Research

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16 pages, 2767 KiB

Open AccessArticle

Analyzing Delay and CO Emissions: A Simulation Study of the Median U-Turn Method at Intersections

by Ziyan Zhao, Caixia Tian, Baohua Guo, Chengming Zhu and Qingwen Guo

Appl. Sci. 2024, 14(9), 3737; https://doi.org/10.3390/app14093737 (registering DOI) - 27 Apr 2024

Viewed by 107

To improve traffic efficiency and reduce pollutant emissions at urban road intersections, VISSIM software was used to simulate traffic states to compare the median U-turn method with the direct left-turn method under various traffic volumes and left-turn ratios. Based on the average delay [...] Read more.

To improve traffic efficiency and reduce pollutant emissions at urban road intersections, VISSIM software was used to simulate traffic states to compare the median U-turn method with the direct left-turn method under various traffic volumes and left-turn ratios. Based on the average delay and CO emissions, suitable conditions were identified for using the median U-turn method at intersections. The results show that there are three critical left-turn ratio boundary curves named ,, and based on the relatively smaller average delay and there is a critical left-turn ratio boundary curve based on the lower average CO emissions at the intersection when the through traffic volume is in the range of 0–3000 veh/h and the left-turn ratio is in the range of 0–4. The median U-turn method is considered applicable at the intersection when the through traffic volumes are in the range of 0–87 veh/h, 87–400 veh/h, 400–416 veh/h, 416– veh/h, and 934–3000 veh/h, respectively, and, accordingly, the left-turn ratios are in the range of 0–, 0– or –4, 0–4, 0–, and . These findings can provide a reference for traffic managers to organize the left-turn traffic at an intersection reasonably. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

14 pages, 2886 KiB

Open AccessArticle

A Helly Model-Based MPC Control System for Jam-Absorption Driving Strategy against Traffic Waves in Mixed Traffic

by Haizhen Li, Claudio Roncoli and Yongfeng Ju

Appl. Sci. 2024, 14(4), 1424; https://doi.org/10.3390/app14041424 - 09 Feb 2024

Viewed by 718

Abstract

Traffic waves in traffic flow significantly impact road throughput and fuel consumption and may even lead to severe safety issues. Currently, in connected and autonomous environments, the jam-absorption driving (JAD) strategy shows good performance in dissipating traffic waves. However, the previous JAD strategy [...] Read more.

Traffic waves in traffic flow significantly impact road throughput and fuel consumption and may even lead to severe safety issues. Currently, in connected and autonomous environments, the jam-absorption driving (JAD) strategy shows good performance in dissipating traffic waves. However, the previous JAD strategy has mostly focused on wave dissipation without adequately assessing traffic efficiency and safety. To address this gap, an optimal control problem for JAD in mixed traffic is proposed to reduce traffic waves. The prediction model is developed using the car-following model within a model predictive control (MPC) framework. The Helly model is selected for the manual vehicle. This is because the Helly model is a linear model that describes the car-following phenomenon accurately without delay effect. In addition, the objective function of the prediction model considers both traffic safety and efficiency while satisfying mechanical and safety constraints. Simulation results indicate that the proposed methodology can effectively reduce traffic jams and improve traffic performance on a one-lane freeway. The optimal method is more applicable to complex traffic wave scenarios, providing a new perspective for reducing traffic jams on the freeway. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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15 pages, 2997 KiB

Open AccessArticle

Numerical Analysis of Low-Cost Recognition of Tunnel Cracks with Compressive Sensing along the Railway

by Jinfeng Chen and Meng Mei

Appl. Sci. 2023, 13(24), 13007; https://doi.org/10.3390/app132413007 - 06 Dec 2023

Cited by 1 | Viewed by 570

Abstract

Currently, the use of microseismic detection technology for crack detection and localization in rock masses has great potential in detecting structural damage. As engineering safety has always been a very important issue, this study investigated the problem of multi-crack identification in rock masses [...] Read more.

