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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 (30 November 2023) | Viewed by 12395

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Special Issue Editors

Dr. Wen-Long Shang

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Guest Editor

1. Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing, China
2. Department of Civil and Environmental Engineering, Imperial College London, London, UK
Interests: intelligent transportation system; transportation system emergency management; transportation system energy conservation and emission reduction; transportation big data; large-scale optimization

Dr. Kun Wang

E-Mail Website
Guest Editor

Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: aviation transport management; international maritime shipping and logistics; intermodal competition and policy; green supply chain and logistics management

Dr. Haoran Zhang

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Guest Editor

School of Urban Planning and Design, Peking University, Shenzhen 518055, China
Interests: urban energy system; urban logistics system; urban flow space
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yanyan Chen

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Guest Editor

Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing 100124, China
Interests: big ransportation data; transportation planning and management; intelligent transportation; system simulation; road network optimization; transportation energy saving and emission reduction

Prof. Dr. Washington Yotto Ochieng

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Guest Editor

Department of Civil and Environmental Engineering, Imperial College London, London, UK
Interests: navigation; positioning; transport; aviation; geomatics
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Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is a constellation of many different technologies working together to perform tasks with human-like levels of intelligence. AI techniques, such as machine learning and natural language processing, are being used in a wide range of applications, from autonomous vehicles to healthcare, and from finance to manufacturing. AI combines multiple disciplines such as mathematics, computer science, and engineering to create systems that are able to learn from experience, recognize patterns, and make decisions. AI is expected to have a profound impact on the world in the recent future. Meanwhile, intelligent transportation systems (ITS) are a set of technologies and applications that are used to improve the safety, efficiency, and sustainability of transportation systems. Nowadays, the relationship between AI and ITS is growing much closer, as AI-powered technologies are increasingly being used to automate and optimize many aspects of transportation systems. AI-based ITS are being used to analyze large amounts of traffic data to generate more efficient routes and to improve traffic control. AI-driven sensors are also being used to detect potential hazards and provide maintenance information. By leveraging the capability of AI, ITS is a becoming more efficient, reliable, and safer, transforming the way we travel.

This Special Issue is intended to encourage experts and scholars to discuss and explore how to understand and employ AI technologies in ITS, which may involve all aspects of transportation systems. We also encourage innovative attempts to develop new ITS technologies and methods based on AI to solve traffic problems such as congestion, safety and the environment. The targeted audience includes academia, policy makers, traffic managers, the public, and even project developers.

Suitable topics include, but are not limited to, the following:

Machine learning algorithms for traffic flow optimization;
Traffic data detection and data acquisition based on AI;
Intelligent scheduling of public transportation using AI;
Autonomous vehicle navigation based on AI;
Real-Time traffic monitoring with AI;
Solutions of road safety based on AI;
Intelligent signal control based on AI;
Traffic congestion detection and management based on AI;
Security and privacy challenges for AI-based ITS;
Data fusion, sharing and visualization in AI-based ITS;
Low-carbon transportation based on AI;
Emergence management of transportation systems based on AI.

Dr. Wen-Long Shang
Dr. Kun Wang
Dr. Haoran Zhang
Prof. Dr. Yanyan Chen
Prof. Dr. Washington Yotto Ochieng
Guest Editors

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Keywords

AI
intelligent transportation systems
traffic management
autonomous vehicles
low-carbon transportation

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Published Papers (9 papers)

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Research

13 pages, 1676 KiB

Open AccessArticle

Analyzing Parking Demand Characteristics Using a Bayesian Model Averaging

by Bo Liu, Peng Zhang, Shubo Wu, Yajie Zou, Linbo Li and Shuning Tang

Appl. Sci. 2023, 13(24), 13245; https://doi.org/10.3390/app132413245 - 14 Dec 2023

