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ABSTRACTThis study investigates the impact of heterogeneous psychological inertia of travelers on link flow evolution process on a day-to-day basis. The psychological inertia of a traveler is defined as his/her reluctance to reconsider his/her route choice, and is characterized by a sequence of binary parameters along time. Travelers are grouped into different classes by their inertia patterns. Based on these classes, a variational inequality formulation for the multi-class user equilibrium problem is presented. We develop a generic day-to-day link flow dynamic model by considering heterogeneous inertia patterns of the travelers. The convergence properties of the model are rigorously demonstrated. The developed model is formulated in a general form under mild assumptions. We further consider a special case of the generic model. We examine properties of this special case model, and investigate its relationship with existing models in the literature. Numerical experiments are conducted to demonstrate our theoretical results.

This study formulates a Monkeypox model with protected travelers. The fixed point theorem is used to obtain the existence and uniqueness of the solution with Ulam–Hyers stability for the analysis of the solution to the model. The Newton polynomial interpolation scheme is employed to solve an approximate solution of the fractional Monkeypox model. The numerical simulations and the graphical representations suggest that the fractional order affects the dynamics of the Monkeypox. The fractional order shows other underlining transmission trends of the Monkeypox disease. We conclude that the result obtained for each compartment conforms to reality as the fractional order approaches unity.

In this paper, we examined travelers’ dynamic mode choice behavior under travel time variability. We found travelers’ inconsistent risk attitudes through a binary mode choice experiment. Although the results deviated from the traditional utility maximization theory and could not be explained by the payoff variability effect, they could be well captured in a cumulative prospect theory (CPT) framework. After considering the imperfect memory effect, we found that the prediction ability of the cumulative prospect theory learning (CPTL) model could be significantly improved. The experimental results were also compared with the CPTL model and the reinforcement learning (REL) model. This study empirically showed the potential of alternative theories to better capture travelers’ day-to-day mode choice behavior under uncertainty. A new definition of willingness to pay (WTP) in a CPT framework was provided to explicitly consider travelers’ perceived value increases in travel time.

The modeling of traveler’s daily travel behaviour is a complex problem. We propose a general activity-based traveler agent behaviour model for microscopic traffic flow simulation, inspired by the ideas of activity-based traffic demand model, hierarchical structure in behaviours, agent approach and subjective utility optimization method. In the case study, the general model was applied to an unsignalized intersection mixed traffic flow simulation model, and the validation results were promising.

This paper studies commuters' departure time choice behavior along a bottleneck constrained highway connecting a resident area and a workplace. Commuters are differentiated by their value of time, and the bottleneck capacity is assumed to follow a uniform distribution within a range. The equilibrium properties of the proposed model are derived and individual travel cost is obtained. It is found that at equilibrium, the increase of capacity variability would change the commuters' travel behavior by increasing the expected equivalent travel time and lengthening the peak period. Every commuter is better off by reducing the individual travel cost if the stochasticity of the capacity decrease, and commuters with high value of time benefit more than those with low values. Numerical results are presented to support analytic findings.

The main goal of the study was to integrate demand models into weather-responsive network traffic estimation and prediction system methodologies. The study examined the behavioral responses of travelers along several dimensions in response to weather-related transportation management strategies in conjunction with active travel demand management strategies before and during severe weather events. Specific management interventions included pretrip, information-based mode, and departure time choice adjustments, as well as policy-based rescheduling of school hours. The paper presents a case study of the Chicago, Illinois, area network under snow conditions to assess the effect of a combination of demand management strategies to maintain the same level of network performance as under clear weather conditions. A combination of earlier dissemination of information and school-opening policy resulted in a similar level of network performance maintained under a median snow day as compared with a clear weather day. The paper presents integrated supply and demand models for simulation and an assessment of demand management strategies in conjunction with weather-related congestion.

