سیداحسان سیدابریشمی

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.

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.

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.

One of the important ways of identifying road accident potentialities is road safety audits. After identifying these points, appropriate strategies to reduce the number and severity of accidents can be considered for them. In this study, using road safety audits, access, pavements, signs, roadside, and geometric problems and accidents leading to the deaths of outlying urban road sections were identified and using these data, the model of crashes in them Was presented. In this paper, the Hamadan-Kermanshah axis was chosen as a case study. In this axis, Poisson's regression model was more reliable and the problems of drainage ditches and vertical alignment were the most important reasons for the accident and resulted in death.

Maintaining the current share of public transportation has direct relationship with users’ satisfaction. Determining users` satisfaction depends on many factors, which usually are divided into 5 categories: safety, comfort, reliability, service characteristics, and quality of providing information for users. This article suggests. The model used the data collected by a questionnaire which has three mail sections. The first section questions about satisfaction of users and the importance of each of these attributes have been asked, the second part is about the trip characteristics and the third part collected passengers’ socioeconomic characteristics. The proposed model studies Satisfaction Index with using the mean and variance, and simultaneous effect of satisfaction and importance of attributes. Analyzing of this model shows that those attributes which have the most impact on the users` satisfaction of system are Travel Safety, reaching the destination on expected time, and apt air conditioning of bus. While in developed countries reliability of service has the most important role in users` satisfaction. The proposed methodologies show some advantages compared to the others adopted for measuring service quality, because it can be easily applied by the transit agencies

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.

In this paper by using the aggregate socioeconomic features of each zone and considering the land use prosperities, the aggregate zonal tours were modeled. For that, classic 4-step modeling database was utilized in which less data collection cost needs. Due heteroscedasticity , appling weighted least square (WLS) method led to multiple weighted regressions by two exogenous independent variables; population and number of employers in zone. Heteroscedasticity affect the efficiency of regression. In comparing of two model, the F test of WLS method growth 27 percent and the amount t students for population reduced from 2.79 to 2.27 that in negligible. On the other hand, the amount of t students for number of employers increases from 2.62 to 3.40 (30 percent). Constant coefficient is negative but in comparison with the maximum number of observed tours (7202 tours) is 0.069 percent and the average number of observed tours (1386 tours) is 0.36 percent that is negligible and the R2 goodness of fit index is 0.927 and acceptable.

Today,transportationdemandmanagement(TDM)policy tools are accepted as practical solutions for decreasing the costs of congestion in urban regions, and more ecient using of transport infrastructures. This paper investigates the role of a \time-of-day congestion pricing scheme" as a pull TDM policy and two push TDM policies including \bus travel time reduction" and \bus access time reduction" in user's car use behavior. The main goal of this research is to estimate the impacts of these policy-tools on the probability of choosing car at morning peak, when they are applied separately or simultaneously. The analysis is based on the results of a stated preferences survey developed through the experimental design approach and was completed by 231 users, who travel intoTehranseven-oddzoneforworkbycar. Theadvantage of data gathering in even-odd zone was that these commuters were familiar with the boundaries of pricing area and so, they could make a more realistic decision(forexample, decisionaboutchoosingpark-and-ride mode). For considering a time-of-day congestion pricing policy, we introduced a cordon pricing scheme from 6:30 AM with a discount on entering after peak period (in this case study, between 6:30 AM to 9 AM). Like other policies, the discount policy has three levels containing 50%, 25% and 0% of peak period tolls. The independent and interaction eects of these policies are assessed by developing a two-level mode choice nested logit model and estimating marginal eects. This model has 8 alternatives, three of which are related to driving a car: drive before 6:30, drive between 6:30 and 9, and drive after 9 AM. Results show that cordon pricingschemefrom6:30AMhasthelargesteectandcould decrease share of drive between 6:30 and 9 AM by 0.408. Congestion charging scheme at 6:30-9 AM and bus access time reduction, are also the most eective policytools with a 0.49 decrease in car share when applied simultaneously.

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.

