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Application of System Engineering and Complex Theory in Transportation
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Application of System Engineering and Complex Theory in Transportation

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Engineering".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 15743

Special Issue Editors

Dr. Shuqi Xue

E-Mail Website
Guest Editor
School of Modern Posts, Xi'an University of Posts and Telecommunications, Xi’an 710121, China
Interests: transportation system optimization; traffic flow theory; pedestrian safety; passenger crowd dynamics and evacuation management
Special Issues, Collections and Topics in MDPI journals
Dr. Yun Wang

E-Mail Website
Guest Editor
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Interests: public transport planning and management; transportation modeling and optimization; transportation emergency management
Dr. Xiaomeng Shi

E-Mail Website
Guest Editor
Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, 2 Dongnandaxue Rd, Nanjing 211189, China
Interests: active mode traffic; active traffic safety; human factors; intelligent transportation system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

System engineering, with its comprehensive approach to managing complex systems, plays a crucial role in addressing the inherent complexities of modern transportation systems. Complex theory, on the other hand, provides a framework for understanding the unpredictable and dynamic nature of transportation systems, enabling us to delve into the interactions among various elements within transportation system. This Special Issue focuses on exploring the latest research developments in the application of system engineering and complexity theory in the field of transportation, aiming to determine the potential of these disciplines in enhancing the efficiency, sustainability, and adaptability of transportation systems. The contents of this Special Issue will concentrate on new methods and approaches in system engineering for designing, analyzing, and implementing transportation solutions. We will also explore the role of complex theory in helping to understand and model nonlinear behaviors within transportation systems, aiming for more accurate predictions and management strategies. Additionally, this Special Issue pays special attention to potential research areas such as the integration of autonomous vehicles into existing transportation ecosystems, the application of intelligent technologies for dynamic traffic control, and the development of resilient infrastructure capable of adapting to changing demands and environmental conditions. Through these topics, we hope to provide a comprehensive overview of the current trends, challenges, and future directions in the application of system engineering and complexity theory in transportation. We eagerly invite researchers and practitioners who are pushing the boundaries in these fields to share their insights and discoveries. Your contributions will undoubtedly help shape the future of transportation systems, making them more intelligent, adaptable, and in-tune with rapidly evolving global needs.

Both original research and review works are welcome for submission. Research topics of interest include, but are not limited to, the following:

  • Smart city transportation systems;
  • Passenger behavioral analysis and travel mode choice in transportation systems;
  • Coordination and synchronization of transportation systems;
  • Relicense assessment and enhancement of transportation systems;
  • Disruption management in transportation systems;
  • Human factors and behavioral analysis in transportation systems;
  • Collaborative and cooperative systems in transportation;
  • Sustainable transportation planning and policy making;
  • Application of complex theory in emergency response and disaster management;
  • System engineering approaches to multimodal transportation networks;
  • Economic and environmental impact assessments of transportation systems;
  • Integration of renewable energy sources in transportation systems;
  • Advanced traffic prediction models using ai and machine learning;
  • Impact of autonomous vehicles on traffic dynamics and control;
  • Innovative materials and technologies for transportation infrastructure.

Dr. Shuqi Xue
Dr. Yun Wang
Dr. Xiaomeng Shi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • system engineering
  • complex theory
  • transportation systems
  • transportation optimization
  • transportation control
  • transportation management
  • sustainable transportation
  • traffic dynamics
  • traffic behaviors
  • intelligent transportation

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Published Papers (12 papers)