Viewed by 1021

Abstract

Parking duration analysis is an important aspect of evaluating parking demand. Identifying accurate distribution characteristics of parking duration can not only enhance parking efficiency and parking facility planning, but also provide essential support for parking delicacy management. Previous studies have proposed various statistical distributions to depict parking duration data. However, it is difficult to find a certain type of distribution to describe the characteristics of parking duration in diverse parking facilities, since model uncertainty is caused by stochastic parking behaviors and diverse parking environments. To address the model uncertainty, a Bayesian model averaging (BMA) was applied to integrate the advantages of different statistical distributions to depict parking duration characteristics. The parking dataset was collected from a commercial parking lot in Chengdu, China, and the dataset was categorized into two groups (i.e., temporary users and long-term users) to analyze. A set of statistical distributions was chosen as candidate models, and their corresponding unknown parameters were estimated. The posterior model probability for each candidate model was calculated according to the goodness-of-fit (GOF) metric. The findings of the study illustrate that there is no universally applicable distribution form (e.g., log-normal distribution) to depict the parking duration distribution for both user types, whereas the BMA approach assigns weights to candidate models and always provides an accurate description of the parking duration characteristics. The parking duration analysis is useful for improving parking management strategies and optimizing parking pricing policies. Full article

(This article belongs to the Special Issue AI Techniques in Intelligent Transport Systems)

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15 pages, 6025 KiB

Open AccessArticle

A Strategy for Integrated Multi-Demands High-Performance Motion Planning Based on Nonlinear MPC

by Yu Han, Xiaolei Ma, Bo Wang, Hongwang Zhang, Qiuxia Zhang and Gang Chen

Appl. Sci. 2023, 13(22), 12443; https://doi.org/10.3390/app132212443 - 17 Nov 2023

Cited by 1 | Viewed by 1061

Abstract

Nonlinear Model Predictive Control (NMPC) is an effective approach for motion planning in autonomous vehicles that need to satisfy multiple driving demands. Within the realm of planner design, current strategies inadequately address the issues related to redundancy and conflicts among these diverse demands. This shortcoming leads to low efficiency and suboptimal performance, particularly when faced with a high volume of demands. In response to this challenge, this paper introduces the Hierarchical and Multi-Domain (HMD) strategy as a solution for designing a multi-objective NMPC planner. This strategy enables the dynamic adjustment of the integration method for demand indicators based on their priority. To evaluate the risk of breaching driving demands, several risk functions are established. The constraints and objective function of the planner are meticulously designed in accordance with the HMD strategy and evaluation functions. Simulation results attest to the advantages of the HMD-based planner, which, compared to planners based on traditional multi-objective (TMO) strategies, exhibits a 68.5% improvement in solution efficiency and the simultaneous enhancement of driving safety. Additionally, the HMD approach reduces the maximum jerk by 58.8%. Full article

(This article belongs to the Special Issue AI Techniques in Intelligent Transport Systems)

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13 pages, 2654 KiB

Open AccessArticle

How Does the Built Environment Affect Drunk-Driving Crashes? A Spatial Heterogeneity Analysis

by Shaohua Wang, Jianzhen Liu, Ning Chen, Jinjian Xiao and Panyi Wei

Appl. Sci. 2023, 13(21), 11813; https://doi.org/10.3390/app132111813 - 29 Oct 2023

Cited by 4 | Viewed by 1191

Abstract

In this research, 3356 alcohol-related traffic crashes were obtained from blood-alcohol test reports in Tianjin, China. Population density, intersection density, road density, and alcohol outlet densities, including retail density, entertainment density, restaurant density, company density, hotel density, and residential density, were extracted from 2114 traffic analysis zones (TAZs). After a spatial autocorrelation test, the multiple linear regression model (MLR), geographically weighted Poisson regression model (GWPR), and semi-parametric geographically weighted Poisson regression model (SGWPR) were utilized to explore the spatial effects of the aforementioned variables on drunk-driving crash density. The result shows that the SGWPR model based on the adaptive Gaussian function had the smallest AICc value and the best-fitting accuracy. The residential density and the intersection density are global variables, and the others are local variables that have different influences in different regions. Furthermore, we found that the influence of local variables in the economic–technological development area shows significantly different characteristics compared with other districts. Thus, a comprehensive consideration of spatial heterogeneity would be able to improve the effectiveness of the programs formulated to decrease drunk driving crashes. Full article

(This article belongs to the Special Issue AI Techniques in Intelligent Transport Systems)

