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As urban areas grow and city populations expand, traffic congestion has become a significant problem, particularly in regions with substantial truck traffic. This study presents a coordinated freight routing system designed to optimize network utility and reduce congestion through personalized routing guidance and incentive mechanisms. The system customizes incentives and payments for individual drivers based on current traffic conditions and their specific routing preferences. Using a mixed logit model with a linear utility specification, the system captures drivers' route choice behaviors and decisions accurately. Participation is voluntary, ensuring most drivers receive a combined expected utility, including incentives, exceeding their anticipated utility under User Equilibrium (UE). This structure encourages drivers to follow suggested routes. Data collection on drivers' routing choices allows the system to update utility parameter estimates using a hierarchical Bayes estimator, ensuring routing suggestions remain relevant and effective. The system operates over defined intervals, where truck drivers submit their intended Origin-Destination (OD) pairs to a central coordinator. The coordinator assigns routes and payments, optimizing overall system costs and offering tailored incentives to maximize compliance. Experimental results on the Sioux Falls network validate the system's effectiveness, showing significant improvements in the objective function. This study highlights the potential of a coordinated routing system to enhance urban traffic efficiency by dynamically adjusting incentives based on drivers’ choice data and driver behavior. View the NCST Project Webpage

A major limitation of amine-based post-combustion carbon capture technology is the necessity to regenerate amines at high temperatures, which dramatically increases operating costs. This paper concludes the effect of solvent choice as a possible route to modify the thermodynamics and kinetics characterizing the involved amine regeneration reactions and discusses whether these modifications can be economically beneficial. We report experimentally benchmarked computational chemistry calculations of monoethanolamine regeneration reactions employing aqueous and non-aqueous solvents with a wide range of dielectric constants. Unlike previous studies, our improved computational chemistry framework could accurately reproduce the right experimental activation energy of zwitterion formation. From the thermodynamics and kinetics of the predicted reactions, the use of non-aqueous solvents with small dielectric constants led to reductions in regeneration Gibbs free energies, activation barriers, and enthalpy changes. This can reduce energy consumption and give an opportunity to run desorption columns at relatively lower temperatures, thus offering the possibility of relying on low-grade waste heat as an energy input. [ABSTRACT FROM AUTHOR]

Background and ObjectivesDesign and MethodsResultsConclusionsThis multimodal two-phase study investigated the impact of trait social anxiety on exploration, social attention, and autonomic responses in a naturalistic setting. We expected higher avoidance of potentially crowded spaces, reduced visual attention on other people, and heightened physiological arousal in social situations for participants with higher social anxiety levels.Eighty-seven participants, pre-screened for high variance in trait social anxiety, first completed a half-hour walk on a freely chosen route and subsequently had a staged social interaction with a confederate consisting of a non-interactive waiting phase and a short conversation.While social anxiety did not modulate the choice of route during the walk phase, socially anxious participants avoided gazing at other individuals in non-interactive situations, i.e., during the walk and the waiting phase. In contrast, during actual interaction, they showed increased visual attention towards the confederate’s face. Across all experimental phases, highly socially anxious individuals showed elevated heart rates, but this effect was independent of the social context.This study showed that social anxiety affects social exploration behavior not in a way of general avoidance, but rather in nuanced adaptations depending on the concrete situation, likelihood of interaction and associated socio-evaluative threat. [ABSTRACT FROM AUTHOR]

Summary: In recent years, the markets for plant‐based products and plant protein have experienced a remarkable global expansion. The demand for plant‐based products, ranging from meat substitutes and dairy alternatives to plant‐based snacks and beverages, has surged exponentially. Soy protein, among all the plant protein resources, held a significant market share in the plant‐based industry, reflecting its popularity and widespread use. The selection of an appropriate soy fractionation process is essential for the development of cleaner and more sustainable soy‐based food products. This study compared three different soy protein fractions (SPFs) obtained through mild fractionation processes, each with unique chemical compositions and functionalities. Aqueous ethanol washing process resulted in the fractions with the highest protein purity and water holding capacity, while the simplified aqueous extraction method coupled with either an alkaline solubilisation step (SAE‐Alkaline) or an acid precipitation step (SAE‐Acid) yielded protein fractions with superior solubility, thermal stability, and gel strength. The choice of fractionation route has significant implications for the production of protein‐rich ingredients, and prioritising sustainable processing methods can contribute to the development of innovative and environmentally friendly soy‐based foods. [ABSTRACT FROM AUTHOR]

Accurate assessment of crowd evacuation inside the post-earthquake environment is critical from many perspectives, but this issue receives much less attention compared to the seismic losses of structural and non-structural components. This could be attributed to the fact that post-earthquake evacuation analysis is complex due to the interaction between human behavior and the actual built environment induced by different building contents. This study attempts to tackle this problem by investigating the impacts of different building contents on post-earthquake evacuation time by using an agent-based model that considers turning behavior. To this end, the agent-based model is first described, including: properties of the agent-based model with turning behavior, key aspects in its formulation considering different evacuation stages, and influence of different building contents (namely, debris from partition walls and ceiling systems, and various types of equipment) on the agent's behavior. Subsequently, a school building is used as a benchmark problem to validate the model without earthquake, and the findings indicate that the agent-based model can match the real safety drill results reasonably well. After the validation, the school building is subsequently designed in accordance with modern seismic design codes, and the influence of debris and equipment on post-earthquake evacuation time is quantitatively studied using a suite of pulse-type ground motions as input. Based on this case study, recommendations are made for structural and architectural designers in an effort to reduce the potential evacuation time. Specifically, debris induced by partition walls or ceiling systems should be controlled as it has the greatest impact on the total evacuation time. [ABSTRACT FROM AUTHOR]

In the context of transportation development, the simultaneous emergence of automated vehicles (AVs) and human-driven vehicles (HDVs) can lead to varied traffic system performance. For the purpose of improving traffic systems, this paper proposes a traffic restriction scheme only for HDVs. We develop a variational inequality (VI) model to describe travel mode and route choices under this restriction scheme and design an algorithm to solve the model. The proposed model and algorithm are applied to a Sioux Falls network example to evaluate the effects of the traffic restriction scheme. Our findings indicate that the scheme improves overall social welfare, with a higher proportion of restricted travelers leading to greater social welfare as well as increased travel demand due to changes in capacity. However, some lower exogenous monetary factors lead to negative social welfare, as the presence of AVs may exacerbate road congestion. Additionally, advancements in technology are needed to adjust the weightings of travel time and congestion level estimates to further enhance social welfare. These results offer valuable insights for traffic demand management in traffic systems with a mix of AVs and HDVs. [ABSTRACT FROM AUTHOR]