Currently, the use of microseismic detection technology for crack detection and localization in rock masses has great potential in detecting structural damage. As engineering safety has always been a very important issue, this study investigated the problem of multi-crack identification in rock masses within the environment of track tunnels using transient waves. A tunnel rock was modeled using MIDAS GTS NX software (2019.v1.2) and a crack transient wave model in the frequency domain was obtained through data analysis and simulation. Then, this was combined with compressive sensing techniques to locate and detect multiple cracks in tunnel rock. The performance of the proposed approach was validated through experimental simulations, which included experiments on differences in the number of cracks, as well as spatial samples. The experimental results indicate that the technique performs well for single-crack localization in tunnel rock mass, where the average localization error is 4 m. Meanwhile, the localization error is larger in multi-crack localization, and the number of spatial sample points set using compressive sensing also has a large impact on the experimental results. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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19 pages, 1587 KiB

Open AccessArticle

Fault Diagnosis Method for Railway Turnout with Pinball Loss-Based Multiclass Support Matrix Machine

by Mingyi Geng, Zhongwei Xu and Meng Mei

Appl. Sci. 2023, 13(22), 12375; https://doi.org/10.3390/app132212375 - 15 Nov 2023

Viewed by 659

Abstract

The intelligent maintenance of railway equipment plays a pivotal role in advancing the sustainability of transportation and manufacturing. Railway turnouts, being an essential component of railway infrastructure, often encounter various faults, which present operational challenges. Existing fault diagnosis methods for railway turnouts primarily [...] Read more.

The intelligent maintenance of railway equipment plays a pivotal role in advancing the sustainability of transportation and manufacturing. Railway turnouts, being an essential component of railway infrastructure, often encounter various faults, which present operational challenges. Existing fault diagnosis methods for railway turnouts primarily utilize vectorized monitoring data, interpreted either through vector-based models or distance-based measurements. However, these methods exhibit limited interpretability or are heavily reliant on standard curves, which impairs their performance or restricts their generalizability. To address these limitations, a railway turnouts fault diagnosis method with monitoring signal images and support matrix machine is proposed herein. In addition, a pinball loss-based multiclass support matrix machine (PL-MSMM) is designed to address the noise sensitivity limitations of the multiclass support matrix machine (MSMM). First, the time-series monitoring signals in one dimension are transformed into images in two dimensions. Subsequently, the image-based feature matrix is constructed. Then, the PL-MSMM model is trained using the feature matrix to facilitate the fault diagnosis. The proposed method is evaluated using a real-world operational current dataset, achieving a fault identification accuracy rate of 98.67%. This method outperforms the existing method in terms of accuracy, precision, and F1-score, demonstrating its superiority. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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16 pages, 11336 KiB

Open AccessArticle

Measurement of CO₂ Emissions by the Operation of Freight Transport in Mexican Road Corridors

by Juan F. Mendoza-Sanchez, Elia M. Alonso-Guzman, Wilfrido Martinez-Molina, Hugo L. Chavez-Garcia, Rafael Soto-Espitia, Saúl A. Obregón-Biosca and Horacio Delgado-Alamilla

Appl. Sci. 2023, 13(20), 11391; https://doi.org/10.3390/app132011391 - 17 Oct 2023

Viewed by 724

Abstract

The freight transport industry in Mexico has grown significantly since the establishment of trade agreements in North America, which has brought significant environmental consequences to the main transport corridors. This paper proposes a methodology for the estimation of emissions for freight vehicles on [...] Read more.

The freight transport industry in Mexico has grown significantly since the establishment of trade agreements in North America, which has brought significant environmental consequences to the main transport corridors. This paper proposes a methodology for the estimation of emissions for freight vehicles on road transportation corridors. The variables included in this analysis allow adequate characterization of the conditions of the vehicle fleet, the geometry and the quality of the road, the environment, and the average annual daily traffic (AADT) of heavy vehicles. The results were structured to show two indicators, the amount of CO₂ emissions per kilometer and the amount of emissions per tonne transported. These results will allow establishing a baseline of CO₂ emissions through which we can implement actions in the road transport sector to reduce greenhouse gases (GHG) to mitigate climate change and develop parameter values for use in Cost Benefit Analysis. The indicators can also be applied to geospatial modeling of emissions in road transport corridors and forecast its growth. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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17 pages, 1409 KiB

Open AccessArticle

Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm

by Dan-Dan Yang, Meng Mei, Yu-Jun Zhu, Xin He, Yong Xu and Wei Wu

Appl. Sci. 2023, 13(20), 11252; https://doi.org/10.3390/app132011252 - 13 Oct 2023

Viewed by 610

Abstract

In complex two-dimensional monitoring environments, how to enhance network efficiency and network lifespan while utilizing limited energy resources, and ensuring that wireless sensor networks achieve the required partial coverage of the monitoring area, are the challenges of optimizing coverage in wireless sensor networks.With [...] Read more.

