Optimization and Simulation Techniques for Transportation

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

Deadline for manuscript submissions: 20 July 2024 | Viewed by 11276

Special Issue Editors

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
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

Published Papers (7 papers)

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Research

16 pages, 2446 KiB  
Article
PMMTss: A Parallel Multi-Way Merging-Based Trajectory Similarity Search for a Million Metro Passengers
Appl. Sci. 2023, 13(13), 7988; https://doi.org/10.3390/app13137988 - 07 Jul 2023
Viewed by 642
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 [...] Read more.
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  
Article
Human and Environmental Factors Analysis in Traffic Using Agent-Based Simulation
Appl. Sci. 2023, 13(6), 3499; https://doi.org/10.3390/app13063499 - 09 Mar 2023
Viewed by 1061
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 [...] Read more.
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  
Article
LSTM-Based Transformer for Transfer Passenger Flow Forecasting between Transportation Integrated Hubs in Urban Agglomeration
Appl. Sci. 2023, 13(1), 637; https://doi.org/10.3390/app13010637 - 03 Jan 2023
Cited by 3 | Viewed by 2252
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 [...] Read more.
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  
Article
Hierarchical Segmentation Method for Generating Road Intersections from Crowdsourced Trajectory Data
Appl. Sci. 2022, 12(20), 10383; https://doi.org/10.3390/app122010383 - 14 Oct 2022
Viewed by 1090
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 [...] Read more.
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  
Article
Pedestrians’ Microscopic Walking Dynamics in Single-File Movement: The Influence of Gender
Appl. Sci. 2022, 12(19), 9714; https://doi.org/10.3390/app12199714 - 27 Sep 2022
Cited by 4 | Viewed by 1356
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 [...] Read more.
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  
Article
Short-Time Traffic Forecasting in Tourist Service Areas Based on a CNN and GRU Neural Network
Appl. Sci. 2022, 12(18), 9114; https://doi.org/10.3390/app12189114 - 10 Sep 2022
Cited by 1 | Viewed by 1079
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 [...] Read more.
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  
Article
Research on Landscape Perception and Visual Attributes Based on Social Media Data—A Case Study on Wuhan University
Appl. Sci. 2022, 12(16), 8346; https://doi.org/10.3390/app12168346 - 20 Aug 2022
Cited by 5 | Viewed by 2507
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 [...] Read more.
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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