Choice of trade route and destination by cross border traders is an important decision in today's increasingly cross border trade of products including fish. The current study examined the geographical factors influencing choice of informal trade route and destination by fish traders. Both qualitative and quantitative approaches were adopted for the study. These included semi-structured questionnaires through personal interviews and key informant interviews with fish border inspectors. Data was analyzed through various techniques for trader's choice and destination. Specifically, principal component analysis and spearman correlation, and Huff's gravity model were used to identify significant factors influencing choice of trade route and destination, and market attractiveness, respectively. Fish traders bypass official border posts using informal routes connecting the main route before and after the border post. Despite attempts and awareness about formal trade, stringent regulations forced fish traders to use informal trade routes. However, investigations into choice of trade routes and destination found that route distance, presence of alternative destination, mode of transport, demand of the fish product, and personal safety and risks were the factors influencing choice of both trade route and destination by informal fish traders. This geographic analysis provides a basis for addressing challenges in informal cross border trade in Malawi. It is recommended that strategic efforts and investments should be made in the development of transportation infrastructure, such as roads and railways to improve connectivity between secondary routes and one stop border post. The government should also intensify awareness initiatives on COMESA simplified trade regime (STR) to encourage informal traders to transition to the formal sector through use of formal routes. [ABSTRACT FROM AUTHOR]

Validation of travel demand models, although recognised as important, is seldom undertaken. This study adds to the scarce literature in this field by undertaking an external validation of a multi-modal transit route choice model. The model was estimated using smart card data for the urban transit network of Amsterdam before the introduction of a new metro line and is used to predict changes in travel behaviour after the network change. To validate, the model was checked for changes in estimated parameters between the two time periods, and predictive ability was evaluated at different aggregation levels. Although most model parameters were found to be unstable between the two contexts, the predictive performance at all levels was similar to the locally estimated model. Moreover, individual choices and transit mode-share predictions were found to be close to the observed ones. The errors were relatively larger for the link and route-level predictions, some of which could be attributed to the assumptions made regarding consideration choice set given as input to the model. On comparing alternative model specifications, using generic instead of mode-specific travel attributes lead to a strong degradation in predictive performance. Conversely, a model incorporating overlap between routes, with a better model fit in the base period, did not offer a clear improvement in prediction performance. The study highlights the need to validate transit route choice models before using them for deriving policy recommendations, especially in this data-rich age in which it can often be undertaken at a relatively low additional cost. [ABSTRACT FROM AUTHOR]

As one of the most fundamental concepts in transportation science, Wardrop equilibrium (WE) has always had a relatively weak behavioral underpinning. To strengthen this foundation, one must reckon with bounded rationality in human decision-making processes, such as the lack of accurate information, limited computing power, and suboptimal choices. This retreat from behavioral perfectionism in the literature, however, was typically accompanied by a conceptual modification of WE. Here, we show that giving up perfect rationality need not force a departure from WE. On the contrary, WE can be reached with global stability in a routing game played by boundedly rational travelers. We achieve this result by developing a day-to-day (DTD) dynamical model that mimics how travelers gradually adjust their route valuations, hence choice probabilities, based on past experiences. Our model, called cumulative logit (CumLog), resembles the classical DTD models but makes a crucial change; whereas the classical models assume that routes are valued based on the cost averaged over historical data, our model values the routes based on the cost accumulated. To describe route choice behaviors, the CumLog model only uses two parameters, one accounting for the rate at which the future route cost is discounted in the valuation relative to the past ones and the other describing the sensitivity of route choice probabilities to valuation differences. We prove that the CumLog model always converges to WE, regardless of the initial point, as long as the behavioral parameters satisfy certain mild conditions. Our theory thus upholds WE's role as a benchmark in transportation systems analysis. It also explains why equally good routes at equilibrium may be selected with different probabilities, which solves the instability problem posed by Harsanyi. Funding: This research is funded by the National Science Foundation [Grants CMMI #2225087 and ECCS #2048075]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0132. [ABSTRACT FROM AUTHOR]

This study explores asymmetric preferences in route switching behavior on intercity travel. A reference-dependent travel choice model reflecting the endowment effect is empirically compared with the classical symmetric travel choice model to investigate the significance of the reference-dependency. Based on the application to stated preference data, our results reveal that the reference-dependent travel choice model outperforms the classical symmetric travel choice model in route switching decision, indicating the significant disparities between WTA and WTP. It implies that policy making based on the results of the classical symmetric model assuming equivalent WTA and WTP could lead to the overestimation of switching demand. Furthermore, our results show that drivers are more heterogeneous in terms of WTA than WTP. Moreover, our applications of the latent class mixed logit structure indicate that most of the responded drivers show reference-dependency in route switching decision. Our findings in this study are essential in transport network policy making. • Proposing of a reference dependent RUM model reflecting endowment effect. • Analysis of disparities between WTA and WTP caused by asymmetric preferences in route switching behavior on intercity travel. • Design of face-to-face stated preference survey in route switching decision. • Application of advanced latent class mixed logit structure to better understand drivers' heterogeneity. [ABSTRACT FROM AUTHOR]

Travelers always perform some preference during the decision-making process. The preference will affect the decision results and can be improved by continuously learning. In order to understand the influence of individual preference on travel behavior choice , two individual preferences, including indifference preference and compulsive preference are considered in the paper. Two updating mechanisms of compulsive preference are proposed to obtain the choosing probability of all alternatives. Reinforcement learning models are established integrating the gain stimulating and loss stimulating considering expected utility. Nguyen Dupuis network is adopted for numerical simulation to study the updating process. Simulation results denote that the equilibrium state is much more efficient when preference learning mechanism is considered comparing with the traditional stochastic user equilibrium model, and can decrease the total travel time greatly, which can be applied for urban traffic management. Personalized traffic guidance is the effective solution to traffic congestion in the future [ABSTRACT FROM AUTHOR]

Despite many years of research and rapid changes in the field of bicycle route choice modelling, a review of factors associated with cyclists' route choice decisions is missing. This paper reviews 33 studies that employed GPS data and statistical modelling to explain the route choice behaviour of cyclists on utilitarian trips. Factors associated with bicycle route choice are divided into network, contextual, and individual factors. Findings from this review demonstrate that a refined categorisation of model attributes is essential, as the preferences vary greatly between different route attributes. Moreover, the analysis indicates a strong influence of the local context on the attribute definitions and route choice preferences. Although some commonalities between studies w.r.t. the network factors (e.g. a preference towards bicycle infrastructure or avoidance of slopes) can be determined, many results are contradictory or intertwined with other factors. Specifically, results related to intersections, land use characteristics, and interaction with other modes of transport are not always conclusive and are strongly dependent on the local context. However, the explanation of some of the unclear effects can be facilitated by incorporating contextual or individual information in the models. Based on the analysis of the existing studies, this review encourages a joint consideration of studies employing quantitative and qualitative methods to explain cyclists' behaviour. Moreover, reporting model results in an accessible form (e.g. through relatable values or visualisation) is essential to build bridges between bicycle research and policymaking. Future research is encouraged to study cyclists' route choice decisions from the perspective of objective well-being, quantifying and modelling the influence of factors such as safety, health, energy expenditure, or noise exposure. [ABSTRACT FROM AUTHOR]

