arash rasaizadi

Nowadays, traffic congestion is a major problem in metropolises that affect daily life. Imbalance between supply and demand causes conjestion. Demand management is used to balance demand and supply, since has more reasonable cost and less restrictions in comparison with increasing transportation supply. One policy in order to manage the demand and reduce Tehran traffic conjestion is to implement flexible working hours since Mehr 1401. In this study, the effect of flexible working hours on traffic congestion has been investigated by using length of Tehran congested roads. The linear regression has been modeled based on the length of traffic congestion of the third week of Mehr 1398 before the Covid-19 epidemic in comparison with the third week of Mehr 1401. It was expected to face more road congestion due to the traditional education after the virtual education during Covid19 epidemic, user behavior changes, increase in usage of private cars. Nevertheless by implementing flexible working hours, Tehran traffic congestion has been far more suitable except the Limited traffic zone than the urban management's prediction.

One of the information needed for all planning problems and specifically transportation planning is to have accurate prediction about the future. Traffic variables prediction is one of the efficient tools in travel demand management. Using this tool and advanced traveler information systems (ATIS), the predicted traffic variables are informed to the users and transportation system operators to make plans and set policies. In this study, the average speed and traffic volume of the Karaj to Chalus suburban road with the high variation of traffic variables in the north of Iran is predicted. The Karaj to Chalous road is part of the route from Tehran as the capital of Iran to the country's northern coast. Along the Karaj to Chalous road, three parallel roads, with different lengths, connect Tehran with the cities of the north. In general, finding the pattern of non-mandatory trips is more complicated than mandatory trips. Generally, the predictive methods are divided into three groups, naïve, parametric and non-parametric methods. Among the various predictive models, the SARIMA as a parametric model and the artificial neural network and the support vector machine as nonparametric models are employed. In the data pre-processing step, the variables affecting the average speed and traffic volume are extracted and added to the dataset as predictor variables. These variables are related to time, calendar, holidays, weather, and roads blockage. Also, because of the importance of the maximum and minimum values of traffic speed and volume, as critical values and rare events, models are evaluated with emphasis on the prediction of rare events compared to normal values. The results show that, for the test data, the lowest root mean square error of predicting the average traffic speed and traffic volume are obtained using artificial neural network and support vector machine models equals 139 vehicles per hour and 5 kilometers per hour, respectively. In terms of R2 of prediction-observation plot, the performance of SARIMA for predicting the average speed and traffic volume is the same for the test dataset. In contrast the R2 of hourly traffic volume prediction is higher for the training data. The R2 of artificial neural network model and the support vector machine for traffic volume prediction is higher than traffic speed prediction. The lowest root mean square error of predicting the first and fourth quartile of the observed average traffic speed values is obtained by support vector machine models and artificial neural network, respectively. Also, predicting the first quartile and fourth quartile of the observed traffic volume values by the support vector machine model is more accurate than two other models. Using predicted traffic parameters and providing them to travelers and transportation agencies by intelligent transportation systems leads to make a balance between travel demand and travel supply in the near future which is the main aim of this study. Travelers can have a better personal plan for their future trips based on these predictions. Also, the transportation agencies are more prepared to deal with critical traffic situations and can prevent traffic congestion.

Nowadays, traffic congestion is a major problem in metropolises that affect daily life. The imbalance between supply and demand causes congestion. Demand management is used to balance demand and supply since has more reasonable costs and fewer restrictions in comparison with increasing transportation supply. One policy in order to manage the demand and reduce Tehran traffic congestion is to implement flexible working hours since October 2022. In this study, the effect of flexible working hours on traffic congestion has been investigated by using the length of Tehran's congested roads. The linear regression has been modeled based on the length of traffic congestion of the second week of October 2019 before the Covid-19 epidemic in comparison with the second week of October 2022. By flexible working hours, the graphs of the average hourly queue length have become smoother. In 2022 this chart has two prominent peaks as morning peak and evening peak. In 2022, the height of these two peaks has decreased and the trips have been more evenly distributed over time. In 2022, the height of these two peaks has decreased and the trips have been more evenly distributed over time. It was expected to face more road congestion due to the traditional education after the virtual education during the Covid-19 epidemic, user behavior changes, and an increase in the usage of private cars. Nevertheless, by implementing flexible working hours, Tehran's traffic congestion has been far more suitable except for the Limited traffic zone than the urban management's prediction.

