Traffic Demand Research Articles

Comparative Assessment of Expected Safety Performance of Freeway Automated Vehicle Managed Lanes

The use of dedicated lanes, known as managed lanes (MLs), on freeways is an established traffic management strategy to reduce congestion. Allowing automated vehicles (AVs) in existing MLs or dedicating MLs for AVs, referred to as AVMLs, has been suggested in the literature as a tool to improve traffic operation and safety performance as AVs and driver-operated vehicles (DVs) coexist in a mixed-vehicle environment. This paper focuses on investigating the safety impacts of deploying AVMLs on freeways by repurposing general-purpose lanes (GPLs). Four ML strategies considering different lane positions and access controls were implemented in a traffic microsimulation under different AV market adoption rates (MARs) and traffic demand levels, and trajectories were used to extract rear-end and lane change conflicts. The time-to-collision (TTC) surrogate safety measure was used to identify critical conflicts using a time threshold dependent on the type of following vehicle. Rates of conflicts involving different vehicle types for all ML strategies were compared to the case of heterogeneous traffic. The results indicated that the rates of rear-end conflicts involving the same vehicle type as the lead and following vehicle, namely DV-DV and AV-AV conflicts, increased with ML implementation as more vehicles of the same type traveled in the same lane(s). By comparing the aggregated conflict rates, the design options that were deemed to negatively impact traffic efficiency and capacity were also found to negatively impact traffic safety. However, other ML options were found to be feasible in terms of traffic operation and safety performance, especially at traffic demand levels below capacity. Specifically, one left-side AVML with continuous access was found to have lower or comparable aggregated conflict rates compared to heterogenous traffic at 25% and 50% MARs, and, thus, it is expected to have positive or neutral safety impacts.

The Forecasting Model of the Impact of Shopping Centres in Urban Areas on the Generation of Traffic Demand

The increase in traffic caused by new development affects the change in traffic conditions on the surrounding roads, and shopping centres are significant traffic generators. The development of local travel generation rates and their characteristics for individual land uses from the aspect of traffic demand is a reliable way to plan traffic in order to come up with preventive solutions to traffic problems, that is, prevention of possible negative consequences on traffic conditions in the street network occurring due to the construction of shopping centres. One of the main aims of this paper is to develop a model for objective assessment of the generated traffic demand for significant changes in land use, such as the construction of shopping centres in medium-sized towns. All these would be steps in the right direction for the promotion of reliable traffic planning and adoption of TIA for every new development before a decision regarding the change in land purpose has been made. This kind of process still has not been established systematically in either Bosnia and Herzegovina and the Republic of Serbia, or in surrounding countries. This paper focuses on the formulation of a model for determining the volume of traffic generated by shopping centres in medium-sized towns in two countries of the Southeast Europe region. The survey was conducted in eight different locations (cities) where there are shopping centres with common facilities. The analysis showed that the number of visitors and vehicles attracted by the shopping centre zone can be determined by a model based on a linear regression analysis. The analysis included exploring several different factors of trip generation in shopping centres, including the relationship between trip generation and combinations of several independent variables. The verification of the model was conducted in real conditions of the traffic flow generated by a shopping centre which was not the analysis subject when forming the forecasting model. In this way, the validity of the proposed model is credibly assessed. The developed model can be applied in the procedures of planning the construction of shopping centres in medium-sized cities in the Republic of Serbia and Bosnia and Herzegovina, and wider, in the region of Southeast Europe, in order to estimate the volume of generated traffic demand, that is, its impact on the conditions of traffic on the surrounding traffic network.

A cooperative perception based adaptive signal control under early deployment of connected and automated vehicles

