User Comfort Level Research Articles

Analysis of Traffic Impact Due to Construction of Village Government Offices on MT Haryono Road Samarinda City, Indonesia

Development of the Village Government Office Area located on MT. Haryono Street Samarinda City based on Minister of Transportation Regulation no. 17 of 2021 is included in the Low Andalalin category, where the development activities do not have a significant impact on traffic on roads and intersections. The results of the analysis by modeling the increase in traffic growth each year also did not cause a significant decrease in the performance and level of service of a road section or intersection. However, to create sustainable and well-implemented development, efforts are made to maximize the level of safety and comfort for road users around the activity area using vehicle circulation, pedestrian facilities, signs or markings, and traffic engineering management. as well as other safety and comfort supporting facilities. Considering that Andalalin is a requirement that is integrated with the Amdal study in arranging a Building Construction Permit (IMB), the initiator must take care of the traffic impact analysis recommendations, which of course apart from maintaining human and environmental safety is also an administrative requirement that must be fulfilled.

Design and deployment of a novel decisive algorithm to enable real-time optimal load scheduling within an intelligent smart energy management system based on IoT

Consumers routinely use electrical devices, leading to a disparity between consumer demand and the supply side a significant concern for the energy sector. Implementing demand-side energy management can enhance energy efficiency and mitigate substantial supply-side shortages. Current energy management practices focus on reducing power consumption during peak hours, enabling a decrease in overall electricity costs without sacrificing usage. To tackle the mentioned challenges and maintain system equilibrium, it is essential to develop a flexible and portable system. Introducing an intelligent energy management system could pre-empt power outages by implementing controlled partial load shedding based on consumer preferences. During a demand response event, the system adapts by imposing a maximum demand limit, considering various scenarios and adjusting appliance priorities. Experimental work, incorporating user comfort levels, sensor data, and usage times, is conducted using Smart Energy Management Systems (SEMS) integrated with cost-optimization algorithms.

Optimal smart contracts for controlling the environment in electric vehicles based on an Internet of Things network

The scientific community has recently focused on intelligent models for predicting and optimizing EV energy management. Despite numerous studies in energy management optimization, there is a critical need to address the trade-off between energy consumption and occupant comfort. Existing IoT systems face challenges in data analytics security and authenticity, highlighting the need for contemporary models to overcome data privacy and cost-related issues. This study introduces a smart contract model based on optimization and control modules, aiming to manage energy consumption while satisfying user comfort requirements intelligently. Introducing a smart contract model with hierarchical layers—prediction, optimization, control, and Blockchain—the proposed approach intelligently manages energy consumption while meeting user comfort requirements. Utilizing a Kalman filter for prediction and the BAT algorithm for optimization, the model integrates modules to tailor user preferences and enhance comfort. The synergy between the optimization module and a convolutional FLC enhances system performance, ensuring minimized energy usage and elevated user comfort levels. The study also evaluates the model’s implementation of the Hyperledger Fabric network, assessing outcomes regarding caliper, latency, throughput, and resource utilization.

Using virtual and augmented reality to study architectural lighting

This paper presents industry stakeholder insights from the implementation of a dual modality intervention using virtual and augmented reality simulation to study complex lighting theory in architecture design. Using a design based research method the aim is to evaluate these insights and inform a pilot study to educate first year architectural design students on the complexities of lighting the built environment and methods to improve architectural workflow. The aim is to enable learners to experience natural and artificial lighting methods comparatively in real-time through multiple comparative visualisation methods. This is important to make informed evaluations regarding architectural designs in terms of spatial quality, character, performance, and user-comfort levels. This in turn allows architects to rapidly modify their designs to accommodate or mitigate the environmental effects. Outcomes from the initial usability test highlight the ability to switch back and forth between the virtual and augmented reality simulation technology, and between lighting visualisation modes as a huge step forward by the industry stakeholders. Additionally, the idea of representing the physical building where the simulation took place virtually using a detailed mapping gave a real-world anchor that made the simulations easy to navigate, leading to improved satisfaction and engagement. However, the study also highlighted improvements in the delivery of the simulation is required to improve simulation learnability and efficiency.