Over the past years, aviation policies are more and more focussed on sustainability rather than connectivity, putting pressure on airlines to re-design the network. For this, evaluating air travellers their preferences on direct routing compared to lay-overs is needed to make well-informed design choices. This paper examines the preferences of air travellers on direct itineraries by applying a data-driven approach. To this end, a stated preference choice experiment is developed and implemented in an online survey. The results of the latent class choice model show two types of flyers with different preferences, labelled as time sensitive and price sensitive flyers. The model outcomes indicate that both are willing to pay for direct itineraries, while time sensitive flyers are willing to pay substantially more. Other flight characteristics also play an important role in air travellers’ route choice, such as the distance to the airport, travel time, and transfer time. Travel motive, ticket price compensation by the employer, and experience with layovers drive whether air travellers belong to the time or price sensitive flyers. The distinction between time and price sensitive flyers and their preferences is expected to yield more tailored transport policies than a distinction between, for instance, business and non-business. Future research should focus on the societal impact of a reduction in direct itineraries for air travellers, using insights gained in this study.

This study employed Virtual Reality (VR) technology to investigate the influence of corridor directional configuration on evacuation wayfinding behavior in underground spaces. The study designed two virtual underground space fire evacuation scenarios with different forms of intersections, and recruited 115 volunteers to participate in the experiment.The results indicated that corridor directional configuration significantly affected participants' fire evacuation wayfinding behavior. At Y-shaped and T-shaped intersections with left and right turning options, participants showed a preference for choosing the right-side corridor. At ┡-shaped and ┩-shaped intersections with straight and turning options, participants tended to choose the straight path. Individual factors (such as gender, evacuation experience, and professional background) did not demonstrate significant effects on wayfinding choices in this study, though they may produce different evacuation outcomes in various scenarios. In practical evacuation design, corridor directional configuration should be organically integrated with other environmental factors to reinforce directional preferences and more effectively guide evacuation. The findings provide scientific evidence for underground space evacuation route design, which can be used to optimize evacuation signage and path configuration, thereby improving evacuation efficiency and safety. Future research could be conducted in more complex environments, considering additional variables to gain a more comprehensive understanding of evacuation behavior. [ABSTRACT FROM AUTHOR]

In this study, we aimed to assess the efficacy of different ways of administration and types of beta-lactams for hospitalized community-acquired pneumonia (CAP). In this post-hoc analysis of randomized controlled trials (RCT) on patients hospitalized for CAP (pneumonia short treatment trial) comparing 3-day vs. 8-day durations of beta-lactams, which concluded to non-inferiority, we included patients who received either amoxicillin-clavulanate (AMC) or third-generation cephalosporin (3GC) regimens, and exclusively either intravenous or oral treatment for the first 3 days (followed by either 5 days of oral placebo or AMC according to randomization). The choice of route and molecule was left to the physician in charge. The main outcome was a failure at 15 days after the first antibiotic intake, defined as temperature >37.9°C, and/or absence of resolution/improvement of respiratory symptoms, and/or additional antibiotic treatment for any cause. The primary outcome according to the route of administration was evaluated through logistic regression. Inverse probability treatment weighting with a propensity score model was used to adjust for non-randomization of treatment routes and potential confounders. The difference in failure rates was also evaluated among several sub-populations (AMC vs. 3GC treatments, intravenous vs. oral AMC, patients with multi-lobar infection, patients aged ≥65 years old, and patients with CURB65 scores of 3–4). We included 200 patients from the original trial, with 93/200 (46.5%) patients only treated with intravenous treatment and 107/200 (53.5%) patients only treated with oral therapy. The failure rate at Day 15 was not significantly different among patients treated with initial intravenous vs. oral treatment [25/93 (26.9%) vs. 28/107 (26.2%), adjusted odds ratios (aOR) 0.973 (95% CI 0.519–1.823), p 0.932)]. Failure rates at Day 15 were not significantly different among the subgroup populations. Among hospitalized patients with CAP, there was no significant difference in efficacy between initial intravenous and exclusive oral treatment. This trial is registered with ClinicalTrials.gov , NCT01963442. [ABSTRACT FROM AUTHOR]

Travel is an activity that everyone carries out in their daily life. While travelling, commuters are exposed to air pollution, which is likely to impact their mode, route, and/or departure time choice. Various review studies on vehicular emissions have been conducted in the past, but the aspects of its impact on resulting air quality and travel behaviour are sporadic and largely unorganized. In an effort to close this gap, this study carried out an extensive review of 63 studies to document the influence of air pollution on travel behaviour. The findings reveal that a majority of the research in this arena has been carried out in the USA and China, and the highest instances of behaviour change was observed when users shifted their routes to reduce exposure. The awareness about AQI and its real-time pre-trip information plays a vital role in lowering commuters' air pollution exposure. Finally, strategies such as congestion/emission pricing schemes, MaaS, etc. could provide a clean travel environment to all commuters by reducing their exposure during travel. • Reviews best practices in air quality exposure and travel behaviour. • Smart route choice helps users minimize their exposure to poor air quality. • Integrating technological advances in TDM would provide a cleaner environment. [ABSTRACT FROM AUTHOR]

Traffic congestion has caused great concern among policymakers in large cities around the world. In contrast with constantly increasing transport supply, individual-based active travel demand management (ATDM) has been proposed as a more efficient policy alternative for combating congestion. However, how individuals make routing choices during commuting in response to ATDM incentives is still largely unknown, given the lack of individual travel data. Using a desensitized one-week travel trajectory data set involving 5641 personal electric vehicles, we examine the major influencing factors of commuting route stability during working days and make suggestions on targeting the most responsive commuters. We first filter family-used vehicles by clustering vehicle usage patterns through employing the Gaussian mixture model and interpret drivers' route choice behaviors during morning peak hours. To look for factors affecting route stability, we develop a generalized additive model and find that route stability is significantly associated with road network density of origins and destinations, departure time, travel duration, commuting distance, reliability of the expressway, and volatility of the congested sections by which the routes passed. The empirical results may contribute to the understanding of individual route choices, and help urban managers develop more refined ATDM policies to alleviate traffic congestion in the future. [ABSTRACT FROM AUTHOR]

