seyed ehsan seyedabrishami

For several decades, finding the optimal location of counting sensors in a traffic network to obtain the best estimates of the O-D matrix has attracted a growing amount of attention. The availability and the accuracy of a priori data in a network such as O-D matrix and route choice probabilities on one hand, and the complexity of the mathematical operations for solving the location problem even in not a large network, on the other hand, are two main concerns of the presented methods. This paper aims to propose a method that identifies optimum locations for counting sensors without utilizing any a priori data. Relying on the network topological characteristics and link travel times as the representation of the network’s pattern of trips is the core concept of this study. By taking benefit of the frame theory algebraic operations, needless of any pre-given a priori data, the location set vector with higher coverage on the network route vectors is identified as the optimal location set of the sensor-equipped network links based on its representation in the route-vectors frame. The most probable used paths are identified utilizing an efficient path algorithm. Additionally, by taking advantage of the matrix operations, the novel method obviates the calculations required in methods using linear or non-linear programming solutions. The presented method is applied on a test network and the results show that in comparison to the non-linear programming method, the proposed method finds a better solution.

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.

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.

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.

Traffic accidents, as one of the most important threats to human beings, have shown themselves as a dangerous phenomenon. The impact of various factors on the incidence of accidents with different contributions has made it difficult to reduce the consequences of this unpleasant event in the safety analysis. Calibrated simulation models by taking into account the interaction effects of factors such as various behavioral characteristics of users in interaction with road have the potential to assess the safety performance of traffic facilities in different situations. Identifying the effective calibration parameters in simulation models due to their different impacts according to the purpose of the study as well as computational constraints is one of the essential steps in the calibration process. This study aims to provide a solution to identify the effective calibration parameters in the safety microscopic simulation model. For this purpose, 4 detectors located at the Mashhad-Sabzevar-Tehran route and vice versa with 23 accidents and 306 traffic records were selected as case studies and by deriving geometric information from available databases, the simulation model of the study area was built in the SUMO microscopic simulation software. By logically executing the simulation model on selected accidents, the effective calibration parameters in the car-following model, as well as the lane-changing model, were identified and by adjusting them in multiple scenarios, the simulation model in the case study routes was calibrated and validated with the use of the safety effective calibration target parameters such as Surrogate Safety Measures (SSMs). Among all the parameters of the simulation model, the global parameters of the normal distribution of permissible speed coefficient index with a mean and standard deviation of 0.95 and 0.10, respectively, and also local parameters of the cooperative, assertive and keep right lane-changing indexes were identified as effective.

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.

Intersections are one of the elements that play an important role in urban networks. Analysis of drivers’ performance at unsignalised intersections is crucial, especially in left-turning movements due to their several inherent conflicts and variety of drivers’ maneuver types which affect traffic safety and capacity at such intersections, so the purpose of this paper is to introduce how the behaviour of drivers will be specified in left-turning at unsignalized intersection. For this study, traffic data were collected using a fixed digital camera. First, the vehicle speed profiles are categorized into descending-ascending slope (type (A)), the smooth descending-ascending slope (type (B)) and ascending slope (type (C)).The effects of the initial speed of left-turning vehicles, the exposure with other vehicles, and the vehicle type (i.e., taxi versus other vehicles) are investigated on the choice of speed profile. A multinomial logit model is utilized to explain how various variables influence the choice of speed profile. The estimated model indicates that the initial speed and the exposures are influential parameters. Also, vehicles with left exposure at intersections increase the drivers’ tendency for selecting type (A) profile while they have low to medium initial speeds when entering the intersections. For the vehicles with high initial speeds, most drivers pass the intersection with type (B) profile. Vehicles with low initial speeds and a low number of exposures increase the probability of selecting type (C) profile. Introduced method can be applied for simulation models at unsignalised intersection to show how drivers will behave in left-turning movements.

