Search Results (33)

This paper proposes an incentive scheme based on a public–private partnership (PPP) to encourage low-carbon travel behavior by inducing the mode choice shift from private cars to public transit. The scheme involves three key entities: travelers, the government, and the private sector. Travelers can choose between private cars and public transit, producing different emissions. As the leader, the government aims to reduce total emission to a certain level with limited budgets. The private sector, as an intermediary, invests subsidies in low-carbon rewards to attract green travelers and benefits from a larger user pool. A two-layer multi-objective optimization model is proposed, which includes travel time, monetary cost, and emission. The objective of the upper level is to maximize the utilities of the private sector and minimize social costs to the government. The lower layer is the user equilibrium of the travelers. The numerical results obtained through heuristic algorithms demonstrate that the proposed scheme can achieve a triple-win situation, where all stakeholders benefit. Moreover, sensitivity analysis finds that prioritizing pollution control strategies will be beneficial to the government only if the unit pollution control cost coefficient is below a low threshold. Contrary to intuition, larger government subsidies do not necessarily lead to better promotion of low-carbon travel. Full article

Empirical studies have suggested that travelers’ risk attitudes affect their choice behavior when travel conditions are stochastic. By considering the travelers’ risk attitudes, we extend the classical two-route model, in which road capacities vary due to such shocks as bad weather, accidents, and special events. Two information regimes have been investigated. In the zero-information regime, we postulate that travelers acquire the variability in route travel time based on past experiences and choose the route to minimize the travel time budget. In the full-information regime, travelers have pre-trip information of the road capacities and thus choose the route to minimize the travel time. User equilibrium states of the two regimes have been analyzed, based on the canonical BPR travel time function with power coefficient $p$. In the special case $p=1$, the closed form solutions have been derived. Three cases and eleven subcases have been classified concerning the dependence of expected total travel times on the risk attitude in the zero-information regime. In the general condition $p>0$, although we are not able to derive the closed form solutions, we proved that the results are qualitatively unchanged. We have studied the benefit gains/losses by shifting from the zero-information to the full-information regime. The circumstance under which pre-trip information is beneficial has been identified. A numerical analysis is conducted to further illustrate the theoretical findings. Full article

Tourism refers to the activity of traveling for pleasure, recreation, or leisure purposes. It encompasses a wide range of activities and experiences, from sightseeing to cultural exploration. In today’s digital age, tourists often organize their excursions independently by utilizing information available on websites. However, due to constraints in designing customized tour routes such as travel time and budget, many still require assistance with vacation planning to optimize their experiences. Therefore, this paper proposes an algorithm for personalized tourism planning that considers tourists’ preferences. For instance, the algorithm can recommend places to visit and suggest activities based on tourist requirements. The proposed algorithm utilizes an extended model of the team orienteering problem with time windows (TOPTW) to account for mandatory locations and activities at each site. It offers trip planning that includes a set of locations and activities designed to maximize the overall score accumulated from visiting these locations. To solve the proposed model, the Adaptive Neighborhood Simulated Annealing (ANSA) algorithm is applied. ANSA is an enhanced version of the well-known Simulated Annealing algorithm (SA), providing an adaptive mechanism to manage the probability of selecting neighborhood moves during the SA search process. The computational results demonstrate that ANSA performs well in solving benchmark problems. Furthermore, a real-world attractive location in Tak Province, Thailand, is used as the case study in this paper to illustrate the effectiveness of the proposed model. Full article

The Portuguese Institute for Quality is responsible for the realization and dissemination of the frequency standard in Portugal. There are several techniques for frequency transfer, but we use a frequency mixer to detect phase variations between two light signals with different wavelengths, traveling along an optical fibre. In this paper, we present the development of a digital twin (DT) that replicates the use of a frequency mixer to improve the frequency transfer problem. A setup was built to train and validate the technique: a frequency mixer was used to determine the phase difference between the two signals, which are caused by temperature gradients in the fibre, together with real-time temperature data from sensors placed along the fibre and on the mixer itself. The DT was trained with two machine learning algorithms, in particular, ARIMA and LSTM networks. To estimate the accuracy of the frequency mixer working as a phasemeter, several sources of uncertainty were considered and included in the DT model, with the goal of obtaining a phase value measurement and its uncertainty in real time. The JCGM 100:2008 and JCGM 101:2008 approaches were used for the estimation of the uncertainty budget. With this work, we merge DT technology with a frequency mixer used for phase detection to provide its value and uncertainty in real time. Full article