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Research

20 pages, 1087 KiB  
Article
Applying a Modified Technology Acceptance Model to Explore Individuals’ Willingness to Use Mobility as a Service (MaaS): A Case Study of Beijing, China
by Shuai Yu, Bin Li, Haipeng Wang, Yuqing Liu and Song Hu
Systems 2024, 12(12), 511; https://doi.org/10.3390/systems12120511 - 21 Nov 2024
Viewed by 285
Abstract
The sustainable development of urban transportation is facing various challenges, including traffic congestion, increasing carbon emissions, and diversified travel demands. New concepts of mobility and business models are emerging to address these challenging conditions, such as mobility as a service (MaaS). As a [...] Read more.
The sustainable development of urban transportation is facing various challenges, including traffic congestion, increasing carbon emissions, and diversified travel demands. New concepts of mobility and business models are emerging to address these challenging conditions, such as mobility as a service (MaaS). As a new paradigm of travel services, users’ recognition, acceptance, and continuous use of MaaS are prerequisites for its survival and development. Hence, to ensure the successful implementation of MaaS, it is crucial to precisely identify the key factors influencing individuals’ willingness to use MaaS. In order to analyze the mechanisms that influence individuals’ willingness to use MaaS, this study first conceptualized a behavioral model by drawing on the modified Technology Acceptance Model (TAM) and the fundamental characteristics of MaaS. Based on the behavioral model, a structured questionnaire consisting of eight sections and thirty-three questions was designed and conducted online in Beijing, China. A total of 1260 valid questionnaire data were collected, and a descriptive analysis was conducted on the collected data, including the frequency distribution and intention to use MaaS based on the socioeconomic and mobility characteristics. Then, reliability and validity analyses were conducted on the questionnaire data using Cronbach’s alpha coefficient method and the Confirmatory Factor Analysis (CFA) method, respectively. Finally, the behavioral model was analyzed quantitatively using the Structural Equation Model (SEM). The results show that 77.62% of the respondents are willing to use MaaS after it is implemented, and 44.29% of them strongly agree to using it, while 2.06% of them strongly disagree to using it. Travel philosophy, travel preference, and perceived usefulness have positive impacts on individuals’ behavioral intention to use MaaS, while perceived usefulness exerts the greatest influence, with a coefficient of 0.364. Meanwhile, the latent variable of perceived risk has a significantly negative impact on behavioral intention, with a coefficient of −0.141. From the perspective of observed variables, convenience and efficiency are the most important factors affecting intention to use MaaS, while environment protection is the least influential factor. The results of this study can provide a decision-making basis for transportation planners, MaaS service providers, and policymakers, enhancing the level of sustainable development of urban transportation. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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22 pages, 4927 KiB  
Article
Simulation and Optimization of Automated Guided Vehicle Charging Strategy for U-Shaped Automated Container Terminal Based on Improved Proximal Policy Optimization
by Yongsheng Yang, Jianyi Liang and Junkai Feng
Systems 2024, 12(11), 472; https://doi.org/10.3390/systems12110472 - 5 Nov 2024
Viewed by 520
Abstract
As the decarbonization strategies of automated container terminals (ACTs) continue to advance, electrically powered Battery-Automated Guided Vehicles (B-AGVs) are being widely adopted in ACTs. The U-shaped ACT, as a novel layout, faces higher AGV energy consumption due to its deep yard characteristics. A [...] Read more.
As the decarbonization strategies of automated container terminals (ACTs) continue to advance, electrically powered Battery-Automated Guided Vehicles (B-AGVs) are being widely adopted in ACTs. The U-shaped ACT, as a novel layout, faces higher AGV energy consumption due to its deep yard characteristics. A key issue is how to adopt charging strategies suited to varying conditions to reduce the operational capacity loss caused by charging. This paper proposes a simulation-based optimization method for AGV charging strategies in U-shaped ACTs based on an improved Proximal Policy Optimization (PPO) algorithm. Firstly, Gated Recurrent Unit (GRU) structures are incorporated into the PPO to capture temporal correlations in state information. To effectively limit policy update magnitudes in the PPO, we improve the clipping function. Secondly, a simulation model is established by mimicking the operational process of the U-shaped ACTs. Lastly, iterative training of the proposed method is conducted based on the simulation model. The experimental results indicate that the proposed method converges faster than standard PPO and Deep Q-network (DQN). When comparing the proposed method-based charging threshold with a fixed charging threshold strategy across six different scenarios with varying charging rates, the proposed charging strategy demonstrates better adaptability to terminal condition variations in two-thirds of the scenarios. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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16 pages, 891 KiB  
Article
Low-Carbon Water–Rail–Road Multimodal Routing Problem with Hard Time Windows for Time-Sensitive Goods Under Uncertainty: A Chance-Constrained Programming Approach
by Yan Sun, Yan Ge, Min Li and Chen Zhang