In complex two-dimensional monitoring environments, how to enhance network efficiency and network lifespan while utilizing limited energy resources, and ensuring that wireless sensor networks achieve the required partial coverage of the monitoring area, are the challenges of optimizing coverage in wireless sensor networks.With the premise of ensuring connectivity in the target network area, an enhanced multi-objective salp swarm algorithm based on non-dominated sorting (EMSSA) is proposed in this paper, by jointly optimizing network coverage, node utilization, and network energy balance objectives. Firstly, the logistic chaotic mapping is used to maintain the diversity of the initial salp swarm population. Secondly, to balance global and local search capabilities, a new dynamic convergence factor is introduced. Finally, to escape local optima more effectively, a follower updating strategy is implemented to reduce the blind following of followers while retaining superior individual information. The effectiveness of the strategy is validated through comparative experiments on ZDT and DTLZ test functions, and the proposed algorithm is applied to coverage optimization in WSNs in complex environments. The results demonstrate that the algorithm can adjust coverage thresholds according to different application requirements, providing various effective coverage optimization configurations. With the same preset requirements for partial coverage achieved, both network efficiency and lifespan have been significantly improved. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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13 pages, 2609 KiB

Open AccessArticle

Optimal Fleet Transition Modeling for Sustainable Inland Waterways Transport

by Matteo Giacomo Prina, Alyona Zubaryeva, Giuseppe Rotondo, Andrea Grotto and Wolfram Sparber

Appl. Sci. 2023, 13(17), 9524; https://doi.org/10.3390/app13179524 - 23 Aug 2023

Cited by 1 | Viewed by 829

Abstract

The transition to sustainable waterways transport is imperative in the face of environmental and climate challenges. Local lakes, often overlooked, play a significant role in regional transportation networks and ecosystems. This study focuses on Orta lake, Italy, and aims to facilitate its transition [...] Read more.

The transition to sustainable waterways transport is imperative in the face of environmental and climate challenges. Local lakes, often overlooked, play a significant role in regional transportation networks and ecosystems. This study focuses on Orta lake, Italy, and aims to facilitate its transition to sustainable inland waterways transport by substituting its diesel-based fleet with electric vessels. Firstly, a comprehensive market analysis was conducted to understand the available electric vessel models and their technical characteristics. This included parameters such as capacity, range, and charging time. Based on the market analysis, an optimization model was developed to determine the minimum number of electric vessels required to completely replace the existing diesel-based fleet. This model considers various constraints and objectives, such as meeting transport demand, minimizing the number of vessels, and reducing environmental impact. The developed model was then applied to the case study of Orta lake using the collected market data. The results indicate an optimal fleet configuration and provide insights into the feasibility and implications of the transition. This study contributes to the growing body of knowledge on sustainable inland waterways transport and offers a methodology that can be replicated and adapted for other local lakes or maritime settings. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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23 pages, 3700 KiB

Open AccessArticle

Enhanced Evaluation Model Based on Classification Selection Applied to Value Evaluation of Waste Household Appliances

by Yi-Zhan Chen, Yi Huang, Chen-Ye Huang, Peng-Fei Li and Xin-Lin Huang

Appl. Sci. 2023, 13(13), 7434; https://doi.org/10.3390/app13137434 - 23 Jun 2023

Viewed by 881

Abstract

In the process of recycling, dismantling, and reusing household appliances, implementing extended producer responsibility (EPR) has become increasingly important. Designing a reasonable pricing mechanism for waste household appliance recycling is critical for the implementation of EPR. To address the problem of labor-intensive and [...] Read more.

