Route Selection Research Articles

Attitudes, Time Pressure, and Behavior Change Techniques Affect Route Journey Planning Decisions: Evidence from an RCT

Transport emissions are a major contributor to global CO2 emissions, requiring interventions to promote sustainable travel behaviors. This study examines how behavior change techniques (BCTs), attitudinal and behavioral segmentation, and time pressure influence green route selection in a simulated journey-planning app. Using a randomized 2 × 3 × 3 factorial design, 600 UK participants completed travel booking tasks under three time-pressure scenarios (low, moderate, high) using either a control app or a BCT-enhanced intervention app. Participants were segmented based on environmental attitudes, public transport preferences, and travel needs. Multilevel logistic regression showed significant main effects for condition, segment, and time pressure. Participants using the intervention app were more likely to select green routes (5.39, p < 0.001). Segments with a more positive attitude to public transport demonstrated higher baseline green route selection compared to those with low public transport attitudes (odds ratio [OR] = 0.31, p = 0.020). Moderate time pressure facilitated the highest likelihood of green route selection, while low (OR = 0.16, p < 0.001) and high (OR = 0.48, p < 0.001) time pressures reduced green bookings. Interaction effects were non-significant, potentially reflecting the sample size. The findings highlight the potential of BCT-enhanced apps to promote sustainable travel, particularly when tailored to user segments and designed to address time pressure. Future research should explore real-world applications and intervention durability.

Cognitive Fish Swarm Optimization for Multi-Objective Routing in IoT-based Wireless Sensor Networks utilized in Greenhouse Agriculture

This research presents the working mechanism of Cognitive Fish Swarm Optimization (CFSO) for multi-objective routing and channel selection in Internet of Things (IoT)-based Wireless Sensor Networks (IWSNs). CFSO is inspired by the collective intelligence and cooperation observed in fish swarms. The model involves three main components: perception, cognition, and behavior. Each fish in the swarm perceives the network conditions by gathering information from its surrounding environment, including signal strength, channel availability, and network congestion. The fish then utilizes its cognitive abilities to evaluate different routing paths and channel options based on specific objectives, namely energy efficiency, packet delivery ratio, and delay. This evaluation process involves analyzing historical information and utilizing heuristics to create notified results. Each fish adapts its behavior by adjusting its movement pattern and selecting optimal routing paths and channels. This adaptive behavior is critical for achieving reliable and efficient data transmission in IWSNs. The fish swarm balances exploration and exploitation strategies to search for optimal solutions comprehensively. Exploration allows for discovering new paths and channels, while exploitation focuses on refining the best-known solutions. The efficiency of the CFSO method in enhancing data transmission efficiency in greenhouse agriculture applications was validated through extensive simulations in the NS-3 network simulation framework. The findings suggest that the CFSO method is a promising technique for addressing routing and channel selection challenges in IWSN by leveraging the collective intelligence of fish swarms. The CFSO model portrayed a superior throughput and Network Lifetime (NLT) values of 71.34% and 77.20%, respectively, significantly outpacing SSEER and CRP across overall node counts.

Barriers and Benefits: Understanding Riders’ Views on Pooled Rideshare in the U.S

This manuscript provides actionable recommendations to enhance user satisfaction and address existing barriers regarding pooled rideshare (PR) in the United States. Despite PR’s intended benefits, such as reduced traffic congestion and cost savings, its adoption remains limited. To identify these actionable items, a U.S. nationwide survey with 5385 participants explored transportation preferences, barriers, and motivators for PR use in the summer of 2021. First, two factor analyses were conducted. The first factor analysis identified the five factors associated with one’s willingness to consider PR (time/cost, traffic/environment, safety, privacy, and service experience). The second factor analysis revealed the four factors related to ways to optimize one’s PR experience (comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety). Privacy concerns, for instance, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. A structural equation model evaluated the relationships among these nine key factors influencing PR usage to develop the Pooled Rideshare Acceptance Model (PRAM). The privacy, safety, trust service, and convenience factors each had a significant large effect (Cohen’s f2 > 0.35) on the model. PRAM was extended using multigroup analyses to reveal the nuanced impact of 16 demographics, including gender, generation, rideshare experience, etc., highlighting the need for tailored strategies to improve PR acceptance through the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMAs). Multiple workshops were held with diverse audiences to translate the team’s findings to date into 84 actionable recommendations, categorized across topical areas like safety, routing, driver and passenger selection, user education, etc. These findings are a foundation for a future study to determine which items resonate with different user groups. In the meantime, the actional items serve as a user-driven resource for policymakers, transportation network companies, and researchers, offering a roadmap to potential improvements to PR services to address existing concerns with the goal of increasing the usage of PR.