Systems 2024, 12(11), 468; https://doi.org/10.3390/systems12110468 - 1 Nov 2024
Viewed by 684
Abstract
In this study, a low-carbon freight routing problem for time-sensitive goods is investigated in the context of water–rail–road multimodal transportation. To enhance the on-time transportation of time-sensitive goods, hard time windows are employed to regulate both pickup and delivery services at the start [...] Read more.
In this study, a low-carbon freight routing problem for time-sensitive goods is investigated in the context of water–rail–road multimodal transportation. To enhance the on-time transportation of time-sensitive goods, hard time windows are employed to regulate both pickup and delivery services at the start and end of their transportation. The uncertainty of both the demand for time-sensitive goods and the capacity of the transportation network are modeled using L-R triangular fuzzy numbers in the routing process to make the advanced routing more feasible in the actual transportation. Based on the carbon tax policy, a fuzzy linear optimization model is established to address the proposed problem, and an equivalent chance-constrained programming formulation is then obtained to make the solution to the problem attainable. A numerical experiment is carried out to verify the feasibility of incorporating the carbon tax policy, uncertainty, and water–rail–road multimodal transportation to optimize the low-carbon freight routing problem for time-sensitive goods. Furthermore, a multi-objective optimization is used to reveal that lowering the transportation costs, reducing the carbon emissions, and avoiding the risk are in conflict with each in the routing. We also analyze the sensitivity of the optimization results concerning the confidence level of the chance constraints and the uncertainty degree of the uncertain demand and capacity. Based on the numerical experiment, we draw several conclusions to help the shipper, receiver, and multimodal transportation operator to organize efficient water–rail–road multimodal transportation for time-sensitive goods. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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35 pages, 15840 KiB  
Article
An Integrated Framework for Estimating Origins and Destinations of Multimodal Multi-Commodity Import and Export Flows Using Multisource Data
by Muhammad Safdar, Ming Zhong, Zhi Ren and John Douglas Hunt
Systems 2024, 12(10), 406; https://doi.org/10.3390/systems12100406 - 30 Sep 2024
Viewed by 870
Abstract
Estimating origin-destination (OD) demand is integral to urban, regional, and national freight transportation planning and modeling systems. However, in developing countries, existing studies reveal significant inconsistencies between OD estimates for domestic and import/export commodities derived from interregional input-output (IO) tables and those from [...] Read more.
Estimating origin-destination (OD) demand is integral to urban, regional, and national freight transportation planning and modeling systems. However, in developing countries, existing studies reveal significant inconsistencies between OD estimates for domestic and import/export commodities derived from interregional input-output (IO) tables and those from regional IO tables. These discrepancies create a significant challenge for properly forecasting the freight demand of regional/interregional multimodal transportation networks. To this end, this study proposes a novel integrated framework for estimating regional and international (import/export) OD freight flows for a set of key commodities that dominate long-distance transportation. The framework leverages multisource data and follows a three-step process. First, a spatial economic model, PECAS activity allocation, is developed to estimate freight OD demand within a specific region. Second, the international (import and export) freight OD is estimated from different zones to foreign countries, including major import and export nodes such as international seaports, using a gravity model with the zone-pair friction obtained from a multimodal transportation model. Third, the OD matrices are converted from monetary value to tonnage and assigned to the multimodal transportation super network using the incremental freight assignment method. The model is calibrated using traffic counts of the highways, railways, and port throughput data. The proposed framework is tested through a case study of the Province of Jiangxi, which is crucial for forecasting freight demand before the planning, design, and operation of the Ganyue Canal. The predictive analytics of the proposed framework demonstrated high validity, where the goodness-of-fit (R2) between the observed and estimated freight flows on specific links for each of the three transport modes was higher than 0.9. This indirectly confirms the efficacy of the model in predicting freight OD demands. The proposed framework is adaptable to other regions and aids practitioners in providing a comprehensive tool for informed decision-making in freight demand modeling. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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22 pages, 7166 KiB  
Article
An Improved Driving Safety Field Model Based on Vehicle Movement Uncertainty for Highway Ramp Influence Areas
by Yueru Xu, Wei Ye, Yalin Luan and Bingbo Cui
Systems 2024, 12(9), 370; https://doi.org/10.3390/systems12090370 - 14 Sep 2024
Viewed by 766
Abstract
Road traffic accidents result in numerous fatalities and injuries annually. Advanced driving assistance systems (ADASs) have garnered significant attention to mitigate these harms. An accurate safety assessment can significantly improve the effectiveness and credibility of ADASs. However, a real-time safety assessment remains a [...] Read more.
