Journal of Transportation Engineering
Year:2012 | Volume:3 | Issue:4 (12)|Papers Count:7

Detection and ranking accident-prone locations or black spots in a transportation network is a basic step in the process of traffic safety improvement. The current study uses two different methods; data envelopment analysis and concordance analysis, as alternative ways for detecting and ranking black spots. The methods are branches of multi-criteria decision making analysis. One of the important characteristics of these methods is their capability of using different factors affecting accidents, considering crashes’ intensities, in ranking hotspots in the network. The current paper is devoted to study hotspots in Qazvin based on two aforesaid methods. These locations are considered to be decision-making units in data envelopment analysis in which inputs and outputs are effective factors on accidents, and different kinds of accidents, respectively. On the other hand, concordance analysis by using a combination of available variables and defining concordance and discordance sets and indices, ranks the locations of interest according to their risks. One of the advantages of these two methods is their multi-criteria nature, which appear to conform better to the pattern of hotspot identification, than the traditional methods. The other advantage (particularly for data envelopment analysis) is to devote attention to the systems’ inputs as well as the concept of efficiency, what is not considered in the conventional methods of hot spot identification. The study on two methods showed that the accuracy of proposed methods is higher due to its tendency to the effective factors on accidents, although having similar results from the two methods, because of different approaches, is not expectable.

In this study the effect of fuel price on the share of rail freight transport to total freight transport for the years 1981 to 2007 is investigated. The authors used data including freight rates by rail, freight rates by road, fuel prices, national income and number of wagons as model explanatory variables. The method estimation was Co-integration and the result showed that fuel prices could increase the railroad’s share of total freight. Coefficient of freight rate by road showed that road and rail transport are complementary in Iran. Due to the lack of coverage in all parts of the country’s rail, in some of the routes the authors had to travel some miles to access to the railway station.It was concluded that if rail freight rate was increased, railroad share of total freight transport was decreased.

Traffic accidents are one of the most substantial concerns that call for improving road safety. In this paper, research work was performed to recognize the factors affecting crash frequency in rural freeway. Therefore crash data of Tehran-Ghom freeway were used as a case study. In this research, artificial neural network and Log-Normal model were proposed to estimate the number of road accidents in Tehran-Qom freeway. Average daily traffic volume, percentage of heavy vehicle, average speed and environmental effects were considered as independent variables. Thirty four-month period of accident data and parameters, were collected to use in analytical process of modeling and validation of them.To address influences of the main contributing factors on accident frequency, Principal Component Analysis (PCA) and Factor Analysis (PFA) techniques appeared to be useful l in analyzing variables in order to identify the most significant in accident-prediction model. Also sampling adequacy was measured by the Kaiser-Meyer-Olkin (KMO) and Bartlet test statistics in PCA technique.With the aim of evaluating efficiency of artificial neural network model against Log-Normal model in crashes modeling on rural freeways, these models were compared and the results revealed that artificial neural network model was more capable to estimate the number of road accidents in freeways. Results also showed that average speed of vehicles and average daily traffic volume were the most effective parameters in freeway accidents.

A major problem in highway construction is the provision of materials to be used in pavement layers. In many regions, the materials with required specifications are not available or need to be transported in long distances. Therefore, improving the inappropriate materials available in the construction sites to make them usable in pavement layers is a measure for reducing the costs.In this research, a nano polymer stabilizer, called CBR PLUS, has been used for improving the physical and mechanical properties of three combinations of clay, sand and gravel, neither of which have the required properties defined by the specifications. Each combination is mixed with different contents of the additive and the properties of compaction, Atterberg limits, uniaxial compressive strength, California Bearing Ratio and permeability are evaluated. It is found that the stabilizer decreases the plasticity index and permeability and increases the compressive strength and CBR of the combinations. The combination of 50% gravel, 30%sand and 20% of clay is found to attain the highest strength with the stabilizer.

With increasing traffic density on Iran’s regional corridors, the probability of accident occurrence has increased accordingly. The main objective of this paper is to develop an ontology-driven geospatial information system to induct major crash rules for vehicles accident severity prediction which has the full domain knowledge and logical reasoning ability based on ontology. In the proposed approach, Geographic Information System (GIS) provides a platform both for spatial data analysis and for visualizing relationships between spatial and non-spatial data. Furthermore, the ontology is employed to represent geospatial and attribute domain knowledge related to road, environment and vehicle. Crash rules are acquired by integrating experts knowledge with the rules which are extracted using the Separate-and-Conquer rule induction approach. These rules are transformed to Semantic Web Rule Language (SWRL) syntax for reasoning in crash severity estimation engine. To evaluate the proposed method, a system prototype in the Qazvin-Rasht (Iran) regional transportation corridor as a case study is implemented. The results show that the proposed approach can efficiently induce major crash rules and predict accident prone vehicles crash severity with respect to real-time road, driver and environmental information in the vicinity of vehicle current location.

One of the major solutions for sustainable use of resources is official transportation system. Nowadays, the current transportation systems are determined optionally by people opinions, whereas this choice is not optimum. Therefore, a method must be taken due to a model to solve this problem efficiently. On the other hand, if the number of employees is considerable in a company, the problem area will be increased and using the mathematic algorithms will be difficult. Therefore in this paper the authors tried to reduce the problem’s search area by simple clustering method and then searched optimum path for employees in each cluster by population-based Genetic Algorithm.But one of problems about Genetic Algorithm using operations are appropriate for problematic conditions. In this paper the authors tried to develop the problem- solving conditions by using the appropriate cross over and mutation operations and then decrease spend time for finding the optimum solution. This algorithm is used in a part of Tehran city, and the information refers to 2006. By using the developed algorithm, on one hand, problem is responsive and the on the other hand problem is converged to optimum answer with lower repetition number in comparison with genetic method with simple operations and it has high repeatable test. At the end, the authors propose some suggestions to close the problem’s condition to real world condition and using some other population-based algorithms.

The time headway is an important characteristic of drivers’ behavior and traffic flow, with a great effect on traffic safety, level of service and capacity of transportation systems. In this paper, different groups of the headways are analyzed based on different combination of a pair of leading following vehicles in a car following situation. Statistics deductions indicate that drivers in car following situations select proper headways based on the type of a specified vehicle and its leading vehicle type. Based on I-80 data, results indicate that there is a significant difference between the headways of different pair of vehicles. When a heavy vehicle follows another one, the headway is the most and headway for groups includes motorcycles and passenger cars which follow a passenger car is the least. In addition, groups include heavy vehicles (a heavy vehicle following a passenger car, a heavy vehicle following another one and a passenger car following a heavy vehicle) have the most headway in comparison to other pair of vehicles. So when heavy vehicles increase in traffic flow, average time headway would also increase. Results of this research can be used for improving microscopic traffic simulation models.