The transfer city is a key point in air-to-rail intermodal travel (ARIT) that directly influences the service level of the entire system. Although some studies have investigated factors that influence passengers' ARIT preferences based on subjective surveys, an in-depth understanding of their nonlinear and interactive impacts on passengers' actual behavior is still lacking. Using passengers' online booking data in China, this study implements an interpretable ensemble machine learning framework that incorporates decision-making theory to unveil feature importance and the complex nonlinear and interactive effects of various attributes on passengers' choice of transfer city in the crucial ARIT scenario. The results show that (1) the extreme gradient boosting (XGBoost) model achieves better performance in predicting ARIT transfer city choice than the conventional discrete choice model; (2) attributes related to intermodal services (e.g., ticket price, in-vehicle duration, transfer duration, quantities of flights and trains) are more important than personal demographic characteristics (e.g., age and gender); (3) factors related to service economy and efficiency display nonlinear impacts with fluctuations and critical thresholds; and (4) individuals with different characteristics present heterogeneous preferences for ARIT transfer cities. These findings can provide useful managerial implications for policymakers. [ABSTRACT FROM AUTHOR]

In recent years, global trade volume has been increasing, and marine transportation plays a significant role here. In marine transportation, the choice of transportation route has been widely discussed. Minimizing fuel consumption, minimizing voyage time, and maximizing voyage security are concerns of the International Maritime Organization (IMO) regarding Maritime Autonomous Surface Ships (MASS). These goals are contradictory and have not yet been effectively resolved. This paper describes the ship path-planning problem as a multi-objective optimization problem that considers fuel consumption, voyage time, and voyage security. The model considers wind and waves as marine environmental factors. Furthermore, this paper uses an improved Whale Optimization Algorithm to solve multi-objective problems. At the same time, it is compared to three advanced algorithms. Through seven three-objective test functions, the performance of the algorithm is tested and applied in path planning. The results indicate that the algorithm can effectively balance the fuel consumption, voyage time, and voyage security of the ship, offering reasonable paths. [ABSTRACT FROM AUTHOR]

The fastest route search, which is to find a path with the shortest travel time when the user initiates a query, has become one of the most important services in many map applications. To enhance the user experience of travel, it is necessary to achieve accurate and real-time route search. However, traffic conditions are changing dynamically, and the frequent occurrence of traffic congestion may greatly increase travel time. Thus, it is challenging to achieve the above goal. To deal with it, we present a congestion-aware spatio-temporal graph convolutional network-based A* search algorithm for the task of fastest route search. We first identify a sequence of consecutive congested traffic conditions as a traffic congestion event. Then, we propose a spatio-temporal graph convolutional network that jointly models the congestion events and changing travel time to capture their complex spatio-temporal correlations, which can predict the future travel-time information of each road segment as the basis of route planning. Further, we design a path-aided neural network to achieve effective origin-destination (OD) shortest travel-time estimation by encoding the complex relationships between OD pairs and their corresponding fastest paths. Finally, the cost function in the A* algorithm is set by fusing the output results of the two components, which is used to guide the route search. Our experimental results on the two real-world datasets show the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

At present, the optimization of public transportation networks and vehicle scheduling are carried out independently in stages. However, through analysis, it has been found that scheduling information such as route schedules is an important factor related to passenger route selection. Therefore, in order to further improve the optimization effect, this article proposes an innovative idea of simultaneously optimizing the line network and scheduling. Based on the construction of a real–virtual public transportation network, this article constructs a synchronous optimization model for the line network and scheduling by considering both passenger waiting and on-board time. To achieve the consideration of passengers for different route choices, a shortest path traversal algorithm based on Yen was proposed to analyze the number and weight of the shortest paths between the same OD, and a genetic algorithm was used to solve the model. Finally, the effectiveness of the model was verified through numerical examples, and the results showed that synchronous optimization was superior to phased optimization: the passenger time cost was reduced by 21.5%, the bus operation cost was reduced by 13.7%, and the total bus system cost was reduced by 18.0%. [ABSTRACT FROM AUTHOR]

Despite the introduction of serological neoplastic biomarkers and typical radiological characteristics in clinical practice, liver biopsy (LB) is often still necessary to establish a histological diagnosis, especially in ambiguous cases. Nowadays, LB via the percutaneous approach (PC-LB), under computed tomography (CT) scan or ultrasonography (US) guidance, is the route of choice. However, certain focal liver lesions can be challenging to access percutaneously. In such cases, endoscopic ultrasound (EUS)-guided fine needle biopsy (FNB) may represent an attractive, minimally invasive alternative. This retrospective observational study aimed to evaluate the efficacy, diagnostic performance, and safety of EUS-FNB conducted on 58 focal liver lesions located in both liver lobes. The adequacy of FNB samples for focal liver lesions located in the left and right lobes was 100% and 81.2%, respectively, and the difference was statistically significant (p = 0.001). Technical success was 100% for both liver lobes. The overall sensitivity and specificity were 95% and 100%, respectively. EUS-FNB is effective in making an accurate diagnosis with an excellent safety profile for focal liver lesions located in both liver lobes. [ABSTRACT FROM AUTHOR]

• EV driver's long-distance charging and route choice preferences were shown. • Insights on customized long-distance trip and charging facility planning were given. • Personal attitudes such as planning ability affect drivers' charging times choice. • People with strong planning ability are more likely to choose charging once. • Simultaneous charging and resting is best for meeting needs and reducing travel time. The increasing demand for long-distance and intercity travel is driven by the progress in regional transportation integration and the establishment of charging infrastructure. This increase suggests that the use of electric vehicles (EVs) for long-distance trips is likely to become more prevalent in the future. Therefore, there is an urgent need for more studies on the travel behavior of EV drivers for long-distance trips. However, there are few studies looking at the travel behavior of EV drivers on long-distance trip and multiple charging during trips. To fill this gap, this study identified EV drivers' preferences for charging and route choice in long-distance or intercity travel on the basis of a stated preference survey, with the Shandong highway network as the travel scenario. A nested logit (NL)-based hybrid choice model was used to investigate the impacts of personal attitudes, namely planning ability, risk aversion and confidence in battery, on the drivers' charging times choice behavior. The NL structure consists of two levels, with the upper level focusing on the charging times decision process, and the lower level analyzing the choice behavior of routes with no charging, routes with charging once, and routes with charging twice. The results show that the initial state of charge, and attitudes significantly influence the decision on charging times. In addition, EV drivers who are risk-averse and lack confidence in the battery tend to charge, whereas drivers with strong planning ability prefer to charge once in long-distance travel. Charging and resting simultaneously is the best way to meet the needs of charging and resting, and reduce the time lost while driving. These results provide insights on drivers' travel planning and charging infrastructures planning for long-distance trips. [ABSTRACT FROM AUTHOR]

