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Optimization and Simulation Techniques for Transportation

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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: 10 March 2025 | Viewed by 19437

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Special Issue Editors

Dr. Xue Yang

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Guest Editor

School of Geography and Information Engineering, China University of Geosciences, Wuhan 100083, China
Interests: traffic information mining; road information updating; moving computing; trajectory data-driven techniques; change detection

Prof. Dr. Luliang Tang

E-Mail Website
Guest Editor

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
Interests: gis for transportation; Internet of Vehicles; vehicle-mounted pavement detection

Special Issue Information

Dear Colleagues,

Transportation techniques play an important role in the daily lives of humans. In the era of big data, with the development of artificial intelligence technology and big data, optimization and simulation techniques for transportation should go deep into the following aspects: first, AI techniques used in transportation simulation, including road traffic simulation by considering traffic lights and historical traffic information, pedestrian moving simulation based on pedestrian moving behavior and the surrounding environment, and human–car interaction simulation, especially for shared places of vehicles and humans, etc.; second, transportation information mining based on data-driven techniques, including traffic predication, travel mode detection, and road network refining, especially for detailed road information, such as lane-level road information or pedestrian road containing semantic attribute information such as road type, slop, topology, etc.; and third, the function structure optimization of traffic space, which should be linked to space heat estimation, space utilization, and so on.

Dr. Xue Yang
Prof. Dr. Luliang Tang
Guest Editors

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Keywords

artificial intelligence technology
big data
moving computing
road traffic prediction and simulation
human–car interaction simulation
pedestrian moving simulation
traffic light
detailed road information
traffic space optimization
traffic space heat estimation
traffic space utilization

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Published Papers (10 papers)

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Research

19 pages, 2620 KiB

Open AccessArticle

Research on the Application of Pruning Algorithm Based on Local Linear Embedding Method in Traffic Sign Recognition

by Wei Wang and Xiaorui Liu

Appl. Sci. 2024, 14(16), 7184; https://doi.org/10.3390/app14167184 - 15 Aug 2024

Viewed by 612

Abstract

Efficient traffic sign recognition is crucial to facilitating the intelligent driving of new energy vehicles. However, current approaches like the Vision Transformer (ViT) model often impose high storage and computational demands, escalating hardware costs. This paper presents a similarity filter pruning method based on locally linear embedding. Using the alternating direction multiplier method and the loss of the locally linear embedding method for the model training function, the proposed pruning method prunes the operation model mainly by evaluating the similarity of each layer in the network layer filters. According to the pre-set pruning threshold value, similar filters to be pruned are obtained, and the filter with a large cross-entropy value is retained. The results from the Belgium Traffic Sign (BelgiumTS) and German Traffic Sign Recognition Benchmark (GTSRB) datasets indicate that the proposed similarity filter pruning based on local linear embedding (SJ-LLE) pruning algorithm can reduce the number of parameters of the multi-head self-attention module and Multi-layer Perceptron (MLP) module of the ViT model by more than 60%, and the loss of model accuracy is acceptable. The scale of the ViT model is greatly reduced, which is conducive to applying this model in embedded traffic sign recognition equipment. Also, this paper proves the hypothesis through experiments that “using the LLE algorithm as the loss function for model training before pruning plays a positive role in reducing the loss of model performance in the pruning process”. Full article

(This article belongs to the Special Issue Optimization and Simulation Techniques for Transportation)

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18 pages, 484 KiB

Open AccessArticle

Collaborative Scheduling for Yangtze Riverport Channels and Berths Using Multi-Objective Optimization

by Shiting Yang, Helong Shen, Zhenyang Zhong, Xiaobin Qian and Yufei Wang

Appl. Sci. 2024, 14(15), 6514; https://doi.org/10.3390/app14156514 - 25 Jul 2024

Viewed by 754

Abstract

Efficient coordinated scheduling has long been a focal point in port research, complicated by the diverse optimization goals dictated by different port characteristics. This study focuses on Yangtze River ports, exploring coordinated scheduling amidst river–sea intermodal transportation. Our research aims to reduce berth deviation costs and shorten the total scheduling time for ships, while maximizing berth utilization rates for ports. Initially, we analyzed the operational realities of Yangtze River ports and waterways. Subsequently, we innovatively introduced three key factors influencing scheduling: berth preferences, seagoing ship inspections, and planning cycles. Finally we proposed the optimized Non-dominated Sorting Genetic Algorithm III (NSGA-III). Evaluating the model using a seven-day dataset of vessel activities at Yangtze River ports revealed significant improvements: the optimized NSGA-III enhanced objective values by 30.81%, 13.73%, and 12.11% compared to the original scheduling approach, surpassing both conventional NSGA-III and NSGA-II algorithms. This study underscores the model’s efficacy in not only reducing operational costs through optimized ship and berth sequencing but also in enhancing clearance efficiency for relevant authorities. Full article

