CN103761430A - Method for identifying peak periods of road networks on basis of floating cars - Google Patents

Method for identifying peak periods of road networks on basis of floating cars Download PDF

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CN103761430A
CN103761430A CN201410012796.2A CN201410012796A CN103761430A CN 103761430 A CN103761430 A CN 103761430A CN 201410012796 A CN201410012796 A CN 201410012796A CN 103761430 A CN103761430 A CN 103761430A
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road
speed
tci
sections
link
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CN103761430B (en
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邹娇
陶刚
刘俊
高万宝
方林
李立超
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安徽科力信息产业有限责任公司
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Abstract

The invention relates to a method for identifying peak periods of road networks on the basis of floating cars. The method includes steps of computing speeds of single vehicle samples of road sections by the aid of GPS (global positioning system) data of the floating cars; extracting average travel speeds of the road sections; computing periodic traffic congestion indexes TCI; extracting morning and evening peak hour starting and ending time points. Compared with the prior art, the method has the advantages that the shortcoming of deficiency of a method for identifying peak periods of road networks on the basis of a floating car technology at present can be overcome; time-varying traffic laws of the road networks can be analyzed via the existing floating car technology and an urban road congestion analysis system, so that the peak periods of the road networks can be extracted.

Description

—种基于浮动车的路网高峰时段识别方法技术领域 - TECHNICAL FIELD-identification period based on the road network peak floating cars

[0001] 本发明涉及道路交通规划技术领域,具体来说是一种基于浮动车的路网高峰时段识别方法。 [0001] The present invention relates to the technical field of road traffic planning, particularly road network recognition method based on peak hours floating cars.

背景技术 Background technique

[0002] 浮动车技术是根据道路路面运行车辆动态位置信息获取道路通行状况的一种技术,利用带有GPS信息的浮动车(出租车或公交车)可以实时采集车辆的位移信息,将时间序列的车辆位置坐标与地图进行匹配,可以得到浮动车辆的速度数据。 [0002] floating car technology is acquired road traffic condition of the road surface according to operation of the vehicle dynamic location information of a technique using floating car with GPS information (taxi or bus) can collect real-time information on the vehicle displacement, time series the position coordinates of the vehicle with the map matching can be obtained the vehicle speed data float. 浮动车技术能够将采集一年的数据存储到数据库中,利用周期路段速度信息得到周期路段流量信息。 Floating Car Data can be collected year stored in the database, the information obtained using the cycle period link speed link traffic information.

[0003] AADT (道路的年平均日交通流量,annual average daily traffic)是交通模型和管理决策非常重要的参数,在交通规划、道路设计、交通安全、交通需求分析、交通控制等研究领域都有着关键的作用。 [0003] AADT (annual average daily traffic roads, annual average daily traffic) is very important parameter traffic model and management decisions in the field of transport planning, road design, traffic safety, traffic demand analysis, traffic control, etc. all have key role. 现有的AADT计算不再利用传统的人工调查法,也通过利用浮动车技术获取的路段平均速度,通过一系列模型计算实现道路AADT的智能化准确估计。 Existing AADT computing is no longer using the traditional manual survey, also by using the average speed of floating car technology acquisition, road AADT achieved through a series of intelligent computing model accurately estimated. 满足了交通规划、交通设计、交通管理的数据需求,提高了工作效率。 Meet the data needs of transportation planning, traffic design, traffic management, improve work efficiency.

