فهرست مطالب

Transportation Engineering - Volume:6 Issue:1, 2018
  • Volume:6 Issue:1, 2018
  • تاریخ انتشار: 1396/11/25
  • تعداد عناوین: 6
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  • Farshidreza Haghighi *, Esmaeil Karimi Pages 1-15
    Despite the identification of crash hotspots as a first step of the roads safety management process, with various effective black spots identification (HSID) methods, only a few researchers have compared the performance of these methods; also it is not clear which test is the most consistent in the black-spots identification. In this research, seven commonly applied HSID methods (accident frequency (AF), PIARC coefficient based equivalent property damage only (EPDO), P-value (Islamic Republic of Iran Ministry Roads and Urban development), accident rate (AR), combined criteria, empirical Bayes (EB), societal risk-based) were compared against six robust and informative quantitative evaluation criteria (site consistency test, method consistency test, total rank differences test, total score test, sensitivity test and specificity test). These tests evaluate each method performance in a variety of areas, such as efficiency in identifying sites that show consistently poor safety performance, reliability in identifying the same black spots in subsequent time periods. To evaluate the HSID methods, three years of crash data from the Kerman state were used. Analytical Hierarchy Process (AHP) method has been used for determination the importance coefficients of evaluation tests and as a result, showed that the total rank differences test is the most appropriate test. The quantitative evaluation tests showed that the EB method performs better than the other HSID method. Test results highlight that the EB method is the most consistent and reliable method for identifying priority investigation locations. Overall, this result is consistent with the results of previous studies. The societal risk-based method performed worst in the all of the tests. It should be noted that advantages associated with the EB method were based on crash data from one of the road in Iran country, so the relative performances of HSID methods may change when using other crash data. However, the study results are consistent with earlier findings.
    Keywords: safety, black spots, crash hotspots, HSID
  • Mohammadreza Elyasi *, Seyed Farzin Faezi, Maryam Haghsheno-Sabet, Mehdi Mazaheri Pages 17-34
    Speeding is a major cause of traffic accidents and is estimated to be the cause of about 40% of fatalities in road accidents. Speed is a major accident risk factor and affects both rate and severity of traffic accidents. This importance has led to universal use of intelligent control systems for maintaining road safety by enforcing speed regulations. In this study, a modeling is carried out by the use of Analytic Network Process (ANP) based on the expert opinion to determine the importance of several criteria for location of speed cameras. Modeling was carried out using the opinions expressed in a meeting with an expert group consisting of two credible experts on traffic and transportation, one traffic officer, and one expert on traffic accident. Criteria, alternatives, and the relation between factors were discussed in meeting and the results were recorded only after reaching a consensus. The basic model was developed, and then quantified by introducing the numbers and relations suggested by experts. Camera location criteria considered in this study are police presence, effect of traffic control equipment, lighting conditions, history of accidents (in the last three years), and land use adjacent to the studied road segment. Accident statistics was used as the measure of road segment’s recent safety. For this purpose, history of accidents in each segment in the last three years was collected. The accidents resulting no injury or fatality were given a weight of 1, accidents leading to injury were given a weight of 3, and fatal accidents were given a weight of 9. Hamedan’s third ring road is then used as a case study to evaluate the model. The results show that Ahmadi Roshan Blvd. is the top priority for installation of speed cameras.
    Keywords: Accident, speeding, speed camera, Analytical Network Process
  • Mohammadsina Semnarshad *, Mohammadreza Elyasi, Mahmoud Saffarzadeh, Arman Saffarzadeh Pages 35-48
    During last decades, owing to the increase in a number of vehicles, the rate of accident occurrence grows significantly. Efforts must be made to provide efficient tools to prioritize segments requiring safety improvement and identify influential factors on accidents. This objective of the research was to determine the safety oriented threshold of International Roughness Index (IRI) to recognize Accident-Prone Segments (APSs) using new segmentation method. The modified Floating Fixed-length Segmentation (FFLS) was performed based upon the determined safety oriented IRI threshold with respect to the available literature. Floating fixed-length patterns with lengths of 100, 200 and 500 meters were moved over an entire length of a selected highway to detect segments with IRI values higher than the threshold. To diminish the lack of heterogeneity in characteristics of segments, it was proposed to analyze adjacent road segments with a similar pattern of IRI variation, as a unit. Owing to the limitation in road maintenance and rehabilitation costs for safety improvement, the entire APSs cannot be treated. Therefore, prioritization and selection of APSs were followed by imposing constraints upon the preservation of different percentages of the highway. Results indicated that the assumed safety oriented threshold of IRI and the modified segmentation method led to correct recognition of segments with high IRI associated with low level of safety. Application of the proposed method using 200-meter floating segment resulted in the shortest length of APSs for safety improvement. The outcomes lead to preserving the most deteriorated segments considering budget constraints. Furthermore, the validation supported the outcomes in which most of the segments were selected from sections with PCI values of 30 or 19. The latter supports the results achieved by the determined IRI threshold and segmentation method. Therefore, considering safety issues as well as maintenance operations would result in optimal use of available budget.