Road traffic accidents result in numerous fatalities and injuries annually. Advanced driving assistance systems (ADASs) have garnered significant attention to mitigate these harms. An accurate safety assessment can significantly improve the effectiveness and credibility of ADASs. However, a real-time safety assessment remains a key challenge due to the complex interactions among humans, vehicles, and the road environment. Traditional safety assessment methods, relying on crash data and surrogate safety measures (SSMs), face limitations in real-time applicability and scenario coverage, especially in freeway ramp areas with frequent merging and lane changing. To address these gaps, this paper develops a driving safety field based on the uncertainty of vehicle movements, which integrates the characteristics of driving behaviors, vehicles, and the road environment. The proposed method is validated with a simulation of driving scenarios and ROC curves obtained from the NGSIM dataset. The results demonstrate that our proposed driving safety field effectively quantifies the real-time risk in ramp influence areas and outperforms Time to Collision (TTC), making it suitable for integration into collision warning systems of ADASs. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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24 pages, 5173 KiB  
Article
Sharing a Ride: A Dual-Service Model of People and Parcels Sharing Taxis with Loose Time Windows of Parcels
by Shuqi Xue, Qi Zhang and Nirajan Shiwakoti
Systems 2024, 12(8), 302; https://doi.org/10.3390/systems12080302 - 14 Aug 2024
Viewed by 977
Abstract
(1) Efficient resource utilization in urban transport necessitates the integration of passenger and freight transport systems. Current research focuses on dynamically responding to both passenger and parcel orders, typically by initially planning passenger routes and then dynamically inserting parcel requests. However, this approach [...] Read more.
(1) Efficient resource utilization in urban transport necessitates the integration of passenger and freight transport systems. Current research focuses on dynamically responding to both passenger and parcel orders, typically by initially planning passenger routes and then dynamically inserting parcel requests. However, this approach overlooks the inherent flexibility in parcel delivery times compared to the stringent time constraints of passenger transport. (2) This study introduces a novel approach to enhance taxi resource utilization by proposing a shared model for people and parcel transport, designated as the SARP-LTW (Sharing a ride problem with loose time windows of parcels) model. Our model accommodates loose time windows for parcel deliveries and initially defines the parcel delivery routes for each taxi before each working day, which was prior to addressing passenger requests. Once the working day of each taxi commences, all taxis will prioritize serving the dynamic passenger travel requests, minimizing the delay for these requests, with the only requirement being to ensure that all pre-scheduled parcels can be delivered to their destinations. (3) This dual-service approach aims to optimize profits while balancing the time-sensitivity of passenger orders against the flexibility in parcel delivery. Furthermore, we improved the adaptive large neighborhood search algorithm by introducing an ant colony information update mechanism (AC-ALNS) to solve the SARP-LTW efficiently. (4) Numerical analysis of the well-known Solomon set of benchmark instances demonstrates that the SARP-LTW model outperforms the SARP model in profit rate, revenue, and revenue stability, with improvements of 48%, 46%, and 49%, respectively. Our proposed approach enables taxi companies to maximize vehicle utilization, reducing idle time and increasing revenue. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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19 pages, 1439 KiB  
Article
A Testing and Evaluation Method for the Car-Following Models of Automated Vehicles Based on Driving Simulator
by Yuhan Zhang, Yichang Shao, Xiaomeng Shi and Zhirui Ye
Systems 2024, 12(8), 298; https://doi.org/10.3390/systems12080298 - 12 Aug 2024
Viewed by 1286
Abstract
The continuous advancement of connected and automated driving technologies has garnered considerable public attention regarding the safety and reliability of automated vehicles (AVs). Comprehensive and efficient testing is essential before AVs can be deployed on public roads. Current mainstream testing methods involve high [...] Read more.
The continuous advancement of connected and automated driving technologies has garnered considerable public attention regarding the safety and reliability of automated vehicles (AVs). Comprehensive and efficient testing is essential before AVs can be deployed on public roads. Current mainstream testing methods involve high costs in real-world settings and limited immersion in numerical simulations. To address these challenges and facilitate testing in mixed traffic scenarios involving both human-driven vehicles (HDVs) and AVs, we propose a testing and evaluation approach using a driving simulator. Our methodology comprises three fundamental steps. First, we systematically classify scenario elements by drawing insights from the scenario generation logic of the driving simulator. Second, we establish an interactive traffic scenario that allows human drivers to manipulate vehicles within the simulator while AVs execute their decision and planning algorithms. Third, we introduce an evaluation method based on this testing approach, validated through a case study focused on car-following models. The experimental results confirm the efficiency of the simulation-based testing method and demonstrate how car-following efficiency and comfort decline with increased speeds. The proposed approach offers a cost-effective and comprehensive solution for testing, considering human driver behavior, making it a promising method for evaluating AVs in mixed traffic scenarios. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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20 pages, 2394 KiB  