The impact of vessels on dolphin populations has been extensively studied worldwide. The common dolphin, Delphinus delphis, has been observed in the Tagus estuary for the past two centuries, and during the last several years, these sightings seem to have increased. This area has high levels of maritime traffic throughout the year, both commercial and recreational. To understand the possible effects of vessel traffic on dolphins’ behavior, land-based observations were carried out from March 2022 to March 2023. For a total of 67 events (48.9 h of dolphin sightings), differences in behavioral budgets were noted. Although “neutral reaction” was the most observed response when vessels were in the vicinity of dolphins, “negative reaction” was also common and five times more abundant than “positive reaction”. The GEE model showed statistical differences between these reaction types (positive, neutral, and negative). Markov chains’ analysis revealed distinct patterns in the behavioral transition probabilities, as dolphins were more likely to switch to a traveling state when vessels were nearby. This study is the first step towards understanding a potential impact source in the area since it is expected that tourism companies expand due to the increase in dolphin sightings in the estuary. Full article

There have been numerous theoretical and empirical transportation studies contesting the stability of commuting time over time. The constant commuting time hypothesis posits that people adjust trip durations, shift across modes, and sort through locations, so that their average commuting time remains within a constant budget. There is a discrepancy between studies applying aggregate analysis and those using disaggregate analysis, and differences in data collection may have contributed to the varying conclusions reported in the literature. This study conducts both aggregate and disaggregate analyses with two travel surveys of the Portland region. We employ descriptive analysis and t-tests to compare the aggregate commuting times of two years and use regression models to explore factors affecting the disaggregate commuting time at the individual trip level to examine whether the stability of the commuting time remains after substantial changes in the transportation and land use systems. Our study indicates that the average commuting time, along with the average commuting distance, increased slightly, as the mode share shifted away from driving during the examined period. The growth in shares of non-driving modes, which are slower than driving, coupled with an increased travel distance, contributed to the small increase in the average commuting time. Our analysis also indicates that the average travel speed improved for transit riders as well as drivers, contradicting earlier research that claims that public transit investment has worsened the congestion in Portland. Full article

Affected by climatic factors (e.g., low temperature and intense ultraviolet radiation), high-altitude regions experience numerous pavement diseases, which compromise driving safety and negatively impact user travel experience. Timely planning and execution of pavement maintenance are particularly critical. In this paper, considering the characteristics of pavement maintenance in high-altitude regions (e.g., volatility of traffic volume, seasonality of maintenance timing, and fragility of the ecological environment), we aim to derive optimal monthly maintenance plans. We develop a multi-objective nonlinear optimization model that comprehensively accounts for minimizing maintenance costs, affected traffic volume and carbon emissions, and maximizing pavement maintenance effectiveness. Utilizing linearization methods, the model is reconstructed into a typical mixed-integer linear programming (MILP) model, enabling it to be solved directly using conventional solvers. We consider five types of decision strategies to reflect the preferences of different decision-makers. Given the uncertainty of maintenance costs, we also utilize the robust optimization method based on the acceptable objective variation range (AOVR) to construct a robust optimization model and discuss the characteristics of optimistic, robust, and pessimistic solutions. The results suggest that different decision strategies show differences in the indicators of maintenance costs, affected traffic volume, carbon emissions, and pavement performance. When multiple decision objectives are comprehensively considered, the indicators are between the maximum and minimum values, which can effectively balance the decision needs of maintenance effectiveness, maintenance timing, and environmental protection. The number of maintenance workers, the requirement of the minimum pavement condition index (PCI), and the annual budget influence the maintenance planning. The obtained robust solution can somewhat overcome the conservative nature of the pessimistic solution. The method proposed in this paper helps address the complexities of pavement maintenance decisions in high-altitude regions and provides guidance for pavement maintenance decisions in such areas. Full article

