




版權(quán)說明:本文檔由用戶提供并上傳,收益歸屬內(nèi)容提供方,若內(nèi)容存在侵權(quán),請(qǐng)進(jìn)行舉報(bào)或認(rèn)領(lǐng)
文檔簡(jiǎn)介
1、 The Keep-Right-Except-To-Pass RuleSummaryAs for the first question, it provides a traffic rule of keep right except to pass, requiring us to verify its effectiveness. Firstly, we define one kind of traffic rule different from the rule of the keep right in order to solve the problem clearly; then, w
2、e build a Cellular automaton model and a Nasch model by collecting massive data; next, we make full use of the numerical simulation according to several influence factors of traffic flow; At last, by lots of analysis of graph we obtain, we indicate a conclusion as follow: when vehicle density is low
3、er than 0.15, the rule of lane speed control is more effective in terms of the factor of safe in the light traffic; when vehicle density is greater than 0.15, so the rule of keep right except passing is more effective In the heavy traffic. As for the second question, it requires us to testify that w
4、hether the conclusion we obtain in the first question is the same apply to the keep left rule. First of all, we build a stochastic multi-lane traffic model; from the view of the vehicle flow stress, we propose that the probability of moving to the right is 0.7and to the left otherwise by making full
5、 use of the Bernoulli process from the view of the ping-pong effect, the conclusion is that the choice of the changing lane is random. On the whole, the fundamental reason is the formation of the driving habit, so the conclusion is effective under the rule of keep left. As for the third question, it
6、 requires us to demonstrate the effectiveness of the result advised in the first question under the intelligent vehicle control system. Firstly, taking the speed limits into consideration, we build a microscopic traffic simulator model for traffic simulation purposes. Then, we implement a METANET mo
7、del for prediction state with the use of the MPC traffic controller. Afterwards, we certify that the dynamic speed control measure can improve the traffic flow . Lastly neglecting the safe factor, combining the rule of keep right with the rule of dynamical speed control is the best solution to accel
8、erate the traffic flow overall.Key words:Cellular automaton model Bernoulli process Microscopic traffic simulator model The MPC traffic controlContentContent21. Introduction32. Analysis of the problem33. Assumption34. Symbol Definition45. Models55.1 Building of the Cellular automaton model55.1.1 Ver
9、ify the effectiveness of the keep right except to pass rule65.1.2 Numerical simulation results and discussion95.1.3 Conclusion145.2 The solving of second question155.2.1 The building of the stochastic multi-lane traffic model155.2.2 Conclusion165.3 Taking the an intelligent vehicle system into a acc
10、ount165.3.1 Introduction of the Intelligent Vehicle Highway Systems165.3.2 Control problem175.3.3 Results and analysis185.3.4 The comprehensive analysis of the result216. Improvement of the model226.1 strength and weakness226.1.1 Strength226.1.2 Weakness226.2 Improvement of the model227. Reference24
11、1. IntroductionAs is known to all, its essential for us to drive automobiles, thus the driving rules is crucial important. In many countries like USA, China, drivers obey the rules which called “The Keep-Right-Except-To-Pass (that is, when driving automobiles, the rule requires drivers to drive in t
12、he right-most unless they are passing another vehicle)”.2. Analysis of the problemFor the first question, we decide to use the Cellular automaton to build models, then analyze the performance of this rule in light and heavy traffic. Firstly, we mainly use the vehicle density to distinguish the light
13、 and heavy traffic; secondly, we consider the traffic flow and safe as the represent variable which denotes the light or heavy traffic; thirdly, we build and analyze a Cellular automaton model; finally, we judge the rule through two different driving rules, and then draw conclusions.3. AssumptionIn
14、order to streamline our model we have made several key assumptionsl The highway of double row three lanes that we study can represent multi-lane freeways.l The data that we refer to has certain representativeness and descriptive l Operation condition of the highway not be influenced by blizzard
15、;or accidental factorsl Ignore the driver's own abnormal factors, such as drunk driving and fatigue drivingl The operation form of highway intelligent system that our analysis can reflect intelligent systeml In the intelligent vehicle system, the result of the sampling data has high accuracy.4.