Article
Commuting Behavior Changes at Different Stages of Localized COVID-19 Outbreak: Evidence from Nanjing, China
by Pei Chen, Tao Wu, Yurui Yin and Xinwei Ma
Systems 2024, 12(8), 271; https://doi.org/10.3390/systems12080271 - 28 Jul 2024
Viewed by 1063
Abstract
Commuting behaviors have been changed by the COVID-19 pandemic. To investigate the impacts at different stages of sudden and localized COVID-19 outbreak, this paper carries out an online survey to obtain data, targeting the residents in Nanjing China, where there had been COVID-19 [...] Read more.
Commuting behaviors have been changed by the COVID-19 pandemic. To investigate the impacts at different stages of sudden and localized COVID-19 outbreak, this paper carries out an online survey to obtain data, targeting the residents in Nanjing China, where there had been COVID-19 outbreaks and proposes a sequential analysis method to calculate the complexity of commuting behavior changes. The Tobit model is used to explore the factors that influence the complexity of commuting behavior changes. Results show that commuters using public transportation drop significantly when sudden outbreaks occur, with 43.5% of them switching to private cars or working from home. The number of residents working from home increases by 14 times. While an outbreak gradually subsides, commuting modes tend to recover, but does not immediately return to the state before the outbreak. Regression model results indicate that commuters aged 40–60 tend to maintain their commuting habits, while younger workers are more flexible on their commuting options. Middle-income commuters, or those living in low-risk areas or near a subway within 800 m prefer to change commuting modes, opting for what they perceive to be safer ways to commute. For commuters living in medium- or high-risk areas and those who are living with people who have non-green health codes, they tend to adjust their commuting modes in real time based on the color change in the health codes and the risk level of the areas they live. The research findings contribute to our understanding of commuting behaviors and targeted management needs during local outbreaks, and can help the government formulate a comprehensive and more effective pandemic prevention policy. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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29 pages, 5960 KiB  
Article
Strategies for Humanitarian Logistics and Supply Chain in Organizational Contexts: Pre- and Post-Disaster Management Perspectives
by Amir Aghsami, Simintaj Sharififar, Nader Markazi Moghaddam, Ebrahim Hazrati, Fariborz Jolai and Reza Yazdani
Systems 2024, 12(6), 215; https://doi.org/10.3390/systems12060215 - 18 Jun 2024
Viewed by 2747
Abstract
Every organization typically comprises various internal components, including regional branches, operations centers/field offices, major transportation hubs, and operational units, among others, housing a population susceptible to disaster impacts. Moreover, organizations often possess resources such as staff, various vehicles, and medical facilities, which can [...] Read more.
Every organization typically comprises various internal components, including regional branches, operations centers/field offices, major transportation hubs, and operational units, among others, housing a population susceptible to disaster impacts. Moreover, organizations often possess resources such as staff, various vehicles, and medical facilities, which can mitigate human casualties and address needs across affected areas. However, despite the importance of managing disasters within organizational networks, there remains a research gap in the development of mathematical models for such scenarios, specifically incorporating operations centers/field offices and external stakeholders as relief centers. Addressing this gap, this study examines an optimization model for both before and after disaster planning in a humanitarian supply chain and logistical framework within an organization. The affected areas are defined as regional branches, operational units, major transportation hubs, operations centers/field offices, external stakeholders, and medical facilities. A mixed-integer nonlinear model is formulated to minimize overall costs, considering factors such as penalty costs for untreated injuries and demand, delays in rescue and relief item distribution operations, and waiting costs for the injured in emergency medical vehicles and air ambulances. The model is implemented using GAMS software 47.1.0 for various test problems across different scales, with the Grasshopper Optimization Algorithm proposed for larger-scale scenarios. Numerical examples are provided to show the effectiveness and feasibility of the proposed model and to validate the metaheuristic approach. Sensitivity analysis is conducted to assess the model’s performance under different conditions, and key managerial insights and implications are discussed. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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21 pages, 1726 KiB  
Article
Modeling a Multimodal Routing Problem with Flexible Time Window in a Multi-Uncertainty Environment
by Yan Ge, Yan Sun and Chen Zhang
Systems 2024, 12(6), 212; https://doi.org/10.3390/systems12060212 - 15 Jun 2024
Cited by 2 | Viewed by 1108
Abstract