• A framework is presented to explore the influence of friendships in a social network on drivers' route choice. • This paper simulates the evolution of drivers' route choice in a hypothetical transportation network under different assumptions. • This research offers insight into the route choice process when drivers can access information about other drivers' travel experience in a social network. • The simulation results show that the information types mainly affect the change rate, and the memory length mainly affects the time for convergence. Route choice is a decision that drivers face daily under varying traffic conditions. Although many studies have focused on route choice, the actual behavior considering real-world networks and the mechanisms that govern it have eluded complete characterization. Part of the difficulty has been in accounting for the growing availability of information sources that may influence drivers' route choices, precluding collection of adequate observational data. In addition, with the continuing emergence of new forms and sources of information, such as social media with varying degrees of interactivity, engagement, and immediacy, existing frameworks and models for representing these choices and the influence of new information sources have been lacking. This study uses an agent-based modeling approach to investigate the effects of social influence on drivers' route choices. In the model, all drivers communicate with each other in the same social network, and this information is combined with their previous experiences. The first aim of this study is to investigate the impact of social network connectivity and information exchange on route choice. The second is to characterize situations in which all drivers in the social network are satisfied with their route choices. The simulation results show that an equilibrium is achieved when all drivers in the given social network are satisfied with their current route choices. [ABSTRACT FROM AUTHOR]

Imbalances between limited police resource allocations and the timely handling of road traffic crashes are prevalent. To optimize resource allocations and route choices for traffic police routine patrol vehicle (RPV) assignments, a dynamic crash handling response model was developed. This approach was characterized by two objective functions: the minimum waiting time and the minimum number of RPVs. In particular, an adaptive large neighborhood search (ALNS) was designed to solve the model. Then, the proposed ALNS-based approach was examined using comprehensive traffic and crash data from Ningbo, China. Finally, a sensitivity analysis was conducted to evaluate the bi-objective of the proposed model and simultaneously demonstrate the efficiency of the obtained solutions. Two resolution methods, the global static resolution mode, and real-time dynamic resolution mode, were applied to explore the optimal solution. The results show that the optimal allocation scheme for traffic police is 13 RPVs based on the global static resolution mode. Specifically, the average waiting time for traffic crash handling can be reduced to 5.5 min, with 53.8% less than 5.0 min and 90.0% less than 10.0 min. [ABSTRACT FROM AUTHOR]

This paper aims at automatic route choice in wargame deduction, that is, using computer algorithms to mimic the route choice of human players. Route choice is the process by which a player of a game chooses the optimal route from a set of candidate routes. Essentially, it can be viewed as a multi-attribute decision making problem. However, due to the uncertain and dynamic decision making environment in wargaming, the existing decision making theory and methods face some challenges in addressing such problems. We summarize these challenges in two aspects. On the one hand, from a psychological point of view, the process of route choice by a human player is a dynamic decision making process. However, most of the multi-attribute decision making methods widely used today were developed under static conditions and do not accurately describe the decision making behaviors of individuals in dynamic and real-world settings. On the other hand, in the real dynamic decision making environment of wargaming, there is a large amount of ambiguous and uncertain information. How to imitate human thinking to quantify the information is crucial for making scientific decisions and for automating decision making. Based on the above two aspects of the analysis, in this paper, we first construct a fuzzy dynamic route choice model of wargame. The model simulates the decision making process of humans from two stages of "information preprocessing" and "information processing". Then we propose a fuzzy dynamic route choice algorithm of wargame. Furthermore, the model is applied to a practical case of route choice of wargame, and the effectiveness and advantages of the model are illustrated through a comparative analysis. Finally, the model is further quantitated and analyzed in combination with the actual case, and the approximation of the model to human decision making psychology is illustrated from both a simulation and a mathematical justification perspective. These results demonstrate that the proposed model can not only handle a large amount of fuzzy and uncertain information, but also simulate the decision making psychology of wargame players. This research result is expected to provide a new effective method for automatic route choice in wargame simulations. [ABSTRACT FROM AUTHOR]

The station–city integration development strategy, guided by the Transit-Oriented Development (TOD) model, has enhanced the coupling relationship between rail transit stations and urban areas. Walking, as a core mode of low-carbon urban transport, plays a significant role in the connectivity between stations and urban areas and in the rate of passenger flow dispersal. During peak periods, large volumes of passengers flood the streets, forming complex and diverse walking paths that penetrate urban neighborhoods. Route choice is a complex decision-making process influenced by both subjective and objective factors. Previous studies on pathfinding activities have often focused on either subjective or objective factors in isolation, with limited research on their interaction. This paper establishes a comparative analysis framework based on the translation of subjective and objective information and behavioral feedback mechanisms. Using Lvcuo Station, a transit station on Xiamen's Metro Line 1 in Fujian Province, China, as a case study, we collected walking data from 410 passengers through field surveys. We used the Kappa consistency coefficient test method to analyze the performance of the interaction between the objective environment and subjective environmental cognitive factors when pedestrians exit the rail transit station and walk to their destinations. We also used multinomial logistic regression (MLR) to analyze the correlation between subjective perception variables and objective built environment variables and to consider the influence of individual pedestrian characteristics and attributes on path selection. The results revealed the following: (1) Overall, there is a significant deviation effect of subjective and objective factors on passengers' pathfinding behavior, with some spatial correlation with the built environment of the streets. (2) The consistency of influences on walking activities varies significantly across different travel periods, distances, and purposes. (3) Visual elements, urban life with a bustling atmosphere, street permeability, and route connectivity positively correlate with subjective perception while "supporting walking facilities" and "meeting consumption needs along the way" negatively correlate with subjective perception. These findings underscore the need to enhance the understanding of the built environment in the street spaces within rail transit station areas from the perspectives of pedestrians to improve the walkability of these streets. [ABSTRACT FROM AUTHOR]

Maintaining driving independence is important for older adults. However, cognitive decline, a common issue in older populations, can impair older adults' driving abilities and overall safety on the roads. This study explores how cognitive impairment influences driving patterns and driving choices among older adults. We analyzed real-world driving patterns of 246 older adults using GPS dataloggers. Our sample included 230 cognitively normal older adults (CN; Clinical Dementia Rating R [CDR] = 0) and 16 older adults with incident cognitive impairment (ICI; CDR = 0.5). The CN group had an average age of 68.2 years, with 46% females and an average of 16.5 years of education, while the ICI group's average age was 69.2 years, with 36% females and an average of 16.0 years of education. We employed spatial clustering and hashing algorithms to evaluate driving behaviours. Significant differences emerged: The ICI group used fewer distinct routes to their most common destination. These differences can be leveraged to develop driving as a digital biomarker for the early detection and continuous monitoring of cognitive impairment. [ABSTRACT FROM AUTHOR]

