【摘要】：摘要：隨著我國城鎮(zhèn)化的加速發(fā)展,城市機(jī)動車保有量急劇增長,機(jī)動車尾氣污染物排放日益成為影響人們?nèi)粘Ｉ畹闹饕獑栴}之一。微觀交通仿真模型作為銜接城市交通流運(yùn)行評價與機(jī)動車尾氣污染物測算的重要手段,其對城市機(jī)動車污染物排放評價的功能越來越受到眾多學(xué)者的應(yīng)用與研究。然而,由于內(nèi)部仿真機(jī)理存在系統(tǒng)缺陷,微觀交通仿真模型的核心模塊,車輛跟馳模型的排放測算精度尚不能滿足排放測算要求。為此,本文以機(jī)動車排放測算的最佳解釋變量VSP分布作為研究基礎(chǔ),重點(diǎn)分析與探討了提高Wiedemann模型與Fritzsche模型排放測算精度的方法與途徑,并對優(yōu)化效果進(jìn)行驗(yàn)證。 首先,對生理-心理跟馳模型進(jìn)行重點(diǎn)分析,研究Wiedemann模型和Fritzsche模型的內(nèi)部跟馳邏輯與仿真機(jī)理。此外,對國內(nèi)外的微觀排放模型和面向排放測算的車輛跟馳模型優(yōu)化研究展開綜述與分析。 其次,利用便攜式車載GPS展開數(shù)據(jù)收集和仿真方法設(shè)計(jì)。本文利用便攜式車載GPS收集了大量的北京市快速路上實(shí)際駕駛行為數(shù)據(jù)和典型的跟馳駕駛行為數(shù)據(jù),利用收集到的實(shí)際駕駛行為數(shù)據(jù)和實(shí)測跟馳駕駛行為數(shù)據(jù),本文根據(jù)相同道路等級下的駕駛行為數(shù)據(jù)具有相同的分布特征,研究設(shè)計(jì)了Wiedemann模型和Fritzsche模型的Matlab數(shù)值仿真方法。 在此基礎(chǔ)上,基于仿真得出的Wiedemann模型和Fritzsche模型在各個跟馳狀態(tài)下的比例,對Wiedemann模型的速度跟馳閾值SDV、CLDV以及距離跟馳閾值SDX展開相關(guān)敏感性分析。根據(jù)敏感性分析結(jié)果,重點(diǎn)對距離跟馳閾值SDX展開優(yōu)化研究,并提出了跟馳域SDX改進(jìn)的Wiedemann模型。此外,基于本文實(shí)際采集的大量駕駛行為數(shù)據(jù),擬合建模車輛行駛最大加速度與瞬時速度的關(guān)系式,并以此作為Wiedemann模型和Fritzsche模型最大加速度的改進(jìn)關(guān)系式。經(jīng)過模型整體優(yōu)化效果分析,Wiedemann模型仿真輸出的VSP分布在各個速度區(qū)間下的平均相對均方根誤差,從原來的0.565減低到0.102,降幅明顯；改進(jìn)后的Fritzsche模型相比于原模型,其仿真輸出的VSP分布在各個速度區(qū)間下的平均相對均方根誤差降低了57.23%。 最后,以實(shí)測跟馳駕駛行為數(shù)據(jù)為基礎(chǔ),設(shè)計(jì)了車輛跟馳模型的模型穩(wěn)定性控制算法,并分別從車頭時距穩(wěn)定性、加速度合理性和仿真速度RMSE等角度對改進(jìn)后的Wiedemann模型和Fritzsche模型進(jìn)行模型穩(wěn)定性驗(yàn)證。結(jié)果表明,改進(jìn)后的模型仍具備較高的穩(wěn)定性。
[Abstract]:Abstract: with the rapid development of urbanization in China, the number of urban motor vehicle ownership has increased dramatically, and the emission of vehicle exhaust pollutants has become one of the main problems affecting people's daily life. As an important means of linking urban traffic flow evaluation with vehicle exhaust pollutant estimation, microscopic traffic simulation model has been applied and studied more and more by many scholars in the evaluation of urban vehicle pollutant emission. However, due to the defects of the internal simulation mechanism, the emission measurement accuracy of the vehicle-following model, the core module of the microscopic traffic simulation model, can not meet the requirements of emission measurement. Therefore, based on the VSP distribution of the best explanatory variable of vehicle emission measurement, this paper analyzes and discusses the methods and ways to improve the precision of Wiedemann model and Fritzsche model, and validates the optimization effect. Firstly, the physiological-psychological following model is analyzed, and the internal following logic and simulation mechanism of Wiedemann model and Fritzsche model are studied. In addition, domestic and foreign microscopic emission models and vehicle-following model optimization for emission measurement are reviewed and analyzed. Secondly, the method of data collection and simulation is designed by using portable vehicle GPS. In this paper, a large number of actual driving behavior data and typical following driving behavior data on Beijing expressway are collected by portable vehicle GPS, and the actual driving behavior data and measured driving behavior data are used. In this paper, according to the same distribution characteristics of driving behavior data under the same road grade, the Matlab numerical simulation method of Wiedemann model and Fritzsche model is studied and designed. On this basis, based on the proportions of the Wiedemann model and the Fritzsche model under each following state, the sensitivity analysis of the velocity-following threshold SDV,CLDV and the distance-following threshold SDX of the Wiedemann model is carried out. According to the results of sensitivity analysis, the optimization of distance-following threshold (SDX) is focused on, and an improved Wiedemann model based on SDX in the range-following region is proposed. In addition, based on a large number of driving behavior data collected in this paper, the relationship between the maximum acceleration and the instantaneous velocity of the vehicle is modeled and used as an improved formula for the maximum acceleration of the Wiedemann model and the Fritzsche model. Through the analysis of the whole optimization effect of the model, the average relative root mean square error of the VSP distribution of the simulation output of the Wiedemann model in each velocity range is reduced from 0.565 to 0.102, and the decrease is obvious. Compared with the original model, the average relative root mean square error of the VSP distribution of the improved Fritzsche model is reduced by 57.23% in each velocity range. Finally, based on the measured driving behavior data, the model stability control algorithm of the vehicle following model is designed, and the stability of the vehicle following model is obtained from the time-distance between the front of the car and the vehicle. The stability of the improved Wiedemann model and Fritzsche model is verified from the angle of acceleration rationality and simulation velocity RMSE. The results show that the improved model still has high stability.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U491

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