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Applications of Artificial Intelligence to Improve Road Traffic Performance

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 25627

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Special Issue Editors

Prof. Dr. Yuchuan Du

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Guest Editor

School of Transportation Engineering, Tongji University, Shanghai 200092, China
Interests: traffic holographic perception and intelligent computing; Intelligent Transportation System (ITS); transportation economic analysis; transport infrastructure management system

Dr. Yu Shen

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Guest Editor

Department of Transportation Information and Control Engineering, College of Transportation Engineering, Tongji University, Shanghai, China
Interests: shared mobility; public transit; data mining; machine learning
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Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has been proved as an effective and solid tool for tackling transportation problems. Based on the massive amounts of data generated every day, there is currently a great deal of interest in developing AI algorithms, models, and techniques to improve road traffic performance, such as urban road, freeway, parking, and road infrastructure. Although traditional analytical algorithms based on probability statistics can describe the performance of the road traffic system, it is difficult to accurately predict and optimize its dynamic status under complex transport conditions. The emerging AI technologies combine analytical models with data models, and convert model-based frameworks into model-free or model-data mixed frameworks, thereby effectively improving data analysis efficiency and result accuracy.

This Special Issue will be dedicated to soliciting high-quality research to better evaluate and improve the performance of the current road transport system. The scope of the Special Issue includes, but is not limited to, AI applications in road traffic and asset management, data-driven techniques for solving traffic congestion, novel concurrent algorithms and applications, cyber–physical–social transportation system, and other relevant subjects. Future-oriented electrification, automation, shared (3Rs) mobility, system design for connected and automated vehicles, blockchain-based ITS, internet of things (IoT), and large-scale deployment of AI-based distributed sensors are also topics of interest.

Prof. Dr. Yuchuan Du
Dr. Yu Shen
Guest Editors

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Keywords

AI applications in road asset management
AI and deep learning in road transport system
data-driven traffic control and management
Cyber–Physical–Social transportation systems
electrification, automation, and shared mobility
infrastructure performance measurement using AI techniques
large-scale traffic infrastructure maintenance and optimization
emerging technologies for traffic perception and surveillance
AI-based traffic state estimation and forecasting
AI empowered trajectory analytics and traffic risk modeling
optimization for connected road transport systems
road traffic big data management and quality control
intelligent urban parking systems based on IoT and AI techniques
data-driven road traffic parallel simulation and emerging decision-making algorithms
cooperative vehicle–infrastructure systems

Published Papers (13 papers)

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Research

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16 pages, 3495 KiB

Open AccessArticle

Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network

by Xubin Zhai and Yu Shen

Appl. Sci. 2023, 13(8), 4910; https://doi.org/10.3390/app13084910 - 13 Apr 2023

Cited by 1 | Viewed by 1597

Abstract

The short-term bus passenger flow prediction of each bus line in a transit network is the basis of real-time cross-line bus dispatching, which ensures the efficient utilization of bus vehicle resources. As bus passengers transfer between different lines, to increase the accuracy of prediction, we integrate graph features into the recurrent neural network (RNN) to capture the spatiotemporal dependencies in the bus network. The diffusion convolution recurrent neural network (DCRNN) architecture is adopted to forecast the future number of passengers on each bus line. The demand evolution in the bus network of Jiading, Shanghai, is investigated to demonstrate the effectiveness of the DCRNN model. Compared with classic RNN models, our proposed method has an advantage of about 5% in mean average percentage error (MAPE). The incorporation of diffusion convolution shows that the travel demand in a bus line tends to be similar to that in the closely related lines. In addition, the improvement in MAPE shows that this model outputs more accurate prediction values for low-demand bus lines. It ensures that, for real-time cross-line bus dispatching with limited vehicle resources, the low-demand bus lines are less likely to be affected to maintain a decent level of service of the whole bus network. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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15 pages, 3380 KiB

Open AccessArticle

A General Framework for Reconstructing Full-Sample Continuous Vehicle Trajectories Using Roadside Sensing Data

by Guimin Su, Zimu Zeng, Andi Song, Cong Zhao, Feng Shen, Liangxiao Yuan and Xinghua Li