(This article belongs to the Special Issue Optimization and Simulation Techniques for Transportation)

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14 pages, 658 KiB

Open AccessArticle

Using Machine Learning to Predict Pedestrian Compliance at Crosswalks in Jordan

by Madhar M. Taamneh, Ahmad H. Alomari and Salah M. Taamneh

Appl. Sci. 2024, 14(11), 4945; https://doi.org/10.3390/app14114945 - 6 Jun 2024

Viewed by 1338

Abstract

This study employs machine learning (ML) techniques to predict pedestrian compliance at crosswalks in urban settings in Jordan, aiming to enhance pedestrian safety and traffic management. Utilizing data from 2437 pedestrians at signalized intersections in Amman, Irbid, and Zarqa, four models based on different ML algorithms were developed: an artificial neural network (ANN), a support vector machine (SVM), a decision tree (ID3), and a random forest (RF). The results have shown that local infrastructure and traffic conditions influence pedestrian behavior. The RF model, with its excellent accuracy and precision, has proven to be an excellent choice for accurately predicting pedestrian behavior. This research provides valuable insights into the demographic and spatial aspects that influence pedestrian compliance with laws and regulations in the local environment. Additionally, this work highlights the ability of ML algorithms to improve urban traffic dynamics. Policymakers and urban planners, particularly with the rise of theories and trends toward the humanization of urban roads, should firmly establish this understanding among themselves to create environments that make pedestrians safer. This strategy could be a measurable solution for international urban situations if future research focuses on integrating these prediction models with real-time traffic management systems to improve pedestrian safety dynamically. Full article

(This article belongs to the Special Issue Optimization and Simulation Techniques for Transportation)

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16 pages, 2446 KiB

Open AccessArticle

PMMTss: A Parallel Multi-Way Merging-Based Trajectory Similarity Search for a Million Metro Passengers

by Wanbing Huang, Wen Xiong and Xiaoxuan Wang

Appl. Sci. 2023, 13(13), 7988; https://doi.org/10.3390/app13137988 - 7 Jul 2023

Cited by 1 | Viewed by 1065

Abstract

Trajectory similarity search (TSS) is a common operation for spatiotemporal data analysis. However, the existing TSS methods are mainly focused on GPS trajectories produced by moving objects such as vehicles. Further, these corresponding optimization strategies cannot be directly applied in the metro scenario because the metro passenger trajectory is totally different from the GPS trajectory. To fill this gap, we systematically analyze the unique spatiotemporal characteristics of metro passenger trajectories and propose a similarity search solution named PMMTss for the metro system. The core idea of this solution has two key points: first, we design a multi-layer index based on the spatiotemporal feature of metro trajectories, and all points of a trajectory are stored in this index. Second, we design a parallel multi-way merging-based trajectory similar search method, in which the candidate trajectory segments are merged and filtered. We evaluate this solution on a large dataset (Shenzhen Metro data for 3 consecutive months, 6.976 million trajectories with 260 million records). When lengths of input trajectories are 16, 32, and 64, respectively, the corresponding search times are 0.004 s, 0.016 s, and 0.036 s, respectively. Compared to the baseline PPJion+, the query times are reduced by 99.7%, 98.8%, and 97.6%, respectively. Full article

(This article belongs to the Special Issue Optimization and Simulation Techniques for Transportation)

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18 pages, 984 KiB

Open AccessArticle

Human and Environmental Factors Analysis in Traffic Using Agent-Based Simulation

by Ariadna Claudia Moreno, Mailyn Moreno, Cynthia Porras and Juan Pavón

Appl. Sci. 2023, 13(6), 3499; https://doi.org/10.3390/app13063499 - 9 Mar 2023

Viewed by 1643

Abstract

Traffic congestion is a frequent problem on most urban roads. This may be due to incorrect configuration of traffic signals but planning analysis should also include a study of human behavior, which, often imprudent, contributes to traffic congestion. The aim of this paper is to analyze the influence of human factors and their reaction to the environment on the impact of traffic performance through waiting time. For this purpose, an agent-based simulation is developed to represent the autonomous and social behavior of road users. The waiting of vehicles at signals is modeled on the basis of a queuing system. Simulations and experiments are based on the analysis of the age of the people and the condition of the pavement. Results show that people’s age is the most important factor influencing their behavior on the road. It is also shown external factors that also affect driver response and thus signal impact, such as the condition of the pavement. Finally, traffic performance, measured by waiting time, depends strongly on the behavior of people facing signals, according to their characteristics and factors present in the environment. Full article