    Keywords: Road safety, International Roughness Index, road segmentation, accident, prone segments, Prioritization
  • Sina Abolhoseiniorcid, Abolghasem Sadeghi-Niaraki * Pages 49-64
    Intelligent Transportation System (ITS) is one of the most important urban systems that its functionality affects other urban systems directly and indirectly. In developing societies, increasing the transportation system efficiency is an important concern, because variety of problems such as heavy traffic condition, rise of the accident rate and the reduced performance happen with the rise of population. Route finding and navigation are two effective tools to reduce the pressure on the transportation system. Better navigation methods can reduce the traffic concentration in specific areas. In most of the cases, transportation networks are changing through time and they don’t have a static status. On the other hand, different users consider different objectives when they want to move through the transportation network. So, this paper proposed a novel method to solve dynamic navigation and route finding problem while considering different objectives. This new method is based on multi-objective Ant Colony Optimization (ACO). Experiments are designed in a simulated network and results are compared with static navigation in single-objective and multi-objective mode. Results indicated that the proposed method is performing very accurate in finding the optimal paths. Also the proposed method for dynamic navigation is performing better than the static navigation. It has improved the trip duration of the 80% of the altered routes and decreased the trip duration of some experiments up to 50%. These results indicate that the proposed method has the ability to solve multi-objective dynamic navigation in urban transportation systems in the presence of high rate traffic information.
    Keywords: Intelligent Transportation System, dynamic route finding, navigation, ant colony optimization, multi, objective problem
  • Hamid Shirmohammadi *, Esfandiar Mardani, Majid Emdadian Ghane, Araz Hasheminezhad Pages 65-83
    One of the major reasons for accidents is speed. Top of Form Inappropriate speed has been identified as the most important causal factor for serious traffic accidents. Traffic calming measures (TCMs) are engineering measures that are widely implemented to improve road safety by considerably reducing vehicle speed. TCMs have been widely used in urban areas to reduce vehicle flow rates and especially speed, and hence, to bring down the number and severity of traffic accidents. Despite numerous studies in literature, little attention has been paid to the effects of speed reduction using TCMs on network capacity regarding the main traffic operation parameters, including delay time, average speed of vehicles passing through the network, and vehicle fuel consumption. In response to this need, in this study drivers’ behavior in the presence of TCMs, based on different types and spacing of TCMs, was investigated using Aimsun software. For this purpose, the effects of a number of TCMs and their locations in the network were simulated in various scenarios and their capacity and operation performance parameters on urban roads were calculated accordingly. Consequently, according to the technical criteria, the scenarios in which the TCMs were implemented presented a better design quality. On the other hand, according to the results obtained in the different scenarios of this research, delay time and average fuel consumption of vehicles substantially increased while the average speed was reduced. It was found that TCMs have a direct effect on the network capacity and changing in the number and location of measures can change network capacity.
    Keywords: Traffic simulation, traffic calming measures, safety, capacity, fuel consumption
  • Hamid Torfehnejad *, Ali Jalali Pages 85-98
    Traffic conditions vary over time, and therefore, traffic behavior should be modeled as a stochastic process. In this study, a probabilistic approach utilizing Autocorrelation is proposed to model the stochastic variation of traffic conditions, and subsequently, predict the traffic conditions. Using autocorrelation of the time series samples of density and flow which are collected from segments with predefined specifications is the main technique to detect the trend in flow and density changes if exist. A table of possibilities for flow and density changes in two sequential segments will help to detect congestion or any other abnormal traffic events.
    In this study proposes a stochastic approach to predict the traffic situation in freeway. The dynamic changes of freeway traffic conditions are addressed with state transition probabilities. For sequence trends of density and flow change, using autocorrelation of speed and flow series will estimate the most likely sequence of traffic states. This is the novelty in this paper that introduces a robust method to recognize the traffic state in a segmented freeway. According to the model definitions 3-state traffic pattern prediction implemented as No Risk (NR), Risk (R) and High risk (HR). We evaluated the proposed method using different data sources of real traffic scenes from Tehran-Qom freeway, Iran. A total of 480 minutes, which corresponds to interstate highways, are chosen for testing. The number of passed vehicle and mean speed are collected by six traffic counter every 1 minute. The estimation rate of this model is 95% over a short time period for the month of July 2014.
    Keywords: flow, density, Autocorrelation, traffic detection, prediction