16、Symbol Definition The number of vehicles The time5. ModelsBy analyzing the problem, we decided to propose a solution with building a cellular automaton model.5.1 Building of the Cellular automaton modelThanks to its simple rules and convenience for computer simulation, cellular automaton model has b
17、een widely used in the study of traffic flow in recent years. Let be the position of vehicle at time , be the speed of vehicle at time , be the random slowing down probability, and R be the proportion of trucks and buses, the distance between vehicle and the front vehicle at time is:, if the front v
18、ehicle is a small vehicle., if the front vehicle is a truck or bus. Verify the effectiveness of the keep right except to pass rule In addition, according to the keep right except to pass rule, we define a new rule called: Control rules based on lane speed. The concrete explanation of the new rule as
19、 follow: There is no special passing lane under this rule. The speed of the first lane (the far left lane) is 120100km/h (including 100 km/h);the speed of the second lane (the middle lane) is 10080km8/h (including80km/h);the speed of the third lane (the far right lane) is below 80km/ h. The speeds o
20、f lanes decrease from left to right.l Lane changing rules based lane speed control If vehicle on the high-speed lane meets , , , the vehicle will turn into the adjacent right lane, and the speed of the vehicle after lane changing remains unchanged, where is the minimum speed of the corresponding lan
21、e.l The application of the Nasch model evolutionLet be the lane changing probability (taking into account the actual situation that some drivers like driving in a certain lane, and will not take the initiative to change lanes), indicates the distance between the vehicle and the nearest front vehicle
22、, indicates the distance between the vehicle and the nearest following vehicle. In this article, we assume that the minimum safe distance gap safe of lane changing equals to the maximum speed of the following vehicle in the adjacent lanes.l Lane changing rules based on keeping right except to passIn
23、 general, traffic flow going through a passing zone (Fig. 5.1.1) involves three processes: the diverging process (one traffic flow diverging into two flows), interacting process (interacting between the two flows), and merging process (the two flows merging into one) 4.Fig. Control plan of overtakin
24、g process(1) If vehicle on the first lane (passing lane) meets and , the vehicle will turn into the second lane, the speed of the vehicle after lane changing remains unchanged. Numerical simulation results and discussion In order to facilitate the subsequent discussions, we define the space occupati
25、on rate as, where indicates the number of small vehicles on the driveway, indicates the number of trucks and buses on the driveway, and L indicates the total length of the road. The vehicle flow volume is the number of vehicles passing a fixed point per unit time, where is the number of vehicles obs
26、erved in time duration.The average speed , is the speed of vehicle at time . Take overtaking ratio as the evaluation indicator of the safety of traffic flow, which is the ratio of the total number of overtaking and the number of vehicles observed. After 20,000 evolution steps, and averaging the last
27、 2000 steps based on time, we have obtained the following experimental results. In order to eliminate the effect of randomicity, we take the systemic average of 20 samples 5.l Overtaking ratio of different control rule conditions Because different control conditions of road will produce different ov
28、ertaking ratio, so we first observe relationships among vehicle density, proportion of large vehicles and overtaking ratio under different control conditions.(a) Based on passing lane control (b) Based on speed control Fig.5.1.3Fig.5.1.3 Relationships among vehicle density, proportion of large vehic
29、les and overtaking ratio under different control conditions.It can be seen from Fig. 5.1.3: (1) when the vehicle density is less than 0.05, the overtaking ratio will continue to rise with the increase of vehicle density; when the vehicle density is larger than 0.05, the overtaking ratio will decreas
30、e with the increase of vehicle density; when density is greater than 0.12, due to the crowding, it will become difficult to overtake, so the overtaking ratio is almost 0.(2) when the proportion of large vehicles is less than 0.5, the overtaking ratio will rise with the increase of large vehicles; wh
31、en the proportion of large vehicles is about 0.5, the overtaking ratio will reach its peak value; when the proportion of large vehicles is larger than 0.5, the overtaking ratio will decrease with the increase of large vehicles, especially under lane-based control condition s the decline is very clea
