基于實(shí)測(cè)微觀駕駛狀態(tài)的交通安全風(fēng)險(xiǎn)分析及模型校正
發(fā)布時(shí)間：2018-11-22 15:39
【摘要】：隨著經(jīng)濟(jì)的發(fā)展,越來越多的車輛進(jìn)入普通家庭中。伴隨著車輛的普及,交通安全問題日益嚴(yán)重。不安全的交通環(huán)境不僅會(huì)帶來財(cái)產(chǎn)的損失,還會(huì)威脅人的生命安全。而在交通安全問題中,與事故發(fā)生后再補(bǔ)救相比,如何在事故發(fā)生之前及時(shí)采取措施,避免危險(xiǎn)的發(fā)生則顯得尤為重要。論文首先在NGSIM數(shù)據(jù)的基礎(chǔ)上,分別對(duì)宏觀與微觀的交通狀態(tài)和風(fēng)險(xiǎn)性進(jìn)行了分析。而后根據(jù)Fuzzy C-means clustering method(簡(jiǎn)稱FCM)聚類方法將實(shí)測(cè)數(shù)據(jù)劃分了不同的聚類,并應(yīng)用Helly模型對(duì)不同聚類進(jìn)行了校正,并分析了不同風(fēng)險(xiǎn)形成的不同特點(diǎn)。最后將風(fēng)險(xiǎn)指標(biāo)引入了 Helly模型中來避免出現(xiàn)較高的交通安全風(fēng)險(xiǎn)。仿真分析發(fā)現(xiàn):引入風(fēng)險(xiǎn)指標(biāo)之后,駕駛員能夠根據(jù)自身駕駛狀態(tài)及時(shí)調(diào)整駕駛行為來模擬避免潛在風(fēng)險(xiǎn)。其次,從NGSIM數(shù)據(jù)提取出了多個(gè)車輛組,分析了車輛組中頭車的駕駛狀態(tài)和風(fēng)險(xiǎn)對(duì)后續(xù)跟隨車的影響,以及車輛組中相鄰兩輛前后車之間的相互影響。實(shí)測(cè)數(shù)據(jù)表明:頭車的速度和速度差與跟隨車的速度和車間距有明顯的相關(guān)性,而且頭車的風(fēng)險(xiǎn)大約能夠影響到第5輛跟隨車;因此,我們根據(jù)頭車的速度和速度差,跟隨車的速度和車間距進(jìn)一步對(duì)車輛組進(jìn)行聚類的劃分。結(jié)果發(fā)現(xiàn),隨著跟隨距離的逐漸增大,分類1的風(fēng)險(xiǎn)變化不大,分類2的風(fēng)險(xiǎn)逐漸減小,分類3的風(fēng)險(xiǎn)逐漸增加。整體來看,分類2的風(fēng)險(xiǎn)最高。最后我們對(duì)不同車輛組進(jìn)行了模型校正,并分析車輛組中不同位置車輛的風(fēng)險(xiǎn)性。結(jié)果發(fā)現(xiàn),對(duì)于不同位置的車輛,應(yīng)特別關(guān)注速度差和速度的變化,來降低或避免風(fēng)險(xiǎn)。最后,我們利用中國(guó)合肥郊區(qū)跟馳實(shí)驗(yàn)數(shù)據(jù)分析了宏觀和微觀狀態(tài)及風(fēng)險(xiǎn)的差別,發(fā)現(xiàn)微觀狀態(tài)能夠更好地體現(xiàn)駕駛狀態(tài)變化的瞬時(shí)性。聚類結(jié)果發(fā)現(xiàn):分類3具有較小的速度差和較大的車間距,為低風(fēng)險(xiǎn)狀態(tài);分類1的速度差和間距都較小,而分類2的速度差和間距都較大,因此與分類3相比,都具有一定程度的風(fēng)險(xiǎn)。接著,我們將NGSIM數(shù)據(jù)和跟馳實(shí)驗(yàn)數(shù)據(jù)進(jìn)行對(duì)比,以分析不同數(shù)據(jù)源的交通狀態(tài)和風(fēng)險(xiǎn)差異。交通狀態(tài)方面,宏觀和微觀狀態(tài)下,NGSIM數(shù)據(jù)的速度、密度分布范圍都比跟馳實(shí)驗(yàn)數(shù)據(jù)的小得多,而風(fēng)險(xiǎn)則比跟馳實(shí)驗(yàn)的略小。車輛組狀態(tài)和風(fēng)險(xiǎn)相關(guān)性方面,跟馳實(shí)驗(yàn)數(shù)據(jù)頭車速度與跟隨車具有更強(qiáng)的相關(guān)性,而車輛組之間的風(fēng)險(xiǎn)相關(guān)性更小。在不同的分類下,NGSIM數(shù)據(jù)的間距分布范圍更大,跟馳實(shí)驗(yàn)數(shù)據(jù)的速度差分布范圍更大,因此相應(yīng)的NGSIM數(shù)據(jù)的風(fēng)險(xiǎn)更低。
[Abstract]:With the development of economy, more and more vehicles enter ordinary families. With the popularity of vehicles, traffic safety problems are becoming more and more serious. Unsafe traffic environment will not only bring loss of property, but also threaten the safety of human life. In the traffic safety problem, how to take measures to avoid the danger is more important than remedying after the accident. Firstly, based on the NGSIM data, the traffic state and risk are analyzed. Then according to the Fuzzy C-means clustering method (FCM) clustering method, the measured data are divided into different clusters, and the Helly model is used to correct the different clustering, and the different characteristics of the formation of different risks are analyzed. Finally, the risk index is introduced into the Helly model to avoid the high traffic safety risk. The simulation results show that the driver can adjust his driving behavior according to his driving state to avoid the potential risk by introducing the risk index. Secondly, several vehicle groups are extracted from the NGSIM data, and the influence of the driving state and risk of the first vehicle in the vehicle group on the follower vehicle is analyzed, as well as the interaction between the two adjacent front and rear vehicles in the vehicle group. The measured data show that the velocity and velocity difference of the head car have obvious correlation with the speed and the distance of the vehicle, and the risk of the first car can affect the fifth car. Therefore, according to the speed and speed difference of the vehicle, the speed and the distance of the vehicle are further divided into clusters. The results show that with the increasing of the following distance, the risk of classification 1 does not change much, the risk of category 2 decreases gradually, and the risk of category 3 increases gradually. Overall, Category 2 has the highest risk. Finally, we calibrate the models of different vehicle groups and analyze the risk of vehicles in different positions. It is found that for vehicles in different positions, special attention should be paid to the variation of speed and speed to reduce or avoid risks. Finally, we analyze the difference between macro and micro states and risks by using the experimental data from the suburb of Hefei, China, and find that the microscopic state can better reflect the instantaneous change of driving state. The clustering results show that classification 3 has a small speed difference and a large vehicle spacing, which is a low risk state; The velocity difference and spacing of classification 1 are small, but the velocity