فصلنامه مهندسی حمل و نقل
سال چهاردهم شماره 4 (پیاپی 57، تابستان 1402)

Lack of uncertainty studies is one of the common shortcomings in travel demand forecasts. This has led to overestimation of demand and has made some transportation infrastructures unprofitable. Investing in projects with less but more reliable profit is important. Mount Carlo simulation is one of the most common methods for uncertainty and sensitivity analysis. One of the main requirements of Monte Carlo is the use of efficient random number generators to generate random numbers with high uniformity. But the uniformity of random numbers produced by pseudo-random number generators (PRNG’s) may be sometimes good and sometimes poor. But quasi-random number generators (QRNG’s) produce a sequence of deterministic random numbers with more uniformity and thus better filling the unit hypercube. Therefore, it is necessary to examine and quantify the uncertainty of travel demand models and investigate the effects of RNG’s. The aims of this research article are threefold: 1. Measure the uncertainty of travel demand (production and attraction) models; 2- Using sensitivity analysis, rank the input variables that play the most important role in the uncertainty of the model outputs; and 3- Investigate the effect of quasi-random and pseudo-random generators on uncertainty. The results of this study showed that the travel production and attraction models reduce the uncertainty of inputs and the uncertainty of the attraction models is higher than the production models. The Latin hypercube sampling (LHS) and shuffled Halton are marginally more stable than other methods assessed in this study.

Appreciating the crucial importance of roads in keeping different areas connected to one another, road maintenance and repair represent a vital role of any road transportation management. Microsurfacing is an important preventive method for paved road maintenance. The present research is aimed at investigating the effect of adding polypropylene fibers into microsurfacing mix on its performance. For this purpose, performance evaluation of the microsurfacing mix was conducted by preparing five different mixes containing polypropylene fibers at different dosages, namely 0, 0.05, 0.10, 0.15, and 0.20% by weight to the weight of total aggregate. Analyses were performed by doing wet cohesion test, loaded wheel test, and wet track abrasion test according to ASTM D6372. Results showed that the polypropylene fiber-reinforced mixes exhibited improved microsurfacing performance. Among other mixes, the one containing the fibers at 0.10% showed 20.6 and 21.1% higher bitumen cohesion in the 30- and 60-min cohesion tests, respectively. Also using this mix, vertical and lateral displacements could be reduced by 38.2 and 28.9%, respectively, resistance to abrasion improved by 35.6%, and bleeding decreased by 25.4%. Compared to the control specimen, the mix containing polypropylene fibers at 0.10% exhibited 1.6% higher bitumen emulsion, providing for achieving the desired cohesion within a predefined period of time. The other results showed that excessive incorporation of the polypropylene fibers into the microsurfacing mix would inhibit optimal dispersion of the fibers across the mix, leading to accumulation of the fibers in the form of nodules and hence deteriorating the mix performance.

In this research, in order to construct environmentally friendly green asphalt mixtures, steel slag with a size of 9.5-4.75 mm to a volume ratio of 60% of aggregates and red mud waste with 50 and 100% ratios of filler has been used as an alternative to limestone filler, and self-healing index and moisture sensitivity of these mixtures have been evaluated. To do that, the semi-circular bending test (SCB) was performed by seven damage-healing cycles using the microwave irradiation heating method. Results indicated that the steel slag and 50% red mud waste in the first healing cycle increased the optimum healing ratio compared to the control sample, respectively; 53.01% and 32.35%. Also, the moisture susceptibility test results showed that the amount of moisture resistance in mixtures containing steel slag and 50% red mud decreased by 16% and 21%, respectively, compared to the control sample. Using 50% and 100% carbonation cake as a replacement for limestone filler in mixtures containing steel slag has increased the moisture resistance by 9% and 13%, respectively. Also, this weakness of moisture resistance in the sample containing 50% of red mud was compensated by using 50% of carbonation cake, and moisture resistance was increased by 12%. According to the obtained results, the use of these waste materials instead of non-renewable natural resources such as aggregates, in addition to preserve the non-renewable natural resources, will improve the healing performance of asphalt mixtures.