32、r. l Concrete impact of under different control rules on overtaking ratioFig.5.1.4Fig. Relationships among vehicle density, proportion of large vehicles and overtaking ratio under different control conditions. (Figures in left-hand indicate the passing lane control, figures in right-hand indicate th
33、e speed control. is the overtaking ratio of small vehicles over large vehicles, is the overtaking ratio of small vehicles over small vehicles, is the overtaking ratio of large vehicles over small vehicles, is the overtaking ratio of large vehicles over large vehicles.).It can be seen from Fig. : (1)
34、 The overtaking ratio of small vehicles over large vehicles under passing lane control is much higher than that under speed control condition, which is because, under passing lane control condition, high-speed small vehicles have to surpass low-speed large vehicles by the passing lane, while under s
35、peed control condition, small vehicles are designed to travel on the high-speed lane, there is no low- speed vehicle in front, thus there is no need to overtake.l Impact of different control rules on vehicle speed Fig. Relationships among vehicle density, proportion of large vehicles and average spe
36、ed under different control conditions. (Figures in left-hand indicates passing lane control, figures in right-hand indicates speed control. is the average speed of all the vehicles, is the average speed of all the small vehicles, is the average speed of all the buses and trucks.).It can be seen from
37、 Fig. : (1) The average speed will reduce with the increase of vehicle density and proportion of large vehicles. (2) When vehicle density is less than 0.15,andare almost the same under both control conditions. l Effect of different control conditions on traffic flowFig.Fig. Relationships among vehic
38、le density, proportion of large vehicles and traffic flow under different control conditions. (Figure a1 indicates passing lane control, figure a2 indicates speed control, and figure b indicates the traffic flow difference between the two conditions.It can be seen from Fig. :(1) When vehicle density
39、 is lower than 0.15 and the proportion of large vehicles is from 0.4 to 1, the traffic flow of the two control conditions are basically the same.(2) Except that, the traffic flow under passing lane control condition is slightly larger than that of speed control condition. ConclusionIn this paper, we
40、 have established three-lane model of different control conditions, studied the overtaking ratio, speed and traffic flow under different control conditions, vehicle density and proportion of large vehicles.5.2 The solving of second question 5.2.1 The building of the stochastic multi-lane traffic mod
41、el ConclusionOn one hand, from the analysis of the model, in the case the stress is positive, we also consider the jam situation while making the decision. More specifically, if a driver is in a jam situation, applying results with a tendency of moving to the right lane for this driver. However in r
42、eality, drivers tend to find an emptier lane in a jam situation. For this reason, we apply a Bernoulli process where the probability of moving to the right is 0.7and to the left otherwise, and the conclusion is under the rule of keep left except to pass, So, the fundamental reason is the formation o
43、f the driving habit.5.3 Taking the an intelligent vehicle system into a accountFor the third question, if vehicle transportation on the same roadway was fully under the control of an intelligent system, we make some improvements for the solution proposed by us to perfect the performance of the freew
44、ay by lots of analysis. Introduction of the Intelligent Vehicle Highway SystemsWe will use the microscopic traffic simulator model for traffic simulation purposes. The MPC traffic controller that is implemented in the Matlab needs a traffic model to predict the states when the speed limits are appli
45、ed in Fig. We implement a METANET model for prediction purpose14. Control problemAs a constraint, the dynamic speed limits are given a maximum and minimum allowed value. The upper bound for the speed limits is 120 km/h, and the lower bound value is 40 km/h. For the calculation of the optimal control
46、 values, all speed limits are constrained to this range. When the optimal values are found, they are rounded to a multiplicity of 10 km/h, since this is more clear for human drivers, and also technically feasible without large investments. Results and analysisWhen the density is high, it is more dif
47、ficult to control the traffic, since the mean speed might already be below the control speed. Therefore, simulations are done using densities at which the shock wave can dissolve without using control, and at densities where the shock wave remains. For each scenario, five simulations for three diffe