Concerns for climate change, reduction of greenhouse gas emission and environmental pollution, besides economically dependency on fossil fuels and political aspect motivate governments and policy makers to take into account replacing usual vehicle with alternative fuel vehicles (AFVs) such as Compressed Natural Gas Vehicles (CNGV) and Gasoline-Electric Hybrid Vehicles (GEHV). The air pollution in Tehran is a serious concern that based on this problem, CNGV has been introduced to Iranian market from 10 years ago. On the other hand, with the approval and notification of the removal of the electric vehicle importation’s tariff law, GEHV has been entered into the market of Iran as a new entrant. The purposes of this paper is to identify the effective factors to choose AFVs for drivers in Tehran and the assessment of effects of the incentive policies that increase AFV shares and computing their willingness to pay (WTP) for AFV under different incentives. This study designed a questionnaire which includes 3 parts: current vehicle features, dominant travel characteristics, socio-economic properties and the prioritization of effective factors on new vehicle purchase, and the tendency of AFVs choice with different scenarios representing different features. A random sample of 365 respondents was interviewed in a face-to-face survey in February 2016 in the technical inspection centers and in compressed natural gas stations. Finally, for the determination of effective factors on current and new vehicle purchase with revealed preference information and the assessment of AFVs usage tendency with stated preference information, the Multinomial Logit models have been used and WTPs are calculated. The incentive policy in Tehran, like previous studied countries, was the most influential factor in motivating consumers to buy AFVs in comparison to improvement of AFV specifications. The results show that drivers’ WTP is 5 MT for free access to even-odd area for CNGV and 12 MT for GEHV, also WTP for free access to pricing area in Tehran central business district is equal to 10 MT for CNGV; i.e. people tend to pay this extra cost for AFVs to access to pricing areas. These values are comparable with similar studies in cities located in developed countries. However, the results of this study show that WTP for fuel cost in Iran is considerably less than WTP of people driving in developed countries. The fuel cost and access time to gas stations are influential variables on CNGV choice. The vehicle acceleration and driving range are influential variables on GEHV choice.

These days, emergency request responding in a short time is a highly challenging issue. The main goal of current research is modeling an optimization problem for emergency vehicle routing on small and medium-sized networks and solving the problem by a novice algorithm. The suggested algorithm inputs are the network, location and number of requests, vehicles, and hospitals. The objective is to minimize total travel time of vehicles plus the time that the last request is responded completely. The algorithm output is the vehicle and hospital assignment to each request and requests' responding order on the network. After developing the mathematical model of the problem, 138 random samples are produced on a network with 7 nodes and 16 links. The number of feasible answers, which is depended on the number of requests, vehicles, and hospitals, is tried to be reduced by assuming a fixed order for responding the requests. Hence, 3 different scenarios of request order are examined and it is found out that arranging the requests based on distance from the nearest vehicle results in better answers (lower total travel times). An innovative algorithm is presented for solving the problem which reduces the solving time to 5 seconds; while finding the precise answer needs time up to 810 seconds. The results show that the difference between more than 80 percent of answers found with the suggested algorithm and the precise answer is lower than 10 percent and this difference is less than 30 percent in more than 94 percent of the answers.

The main role of feeder in urban networks is to increase the coverage of rapid public transportation systems such as subway and bus rapid transit. The high complexity of the exact methods of designing feeder networks motivates researchers to apply heuristic and meta-heuristic methods to find approximate solution of feeder network design. This paper presents an innovative approach to design of feeder network for a subway line using a two-stage heuristic method. The proposed method has been applied in Sioux Falls as a test network which is often used in transit network design. According to the proposed method, at the first stage, a number of primary routes are generated as initial solutions; then these routes are screened according to a set of parameters such as the ratio of the covered demand to travel time, maximum and minimum limit for feeder network length. At second stage, frequency restrictions, the number of routes and covered percentage have been used to select final routes. The proposed method was used in the test network Sioux Falls that improved the demand coverage for public transport system. The demand coverage with the limit of 3 and 4 lines for feeder network are equal to 65.22 and 78.42 percent, respectively.

In recent decades, increasing urban population, urbanization development and the lack of public transportation in response to increasing demand for inter-city trips have led to an increase in the use of private cars in Tehran. Therefore, changes in urban transport policies and efforts to develop and improve public transportation, especially the bus, are one of the most important strategies in the urban transport planning and require the necessary surveys to have a growth in share of public transportation. Studies show that planning for the optimal use of bus infrastructure and the use of solutions to increase the efficiency of this system in the world, requires information on infrastructure and bus lines demand. Accordingly, it is necessary to carry out studies to predict bus lines passenger demand in Tehran, for operational lines planning which are currently empirically carried out by the line manager and there is no planning based on the existing conditions for the future. Thus, this study predicts bus lines passenger demand for the short-term future, using AFC data (data recorded from smart credit card transactions for fares payment) and calibration of a suitable model. For this purpose, AFC information is firstly arranged for each bus line, then Sarima and neural network model (multi- layer perceptron) are calibrated based on. The model results show that the multi- layer perceptron model in terms of error indices in prediction of bus passenger demand is better than Sarima model and it is a more appropriate method for predicting short-term bus lines passenger demand.

