Transportation Planning, Management and Optimization

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 April 2024 | Viewed by 13567

Special Issue Editor

Prof. Dr. Xinlin Huang
E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Tongji University, Shanghai 201804, China
Interests: cognitive radio, multimedia transmission, and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transportation, along with manufacturing and warehousing, is one of the main components in supply chain processes. It comprises a lot of activities—from delivery planning to carrier management to reverse logistics for recycling—that have to be properly handled. In recent years, the new realities of the logistics environment have made transportation optimization more important than ever before. For example, the recycling process in reverse logistics is known as the process for allowing different materials in products to be reused in future manufacturing processes, which is essential for sustainable industrial manufacturing. However, the current process may produce a certain loss of materials and result in environmental pollution due to the lack of recycling efficiency.

Hence, research on intelligent transport planning, management and optimization has recently attracted more attention from academia and industry. Transportation planning and management is the process of looking at the current state of transportation in the region, designing for future transportation needs, and combining all of that with the requirement of commercial, political, and other objectives, e.g., study of more efficient and environmentally friendly reverse logistics technologies in recycling. On the other hand, artificial intelligence has been widely deployed for improving the efficiency of manufacturing, transportation, recycling of energy and materials, etc., while the design of intelligent transportation technologies relies on a great amount of high-quality data.

In this Special Issue, recent efforts and advances made for intelligent transport planning, management, and optimization will be discussed. The topics of interest include but are not limited to the following research areas:

  • AI for sustainable transportation and manufacturing;
  • Deep learning for recyclable material transportation;
  • Plan and forecast network needs;
  • Smart transportation route optimization;
  • Machine learning for transportation efficiency improvement;
  • Intelligent reverse logistic technology;
  • Integration of manufacturing, transportation, and recycling;
  • Related value assessment and pricing strategy;
  • IoT for smart transportation, manufacturing, and warehousing.

Prof. Dr. Xinlin Huang
Guest Editor

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Keywords

  • intelligent transportation system
  • transportation planning
  • transportation management
  • reverse logistic efficiency
  • pricing strategy
  • route optimization
  • machine learning
  • neuron network

Published Papers (13 papers)

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Research

14 pages, 2886 KiB  
Article
A Helly Model-Based MPC Control System for Jam-Absorption Driving Strategy against Traffic Waves in Mixed Traffic
Appl. Sci. 2024, 14(4), 1424; https://doi.org/10.3390/app14041424 - 09 Feb 2024
Viewed by 482
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  
Article
Numerical Analysis of Low-Cost Recognition of Tunnel Cracks with Compressive Sensing along the Railway
Appl. Sci. 2023, 13(24), 13007; https://doi.org/10.3390/app132413007 - 06 Dec 2023
Cited by 1 | Viewed by 464
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  
Article
Fault Diagnosis Method for Railway Turnout with Pinball Loss-Based Multiclass Support Matrix Machine
Appl. Sci. 2023, 13(22), 12375; https://doi.org/10.3390/app132212375 - 15 Nov 2023
Viewed by 498
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  
Article
Measurement of CO2 Emissions by the Operation of Freight Transport in Mexican Road Corridors
Appl. Sci. 2023, 13(20), 11391; https://doi.org/10.3390/app132011391 - 17 Oct 2023
Viewed by 585
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  
Article
Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
Appl. Sci. 2023, 13(20), 11252; https://doi.org/10.3390/app132011252 - 13 Oct 2023
Viewed by 479
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  
Article
Optimal Fleet Transition Modeling for Sustainable Inland Waterways Transport
Appl. Sci. 2023, 13(17), 9524; https://doi.org/10.3390/app13179524 - 23 Aug 2023
Cited by 1 | Viewed by 691
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  
Article
Enhanced Evaluation Model Based on Classification Selection Applied to Value Evaluation of Waste Household Appliances
Appl. Sci. 2023, 13(13), 7434; https://doi.org/10.3390/app13137434 - 23 Jun 2023
Viewed by 766
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  
Article
Identification of Critical Road Links Based on Static and Dynamic Features Fusion
by and
Appl. Sci. 2023, 13(10), 5994; https://doi.org/10.3390/app13105994 - 13 May 2023
Cited by 1 | Viewed by 948
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  
Article
Priority of Emergency Vehicle Dynamic Right-Of-Way Control Method in Networked Environment
Appl. Sci. 2023, 13(10), 5883; https://doi.org/10.3390/app13105883 - 10 May 2023
Cited by 2 | Viewed by 1068
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  
Article
Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks
Appl. Sci. 2023, 13(9), 5476; https://doi.org/10.3390/app13095476 - 27 Apr 2023
Cited by 2 | Viewed by 1237
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  
Article
Selection of Potential Regions for the Creation of Intelligent Transportation Systems Based on the Machine Learning Algorithm Random Forest
Appl. Sci. 2023, 13(6), 4024; https://doi.org/10.3390/app13064024 - 22 Mar 2023
Viewed by 1000
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  
Article
A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles
Appl. Sci. 2023, 13(4), 2750; https://doi.org/10.3390/app13042750 - 20 Feb 2023
Cited by 4 | Viewed by 2672
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  
Article
Exclusive Bus Lane Allocation Considering Multimodal Traffic Equity Based on Bi-Level Programming
Appl. Sci. 2023, 13(4), 2047; https://doi.org/10.3390/app13042047 - 04 Feb 2023
Cited by 1 | Viewed by 1252
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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