In the highly competitive tourist destination marketplace, destination marketers are constantly looking to enhance their brand equity by focusing on a number of key factors to strengthen their brand values. Because tourists' interests and involvement, adoption and visit intentions are fundamental elements to destination brand building, changes in these elements can serve as predictor variables for understanding the changes in destination demand. This paper examines the relationship among tourists' enduring travel involvement, destination brand equity, and visit intentions during pretrip information search. It was found that customer-based destination brand equity is a combination of key factors that can derive the overall utility that tourists place in the destination brand, some of which are impacted by enduring travel involvement. Furthermore, different facets of destination brand equity have positive impacts on destination visit intentions. This study provides new insights into the theory and practice of travelers' behavior and destination brand equity management. The findings are particularly valuable to destination marketers with direct or indirect responsibility to develop and protect the equity in their destination brands.

Substantial evidence exists to support the proposition that consumer perceptions and preferences should be the basis for tourism marketing and consumerpolicy. Some of this evidence is reviewed, and a general model of traveler destination awareness and choice is presented. Results of an empirical test of the model provide some supportfor the hypotheses presented in the model.

There is an application need for seamless multimodal advanced traveler information systems. Currently, no comprehensive network modeling approach exists to deal with routing queries for different private and public transport modes taking into account multiple attributes, dynamic travel times and time tables in large-scale transport networks. The goal of this paper is to develop and test a generic multimodal transport network model for ATIS applications. First, we model multimodal transport networks from an abstract point of view and categorize networks into private and public modes. Then we use a generic method to construct a multimodal transport network representation by using transfer links which is inspired by the so-called supernetwork technique. Among all modes, pedestrian networks play an important role in modeling transfer connections. We test our model and algorithm based on a case study in the Eindhoven region. The results indicate that our model and algorithms provide a suitable basis for ATIS applications. One current limitation is that much time is required for data reading and compiling. This can be solved by implementing existing computational strategies to increase efficiency.

The purpose of this paper is to contribute to the theoretical work on service-perceived risk management of knowledge-based services (i.e., intangible and heterogeneous) and experience-based services and to suggest a framework that helps to formalize these risks and the value associated with their management, by arguing that this risk management relies on a sequence of risks (Threat, Event, Ignorance and Damage, called the TEID model) and on three categories of control measures (preventative, detective and protective). By categorizing customer-perceived risks, and by integrating control measures and assurances into their offer, providers can design new and valuable services. Service value-chains involve various providers (implicitly or otherwise) who may engender annoyance and damages as risks are a sequence of events. This paper holds the potential to contribute to extending an understanding and management capacity of customer-perceived risks of knowledge-based services. It brings into play a new framework and new risk management process. It also helps with formalizing and making tangible customer added-value.

In this paper a new robust optimization (RO) model is proposed for route guidance based on the advanced traveler information system. The arc travel time is treated as a random variable, and its distribution is estimated from historical data. Traditional stochastic routing models just minimize the expected travel time between the origin and the destination. Such approaches do not account for the fact that travelers often incorporate travel time variability in their decision making. Recently some RO models were proposed to incorporate more statistical information into the models, but these models still ignore much information available from historical travel time data. The probability measurement, time at risk (TaR), is introduced in this paper, and a multiobjective model is built up that allows a trade-off between the expected travel time and the TaR. Thus, the skewness and kurtosis of the arc travel time distribution are taken into consideration; that is important because the travel time distributions of typical arcs show high asymmetry and long tails on the right side as a result of the impact of random incidents and events. This approach is applied in two examples, one of which is a real traffic network.

In this paper a new robust optimization (RO) model is proposed for route guidance based on the advanced traveler information system. The arc travel time is treated as a random variable, and its distribution is estimated from historical data. Traditional stochastic routing models just minimize the expected travel time between the origin and the destination. Such approaches do not account for the fact that travelers often incorporate travel time variability in their decision making. Recently some RO models were proposed to incorporate more statistical information into the models, but these models still ignore much information available from historical travel time data. The probability measurement, time at risk (TaR), is introduced in this paper, and a multiobjective model is built up that allows a trade-off between the expected travel time and the TaR. Thus, the skewness and kurtosis of the arc travel time distribution are taken into consideration; that is important because the travel time distributions of typical arcs show high asymmetry and long tails on the right side as a result of the impact of random incidents and events. This approach is applied in two examples, one of which is a real traffic network.