Nowadays, cities as a place of living and human activity are facing serious challenges in providing human needs. Increasing in population growth, vehicle ownership and communication development has led to complexity of the transportation system and its problems, including congestion, environmental pollution and the consumption of non-renewable resources. Therefore, changes in urban transport policies and efforts to develop and more use of the public transport, especially the bus, are one of the most important concerns in urban transport planning. A review of various studies suggests that planning for efficient use of bus infrastructures and enhancing the efficiency of public transportation operation in the world, require information on the infrastructure and passenger demand for lines and bus stations. Accordingly, it is necessary to carry out studies to predict passenger demand for bus stations in Tehran. Thus, this study predicts bus stations passenger demand for future short-term periods, using data gathered by AFC (Automated Fare Collection) and AVL (Automatic Vehicle Location). For this purpose, firstly AFC and AVL data was sorted according to the time for each bus line. Since passengers use their smart card while they are getting off the bus it means at the exit station thus identifying their origin station is vital, so that in second step, data of two data bases is compared and matched by writing computer code in Matlab software to determine the origin stations of passengers and then forming origin-destination demand matrix for each bus line in terms of its stations. This matrix is considered as the main data base of the study, a time series analysis, a seasonal autoregressive integrated moving average (SARIMA) and neural network as an artificial model are calibrated based on the available data. Both models’ goodness of fit indices are compared in terms of learning and generalization capabilities. For this purpose, initial data is divided into two subsets called learning and test data sets and comparison indices are computed for both aforementioned sets. The models’ results show that the multi-layer perceptron neural network model in terms of goodness of fit indices in both learning and generalization capabilities in prediction of bus station passenger demand is better than SARIMA model; however, the manner of influencing different factors such as day of week or month of year in passenger demand in each station is more clear in time series analysis. The passenger demand for each stations in first month in spring is different from the rest months in this season. Months in summer is also show different trends for passenger demand, while all months in fall and the first two months in winter have similar passenger demand in various stations. Official holidays has also significant influence on passenger demand so that reduce passenger demand by approximately 256 persons on average. All days in week have meaningful effects on passenger demand in comparison with Friday so that Monday and Thursday have the highest and the lowest effect on weekday passenger demand in bus stations in comparison with Friday, respectively. This analysis comparison show that if the precision of future prediction is important then neural network outweigh time series regression, while the identification of influential variables on passenger demand is better done by time series analysis.

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.

Public transportation network design is one of the most complex issues in transportation engineering where researchers usually apply simple heuristic methods to solve the complicated problem. Route generation algorithm is one of the heuristic methods that use the shortest path between high transit demand O-D pairs and expand the shortest path, called route generation algorithm, to cover more transit demand. In this paper, the expanding route generation algorithm has been revised in order to consider restrictions on minimum route travel time and length to the primary route generation algorithm. The proposed algorithm for route generation has been coded in a computer programs and used for transit network design in Sioux Falls test network. The results show that the algorithm reduces the number of transit routes in the test network by 70 percent and route overlaps length by one-third compare with the previous route generation algorithms used for transit network design on Sioux Falls.

Catastrophic consequences of accidents related to the hazardous materials transportation and significant growth in the use of these materials to meet the industry needs, show the necessity of studying in this field. In the context of hazmat transportation, risk is a measure of the probability and severity of harm to an exposed receptor. The purpose of this study is to provide an approach for route evaluation of hazmat based on risk analysis. To reach this goal, after route segmentation, the risk is calculated in different segments. The calculation includes quantification of two main components of risk which are probability of occurrence (frequency) and consequences of occurrence for the events related to hazmat. As an output for the approach, the critical segments of a road according to the risk of hazmat transportation can be determined. The suggested approach was applied in Haraz and Firouzkooh roads as case study. Calculating risk for segments, critical segments were determined for the roads. The total risk of the roads was also calculated and the results were used to evaluate the danger level of the roads.

In general, freeways, due to geometric conditions, the speed and number of overpass lines, pass a large percentage of traffic flow volume. Lane change phenomenon can decreases their functional capacity and as a result, the travel time increased traffic flow and reduces speed and safety. Line change is divided into two types of optional and mandatory line change. Mandatory lane change (MLC) occurs when a driver must change lane to follow a specified path and Discretionary lane change (DLC) occurs when a driver changes to a lane perceived to offer better traffic conditions, he attempts to achieve desired speed, avoid following trucks, avoid merging traffic, etc. The aim of this research is predicting the number of the Discretionary lane change in freeways with count regression models.

