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The public transportation system is now dealing with a number of problems brought on by the sharp increase in automobile ownership in cities as well as the buildup of vehicles as a result of events and accidents. However, the city’s limited road network capacity cannot keep up with the increasing traffic demand, which further worsens travel conditions and results in a waste of time and money. Given that it is challenging to enhance the capacity of the road network in practice, efficient vehicle travel and evacuation using algorithms has emerged as a recent study focus. It is crucial to learn how to manage urban traffic issues during emergencies and maintain smooth and safe traffic flow. The existing studies only consider the optimized route selection for individual vehicles, signal cycle of traffic lights and deploy historical data to disperse the vehicles on alternative routes. However, such works do not consider the conflict of routes between vehicles, the customized traffic demand of each vehicle and uncertain traffic conditions. Therefore, this paper proposes a novel approach to facilitate the user to select the optimal route with real-time traffic scenario. Furthermore, the Nash equilibrium is established by mutual information swapping and self-adaptive learning method. Simulation results show that the proposed algorithm has better route selection capability in real-time personalized road traffic as compared with existing algorithms.

With the significant rise in demand for network utilization, such as data transmission and device-to-device (D2D) communication, fifth-generation (5G) networks have been proposed to fill the demand. Deploying 5G enhances the utilization of network channels and allows users to exploit licensed channels in the absence of primary users (PUs). In this paper, a hybrid route selection mechanism is proposed, and it allows the central controller (CC) to evaluate the route map proactively in a centralized manner for source nodes. In contrast, source nodes are enabled to make their own decisions reactively and select a route in a distributed manner. D2D communication is preferred, which helps networks to offload traffic from the control plane to the data plane. In addition to the theoretical analysis, a real testbed was set up for the proof of concept; it was composed of eleven nodes with independent processing units. Experiment results showed improvements in traffic offloading, higher utilization of network channels, and a lower interference level between primary and secondary users. Packet delivery ratio and end-to-end delay were affected due to a higher number of intermediate nodes and the dynamicity of PU activities.

An appropriate road network imposes on planners take into account factors such as land use, slope, soil type, hydrology, and agricultural area. Due to various considerations and desires, the planning process is difficult hence there may be confusion in interest in the decision-making process. The use of a geographic information system (GIS) and Multi-Criteria Decision Analysis (MCDA) assist planners in achieving more detailed and desirable results. Thus, reducing the complexity of the planning process and helping various stakeholders for drawing to general conclusion. The study site was chosen on an area between the cities of Ramadi and Heet in Anbar Province, western Iraq, where it suffers from congestion and traffic accidents. This research aims to integrate a set of evaluation criteria using the Analytical Hierarchy Process (AHP) and a spatial multicriteria model to find the optimal path in the study area. In this study, two alternate paths were proposed and compared with the current path to find the best path. Finally, the results indicated that the first alternative is 36% better. This research succeeded in proving that it is possible to decide a rural highway route between two cities using GIS and MCDA.

Unstable Mobile nodes in the network does not maintain the accuracy of data transmission at the maximum level since the node’s characteristics are updated, then nodes receive data’s are intruded, its packet information is missed. Since that time, congestion is made for current routing path, so consider that path is a failure, also provide re transmission. It occupies more energy, and packet drop rate. In proposed Enhanced data Accuracy based Path Discovery (EAPD) technique is used to provide transmitting and receiving data has higher accuracy. It verifies the every node communication in routing path has maximum data accuracy, they are selected, otherwise, communication data have minimum data accuracy is rejected. The backing route selection algorithm is constructed to avoid intrusion for communication period; it’s discovering the path, which does not lose the data from packets, since congestion is easily identified. It reduces energy consumption, and packet drop rate.

PurposeThe development of urban underground complexes (UUCs) has great positive significance for improving urban safety. Therefore, it is necessary to identify the key factors of the people's behavior of evacuation route selection (BERS) for fire emergencies and UUCs’ development. This study aims to find out the factors affecting people's BERS in the evacuation process of UUCs.Design/methodology/approachThis study aims to find out the factors affecting people's BERS in the evacuation process of UUCs. To achieve this goal, the authors conducted a field experiment in F City. Furthermore, the people's BERS are obtained by using a structural equation model and compared with the field test results.FindingsThe authors found that the key factors for people's BERS are lighting conditions, route distance, flow direction guidance and indication. The results of this study contribute to the safety field by providing key factors for fire emergencies. It can also be used to improve fire safety management, evacuation strategies and assist in the development of intelligent evacuation systems.Originality/valueThe results of this study contribute to the safety field by providing key factors for fire emergencies. It can also be used to improve fire safety management, evacuation strategies and assist in the development of intelligent evacuation systems.

Mobile ad hoc network (MANET) is the collection of mobile nodes, which might act as the hosts and routers in an ad hoc network. They can self-organize dynamically without using any pre-established communications. Mobile nodes alter their position based on certain constraints while transmitting the packets. Here, the location or position-based routing is essential to transmit the packet which intends to enhance the network lifetime. This paper proposes the implementation of a new geographic routing protocol in MANET, where the optimal route selection process is carried out by Hybrid Roxes Whale Optimization (HRWO) algorithm that hybridizes the Whale Optimization Algorithm (WOA) and Rock Hyraxes Swarm Optimization (RHSO) algorithm. Moreover, the optimal routing is done by considering several parameters like the average distance between nodes (mobility-related information based on the location), delay, link lifetime, risk evaluation, and packet loss, respectively. At the end, the performance of the adopted routing approach is evaluated over other traditional schemes based on various metrics like average distance, packet loss, delay, risk, and link lifetime, respectively.