In the process of recycling, dismantling, and reusing household appliances, implementing extended producer responsibility (EPR) has become increasingly important. Designing a reasonable pricing mechanism for waste household appliance recycling is critical for the implementation of EPR. To address the problem of labor-intensive and experience-dependent traditional manual methods for assessing the value of waste household appliances, in this paper, we propose an evaluation method based on the subtractive clustering method and an adaptive neuro fuzzy inference system (SCM–ANFIS), which outperforms traditional neural networks such as LSTM, BP neural network, random forest and Takagi–Sugeno fuzzy neural network (T–S FNN). Moreover, in this paper, we combine the five aforementioned algorithms to design a combination evaluation model based on maximum ratio combination (CEM–MRC), which can achieve a performance improvement of 0.1% in terms of mean absolute percentage error (MAPE) compared to the suboptimal BP neural network. Furthermore, an enhanced evaluation model based on classification selection (EEM–CS) is designed to automatically select the evaluation results between the optimal SCM–ANFIS and the suboptimal CEM–MRC, resulting in a 0.73% reduction in MAPE compared to the optimal SCM–ANFIS and a 1.42% reduction compared to the suboptimal CEM–MRC. In this paper, we also validate the performance of the proposed algorithms using a dataset of waste television recycling, which demonstrates the high accuracy of the proposed value assessment mechanisms achieved without human intervention and a significant improvement in evaluation accuracy as compared to conventional neural-network-based algorithms. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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15 pages, 1820 KiB

Open AccessArticle

Identification of Critical Road Links Based on Static and Dynamic Features Fusion

by Yi Li and Min Huang

Appl. Sci. 2023, 13(10), 5994; https://doi.org/10.3390/app13105994 - 13 May 2023

Cited by 1 | Viewed by 1066

Abstract

Traffic congestion is a significant challenge in modern cities, leading to economic losses, environmental pollution, and inconvenience for the public. Identifying critical road links in a city can assist urban traffic management in developing effective management strategies, preserving the efficiency of critical road [...] Read more.

Traffic congestion is a significant challenge in modern cities, leading to economic losses, environmental pollution, and inconvenience for the public. Identifying critical road links in a city can assist urban traffic management in developing effective management strategies, preserving the efficiency of critical road links, and ensuring the smooth operation of urban transportation systems. However, the existing road link importance evaluation metrics mostly rely on complex network metrics and traffic metrics, which may lead to biased results. In this paper, we propose a critical road link identification framework based on the fusion of dynamic and static features. First, we propose a directed dual topological traffic network model that considers the subjectivity of road links, traffic circulation characteristics, and time-varying characteristics, which addresses the limitations of existing traffic network topology construction. Subsequently, we employ a novel graph representation learning network to learn the road link node low-dimensional embeddings. Finally, we utilize clustering algorithms to cluster each road link node and evaluate critical road links using the average importance evaluation indicator of different categories. The results of comparison experiments using real-world data demonstrate the clear superiority and effectiveness of our proposed method. Specifically, our method is able to achieve a reduction in traffic network efficiency of 70–75% when less than 25% of the road links are removed. In contrast, the other baseline methods only achieve a reduction of 50–70% when removing the same proportion of road links. These findings highlight the significant advantages of our approach in identifying the critical links. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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20 pages, 6209 KiB

Open AccessArticle

Priority of Emergency Vehicle Dynamic Right-Of-Way Control Method in Networked Environment

by Zhihui Kuang, Xin Zhao and Lei Feng

Appl. Sci. 2023, 13(10), 5883; https://doi.org/10.3390/app13105883 - 10 May 2023

Cited by 2 | Viewed by 1241

Abstract

This paper proposes a dynamic right-of-way priority control approach for emergency vehicles (PDR-EVs) to improve their efficiency on basic road sections in the city based on a cooperative vehicle infrastructure system. Specifically, a movable physical function area was set in front of the [...] Read more.

This paper proposes a dynamic right-of-way priority control approach for emergency vehicles (PDR-EVs) to improve their efficiency on basic road sections in the city based on a cooperative vehicle infrastructure system. Specifically, a movable physical function area was set in front of the EVs to prohibit connected vehicles (CVs) from entering a lane or to request them to change lanes to avoid a collision. Setting up a dynamic monitoring area at the EV’s front end affords real-time monitoring of the CV’s headway distribution in the inner lane. Moreover, a lane change request is sent when the CVs enter the buffer area, and the traversal search method predicts the optimal time and rate of speed to change the lane change and guides the CVs ahead of the EVs to merge into the target gap. Extensive simulations using the SUMO platform revealed that the priority of the dynamic right-of-way (PDR) control method reduced the average delay of the EVs by more than 70%, given that the road saturation did not exceed 0.8 and hardly increased the delay of the CVs (not more than 8%). Moreover, the simulations revealed that the long buffer area was suitable for low-volume conditions, and the short one was suitable for high-volume conditions. The proposed methodology fully employs the road space resources and enhances the EV’s operating efficiency on basic road sections while considering the CV’s operating efficiency. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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25 pages, 3004 KiB

Open AccessArticle

Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks

by Qinghua Tang, Demin Li, Yihong Zhang and Xuemin Chen

Appl. Sci. 2023, 13(9), 5476; https://doi.org/10.3390/app13095476 - 27 Apr 2023

Cited by 2 | Viewed by 1519

Abstract

With the growing popularity of autonomous electric vehicles (AEVs), optimizing their path-planning and charging strategy has become a critical research area. However, the dynamic nature of transport networks presents a significant challenge when ensuring their efficient operation. The use of vehicle-to-everything (V2X) communication [...] Read more.