Fatality, injury, disability, and medical expense, road and vehicle damage, and mental aspects are the main disadvantages of crashes in a community. This study tries to find the influential factors, which are predictable on the severity of crashes in the rural highway using the descriptive logit model family. First, crash data are integrated with traffic data obtained from loop detectors in rural road. Second, the Ordered logit (OL) and Multinomial logit (MNL) models are developed to identify the influential factors on crash severity. Third, the models are evaluated in terms of goodness of fit. They are applied for the short-term prediction of crash severity on rural road network in Khorasan Razavi, one of the most populated province in the northeast of Iran. Results show that the overall goodness of fit for both OL and MNL models are 0.017 and 0.019, respectively that expectedly indicates the MNL model is more accurate. The independent traffic variables "heavy vehicle flow" and "speed above 85 km/h" are significant in the suggested OL model. In the MNL model, the independent variable "minimum headway" is also significant for accidents with serious damage, in addition to the significant variables "heavy vehicle flow" and "speed above 85 km/h" for the severity levels of injury and fatality.

Over the past few decades, there has been a lot of interest in short-term traffic prediction which is a crucial part of transportation systems. One of the major considerations that travelers evaluate when making travel plans is information regarding the near future. Transportation planners have investigated a number of methods to produce more reliable travel time predictions in the future. However, there have not been enough in-depth and comprehensive surveys in this area yet. In this paper, a comprehensive review of the literature has been conducted, and various traffic parameter prediction algorithms have been investigated. The methods can be divided into two main groups: parametric, and nonparametric. Parametric models are those that require the specification of some parameters before they can be used to make predictions. In contrast to nonparametric procedures, parametric approaches have a distribution with a defined number of parameters. The parametric approaches are related to statistical methods like time-series, while the nonparametric approaches are related to machine learning methods like neural networks. Predicting flow, volume, speed, density, and occupancy are the main emphasis of the majority of the literature. A detailed methodology is first presented for each of the techniques mentioned in this article, and then the outcomes of numerous articles that have used the technique are discussed.

Predicting traffic variables and informing the passengers and the transportation network operators is one way to manage the travel demand. By informing the future state of traffic through intelligent transportation systems, there is more readiness to avoid congestion. In this study, three machine learning models, including support vector machine (SVM), random forest (RF), and long short-term memory (LSTM), were used to predict the hourly traffic state, consist of light, semi-heavy and heavy states, for Karaj to Chalus rural road in the north of Iran. Predictor variables of mid-term models are calendar information, weather, and road blockage policies. In contrast, in short-term models, in addition to mentioned variables, the observed traffic states in the past three to eight hours have been used, and these models can only predict the future of one and two hours of future. The results show that short-term LSTM is the most accurate traffic state predictor model with accuracy equal to 90.11%. Among the mid-term models, the LSTM model has predicted traffic state more accurately than SVM and Rf, and its accuracy is equal to 82.07%. Also, LSTM has the highest values of f1 to predict light, semi-heavy, and heavy, which are equivalent to 0.86, 0.93, and 0.81, respectively. Also, the hour, holiday, and type of holiday variables and traffic state observed in 3 to 8 hours later variables have the greatest effect on increasing the accuracy of mid-term and short-term models, respectively.

Speed violation is one of the main causes of accidents. High speed not only increases the probability of occurrence of accidents but also increases the severity of accidents. So a vital point is trying to prevent the occurrence of speed violations. In this study, the hourly average traffic speed for Khorramabad to Arak highway is predicted for the future. If the predicted speed is near or exceeds the permitted speed, it is necessary to consider arrangements and preparations to reduce the average speed of traffic by users or the transportation network operators. In order to predict hourly average traffic speed, related traffic data was recorded in recent years. Many new features that affect traffic speed are extracted and used in predictive models. Three machine learning methods, including support vector machine, artificial neural network, and recurrent neural network, have been used. All three methods have the ability to analyze big traffic data, and in addition, the recurrent neural network has more consistency with the time-series nature of data. The results show that for both directions of this highway, by using only calendar and weather features, the mean absolute percentage error of the models is varied between 2.8 to 5.1 percent. Models can predict speeds over 80 kilometers per hour with precision over 80 percent. By adding the observed speed of the previous 3 to 8 hours as predictive features, the mean absolute percentage error of the models is decreased to 2.5 to 4.6.