[0004] 高峰时段(peak hours)是指由于通勤交通造成的道路交通早晚高峰时间段。 [0004] peak hours (peak hours) means the road due to the commuter traffic caused by traffic morning and evening peak periods. 早高峰时段通常为:7:00-9:00,晚高峰通常为17:00-19:00,具体时间断点随区域、道路等级、路段的不同而存在差异。 Morning peak hours is usually: 7: 00-9: 00 and evening peak is usually 17: 00-19: 00, the specific time break with different areas, road grade, road sections and different. 高峰时段估计算法可以实现城市路网以及区域的高峰时段计算,为路况发布服务提供基础数据源,在交通管理和交通信息服务中发挥着重要的作用。 Peak periods estimation algorithm can achieve peak periods city and regional road network computing, to provide basic data source for traffic distribution services play an important role in traffic management and traffic information services. 在利用浮动车技术计算AADT的过程中,当计算小时交通流量时,其高峰时段的判定则依据《城市道路交通拥堵评价指标体系》(征求意见稿)中所阐述的道路交通早晚高峰期标准。 In calculating AADT use floating car technology, when calculating hour traffic, it is determined that the peak hours of the morning and evening peak period road traffic standards "urban road traffic congestion evaluation system" (draft) set forth basis. 但是各个城市的高峰时段均不相同,若利用统一的标准显然与各个城市的实际高峰时段有差异,从而影响AADT的智能估算精度。 But the rush hour cities are not the same, if the use of a unified standard is clearly different from the actual peak times in various cities, thus affecting intelligent AADT estimation accuracy. 如何开发出一种可以基于浮动车技术针对不同城市而进行路网高峰时段的识别方法已经成为急需解决的技术问题。 How can develop a floating vehicle technology and road network identification method peak times for different cities based technology has become urgent problem. `发明内容 `SUMMARY

[0005] 本发明的目的是为了解决现有技术中没有基于浮动车技术的路网高峰时段识别方法的缺陷,提供一种基于浮动车的路网高峰时段识别方法来解决上述问题。 [0005] The object of the present invention is to solve the defect peak period road network identification method is not based on the prior art floating car technology, there is provided a road network during peak hours floating car based recognition method to solve the above problems.

[0006] 为了实现上述目的,本发明的技术方案如下: [0006] To achieve the above object, the technical solution of the present invention is as follows:

[0007] 一种基于浮动车的路网高峰时段识别方法,包括以下步骤: [0007] A road network during peak hours floating cars based recognition method, comprising the steps of:

[0008] 利用浮动车GPS数据计算路段单车样本速度; [0008] Sample Calculation road bicycle speed using GPS floating car data;

[0009] 提取路段平均行程速度; [0009] Extraction link average travel speed;

[0010] 计算周期交通拥堵指数TCI ; [0010] index calculation cycle TCI traffic congestion;

[0011] 提取早晚高峰小时起止时间点。 [0011] extraction morning and evening peak hours start and end time point.

[0012] 所述的利用浮动车GPS数据计算路段单车样本速度包括以下步骤: [0012] The floating car using GPS data calculated link speed bicycle sample comprising the steps of:

[0013] 通过浮动车GPS数据得到样本车辆j所经过的前后相邻两点的路径信息{Pi; i = [0013] The samples obtained before and after the vehicle passes the two adjacent points j path information {Pi by GPS floating car data; i =

I, 2, L, η};[0014] 通过路径长度Adj和时间差Atj得到此段路径的平均旅行速度; = I, 2, L, η}; [0014] This section is obtained by the path time difference and the path length Adj Atj average travel velocity; =

[0015] 若途径路段数只有一个或[>30公里/小时时,将&赋给路段P1 ;否则,按四种交 [0015] If only a road or route number [> 30 km / h, the Pl & assign sections; otherwise, according to four kinds of cross-

通状态原则结合起点的瞬时速度V1和终点的瞬时速度V2,对途径的每个路段速度分别赋值。 State principle in conjunction with the start of the instantaneous velocity and instantaneous velocity V1 end of V2, each link speed routes were assigned.

[0016] 所述的四种交通状态原则的判断方法如下: [0016] The method of determining the traffic state four principles as follows:

[0017] 减速状态,满足V, 2 V2时,起始路段速度值赋为(V1 +v)i2f其它路段速度值为总的出行时间减去起始路段的出行时间,然后通过距离除以该时间得到速度; [0017] deceleration state, meet V, 2 V2, the initial value assigned to the link speed (V1 + v) i2f other road speed value of the total travel time by subtracting the initial section of the travel time, and then dividing by the distance time to obtain velocity;

[0018] 加速状态,满足V1 SfSv2时,终止路段速度值赋为(V2+;)/2其它路段速度值为 [0018] The acceleration state, when satisfied SfSv2 V1, the speed value assigned to the termination sections (V2 +;) / 2 Other road speed value

r 7 r 7

总的出行时间减去起始路段的出行时间,然后通过距离除以该时间得到速度; The total travel time by subtracting the initial section of the travel time, and speed obtained by dividing the time distance;