Connected vehicle-based adaptive traffic signal control requires certain market penetration rates (MPRs) to be effective, usually exceeding 10%. Cooperative perception based on connected and automated vehicles (CAVs) can effectively improve overall data collection efficiency and reduce required MPR. However, the distribution of observed vehicles under cooperative perception is highly skewed and imbalanced, especially under very low CAV MPRs (e.g., 1%). To address this challenge, this paper proposes a novel deep reinforcement learning-based adaptive traffic signal control (RL-TSC) method that integrates a traffic flow model, known as the cell transmission model (CTM), denoted as CAVLight. Traffic states estimated from the CTM are integrated with the data collected from the cooperative perception environment to update the states in the CAVLight model. The design of reward function aims for reducing total vehicle delays and stabilizing agent behaviors. Extensive numerical experiments under a real-world intersection with varying traffic demand levels and CAV MPRs are conducted to compare the performance of CAVLight and other benchmark algorithms, including a fixed-time controller, an actuated controller, the max pressure model, and an optimization-based adaptive TSC. Results demonstrate the superiority of CAVLight in performance and generalizability over benchmarks, especially under 1% CAV MPR scenario with high traffic demands. The influence of CTM integration on CAVLight is further explored through RL agent policy visualization and sensitivity analysis in CTM parameters and CAV perception capabilities (i.e., detection range and detection accuracy).

Analysis of Low-Carbon, Energy-Saving, and Emission Reduction Models Based on Rail Transit

It is difficult to define scientific applicability conditions when dealing with different evaluation objects and scope. This article is based on multi-source big data such as the Automatic Fare Collection System (AFC) of urban rail transit, comprehensively considering the multidimensional impact of urban rail transit on the urban transportation system, and conducting a quantitative analysis of the energy-saving and emission reduction effects of urban rail transit. This study adopts a traffic emission model based on specific driving forces and a traffic demand prediction model, coupling the model and data to establish an urban rail transit energy conservation and emission reduction evaluation model suitable for different urban rail transit setting scenarios. Finally, this study selected six districts in Beijing as model application cases and used a combination of RP (display preference) survey and SP (state preference) survey to complete model parameter calibration for application cases.

Flow prediction of mountain cities arterial road network for real-time regulation

This study aims to enhance the understanding of vehicle path selection behavior within arterial road networks by investigating the influencing factors and analyzing spatial and temporal traffic flow distributions. Using radio frequency identification (RFID) travel data, key factors such as travel duration, route familiarity, route length, expressway ratio, arterial road ratio, and ramp ratio were identified. We then proposed an origin–destination path acquisition method and developed a route-selection prediction model based on a multinomial logit model with sample weights. Additionally, the study linked the traffic control scheme with travel time using the Bureau of Public Roads function—a model that illustrates the relationship between network-wide travel time and traffic demand—and developed an arterial road network traffic forecasting model. Verification showed that the prediction accuracy of the improved multinomial logit model increased from 92.55 % to 97.87 %. Furthermore, reducing the green time ratio for multilane merging from 0.75 to 0.5 significantly decreased the likelihood of vehicles choosing this route and reduced the number of vehicles passing through the ramp. The flow prediction model achieved a 97.9 % accuracy, accurately reflecting actual volume changes and ensuring smooth operation of the main airport road. This provides a strong foundation for developing effective traffic control plans.

Enhancing air traffic control: A transparent deep reinforcement learning framework for autonomous conflict resolution

Rising traffic demands, advancements in automation, and communication breakthroughs are opening new design frontiers for future air traffic controllers (ATCs). This article introduces a deep reinforcement learning (DRL) controller to support conflict resolution in autonomous free flight. While DRL has made significant strides in this domain, there is scant focus in existing research on the explainability and safety of DRL controllers, especially their robustness against adversarial attacks. To tackle these concerns, we have engineered a fully transparent DRL framework that: (i) separates the intertwined Q-value learning model into distinct modules for safety-awareness and efficiency (target attainment); and (ii) incorporates data from nearby intruders directly, thereby obviating the need for central control. Our simulated experiments demonstrate that this bifurcation of safety-awareness and efficiency not only enhances performance in free flight control tasks but also substantially boosts practical explainability. Moreover, the safety-oriented Q-learning component offers detailed insights into environmental risk factors. To assess resilience against adversarial attacks, we present a novel attack strategy that induces both safety-centric and efficiency-centric disruptions. This adversary aims to degrade safety/efficiency by targeting the agent at select time intervals. Our experimental results reveal that this targeted approach can provoke as many collisions as a uniform attack strategy — that is, attacking at every opportunity — while engaging the agent four times less frequently, shedding light on the potential and limitations of DRL in future ATC system designs. The source code is available to the public at https://github.com/WLeiiiii/Gym-ATC-Attack-Project.

