JOURNAL OF TRANSPORTATION RESEARCH
Year:2010 | Volume:7 | Issue:3 (24)|Papers Count:8

The aim of the present study is to analyze the capacity of the basic Multi-Lane Highway Segments and to investigate the relationship between the capacity and the behavioral characteristics of Iranian driver population. The case study was Shahid Kharrazi multi-lane highway in Isfahan. Under uninterrupted flow conditions, the capacity of different lanes was calculated using both macroscopic and microscopic approach. For the first approach, three macroscopic speed- density models were used including the Greenshields linear model, Greenberg logarithmic model and Underwood exponential model. For this purpose, the main traffic parameters such as flow rate, mean speed and density were obtained for different day-time and night-time periods. In order to implement the capacity analysis through the microscopic approach, the vehicle time headway data were extracted. The results related to the both approaches indicate that the capacity values of Iranian multi-lane highways lead to higher amounts than the value proposed by Highway Capacity Manual (HCM2000). It seems that the most important reason is the distinct behavioral characteristics of Iranian driver population, rather than HCM study-population. The inclination of a large part of Iranian drivers to choose unsafe headways and non-compliance with safe distance through car-following process has caused increased capacity. Nevertheless it has increased the risk of accident and has caused reduction in the driving safety. Other results of this paper indicate that the driver's behavior patterns in different lanes are heterogeneous. This has led to the fact that traffic parameters such as capacity have had considerable discrepancy for different lanes. It is also inferable that the capacity decreases at night-time hours about 2 till 8 % due to more cautious behavior of drivers.

In statistical modeling of accidents, exact determination of model coefficients is crucial because it expresses how and how much the dependent variable relates to the independent variables (an incorrect coefficient can result in an incorrect modeling).To determine these coefficients, usually a log likelihood function is optimized using the "gradient vector", "Quasi- Newton" and "Newton-Raphson" numerical methods which all depend on the "Hessian Matrix" and have drawbacks such as slow convergence, high dependency on the initial value and possibility of convergence at a point other than the real highest peak.In this paper, to minimize the above mentioned drawbacks and make a more appropriate model for determination of the coefficients, a new method has been presented in which the "Hessian Matrix" has been eliminated from the "optimized pace length function" of the "vector gradient method" using some mathematical techniques, the results of applying this method on a "road safety performance function", (using the accident and traffic volume data in 160 sections of Iranian urban roads), show that, for the optimization process, the proposed method is independent of the starting moving point  and creates convergence of a log likelihood function at the highest possible peak.

Incidence of natural disasters is inevitable and cause many losses. Transportation network connectivity, considering the important role of highways, under emergency conditions is very important. Proper function of road networks could reduce the adverse effects of disasters. Road networks which fail to provide the necessary access have a little reliability. They will contribute to more widespread losses and damages. Kurdistan province geographic and natural conditions make it quite prone to natural disasters. To reduce losses and damages, it is imperative to provide access. Hence, the provision of access and reducing injuries is important. In this study, based on large volume of information, a risk index for Kurdistan road network is estimated. To validate this index, a sensitivity analysis is done. The results are compared with data from past events. With this index, the routes are prioritized for investment based on their risk of failure.

Metro is an efficient public transportation mode. Although it would decrease overall on-ground traffic levels, it may locally have adverse effects both in terms of local traffic, as well as social relations within the neighborhoods adjacent to the stations. As a consequence, residential satisfaction in neighborhoods might be threatened.Social problems including social transformation such as presence of non-locals in the neighborhoods, creates a sense of insecurity.  Problems in terms of traffic includes increase in local traffic and disruption of environmental pleasantness, increase in noise pollution and on street parking as well as reduction of safety in neighborhoods.  These in turn have negatively affected residential satisfaction levels in neighborhoods adjacent to metro stations.The aim of this article is to review the effective factors on satisfaction of residents in neighborhoods after launching metro stations, including personal characteristics of metro users (gender, age, education…) and indices of social and traffic conditions to provide a predictive model to measure the satisfaction level of local residence.For this purpose, field data were collected and analyzed, using questionnaire techniques.  In this study, two major metro stations, Sharif University and Science and Technology University were selected and 160 residents of adjacent neighborhoods responded to a structured questionnaire.The results of this model show that gender and age indices among personal characteristics and transformation in socio-physical indices due to launching metro stations have affected residential satisfaction levels within the surveyed neighborhoods. Therefore, personal characteristics and physical as well as social characteristics of neighborhoods are significant indices that should be considered in measuring residential satisfaction levels.