[0019] 先减速后加速,起始路段速度值赋为V1,终止路段速度值赋为V2,中间路段速度值为总的出行时间减去起始路段的出行时间,然后通过距离除以该时间得到速度; [0019] After the first deceleration acceleration, the speed value assigned to the starting segment V1, the speed value assigned terminating segment is V2, the intermediate link speed travel time by subtracting the value of the total link travel time of starting, then the distance divided by time velocity obtained;

[0020] 先加速后减速,途径路段速度值赋为Ϋ。 [0020] After the first acceleration and deceleration, the speed value assigned to route sections Ϋ.

[0021] 所述的提取路段平均行程速度计算公式为 Extracting sections [0021] The average travel speed is calculated as

Figure CN103761430AD00071

[0023] 其中,Vi为弧段Pi的平均速度,Ii为弧段Pi的长度,tj为第j辆车在路径中弧段Pi上的出行时间,Hi为弧段Pi上参与计算的车辆数目。 The number of the vehicle [0023] wherein, Vi is the average speed of the arc segment Pi, Ii is the length of the arc segment Pi, tj j for the first vehicle travel time Pi in the arc path, Hi Pi of the arc involved in the calculation .

[0024] 所述的计算周期交通拥堵指数TCI包括以下步骤: [0024] The calculation cycle TCI traffic congestion index comprising the steps of:

[0025] 基于路段平均行程速度Vi进行拥堵状态识别,判断出拥堵路段; [0025] Vi performed based on the congestion status identification average travel speed of the link, it is determined that the congested road;

[0026] 计算路段拥堵里程比例RCR,分别计算快速路拥堵里程比例RCRf、主干路拥堵里程比例RCRa、次干路拥堵里程比例RCRm和支路拥堵里程比例RCR1,计算公式如下: [0026] mileage is calculated road congestion ratio RCR, calculates congestion on mileage expressway RCRf, trunk road congestion on mileage RCRa, secondary road congestion and mileage branch ratio RCRm congestion on mileage RCR1, calculated as follows:

[0027] [0027]

Figure CN103761430AD00072
Figure CN103761430AD00081

[0031 ] RCR=RCRf* ω JRCRa* ω 2+RCRm* ω 3+RCRl* ω 4 ; [0031] RCR = RCRf * ω JRCRa * ω 2 + RCRm * ω 3 + RCRl * ω 4;

[0032]其中, [0032] wherein,

[0033] L⑴为路段i的长度,Lc⑴为发生拥堵的路段i的长度, [0033] L⑴ length of link i, Lc⑴ length i of the road congestion occurs,

[0034] nf:快速路路段总个数, [0034] nf: total number of sections Expressway,

[0035] na:主干路路段总个数, [0035] na: total number of sections of trunk road,

[0036] nffl:次干路路段总个数, [0036] nffl: total number of secondary road sections,

[0037] Ii1:支路路段总个数, [0037] Ii1: total number of branch sections,

[0038] wl, w2, w3, w4分别代表各个等级道路的权重, Right [0038] wl, w2, w3, w4 represent various levels of heavy road,

[0039] [0039]

Figure CN103761430AD00082

[0040] 计算路网交通拥堵指数TCI,计算公式如下: [0040] calculation of road network traffic congestion index TCI, is calculated as follows:

Figure CN103761430AD00083

[0042]其中:a=RCR*100。 [0042] where: a = RCR * 100.

[0043] 所述的提取早晚高峰小时起止时间点包括以下步骤: [0043] The extraction morning and evening peak hours starting and ending time points include the steps of:

[0044] 判断一天24小时内TCI曲线是否服从正态分布,如果服从正态分布进入下一步计算,如果不服从正态分布,则表示当天交通异常,剔除数据并重新选择数据; [0044] Analyzing 24 hours a day TCI follow a normal distribution curve, the next step if the calculated normal distribution, normal distribution if not, it indicates an abnormal traffic that day, and re-select the data excluding the data;

[0045] 设定置信度值C,c为估计值与总体参数允许的误差范围; [0045] Setting a confidence value C, c is the estimated value of the parameter allows the overall error range;