Understanding behavioral intentions in urban travel reservation: a combined model of rational motivation and social norms

ABSTRACT The travel reservation strategy (TRS) is more flexible and refined than traditional traffic demand management methods. There is limited research discussing the implementation of travel reservations during peak hours for congested bottleneck roads in urban areas. Furthermore, previous studies have not explored the behavioral intentions of urban residents regarding travel reservations. This study explored the essential psychological characteristics affecting travelers’ acceptance of TRS and the potential causal relationships between these variables. We proposed the Tech-Norm-Behavior Integration model that integrates the Theory of Planned Behavior, Technology Acceptance Model, and Norm Activation Model to examine travelers’ intentions to use travel reservations driven by rational and normative motivations. We used the structural equation model to verify the integrated model's validity. The results demonstrate that personal norms significantly impact behavioral intentions, followed by attitude and perceived behavioral control. It confirms that TRS is a prosocial traffic demand management strategy have significant mediating effects.

Travel Time Variability in Urban Mobility: Exploring Transportation System Reliability Performance Using Ridesharing Data

Travel time variability (TTV) is a crucial indicator of transportation network performance, assessing travel time reliability and delays. This study investigates TTV metrics within the context of shared mobility using probe data from transportation network companies (TNCs) in Chicago, Los Angeles, and Dallas–Fort Worth. Eight reliability metrics are analyzed and compared for each origin–destination (OD) pair in the network, including standard deviation (SD), the Planning Time Index (PTI), the Travel Time Index (TTI), the Buffer Index (BI), On-time Measures PR (alpha), and the Misery Index (MI), to evaluate their effectiveness in clustering OD pairs using K-means clustering. The findings confirm that SD, PTI, and MI are particularly effective in measuring travel time reliability and clustering within urban systems. This study identifies the most unbalanced supply–demand OD pairs/regions in each city, noting that low/medium-SD clusters around metropolitan airports indicate stable travel times even in high-demand zones, while high-SD clusters in downtown areas reveal significant traffic demands and unreliability. These patterns become more pronounced in study areas with multiple city centers. This study highlights the need for targeted strategies to enhance travel time reliability, particularly in regions like Dallas–Fort Worth, where public transportation alternatives are limited.

A multi‐objective optimization model for RSU deployment in intelligent expressways based on traffic adaptability

AbstractThe intelligent expressway exemplifies a prominent application of intelligent transportation systems. Roadside units (RSUs), strategically deployed alongside roadways, serve as pivotal infrastructure in facilitating interactions within intelligent expressways. A well‐planned RSU deployment strategy is crucial for enhancing service quality, it necessitates balancing performance improvements with significant financial costs due to the limited transmission range and high deployment expenses of RSUs. To tackle these challenges, an adaptive approach for RSU deployment is proposed, which takes into account economic feasibility, service requirements, and dynamic traffic demands. A traffic adaptability‐based RSU deployment (TARD) model, which integrates factors such as deployment cost, the effectiveness of information coverage, road network topology, and traffic flow characteristics have been devised. The TARD aims to minimize deployment expenses while maximizing the benefits of information coverage and alignment with road traffic demands. The Non‐dominated Sorting Genetic Algorithm II (NSGA‐II) is employed to solve this optimization model. To validate its efficacy, simulations are conducted on the G2 expressway in Shandong Province, China, demonstrating the superior performance of the TARD compared to three other deployment strategies. Ablation experiments further underscore the critical role of tunnel deployments and comprehensive coverage along long sections in bolstering network connectivity and elevating service quality.

Dynamic Multi-Function Lane Management for Connected and Automated Vehicles Considering Bus Priority

Bus lanes are commonly implemented to ensure absolute priority for buses at signalized intersections. However, while prioritizing buses, existing bus lane management strategies often exacerbate traffic demand imbalances among lanes. To address this issue, this paper proposes a dynamic Multi-Function Lane (MFL) management strategy. The proposed strategy transforms traditional bus lanes into Multi-Function Lanes (MFLs) that permit access to Connected and Automated Vehicles (CAVs). By fully utilizing the idle right-of-way of the MFL, the proposed strategy can achieve traffic efficiency improvement. To evaluate the proposed strategy, some experiments are conducted under various demand levels and CAV penetration rates. The results reveal that the proposed strategy (i) improves the traffic intensity balance degree by up to 52.9 under high demand levels; (ii) reduces delay by up to 80.56% and stops by up to 89.35% with the increase in demand level and CAV penetration rate; (iii) guarantees absolute bus priority under various demand levels and CAV penetration rates. The proposed strategy performs well even when CAV penetration is low. This indicates that the proposed strategy has the potential for real-world application.