Route Optimization for Motorcycle Riders Using A* Algorithm to Enhance Fuel Efficiency

This research focuses on the development of a route optimization application designed to help motorcycle users reduce fuel consumption. Leveraging the Google Maps API and the A* algorithm, the application identifies the most efficient routes by analyzing factors such as road distance, traffic conditions, and elevation. The system is tailored to benefit motorcyclists, both those using carburetors and fuel injection systems, who frequently face fuel inefficiency due to improper route selection. By integrating real-time data from Google Maps, the application provides users with optimal route recommendations that not only minimize fuel usage but also reduce travel time. The A* algorithm was selected due to its high efficiency in determining the shortest paths by considering multiple variables simultaneously. Through simulations and testing, the system demonstrates significant potential to improve navigation efficiency, particularly for long-distance commuters. This solution is expected to contribute to cost savings and more environmentally friendly transportation choices.

Optimal routing and request selection for multiple service routes in a demand-adaptive transit system

Optimal routing and request selection for multiple service routes in a demand-adaptive transit system

Analyzing Travel and Emission Characteristics of Hazardous Material Transportation Trucks Using BeiDou Satellite Navigation System Data

Road hazardous material transportation plays a critical role in road traffic management. Due to the dangerous nature of the cargo, hazardous material transportation trucks (HMTTs) have different route selection and driving characteristics compared to traditional freight trucks. These differences lead to unique travel and emission patterns, which in turn affect traffic management strategies and emission control measures. However, existing research predominantly focuses on safety aspects related to individual vehicle behavior, with limited exploration of the broader travel and emission characteristics of HMTTs. To bridge this gap, this study develops a comprehensive framework for analyzing the travel patterns and emissions of HMTTs. The methodology begins by applying a Gaussian mixture distribution model to identify vehicle stop points, eliminating biases associated with subjective settings. Origin–destination (OD) pairs are then determined through stop time clustering, followed by the extraction of travel characteristics using non-negative matrix factorization. Emissions are subsequently calculated based on the identified trip data. The relationship between emissions and land use characteristics is further analyzed using geographically weighted regression (GWR). Crucially, this study leverages data from the BeiDou Satellite Navigation System, focusing on HMTTs operating within Shanghai. The processed data reveal three distinct travel modes of HMTTs, categorized by spatiotemporal patterns: Daytime—Surrounding cities, Early morning—In-city, and Midnight—Scattered. Moreover, unlike other road vehicles, HMTT emissions are heavily influenced by industrial and company-related points of interest (POIs). These findings highlight the significant role of BeiDou Satellite Navigation System data in optimizing HMTT management strategies to reduce emissions and improve overall safety.

Optimization in Routing and Vehicle Selection for E-commerce Last Mile Logistics: Bibliometric Analysis

Optimization in Routing and Vehicle Selection for E-commerce Last Mile Logistics: Bibliometric Analysis

An Energy-Based Model for the Micro-Simulation of a Synthetic Population of Free Cyclists

Recent trends in mobility and transportation underscore the growing importance of promoting alternative, flexible, and environmentally friendly modes of transport—such as cycling—that not only contribute significantly to users’ health and well-being but also enable urban concepts like the 15-minute city. For cyclists, travel time is a critical factor influencing both route selection and the decision to choose cycling as a preferred mode of transportation. This paper presents an energy-based model of a synthetic population of free cyclists to analyze their speed profile characteristics, summarized in terms of average speeds. The proposed model is intended for use in evaluating infrastructure planning, optimizing green wave traffic light systems, and assessing the health benefits of cycling. The model is built on general data (not fitted to a certain dataset) and accounts for key factors such as rolling resistance and aerodynamic drag—both of which vary with ambient temperature—along with acceleration, road gradient, and other influences, including free rolling and deceleration or stopping at intersections and traffic lights. The results reveal a distribution of cycling speeds that show good agreement with field observations.