[0046] 依据24小时TCI变化值,取TCI的最大值与最小值, [0046] 24 hours based on the change value TCI, TCI takes the maximum and minimum values,

[0047] 以O点至12点为划分,TCI最大值为max_a,其中a为1-288的周期个数,周期为5分钟;TCI前部最小值为min_tl,其中tl是最小值对应的周期数;TCI后部最小值min_t2,其中t2是最小值对应的周期数;[0048] 以12点至24点为划分,TCI最大值max_p,其中P是1-288的周期个数,周期为5分钟;TCI前部最小值min_t3,其中t3是最小值对应的周期数;TCI后部最小值min_t4,其中t4是最小值对应的周期数; [0047] In point O is divided to 12, a maximum value of TCI max_a, wherein a is the number of cycles 1-288 period of 5 minutes; TCI front portion of minimum min_tl, where tl is the period corresponding to the minimum number; TCI minimum rear min_t2, where t2 is the cycle number corresponding to the minimum; [0048] of 12 to 24 points of division, the maximum Max_p TCI, where P is the number of 1-288 cycle period of 5 TCI minimum number of rear min_t4, wherein t4 is a period corresponding to the minimum; min; minimum number TCI front portion min_t3, wherein t3 is a period corresponding to the minimum;

[0049] 计算区域总面积S1、S2、S3、S4, [0049] Calculation of the total area of ​​regions S1, S2, S3, S4,

Figure CN103761430AD00091

[0054]计算方差面积 S/、S2'、S3'、S4', [0054] The variance is calculated area S /, S2 ', S3', S4 ',

Figure CN103761430AD00092

[0059] 将S1、S2、S3、S4和S1'、S2'、S3'、S4'分别对应的代入公式c=S/ /Si求解,通过求解分别得到jl、j2、j3、j4,其中,i=l, 2, 3, 4, S/是方差面积、Si是区间面积; [0059] The S1, S2, S3, S4 and S1 ', S2', S3 ', S4', respectively, corresponding to the equation c = S / / Si solved to give jl, j2, j3 by solving respectively, J4, wherein i = l, 2, 3, 4, S / variance area, Si is the area of ​​the section;

[0060] 确定早高峰时段为Tl至T2,确定晚高峰时段为T3至T4,其中Tl、T2、T3、T4分别依次对应jl、j2、j3、j4的周期开始时间。 [0060] Tl is determined to early peak times T2, T3 is determined evening rush hour to T4, wherein Tl, T2, T3, T4 are sequentially correspond jl, j2, j3, j4 cycle start time.

[0061] 所述的判断TCI曲线是否服从正态分布的公式为: [0061] The determination formulas whether TCI normal distribution curve is:

[0062] 0.9 <M/X <1.1 且x >.3.S, [0062] 0.9 <M / X <1.1 and x> .3.S,

[0063] 其中:X是算术平均值,M是中位数,s是标准差。 [0063] wherein: X is the arithmetic mean, M is the median, s is the standard deviation.

[0064] 有益效果 [0064] beneficial effects

[0065] 本发明的一种基于浮动车的路网高峰时段识别方法,与现有技术相比可以通过现有的浮动车技术和城市道路拥堵分析体系,从中分析路网交通时变规律,提取路网高峰时段。 [0065] A road network of the present invention peak hours floating car based recognition method, compared with the prior art by conventional techniques and floating car urban road congestion analysis system, the analysis of the road network from the time variation laws, extraction road network during peak hours. 得到路网交通负荷最严重的时段,为交通管理者和交通规划者提供数据支持,提高AADT的智能估算精度。 Get traffic load worst period road network, providing data support for traffic management and traffic planners, improve intelligence AADT estimation accuracy. 附图说明 BRIEF DESCRIPTION

[0066] 图1为本发明的方法流程图 [0066] FIG. 1 is a flowchart of a method of the present invention

[0067] 图2为TCI24小时曲线变化图及相应参数标注图 [0067] FIG. 2 is a view and a curve h TCI24 corresponding parameters denoted in FIG.