Total capacity and LOS estimation of innovative and conventional roundabouts through macroscopic fundamental diagrams

Traffic congestion remains a significant problem, especially in urban areas. To avoid congestion phenomena, several measures can be adopted, including the construction of roundabouts and the control of the measures of effectiveness (MOEs) at intersections. This article compares different types of roundabouts by estimating the deterministic fundamental diagram (MFD) and using microscopic traffic simulations. The deduction of MFD for different roundabout types (single and double-lane, flower and turbo-roundabout) is based on reasonably simplified traffic and boundary conditions. MFDs are estimated through several OD traffic matrices, considering as outputs the values of the macroscopic traffic variable deduced in time intervals of 5 min and 1 h. The findings from this research, in terms of total capacity, travel times, and level of service (LOS) estimation, may be useful for traffic and highway engineers in many realistic applications to partially face congestion phenomena in urban areas. MFDs allow us to determine the best roundabout type based on the traffic demand and represent a parsimonious method for monitoring and controlling traditional and unconventional or smart roundabouts, even in the presence of connected and automated vehicles (CAVs).

Performance Enhancement of Solar Still Couples With Solar Water Heater by Using Different PCM's and Nanoparticle Combinations

ABSTRACTThe majority of the water on Earth roughly 97% is contaminated or salty, only 3% is fresh water, and only 1% of pure water is easily available for human use. In rural areas and remote locations suffering from pure drinking water scarcity.Water purification techniques are generally dependent on electricity, which relies on coal and gas plants, these poses a risk to the environment and society. Solar desalination is being recognized as the most practical way to deal with the scarcity of pure drinking water in all aspects of sustainable development. This paper describes the creation of a single slope solar still (SSSS) using opaque and crystal‐clear toughened glass with a thickness of 6 mm as a cover and also another setup of a single slope phase change material (PCM)‐based solar still (MSSSS). In this paper, a flat plate solar collector coupled with water heater is used to enhance the productivity of still. In this study, an experimental model has been developed to experimentally analyze exercise productivity performance of SSSS and MSSSS of MMIT Kushinagar, India on April 10, 2023 to April 15, 2023. Combination of stearic acid, lauric acid, and paraffin wax combined with CuO (nanoadditive) is used to enhance the solar still productivity. Also, basin temperature for different PCM's such as paraffin wax, lauric acid, and stearic acid are compared. Different combinations of PCM's and nanoadditives are compared to find the better productivity. It is observed that maximum output is obtained at 3:00 p.m. afternoon on experimental setup. Paraffin wax, stearic acid, and lauric acid still increases productivity by 38.8%, 20.3%, and 30.5%, respectively, when compared to simple solar still. On experimentation of various combinations, it is found that the use of PCM paraffin wax and nanoadditives CuO gives 55% better productivity compared to other combinations. This innovative system is suitable and ideal for desalinating water in isolated and rural locations with low traffic and limited demand.

Estimating traffic demand of different transportation modes using floating smartphone data

Traffic demand is crucial for traffic planning, helping to understand traffic volume, identify congestion points, and develop strategies for efficient and sustainable transportation. This paper introduces a method to estimate origin-destination (OD) matrices for drivers, cyclists, and pedestrians using floating smartphone data (FSD) and the information minimisation (IM) model. The FSD is used in the gravity model (GM) to provide more accurate inputs for the IM model by estimating an a priori matrix and traffic demand at intersections. Additionally, FSD allowed a direct route choice proportions deriviation without needing a traffic assignment model. The proposed estimation model was tested with real data and analyzed in a simulation environment, examining the impact of increasing the penetration rate (PR) of FSD. Results indicate that traffic demand in the study area was accurately estimated with an average Rxy value of 92%, aligning well with simulation results.