Research on Robot Membrane Material Selection and Distribution Route Optimization on College Campuses

An e-commerce logistics park, as a hub for e-commerce logistics, possesses the traits of high task volume and intensive package delivery. It is inefficient and dangerous to rely solely on manual work. Thus, there is some practical significance and benefit in figuring out how to implement cutting-edge delivery techniques to boost the delivery effectiveness of e-commerce logistics parks. This study combined the features of the e-commerce logistics and distribution environment, identified and eliminated the three robot types that are currently suitable for intelligent distribution, built an evaluation index system to assess the robot model selection scheme appropriate for e-commerce logistics and distribution. To assess the robot model selection strategy, we built an entropy weight-material element expansion model and computed the weighted correlation degree and proximity to various grades of the three robot kinds and five assessment grades, as well as the degree of proximity with various grades, determined the distribution robot model. Following example verification, the research presented in this paper can successfully conduct a reasonable and scientific evaluation of the distribution robot selection and can offer specific references for the more thorough decision-making process of robot selection for logistics and distribution in e-commerce parks.

Joint design of routing selection and user association in multi-hop mmWave IABN: a multi-agent and double DQN framework

Joint design of routing selection and user association in multi-hop mmWave IABN: a multi-agent and double DQN framework

Hybrid Archimedes and Arithmetic Optimization Algorithm for Cluster Head Selection and Multipath Routing in Wireless Sensor Network

Hybrid Archimedes and Arithmetic Optimization Algorithm for Cluster Head Selection and Multipath Routing in Wireless Sensor Network

A traffic prediction method for missing data scenarios: graph convolutional recurrent ordinary differential equation network

Traffic prediction plays an increasingly important role in intelligent transportation systems and smart cities. Both travelers and urban managers rely on accurate traffic information to make decisions about route selection and traffic management. Due to various factors, both human and natural, traffic data often contains missing values. Addressing the impact of missing data on traffic flow prediction has become a widely discussed topic in the academic community and holds significant practical importance. Existing spatiotemporal graph models typically rely on complete data, and the presence of missing values can significantly degrade prediction performance and disrupt the construction of dynamic graph structures. To address this challenge, this paper proposes a neural network architecture designed specifically for missing data scenarios—graph convolutional recurrent ordinary differential equation network (GCRNODE). GCRNODE combines recurrent networks based on ordinary differential equation (ODE) with spatiotemporal memory graph convolutional networks, enabling accurate traffic prediction and effective modeling of dynamic graph structures even in the presence of missing data. GCRNODE uses ODE to model the evolution of traffic flow and updates the hidden states of the ODE through observed data. Additionally, GCRNODE employs a data-independent spatiotemporal memory graph convolutional network to capture the dynamic spatial dependencies in missing data scenarios. The experimental results on three real-world traffic datasets demonstrate that GCRNODE outperforms baseline models in prediction performance under various missing data rates and scenarios. This indicates that the proposed method has stronger adaptability and robustness in handling missing data and modeling spatiotemporal dependencies.

A Load‐Balancing Enhancement to Schedule‐Aware Bundle Routing

ABSTRACTDelay‐ and disruption‐tolerant networking (DTN) enables communication in networks afflicted by long propagation delays and sporadic connectivity. DTN routing techniques such as schedule‐aware bundle routing (SABR) exist to route data bundles in deterministic networks, such as those found in deep‐space environments, where node contacts are predictable. This article begins with an overview of DTN architecture and SABR. SABR's method of final route selection (forwarding rules) is closely examined. The article then addresses a limitation of SABR whereby the algorithm may overlook parallel channels, leading to network congestion. To mitigate this, an enhancement is proposed. This enhancement aims to optimize data bundle distribution across candidate routes in networks with parallel channels, thus alleviating congestion and enhancing overall network performance. This is achieved with simple modifications to SABR's forwarding rules to avoid the concentration of data bundles on a minority of node contacts. The enhancement is demonstrated through simulations in a reference scenario implemented in DtnSim.