48、rent cases are done, each with a duration of one hour. The results of the simulations are reported in Table 5.1, 5.2, 5.3.Table.5.1 measured results for the unenforced speed limit scenariocase#1#2#3#4#5TTS:mean(std)TPN4700no shock wave494.73452.15435.98414.88428.30445.21(6.9%)5:414700no controlled52
49、0.42517.48536.13475.98539.58517.92(4.9%)6:364700controlled513.45488.43521.35479.75486.50500.75(4.0%)6:244700no shock wave493.90472.60492.78521.10489.43493.96(3.5%)6:034700uncontrolled635.10584.92643.72571.85588.63604.84(5.3%)7:244700controlled575.30654.12589.77572.15586.46597.84(6. 4%)7:19l Enforced
50、 speed limitsl Intelligent speed adaptationFor the ISA scenario, the desired free-flow speed is about 100% of the speed limit. The desired free-flow speed is modeled as a Gaussian distribution, with a mean value of 100% of the speed limit, and a standard deviation of 5% of the speed limit. Based on
51、this percentage, the influence of the dynamic speed limits is expected to be good19. The comprehensive analysis of the resultFrom the analysis above, we indicate that adopting the intelligent speed control system can effectively decrease the travel times under the control of an intelligent system, i
52、n other words, the measures of dynamic speed control can improve the traffic flow. Evidently, under the intelligent speed control system, the effect of the dynamic speed control measure is better than that under the lane speed control mentioned in the first problem. Because of the application of the
53、 intelligent speed control system, it can provide the optimal speed limit in time. In addition, it can guarantee the safe condition with all kinds of detection device and the sensor under the intelligent speed system. On the whole, taking all the analysis from the first problem to the end into a acc
54、ount, when it is in light traffic, we can neglect the factor of safe with the help of the intelligent speed control system. Thus, under the state of the light traffic, we propose a new conclusion different from that in the first problem: the rule of keep right except to pass is more effective than t
55、hat of lane speed control. And when it is in the heavy traffic, for sparing no effort to improve the operation efficiency of the freeway, we combine the dynamical speed control measure with the rule of keep right except to pass, drawing a conclusion that the application of the dynamical speed contro
56、l can improve the performance of the freeway. What we should highlight is that we can make some different speed limit as for different section of road or different size of vehicle with the application of the Intelligent Vehicle Highway Systems. In fact, that how the freeway traffic operate is extrem
57、ely complex, thereby, with the application of the Intelligent Vehicle Highway Systems, by adjusting our solution originally, we make it still effective to freeway traffic.6. Improvement of the model6.1 strength and weakness6.1.1 Strengthl it is easy for computer simulating and can be modified flexib
58、ly to consider actual traffic conditions ,moreover a large number of images make the model more visual.l The result is effectively achieved all of the goals we set initially, meantime the conclusion is more persuasive because of we used the Bernoulli equation.l We can get more accurate result as we apply Matlab.6.1.2 Weaknessl The relationship betwee
溫馨提示
- 1. 本站所有資源如無特殊說明,都需要本地電腦安裝OFFICE2007和PDF閱讀器。圖紙軟件為CAD,CAXA,PROE,UG,SolidWorks等.壓縮文件請(qǐng)下載最新的WinRAR軟件解壓。
- 2. 本站的文檔不包含任何第三方提供的附件圖紙等,如果需要附件,請(qǐng)聯(lián)系上傳者。文件的所有權(quán)益歸上傳用戶所有。
- 3. 本站RAR壓縮包中若帶圖紙,網(wǎng)頁內(nèi)容里面會(huì)有圖紙預(yù)覽,若沒有圖紙預(yù)覽就沒有圖紙。
- 4. 未經(jīng)權(quán)益所有人同意不得將文件中的內(nèi)容挪作商業(yè)或盈利用途。
- 5. 人人文庫網(wǎng)僅提供信息存儲(chǔ)空間,僅對(duì)用戶上傳內(nèi)容的表現(xiàn)方式做保護(hù)處理,對(duì)用戶上傳分享的文檔內(nèi)容本身不做任何修改或編輯,并不能對(duì)任何下載內(nèi)容負(fù)責(zé)。
- 6. 下載文件中如有侵權(quán)或不適當(dāng)內(nèi)容,請(qǐng)與我們聯(lián)系,我們立即糾正。
- 7. 本站不保證下載資源的準(zhǔn)確性、安全性和完整性, 同時(shí)也不承擔(dān)用戶因使用這些下載資源對(duì)自己和他人造成任何形式的傷害或損失。
最新文檔
- 公司新年開班活動(dòng)方案
- 公司福利拼圖活動(dòng)方案
- 公司組織打排球活動(dòng)方案
- 公司現(xiàn)場(chǎng)搶紅包活動(dòng)方案
- 公司春節(jié)團(tuán)隊(duì)活動(dòng)方案
- 2025年影響力與傳播學(xué)綜合能力考試試題及答案
- 2025年文化遺產(chǎn)保護(hù)與管理考試題及答案
- 2025年摩托車駕駛技術(shù)培訓(xùn)和考核試卷及答案
- 2025年農(nóng)村經(jīng)濟(jì)管理考試試卷及答案
- 2025年計(jì)算機(jī)設(shè)計(jì)師職業(yè)資格考試題及答案
- 肌少癥的診治淺析
- 2024年海南省中考數(shù)學(xué)試卷真題及答案詳解(精校打?。?/a>
- 三菱FX3u-PLC應(yīng)用實(shí)例教程全套課件配套課件完整版電子教案
- 廣東省深圳市福田區(qū)2023-2024學(xué)年七年級(jí)下學(xué)期期末數(shù)學(xué)試題
- 新疆省新疆生產(chǎn)建設(shè)兵團(tuán)2024年六年級(jí)下學(xué)期5月模擬預(yù)測(cè)數(shù)學(xué)試題含解析
- 北京市昌平區(qū)2022-2023學(xué)年四年級(jí)下學(xué)期數(shù)學(xué)期末試卷(含答案)
- 《第14課 明至清中葉的經(jīng)濟(jì)與文化》教學(xué)設(shè)計(jì)教學(xué)反思-2024-2025學(xué)年高中歷史統(tǒng)編版必修中外歷史綱要上
- 2025屆自貢市重點(diǎn)中學(xué)高一下數(shù)學(xué)期末統(tǒng)考模擬試題含解析
- 河南省南陽市鄧州市2023-2024學(xué)年六年級(jí)下學(xué)期6月期末英語試題
- 一年級(jí)下冊(cè)《讀讀童謠和兒歌》試題及答案共10套
- DG∕TJ 08-87-2016 道路、排水管道成品與半成品施工及驗(yàn)收規(guī)程
評(píng)論
0/150
提交評(píng)論