With the continuous development of education level and the downturn of economic situation, employment competition is intensifying, more and more high-quality talents appear, and the misfit between people and posts has become a common phenomenon. However, there is no consensus on the relationship between perceived overqualification and employee creativity. Based on the conservation of resource theory, this study reveals the micro mechanism and boundary conditions of the influence of excessive qualification on employee creativity. This study analyzed 487 valid samples obtained in three stages. The results show that: (1) Job crafting has a positive mediating effect on perceived overqualification and creativity, and the path of the two halves is positive; (2) Work withdrawal behavior plays a negative mediating role between the perceived overqualification and creativity. The path in the first half is positive, and the path in the second half is negative; (3) Organizational identity moderates the effect of perceived overqualification on job crafting and work withdrawal behavior. Specifically, the higher the sense of organizational identification, the stronger the positive effect of perceived overqualification on job crafting and the weaker the positive effect on work withdrawal behavior; (4) Organizational identification moderates the mediating role of job crafting and work withdrawal behavior in the relationship between overqualification and creativity. Specifically, the higher the organizational identity, the stronger the indirect positive effect of perceived overqualification on creativity through job crafting, and the weaker the indirect negative impact of perceived overqualification on creativity through work withdrawal behavior. The study conclusion deepens the research on the mechanism of the influence of the perceived overqualification on employees' work behavior, and provides practical enlightenment for the organization and management of employees with excess qualification. [ABSTRACT FROM AUTHOR]

Analyzing the preference of route choice not only facilitates the understanding of individuals' decision‐making behavior, but also provides valuable information for improving traffic management strategies. As the layout of the road network, the variability of individual preferences and the spatial distribution of origins and destinations all play a role in route choice, it is a great challenge to reveal the interplay of such numerous complex factors. In this paper, we propose RouteVis, an interactive visual analytics system that enables traffic analysts to gain insight into what factors drive individuals to choose a specific route. To uncover the relationship between route choice and influencing factors, we design a quantitative analytical framework that supports analysts in conducting closed‐loop analysis of various factors, i.e., data preprocessing, route identification, and the quantification of influence and contribution. Furthermore, given the multidimensional and spatio‐temporal characteristics of the analysis results, we customize a set of coordinated views and visual designs to provide an intuitive presentation of the factors affecting people's travels, thus freeing analysts from tedious repetitive tasks and significantly enhancing work efficiency. Two typical usage scenarios and expert feedback on the system's functionality demonstrate that RouteVis can greatly enhance the capabilities of understanding the travel status. [ABSTRACT FROM AUTHOR]

Pavement management systems (PMS) are considered to be crucial tools for agencies in managing their pavement networks and using accessible budgets in an appropriate technique. In this paper, a newly developed condition rating index [modified pavement condition rating (MPCR)] was used to assess the current and future pavement performance. The pavement future performance in terms of MPCR was predicted using two models: (1) a stochastic Markov chain model, and (2) an artificial neural network (ANN) model. In Markov chain modeling, the Long-Term Pavement Performance (LTPP) database was used to develop the transition probability matrix (TPM). An ANN model for future pavement performance prediction was developed based on the same LTPP data. For both models, 245 data points from the LTPP data were used for deterioration modeling. Both models were verified using LTPP data and data obtained from the General Authority of Roads, Bridges and Land Transport (GARBLT), Egypt. The Markov chain and ANN results were compared. The Markov chain model performed better than the ANN model, with a coefficient of determination, R2 , of 0.9 and RMS Error (RMSE) of 0.1268. the ANN model yielded an R2 of only 0.53 and a RMSE of 0.2205. Increasing the number data points did not lead to a significant improvement in the ANN model accuracy. A multiobjective particle swarm optimization (PSO) model is used for fund allocation to maximize the average pavement condition and minimize the maintenance cost. The developed optimization model was tested on two benchmark optimization problems to ensure its validity for real-life applications. The developed optimization model was used on a network of roads and was found to be capable of generating optimal or near-optimal solutions for the maintenance decisions to keep the pavement in good workable condition. As such, the current study presents a comprehensive development tackling different modules of a pavement management system. [ABSTRACT FROM AUTHOR]

• 52 participants reached 4 predefined destinations via shared e-scooter or bicycle. • E-scooter riders prioritize road surface, safety, and rule compliance. • GPS riding data is comparable for both user groups. • E-scooter riders tend to take longer routes for routes with multiple turns. • Traffic planning should prioritize high-quality cycling infrastructure. Problem : The surge in popularity of electric kick scooters (e-scooters) poses new challenges for traffic planning, demanding a comprehensive understanding of route choice behavior to see how e-scooters are used, how they affect traffic flow, and where improvements can be made to the road infrastructure. Therefore, this study aimed to analyze route choices and preferences of e-scooter riders and cyclists in a quasi-experimental setup with both user groups having the same trip destinations. Method : Two groups of participants (n = 52) completed a ride with either a shared e-scooter or bicycle to reach four predefined destinations in Dresden, Germany. The riders were supposed to choose their routes and subsequently reported the difficulty of decision-making and the importance of several route choice factors related to the ride. Results : E-scooter riders rated road surface and safety as significantly more important for route choice than cyclists and tended to perceive the decision-making as more difficult. Riding data revealed broad comparability between the groups, with e-scooter riders tendentially having longer routes for complex decisions (unknown destinations, scenic routes, more turns required). Discussion : The study suggests that the route preferences of e-scooter riders may be influenced by a combination of road surface and safety considerations, highlighting the need for high-quality cycling infrastructure. Limitations exist regarding the naturally occurring differences in riding experience in e-scooter riding versus cycling. Practical implications indicate that planning for e-scooter riders can benefit from insights drawn from activities designed for cyclists. The provision of real-time road quality information is proposed, considering its potential impact on overall road safety. Summary : This study contributes to a better understanding of how e-scooter riders navigate through cities and delivers a valuable foundation for transport planners and engineers considering the rise in cycling and micro-mobility use. [ABSTRACT FROM AUTHOR]