Optimizing a courtyard microclimate with adaptable shading and evaporative cooling in a hot mediterranean climate

While in recent years significant improvements have been made in the thermal adaptation of indoor building environments, less progress has been made in improving thermal comfort in transitional spaces next to buildings. This makes it imperative to investigate the thermodynamics of such outdoor urban spaces, particularly in warm locations such as southern Spain. The application of suitable passive systems for the regulation of thermal exchanges and the creation of temperate conditions is explored. In order to determine the thermal comfort of users this study focuses on the assessment of two key parameters, mean radiant temperature and relative humidity, and how these are impacted by shading and misting systems in a courtyard. Field measurements analysed the extent to which shading and misting systems enhance user comfort levels in a Seville courtyard. The results indicate a considerable decrease in temperature, with thermal mitigation of up to 11.7 °C below reference points outside the courtyard. The percentage of hours within comfort ranges during heat waves increased by 100% by optimizing the results of an additional experimental campaign and considering results from previous ones. The main novelty of this research lies in the comparative study of standalone and integrated passive cooling strategies aiming to provide optimal results in high temperature and heat wave scenarios. The study findings help understand and estimate the effectiveness of low-cost and easily applicable strategies for maintaining comfort conditions during diurnal and nocturnal cycles in both hot seasons and heat waves.

Performance of a novel structural insulated panel in tropical climates: Experimental and numerical studies

This article investigates experimentally and numerically the thermal performance of a new type of structural insulated panel (SIP) termed ‘UWall’. The panel core consists of an ad-hoc waffle skeleton of Expanded Polystyrene (EPS), EPS beads mixed with cement mortar (Portland cement type I and recycled concrete aggregate), and two external water-proof cement boards. The experimental programme examined the panels in three Phases. In Phase 1, 200×200×100 mm block specimens were exposed to a temperature of 70°C for 12 hrs. It was found that the new UWall specimens had a better thermal performance (by up to 20%) over other types of walls typically used in house construction in Southeast Asia such as Mon block wall. In Phase 2, five scaled-down house units (1.5×1.5×2.8 m) were built in Thailand. Temperature and relative humidity outside/inside the units were continuously monitored for 7 days during the summer season. It was found that the new UWall has a good thermal resistance, reducing temperatures by up to 4°C compared to mon-block wall material. The house units were subsequently modelled in Abaqus® software, and the modelling approach proved accurate (within 10%) at simulating the thermal performance of the house units. UWall panels were also tested in bending to determine their structural capacity. It was found that the panels can be safely used as load-bearing walls for single-storey houses. Design charts are then proposed and used to design and build a full-scale house in Phase 3. In Phase 3, a 10×7 m full-scale single-storey house was built using the new UWall panels. Based on results from bioclimatic charts, it was found that daytime temperature and humidity in the full-scale house were deemed as uncomfortable. However, if air movement was provided, the house remained within the comfortable zone at all times. New design charts for full-scale houses with UWall are proposed to meet a desired user-comfort level. This study is expected to promote the use of SIPs in house construction in tropical climates, which in turn is envisaged to save energy and make construction more sustainable.

Data-driven approaches to achieve energy transition: Statistical analysis of demand-side management and smart decision making in urban energy hub networks

Energy hubs (EHs) have emerged as a crucial solution to address resource scarcity and environmental concerns by integrating various technologies for energy production, conversion, and storage. These hubs encompass diverse elements such as integrated heat, electricity, and cooling systems, renewable energy sources, batteries, and thermal energy storage units (TES). This study focuses on a residential EH model that receives inputs in the form of electricity, gas, and solar energy to satisfy the household's electricity, heat, and cooling requirements. To enhance the operational flexibility of this energy hub, the study employs a Demand Response Program (DRP), which facilitates load shifting, load curtailment, and the modeling of flexible heating loads. The primary objective is to develop an efficient heat and power management system that optimally schedules household devices, generation equipment, and storage units. In pursuit of this goal, Long Short-Term Memory (LSTM) techniques have been selected for renewable energy, market price, and load forecasting, enhancing the system's realism and predictive accuracy. The Wild Goats algorithm is employed to solve an optimization problem for three distinct scenarios, aiming to minimize overall power costs while maintaining user comfort levels with regard to temperature and hot water availability. Furthermore, this study incorporates an environmentally conscious approach by considering the reduction of CO2, SO2, and NO pollution originating from consumers in a multi-objective optimization framework. The findings reveal that the implementation of DRP, intelligent Plug-in Hybrid Electric Vehicle (PHEV) management, TES technologies, and LSTM-based forecasting significantly contribute to reducing power costs within the proposed EH scheme. This research underscores the potential of data-driven strategies in advancing energy transition initiatives and fostering sustainable urban energy hubs while acknowledging the role of advanced forecasting techniques in enhancing system realism and efficiency.