The information used in this study has been processed by the data of an interstate freeway in America. Then, using this data, two types of linear regression models, poisson regression has been paid and then, for a good evaluation of the models are presented their observation and estimation charts. One of the most important usage of this model is its use in macroscopic simulation software and use in service-level design models.

The virtual variables of the number of crossing line, the number of vehicles on the current crossing line and then the average speed of the current crossing line and the target crossing line have the most impact on the estimation of this model.

In the comparison between the two linear regression and count regression models, it was observed that The count regression model describes this phenomenon better because of the proximity of the characteristics to the line change feature. It is also suggested that in future research should also be considered the mandatory line change and the differences between the two models.

Establishing a network of transit routes with satisfactory demand coverage is one of the main goals of transit agencies in moving towards a sustainable urban development. A primary concern in obtaining such a network is reducing operational costs. This paper deals with the problem of minimizing construction costs in a grid transportation network while satisfying a certain level of demand coverage. An algorithm is proposed following the general idea of “constructive algorithms” in related literature. The proposed algorithm, in an iterative approach, selects an origin-destination with maximum demand, generates a basic shortest-path route, and attempts to improve it through a route expansion process. The paper reports the scenarios and further details of the algorithm considered for expanding a transit route in a grid network. A random 6×10 grid network is applied to report the results. The results support that application of the proposed algorithm notably reduces the operational costs for various amounts of demand coverage.

In this article, a new approach is presented to solve the double-track railway rescheduling problem, when an incident occurs into one of the block sections of the railway. The approach simultaneously considers three rescheduling policies: cancelling, delaying and reordering. To find the optimal conflict-free timetables compatible with the approach, a mathematical model and an exact three-phase solution method are proposed. The method is based on Branch-and-Bound (B&B) algorithm. The lower bound consists of two cost parts: the cost of deviation from the primary timetable and the cost of train cancellation. To generate an appropriate upper bound, the method exploits an innovative algorithm called «Local Left Shifting». A heuristic beam search technique is also developed for tackling the large-scale problems. An experimental analysis on two double-track railways of the Iranian network indicates that the proposed solution method provides the optimal solution in much shorter time, compared with the time taken to solve the mathematical model by CPLEX software. Based on the findings of this research, it is possible to optimally retrieve the primary timetable after incident occurrence during a pre-determined time horizon.

This paper presents a novel approach to solve the double-track railway rescheduling problem, when an incident occurs into one of the block sections of the railway. The approach restricts the effects of an incident to a specific time, based on which the trains are divided into rescheduled and unchanged ones, so that the latter retain their original time-table after the incident. The main contribution of this approach is the simultaneous consideration of three rescheduling policies: cancelling, delaying and re-ordering. A mixed-integer optimization model is developed to find optimal conflict-free time-table compatible with the proposed approach. The objective function minimizes two cost parts: the cost of deviation from the primary time-table and the cost of train cancellation. The model is solved by CPLEX 11 software which automatically generates the optimal solution of a problem. Also, a meta-heuristic solution method based on simulated annealing algorithm is proposed for tackling the large-scale problems. The results of an experimental analysis on two double-track railways of the Iranian network show an appropriate capability of the model and solution method for handling the simultaneous train rescheduling. The results indicate that the proposed solution method can provide good solutions in much shorter time, compared with the time taken to solve the mathematical model by CPLEX software.

Travel demand is well announced as a crucial component of transportation planning. This paper aims to develop a direct demand model, denoting a more acceptable abstraction of reality, for intercity passengers in daily work and leisure trips in Tehran province. The model utilizes combined estimation across the data source, collected in 2011, of travelers originating from the city of Tehran and heading toward two destination clusters: intra-province and inter-province. The paper sketches a way to predict simultaneous choice of departure time and travel mode under the influence of zonal (origin, destination, and residence), individual and household socio-demographic, and trip-related variables. The time frame for analysis of departure time is [5-19] and available modes are auto, taxi, bus, and metro. Multinomial Logit (MNL) and Nested Logit (NL) models as behavioral models are selected from discrete choice family to provide appropriate direct demand structure. Besides, the paper discusses Independent Irrelative Alternative (IIA) assumption of the models and demonstrates choice order of NL; Travelers choose departure time prior to mode at first level and then decide on mode at second level. Finally, travel demand elasticity and marginal effect with respect to travel time, age, and auto cost are also highlighted.