Spatial design greatly influences fire evacuation in buildings. Although existing works have comprehensively assessed building spatial design in terms of building circulation, they overlooked how the building layout and emergency signage system affect occupants' attention allocation during fire emergencies. Therefore, this paper proposes a framework to investigate the effectiveness of building evacuation impacted by building spatial design by integrating building information model (BIM), fire simulation, and virtual reality (VR) technology. A case study was carried out to evaluate building spatial design in terms of building circulation and emergency signage. The results demonstrated that when the signage sizes were relatively small but the spacing and placement heights were reasonable, participants were able to recognize and follow the guide. However, insufficient continuity caused the participants to waste much effort searching for helpful information on evacuation-irrelevant objects, reducing fire evacuation efficiency. Moreover, the signage placed at a higher position has a positive effect on route choice. [ABSTRACT FROM AUTHOR]

Purpose: Public–Private Partnerships (PPPs) are crucial to the procurement of global infrastructure projects. Moreover, a price mode based on a cluster of core concessionary items is key to the delivery of value-for-money and successful project outcomes. However, existing research has yet to fully identify PPP concessionary items, nor yet described the range of practical price modes. This study provides taxonomy of core concessionary items impacting PPP projects, systematically classifies price modes, and assesses the applicability and risk impacts of those price modes on PPP projects. Design/methodology/approach: This study adopts a comparative case study method in analyzing core concessionary items and alternative price modes. China is taken as the context, as it is one of the world's largest PPP markets. In ensuring research validity and reliability, diverse data sources are utilized, with a graphic content analysis tool developed to capture the structure of price modes. Findings: Eight PPP price modes are identified. These are: (1) UP (Unit Price) mode, (2) ALS (Annual Lump Sum) mode, (3) IRR (Internal Rate of Return) mode, (4) RP (Return for Investing Capital (RIC) - Profit Rate of O&M (PROM)) mode, (5) RFP (RIC - Financing Interest Rate (FR) - PROM) mode, (6) RFPL (RIC - FR - PROM - Lower Limit of User Charge (LLoUC)) mode, (7) RFL (RIC - FR - Lump Sum/Fixed Unit Price O&M Contract (LSOM/FUP)) mode, and (8) RFLL (RIC - FR - LSOM/FUP - LLoUC) mode. Other main findings are as follows: (1) Five risk allocation configurations can be achieved via these price modes. Yet while different price modes enable the allocation of specific risks, these do not always align with contracting parties' original intentions. (2) IRR and RP modes may be less applicable in general because of their vulnerability in allocating critical risks and capacity for spurring opportunistic behavior. Originality/value: By depicting the paths by which concessionary items in price modes affect cash flow, a systematic analysis of price modes was conducted exposing structural characteristics, along with risk allocation choice implications. The study is unique in: (1) Providing a systematic classification of PPP price modes used in PPP projects, (2) Presenting a comprehensive identification and streamlining of concessionary items in PPP practice, and (3) Analyzing the risk effects of different price modes. Together, these outcomes offer a hitherto unavailable perspective on PPP project risk management. The value of the study lies in the following: (1) Existing studies employ diverse concessionary items, but their applicability varies. This study offers an overarching framework facilitating decision-making in selecting appropriate PPP price modes and in determining concessionary items. (2) This study adds to the understanding of PPP price modes in significant ways that will aid local governments and potential sponsors in crafting and administrating more workable contract designs. [ABSTRACT FROM AUTHOR]

The 60 GHz band has been selected for short-range communication systems to meet consumers' needs for high data rates. However, this frequency is attenuated by obstacles. This study addresses the limitations of the 60 GHz band by modifying indoor environments with square loop (SL) frequency selective surfaces (FSSs) wallpaper, thereby increasing its utilization. The SL FSS wallpaper response at a 61.5 GHz frequency has been analyzed using both MATLAB and CST Studio Suite software. 'Wireless InSite' is also used to demonstrate enhanced wave propagation in a building modified with SL FSSs wallpaper. The demonstration is applied to multiple input multiple output system to verify the effectiveness of FSSs on such systems' capacity, as well as the effect of the human body on capacity. Simulation results presented here show that modifying a building using SL FSS wallpaper is an attractive scheme for significantly improving the indoor 60 GHz wireless communications band. This paper also presents and compares two large-scale indoor propagation path loss models, the close-in (CI) free space reference distance model and the floating intercept (FI) model. Data obtained from 'Wireless InSite' over distances ranging from 4 to 14.31 m is analyzed. Results show that the CI model provides good estimation and exhibits stable behavior over frequencies and distances, with a solid physical basis and less computational complexity when compared to the FI model. [ABSTRACT FROM AUTHOR]

In ride-sharing, commuters with similar itineraries share a vehicle for their trip. Despite its clear benefits in terms of reduced congestion, ride-sharing is not yet widely accepted. We propose a specific ride-sharing variant, where drivers are completely inflexible. This variant can form a competitive alternative against private transportation, due to the limited efforts that need to be made by drivers. However, due to this inflexibility, matching of drivers and riders can be substantially more complicated, compared to the situation where drivers can deviate. In this work, we propose a four-step procedure to identify the effect of such a ride-sharing scheme. We use a dynamic mesoscopic traffic simulator which computes departure-time choices and route choices for each commuter. The optimal matching of potential drivers and riders is obtained outside the simulation framework through an exact formulation of the problem. We evaluate the potential of this ride-sharing scheme on a real network of the Paris metropolitan area for the morning commute. We show that even with inflexible drivers and when only a small share of the population is willing to participate in the ride-sharing scheme, ride-sharing can alleviate congestion. Further improvements can be obtained by increasing the capacity of the vehicles or by providing small monetary incentives, but without jeopardizing the inflexibility of the drivers. Thereby, we show that ride-sharing can lead to fuel savings, CO 2 emission reductions and travel time savings on a network level, even with a low participation rate. [ABSTRACT FROM AUTHOR]

A discrete choice experiment (DCE) was used to investigate students' preferences for mobile phone plans at a South African university. Upon obtaining the data, this study compares the predictive performance of two machine-learning models for discrete choice analysis and makes recommendations for model selection. Using concepts from blocked fractional factorial designs, a locally optimal DCE was created for the choice sets. This contrasts with alternative ways that, in practice, could be more difficult, especially when there is a large number of attributes. The call rate, data speed, customer service, premiums, and network coverage were the features considered. A total of 180 respondents were chosen from the student population using a two-stage sample approach, and data were gathered through face-to-face interviews. In this study, two deep-learning models are examined to analyze the data, which are the artificial neural network (ANN) and the extreme gradient boosting (XGBoost) models. Root mean square error (RMSE) and mean absolute error (MAE) are used to assess the model fitness, while accuracy, precision, recall and F1 score were used to compare the models' performance. The results showed that XGBoost performs better compared to ANN in model fitness and prediction. Thus, the use of the XGBoost deep-learning model in choice preference modeling is therefore encouraged. [ABSTRACT FROM AUTHOR]