Efficiency and fuel consumption of mixed traffic flow with lane management of CAVs

The mixed traffic flow of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) will exist on highways for a long time, as the deployment of CAVs is gradual. To reduce the negative impact of HDVs on CAVs, the deployment of dedicated lanes has been considered an effective solution. Along with the dedicated lanes, three different lane management strategies will be formed, which are (C, H) strategy (CAVs dedicated lanes and HDVs dedicated lanes), (C, G) strategy (CAVs dedicated lanes and general lanes), and (G, H) strategy (general lanes and HDVs dedicated lanes). To evaluate the influence of dedicated lane settings on mixed traffic flow comprehensively, this paper proposes a framework for evaluating road segment efficiency and fuel consumption by considering lane management strategies. First, the possible traffic flow equilibrium states under three lane management strategies are discussed, and the characteristics of five car-following modes in mixed traffic flow are analyzed. Then, a mixed traffic flow capacity model considering platoon size is introduced to the traditional BPR function to establish a speed estimation model for mixed traffic flow and a fuel consumption estimation model for mixed traffic flow. Next, the traffic flow distribution model at the lane level in a steady state is derived for different lane management strategies. Based on the traffic flow distribution model, the speed estimation model and the fuel consumption estimation model for mixed traffic flow, which consider lane management strategies, are proposed. Finally, a numerical simulation is conducted to analyze the effects of different lane management strategies and configuration schemes on road segment efficiency and fuel consumption. The results of numerical experiments show that (1) at the same traffic demand, the operational speeds of vehicles under the (C, H) strategy and (G, H) strategy tend to increase and then decrease with the increase in the penetration rate of CAVs. While the speed of the vehicle under the (C, G) strategy increases with the increase in the penetration rate of CAVs. (2) Compared with the baseline strategy, all three management strategies can improve the operating efficiency of vehicles under certain traffic conditions. (3) At the same traffic demand, the average fuel consumption under the three strategies tends to decrease first and then increase slightly as the penetration rate increases. Increasing the number of dedicated lanes under specific traffic conditions can significantly increase the fuel consumption reduction rate under each strategy. At the same penetration rate, this advantage diminishes with the increase in traffic demand. (4) The increase in platoon size favors the efficiency of vehicle operations under different strategies. However, as platoon size increases, the marginal benefit of increasing platoon size becomes smaller and smaller. In addition, the average fuel consumption of vehicles has a low sensitivity to platoon size, and increasing platoon size may not always reduce fuel consumption.

A hybrid intersection control strategy for CAVs under fluctuating traffic demands: A value approximation approach

Confronted with growing and fluctuating traffic demands, urban transportation system has been encountering mounting challenges in traffic congestion, especially at intersections. With enhanced traffic control precision enabled by the emerging Connected and Automated Vehicle (CAV) technologies, this study proposes a hybrid control strategy for connected and automated traffic at urban intersections, which enables the integration of diverse control schemes to harness their strengths and mitigate their weaknesses. With rolling horizon strategy, a nonlinear optimization model is developed to determine the optimal traffic control plans considering both current status and forthcoming vehicle arrivals. Vehicle delays are elaborately characterized without relying on any empirical assumptions. The original model is converted to a Mixed Integer Programming with Quadratic Constraints (MIP-QC) by employing appropriate linearization techniques, which could be solved by commercial solvers. For the acquisition of instant and reliable solutions, a multilayer feedforward network-based approximate algorithm is developed, referred as Value Approximation Control (VAC) algorithm. Theoretical derivation is provided to validate the capability of VAC algorithm in the precise approximations of the value function in the traffic plan optimization problem, and ultimately enabling to acquire global optimal solutions via specific network design and training techniques. Numerical experiments on both artificial and researcher-collected datasets demonstrate that our proposed VAC algorithm achieves performance nearly equivalent to the mathematical model. Significantly, it outperforms current state-of-art traffic control methods in terms of both intersection throughput and average vehicle delay. Moreover, sensitivity analysis reveals the robustness of the VAC algorithm against inaccuracy in vehicle arrival information, and the stable performance even in the presence of significant disturbances.

Electric vehicle eco-driving strategy at signalized intersections based on optimal energy consumption

Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, we develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, we employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, we formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin’s minimum principle (PMP). Finally, we evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. Our proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time.