This paper presents a formulation and solution algorithm for a composite dynamic user-equilibrium assignment problem with multi-user classes, in order to assess the impacts of Advanced Traveler Information Systems (ATIS) in general networks with queues. Suppose that users equipped with ATIS will receive complete information and hence be able to choose the best departure times and routes in a deterministic manner, while users not equipped with ATIS will have incomplete information and hence may make decisions on departure times and routes in a stochastic manner. This paper proposes a discrete-time, finite-dimensional variational inequality formulation that involves two criteria regarding the route and departure time choice behaviors, i.e., the deterministic dynamic user equilibrium and the nested logit-based stochastic dynamic user equilibrium. The formulation is then converted to an equivalent “zero-extreme value” minimization problem. A heuristic algorithm based on route/time-swapping process is proposed, which iteratively adjusts the route and departure time choices to reach closely to an extreme point of the minimization problem. A numerical example is used to demonstrate the effectiveness of the proposed approach for assessing the ATIS impacts such as changes in individual travel costs, departure times, route inflows, queuing peaks and total network travel cost.

Traveler attitudes and behavior have been shown to correlate in numerous previous studies. However, the correlation by itself leaves open the nature of the interrelationships between traveler attitudes and behavior. For example, attitudes could either cause or be caused by behavior. In fact, both options are concurrently possible. Structural equations are applied to a set of data gathered from Los Angeles central business district workers to ascertain the direction and nature of interrelationships between attitudes and behavior with respect to frequency of taking the bus to work. A mutual dependence between attitudes and behavior is demonstrated in the context of this dataset and behavioral choice situation; behavior and attitudes concurrently cause each other. In addition, it is found that two attitudinal components, perceptions of and affect toward a mode, function differently with respect to travel behavior.

A substantial number of tourism studies concerned with destination awareness and intention to visit a specific destination have sought to identify the dominant factors influencing consumer choice behavior. The distinguishing characteristic of this study is its use of an alternative approach to understanding consumer choice behavior derived from symbolic interactionist theory. The study represents the first test of a drama-based theory in a tourism destination choice context. The motorcycle tourism market for West Virginia serves as an exemplar. The model allowed for the correct classification of 65.6 percent of respondents who had toured the state (classification error reduction of 31.2 percent) and 69.2 percent of respondents who plan to tour the state in the future (classification error reduction of 38.5 percent). Suggestions for further development of drama-based choice models and their application in the context of tourism research are discussed.

This paper explores an important problem under the domain of network modeling, the optimal configuration of charging infrastructure for electric vehicles (EVs) in urban networks considering EV users’ daily activities and charging behavior. This study proposes a charging behavior simulation model considering different initial state of charge (SOC), travel distance, availability of home chargers, and the daily schedule of trips for each traveler. The proposed charging behavior simulation model examines the complete chain of trips for EV users as well as the interdependency of trips traveled by each driver. The problem of finding the optimum charging configuration is then formulated as a mixed-integer nonlinear programming problem that considers the dynamics of travel time and travel distance, the interdependency of trips made by each driver, limited range of EVs, remaining battery capacity for recharging, waiting time in queue, and detour to access a charging station. This problem is solved using a metaheuristic approach for a large-scale case network. A series of examples are presented to demonstrate the model efficacy and explore the impact of energy consumption on the final SOC and the optimum charging infrastructure.

