr. Xiaobing Li
Guest Editor

Manuscript Submission Information

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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. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

automated vehicles
big data
traffic operations
sustainable transportation
mobility
safety
environment
UAV

Published Papers (5 papers)

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Research

14 pages, 2387 KiB

Open AccessArticle

A Data-Driven Approach to W-Beam Barrier Monitoring Data Processing: A Case Study of Highway Congestion Mitigation Strategy

by Weiguang Mu and Chengzhu Gong

Sustainability 2023, 15(5), 4078; https://doi.org/10.3390/su15054078 - 23 Feb 2023

Cited by 3 | Viewed by 1039

Abstract

In this paper, a data-driven approach is used to process W-Beam Barrier monitoring data, expecting to achieve online estimation of the number of trucks and accurate identification of barrier impact events. By analyzing the data features, significant noise was found in the original data, hiding the useful information, so this paper proposes an improved wavelet thresholding algorithm to achieve data denoising. As there is no study of the same application, this paper compares three commonly used data fault diagnosis algorithms: Principal Component Analysis (PCA), Partial Least Squares (PLS) and Fisher Discrimination Analysis (FDA). By designing and conducting comparison experiments, the results show that the PCA model is more suitable for estimating the number of trucks and the FDA model is more suitable for identifying barrier impact events. The data processing results are shared with the highway operation management system as a trigger condition to enable the strategy of forbidden truck overtaking. Through long-term application, the results show that highway capacity is improved by 12.7% and the congestion index and emissions are slightly reduced after adopting this paper’s method. Full article

(This article belongs to the Special Issue Sustainable Transportation for the Future: Automated Vehicles and Big Data on Traffic Operations)

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21 pages, 2737 KiB

Open AccessArticle

Modeling and Analysis of Driving Behaviour for Heterogeneous Traffic Flow Considering Market Penetration under Capacity Constraints

by Zhaoming Zhou, Jianbo Yuan, Shengmin Zhou, Qiong Long, Jianrong Cai and Lei Zhang

Sustainability 2023, 15(4), 2923; https://doi.org/10.3390/su15042923 - 6 Feb 2023

Cited by 1 | Viewed by 1509

Abstract

Based on analytical and simulation methods, this paper discusses the path choice behavior of mixed traffic flow with autonomous vehicles, advanced traveler information systems (ATIS) vehicles and ordinary vehicles, aiming to promote the development of autonomous vehicles. Firstly, a bi-level programming model of mixed traffic flow assignments constrained by link capacity is established to minimize travel time. Subsequently, the algorithm based on the incremental allocation method and method of successive averages is proposed to solve the model. Through a numerical example, the road network capacity under different modes is obtained, the impact of market penetration on travel time is analyzed, and the state and characteristics of single equilibrium flow and mixed equilibrium flow are explored. Analysis results show that the road network can be maximized based on saving travel time when all vehicles are autonomous, especially when the autonomous lane is adopted. The travel time can be shortened by increasing the market penetration of autonomous vehicles and ATIS vehicles, while the former is more effective. However, the popularization of autonomous vehicles cannot be realized in the short term; the market penetration of autonomous vehicles and ATIS vehicles can be set to 0.2 and 0.6, respectively, during the introduction period. Full article

(This article belongs to the Special Issue Sustainable Transportation for the Future: Automated Vehicles and Big Data on Traffic Operations)

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21 pages, 7687 KiB

Open AccessArticle

Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning

by Bo Peng, Hanbo Zhang, Ni Yang and Jiming Xie

Sustainability 2022, 14(13), 7912; https://doi.org/10.3390/su14137912 - 29 Jun 2022

Viewed by 1775

Abstract

For accurate and effective automatic vehicle identification, morphological detection and deep convolutional networks were combined to propose a method for locating and identifying vehicle models from unmanned aerial vehicle (UAV) videos. First, the region of interest of the video frame image was sketched and grey-scale processing was performed; sub-pixel-level skeleton images were generated based on the Canny edge detection results of the region of interest; then, the image skeletons were decomposed and reconstructed. Second, a combination of morphological operations and connected domain morphological features were applied for vehicle target recognition, and a deep learning image benchmark library containing 244,520 UAV video vehicle samples was constructed. Third, we improved the AlexNet model by adding convolutional layers, pooling layers, and adjusting network parameters, which we named AlexNet*. Finally, a vehicle recognition method was established based on a candidate target extraction algorithm with AlexNet*. The validation analysis revealed that AlexNet* achieved a mean

