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Ride-sharing services have attracted significant interest due to overcrowding, limited energy resources, and environmental concerns. This study proposes a mathematical programming model that integrates user preferences into the ride-sharing problem with transfer. Passenger transfer in ride-sharing problems addresses the limitations of the matching process, especially in less-populated areas. It allows passengers to be dropped off at meeting points and continue with another driver. Moreover, considering user preferences in ride-sharing systems is crucial for enhancing efficiency and user satisfaction. Accordingly, we propose a Preference-Driven Matching Algorithm for the matching process. Our proposed algorithm captures user preferences and provides potential matches. In addition, we introduce an Iterative Enhance-and-Optimize Algorithm capable of producing high-quality solutions within short computational times. We evaluate the efficiency of these approaches across various instances, focusing on real-scale scenarios. Based on the results, the model with preferences demonstrates effective performance compared to other methods. Our findings underscore the importance of user preferences in optimizing ride-sharing problems and highlight the trade-off between efficiency and user satisfaction. By incorporating user preferences, our approach results in higher user satisfaction, a more responsive and efficient system with reduced response times, and increased demand and revenue by servicing more users. [ABSTRACT FROM AUTHOR]

Given the utmost importance of global climate change as one of the most extensively discussed subjects in environmental engineering along with its growing impact on human life and the environment, numerous studies have been thus far completed on the sources of greenhouse gas (GHG) emissions, particularly carbon dioxide (CO2). In Iran, the construction industry sector is also a major source of GHGs, accounting for 30% of the country's total GHG emissions. In the present study, the contribution of the construction industry sector in fulfilling Iran's obligations was analyzed based on the Paris Agreement within the United Nations Framework Convention on Climate Change to reduce CO2 emissions. Besides, this study aimed to investigate the potential of reducing CO2 emissions through the replacement of 5%, 7.5%, and 10% of cement with marble waste dust (MWD) or 20%, 25%, and 30% of cement with granite waste dust (GWD). For this purpose, firstly, cement production capacity in different Iranian provinces, over a 12-year period, was collected. Then, an economic analysis of transportation, energy consumption, and landfilling costs was conducted based on the data collected by a researcher-made questionnaire, designed to obtain data about cost saving achieved by recycling MWD and GWD instead of landfilling them. As to consider uncertainty in cement production, the lower, middle, and upper boundaries of cement production per capita as feasible scenarios were also estimated, using bootstrapping at a 95% confidence interval for the target year/or 2030. Finally, the potential to reduce CO2 emissions in each Iranian province and the entire country was estimated. According to Iran's commitments under the Paris Agreement, the country is required to moderate its GHG emissions by 4% if there are international sanctions or by 12% provided that such penalties are lifted between 2021 and 2030. The study findings revealed that the partial replacement of cement in concrete with GWD would allow Iran to meet its 2030 GHG emission reduction obligations under the sanction conditions. The results additionally showed that if the concrete production trends in this country, in the past 12 years, continue in the future, the replacement of 20%, 25%, and 30% of cement with GWD will lower the annual CO2 emissions by 54, 67.6, and 81.1 million tons, respectively. Furthermore, it was demonstrated that the only scenario in which Iran could respect its commitments, for the situation where the sanctions are lifted, is the one involving the replacement of 30% of cement with GWD. [ABSTRACT FROM AUTHOR]

Introduction: Attitudes play a key role in predicting speeding behavior. This study aimed to test: a) whether the positive/negative attitudes towards speeding, and the positive/negative attitudes towards compliance with the speed limit can predict the speed limit compliance behavior; b) which of the positive and negative dimensions of the attitudes have a greater contribution; c) whether the positive/negative attitudes towards compliance with the speed limit can account for variance in the speed limit compliance behavior over and above that explained by the positive/negative attitudes towards speeding. Materials and Methods: In the present study conducted in September 2020 to March 2021, 202 people who drive daily in the city of Mashhad-Iran voluntarily completed self-report questionnaires measuring their driving experiences, positive and negative attitudes towards speeding and compliance with the speed limit, and past behavior of compliance with the speed limit. The data were analyzed by using multiple regression analysis and SPSS-25 software. Results: The positive-negative attitudes towards speeding independently accounted for a significant proportion of the variance of speed limit compliance behavior (R2= 0.32, P< 0.001), and the negative attitude towards speeding was the stronger predictor (β= 0.35, P< 0.001). The positive/negative attitudes towards compliance with the speed limit also accounted for a significant increment (3.6%, P< 0.01) to explain variance in speed limit compliance behavior. Conclusion: The results provide more support for the bi-dimensional attitudes towards speeding in predicting speed limit compliance behavior. It demonstrates that the positive/negative attitudes towards compliance with the speed limit improve the prediction of the behavior independently of the positive/negative attitudes towards speeding. Also, negative attitude has a greater contribution in predicting speed limit compliance behavior. [ABSTRACT FROM AUTHOR]

The present study investigated the relationship between offenses of drivers in terms of number of tickets and their demographic, behavioral, and personality factors. The researchers had the aim of identifying significant contributing predictors of being ticketed and comparing the relationship between aberrant driving behaviors and tickets for those identified as offending. Ticket frequency models were developed to estimate the drivers’ offenses, potential for improvement method was applied to screen out offending drivers and correlation analysis was used to figure out the probability of being ticketed with inclination to commit aberrant driving behavior. A sample including 1,762 drivers was collected. The Iranian drivers responded to a questionnaire aimed at compiling various measures of personality type, aberrant driving behaviors, and demographic and ticket history information. The sample consisted of 78% male and 22% female with a total mean age of 35.6 (SD = 11.987) years. Six models with various independent variables were developed using generalized linear modeling (GLM) approach with a negative binomial error structure. The results indicated that different combinations of variables such as education level, car price, marital status, age, personality type, income level, place of residence, driving experience and exposure and gender in particular influence driving tickets. In the screening procedure, 620 drivers were identified as offending drivers. Comparison analyses between aberrant driving behaviors evidenced that lapses, errors, ordinary and aggressive violations are significantly different for offending drivers at 5% significant level. A correlation comparison between offending and non offending drivers revealed that ordinary violations play a more important role than the others on being ticketed. [ABSTRACT FROM AUTHOR]