Measuring the accessibility of each traffic zone to high-speed railway stations can evaluate the ease of the transportation hub in the transportation system. The utility-based model, which captures individual travel behavior and subjective perception, is often used to quantify the travel impedance on accessibility for a given origin–destination pair. However, existing studies neglect the impacts of travel time uncertainty on utility and possible choice set when measuring accessibility, especially in high-timeliness travel (e.g., railway stations or airports). This study proposes a novel integration of the utility-based model and multimodal space–time prism under travel time uncertainty to measure reliable accessibility to high-speed railway stations. First, the reliable multimodal space–time prism is developed to generate a reliable travel mode choice set constrained by travel time budgets. Then, the reliable choice set is integrated into the utility-based model with the utility function derived from a proposed mean–standard deviation logit-based mode choice model. Finally, this study contributes to measuring reliable accessibility within areas from Beijing’s 5th Ring Road to the Beijing South Railway Station. Based on the results, policymakers can effectively evaluate the distribution of transportation resources and urban planning. Full article

This study proposes a target-oriented method to study travelers’ route choice behavior under travel time variability, and discusses the resulted equilibrium flow patterns. Both travel time reliability and travel time unreliability are considered in this new method, and accordingly, there are two targets. The first one is target for travel time to ensure travel time reliability, and based on this target, another one is target for excess delay to mitigate travel time unreliability. In this model, travel time and excess delay (i.e., the random vector) are stochastically correlated with each other, which is modeled with the copula function based on Sklar’s Theorem, and the exact form of the copula is obtained by the proved comonotonicity relationship of this random vector. The target interaction, i.e., the complementarity relationship, is also modeled based on the utility functions, the meaning of which is that travelers have the will to make more targets achieved so as to obtain more utility. Furthermore, with this model, this paper formulates the user equilibrium as a variational inequality problem to study the long-term effect of the route choice behavior, and solves it with the method of successive average. Finally, numerical testings on the traffic network are conducted to show the convergence of the solution algorithm, and to illustrate the impact of targets on the equilibrium results. Results show that the flow change can be five times more than that with less risk-averse travelers. Full article

Water distribution systems (WDSs) are massive infrastructure systems designed to supply water from sources to consumers. The optimal operation problem of WDSs is the problem of determining pump and tank operation to meet the consumers’ demands with minimal operating cost, under different constraints, which often include hydraulic feasibility, pressure boundaries, and water quality standards. The water quality aspect of WDSs’ operation poses significant challenges due to its complex mathematical nature. Determined by mixing in the systems’ nodes, it is affected by flow directions, which are subject to change based on the hydraulic state of the system and are therefore difficult to either predict, control, or be included in an analytical model used for optimization. Water age, which is defined as the time water travels in the system until reaching the consumer, is often used as a general water quality indicator—high values of water age imply low water quality, whereas low values of water age usually mean fresher, cleaner, and safer water. In this work, we present the effects that tank operation has on water age. As tanks contain large amounts of water for long periods of time, water tends to age there significantly, which translates into older water being supplied to consumers. By constraining the tank operation, we aim to present the trade-off between water age, tank operation, and operational cost in the WDS optimal operation problem and provide an operational tool that could assist system operators to decide how to operate their system, based on their budget and desired water age boundary. The analysis is applied to three case studies that vary in size and complexity, using MATLAB version R2021b and EPANET 2.2. The presented results show an ability to mitigate high water age in water networks through tank constraints, which varies in accordance with the system’s complexity and tank dominance in supply. The importance of a visual tool that serves as a guide for operators to tackle the complex problem of controlling water age is demonstrated as well. Full article

Road networks play a significant role in each country’s economy, especially in countries such as Afghanistan, which is strategically located in the international transit path from Europe to East Asia. In such a country, pavement performance models are fundamental for the pavement maintenance planning that provides high-quality infrastructure for transporting goods and travelers. However, due to the lack of a budget for pavement monitoring and maintenance in Afghanistan, transportation networks and pavement condition data have not been widely acquired for the development of a pavement performance model. The main aim of this study is to use a machine learning technique to, for the first time, develop a pavement performance model for Afghanistan that uses simple, cost-effective, and fairly accurate data—collected via smartphones—and that is based on a case study of over 550 km of Afghanistan’s highways. First, the current condition of Afghanistan’s road network is investigated using a smartphone. Then, collected data are prepared and analyzed so as to estimate the pavement condition index (PCI). Finally, a pavement performance model for PCI is developed using pavement age with an adequate coefficient of determination of 0.70 and successfully validated. It is concluded that the proposed approach is efficient and effective when developing a performance model in other developing countries encountering such data and budget limitations. Full article

