جستجوی مقالات مرتبط با کلیدواژه « density-based spatial clustering » در نشریات گروه « عمران »

Sliding window is one of the road network screening methods to identify black spots. There are two parameters which are required to be defined by the user: the window length and the minimum number of crashes per black spot. Based on previous studies, changing the window length significantly affects the process of black spots identification. In other words, changing window length and increment length leads to different results. The reviews show that, window length and increment length are selected based on engineering judgment or using the previous experiences, and statistical techniques and scientific methods have not been used. In this research, a new method is proposed based on optimizing the framework of the sliding window method by examining its features. The new method allows the sliding window length to be estimated dynamically. The optimization methodology is as follows: First, the desired road is segmented and for each segment, several different scenarios of window length are selected using the density-based clustering algorithm. Then the safety performance function is developed and the number of predicted and expected accidents and the difference between them as the potential safety improvement is calculated for each window movement for all selected scenarios in the segment. Finally, by calculating average differences using the analysis of variance, the length that has the lowest dispersion of difference values compared to the mean is determined as the optimal length of the window in each segment. The results of this study showed that in all segments, at least in a single movement of the window with the estimated optimal length, the SPF-value of the window has met the desired level of accuracy. Also, by moving the window with optimal dynamic lengths, 122 prone black spots identified, which has more crash density and effective length compared to the results of windows with fixed lengths of 300, 500 and 1000 meters.

Nowadays, one of the most important issues in the metropolises of all around the world is using the modern public transportation systems, research and development in this field which indicates the importance of using these types of systems in most countries. One of the most attractive innovation in urban transportation system is using intelligent taxis and improvement in online ordering systems; while in the metropolis of Tehran, the use of smart taxi and optimization of the network coverage is one of the important issues of the municipality. Therefore, in this paper, a new model based on two different strategies for ordering taxis in Tehran is presented for the first time. First, using the concept of the trip chain, a model called trip chain network is presented in accordance with Tehran's situation. After identifying the demand points, a density-based spatial clustering applications with noise (DBSCAN) is used to evaluate the proposed model. To improve the performance of the algorithm and set its parameters, the design of experiment method (DOE) was used and then cluster centers were designated as intelligent taxi stations. The results show centers of these clusters are the best feasible points for developing intelligent taxi stations in Tehran and 53.12 percent of the points obtained as proposed stations of smart taxis are matched with the current situations. Also, the proposed model can provide the maximum usage of taxis and reduce the wasting time for passengers to access the taxi by striking an equilibrium approach between supply and demand.