Catastrophic consequences of accidents related to the hazardous materials transportation and significant growth in the use of these materials to meet the industry needs, show the necessity of studying in this field. In the context of hazmat transportation, risk is a measure of the probability and severity of harm to an exposed receptor. The purpose of this study is to provide an approach for route evaluation of hazmat based on risk analysis. To reach this goal, after route segmentation, the risk is calculated in different segments. The calculation includes quantification of two main components of risk which are probability of occurrence (frequency) and consequences of occurrence for the events related to hazmat. As an output for the approach, the critical segments of a road according to the risk of hazmat transportation can be determined. The suggested approach was applied in Haraz and Firouzkooh roads as case study.Calculating risk for segments, critical segments were determined for the roads. The total risk of the roads was also calculated and the results were used to evaluate the danger level of the roads.

Sixty percent of motorcyclist fatalities in traffic accidents of Iran are due to head injuries, but helmet-use is low, despite it being a legal requirement. This study used face-to-face interviews to investigate the factors associated with helmet-use among motorcycle riders in Mashhad city, the second largest city in Iran. PCA and CFA were used for data reduction and identification of consistent features of the data. Ordered and multinomial logit analysis were applied to quantify the influences on helmet user. According to the statements of motorcyclist,s rate of helmet-use is 47 percent, but in 14 percent of cases helmets were not used properly. In addition, 5 percent of motorcyclists believed in malicious impact of helmets on their safety. Duration of daily trips with motorcycle, riding experience and type of job were affecting helmet-use behavior.

60 percent of motorcyclist fatalities in traffic accidents of Iran are due to head injuries. Using motorcycle safety helmet is the main solution to prevent probable head injuries of motorcycle riders. Although، helmet-use law approved in Iran 12 years ago، the prevalence of helmet-use among motorcyclists remains low. This study investigates factors associated with helmet-use among motorcycle riders of Mashhad city based on data collected through face-to-face interviews. Confirmatory factor analysis was used for data reduction and identification of behavioral factors. Ordered and multinomial logit were applied to statistically describe helmet-use as a non-deterministic behavior. According to the statements of motorcyclists، rate of helmet-use is 47 percent، but in 14 percent of cases helmets were not used properly. In addition، 5 percent of motorcyclists believed in malicious impact of helmets on their safety. Norms، attitude toward helmet-use، risky traffic behavior and awareness of traffic rules were found to be the key determinants of helmet-use، while no appropriate significance was found for perception of enforcement strictness. Duration of daily trips with motorcycle، riding experience and type of job were affecting helmet-use. Results indicate that training and cultural programs to improve social norms and attitudes toward the use of motorcycle helmets beside effective law enforcement techniques possibly increase proper use of helmets between Iranian motorcyclists. In addition، special safety courses should be approved for motorcyclists who have committed traffic violations.

Transportation models as tools for transportation planning are critical to such related decisions. Considering the high cost of calibrating and validating such models, effective alternatives are highly sought for; one such alternative being the use of models calibrated for other cities. This calls for transferability analysis which has not been the subject of many researches. Due to criticality of aggregate and disaggregate data in transportation models, this paper tries to compare transferability of models calibrated with data of both groups. Mode choice models for daily work trips in two real-sized cities of Qazvin and Shiraz are analyzed. Models are calibrated employing multinomial logit structure with four modes of private car, taxi, bus, and 2-wheelers. In order to increase reliability of results, the top five best models are selected for each city-data category to be transferred. Based on transferability test statistics, transfer index, and goodness-of-fit of transfer models, aggregate models are not transferable and their results are deceptive. Transferability measures of these models are not in acceptable range; whereas transferability of disaggregate models have relative proper response. According to transfer index and goodness-of-fit of origin models operate similar to destination models. However transferability test statistics rejects the assumption of equality coefficients in both cities models. Using personal variables helps to effectively transfer origin models in addition to improve them.

Congestion pricing is the popular demand management policies in metropolises to eliminate unnecessary travel or change mode choice from private vehicle to public transportation. The purpose of this paper is investigating the effects of disincentive congestion pricing and incentive improvement public transportation policies to mode choice and detecting important factors affecting the users, behaviors. Data collected from 251 questionnaires in the odd and even areas in Tehran and including of two parts of revealed and stated preference. Revealed preference data consist of the existingtransportation facilities and state preference data represents user-selected options in dealing with the new requirements of politics. The ρH index for combined NL model is 1.14 and indicating that the combined weight model has a better description of the data. The policy of reducing travel time compared to other demand management policies has more impact on the choice of users of public transport.