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15 pages, 2077 KiB

Open AccessArticle

A Clustering Routing Algorithm Based on Improved Ant Colony Optimization for an Urban Rail Transit Ad Hoc Network

by Zhaoyang Su, Xiao Xiao, Zijie Han and Liu Liu

Appl. Sci. 2023, 13(21), 11769; https://doi.org/10.3390/app132111769 - 27 Oct 2023

Viewed by 1424

Abstract

As traffic pressure increases, urban rail transit has attracted attention because of its high speed and growing capacity in recent years. However, the traditional vehicle–ground communication systems of urban rail transit have been unable to meet the increasing requirements for communication. In this paper, we introduce an ad hoc network for urban rail transit to improve communication reliability, reduce end-to-end latency, and improve throughput of vehicle–ground communication. A novel clustering algorithm based on improved ant colony optimization is presented, which integrates various variables to elect cluster heads and adopts a low-latency queuing strategy. In addition, the algorithm can also replace the cluster heads adaptively to optimize the communication quality. Through the simulation results, the proposed algorithm can reduce the packet loss rate by about 60%, reduce the delay by about 50%, and improve the throughput by about 40% compared with the classical clustering routing protocol. Full article

(This article belongs to the Special Issue AI Techniques in Intelligent Transport Systems)

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17 pages, 3136 KiB

Open AccessArticle

A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification

by Wenji Yin, Yueping Peng, Zecong Ye and Wenchao Liu

Appl. Sci. 2023, 13(21), 11651; https://doi.org/10.3390/app132111651 - 25 Oct 2023

Cited by 2 | Viewed by 1133

Abstract

Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of unmanned aerial vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complicated shooting angles, occlusions, low discrimination of top–down features, and significant changes in vehicle scales. To address this, we propose a novel dual mixing attention network (DMANet) to extract discriminative features robust to variations in viewpoint. Specifically, we first present a plug-and-play dual mixing attention module (DMAM) to capture pixel-level pairwise relationships and channel dependencies, where DMAM is composed of spatial mixing attention (SMA) and channel mixing attention (CMA). First, the original feature is divided according to the spatial and channel dimensions to obtain multiple subspaces. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. Finally, the features extracted from all subspaces are aggregated to promote their comprehensive feature interaction. In addition, DMAM can be easily plugged into any depth of the backbone network to improve vehicle recognition. The experimental results show that the proposed structure performs better than the representative method in the UAV-based vehicle ReID. Our code and models will be published publicly. Full article

(This article belongs to the Special Issue AI Techniques in Intelligent Transport Systems)

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13 pages, 1810 KiB

Open AccessCommunication

Artificial Intelligence Component of the FERODATA AI Engine to Optimize the Assignment of Rail Freight Locomotive Drivers

by Adrian Brezulianu, Oana Geman and Iolanda Valentina Popa

Appl. Sci. 2023, 13(20), 11516; https://doi.org/10.3390/app132011516 - 20 Oct 2023

Viewed by 1130

Abstract

The optimization of locomotive drivers’ scheduling in rail freight transportation comes as a necessity for minimizing economic expenses and training investments. The Ferodata AI engine, an artificial intelligence (AI)/machine learning (ML) software module, developed by our team, has integrated a supervised random forest model that automatically assigns conductors to freight transportation orders based on the data about locomotive driver’s tiredness score, distance of the driver to the departure point of a transportation order, driver availability, and circulation history. The model proposed by us obtained very good performance metrics on the train set (accuracy: 95%, AUC: 0.9905) and reasonably good and encouraging performance on the test set (accuracy: 84%, AUC: 0.8357). After rigorous testing and validation on external and larger datasets, the automated optimization of locomotive driver assignments could bring operational efficiency, cost savings, regulatory compliance, and improved safety to scheduled rail freight transports. Full article

(This article belongs to the Special Issue AI Techniques in Intelligent Transport Systems)

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22 pages, 3338 KiB

Open AccessArticle

Research on Optimization of Medical Waste Emergency Disposal Transportation Network for Public Health Emergencies in the Context of Intelligent Transportation

by Fei Zhao, Xi Wang, Beibei Liu, Wenzhuo Sun and Zheng Liu

Appl. Sci. 2023, 13(18), 10122; https://doi.org/10.3390/app131810122 - 8 Sep 2023