Appl. Sci. 2023, 13(5), 3141; https://doi.org/10.3390/app13053141 - 28 Feb 2023

Viewed by 1502

Abstract

Vehicle trajectory data play an important role in autonomous driving and intelligent traffic control. With the widespread deployment of roadside sensors, such as cameras and millimeter-wave radar, it is possible to obtain full-sample vehicle trajectories for a whole area. This paper proposes a general framework for reconstructing continuous vehicle trajectories using roadside visual sensing data. The framework includes three modules: single-region vehicle trajectory extraction, multi-camera cross-region vehicle trajectory splicing, and missing trajectory completion. Firstly, the vehicle trajectory is extracted from each video by YOLOv5 and DeepSORT multi-target tracking algorithms. The vehicle trajectories in different videos are then spliced by the vehicle re-identification algorithm fused with lane features. Finally, the bidirectional long-short-time memory model (LSTM) based on graph attention is applied to complete the missing trajectory to obtain the continuous vehicle trajectory. Measured data from Donghai Bridge in Shanghai are applied to verify the feasibility and effectiveness of the framework. The results indicate that the vehicle re-identification algorithm with the lane features outperforms the vehicle re-identification algorithm that only considers the visual feature by 1.5% in mAP (mean average precision). Additionally, the bidirectional LSTM based on graph attention performs better than the model that does not consider the interaction between vehicles. The experiment demonstrates that our framework can effectively reconstruct the continuous vehicle trajectories on the expressway. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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18 pages, 7454 KiB

Open AccessArticle

Negative Effect Prediction and Refueling Traffic Flow Equilibrium of Urban Energy Supply Network during Weekends and Holidays

by Xianlong Ge, Zuofa Yin, Yuanqiu Zou and Bo Wang

Appl. Sci. 2023, 13(4), 2498; https://doi.org/10.3390/app13042498 - 15 Feb 2023

Viewed by 1125

Abstract

With the continuous improvement of people’s living standards, the travel demand for vehicles increases rapidly during weekends and holidays. This situation leads to a number of negative impacts on the transport network, such as traffic congestion, carbon emissions, and long queues at refueling stations. The impact of vehicles requiring energy replenishment on the negative effect is nonlinear. Therefore, the negative effect data of working days cannot be directly used to evaluate the cost, and the traffic equilibrium allocation problem also needs to be solved. An equilibrium allocation model of hybrid vehicles considering the energy replenishment demand in holidays is established, aiming at minimizing the total time cost and energy consumption cost. The Frank–Wolfe algorithm is used to solve the traffic allocation problem in advance, and the negative effect of the urban energy network during holidays is predicted by the Genetic Algorithm—Back Propagation (GA-BP) algorithm. Then, the energy-replenishing vehicles in the traffic network are induced to reduce the total cost. Finally, taking the actual road network in Jiulongpo District of Chongqing City, southwest of China, as an example, the negative effects of multiple stations are predicted. The results show that the prediction method proposed in this paper is effective during holiday periods. In addition, as the market share of electric vehicles increases, the negative cost can decrease gradually. The predicted results can provide a reference for traffic managers. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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19 pages, 41082 KiB

Open AccessArticle

Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic Regression

by Hao Liu, Yishun Li, Chenglong Liu, Guohong Shen and Hui Xiang

Appl. Sci. 2022, 12(22), 11855; https://doi.org/10.3390/app122211855 - 21 Nov 2022

Cited by 3 | Viewed by 1481

Abstract

Pavement condition prediction plays a vital role in pavement maintenance. Many prediction models and analyses have been conducted based on long-term pavement condition data. However, the condition evaluation for road sections can hardly support daily routine maintenance. This paper uses high-frequency pavement distress data to explore the relationship between distress initiation, weather, and geometric factors. Firstly, a framework is designed to extract the initial time of pavement distress. Weather and geometric data are integrated to establish a pavement distress initiation dataset. Then, the time-lag cross-correlation analysis methods were utilized to explore the relationship between distress initiation and environmental factors. In addition, the logistic regression model is used to establish the distress initiation prediction model. Finally, Akaike information criterion (AIC), Bayesian information criterions (BIC), and areas under receiver operating characteristic curves (AUC) of logistic regression models with or without time-lag variables are compared as performance measurements. The results show that pavement distress initiation is susceptible to weather factors and location relationships. Daily total precipitation, minimum temperature, and daily average temperature have a time delay effect on the initiation of the pavement distress. Distress initiation is negatively correlated with the distance from the nearby intersection and positively correlated with adjacent distresses. The weather factors, considering the time-lag effect, can improve the model performance of the distress initiation prediction model and provide support for emergency management after severe weather. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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21 pages, 4719 KiB

Open AccessArticle

Real-Time Risk Assessment for Road Transportation of Hazardous Materials Based on GRU-DNN with Multimodal Feature Embedding

by Shanchuan Yu, Yi Li, Zhaoze Xuan, Yishun Li and Gang Li

Appl. Sci. 2022, 12(21), 11130; https://doi.org/10.3390/app122111130 - 2 Nov 2022