Micro-ring wavelength selective switch device based on contra-DC

This paper presents, to our knowledge, a novel micro-ring wavelength selective switch device based on a contra-directional coupler (contra-DC), which utilizes the transparent window formed by the contra-DC to select the single resonant wavelength of the micro-ring resonator, avoiding the limitation of free spectral range and realizing the function of wavelength selection/switching. The desired wavelength switching function is achieved by changing the crystal state of the Ge2Sb2Te5 (GST) loaded on the micro-ring resonator. When the device is in the on state, the desired wavelength signal is selectively output from the drop port of the bus waveguide; when the device is in the off state, light is output from the through port. By modifying the GST crystal state, selective routing of light can be achieved, which can be used in optical interconnection networks. The simulation is performed using a 3D finite-difference method. The wavelength selection device achieves a 3 dB bandwidthof 1.6 nm with a side lobe suppression ratio of 20 dB at a length of 230 µm; the switching device achieves the extinction ratio of about 14.7 dB and a side lobe suppression ratio of 18.2 dB between the on/off states.

Methodology for Providing Priority to Urban Passenger Transport Traffic

The article considers the efficiency of providing priority conditions for urban passenger transport by allocating separate lanes for public transport on the roadway. The results of modelling the traffic flow on the considered segments of the city street network are complemented by a conclusion about the need to improve road traffic management on these segments, considering the intensity and composition of the traffic flow.Simulation allows creating mathematical or computer models for analysing and assessing the traffic conditions of urban passenger transport. The models are based on various parameters of traffic flows, the schedule of urban passenger transport, the location of public transport stops, etc. Modelling allows conduct of different experiments to assess the effectiveness of various measures and changes in the transport system.The objective of the study is to develop a methodology for analysing traffic conditions that will identify and assign priority to urban passenger transport traffic to achieve efficiency and comfort of its operation, improve transport accessibility and reduce traffic delays.Optimisation of the traffic provides for various management scenarios to find optimal solutions that reduce delays, congestion, traffic intensity, etc.Route planning is necessary for developing an optimal strategy for efficient passenger service. Demand forecasts make it possible to predict not only passenger traffic, but also the need for transportation in different periods, which is necessary for route planning and selection of optimal rolling stock features. All these areas of modelling the urban passenger transport traffic are developed to achieve the goals of increasing efficiency, reducing the risk of road traffic accidents, improving the quality of transport services and the safety of passenger transportation, which should create a sustainable system aimed at satisfying the basic needs of urban transport passengers.

Systemic Clodronate Liposome Treatment for Depletion of Blood-Contacting Phagocytes and Investigation of Their Role in the Control of Bunyavirus Viremia.

During viral infection, phagocytic cells participate in numerous immunological processes. A common approach to elucidate the specific contributions of phagocytic cells is through direct comparison of viral pathogenesis of phagocyte-depleted and phagocyte-intact animals. Clodronate liposomes are a versatile and simple means of depleting phagocytes in any animal model. Selection of clodronate liposome route of delivery can yield systemic or local phagocyte depletion as needed. Here we describe the application of clodronate liposome-mediated depletion of blood-contacting phagocytes to investigate their interactions with La Crosse virus (or other bunyaviruses) in a reductionist model of viremia.

Vitamin D Supplementation in Critically Ill-Narrative Review.

Studies have shown a high prevalence of vitamin D deficiency in critically ill patients, and these patients are at higher risk for pneumonia and have increased incidence of sepsis and mortality. In this study, we reviewed available literature from randomized controlled trials (RCTs) on vitamin D supplementation in critically ill patients and summarized the evidence in this narrative review. Randomized controlled trials that included vitamin D supplementation as an intervention were eligible for inclusion. No limits were set regarding vitamin D dosage or route of administration, as well as for primary and secondary outcomes. A search was conducted in MEDLINE via PubMed for eligible RCTs. References from systematic reviews (SRs) and meta-analyses (MAs) were screened, and Clinicaltrials.gov was searched for ongoing studies. A total of 21 RCTs involving 3166 patients were analyzed. There was a large heterogeneity in terms of patients' characteristics and inclusion criteria. Only six studies included patients with vitamin D levels < 50 nmol/L. Regarding clinically important outcomes, most of the studies did not show differences between the intervention and control group in terms of mortality, intensive care unit (ICU) or hospital length of stay (LoS). There is great variability in trial designs regarding the selection of patients, dosage, dosing intervals and routes of administration of vitamin D supplements. Better study designs are mandatory for future clinical research, with measuring and reporting basal vitamin D levels before randomization. Since variability in supplementation regimes limits the possibility of data synthesis, standardized protocols for vitamin D supplementation should be used in clinical trial settings.