Cited by 2 | Viewed by 1029

Abstract

In order to build a more comprehensive emergency disposal and transportation network system for medical waste, it is necessary to consider various uncertain factors and data characteristics. Therefore, in the context of intelligent transportation, this article considers the uncertainty of the quantity and regional population density of infectious medical waste generation as well as the emergency disposal of infectious medical waste under multi-cycle and multi-objective conditions, and it constructs a multi-cycle emergency disposal logistics network optimization model for infectious medical waste under uncertain conditions. Through deterministic transformation of the model and data mining of the medical waste disposal logistics network in Wuhan, China, the multi-objective model under uncertain conditions was also solved and sensitivity analyzed using the MOPSO-NSGA2 intelligent algorithm, verifying the effectiveness and superiority of the algorithm. Full article

(This article belongs to the Special Issue AI Techniques in Intelligent Transport Systems)

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17 pages, 5543 KiB

Open AccessArticle

Hierarchical Spatial-Temporal Neural Network with Attention Mechanism for Traffic Flow Forecasting

by Qingyun Lian, Wei Sun and Wei Dong

Appl. Sci. 2023, 13(17), 9729; https://doi.org/10.3390/app13179729 - 28 Aug 2023

Cited by 1 | Viewed by 1717

Abstract

Accurate traffic flow forecasting is pivotal for intelligent traffic control and guidance. Manually capturing the intricate dependencies between spatial and temporal dimensions in traffic data presents a significant challenge. Prior methods have primarily employed Recurrent Neural Networks or Graph Convolutional Networks, without fully accounting for the interdependency between spatial and temporal factors. To address this, we introduce a novel Hierarchical Spatial-Temporal Neural Networks with Attention Mechanism model (HSTAN). This model concurrently captures temporal correlations and spatial dependencies using a multi-headed self-attention mechanism in both temporal and spatial terms. It also integrates global spatial-temporal correlations through a hierarchical structure with residuals. Moreover, the analysis of attention weight matrices can depict complex spatial-temporal correlations, thereby enhancing our traffic forecasting capabilities. We conducted experiments on two publicly available traffic datasets, and the results demonstrated that the HSTAN model’s prediction accuracy surpassed that of several benchmark methods. Full article

(This article belongs to the Special Issue AI Techniques in Intelligent Transport Systems)

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16 pages, 3655 KiB

Open AccessArticle

Research on the Optimal Deployment of Expressway Roadside Units under the Fusion Perception of Intelligent Connected Vehicles

by Peng Wang, Youfu Lu, Ning Chen, Luyu Zhang, Weilin Kong, Qingbin Wang, Guizhi Qin and Zhenhua Mou

Appl. Sci. 2023, 13(15), 8878; https://doi.org/10.3390/app13158878 - 1 Aug 2023

Cited by 2 | Viewed by 1145

Abstract

At present, there is still a lack of relevant theoretical guidance on the deployment of roadside RSU on expressways. In the face of the coexistence of V2V and V2I communication in the future, the deployment adjustment after the penetration of intelligent vehicles is not considered. Therefore, this paper proposes a roadside RSU deployment income model in consideration of the influence of V2V and V2I communication. Based on the optimal income of roadside RSU nodes, it achieves the optimization of the RSU deployment range and determines the optimal deployment spacing through the forwarding and relaying role of V2V communication so as to achieve cost savings. In terms of RSU coverage of positive income, it considers the impact of intelligent vehicles and reconstructs the traditional information flow–traffic flow coupling theory to innovatively realize the modeling of income within the information life cycle. In terms of the information transmission deficit, the WSN node energy loss model is reconstructed with permeability. Also, in terms of the construction and maintenance costs, the cost models are constructed for different cluster lengths. In order to provide a basis for expressway sensor network deployment, MATLAB software (version R2016B) is used to analyze the three-dimensional relationship between expressway traffic density, intelligent vehicle permeability, and roadside RSU deployment spacing as well as to determine the optimal roadside RSU deployment spacing with the income model. Finally, the model reliability is validated by the Warshell algorithm and the Kmeans clustering algorithm. Full article

(This article belongs to the Special Issue AI Techniques in Intelligent Transport Systems)

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