The dynamic nature of the Mobile Adhoc network (MANET) is regarded as the primary issue when routing. In MANET, Routing protocol and optimal route selection from various paths are identified for efficient routing. The major goal of this research is the shortest path determination for data transfer in a MANET. This research presents an Energy Efficient-Modified African Vulture and Modified Mayfly (E-MAVMMF) method that contains two sections namely cluster head selection and optimal route selection. In the first stage, the cluster head is selected by using the Modified African Vulture optimization algorithm (AVOA) in which Brownian motion is used so that the effectiveness of AVOA is improved and in the second stage, the optimal route is chosen using a Modified Mayfly algorithm (MMF), which is enhanced by using a modified mutation phase of Symbiotic Organism Search (SOS), which aids in obtaining the global best solution. The suggested approach is built on the NS2 platform. Various performance metrics, such as the delivery ratio of the packet, loss rate of the packet, energy, throughput, etc. are evaluated. The observed outcome shows that the suggested method E-MAV: MMF is more efficient when compared with the existing approach ANFIS-based Group teaching optimization algorithm (O-AeGTA-MANET).

A smart grid is a power distribution network that utilizes information and communication technologies to manage, track, and direct the flow of information between power generators and consumers. Only with dependable communication networks can a smart grid provide a wide range of electrical services while simultaneously streamlining and optimizing energy consumption. In a smart grid network, the Advanced Metering Infrastructure (AMI) sensor nodes detect, analyze, and communicate data; all of this activity necessitates energy, a finite resource that is crucial for the network's upkeep over time. Wireless mesh networks have the same trust issues that plague conventional distributed ad hoc networks. The proposed model considers the multi level trust models for the nodes for mitigating energy fluctuations. This research considers an Energy Efficient Multi Level Trust Model for Multi Route Selection with Rank based Route Clusters (EEMLTM-MRS-RRC) in Smart Grid that maintains multiple routes by considering the trust factors. The proposed model calculates the trust factor of nodes in smart grid by considering the internal and externals factors. The proposed model selects a cluster head node for analyzing and monitoring the internal and external factor of nodes in the network. The proposed model achieved 98.5% accuracy in Energy Consumption Reduction and 98.6% accuracy in Trusted Route Selection. The proposed model, when contrasted with traditional routing models, performs better in energy consumption reduction and route maintenance.

This study aims to analyze the selection of the A.P. Pettarani Elevated Toll Road route. Data collection was carried out by distributing questionnaires and reviewing respondents to describe the travel characteristics of A.P. Pettarani Elevated Toll Road respondents using Ms. Excel to process the data. The results showed that in the selection of A.P. Pettarani elevated toll road to the airport, and the dominant respondents entered the toll road through the Bakti road where the traveler was aged 50-59 years old, with the last education being a bachelor’s degree, working as an officer, with an income of IDR 4.5M-5.5M. The travel costs incurred are IDR20-30K, travel distance of 7-11 km, travel time of 10-20 minutes, and travel frequency of 15-20 times a month to travel for work/business. In the selection of travel routes using the A.P. Pettarani elevated toll road to the port, the dominant respondents entered the overpass toll road through the Rappocini road, where the traveler was aged 20-29 years old, with the last education being a bachelor’s degree, working as an officer, with an income of IDR 4.5M-5.5M. The travel costs incurred are IDR20-30K, travel distance of 7-11 km, travel time of 20-30 minutes, and travel frequency of 15-20 times to travel for work/business.

A mobile ad hoc network (MANET) is a network of moving nodes that can interact with one another without the aid of a centrally located infrastructure. In MANETs, every node acts as a router and as a host, generating and consuming data. However, due to the mobility of nodes and the absence of centralized control, the routing process in MANETs is challenging. Therefore, routing protocols in MANETs are required to be efficient, scalable, and adaptable to the dynamic topology changes of the network. This paper proposes an optimized route selection approach for MANETs via the biomimicry buzz algorithm with the Bellman-Ford-Dijkstra algorithm to improve the effectiveness and accuracy of the routing process. By integrating these behaviors into the algorithm, the approach can select the shortest path in a network, leading to an optimal routing solution. Furthermore, the paper explores the use of Forest Boost Regression (FR), a novel machine learning algorithm, to predict energy consumption in MANETs. Utilizing this will help the network run more efficiently and last longer. Additionally, the paper discusses the use of Artificial Neural Networks (ANNs) to forecast link failure in MANET s, thereby increasing network performance and dependability. The proposed work presents the experimental evaluation by using Ns-3 as the simulation tool. The experimental results indicate a variation in packet delivery ratio from 97% to 90%, an average end-to-end delay of approximately 19 ms, an increase in node speed energy consumption from 60 to 87 joules, and a simulation time energy consumption of 89 joules over 60 seconds. These results provide insights into the performance and efficiency of the proposed strategy in the context of MANETs.