Introducing airplanes powered by renewable energy, such as electricity or hydrogen, is seen as one of the options to contribute to achieving climate neutrality in aviation, especially for regional and short-haul applications. It also brings new possibilities to connect small regional airports and reduce access and egress times to/from airports. This research aimed to gain insights into the acceptance and factors that may affect passenger acceptance of hybrid-electric airplanes for short-haul regional applications. In working toward the aims, a questionnaire was designed and distributed online to active long-distance travelers in Germany. Two structural equation models were estimated to identify the influential factors and their impacts on attitudes and the intention to accept hybrid-electric airplanes. The results confirmed not only the relevance of service attributes, such as travel time, safety, comfort, and climate impact, but also the impacts of psychological and behavioral factors. Generally, safety and security had the highest impact on passenger acceptance. Stronger concerns about the safety and comfort of the new airplanes and of conventional airplanes flying today might reduce the intention to use the new airplanes, whereas stronger climate concerns and trust of new mobility technology might lead to stronger acceptance. Moreover, passengers’ previous experience taking similar types of conventional propeller-driven airplanes might affect their safety concerns about the new airplanes. Information about new aircraft technology and test flying experience may help to overcome psychological barriers.

Congestion pricing policies are increasingly being considered to aid in congestion management and transportation funding in urban areas. This article presents a case study of the optimization of congestion pricing policy design using the Berkeley Integrated System for Transportation Optimization (BISTRO), an open-sourced transportation planning and decision support system (DSS) that uses an agent-based simulation (ABS) and optimization framework to evaluate transportation system interventions. The study exemplifies how the granularity offered by activity-based travel demand models and ABS can be leveraged to enhance the interpretability of multi-objective transportation policy optimization through rich analyses of the effects of policy design on both individual- and system-level outcomes. The location and size of a circular charging zone with two different pricing schemes (a cordon fee and a cordoned mileage fee) are encoded as inputs to an ABS with an activity-based travel model of 15,000 travelers. Through an analysis of the effects of various weighting schemes across congestion-, social-, and revenue-based objectives, we present a method for interpretation of the inherent trade-offs in transportation policy optimization and demonstrate the importance of cultivating transparency in policy DSS that use black-box optimization in order to produce explainable, defensible policy strategies. We find that cordoned mileage fees Pareto-dominate cordon tolls, enabling greater improvements across all objectives studied. However, the prioritization of congestion reduction poses a challenge for pricing optimization in that it may result in unnecessarily large mode shifts away from driving which significantly worsens travel cost burden. We explore the role of weighting schemes in shifting the priority of optimal pricing schemes to social equity while mitigating congestion and maintaining toll revenue.

Activity patterns provide valuable insights into activity-based travel demand modeling and understanding human mobility. However, challenges often arise in accurately recognizing activity patterns, predicting activity patterns, and ensuring the temporal transferability of these prediction models. This paper presents a novel approach that combines natural language processing (NLP) techniques and cutting-edge machine learning algorithms to recognize and predict activity patterns of urban residents using 10-year interval Household Travel Survey data. The proposed method based on NLP to effectively transforms activity sequence information, identifies more categories and provides clearer and more explanatory activity pattern information compared to using only K-Means++ for clustering. Furthermore, the study develops a correlation model between activity patterns and socio-demographic characteristics of traveler by combining various machine learning models and identifies the optimal model with positive predictive performance for both weekdays and weekends. Lastly, the temporal transferability of the model is examined by comparing activity pattern characteristics at 10-year intervals and using machine learning interpretability methods to analyze model performance, relative importance of explanatory variables, and partial dependence plots (PDPs). The results show that the proposed model possesses temporal transferability, indicating its potential for predicting refined activity demand of the population in future years. These findings enhance our understanding of urban residents’ travel and activity demand, and have potential implications for urban planning, transportation management, and policy-making.

People usually travel with different preferences to reach the location of their leisure activities, where transport mode is a decisive factor. Previous studies focus on developing discrete choice models for people where a trip purpose is considered as an explanatory variable. In this paper, discrete choice modeling is applied to model the behavior of leisure travelers, to understand the impact of several factors on the transport mode choice, such as sociodemographic variables. A sample of 1100 travelers from a household survey in Budapest is used in the analysis. The sample includes the daily activity plans of leisure travelers and their sociodemographic as well as economic characteristics. A Multinomial Logit (MNL) model is applied, where the data are examined across travel time variables and travel characteristics, such as travel time, travel cost, age, gender, income, and car ownership. The developed models are used to predict the probability of using the different transport modes based on the characteristics of the travelers and their trips. The model indicates that time and the cost impact travel negatively. While other variables show varied impacts on the transport mode selection. The output of this study is important for decision-makers to understand the patterns of leisure tips in a city.