With the growing popularity of autonomous electric vehicles (AEVs), optimizing their path-planning and charging strategy has become a critical research area. However, the dynamic nature of transport networks presents a significant challenge when ensuring their efficient operation. The use of vehicle-to-everything (V2X) communication in vehicular ad hoc networks (VANETs) has been proposed to tackle this challenge. However, establishing efficient communication and optimizing dynamic paths with charging selection remain complex problems. In this paper, we propose a joint push–pull communication mode to obtain real-time traffic conditions and charging infrastructure information (i.e., charging stations and energy segments). We also analyze the selection of relay vehicles in multi-hop communication routing, considering factors such as link stability, vehicle distance, and reputation values. Furthermore, we formulate a dynamic optimization problem based on real-time information to minimize travel and charging costs. Our proposed algorithm enables AEVs to obtain charging services from charging stations and conduct dynamic wireless charging via energy segments. We present a dynamic real-time A* algorithm to solve the path-optimization problem and a dynamic real-time charging selection algorithm based on dynamic path optimization when the state of charge is lower than the charging threshold. Extensive simulations demonstrate that the proposed joint push-pull communication mode can provide vehicles the up-to-date information and the developed optimization algorithms effectively reduce travel and charging costs. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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16 pages, 1500 KiB

Open AccessArticle

Selection of Potential Regions for the Creation of Intelligent Transportation Systems Based on the Machine Learning Algorithm Random Forest

by Aleksey I. Shinkevich, Tatyana V. Malysheva and Irina G. Ershova

Appl. Sci. 2023, 13(6), 4024; https://doi.org/10.3390/app13064024 - 22 Mar 2023

Viewed by 1103

Abstract

The planning and management of traffic flow networks with multiple input data sources for decision-making generate the need for a mathematical approach. The program of measures for the development of the transport infrastructure of the Russian Federation provides for the selection of pilot [...] Read more.

The planning and management of traffic flow networks with multiple input data sources for decision-making generate the need for a mathematical approach. The program of measures for the development of the transport infrastructure of the Russian Federation provides for the selection of pilot regions for the creation of intelligent transportation systems. With extensive knowledge of theoretical and applied mathematics, it is important to select and adapt mathematical methods for solving problems. In this regard, the aim of the study is to develop and validate an algorithm for solving the problem of classifying objects according to the potential of creating intelligent transportation systems. The main mathematical apparatus for classification is the «random forest» machine learning algorithm method. A bagging machine learning meta-algorithm for high accuracy of the algorithm was used. This paper proposes the author’s method of sequential classification analysis for identifying objects with the potential to create intelligent transportation systems. The choice of using this method is justified by its best behavior under the large number of predictor variables required for an objective aggregate assessment of digital development and quality of territories. The proposed algorithm on the example of Russian regions was tested. A technique and algorithm for statistical data processing based on descriptive analytics tools have been developed. The quality of the classification analysis algorithm was assessed by the random forest method based on misclassification coefficients. The admissibility of retrained algorithms and formation of a «fine-grained» «random forest» model for solving classification problems under the condition of no prediction was proven to be successful. The most productive models with the highest probability of correct classification were «reached» and «finalized» on the basis of logistic regression analysis of relationships between predictors and categorical dependent variables. The regions of class 1 with «high potential for the creation of intelligent transportation systems» are most likely to be ready for the reorganization of infrastructure facilities; the introduction of digital technologies in the management of traffic flows was found. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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23 pages, 6339 KiB

Open AccessArticle

A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles

by Yang Shi, Zhenbo Wang, Tim J. LaClair, Chieh (Ross) Wang, Yunli Shao and Jinghui Yuan

Appl. Sci. 2023, 13(4), 2750; https://doi.org/10.3390/app13042750 - 20 Feb 2023

Cited by 5 | Viewed by 3135

Abstract

The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce congestion, increase fuel efficiency, and enhance [...] Read more.