Short-term prediction of traffic parameters and informing them to travelers and transportation operators is a useful tool for advanced traveler information systems. Also, as an advanced traffic management system, it helps to make or maintains the balance between travel demand and supply for the near future. This paper predicts the hourly traffic level of service, which has easily understandable information for all users. Data used in this study is related to 5 sections of a critical suburban road in the north of Iran. This data was collected for five years, and due to its high volume, it is considered big data. Long short term memory and deep neural network as two deep learning algorithms and support vector machine as a well-known classifier are trained by the first four years records. Results show that in average long short term memory predictions are more accurate for all sections, which compared to the second precise model, long short term memory predictions are higher between 1 and 14%. Using long short term memory for predicting level of services A and C, support vector machine for predicting level of services B and D and deep neural network for predicting E and F, bring the highest accuracy for each level of service.

As one of the main infrastructures of the air transportation system, the air fleet has a significant effect on the operation at a reasonable cost. Deciding on the commensurate airplane to renovate the fleet falls into the multi-criteria decision-making (MCDM) problems. Airplane comparison criteria are very diverse, and no airplane is the best based on all criteria. In this study, selecting commensurate airplanes for airlines that meet domestic air transportation demand was investigated. The alternatives considered were Airbus airplanes, which were evaluated and compared based on six indices: price, maximum takeoff weight, passenger capacity, fuel capacity, the volume of passengers’ space, and volume of the cargo compartment. Also, several MCDM techniques require different levels of computational and maybe produce different outputs. The results of the different methods are not the same. To ensure consistency, accuracy and increase the reliability of the results, several methods were applied. Four different MCDM methods were used to make a comprehensive comparison, including analytic hierarchy process (AHP), simple additive weighting (SAW), and technique for order of preference by similarity to ideal solution (TOPSIS), and elimination et choice translating reality (ELECTRE). The results showed that the Airbus A318 airplane is selected as the top alternative based on these indices and using all four methods. The difference between the results of each method revealed for ranks 2-6. Based on AHP, TOPSIS and SAW, the second rank was designated to Airbus 319. However, ELECTRE had a different rank for this airplane.

Using appropriate variables that can consider individual, family and trip attribute effects on transportation decision is known as one of the most important part of transportation modeling. The goal of this study is using life cycle variables to have more accurate describing of family structure. For example life cycle can discriminate between families with similar number of children and adult but different children’s age, adult’s age and employment status and this discrimination will bring more accuracy for family attribute. This study use life cycle variables in trip mode choice modeling for different trips purpose. One of the most important results of this paper is substantial rule of life cycle variables compared with household size variable in educational, social-recreational and personal trips.

Commute mode choice and number of non-work stops during the commute are joint decisions that have interaction. If an individual chooses a vehicle for the commute, regarding restriction of that vehicle, could has some stops. On the other hand, if an individual need to has some stops, chooses a vehicle for commute regarding number of stops. In this study to consider the interaction between these decisions, we employed copula-based joint modeling framework.
The data used in this study is drawn from origin-destination travels data of Shiraz-Iran conducted in 1997. The commute mode choice modeling is undertaken using a multinomial logit model and the number of non-work stops is modeled using an ordered response formulation. To capturing interactive between these decisions several copula functions have been used. Results approve that mode and number of none-work stop choices are interrelated choices by estimating commonly observed factors and dependence parameters with high statistical significance. By determining common effective factors, we can analyze the current situation in the community. also, we can use results for forecasting future travel demand and set some policies leading to promoting trip chaining.

Optimizing the urban bus networks not only reduces environmental pollution but also effects on costs of transit system. In this study we examined various rapid vehicles (express buses) in terms of capacity and fuel consumption to reduce energy consumption, system costs and empty seat-minute. Our main goal is reduction of energy consumption. The results show that without increasing fleet development and average passenger waiting time, we can optimize reduction of energy consumption and empty seats.