NATURAL LIGHT INTENSITY AND VISUAL COMFORT THROUGH ADJUSTING THE HEIGHT OF THE CURTAIN OPENING

The type of activity, room area, and light intensity influence the visual comfort level of room users. In educational buildings, visual comfort influences the level of performance during the teaching and learning process of students. Students of the Bachelor of Architecture Study Program, FTSP, Trisakti University, use the Studio on the 6th floor (C.608-609) with 85% curtained glass window walls on the North and West, and 10% uncurtained bouvenlight windows on the South. It is estimated that the availability of natural light sources with such capacity is sufficient for the Studio learning process, but in fact users still turn on artificial lighting when working during the day. The hope is that by using curtained windows, students can regulate natural lighting needs to reduce the use of artificial lighting. Until this research was conducted, it was not known how high curtains met visual comfort standards. This research aims to create contour simulation of room’s natural light intensity C.608-609 using dialux kw06-288 software, in relation to adjust of curtains on windows with heights of 30 cm, 60 cm, 90 cm and 120 cm, and their impact on the user's feelings of visual comfort or discomfort. The results show that the distribution of natural light entering the room is not even and varied. According to SNI 03-6575-2001, the light intensity for a studio is 500-750 lux, but the measurement results show that at a curtain opening height of 120 cm, the light reach to the room with the furthest depth of 16.8m is not reached, so to achieve visual comfort the assistance of artificial lighting is needed, especially in the east-south of studio. Meanwhile, the area around the light source causes glare. Keywords: Light Intensity, Visual Comfort, Curtain Opening Height

Hubungan Desain Interior Terhadap Kenyamanan Pemustaka Di Perpustakaan Umum Kabupaten Solok

This research develops three main issues, namely how is the interior design condition in the Solok Regency Public Library, what is the comfort level of the users in the Solok Regency Public Library, is there any influence of the interior design of the library on the comfort level of the users in the Solok Regency Public Library. The purpose of this study was to determine the effect of the relationship between library interior design and the comfort level of users, the interior design includes layout, lighting, and temperature, with partial and simultaneous research. The type of research that researchers use is the type of associative research or relationship (influence) using a quantitative approach. This research was conducted at the Solok District Public Library. The population used in this study were users who visited the Solok District Public Library using the incidental sampling technique. The number of samples taken by the researchers totaled 87 samples. Data collection using questionnaires and questionnaires. Validity test, reliability test and product moment correlation test using IMB SPSS v20 program. The results of the study can be concluded that the r value of 0.667 is categorized as moderate and positive, meaning that there is a relationship between the interior design of the library and the comfort level of users and the results of the simple regression equation, Y = 79.415 + 0.659X. The two levels of influence between variable X and variable Y are 44%. So this can be interpreted that 56% of interior design is influenced by factors other than user comfort. With a regression coefficient b = 0.667 indicating the magnitude of the addition of the user's comfort level for interior design assessment. The results of hypothesis testing are done by t test with an error of 5%, then t table = 1.262 is obtained. If t count = 2.529 > t table = 1.262, then Ho is rejected and Ha is accepted, which shows that there is an influence of interior design on the comfort of users in the public library of Solok Regency.

Level of road user satisfaction with safety and confort on jalan Mohammad hatta

Mohammad Hatta Road is a type of district road that is used as a link between Kurik District and MalindiMalindiMalindi District. The increased activity and mobility of residents on Jalan Mohammad Hatta have not been balanced with safety and comfort for road users, such as damage to the road body and a lack of supporting facilities on the road. The purpose of this study was to determine the factors that influence the lack of safety and comfort levels of road users and the influence between satisfaction variables on safety and comfort variables on Jalan Mohammad Hatta. This research method uses the correlation test method. This test is used to find the relationship between variable X (satisfaction) and variable Y (safety and comfort) on Jalan Mohammad Hatta which has bad road conditions. The data used in this study were obtained by distributing questionnaires with a total sample of 394 respondents. The results of this study showed that people were dissatisfied a percentage of 83.61%, insecure with a percentage of 83.92%, and very uncomfortable with percentage of 84.01%, in terms of several factors, namely road conditions, stability of vehicles, health, travel effectiveness, economy, pavement structure, socio-cultural conditions, facilities, and accessibility. There is a relationship between the satisfaction variable and the safety variable with a correlation value of 0.905 and a relationship between the satisfaction variable and the comfort variable with a correlation value of 0.886. So it shows that the higher the level of safety and comfort, the higher the level of satisfaction of road users.