The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce congestion, increase fuel efficiency, and enhance road safety. The success of CV-based signal control depends on an accurate and computationally efficient model that accounts for the stochastic and nonlinear nature of the traffic flow. Without the necessity of prior knowledge of the traffic system’s model architecture, reinforcement learning (RL) is a promising tool to acquire the control policy through observing the transition of the traffic states. In this paper, we propose a novel data-driven traffic signal control method that leverages the latest in deep learning and reinforcement learning techniques. By incorporating a compressed representation of the traffic states, the proposed method overcomes the limitations of the existing methods in defining the action space to include more practical and flexible signal phases. The simulation results demonstrate the convergence and robust performance of the proposed method against several existing benchmark methods in terms of average vehicle speeds, queue length, wait time, and traffic density. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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19 pages, 6041 KiB

Open AccessArticle

Exclusive Bus Lane Allocation Considering Multimodal Traffic Equity Based on Bi-Level Programming

by Xiaojuan Lu, Jianjun Wang, Choon Wah Yuen and Shiyu Zheng

Appl. Sci. 2023, 13(4), 2047; https://doi.org/10.3390/app13042047 - 04 Feb 2023

Cited by 1 | Viewed by 1389

Abstract

To ensure the equity of exclusive bus lane (EBL) allocation under multimodal traffic conditions, a bi-level programming model is first constructed. The upper-level model is the minimum total system cost considering the Gini coefficient and the lower-level model constructed a stochastic user equilibrium [...] Read more.

To ensure the equity of exclusive bus lane (EBL) allocation under multimodal traffic conditions, a bi-level programming model is first constructed. The upper-level model is the minimum total system cost considering the Gini coefficient and the lower-level model constructed a stochastic user equilibrium (SUE) model based on logit loading. Secondly, a heuristic algorithm combining an improved genetic algorithm (GA) and a method of the successive average method (MSA) is designed. Finally, the Nguyen and Dupuis networks are used as examples to verify and analyze the effectiveness, superiority and sensitivity of the model and algorithm. The results show that the method can effectively obtain the optimal solution of the upper-level model as 15,004 RMB, the Gini coefficient is 0.31, and the equity is at a relatively reasonable level. Compared with the different allocation schemes, the proposed scheme has a higher bus sharing rate and lower Gini coefficient. At the same time, when the actual demand is twice the basic demand, the bus share rate is the largest, 65%, and the Gini coefficient is the smallest at 0.3. The bus share rate decreases with the increase in the proportion of high time value travelers, which fully verifies the sensitivity of the model to the type of traveler. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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Review

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14 pages, 1050 KiB

Open AccessReview

Notes on Bus User Assignment Problem Using Section Network Representation Method

by Ahmed Almutairi, Mahmoud Owais and Abdou S. Ahmed

Appl. Sci. 2024, 14(8), 3406; https://doi.org/10.3390/app14083406 - 17 Apr 2024

Viewed by 312

Abstract

A recurrent solution to consecutive transit assignment problems is typically required to help address the bus network design problem (BNDP). Intriguingly, the transit assignment issue is differentiated by a number of distinctive characteristics. In this article, a complete analysis of one of the [...] Read more.

A recurrent solution to consecutive transit assignment problems is typically required to help address the bus network design problem (BNDP). Intriguingly, the transit assignment issue is differentiated by a number of distinctive characteristics. In this article, a complete analysis of one of the well-known graphical representations of the problem is conducted. The presented design is founded on the representation of the transit network by De Cea and Fernandez (1993). They developed an innovative section-based graph augmentation of the real transit network to overcome many of the mathematical formulation complexities of the problem. This study is organized to thoroughly investigate and review the model to shed light on its capabilities for use in BNDP solution schemes. The review provides the needed information to give the reader a full assessment of the selected bus assignment model. The importance of this review is shown by the fact that the most widely utilized transit assignment models in the BNDP are inadequate in their fundamental assumptions when compared to the model under consideration. The model’s graphical representation and solution technique are described in depth in addition to the constraints that will be integrated into the BNDP solution approaches. We want to refocus emphasis on this approach for further BNDP research since it is infrequently used in BNDP solution frameworks. Full article

(This article belongs to the Special Issue Transportation Planning, Management and Optimization)

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