This study proposes a trip- and area-based tradable credit scheme (TCS) for congestion management in the context of the morning commute problem using a trip-based Macroscopic Fundamental Diagram model with heterogenous travelers. In our proposed TCS, the regulator distributes credits to all travelers and designs a time-varying and trip-based credit tariff. Credits are traded between travelers and the regulator via a credit market at the price determined by credit demand and supply interactions. The TCS is incorporated into a day-to-day modeling framework to examine travelers’ learning process, network state evolution, and credit market properties. The conditions for existence of an equilibrium solution and uniqueness of the equilibrium credit price are established analytically. Simulation results validate the analytical properties, demonstrate that the proposed TCS yields identical social welfare as the congestion pricing while maintaining revenue neutrality, and show the superiority of a trip-based TCS to (trip agnostic) area-based TCS.

Tractor-trailer blow-over crashes caused by strong winds in Wyoming, U.S., can damage roadways, delay goods, and threaten the safety of all members of the traveling public. Identifying roadway sections with high blow-over risk is important for taking appropriate and targeted precautions to avoid these serious problems. This study utilizes a vehicle blow-over model compiled from previous research to quantify incident risk on Wyoming roads and test the appropriateness of existing risk assessments. Data about blow-over crashes from 2012 to 2020 were used to calibrate a new statewide model, and this model was leveraged to observe the evolution of blow-over risk during high wind periods from past blow-over crash events. The results from this model show that blow-over crashes occur in specific zones with elevated risk variables. The model was also especially precise at predicting crash locations within 10 km of nearby weather stations. This computationally efficient model can now be used for real time blow-over risk prediction using road weather information system environmental sensor stations throughout Wyoming. This dynamic risk assessing tool, continuously updated online, can help travelers with high-profile light-weight vehicles through existing traveler information systems, and this model also serves as an addition to the typical wind gust data used by decision-makers managing Wyoming roads.

Purpose: The purpose of this study is to find out the factors that influence dental travelers’ revisit intention (RI) to Tijuana, Mexico. Design/methodology/approach: Based on the push and pull model, five constructs were included: quality service, price, supporting services, cultural proximity and quality information. The information was obtained by applying an online survey to a sample of 384 dental tourists in Tijuana, Mexico, who were repeat patients of a dental clinic in Tijuana after the COVID-19 pandemic. Exploratory factor analysis, average variance explained and composite reliability were conducted to ensure the validity of each construct. Multiple regression analysis was done to identify predictors of travelers’ RI. Findings: The results obtained show that cultural proximity, quality service, price and supporting services influenced the travelers’ revisit behavior after the COVID-19 pandemic lockdowns. Originality/value: This study contributes to the body of knowledge on travel behavior in dental tourism after the COVID-19 pandemic lockdowns, which has been scarcely studied. Also, RI was analyzed, focusing on repeated travelers to propose a model mainly for borders or frontiers where developed and developing countries co-exist and interact.

The use of managed lanes (MLs) is currently predicted using discrete choice models. However, recent research discovered that many travelers were not making choices in their use of MLs—they always used one set of lanes and never varied. Therefore, choice models may not be appropriate. This research examined if machine learning could be used to model ML use by travelers. Machine learning techniques were used to classify approximately 125,000 travelers who made approximately 3.5 million trips on the Katy Freeway over a 2-year period. Different machine learning models were able to classify travelers into those who always used MLs, always used toll-free general purpose lanes (GPLs), and those who used both types of lanes (choosers). Travel time savings, total number of trips made, and the start and end location of the trip were key predictors of the user class. Neural networks with LSTM could classify the trips conducted by choosers as ML trips or GPL trips based on the start and end location of the trips and the drivers’ travel history. A comparison with logit models showed that machine learning models performed better in classifying travelers and trips made by choosers. However, trends obtained from both models were same. This research has shown that a multi-level classification of travelers and machine learning techniques were able to predict real-world traveler choice on a ML facility. This may be an important first step in shifting travel models from how we think travelers are behaving to what is actually happening.