difference and spacing of classification 2 are large. Therefore, compared with classification 3, both have a certain degree of risk. Then, we compare the NGSIM data with the experimental data to analyze the traffic state and risk difference of different data sources. In terms of traffic state, the velocity and density distribution range of NGSIM data is much smaller than that of car-following experiment data, and the risk is slightly smaller than that of car-following experiment. In the aspect of vehicle group status and risk correlation, the first car speed has a stronger correlation with the following vehicle, but the risk correlation between the vehicle group is less. Under different classification, the range of distance distribution of NGSIM data is larger, and the range of velocity difference distribution of NGSIM data is larger, so the risk of corresponding NGSIM data is lower.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491
本文編號(hào)：2349782
[Abstract]:With the development of economy, more and more vehicles enter ordinary families. With the popularity of vehicles, traffic safety problems are becoming more and more serious. Unsafe traffic environment will not only bring loss of property, but also threaten the safety of human life. In the traffic safety problem, how to take measures to avoid the danger is more important than remedying after the accident. Firstly, based on the NGSIM data, the traffic state and risk are analyzed. Then according to the Fuzzy C-means clustering method (FCM) clustering method, the measured data are divided into different clusters, and the Helly model is used to correct the different clustering, and the different characteristics of the formation of different risks are analyzed. Finally, the risk index is introduced into the Helly model to avoid the high traffic safety risk. The simulation results show that the driver can adjust his driving behavior according to his driving state to avoid the potential risk by introducing the risk index. Secondly, several vehicle groups are extracted from the NGSIM data, and the influence of the driving state and risk of the first vehicle in the vehicle group on the follower vehicle is analyzed, as well as the interaction between the two adjacent front and rear vehicles in the vehicle group. The measured data show that the velocity and velocity difference of the head car have obvious correlation with the speed and the distance of the vehicle, and the risk of the first car can affect the fifth car. Therefore, according to the speed and speed difference of the vehicle, the speed and the distance of the vehicle are further divided into clusters. The results show that with the increasing of the following distance, the risk of classification 1 does not change much, the risk of category 2 decreases gradually, and the risk of category 3 increases gradually. Overall, Category 2 has the highest risk. Finally, we calibrate the models of different vehicle groups and analyze the risk of vehicles in different positions. It is found that for vehicles in different positions, special attention should be paid to the variation of speed and speed to reduce or avoid risks. Finally, we analyze the difference between macro and micro states and risks by using the experimental data from the suburb of Hefei, China, and find that the microscopic state can better reflect the instantaneous change of driving state. The clustering results show that classification 3 has a small speed difference and a large vehicle spacing, which is a low risk state; The velocity difference and spacing of classification 1 are small, but the velocity difference and spacing of classification 2 are large. Therefore, compared with classification 3, both have a certain degree of risk. Then, we compare the NGSIM data with the experimental data to analyze the traffic state and risk difference of different data sources. In terms of traffic state, the velocity and density distribution range of NGSIM data is much smaller than that of car-following experiment data, and the risk is slightly smaller than that of car-following experiment. In the aspect of vehicle group status and risk correlation, the first car speed has a stronger correlation with the following vehicle, but the risk correlation between the vehicle group is less. Under different classification, the range of distance distribution of NGSIM data is larger, and the range of velocity difference distribution of NGSIM data is larger, so the risk of corresponding NGSIM data is lower.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491
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