(This article belongs to the Special Issue Optimization and Simulation Techniques for Transportation)

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17 pages, 3596 KiB

Open AccessArticle

LSTM-Based Transformer for Transfer Passenger Flow Forecasting between Transportation Integrated Hubs in Urban Agglomeration

by Min Yue and Shuhong Ma

Appl. Sci. 2023, 13(1), 637; https://doi.org/10.3390/app13010637 - 3 Jan 2023

Cited by 6 | Viewed by 3362

Abstract

A crucial component of multimodal transportation networks and long-distance travel chains is the forecasting of transfer passenger flow between integrated hubs in urban agglomerations, particularly during periods of high passenger flow or unusual weather. Deep learning is better suited to managing massive amounts of traffic data and predicting extended time series. In order to solve the problem of gradient explosion or gradient disappearance that recurrent neural networks are prone to when dealing with long time sequences, this study used a transformer prediction model to estimate short-term transfer passenger flow between two integrated hubs in an urban agglomeration and a long short-term memory network to incorporate previous historical data. The experimental analysis uses two sets of transfer passenger data from the Beijing-Tianjin-Hebei urban agglomeration, collected every 30 min in May 2021 on the transfer corridors between an airport and a high-speed railway station. The findings demonstrate the high adaptability and good performance of the suggested model in passenger flow forecasting. The suggested model and forecasting outcomes assist management in making capacity adjustments in time to correspond with changes, enhance the effectiveness of multimodal transportation systems in urban agglomerations and significantly enhance the service of long-distance multimodal passenger travel. Full article

(This article belongs to the Special Issue Optimization and Simulation Techniques for Transportation)

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16 pages, 4377 KiB

Open AccessArticle

Hierarchical Segmentation Method for Generating Road Intersections from Crowdsourced Trajectory Data

by Yunfei Zhang, Gengbiao Tang, Xiaoliang Fang, Tao Chen, Fangbin Zhou and Yabo Luo

Appl. Sci. 2022, 12(20), 10383; https://doi.org/10.3390/app122010383 - 14 Oct 2022

Cited by 2 | Viewed by 1679

Abstract

Maintaining the data freshness and completeness of road intersection information is the key task of urban road map production and updating. Compared to professional surveying methods, crowdsourced trajectory data provide a low-cost, wide-coverage and real-time data resource for road map construction. However, there may exist the problems of spatio-temporal heterogeneity and uneven density distribution in crowdsourced trajectory data. Hence, in light of road hierarchies, the paper proposes a hierarchical segmentation method to generate road intersections from crowdsourced trajectories. The proposed method firstly implements an adaptive density homogenization processing on raw trajectory data in order to decrease the uneven density discrepancy. Then, a hierarchical segmentation strategy is developed to extract multi-level road intersection elements from coarse scale to fine scale. Finally, the structural models of road intersections are delineated by an iterative piecewise fitting method. Experimental results show that the proposed method can accurately and completely extract road intersections of different shapes and scales, with an accuracy of about 87–90%. Particularly, the precision and recall of road intersection detection are obviously increased by about 7% and 20% by adaptive density homogenization, indicating the advantages of dealing with uneven trajectory data. Full article

(This article belongs to the Special Issue Optimization and Simulation Techniques for Transportation)

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14 pages, 11966 KiB

Open AccessArticle

Pedestrians’ Microscopic Walking Dynamics in Single-File Movement: The Influence of Gender