In the early years of 21st century, the ever-increasing volume of greenhouse gases from fossil fuel consumption have made humans seek alternative, non-polluting fuels as an effective strategy to reduce pollution and prevent related environmental issues. Electric vehicles (EVs) are today known as one of the most effective solutions for this purpose. To inform transportation planning and policies pertinent to EVs, models are needed to capture travelers’ behavior for vehicle type and route choice and the impacts on traffic congestion. The present study proposes a mixed complementarity traffic assignment model for the networks involving both EVs and gasoline vehicles so that the demand for each vehicle class depends on the characteristics of the paths and availability of electric charging stations on the paths. To determine the demand of each class, this study applies a logit choice model, which incorporates the effect of ownership and operating costs on demand of each class under different subsidy policies. This paper further investigates the charging behavior of EVs by considering private and public charging facilities in urban areas. To this end, the complementary traffic assignment algorithm has been used to solve the mentioned assignment problem. Besides, we used a label-setting algorithm for solving the constraint shortest path problem. The results of applying the mixed-user equilibrium to Sioux Falls and Chicago sketch networks demonstrate that our proposed algorithm outperforms existing algorithms for both solution time and accuracy across multiple networks.

After the rapid expansion of the subway system over the past two decades, some cities are preparing to build more suburban railways. The emergence of suburban railways is bound to change the choice of suburban passenger transportation. This paper studies the factors that affect the choice of travel mode at the construction stage of suburban railways, aiming to design a more rational suburban railway network and urban public transport service system. Taking Shanghai as an example, this study first surveyed revealed preference (RP) and stated preference (SP) among urban-suburban travelers. Then, we used discrete choice models (DCM) and machine learning algorithms to build a travel mode choice model based on data collection and analysis. Furthermore, the importance of each factor was analyzed, and the effects were predicted under several traffic demand management schemes. Finally, this study proposed some strategies for increasing the share of public transport. On the one hand, it is suggested that Shanghai should continue to develop suburban railways and maintain low pricing of public transport services. Considering the construction and operation costs, the government needs to provide certain subsidies to stabilize prices. On the other hand, as passengers are very sensitive to the “last mile” trips in their suburban railway travel, transport planners should strengthen the connection from and to the suburban railway stations by developing services such as shared bikes and shuttle buses. In addition, the results indicated that some traffic demand management measures can also contribute to a larger share of public transport.

Public transport network design deals with finding efficient network solution(s) from a set of alternatives that best satisfies the often-conflicting objectives of stakeholders like passengers and operators. This work presents a simulation-based optimization (SBO) model for designing public transport networks. The work’s novelty is in developing such a network design model that fully accounts for the stochastic behavior of commuters on the transit network. The SBO discipline solves decision-based problems like the transit network design problem (TNDP) by combining simulation and optimization models. The proposed model integrates a disaggregated activity-based travel demand simulation with a multi-objective network optimization algorithm. Trip-based travel demand models are commonly used to represent traveler behavior in the literature. The approach limits its ability to accommodate the stochastic realities of traveler behavior in a transit network design solution. Using activity-based simulation instead makes it possible to account for a more realistic traveler behavior, especially real-time decisions made in response to changing network dynamics which ultimately affect the distribution of demand over time on the network. The proposed model is applied to the improved design of the integrated public transport network in the City of Cape Town, South Africa. The results show SBO can design efficient network solutions that reflect the objectives of network stakeholders.