Since models of transport demand have shifted from trip-based to activity-based approaches, prediction of activities and their duration has gained further importance. Activity duration is an important component of activity participation behavior of individuals, and therefore, an important determinant of individual travel behavior. Since duration data is non-negative and often censored, it follows non-normal distributions, hence, linear regression is not a suitable model. Duration models offer vaster suitability for this kind of data and thus have been used for behavioral analysis of individual trips. Since for the past two decades, shopping has been under spotlight as indispensable diurnal activities for human, the current paper has tried to compare the results of several approaches with their different modeling assumptions, by modeling duration shopping activity concurrent with recognition of influential factors on duration variable and proper distributions for such data. case study of this research, is the shopping trips of 2 and 3 home-based trip tours data obtained from inquiry a sample of Qazvin residents comprising personal and household information, the characteristics of transport networks and the features of susceptible sites for doing shopping activity which constitute 99 percent of Qazvin's citizen's daily travels. The analysis and analogy of modeling results indicate estimation of parametric approach with regard to goodness of fit, is better than other approaches and suitable distributions has used for shopping activity duration data is log-logistic distribution. Considering that scale parameter estimated of model is lower than one, the hazard curve is non-monotonic and an inverted U-shape. Worker's female than male and male than other, has associated lower duration to shop with significant difference (p=0.005). Non- motoric travel modes has positive influence to reduction of shopping duration (p

Competitive environment has increased among Iranian and foreign carriers due to the recent changes in the field of pricing and foreign exchange rate. In addition, entry of new airlines including Low-Cost airlines can complicate competitive conditions more than before. Therefore, study of passenger's choice behavior and forecast market demand for air travels can improve planning process of airlines. For this purpose by using the stated preference statistics, the competition of Low-Cost Carriers in contrast to Full Service Carriers and based on the designed scenarios has been analyzed. Fractional factorial design technique has been used in order to design a basic mechanism for transferring information about the selection options, their attributes and each attribute level to passengers. Then, outgoing passengers who were traveling to the destinations of five south-east Asian countries were individually interviewed in Imam Khomeini (RA) International Airport and 480 questionnaires collected. The collected data were analyzed and investigated by using Multinomial Logit Model (MNL). The results indicate that ticket price and the variable of passenger's flight possibility on the desired day and hour for the Low-cost Carriers and the Full Service Carrier are quite different. The effect of these variables on passenger's category with business/non-business, the passengers who paid themselves for the tickets and others is statistically different. Also according to statistical results of model; gender, marital status, international travel experience and Membership of Flight Frequency programs (FFP) have been effective in selecting types of airlines. In addition, the rate of passenger's tendency to pay is different in various passenger's categories and in types of aviation companies.

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.

Transportation Discrete Network Design Problem (TDNDP) is the problem of selecting a feasible subset of proposed projects، i. e. highways، so as to minimize the total travel time of the network users. This problem falls into the NP-Hard complexity class of problems for which no efficient algorithm exists for exact solution in practical cases. As a result، to find a rather good solution for the problem in a reasonable amount of time، many studies addressed TDNDP through heuristic and meta-heuristic approaches. However، application of parallel computing is still another way to further speedup TDNDP solution approaches. This paper is going to explore the application of parallel computation in a meta-heuristic algorithm in TDNDP. A parallel Ant Colony Algorithm (ACA)، based on the study of Poorzahedy and Abulghasemi، is proposed with the master-worker parallelization paradigm. The Chicago Sketch transportation network is considered as a case study with 16 bi-directional proposed projects. The results of parallelization over a cluster of 8 processing cores support that parallel algorithms can achieve high quality solutions in 4000 seconds، while this happens for the single-core algorithm in 10000 seconds. The parallel ACA finds the exact solution of the problem in two instances out of three runs and in the other instance it converges to a solution with 0. 07 percent error from the exact solution. The parallel performance of ACA is also compared with that of the branch and bound algorithm. The comparison indicates that the parallel branch and bound algorithm requires more that 32000 seconds running time to find the exact solution of the problem، while the parallel ACA reveals a much faster performance. Transportation Discrete Network Design Problem (TDNDP) is the problem of selecting a feasible subset of proposed projects، i. e. highways، so as to minimize the total travel time of the network users. This problem falls into the NP-Hard complexity class of problems for which no efficient algorithm exists for exact solution in practical cases. As a result، to find a rather good solution for the problem in a reasonable amount of time، many studies addressed TDNDP through heuristic and meta-heuristic approaches. However، application of parallel computing is still another way to further speedup TDNDP solution approaches. This paper is going to explore the application of parallel computation in a meta-heuristic algorithm in TDNDP. A parallel Ant Colony Algorithm (ACA)، based on the study of Poorzahedy and Abulghasemi، is proposed with the master-worker parallelization paradigm. The Chicago Sketch transportation network is considered as a case study with 16 bi-directional proposed projects. The results of parallelization over a cluster of 8 processing cores support that parallel algorithms can achieve high quality solutions in 4000 seconds، while this happens for the single-core algorithm in 10000 seconds. The parallel ACA finds the exact solution of the problem in two instances out of three runs and in the other instance it converges to a solution with 0. 07 percent error from the exact solution.