As society advances and various technologies like AI and LLMs are further developed, the proportion of human labor contributing to the productivity of nations and societies is gradually decreasing. This has led to increased attention to the quality of life of individuals, and cases of implementing policies such as a four-day work week are on the rise. Therefore, the objective of this study was to analyze the patterns of how people are spending their increased leisure time amid this social trend and to identify the factors influencing these patterns. Building upon the need-based theory proposed in previous studies, this research analyzed people’s recurrent discretionary activity patterns. Multiday analysis was conducted considering the characteristics of leisure activity patterns, and a hazard-based duration model was estimated for statistical analysis. The research results revealed that people’s patterns of consecutive activities are influenced by various factors, such as socio-economic attributes, time–space budgets, previous activity experiences, and preferences for specific days of the week. Through this, we were able to confirm that socio-demographic and household characteristics, as well as attributes of time/space budgets, influence the growth speed and threshold of needs as suggested in need-based theory. Additionally, we observed a preference for specific days of the week for different types of activities. As a result, people tend to either postpone activities until specific days even when their need has accumulated sufficiently or engage in activities on specific days even when the need has not yet accumulated to the desired level. This study demonstrates novelty in that it utilizes the need-based theory proposed in prior research to identify factors influencing multiday activity participation patterns. Additionally, it presents the first study providing model estimation results from the perspective of need-based theory. The correlation between the time–space budget and discretionary activity patterns identified in this study is expected to serve as a guideline for future transportation-related policies, including regional balanced development. This study demonstrates a unique contribution compared to existing research in that it established that, with improvements in activity/travel conditions, there can be an induced demand for activities. This finding can contribute to the feasibility study of transportation projects and the establishment of policies related to regional balanced development. Full article

Participatory budgeting has been advocated as an advanced tool of civic participation and a travelling innovation for more than three decades. This paper provides a bibliometric review of the concurrent body of knowledge on participatory budgeting (PB), explaining how this democratic innovation ‘travelled’ through time and over different scientific fields. This study was based on a dataset of 396 papers on PB published from 1989 to January 2023. The study finds that research in PB has reached its peak of scholarly attention in pre-COVID-19 pandemic years. The study also finds that the research on PB has migrated from the field of political science to other fields, such as economics, management science, law, urban planning, environmental science, and technology. Full article

This paper examines the effects of boundedly rational decision characteristics on travelers’ route choice behavior. The concept of boundedly rational confidence level (BRCL) is redefined, which is the probability that a trip arrives between the acceptable earliest arrival time and the acceptable latest arrival time on the shortest travel time budget (TTB). Mathematically, the acceptable boundedly rational arrival thresholds are proposed. Then, a reliability-based boundedly rational traffic equilibrium model (R-BRTE) considering both travel time reliability and acceptable arrival thresholds is developed. Moreover, the equivalent variational inequality problem and uniqueness of solution on the proposed model are proved. A route-based solution algorithm is used to solve the proposed R-BRTE model. Numerical results present the important decision ideas of the proposed model. The results demonstrate that travelers’ bounded rationality has a great impact on their route choice behavior and network performance. Full article

In this paper, the allocation of charging station (CS) is optimized to alleviate the “range anxiety” of electric vehicle (EV) drivers by reducing the time of medium-to-long distance travel, which is raised due to the potential en-route charging. The problem is defined to explicitly consider the spatial differences in urban land price. Although many works take spatial land price into consideration, few of them notice what the gap of spatial land price bring to the charging system. Our objective function is the expected traveling time under an optimized distribution of urban EV flows, and models of spatial network and CSs allocation are then established. Based on Tabu Search algorithm (TSA), a fixed budget charging resources planning algorithm (FBCRPA) is proposed. The proposed method is compared with methods based on betweeness centrality, and results show that our method can find more effective allocation strategy. It is found that users’ traveling time would decrease with increase in difference in land price. Meanwhile, budget would transfer from central region to other regions and carrying capacity of charging system would improve in the above situation. This paper also finds that increase in budget is beneficial to a reduction in drivers’ time, but the improvement is limited. Full article