Cited by 3 | Viewed by 1575

Abstract

In this paper, a gated recurrent unit–deep neural network (GRU-DNN) model integrated with multimodal feature embedding (MFE) is developed to evaluate the real-time risk of hazmat road transportation based on various types of data for contributing factors. MFE was incorporated into the framework of a deep learning model in which discrete variables, continuous variables, and images were uniformly embedded. GRU is a pre-trained sub-model, and the DNN is able to directly use the relative structure and weights of the GRU, improving the poor classification and recognition results due to insufficient samples. Additionally, the model is trained and validated based on hazmat road transportation database consisting of 2100 samples with 20 real-time contributing factors and four risk levels in China. The accuracy (ACC), precision (PR), recall (RE), F1-score (F1), and areas under receiver-operating-characteristic curves (AUC) of the proposed model and other commonly used models are compared as performance measurements in numerical examples. Finally, Carlini & Wagner attack and three defenses of adversarial training, dimensionality reduction and prediction similarity are proposed in the training to improve the robustness of the model, alleviating the impact of noise and error on small-sized samples. The results demonstrate that the average ACC of the model reaches 93.51% and 87.6% on the training and validation sets, respectively. The prediction of accidents resulting in injury is the most accurate, followed by fatal accidents. Combined with the RE of 89.0%, the model exhibits excellent performance. In addition, the proposed model outperforms other widely used models based on the overall comparisons of ACC, AUC, F1 and PR-RE curve. Finally, prediction similarity can be used as an effective approach for robustness improvement, with the launched adversarial attacks being detected at a high success rate. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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18 pages, 2154 KiB

Open AccessArticle

Lane-Changing Strategy Based on a Novel Sliding Mode Control Approach for Connected Automated Vehicles

by Chengmei Wang and Yuchuan Du

Appl. Sci. 2022, 12(21), 11000; https://doi.org/10.3390/app122111000 - 30 Oct 2022

Cited by 4 | Viewed by 1337

Abstract

Safe and efficient autonomous lane changing is a key step of connected automated vehicles (CAVs), which can greatly reduce the traffic accident rate and relieve the traffic pressure. Aiming at the requirements of the smoothness and efficiency of the lane-changing trajectory of CAVs, it is necessary to design the lane changing controller to integrate the sensing, decision-making, and control tasks in the driving process. Firstly, based on the vehicle dynamics model, this paper proposes a vehicle lane-changing control strategy based on NNTSMC method (neural network enhanced non-singular fast terminal sliding mode control). The designed lane-changing controller can well realize the designed path tracking, and both lateral position and yaw angle can well track the expected value. This method enables the vehicle to control the front wheel steering angle intelligently, and the lateral acceleration during steering changes in the small scope, which ensures the steering stability of the vehicle. In this study, an improved adaptive RBF neural network with bounded mapping is designed to estimate the upper bound of the total disturbance of the system, which effectively reduces the chattering phenomenon of the control force. The Lyapunov function constructed in this study proves that the designed controller can ensure the stability of the controlled system. Finally, a comparative experiment is performed by the MATLAB/Simulink-CarSim co-simulation. Compared with SMC and TSMC (non-singular fast terminal sliding mode control), the proposed method has a performance improvement of at least 58.0% and 34.1%, respectively. The effectiveness and superiority of the proposed control method were confirmed by the experiments on the co-simulation platform. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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17 pages, 8075 KiB

Open AccessArticle

GPR Data Augmentation Methods by Incorporating Domain Knowledge

by Guanghua Yue, Chenglong Liu, Yishun Li, Yuchuan Du and Shili Guo

Appl. Sci. 2022, 12(21), 10896; https://doi.org/10.3390/app122110896 - 27 Oct 2022

Cited by 5 | Viewed by 2408

Abstract

Deep learning has significantly improved the recognition efficiency and accuracy of ground-penetrating radar (GPR) images. A significant number of weight parameters need to be specified, which requires lots of labeled GPR images. However, obtaining the ground-truth subsurface distress labels is challenging as they are invisible. Data augmentation is a predominant method to expand the dataset. The traditional data augmentation methods, such as rotating, scaling, cropping, and flipping, would change the GPR signals’ real features and cause the model’s poor generalization ability. We proposed three GPR data augmentation methods (gain compensation, station spacing, and radar signal mapping) to overcome these challenges by incorporating domain knowledge. Then, the most state-of-the-art model YOLOv7 was applied to verify the effectiveness of these data augmentation methods. The results showed that the proposed data augmentation methods decrease loss function values when the training epochs grow. The performance of the deep learning model gradually became stable when the original datasets were augmented two times, four times, and eight times, proving that the augmented datasets can increase the robustness of the training model. The proposed data augmentation methods can be used to expand the datasets when the labeled training GPR images are insufficient. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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18 pages, 8275 KiB