F_{1}

of 85.51% for image classification, outperforming AlexNet (82.54%), LeNet (63.88%), CaffeNet (46.64%), VGG16 (16.67%), and GoogLeNet (14.38%). The mean values of

P_{c o r}

P_{r e}

, and

P_{m i s s}

for cars and buses reached 94.63%, 6.87%, and 4.40%, respectively, proving that this method can effectively identify UAV video targets. Full article

(This article belongs to the Special Issue Sustainable Transportation for the Future: Automated Vehicles and Big Data on Traffic Operations)

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15 pages, 2265 KiB

Open AccessArticle

An Injury-Severity-Prediction-Driven Accident Prevention System

by Gulsum Alicioglu, Bo Sun and Shen Shyang Ho

Sustainability 2022, 14(11), 6569; https://doi.org/10.3390/su14116569 - 27 May 2022

Cited by 1 | Viewed by 1784

Abstract

Traffic accidents are inevitable events that occur unexpectedly and unintentionally. Therefore, analyzing traffic data is essential to prevent fatal accidents. Traffic data analysis provided insights into significant factors and driver behavioral patterns causing accidents. Combining these patterns and the prediction model into an accident prevention system can assist in reducing and preventing traffic accidents. This study applied various machine learning models, including neural network, ordinal regression, decision tree, support vector machines, and logistic regression to have a robust prediction model in injury severity. The trained model provides timely and accurate predictions on accident occurrence and injury severity using real-world traffic accident datasets. We proposed an informative negative data generator using feature weights derived from multinomial logit regression to balance the non-fatal accident data. Our aim is to resolve the bias that happens in the favor of the majority class as well as performance improvement. We evaluated the overall and class-level performance of the machine learning models based on accuracy and mean squared error scores. Three hidden layered neural networks outperformed the other models with 0.254 ± 0.038 and 0.173 ± 0.016 MSE scores for two different datasets. A neural network, which provides more accurate and reliable results, should be integrated into the accident prevention system. Full article

(This article belongs to the Special Issue Sustainable Transportation for the Future: Automated Vehicles and Big Data on Traffic Operations)

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21 pages, 3690 KiB

Open AccessEditor’s ChoiceArticle

Risk Identification and Conflict Prediction from Videos Based on TTC-ML of a Multi-Lane Weaving Area

by Yulan Xia, Yaqin Qin, Xiaobing Li and Jiming Xie

Sustainability 2022, 14(8), 4620; https://doi.org/10.3390/su14084620 - 12 Apr 2022

Cited by 6 | Viewed by 2291

Abstract

Crash risk identification and prediction are expected to play an important role in traffic accident prevention. However, most of the existing studies focus only on highways, not on multi-lane weaving areas. In this paper, a potential collision risk identification and conflict prediction model based on extending Time-to-Collision-Machine Learning (TTC-ML) for multi-lane weaving zone was proposed. The model can accurately learn various features, such as vehicle operation characteristics, risk and conflict distributions, and physical zoning characteristics in the weaving area. Specifically, TTC was used to capture the collision risk severity, and ML extracted vehicle trajectory features. After normalizing and dimensionality reduction of the vehicle trajectory dataset, Naive Bayes, Logistic Regression, and Gradient Boosting Decision Tree (GBDT) models were selected for traffic conflict prediction, and the experiments showed that the GBDT model outperforms two remaining models in terms of prediction accuracy, precision, false-positive rate (FPR) and Area Under Curve (AUC). The research findings of this paper help traffic management departments develop and optimize traffic control schemes, which can be applied to Intelligent Vehicle Infrastructure Cooperative Systems (IVICS) dynamic warning. Full article

(This article belongs to the Special Issue Sustainable Transportation for the Future: Automated Vehicles and Big Data on Traffic Operations)

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