In this study, we extend the research on the multimodal routing problem by considering flexible time window and multi-uncertainty environment. A multi-uncertainty environment includes uncertainty regarding the demand for goods, the travel speed of the transportation mode, and the transfer time between different [...] Read more.
In this study, we extend the research on the multimodal routing problem by considering flexible time window and multi-uncertainty environment. A multi-uncertainty environment includes uncertainty regarding the demand for goods, the travel speed of the transportation mode, and the transfer time between different transportation modes. This environment further results in uncertainty regarding the delivery time of goods at their destination and the earliness and lateness caused by time window violations. This study adopts triangular fuzzy numbers to model the uncertain parameters and the resulting uncertain variables. Then, a fuzzy mixed integer nonlinear programming model is established to formulate the specific problem, including both fuzzy parameters and fuzzy variables. To make the problem easily solvable, this study employs chance-constrained programming and linearization to process the proposed model to obtain an equivalent credibilistic chance-constrained linear programming reformulation with an attainable global optimum solution. A numerical case study based on a commonly used multimodal network structure is presented to demonstrate the feasibility of the proposed method. Compared to hard and soft time windows, the numerical case analysis reveals the advantages of the flexible time window in reducing the total costs, avoiding low reliability regarding timeliness, and providing confidence level-sensitive route schemes to achieve flexible routing decision-making under uncertainty. Furthermore, the numerical case analysis verifies that it is necessary to model the multi-uncertainty environment to satisfy the improved customer requirements for timeliness and enhance the flexibility of the routing, and multimodal transportation is better than unimodal transportation when routing goods in an uncertain environment. The sensitivity analysis in the numerical case study shows the conflicting relationship between the economic objective and the reliability regarding the timeliness of the routing, and the result provides a reference for the customer to find a balance between them. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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25 pages, 2727 KiB  
Article
Two-Stage Delivery System for Last Mile Logistics in Rural Areas: Truck–Drone Approach
by Debao Dai, Hanqi Cai, Liang Ye and Wei Shao
Systems 2024, 12(4), 121; https://doi.org/10.3390/systems12040121 - 6 Apr 2024
Viewed by 2538
Abstract
In rural areas of China, the challenges of efficient and cost-effective distribution are exacerbated by underdeveloped infrastructure and low population density, with last mile logistics distribution posing a significant obstacle. To address the gap in drone application for last mile logistics in rural [...] Read more.
In rural areas of China, the challenges of efficient and cost-effective distribution are exacerbated by underdeveloped infrastructure and low population density, with last mile logistics distribution posing a significant obstacle. To address the gap in drone application for last mile logistics in rural areas, a truck–drone distribution model was developed based on the specific conditions of rural regions. The improved fuzzy C-means algorithm (FCM) and genetic simulated annealing algorithm (GASA) were employed to tackle real−world cases in rural areas. The focus of the truck–drone system is to optimize the rural logistics distribution process, reduce delivery time, and minimize costs while considering factors such as maximum mileage of trucks and drones as well as customer priority. Compared to traditional methods, this system has demonstrated notable improvements in distribution efficiency and cost reduction, offering valuable insights for practical drone applications in last mile rural logistics. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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20 pages, 6287 KiB  
Article
A Dynamic Collision Risk Assessment Model for the Traffic Flow on Expressways in Urban Agglomerations in North China
by Bing Li, Xiaoduan Sun, Yulong He and Meng Zhang
Systems 2024, 12(3), 86; https://doi.org/10.3390/systems12030086 - 6 Mar 2024
Cited by 1 | Viewed by 1470
Abstract
Expressways in urban agglomerations are important in connecting cities, thus attracting great attention from researchers in the expressways risk assessment. However, there is a lack of safety assessment models suitable for the characteristics of expressways in Chinese urban agglomerations, and the nature and [...] Read more.
Expressways in urban agglomerations are important in connecting cities, thus attracting great attention from researchers in the expressways risk assessment. However, there is a lack of safety assessment models suitable for the characteristics of expressways in Chinese urban agglomerations, and the nature and mode of dynamic risks on Chinese highways are still unclear. Therefore, this study adopts the Adaptive Neural Fuzzy Inference System (ANFIS) and the method of decision tree, combined with data from the Beijing section of the Beijing Harbin Expressway, to model the risk of accident-prone highways in urban agglomerations. To determine the optimal model, we evaluated the model’s bias at different time intervals. In addition, key factors affecting highway safety were analyzed, providing scientific support for the risk prevention of highways in urban agglomerations in China. Full article
(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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