Open AccessArticle

Performance Analysis of the YOLOv4 Algorithm for Pavement Damage Image Detection with Different Embedding Positions of CBAM Modules

by Li Li, Baihao Fang and Jie Zhu

Appl. Sci. 2022, 12(19), 10180; https://doi.org/10.3390/app121910180 - 10 Oct 2022

Cited by 9 | Viewed by 1748

Abstract

One of the most critical tasks for pavement maintenance and road safety is the rapid and correct identification and classification of asphalt pavement damages. Nowadays, deep learning networks have become the popular method for detecting pavement cracks, and there is always a need to further improve the accuracy and precision of pavement damage recognition. An improved YOLOv4-based pavement damage detection model was proposed in this study to address the above problems. The model improves the saliency of pavement damage by introducing the convolutional block attention module (CBAM) to suppress background noise and explores the influence of the embedding position of the CBAM module in the YOLOv4 model on the detection accuracy. The K-means++ algorithm was used to optimize the anchor box parameters to improve the target detection accuracy and form a high-performance pavement crack detection model called YOLOv4-3. The training and test sets were constructed using the same image data sources, and the results showed the mAP (mean average precision) of the improved YOLOv4-3 network was 2.96% higher than that before the improvement. The experiments indicate that embedding CBAM into the Neck module and the Head module can effectively improve the detection accuracy of the YOLOv4 model. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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20 pages, 8673 KiB

Open AccessArticle

Lane-Changing Recognition of Urban Expressway Exit Using Natural Driving Data

by Lei Zhao, Ting Xu, Zhishun Zhang and Yanjun Hao

Appl. Sci. 2022, 12(19), 9762; https://doi.org/10.3390/app12199762 - 28 Sep 2022

Cited by 7 | Viewed by 1618

Abstract

The traffic environment at the exit of the urban expressway is complex, and vehicle lane-changing behavior occurs frequently, making it prone to traffic conflict and congestion. To study the traffic conditions at the exit of the urban expressway and improve the road operation capacity, this paper analyzes the characteristics of lane-changing behaviors at the exit, adds driving style into the influencing factors of lane-changing, and recognizes one’s lane-changing intention based on driving data. A UAV (unmanned aerial vehicle) is used to collect the natural driving track data of the urban expressway diverge area, the track segments of vehicle lane-changing that meet the standards are extracted, and 374 lane-changing segments are obtained. K-means++ is used to cluster the driving style of the lane-changing segments which is grouped into three clusters, corresponding to “ordinary”, “radical”, and “conservative”. Through the random forest model used to identify and predict driving style, the accuracy reaches 93%. Considering the characteristics of a single time point and the characteristics of the historical time window, XGBoost, LightGBM, and the Stacking fusion model are established to recognize one’s lane-changing intention. The results show that the models can well recognize the lane-changing intention of drivers. The Stacking fusion model has the highest accuracy, while the LightGBM model takes less time; the model considering the characteristics of the historical time window performs better than the other one, which can better improve the prediction accuracy of lane-changing behavior. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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11 pages, 1888 KiB

Open AccessArticle

Intelligent Control Strategies for Vehicle Departure in Urban Complex Parking Lots of the Jinding Area in Shanghai, China

by Shengchuan Jiang, Jindong Wang and Zhouyang Du

Appl. Sci. 2022, 12(17), 8781; https://doi.org/10.3390/app12178781 - 31 Aug 2022

Viewed by 1327

Abstract

The entrances and exits of underground parking lots of large complexes are the key nodes for the conversion between ground-level dynamic traffic and underground static traffic. Since congestion is caused by a large number of vehicles leaving parking lots at peak hours, the departure control strategy can effectively manage vehicle departure and reduce the congestion of ground-level traffic. In this study, we introduce cooperative control in ramp control into parking lot exit management. The frequency of parking lot exit gate lever lift is used as the control and optimization variable. To ensure the efficiency of regional traffic, we designed timing and inductive control strategies to control the speed of departing vehicles. In an experimental model, we took Shanghai Jinding super-large underground parking lot as an example. The changes in the external road network were simulated when different strategies were implemented on the Simulation of Urban Mobility (SUMO) simulation platform. The experimental results show that the proposed control strategies can significantly ease the congestion of the regional road network, improve the average speed of dynamic traffic, and reduce the queue length at intersections. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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15 pages, 22454 KiB