Congestion pricing is one of the main strategies for demand management in urban areas. One of the main questions for decision makers, for implementing this strategy, is the effect of congestion pricing on user's mode split. For defining this, the willingness to pay of users should be estimated. In another word, we should know that how much users still will to pay for using their personal cars and how they intend to perform their mobility according to various prices. Previous studies had pay less attention to paratransit mode according to pricing. In this research, the effect of congestion pricing on user's mode choice has been investigated. The restricted traffic zone of Tehran has been selected for case study. Due to impossibility of direct observation, the stated preference method was applied for data gathering. Various pricing scenarios, based on experimental design concept, were defined and several types of questionnaires were designed. In these questionnaires, 3 types of data were asked from interviewees including trip chain of the users, socio economic characteristics and pricing scenarios. According to pricing scenarios, 4 choices including using personal car, public transportation, taxi and cancelling the trip were presented to users which they should choose only one option due to every pricing scenario. The reliability of questionnaires have been investigated with cronbach's alpha which results showed the proper reliability of questionnaires. More than 3500 interviews were performed and after preliminary validation, were entered in the database. Based on this data, more than 70 variables were defined which their correlation was estimated and proper variables were chosen. For modeling, due to discrete nature of data, multinomial logit model was applied and calibrated for every choice. In this regard, the feasibility of applying nested logit model was also tested which results showed the invalidity of this model. More than 200 models have been calibrated and finally best validated models have been chosen for describing the mode choice of every alternative. Results of modeling showed that having more expansive cars will increase the utility of using personal cars and reduce the public transport utilization. Also the residence location of the users is effective in their mode choice. Living in restricted zone, increase the willingness of users to use transit and paratransit mode. User's education is also important in their choice. People with higher education level have more willingness to pay. The sensitivity analysis showed that pricing can be a proper tool for managing personal vehicles demand. When entrance toll is equal to 3000 tomans, only 35 percents use their personal vehicles. In this condition, 30 percents of users choose transit mode. Results also showed that choosing transit and paratransit mode is similar. When the toll is more than 17000 tomans, the rate of using taxi have higher growth comparing to transit utilization. Also, results showed that the demand for entering to restricted zone can be assumed inelastic. In highest toll, only 10 percents of users cancelled their trip. Finally, the sensitivity analysis for every mode has been accomplished and multiple future researches have been proposed.

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.

Congestion، delay and creation of long queues in urban networks make transportation authorities to take traffic management methods for optimizing the current capacity of infrastructures. One of the effective methods in this case is applying route guidance systems. Information and route guidance systems are a kind of intelligent transportation technologies which aid drivers to choose the proper route to their destinations. In this research، a route guidance system was developed which the operator (Traffic Management Center) can control the network by every level he wishes for. An overlapping parameter was introduced for every route which compares the cost of every route with the cost of the same one in system optimality condition. For modeling purpose، two programming model were applied which the first model produced the inputs of the second model. The second model، considering the overlapping factor and the tolerance limit of the operator، guide the users. In the introduced assignment process، when the overlapping parameter reaches the tolerance limit of the operator، the route will be eliminated from the guidable route set and other routes will be introduced and reachable for users. The developed model is tested for simple illustrative network. Results showed that the proposed pattern has the most effects in operator tolerance period from 1 to 1. 4. When the tolerance parameter of the operator reaches 1. 1، the total travel time of the network is decreased more than 13 percent. Also، results showed that only a little share of users (15 percent of users in most case) in comparison with UE condition will change their routes which promise the problems with disobedience of users and also validity of results.