To forecast the induced passenger flow of intercity high-speed rail, two related multinomial logit models are applied to describe travelers’ travel choice behavior under changes in the travel environment. The first model describes different choices of travel mode by travelers between origin–destination pairs, while the second model describes the choice of travel frequency by different travelers. By introducing the consumption surplus variable of travelers, the two selected models are correlated and the model is calibrated using data from a behavioral survey and an intention survey. The results show that the polynomial logit model is applicable for forecasting induced passenger flow after changes in the travel environment. The consumption surplus of travelers is the key factor influencing changes in travel frequency, and the social characteristics of travelers and the economic development features of origin and destination cities have significant impacts on travel frequency. In-transit time is the key factor affecting the demand elasticity of travel frequency. The range of demand elasticity for short-distance business and non-business travel is between −0.61 and −0.79. The results of the demand elasticity analysis were applied to predict that the travel frequency of people from Nanjing to Huai’an, Suqian, Lianyungang, and Yancheng will increase by about 45% after the opening of the Nanjing-Huai’an intercity high-speed railway.

In recent years, advanced information communication technology (such as beyond 5G and 6G) will enable convenient end-to-end communication. This would allow collections of individual preferences as predictions of travellers’ perceptions, and thus support data analysis and traffic control in a dynamic circumstance. Therefore, analysis of nonlinearly modelled perceptions and perception-based user equilibrium are the necessary problem we encounter. This research explores the existence characteristics of perception-based stochastic user equilibrium (P-SUE), a type of stochastic user equilibrium where travelers make route choices according to their perceptive traffic circumstances instead of actual ones. A nonlinear perception model with stochasticity is developed and the traffic arrivals are approximately transformed from Poisson-distributed to Normal-distributed. From a static perspective, we develop a P-SUE model with a heterogeneous traveler community and discuss the interaction among travelers’ attitude, grouping and static perception-based stochastic user equilibrium (P-SSUE). After embedding a day-to-day learning process into the P-SSUE model, we further explore evolutionary dynamics of the equilibrium. Several scenarios are provided to investigate effects and sensitivity of traveler-related and road-related factors. It is found that learning produces perturbation and makes the existence condition of P-SSUE more complicated; the convergence processes of P-SSUEs are significantly shortened as the learning rate increases to higher than 0.1.

Large volumes of end-user-generated textual data are assembled every day which leads to the evolution of social media in the form of reviews/feedback, and brief description messages. As a consequence, end-user often see it difficult to understand more concerning the subject being discussed or appropriate knowledge from such material. The enormous amount of text-oriented data that is accessible via the online platform is analyzed using machine learning and natural language processing algorithms, including topic modeling techniques that have been more prevalent in current years. The novel approach is proposed to represent travel categories called the Normalized Category Travel Personality (NCTP). The main purpose of the technique is to construct the semantics of category feedback to model travelers’ interests to create the category travel personality (CTP) representation. Likewise, we normalize the CTP to obtain our proposed model. The NCTP category model will apprehend the explicit and implicit aspects of categories shown by the social-circle groups to find sentiment scores. The TripAdvisor dataset was considered to evaluate the performance of the NCTP model based on the topic-model quality, implicit and explicit characteristics, and some legacy statistical evaluation metrics, like Recall, Mean Average Precision, and Mean Reciprocal Rank.

As federal spending and planning for air transportation infrastructure looks to prioritize access for disadvantaged populations, aviation systems planning metrics that measure accessibility at the individual level are necessary. Existing metrics, from the mobility-driven metrics focused on efficiency and on-time performance to geographic accessibility metrics focused on connectivity, lack the detail of the multiple, interlocking constraints that limit potential travelers (especially lower-income travelers) from executing their agency and accessing the aviation system. We seek to develop a methodology, resulting in new analysis metrics, to quantify accessibility on an origin–destination basis based on individual constraints, time- and cost-based impedance, and aviation travel supply. We develop and apply our Aviation-accessibility Integrated Mobility (AIM) metric to empirically model relative accessibility based on traveler-specific constraints, accounting for individual-level sensitivity to travel costs and propensity to travel by ground access modes. We illustrate how equity-focused variables can change the calculus and geographic distribution of accessibility by applying the AIM to our case study region: Philadelphia, Baltimore, and Newark metropolitan areas, a region with significant socioeconomic disparities, to diverse markets (Austin, Atlanta, Nashville). Our findings indicate that incorporating individual constraints greatly influences the calculation of accessibility; additionally, we find that transportation supply and service characteristics alter the distribution of accessibility. Our model supports a national map of accessibility and potential policy recommendations to expand traditional federal airport infrastructure projects, such as targeted air service enhancement.