A sustainable approach for demand side management considering demand response and renewable energy in smart grids

The development of smart grids has revolutionized modern energy markets, enabling users to participate in demand response (DR) programs and maintain a balance between power generation and demand. However, users’ decreased awareness poses a challenge in responding to signals from DR programs. To address this issue, energy management controllers (EMCs) have emerged as automated solutions for energy management problems using DR signals. This study introduces a novel hybrid algorithm called the hybrid genetic bacteria foraging optimization algorithm (HGBFOA), which combines the desirable features of the genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) in its design and implementation. The proposed HGBFOA-based EMC effectively solves energy management problems for four categories of residential loads: time elastic, power elastic, critical, and hybrid. By leveraging the characteristics of GA and BFOA, the HGBFOA algorithm achieves an efficient appliance scheduling mechanism, reduced energy consumption, minimized peak-to-average ratio (PAR), cost optimization, and improved user comfort level. To evaluate the performance of HGBFOA, comparisons were made with other well-known algorithms, including the particle swarm optimization algorithm (PSO), GA, BFOA, and hybrid genetic particle optimization algorithm (HGPO). The results demonstrate that the HGBFOA algorithm outperforms existing algorithms in terms of scheduling, energy consumption, power costs, PAR, and user comfort.

A novel optimal framework for scheduling rooftop solar home appliances considering electricity, real pricing and user comfort

With an increase in electrification trends, the energy consumption of households is expected to increase significantly in the near future. To reduce electricity bills in the electrified era, management of the demand side is inevitable. Different demand response programs, such as real-time pricing can be introduced from the grid side. The consumption side needs to adjust its consumption patterns in accordance with the price signals. Therefore, an innovative home appliance scheduling method is proposed considering the rooftop solar panels as part of energy suppliers. First, an optimization problem is formulated considering different objectives such as energy price, consumer satisfaction, and peak-to-average load ratio. Then, home appliances are divided into three major categories (shiftable, flexible, and normally operated) to adjust schedules of different load types. Then, the developed model is solved using the bald eagle search optimization algorithm (BESOA). Finally, the performance of the proposed BESOA-based method is compared with other well-known heuristic/meta-heuristic methods. In addition, an analysis of different equipment’s maximal operation time is conducted for all the methods. In order to provide feedback on user comfort levels, digital twin technology is used allowing the user to adjust the scheduling of the appliances to ensure that they are comfortable. By leveraging digital twin technology, users would be able to optimize the scheduling of their rooftop solar home appliances to maximize efficiency and minimize costs. Simulation results have shown the superiority of the BESOA method over other methods in terms of daily average energy cost, peak-to-average load ratio, and consumer satisfaction.

IoT-based intelligent energy management system for optimal planning of HVAC devices in net-zero emissions PV-battery building considering demand compliance

To achieve the goal of carbon neutrality, renewable energy resources are now being widely deployed in the building sector, forming the hybrid energy system. For such hybrid energy system, the development of energy management system (EMS) is of great significance for their maximum utilization. In this study, an Internet of things-based (IoT-based) intelligent EMS is developed for a real net-zero emissions photovoltaic-battery (PV-battery) building, where its main purpose is to optimally schedule the heating, ventilation, and air conditioning (HVAC) devices in order to tradeoff between user comfort level and battery remaining energy. The developed EMS mainly consists of two modules, including data-driven forecasting module and mixed-integer programming module. The data-driven forecasting module is firstly developed to forecast future 24-h PV generation, baseload demands, and room thermal dynamic using several data-driven methods. Then, based on the forecast results, the optimal planning problem of HVAC devices is formulated as a multi-objective optimization problem, which is solved by the mixed-integer programming module. Moreover, a novel concept named as demand compliance is proposed, where the user-defined demand important coefficient is considered in the optimization problem. Comprehensive case studies are conducted to compare the developed EMS and existing rule-based control method. The experimental results demonstrated that the developed EMS could achieve similar user comfort level comparing with rule-based method, while its battery remaining energy was significantly higher. This study might provide a new perspective for the demand-side management program of net-zero emissions building.