Composite indicators are the collection of a group of sub-indicators to measure multidimensional principles that can't be measured by one indicator. Utilizing Composite Indicators (CIS) as like beneficial tools to assess overall performance of transportation safety analysis has extended of recent times. Amongst many models, the Data Envelopment Analysis Model (DEA) is these days an essential tool of constructing CIs amongst different performance methodologies these days. But DEA assumes that all data are specific or quantitative values to be used in basic DEA-based CI models. However, in some problems, the observed values of input and output data are sometimes imprecise or fuzzy statistics due to there are many variables in the road safety that follows natural ambiguity and often there ​​are imprecise values, which can only be represented by qualitative data. In this study, we studied two methods within the DEA framework for modeling quantitative and qualitative data within the context of constructing composite indicators. There are Imprecise DEA (IDEA) and Fuzzy DEA (FDEA) models respectively. Based on their principle, new models of IDEA-CIs and FDEA- CIs are obtained in the assessment of road safety management via developing a composite indicator of road safety for 30 countries round the world. Comparing the results are obtained from the two models with the primary data confirms the efficiency and reliability of the two models. In the end, targets were calculated for inefficient countries then Benchmarks were set and road safety strategies were prioritized for them.

Occurrence of road accidents in terms of severity and loss of life and property in various environmental and geometric conditions has always been studied and considered. In this study, a multilayer perceptron model with different architectures including one and two hidden layers with different numbers of neurons has been used to estimate the number of road accidents. In modelling of independent variables (input data) including horizontal arc radius, time of the accident, age of the driver causing the accident, gender, having a driving license, type of vehicle, weather conditions, average daily traffic, ambient light, cause of accident, severity and location of accident used. The dependent variable (model output), the number of accidents was divided into four classes (first class equal to one accident, second class equal to 2 accidents, third class equal to 3 accidents, and fourth class equal to 4 accidents). In this regard, the reports of accidents on the Tehran-Pardis freeway between January 2017 and August 2019, which were received from the Tehran-Pardis freeway company, have been used. The number of accidents that occurred during this period was equal to 368 cases, of which 44 cases were excluded due to lack of recorded information and the neural models were constructed using 324 reports. The results show that the hidden two-layer neural network model with 14 neurons each with an accuracy of 83.3% for training data and a total accuracy of 83.3% showed the best performance in estimating the number of accidents.

After network and flight schedule design, routing and aircraft maintenance and repair issues as well as aircrew planning are among the major decisions that affect airline costs. When making decisions on each of these issues, there are specific operational restrictions and rules and regulations that must be followed. If these decisions are integrated and the optimal response is found, there will be a significant reduction in airline cost. In this research, assuming that an airline has designed the flight program at a strategic level, how to optimally implement this program is discussed; to this end, the optimal integration of aircraft routing decisions and the crew's movement plan (flight team) was considered. The desired problem for an airline is solved with net base and crew diversity, with the goal of minimizing total costs. One of the biggest challenges in solving this problem is the possible disruption of the original plan, which makes aircraft and crew planning ineffective in practice. Therefore, in this research, we define the possible disturbances in the initial plan as several different scenarios and by using the two-step robust scenario-oriented optimization approach, we provide a robust answer to the problem of integrating the decisions of aircraft network routing. And the crew's travel plan. In the proposed stable model, in addition to the decision integration, the adjustable variables of the second stage, including the decision to cancel the flight in the event of a disturbance, are also seen so that the model is adaptable to different disturbance scenarios. . The numerical results of the implementation of the proposed model confirm the applicability of the proposed model to provide an integrated and stable answer to the research problem.

Reducing the consumption of energy carriers is one of the main issues and necessities in the current economic conditions of the country. Natural gas is one of the important carriers that is responsible for most of the thermal energy supply in freight and passenger terminals. In this research, first, a set of solutions to reduce natural gas consumption in terminals have been collected, a significant number of which are related to the powerhouses of terminals. Then in this regard, five criteria of technical ease of implementation of the solution, ease of measuring the reduction of gas consumption through the implementation of the solution, appropriate time to implement the solution, access to specialized technicians to implement the solution and access to parts and tools needed to implement the solution. Evaluation and prioritization of solutions were considered. The weight and importance of each criterion are not equal. Therefore, pair-wise comparisons were made between the two criteria to finally calculate the weight coefficients of the criteria. These strategies were scored based on the above five criteria by a group of experts and were prioritized based on the scores obtained using the Analytic Hierarchy Process (AHP) technique. Finally, using the K-Means clustering technique, the solutions were classified and presented in three groups based on the scores of the five criteria evaluated. Some of these solutions are related to technical adjustments, insulation, equipment replacement, compliance with performance standards, installation of new equipment, and basic measures.