Optimizing cell association in 5G and beyond networks: a modified load-aware biased technique

Cellular networks are moving towards increasing heterogeneity by deploying more small cells into macro base station (MBS) to meet rapidly growing traffic demands. To leverage the advantages of these small cells, mobile users should be offloaded onto small base stations (BSs), which will typically be lightly populated and can give a higher data rate by presenting the mobile users with many more channels than the MBS. Likewise, a more balanced cell association will lessen the pressure on the MBS, allowing it to serve its remaining users more effectively. This paper addresses the cell association challenge for Quality of Service (QoS) provisioning in terms of throughput and load-balancing for 5G and future generation networks. This problem is quite challenging because BSs have varying backhaul capacities and users have varying QoS needs. Most of the previous studies are based on reference signal received power (RSRP), signal to interference and noise ratio (SINR) or its variants and most importantly majority of them are not load-aware. Therefore, a modified load-aware biased cell association scheme based on distance is proposed to attain better QoS provisioning in terms of throughput and load-balancing. Simulation results depict that the proposed load-aware-based method outperforms conventional cell association schemes based on RSRP and its variants, and in terms of throughput and load-balancing. Furthermore, the algorithm’s complexity has been assessed through a comparison and analysis of computational time, demonstrating better performance compared to state-of-the-art techniques.

State-dependent multi-agent discrete event simulation for urban rail transit passenger flow

Urban rail transit passenger flow modeling is the foundation of urban rail transit planning, design, and operation. The motivation of this paper is to accurately and efficiently simulate passenger flow dynamics in urban rail transit systems. To this end, we propose a State-dependent Multi-agent Discrete Event Simulation (SdMaDES). The state-dependence (or congestion-dependence) means that the service abilities (or travel times) of bottleneck facilities (e.g. platform screen doors and transfer corridors) are not constant and they interact with the congestion dynamics. Specifically, we first establish a modular Multi-agent Discrete Event Simulation (MaDES), which includes four types of modules (passenger, train, station, and network modules) and three types of agents (passenger, train, and station agents). The logical connections and event-triggering modes between the modules are analyzed and defined, and thirty types of events are designed. The state-dependence is then captured by an Improved Social Force Model (ISFM), which adds an autonomous obstacle avoidance mechanism. The ISFM reproduces passenger movement behavior within bottleneck facilities of urban rail transit systems at the microscopic level. These state-dependent functions or general rules are explicitly formulated by fitting the results of ISFM and are subsequently applied to the proposed MaDES model, resulting in the SdMaDES. This integration aims to enhance the accuracy of the simulation. We conducted a real case from the Chengdu Metro network. Some interesting results are found. (a) The maximum number of boarding passengers in a train carriage is a complex nonlinear function that is dependent on the state (density inside the train carriage). This challenges the linear function commonly utilized in most studies. (b) Compared to the actual data, the proposed SdMaDES model shows a cumulative error of 9.85% after data smoothing, while the conventional MaDES model exhibits a much higher cumulative error of 21.9% after data smoothing. (c) As the overall traffic demand level increases, the gap between the two simulation models’ results is getting wider and wider due to the amplified nonlinear impact of congestion.

A Fuzzy Inference System for Predicting Air Traffic Demand based on Socioeconomic Drivers

The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a Graphical User Interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed Air Passenger Demand Prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Neural quantile optimization for edge–cloud networking

We seek the best traffic allocation scheme for the edge–cloud networking subject to SD-WAN architecture and burstable billing. First, we formulate a family of quantile-based integer programming problems for a fixed network topology with random parameters describing the traffic demands. Then, to overcome the difficulty caused by the discrete feature, we generalize the Gumbel-softmax reparameterization method to induce an unconstrained continuous optimization problem as a regularized continuation of the discrete problem. Finally, we introduce the Gumbel-softmax sampling neural network to solve optimization problems via unsupervised learning. The neural network structure reflects the edge–cloud networking topology and is trained to minimize the expectation of the cost function for unconstrained continuous optimization problems. The trained network works as an efficient traffic allocation scheme sampler, outperforming the random strategy in feasibility and cost value. Besides testing the quality of the output allocation scheme, we examine the generalization property of the network by increasing the time steps and the number of users. We also feed the solution to existing integer optimization solvers as initial conditions and verify the warm-starts can accelerate the short-time iteration process. The framework is general, and the decoupled feature of the random neural networks is adequate for practical implementations.