Studies on level of service for a proposed cycle tracks in urban arterial roads under mixed traffic flow-A case study

In the present study, research is carried out on provision of cycle tracks in Tiruchirappalli. The study area comprises four roads within K.K Nagar, covering a distance of 5 km and surrounded by schools and residential buildings i.e., Simco Meter Road, Rajaram Salai Road, Kajamalai Road and Race Course Road. Traffic volumes during peak hours are measured in each road segment and video graphic studies, spot speed studies have been done for bicyclists and compared with IRC Standards. Preliminary geometric data were collected for all four roads and analyzed. The typical cross-sections of different roads have been shown and based on the requirement and space availability; the cycle tracks are proposed with and without shy distance including side walk wherever possible along with available widths based on IRC standards. The comfort levels of cycle users on road segment are found in terms of Bicycle Level-Of-Service (BLOS) for the considered study area. It is inferred that, LOS for the proposed geometric details with separate cycle track for bicyclists is better than the LOS of the bicyclists on the existing road.

Reinforcement Learning-Based Bi-Level strategic bidding model of Gas-fired unit in integrated electricity and natural gas markets preventing market manipulation

Due to its efficient operation and environment-friendly characteristic, gas-fired unit (GFU) plays a more and more important role in electric power systems and natural gas systems. To investigate the performance of GFU's participation in integrated electricity and natural gas markets, a bi-level strategic bidding model considering price and quantity factors is proposed. With the increasing participation of consumers in electricity markets, demand response (DR) management is implemented in the electricity market clearing process and user comfort level (UCL) is considered in the market clearing model. Since GFU participates in both the electricity market and the natural gas market, a local marginal price penalty (LMPP) variable is defined in this paper to prevent potential market manipulation (MM) of GFU. Then a modified reinforcement learning (RL)-based method is proposed to solve the model, combining deep deterministic policy gradient (DDPG) algorithm with autocorrelated noise. Test results on an integrated electricity-gas system show that the proposed method can reflect the strategic behaviors of GFU effectively. The proposed method has better performance than traditional DDPG algorithm with Gaussian noise and the Deep Q-Network (DQN) algorithm. And electricity markets with LMPP can save about 3.03% in generation cost by preventing MM of GFU.

Data-driven multi-layered intelligent energy management system for domestic decentralized power distribution systems

Energy management techniques have been immensely adopted in residential, industrial, and commercial sectors to meritoriously flatten the load profile of consumers. In this article, a cutting-edge intelligent energy management system is proposed to analytically monitor and regulate the load demand of a residential area to meritoriously address the load balancing and scheduling optimization problem with an aim to design an autonomous domestic complex, less reliant on the focal power grid. To do so, single- and multi- objective functions are defined for the presented double-layered framework. In the first layer, a day-ahead scheduling problem is evaluated to achieve the mutual revenue of electricity utilities and consumers, while in the second layer, a real-time coordination scheme for robust energy transactions among appliances, multiple microgrids, and the main power system is examined. Herein, a 0−1 multiple knapsack problem incorporated with the mixed-integer linear programming method is used to formulate the system mathematical model. To evaluate the intended system model, three heuristic algorithms (genetic algorithm, wind-driven optimization, and their hybrid) are modified. Furthermore, the Monte–Carlo simulations are performed to mitigate the impact of randomness and avoid pre-convergence. From the results of extensive simulations, it is corroborated that the proposed methodology is robust, reliable, and eco-social compared to prior schemes. This article reveals that the hybrid approach outperformed the other two strategies in terms of energy consumption, electricity cost, peak-to-average ratio, and user comfort level.

Real-time energy management in an off-grid smart home: Flexible demand side control with electric vehicle and green hydrogen production

A real-time energy management system for an off-grid smart home is presented in this paper. The primary energy sources for the system are wind turbine and photovoltaics, with a fuel cell serving as a supporting energy source. Surplus power is used to generate hydrogen through an electrolyzer. Data on renewable energy and load demand is gathered from a real smart home located in the Yildiz Technical University Smart Home Laboratory. The aim of the study is to reduce hydrogen consumption and effectively utilize surplus renewable energy by managing controllable loads with fuzzy logic controller, all while maintaining the user's comfort level. Load shifting and tuning are used to increase the demand supplied by renewable energy sources by 10.8% and 13.65% from wind turbines and photovoltaics, respectively. As a result, annual hydrogen consumption is reduced by 7.03%, and the average annual efficiency of the fuel cell increases by 4.6%