Majority of traffic engineering analyses are based on traffic flow micro-simulation tools. Car-following model is one of the most significant micro-simulation sub-models that control drivers' behavior with respect to the preceding vehicle in the same lane. Multiplicity of the car-following models is so high that there is therefore an obvious need for recognition of these models prior to picking up for utilization. In this research, the special case of well-known General Motors (GM) car-following model is considered and characteristics of this model are described. The considered model is GM model as well as the assumption of drivers' reaction time to be zero for all drivers, namely, Quick Response Driver Model. To achieve this purpose, some usual scenarios are defined in car-following process and these scenarios are simulated with Quick Response Driver Model. Also, utilization of differential equations approach is proposed for prediction and estimation of following vehicle behavior in car-following process that preceding vehicle behavior is given. Furthermore, Quick Response Driver Model is calibrated using real car-following time series data to identify model characteristics. The analyses of model evaluation with real data show the good capability of the model to reproduce real world considerations with RMSPE=1.7%. Additionally, results demonstrate that employment of differential equations is a robust approach in analysis of quick response driver model and decrease computational costs.

In early 1970s European road industry felt a need for a pavement that resists rutting, wearing out and damage caused by heavy traffic load and studded tires. In order to address this need, officials have developed Stone Matrix Asphalt (SMA). High value of coarse aggregate, use of large amount of asphalt binder and thickness of asphalt film around the course aggregate in SMA are the main reasons for drain down, flushing and bleeding during the storage, transportation and placing. Therefore to reduce these weakness points, stabilizers for thickening of binder and increasing the viscosity are used. Fibers are one of the materials used for this purpose, that is, fibers are used to reinforce and stabilize this mixture. Common types of fibers used for these applications are cellulose and mineral. The aim of present paper is to investigate the feasibility of using acrylic, jute, polyester, polypropylene and viscous fibers as stabilizers and to determine their effect on drain down and fatigue strength of the mixtures. In this experimental research, mixtures containing cellulose fibers have higher asphalt content than synthetic fibers, therefore synthetic fibers are more economical. By reducing the amount of the most expensive constituents of an asphalt mixture (asphalt binder), the result would lead to construct more cost effective pavements. Also mixtures containing synthetic fibers have higher drain down potential compared to cellulose fibers. Flexural stiffness of different mixtures was decreased with increasing of strain level especially in higher strain level. One type of fiber stabilizer showed significant effect in fatigue life of stone matrix asphalt mixtures.

One of the major issues in railways is passenger train delays and this issue in railway has many reasons, therefore predicting passenger train delay is a very difficult task. The aim of this paper is to present an artificial neural network based model with high accuracy to predict the delays of passenger trains in the Islamic Republic of Iran Railway. In the proposed model three different methods to define inputs, including normalized real number, binary coding and binary set encoding inputs have been used. To find an appropriate structure for proposed neural network model, three different strategies, called quick, dynamic and multiple are investigated. In this research, the registered data of passenger train delays in Iranian railway within the period (2004-2009) have been used. To eliminate any inconsistent and noisy data which always company with real world data set, a comprehensive preprocessing on this data set was done. To get more knowledge about data, some graphs such as seasonal average of delays, monthly average of delays, and total delays since 1383 to 1387 per year were sketched. To prevent models from over fitting with training data specifications, according to cross validation, the existing passenger train delays data set were divided into three subsets called training set, validation set and testing set, respectively. To evaluate the proposed model, the result of three different data input methods and three different structures were compared to each other and also to some common prediction methods such as decision tree and multinomial logistic regression. In comparison, different neural networks, training time, accuracy of neural network on testing data set and network size were considered and to compare neural networks with other well-known prediction methods, training time and accuracy of neural network on testing data set were considered and compared. To do a fair comparison among all models, a time-accuracy graph was sketched. The results revealed the higher accuracy of the proposed model.