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The actions are oriented to delight travelers where existing protocols fall short of solving problems in the travel and tourism industry, seeking the retention of commuters by offering travel quality. The present study investigates the travel decision-making process by exploring the nexus between travel quality and re-travel intentions (RTI). The study also analyzes the relational switching cost's (RSC) unique role as a sequential mediator in driving travel quality through the theoretical lens of expectancy disconfirmation theory. Furthermore, the study also explores the role of gender in explaining RTI. 434 real-time responses were collected following the lifetime value (LTV) approach. The structural equation modeling (SEM) was used to test the hypotheses. The study reveals that travel quality significantly influences commuter satisfaction. The results also suggest the indirect effect of RSC on commuter satisfaction (CS) and (RTI). Finally, it is suggested that RTI is higher among female commuters than male commuters. The fundamental contribution of this study is developing and testing the travel decision-making process by extending the EDT model. Additionally, the TRAVQUAL scale contributes to measuring perceived and expected travel quality. Commuter satisfaction and relational switching costs act as sequential mediators, while gender emerges as a moderator, emphasizing the greater significance of re-travel intentions among female commuters post-RSC. These insights contribute valuable perspectives for enhancing travel experiences and decision-making processes in the travel and tourism industry.

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.

Climate changes are affecting the control of many vector-borne diseases, particularly in Africa. In this work, a dengue fever model with protected travelers is formulated. Caputo-Fabrizio operator is utilized to obtain some qualitative information about the disease. The basic properties and the reproduction number are studied. The two steady states are determined and the local stability of the states are found to be asymptotically stable. The fixed point theory is used to obtain the existence and uniqueness of solutions of the model. The Adams–Bashforth scheme is employed to solve an approximate solution of the fractional dengue model. The numerical simulation suggests that the fractional-order affects the dynamics of dengue fever.

With governments worldwide emphasizing environmental protection and the global focus on carbon reduction, the battery electric vehicle (BEV) industry has developed rapidly. An urban transportation network with BEVs as the main form of transportation will soon become mainstream. Motivated by the abovementioned background, a BEV transportation network design problem is investigated, and a network design model is established. The model aims to minimize the system travel time of BEV transportation networks and optimize the government’s lane expansion scheme (the location and number of lanes) under a limited budget. To consider the travel characteristics of BEV drivers, the charging time, range anxiety, and bounded rationality factors are simultaneously incorporated into the model. A heuristic algorithm is designed based on the active set algorithm to obtain the local optimal solution to the actual-scale problem. Moreover, a cutting-plane method is used to convert the original problem into a different form, and a column generation technique is embedded in the abovementioned algorithm to avoid the enumeration of paths. Sensitivity analyses of different levels of rationality of BEV drivers and government investment scales are performed. The experimental results demonstrate that the model and algorithm can effectively solve the problem and provide decision support for the government in formulating transportation infrastructure construction policies.

With governments worldwide emphasizing environmental protection and the global focus on carbon reduction, the battery electric vehicle (BEV) industry has developed rapidly. An urban transportation network with BEVs as the main form of transportation will soon become mainstream. Motivated by the abovementioned background, a BEV transportation network design problem is investigated, and a network design model is established. The model aims to minimize the system travel time of BEV transportation networks and optimize the government's lane expansion scheme (the location and number of lanes) under a limited budget. To consider the travel characteristics of BEV drivers, the charging time, range anxiety, and bounded rationality factors are simultaneously incorporated into the model. A heuristic algorithm is designed based on the active set algorithm to obtain the local optimal solution to the actual-scale problem. Moreover, a cutting-plane method is used to convert the original problem into a different form, and a column generation technique is embedded in the abovementioned algorithm to avoid the enumeration of paths. Sensitivity analyses of different levels of rationality of BEV drivers and government investment scales are performed. The experimental results demonstrate that the model and algorithm can effectively solve the problem and provide decision support for the government in formulating transportation infrastructure construction policies., Author(s): Xu Xin [1]; Tao Zhang [1]; Cui Li [2]; Yanran Liu (corresponding author) [3]; Lingyu Gao [4]; Yuchuan Du [5] 1. Introduction As reported by the World Meteorological Organization [...]

Dynamic traffic assignment (DTA) has been a topic of substantial research during the past decade. While DTA is gradually maturing, many aspects of DTA still need improvements, especially regarding its formulation and solution capabilities under the transportation environment impacted by the Advanced Transportation Management and Information Systems (ATMIS). It is necessary to develop a set of DTA models to acknowledge the fact that the traffic network itself is probabilistic and uncertain, and different classes of travelers respond differently under uncertain environment, given different levels of traffic information. This paper aims to advance the state-of-the-art in DTA modeling in the sense that the proposed model captures the travelers(tm) decision making among discrete choices in a probabilistic and uncertain environment, in which both probabilistic travel times and random perception errors that are specific to individual travelers, are considered. Travelers(tm) route choices are assumed to be made with the objective of minimizing perceived disutilities at each time. These perceived disutilities depend on the distribution of the variable route travel times, the distribution of individual perception errors and the individual traveler(tm)s risk taking nature at each time instant. We formulate the integrated DTA model through a variational inequality (VI) approach. Subsequently, we discuss the solution algorithm for the formulation. Experimental results are also given to verify the correctness of solutions obtained.

The paper describes current trends in the traveler information supply system and the prospect for potential markets for advanced traveler information in the United States and Europe. Alternative revenue models are identified that may be able to support the operation of the newly created traveler information centers in the post-field operational test phase. In the past, public agencies have been reluctant to share traffic management responsibilities with the private sector. However, this study found that the current trend is toward a public-private partnership in the form of a franchised or revenue sharing program. In the United States and Europe, many private entrepreneurs are currently involved in beta testing their products and in assessing the business viability of advanced traveler information services. In Japan, several types of in-vehicle devices are already commercially available to the consumer. The market potential for privately offered advanced traveler information services in the United States and Europe is; however, yet to be determined since consumer purchase behavior for traveler information is largely unknown. The study investigated alternative revenue models that could support the operation of the newly created Traveler Information Centers in the post-field operational test phase. The interest was to explore the Traveler Information financing mechanisms for short- and long-term viability in Traveler Information Center operation. Keywords: Revenue Model, Market, Advanced Traveler Information System

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.

Understanding travelers' acceptance of Advanced Traveler Information Systems (ATIS) is crucial to the implementation of Intelligent Transportation Systems (ITS) capable of mitigating traffic congestion and improving network performance. This paper adopted an extended Technology Acceptance Model (TAM) to predict and explain road users' intention to use Variable Message Sign (VMS) information. In addition to the traditional parsimonious TAM constructs (perceived usefulness, perceived ease of use and behavioral intention), the model examined the effects of attitude towards route diversion, familiarity with road network and information quality on road users' acceptance of VMS. 762 drivers were interviewed and the obtained data were processed using Structural Equation Modeling. The results showed that travelers' attitude towards route diversion had a positive effect on perceived usefulness and intention to use VMS. Information quality had a positive direct effect on perceived usefulness, perceived ease of use and attitude towards route diversion. Familiarity with the network had a positive effect on attitude towards route diversion and a negative effect on the perceived usefulness of VMS information. Perceived ease of use significantly and positively affected perceived usefulness and intention to use VMS. Perceived usefulness also had a positive effect on intention. Several academic and practical implications were also discussed.