The issue of how to make roads safer for drivers in different age groups is important due to the constant change in the age distribution of drivers. With this in mind, the purpose of this study is to investigate the different characteristics of accidents and to provide suggestions on how to improve road safety for all age groups. This study uses the Road Safety Information Database (HSIS) to determine the factors influencing the severity of motor vehicle accident injuries for young drivers (25-25 years old), middle-aged drivers (64-26 years old) and elderly drivers (over 64 years old) in California between 2015 and 2017. A multinomial logit model was used to model the accidents related to each age group and to evaluate the weight of different variables predicting the severity of the driver injury. The predictor variables were classified into four characteristics: driver, road, accident and environment. The results show that rear-end collisions significantly increase the risk of death and injury for all three age groups, while clear weather and rainy weather reduce the risk of death and injury for all three age groups. In addition, the results show that there are many similarities between the predictors of accident severity for young and middle-aged drivers, but older drivers should be more careful in environmental and road conditions compared to other age groups. Young drivers improve their driving skills over time due to less driving experience, while middle-aged drivers, especially male drivers, have more driving experience and therefore tend to engage in less risky behaviors. Another result of this study is that the driving behavior pattern of older drivers have less variation than other age groups regardless of the surrounding conditions.

The purpose of this study was to identify the factors affecting the measurement and management of the marketing performance of Iran Airports and Air Navigation Company (Economics in Aviation and Airport). This research is exploratory and qualitative content analysis technique has been used. The study population consisted of university professors in the field of aviation as well as senior managers of airports, who were considered as a sample with the snowball technique of 15 people. Based on the results, 15 main indicators were identified to measure the marketing performance of Iran Airports and Air Navigation Company. These indicators include airport staff, general and operational costs, annual passenger reception capacity, number of passenger reception and dispatch counters, airport runway, airline loyalty to the use of airport space and facilities, regional market share of transit flights, and Deposits, claims and net profit and loss (aviation and non-aviation) By keeping input indicators constant, output indicators can be improved and performance efficiency and thus the economic situation in aviation can be increased.

Automated image processing technologies are being implemented increasingly for infrastructure monitoring applications thanks to their high level of accuracy, versatility and efficiency. Therefore, they can be employed for management, repair, and maintenance of transport meta-structures to reduce costs, enhance accuracy, and accelerate task completion. Moreover, the novel technology of unmanned aerial vehicles (UAVs) had a considerable effect on the improvement of different industries. This study addresses the influx of sand to the rail surface in desert areas. This problems is now analyzed through the conventional method (i.e. field observation), which is the simplest possible technique requiring ongoing inspection and large amounts of time and resources. Hence, the use of novel technologies of image processing and artificial intelligence as well as aerial images helped take a major step in the automated analysis of this problem. In rail networks across desert areas, the influx and accumulation of sand block railways, slow down and stop trains, and sometimes cause accidents. Thus, it is essential to adopt an automated system for the timely detection and resolution of this problems in order to prevent additional costs or further accidents. UAV-based imaging, smart image processing based on convolutional neural networks, and deep learning are employed in this study to design a comprehensive system for monitoring the problems. This system can greatly decrease the costs of monitoring the safety of rail networks and significantly improve the accuracy and efficiency of repair and maintenance systems. The proposed system can also provide the centralized ongoing monitoring of Iran’s transport network.

Walking is the most traditional mode of transportation that still plays an important role in daily life and since the development of motor vehicles, the issue of pedestrian safety has become important. One of the most important factors affecting pedestrian safety is pedestrian characteristics and behaviors. Valid data are needed for investigating the effects of various factors. However, variables related to behavioral data such as mental conditions cannot be found in the crash database. Thus, in this study, instead of crash data, near-crash data was used. in this regard, using a questionnaire, the number of near-crash data and various aspects of 378 pedestrians' data were collected. Then, the effects of different factors on the pedestrian safety were investigated. In this regard, the effects of various personal characteristics (included age, education, average monthly income and driving years), mental health, walking experience, violations and the feeling of safety while walking on the pedestrian safety were investigated by considering near-crash events and using SEM technique and SmartPLS software. Results indicated that, among the study factors, the violations with a loading of 3.809 and the pedestrians' personal characteristics included age, education, average monthly income and driving years, with a loading of 2.127 have the most and the least impacts on the pedestrian safety. Besides, mental health with a 2.515, walking experience with a 2.446 and the feeling of pedestrian safety while walking with a 2.308 loading affect pedestrian safety.

Deep reinforcement learning methods have shown promising results in the development of traffic signal controllers. In this paper, we evaluate the flexibility of a controller based on Deep Reinforcement Learning under high traffic volume and under a variety of environmental disruptions, such as accidents, and propose a reliable controller in a dynamic traffic environment. In this method, using the discretization approach, each of the intersection roads is divided into cells and the effect of the size of these cells in terms of whether they are different or identical is studied on the efficiency of the algorithm. By selecting an extended and dense state space, the agent is given information as input that can provide it with a complete understanding of the environment. The Q-deep learning method and experience replay are used to train the agent, and the proposed model is evaluated in the SUMO traffic simulator. The simulation results confirm the efficiency of the proposed method in reducing the queue length even in the presence of a disruption.