Scientia Iranica
Volume:31 Issue: 19, Nov-Dec 2024

Predicting OD demand has always been a challenging problem in transportation. Conventional demand prediction methods mainly propose procedures for forecasting aggregated temporal Origin-Destination (OD) flows. In other words, they are primarily unable to predict short-term demands. Another limitation of these models is that they do not consider the impact of environmental conditions on trip patterns. Furthermore, OD demand prediction requires two individual steps of modeling: trip generation and trip distribution. This article presents a framework for predicting hourly OD flows using the Neural Network. The proposed method utilizes trip patterns and environmental conditions for predicting demands in single-step modeling. A case study on New York City Green Taxi 2018 trip data is done to evaluate the method, and the results demonstrate that the network has reasonably accurate OD flows predictions.

In order to compute level of service and density in weaving segments, the Highway Capacity Manual (HCM) defined for the first time in 2010 a relationship based on a lane change rate to assess the density of the weaving segment. It is critical to accurately estimate lane changing rate in these situations, but field observations in weaving segments shorter than 250 meters in Tehran, Iran revealed a significant difference between the HCM2016 model estimate and field data. The traffic and geometric data collected at 87 (15-minute) intervals from six weaving segments in Tehran were used to develop models for estimating lane changing rate in weaving segments. These 87 intervals were then divided into 69 (terrain data) for equations and 18 (test data) for model comparisons. Weaving volume and weaving segment area are introduced as two independent variables in the optional lane changing rate model of weaving vehicles in this study, with R2=0.74. Furthermore, for a lane-changing model of non-weaving vehicles with R2=0.95, two new variables of non-weaving volume and traffic solidity were defined. Finally, based on the 18 intervals used to test the results, it showed the improvement of the developed models results compared to HCM2016 models.

High compressive strength concrete is desirable in the construction industry. The valuable effect of high-power ultrasonic in the manufacturing industries is the motivation of this research in the construction industry. For this purpose, high-power ultrasound was employed to increase the compressive strength of concrete. Also, the effect of these waves on the water absorption of concrete has been studied. Ultrasonic waves were tested in two modes, one independently and the other in combination with the conventional concrete compaction method. The results did not show a significant change in the improvement of mechanical properties of concrete when using ultrasonic waves independently. However, in combination with the conventional method, the effect of waves on improving mechanical properties was significant. In this way, the results showed a 12.5 % reduction in water absorption for cubic samples and illustrate a 15.5 % for cylindrical samples. Furthermore, the results showed that the use of ultrasonic waves as an auxiliary process in the conventional method of concrete compaction increases by 12.5% and 15% in the compressive strength of cubic and cylindrical specimens, respectively. The results showed that high-power ultrasonic waves have great potential to be added to 3D concrete printer accessories for further research.

Due to the differences between men and women, they are expected to exhibit different travel behaviors. In developing countries, there are limited studies on citizen travel behavior. The main aim of this study is to clarify the transportation behavior distinctions between men and women by modeling the effect of lifestyles and attitudes on the commuters’ mode choice. For this purpose, an interview questionnaire was designed, and 979 participants were interviewed in Tehran. Questions were posed about attitudes toward transportation, attitudes toward residential location, and lifestyle of individuals. The results of factor analysis indicate a significant difference between men’s and women’s lifestyles and attitudes. The total effect of public oriented, walking oriented and vehicle oriented attitude on car use for men (women) are 0.149 (0.138), -0.145 (0.088), and 0.234 (0.324) respectively. These results show that men with walking-oriented attitude use car less but women with this attitude use car more. These differences verify the importance of making transportation policies addressing gender distinctions.

Gasoline is one of the essential commodities in today's societies and its price can affect different aspects of citizens' lives. This article studies the impacts of the sudden increase in gasoline price on the volume of intercity traffic at national and provincial scales, along with the changes in driving speed distribution and the percentage of speeding violations. The studied period includes four months in two consecutive years. Data was collected from more than 2400 loop detectors placed on the roads of 31 provinces of Iran. This data is used for implementing Regression Discontinuity Design (RDD) on traffic volume and speeding violation percentage. Statistical measures of speed distribution are also used to examine changes in driving speed. According to the literature, the optimal driving speed for fuel consumption is about 50 to 70 km/h. This study showed a 7.4% increase in the probability of driving in the 50 to 70 km/h range after the price increase. Also, according to research results, the volume of intercity traffic and the percentage of speeding violations has decreased due to the rise in gasoline price.