The express/local mode of municipal rail transit provides passengers with multiple alternatives to achieve more efficient and superior travel, in contrast to the conventional all-stop operation mode. However, the various route choices (including direct express trains, direct local trains, or transfers) covering different passenger groups pose a significant challenge to passenger flow assignment. To understand route choice behavior, it is crucial to measure the passenger heterogeneity (variability in individual and trip attributes) in order to propose targeted solutions for operation schemes and service planning. This paper proposes a hybrid model by integrating structural equation modeling and the mixed logit model under express/local mode to estimate the impact of passenger heterogeneity on route choice. An empirical study with revealed preference and stated preference surveys carried out in Shanghai revealed how individual and trip attributes quantitatively impact the sensitivity of factors in route choice. The results show that age and trip purpose are more significant factors. Compared to the control group, the probability of express trains is reduced by 10.22% for the elderly and by 11.36% for non-commuters. Our findings can provide support for more reasonable operation schemes and more targeted services. [ABSTRACT FROM AUTHOR]

User-generated videos (UGVs) are popular in the e-business context. Product-related information in UGVs greatly facilitates online transactions. This study aims to investigate the mechanism of audience information processing in online video scenarios. Drawing on elaboration likelihood model, this study proposed an antecedent model for the perceived helpfulness of video-based online reviews from the perspective of audiences' involvement. In addition, the features of the Danmu are acted as moderating variables, moderating the choice of route by influencing the audience's involvement. The results were obtained by an online survey. Results from partial least squares structural equation modeling (PLS-SEM) indicate that video content (i.e., media richness, perceived expertise, and emotional appeal) and video source (i.e., emotional trust, physical attractiveness, and vocal attractiveness) positively influence its perceived helpfulness and the features of Danmu (i.e., emotional intensity and content similarity) moderate the relationships both positively and negatively. This study provides theoretical contributions from several perspectives and practical implications to marketers. [ABSTRACT FROM AUTHOR]

During disasters, swiftly and efficiently evacuating populations in hazardous situations is crucial to minimize losses. This study proposes a novel framework to address dynamic population evacuation (DPE) problems, which includes planning and online evacuation management phases facilitated by vehicular communication. In the planning phase, a shelter allocation problem (SAP) is solved dynamically for destination choice and a dynamic traffic assignment (DTA) for path choice toward the chosen destinations to obtain an initial evacuation plan. The initial plan is then enhanced by employing a vehicular ad hoc network (VANET) within the vehicular edge computing (VEC) architecture. This enhancement enables communication among evacuees, allowing them to revise their vehicle's route choice and planned destination. These revisions take into account the changing risk and traffic conditions. The proposed online DPE framework is applied to the real evacuation scenario of Mill Valley City, CA. The proposed model is evaluated with different VANET architectures, including vehicular cloud computing (VCC) and VEC. The results show that the VEC framework outperforms other configurations and improves the evacuation process compared to the scenario with an initial plan by more than 30% in network clearance time. Additionally, a performance analysis is carried out for evacuation scenarios with different penetration rates of connected vehicles in VANET. [ABSTRACT FROM AUTHOR]

To investigate pedestrian route choice mechanisms from a perspective distinct from that employed in discrete choice models (DCMs), this study utilizes machine learning models and employs SHapley Additive exPlanations (SHAP) for model interpretation. The data used in this paper come from several pedestrian flow experiments with two routes, which were recorded by UAV. Our findings indicate that logistic regression (similar to a binary logit model) exhibits good computational efficiency but falls short in predictive accuracy when compared to other machine learning models. Among the 12 machine learning models assessed, by calculating the new indicator named OP, we find that eXtreme Gradient Boosting (XGB) and Light Gradient Boosting (LGB) strike the best balance between accuracy and computational efficiency. Regarding feature contribution, our analysis reveals that bottlenecks exert the most significant influence on pedestrian route choice behavior, followed by the time it takes pedestrians to return from the end of the route to the origin (reflecting pedestrian characteristics and attitudes). While the pedestrian density of the shorter route contributes less compared to bottlenecks and return time, it exhibits a threshold effect, meaning that once the density of the shorter route surpasses a certain threshold, most pedestrians opt for the longer route. [ABSTRACT FROM AUTHOR]

Given the disparity in signal transmission properties for both current bandwidths and millimeter wave radio frequencies, the present propagation models used for low frequency range up to few gigahertz are not suitable to be utilized for the path loss modelling techniques as well as modulation schemes for the high frequency ranges such as millimeter wave (mmWave) spectra. As a result, rigorous research on link analysis as well as path loss modeling are needed to create a broad and suitable transmission scheme with modeling variables that can handle a broad spectrum of mmWave frequency spectra. This paper proposes an improved path loss model for estimating the path loss in an indoor space wireless communication at 28 GHz and 38 GHz frequencies. The test results for the interior non-line-of-sight (NLOS) situations were collected every two meters over spacing of 24 m separating the transmitting and receiving antenna locations to make a comparison the well-known and improved large-scale generic path loss models. The results of the experimental studies obviously demonstrate that the improved propagation model works significantly better than the CI model, owing to its simple setup, precision, and accurate function. The results show that the presented improved model gives better performance. It is observed that the standard deviation of shadow fading can be significantly reduced in the NLOS scenario, implying greater accuracy in predicting the path loss in an indoor environment. [ABSTRACT FROM AUTHOR]

This paper innovatively explores BEV (battery electric vehicle) users' risk attitudes and charging inertia, examining their effects on en route charging and charging route choice behavior. An attitudinal survey was conducted to explore the two latent variables of risk attitudes and charging inertia in relation to socioeconomic and travel-related characteristics. ICLV (Integrated choice and latent variable) models are adopted to estimate the latent variables and the charging choice behavior simultaneously. Specifically, uncertainty in energy consumption is first considered in the ICLV model, which is represented by the available range (AR) uncertainty. Multinomial logit (MNL) models directly incorporating socioeconomic attributes are employed as a reference for comparison with ICLV models. Results illustrate that risk attitudes and charging inertia both play significant roles in modeling en route charging choice behavior. Risk-averse users and users having charging inertia value AR uncertainty more. Battery range, charging frequency, and income emerge as the most crucial factors influencing users' intention to charge en route. The results show significant heterogeneity of BEV users in attitudes and charging choice behavior, underscoring the importance of accounting for the heterogeneity in en route charging demand estimation and deployment optimization of public charging stations, particularly for medium-to long-distance trips. [ABSTRACT FROM AUTHOR]