by Charitha Dias, Muhammad Abdullah, Dawood Ahmed and Rudina Subaih

Appl. Sci. 2022, 12(19), 9714; https://doi.org/10.3390/app12199714 - 27 Sep 2022

Cited by 4 | Viewed by 1936

Abstract

Demographics of individuals could largely influence their behaviors and interactions with surrounding pedestrians. This study investigates the influence of pedestrians’ gender on microscopic walking dynamics of single-file movements using the trajectory data collected from a controlled experiment conducted under different density levels. Instantaneous acceleration (with a time lag that varied from 0.12 s to 0.68 s) versus relative speed between the subject pedestrian and the pedestrian in front of him/her plots displayed significant correlations, which is analogous to the car following behavior, indicating that the relative speed is a key determinant of pedestrians’ acceleration behavior. Time-delayed instantaneous accelerations and decelerations of pedestrians were modeled as functions of relative speed and spacing that are used in microscopic behavior models and gender using multiple linear regression. The outcomes revealed that in addition to relative speed, gender has a significant influence on instantaneous acceleration and deceleration for all density levels. Spacing displayed significant influence on acceleration and deceleration only for several density levels, and that influence was not as strong as relative speed. Males were likely to accelerate more and decelerate more compared to females for all density levels. The findings of this study provide important insights into gender dependence on microscopic walking dynamics. Furthermore, the results emphasize the importance of considering gender influence in microscopic behavior models. Full article

(This article belongs to the Special Issue Optimization and Simulation Techniques for Transportation)

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12 pages, 2355 KiB

Open AccessArticle

Short-Time Traffic Forecasting in Tourist Service Areas Based on a CNN and GRU Neural Network

by Yan-Qun Yang, Jie Lin and Yu-Bin Zheng

Appl. Sci. 2022, 12(18), 9114; https://doi.org/10.3390/app12189114 - 10 Sep 2022

Cited by 3 | Viewed by 1600

Abstract

The continuous development of highway construction projects has prompted the function of service areas to be improved day by day. A traditional service area gradually transforms from a single traffic service mode to a complex traffic service mode. The continuous enrichment and perfection of the service area’s function makes the surrounding highway network more attractive, which leads to a sudden increase in highway traffic volume in a short period of time. In order to better improve the service level of a tourist service area by predicting the short-term traffic volume of the toll station around the tourist service area, this paper proposes a model combining a convolutional neural network and a gated recurrent unit (CNN plus GRU) to solve the problem of short-term traffic volume prediction. The data from 17 toll stations of the Yu’an Expressway in Guizhou Province were selected for the experiment to test the prediction effect of the CNN plus GRU-based model. The experimental results show that the prediction accuracy, the MAE and RMSE, are 1.8101 and 2.7021, respectively, for the toll stations with lower traffic volumes, and 3.820 and 5.172, respectively, for the toll stations with higher traffic volumes. Compared with a single model, the model’s prediction accuracy is improved, to different degrees. Therefore, the use of a convolutional neural network operation is better when the total traffic volume is low, considering the algorithm’s time and error. When using the combined convolutional neural network and gated recurrent unit model and when the total traffic volume is high, the algorithm error is significantly reduced and the prediction results are better. Full article

(This article belongs to the Special Issue Optimization and Simulation Techniques for Transportation)

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18 pages, 6219 KiB

Open AccessArticle

Research on Landscape Perception and Visual Attributes Based on Social Media Data—A Case Study on Wuhan University

by Xia Zhang, Danning Xu and Ni Zhang

Appl. Sci. 2022, 12(16), 8346; https://doi.org/10.3390/app12168346 - 20 Aug 2022

Cited by 7 | Viewed by 3672

Abstract

With the rapid rise of social media, the photo-taking behavior of tourists and their uploaded photos provide a new perspective to explore landscape visual characters. In this study, we provide methodological advancements for assessing landscape visual quality based on content analysis of user-generated photographs. The purpose is to demonstrate an empirical method for evaluating visual indicators reflected in photographs through a case study application. This research takes the core cultural landscape area of Wuhan University as the research scope. The photographs shared on a famous Chinese social media platform Sina Weibo during the Cherry Blossom Festival, together with tourists’ trajectory data, were used as data sources. Based on a fixed-point photography experiment, the spatial relationship between the scenic spot and the observation point was illustrated. Utilizing a semi-automatic photo content analysis founded on computer vision technology, landscape visual attributes of each attraction were studied thoroughly regarding complexity, visual scale, and color. The results indicate that the Old Dormitory is the most popular scenic spot with diverse viewing angles, strikingly vivid colors, and rich color combinations. Complexity and color play key roles in landscape visual quality, while the depth of view has a subtle impact, which suggests the depth-to-height ratio of less than 1 is the best distance for viewers to take photographs. In all, the mapping relationship between landscape visual attributes and viewers’ perception was revealed in the present work. Full article

(This article belongs to the Special Issue Optimization and Simulation Techniques for Transportation)

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