Correlates of Acoustic and Visual Comforts in Selected Lecture Theaters in Ladoke Akintola University of Technology, Ogbomoso, Nigeria

The challenge of having good acoustics and visual comfort in lecture theatres was explored in the study. The aim was to evaluate the visual and acoustic comfort levels of users in lecture theatres with a view to establishing their significance in future designs. The study selected seven (7) lecture theatres representing 58.33% out of a total of 12 in Ladoke Akintola University of Technology Ogbomoso as the sample size using simple random sampling techniques. Capacity of each lecture theatres were determined and questionnaires were randomly administered on 4% of the total Users in each. The questionnaire was to determine the hearing audibility levels, visual clarity levels and general comfort/satisfaction levels of users at periods they were receiving lectures. A total of 248 questionnaires were administered. Sources of noise in the lecture theatres were also determined through questionnaire administration. Data were descriptively and inferentially analysed. The study found furniture movements as the major source of noise in the lecture theaters studied. Also, it was realized that there was a significant correlation between users’ satisfaction levels and each of visual clarity and hearing audibility (acoustic comfort) despite that users indicated that it was possible for them to hear speaker’s speeches without seeing the board; It was hence suggested that determinant factors of these two criteria such as shape of buildings, sizes/volume and sources of noise should be critically put into consideration at design inception stages. Other recommendations that could foster appropriate synergy between acoustic and visual comforts in Lecture Theatres were given.

Development of a Management System to Improve the Energy Efficiency of Public Buildings by Integrating IoT and BIM

Question: How can the building information models be more dynamic using IoT, so that it improves the comfort level of end-users in educational settings? Purpose: A major challenge for using BIM during the building’s life cycle, and especially at the post-occupancy stage is the lack of live information in the model, which reflects the need for a dynamic model. The requirements for quality control of the interior of buildings and ensuring the comfort of residents, especially in the educational environment due to their high importance in providing comfort, improving productivity, and lean maintenance, have increased. The lean maintenance process structure is designed based on five lean principles, which guide and support organizations to pursue maintenance excellence and improve the level of comfort and energy. Research Method: This study provides a solution that uses environmental data collected through Internet of Things (IoT) sensors to make the model dynamic. This is done by entering the live data into the building information model of a building. For this purpose, a web-based platform is designed that provides the possibility of displaying the level of comfort and control recommendations to improve the level of comfort. Findings: This study shows that temperature and humidity are the two most effective parameters for the level of comfort. The dynamic building model of the institutional building used in the case study is then created and the level of comfort of its users is evaluated. Limitations/Implications: One of the limitations of this research is the costly and time consuming preparation and installation of sensors because just temperature and humidity were selected as the effective environmental parameters to collect the environmental parameters affecting the comfort level of users of an educational setting and entering this data into the building information model. Value for authors: This study improves the comfort level of institutional project users by monitoring live information on a web-based platform significantly. Keywords: Building Information Modeling (BIM), Internet of Things (IoT), User Comfort, Educational Environments, Institutional Projects, Dynamic Model Paper type: Full paper

Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuning

Abstract Predicting the most favorable traveling routes for Vehicles plays an influential role in Intelligent Transportation Systems (ITS). Shortest Traveling Routes with high congestion grievously affect the driving comfort level of VANET users in populated cities. As a result, increase in journey time and traveling cost. Predicting the most favorable traveling routes with less congestion is imperative to minimize the driving inconveniences. A major downside of existing traveling route prediction models is to continuously learn the real-time road congestion data with static benchmarking datasets. However, learning the new information with already learned data is a cumbersome task. The main idea of this paper is to utilize incremental learning on the Hybrid Learning-based traffic Congestion and Timing Prediction (HL-CTP) to select realistic, congestion-free, and shortest traveling routes for the vehicles. The proposed HL-CTP model is decomposed into three steps: dataset construction, incremental and hybrid prediction model, and route selection. Firstly, the HL-CTP constructs a novel Traffic and Timing Dataset (TTD) using historical traffic congestion information. The incremental learning method updates the novel real-time data continuously with the TDD during prediction to optimize the performance efficiency of the hybrid prediction model closer to real-time. Secondly, the hybrid prediction model with various deep learning models performs better by taking the route prediction decision based on the best sub-predictor results. Finally, the HL-CTP selects the most favorable vehicle routes selected using traffic congestion, timing, and uncertain environmental information and enhances the comfort level of VANET users. In the simulation, the proposed HL-CTP demonstrates superior performance in terms of Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).