Priced managed lanes (MLs) offer travelers an option to choose to pay a toll to travel on the MLs for a generally faster, more reliable travel than on the adjacent, toll-free general-purpose lanes (GPLs). Recent research has shown that many travelers on freeways with MLs choose the same lane type every trip regardless of travel time savings and toll rate. In this paper, a classic experimental economics traffic game was replicated and re-examined to model traveler choice by classifying travelers as “choosers” (people who frequently choose between MLs and GPLs) and “non-choosers” (people who use only one type of lane). This traffic experiment was then augmented with a travel survey to find the individual differences (psychological traits), trip-related variables, and socio-demographic variables that help differentiate travelers into the above two categories. Based on the traffic experiment and travel survey, it was found that: (1) the experiment could identify real life choosers and (2) many more travelers indicate that they are choosers in a survey than in actual travel. Features that are related to the choosing behavior were the time taken by the subject to answer a survey section and time used to verify their answers before submitting. Participants who spent more time on those things tended to be choosers. Travelers who were direct responders in the experiment tended to be choosers. Other factors that differentiated choosers from non-choosers were trip duration, familiarity with the ML facilities, and education level.

•A MIMIC model is developed to measure travelers' perceived usefulness, subjective norms, attitudes, habits, and behavioral intentions towards customized buses.•An improved DBSCAN is utilized to generate carpooling stations.•A hybrid genetic-ant colony algorithm is designed to solve bi-level programming model for CB line optimization.•The study yields benefits for passengers, bus operators and urban sustainable development.

Mathematical models are essential to predict the likely outcome of an epidemic. Various models have been proposed in the literature for disease spreads. Some are individual based models and others are compartmental models. In this study, discrete mathematical models are developed for the spread of the coronavirus disease 2019 (COVID‐19). The proposed models take into account the known special characteristics of this disease such as the latency and incubation periods, and the different social and infectiousness conditions of infected people. In particular, they include a novel approach that considers the social structure, the fraction of detected cases over the real total infected cases, the influx of undetected infected people from outside the borders, as well as contact‐tracing and quarantine period for travelers. The first model is a simplified model and the second is a complete model. From a numerical point of view, the particular case of Lebanon has been studied and its reported data have been used to estimate the complete discrete model parameters using optimization techniques. Moreover, a parameter analysis and several prediction scenarios are presented in order to better understand the role of the parameters. Understanding the role of the parameters involved in the models help policy makers in deciding the appropriate mitigation measures. Also, the proposed approach paves the way for models that take into account societal factors and complex human behavior without an extensive process of data collection. Author summary:Mathematical models use mathematical concepts to describe systems. In epidemiology, models try for instance to predict the evolution of the number of infected in the population or the duration of an epidemic. Such models can show how different public health interventions may affect the outcome of the epidemic. A class of existing models consists of ordinary differential equations that are based on the assumption of homogeneous mixing of the population. To be more realistic, we propose herein two discrete models that take into account heterogeneities in the population such as the social activity level of individuals.

Understanding the travel behavior of individuals grouped by similar time-use activity patterns can contribute greatly to modeling regional spatial and temporal patterns of transport demand. In this paper, we present a comprehensive modeling framework to forecast and replicate individuals’ travel behavior, labeled as the Scheduler for Activities, Locations, and Travel (SALT). The prototype version of the SALT framework comprises a series of modules that employ behaviorally-based econometric, machine-learning, and data-mining techniques. The SALT model is cross-validated with 30% of the out-of-home sample survey data from the large Halifax Space Time Activity Research (STAR) household survey. Results show that the SALT scheduling model is able to assemble the travelers’ 24-hour schedules with an average 82% accuracy compared to the observed data. The proposed simulation modeling framework is useful for deeper understanding of individuals’ activity-travel decisions and may be utilized to examine sensitive policy issues such as transportation control measures and congestion-pricing.