Autonomous Vehicles (AVs) can provide safe, clean and efficient mobility by using advanced communication technologies to create an unprecedented revolution in transportation. Acceptance of AVs has a key role in their successful implementation. Most researchers have used Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB) and Unified Theory of Acceptance and Use of Technology (UTAUT) to identify latent factors affecting, which focus only on individuals' internal schema of beliefs without considering the external factors of acceptance. The current study, uses Trialability (TR), Observability (OB) extracted from Diffusion of Innovations (DOI) theory, Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI) extracted from UTAUT, as well as Perceived Risk (PR), Environmental Concerns (EC) and Consumer Innovativeness (CI)) to identify a wider set of latent factors. A stated preference survey conducted to this purpose in Tehran allowed collecting 641 responses. Considering the latent nature of research variables, Structural Equation Modeling is applied. Results show that PE, EE, PR, OB, SI, TR, CI and EC affect acceptance in decreasing order of regression weights, an explain 72.5% of the variance in the dependent variable..

Due to the many advantages of FRP decks, such as lightweight and high strength, recently, using FRP decks as building deck panels is considered an alternative choice to traditional decks. Accordingly, there is an increasing need for an analysis tool for engineering and academic applications. Finite element is an accurate and reliable method for analyzing FRP decks. However, high computational cost and modeling difficulty somewhat limit its application. To overcome this shortcoming, this study presents an integrated, easy-to-use, computationally-efficient, and yet rather accurate analysis method for FRP decks. This integrated method was implemented in a computer code and can be easily used to analyze building FRP deck panels. To evaluate the deck's applicability as a building floor panel system, some requirements are needed to be met, including maximum allowable elastic deflection, local stability of components, vibration frequency, and ductility of the flooring system. The proposed method uses the Rayleigh-Ritz method to calculate these requirements. Using three different FRP deck examples, it was shown that the proposed method is generic and capable of analyzing various forms of the FRP deck panels, including all-FRP and hybrid decks made of two or more different materials.

Concomitant reduction of cost and duration is recognized as one of the main aspects of construction planning. Expedition of project schedule naturally incurs extra costs due to implementation of more productive and/or high-price construction techniques. Meanwhile, a reduction in time is usually plausible only down to a certain limit, below which renders expeditions either technically or financially unviable. Thus, striking a reasonable balance between project cost and duration remains a desirable yet challenging task for which there has been a myriad of advancements and literature. Despite the many studies associated with this problem – referred to as time-cost trade-off problem (TCTP) – it is observed that only a few exercise TCTPs with generalized logical relationships. This observation holds despite the fact that generalized precedence relationships are imperative to introduce parallelism and to secure a realistic overlap among the activities. In this regard, a Simulated Annealing-based Genetic Algorithm as proposed herein, is specifically designed to provide the capability of exerting TCTPs with properly overlapped activities. Efficiency of this algorithm is tested over a wide range of problems and its performance is validated over a large-scale real-case construction project. Results of the hybridized GA indicate fast and robust convergence to high-quality solutions.

Composite metal deck slabs are designed and reinforced at the bottom for positive moments with minimum thermal reinforcement at top. However, when the dynamic impact loading is applied to these slabs without sufficient upper part reinforcements, it may be failed under negative moments. The current study investigates the performance of composite metal deck slabs under free drop weight impact loading. The study is conducted in two main parts: generating a data set using numerical simulation analysis, and training several machines based on the generated data set using machine learning procedures. In the first part, 165 models were produced and analysed using LS-DYNA commercial software. In machine learning part, the FEM results were used as a data set to train the machines and to make prediction on performance of composite metal deck slabs with high accuracy. The main results of the conducted analyses are reported in terms of the maximum negative moment, maximum deflection, and elastic and plastic behaviour of the slab. It was seen that in the cases with high striker velocity, the specimens experienced an ultimate internal negative moment at the range of 60 to 80 kN.m.

The increase in urban population density, car ownership, and car dependency, especially in developing countries highlights the need for sustainable public transportation development. This paper aims to recognize factors affecting people’s satisfaction with public transportation services including, bus rapid transit (BRT) and metro with a focus on the moderating effect of ‎environmental concerns. ‎Satisfaction is modeled using the structural equation modeling approach developed on the preferences of 625 respondents in Tehran, Iran in 2020. The results show direct, and significant, ‎with conflicting impacts of the perceived values and service quality on the satisfaction of metro and BRT users. ‎Further, the results indicate that in contrast with BRT, service quality is more crucial affecting satisfaction than the perceived value in metro. The service quality is also more affected by the core service than the physical environment and the infrastructure in metro. While concerning BRT, these two variables have similar ‎impacts. Environmental concern negatively moderates the relationship between service quality and satisfaction while positively moderates the relationship ‎between the perceived value and satisfaction of both transit systems.