Open AccessArticle

An Urban Traffic Flow Fusion Network Based on a Causal Spatiotemporal Graph Convolution Network

by Xing Xu, Hao Mao, Yun Zhao and Xiaoshu Lü

Appl. Sci. 2022, 12(14), 7010; https://doi.org/10.3390/app12147010 - 11 Jul 2022

Cited by 3 | Viewed by 1605

Abstract

Traffic flow prediction is an important part of intelligent transportation systems. In recent years, most methods have considered only the feature relationships of spatial dimensions of traffic flow data, and ignored the feature fusion of spatial and temporal aspects. Traffic flow has the features of periodicity, nonlinearity and complexity. There are many relatively isolated points in the nodes of traffic flow, resulting in the features usually being accompanied by high-frequency noise. The previous methods directly used the graph convolution network for feature extraction. A polynomial approximation graph convolution network is essentially a convolution operation to enhance the weight of high-frequency signals, which lead to excessive high-frequency noise and reduce prediction accuracy to a certain extent. In this paper, a deep learning framework is proposed for a causal gated low-pass graph convolution neural network (CGLGCN) for traffic flow prediction. The full convolution structure adopted by the causal convolution gated linear unit (C-GLU) extracts the time features of traffic flow to avoid the problem of long running time associated with recursive networks. The reduction of running parameters and running time greatly improved the efficiency of the model. The new graph convolution neural network with self-designed low-pass filter was able to extract spatial features, enhance the weight of low-frequency signal features, suppress the influence of high-frequency noise, extract the spatial features of each node more comprehensively, and improve the prediction accuracy of the framework. Several experiments were carried out on two real-world real data sets. Compared with the existing models, our model achieved better results for short-term and long-term prediction. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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Review

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26 pages, 1634 KiB

Open AccessReview

A Review of Road Surface Anomaly Detection and Classification Systems Based on Vibration-Based Techniques

by Erick Axel Martinez-Ríos, Martin Rogelio Bustamante-Bello and Luis Alejandro Arce-Sáenz

Appl. Sci. 2022, 12(19), 9413; https://doi.org/10.3390/app12199413 - 20 Sep 2022

Cited by 10 | Viewed by 4438

Abstract

Road surfaces suffer from sources of deterioration, such as weather conditions, constant usage, loads, and the age of the infrastructure. These sources of decay generate anomalies that could cause harm to vehicle users and pedestrians and also develop a high cost to repair the irregularities. These drawbacks have motivated the development of systems that automatically detect and classify road anomalies. This study presents a narrative review focused on road surface anomaly detection and classification based on vibration-based techniques. Three methodologies were surveyed: threshold-based methods, feature extraction techniques, and deep learning techniques. Furthermore, datasets, signals, preprocessing steps, and feature extraction techniques are also presented. The results of this review show that road surface anomaly detection and classification performed through vibration-based methods have achieved relatively high performance. However, there are challenges related to the reproduction and heterogeneity of the results that have been reported that are influenced by the limited testing conditions, sample size, and lack of publicly available datasets. Finally, there is potential to standardize the features computed through the time or frequency domains and evaluate and compare the diverse set of settings of time-frequency methods used for feature extraction and signal representation. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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Other

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10 pages, 2653 KiB

Open AccessBrief Report

Research on Queue Equilibrium Control Algorithm of Urban Traffic Based on Game Theory

by Li Wang, Ke Pan, Qi Zhao, Lili Zhang and Lingyu Zhang

Appl. Sci. 2023, 13(3), 1781; https://doi.org/10.3390/app13031781 - 30 Jan 2023

Cited by 1 | Viewed by 1250

Abstract

The intersection traffic signal control is an essential means of urban traffic. To solve the problem of urban congestion, it is necessary to consider the optimal signal control strategy for intersections. Using the store-and-forward method of traffic control modeling, the in-queue vehicle number of the key signal phase as the payoff index, this paper designs an optimal intersection signal-timing strategy based on the game theory method. In this strategy, each key phase is regarded as a game player, and each player competes for the release time to maximize their own payoff and minimize the queue. To optimize the intersection efficiency, a game strategy is designed to achieve the Nash equilibrium state, which is the queueing equilibrium of each key phase. Finally, by VISSIM simulation, the total number of stops can be decreased by 5% to 10% compared with the MA-DD-DACC method. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Improve Road Traffic Performance)

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