Hybrid Mexican axolotl and bitterling fish optimization algorithm-based spectrum sensing multi‑hop clustering routing protocol for cognitive sensor networks

In clustered cognitive radio sensor networks (CRSNs), availability of free channels, spectrum sensing and energy utilization during clustering and cluster head (CH) selection is essential for fairness of time and event-driven data traffic. The existing multi-hop routing protocols in CRSNs generally adopt a perfect spectrum sensing which is not same in the practical spectrum sensing of nodes in real networks. High imbalance in residual energy between the selected CHs negatively impacts the delivery of data packets. Hence, hybrid mexican axolotl and bitterling fish optimization algorithm-based spectrum sensing multi-hop clustering routing protocol (HMABFOA) is proposed as an imperfect spectrum sensing approach for achieving better utilization of downlink energy harvesting and sustain maximized degree of energy between the nodes in the network. This HMABFOA scheme reduces the negative impact of imperfect spectrum sensing for extended network lifetime which sustains the capabilities of the network surveillance. It helped in constructing a distributed cluster with multi-hop routing selection between clusters depending on a energy level function that explores and exploits the factors associated with CHs selection. The merits of Mexican axolotl optimization algorithm (MAOA) is used for better CH selection and cluster formation with energy stability is sustained in the network. Further bitterling fish optimization (BFOA) algorithm is used for optimized multi-hop route between the clusters with minimal energy consumption and maximized spectrum sensing that proves better channels availability. The simulation results guaranteed maximized network lifetime of 24.38%, spectrum utilization rate of 24.58%, and minimized energy utilization of 25.62%, better than the baseline approaches.

A Two-Stage Approach for Secure Node Localization and Optimal Route Selection for Enhanced Performance in Wireless Sensor Networks

In Wireless Sensor Network (WSN), node localization is a crucial need for precise data gathering and effective communication. However, high energy requirements, long inter-node distances and unpredictable limitations create problems for traditional localization techniques. This study proposes an innovative two-stage approach to improve localization accuracy and maximize route selection in WSNs. In the first stage, the Self-Adaptive Binary Waterwheel Plant Optimization (SA-BWP) algorithm is used to evaluate a node’s trustworthiness to achieve accurate localization. In the second stage, the Gazelle-Enhanced Binary Waterwheel Plant Optimization (G-BWP) method is employed to determine the most effective data transfer path between sensor nodes and the sink. To create effective routes, the G-BWP algorithm takes into account variables like energy consumption, shortest distance, delay and trust. The goal of the proposed approach is to optimize WSN performance through precise localization and effective routing. MATLAB is used for both implementation and evaluation of the model, which shows improved performance over current methods in terms of throughput, delivery ratio, network lifetime, energy efficiency, delay reduction and localization accuracy in terms of various number of nodes and rounds. The proposed model achieves highest delivery ratio of 0.97, less delay of 5.39, less energy of 23.3 across various nodes and rounds.

Application of Big Data Analytics to Determine Traffic Congestion

This study focused on the application of big data analytics to estimate roadway congestion. The implementation of big data allows a convenient approach of data collection without any field or observer biasness. It also relieves the restriction of a limited sample. Highway Capacity Manual 2000 suggested considering travel speed as the most appropriate parameter for describing traffic congestion. Therefore, this research employs a speed performance index with the concept of big data analytics to describe traffic congestion of road. This research estimates speed performance index for different sections of road. The data was gathered for three main roads of Karachi i.e. Rashid Minhas Road, Shahra e Faisal Road and Main Korangi Road. The major achievement of this study project is to propose a novel approach to calculate the speed of a vehicle without field measurements. The data is collected through Smartphone with the aid of an application available online namely My Track. The data that was utilized for this project mainly comprises of GPS Exchange Format (GPX) routes that are converted into Extensible Markup Language (XML) to run the developed script. The script was able to determine the status of congestion on a variety of highways with the help of speed performance index. The same script can be used by administrators and other transportation service providers for estimating the congestion on a real-time basis. The methods and procedures from this study would aid in the transportation planning process, especially for route selection of individuals as well as services.