具体实施方式 Detailed ways

[0068] 为使对本发明的结构特征及所达成的功效有更进一步的了解与认识,用以较佳的实施例及附图配合详细的说明,说明如下: [0068] For a better understanding and knowledge of the structural features and effects of the present invention reached to the preferred embodiment and the detailed description of the embodiments with the accompanying drawings, as follows:

[0069] 如图1所示,本发明所述的一种基于浮动车的路网高峰时段识别方法,包括以下步骤: [0069] 1, according to the present invention, one of said road network during peak hours floating cars based recognition method, comprising the steps of:

[0070] 第一步,利用浮动车GPS数据计算路段单车样本速度。 [0070] The first step, using GPS floating car data sample is calculated link speed bicycle.

[0071] 利用浮动车GPS数据计算路段的一个统计周期内单车样本平均旅行速度,首先,通过浮动车GPS数据得到样本车辆j所经过的前后相邻两点的路径信息{Pi; i = 1,2, L,η}。 [0071] The use of GPS floating car data calculating average travel speed of cycling samples within a section of the statistical period, first, the GPS data obtained by a floating car j samples before and after the vehicle passes the path information adjacent points {Pi; i = 1, 2, L, η}. 其次,基于GPS数据可以通过路径长度Adj和时间差Atj得到此段路径的平均旅行速 Second, the average travel speed may be obtained based on this section of the path through the path length Adj GPS data and the time difference Atj

度[ = 。 Degree [=. 再次,当途径路段数只有一个即表示不跨越路口或;>3()公里/小时即畅 Again, when the number of road way means that not only a crossing or intersection;> () 3 km / h i.e. Chang

通状态时,将[赋给路段P1 ;否则,按结合起点的瞬时速度V1和终点的瞬时速度V2,分四种交通状态对途径的每个路段速度分别赋值。 When the state will [assigned sections P1; otherwise, according to the combination of the start of the instantaneous velocity and instantaneous velocity V1 end of V2, sub-four traffic state for each link speed routes were assigned.

[0072] 当浮动车处于减速状态,即满足vt>;>v2时,起始路段速度值赋为(V1 +V)/2,其 [0072] When the vehicle is in a deceleration state of floating, i.e. meet vt>;> v2, the initial value assigned to the link speed (V1 + V) / 2, which is

它路段速度值为总的出行时间减去起始路段的出行时间,然后通过距离除以该时间得到速度,再将速度赋给路段Pi。 It road speed value of the total travel time by subtracting the initial section of the travel time, and speed obtained by dividing the time distance, and then assign link rate Pi.

[0073] 当浮动车处于加速状态,满足V| 时,终止路段速度值赋为(v2+¾/2,其它 [0073] When the vehicle is in an acceleration state of floating, meet V |, the speed value assigned to the termination sections (v2 + ¾ / 2, other

路段速度值为总的出行时间减去起始路段的出行时间,然后通过距离除以该时间得到速度,再将速度赋给路段Pi。 Road speed value of the total travel time by subtracting the initial section of the travel time, and speed obtained by dividing the distance of time, and then assign link rate Pi.

[0074] 当浮动车处于先减速后加速,起始路段速度值赋为V1,终止路段速度值赋为v2,中间路段速度值为总的出行时间减去起始路段的出行时间,然后通过距离除以该时间得到速度,再将速度赋给路段Pi。 [0074] when the floating vehicle is decelerating to accelerate the initial value assigned to the link speed V1, the speed value assigned to link termination V2, the intermediate link speed travel time by subtracting the value of the total link travel time of starting, then the distance divided by the time to get the speed, and then assign speed sections Pi.

[0075] 当浮动车处于先加速后减速,途径路段速度值赋为[,再将[赋给路段Pp [0075] When the vehicle is floating after the first acceleration and deceleration, the speed value assigned to route sections [, then [Pp assigned link

[0076] 第二步,提取路段平均行程速度。 [0076] The second step, extracting the average link travel speed.

[0077] 提取路段平均行程速度计算公式为 [0077] extracting the average link travel speed is calculated as

Figure CN103761430AD00101

[0079] 其中,Vi为弧段Pi的平均速度,Ii为弧段Pi的长度,tj为第j辆车在路径中弧段Pi上的出行时间Ai为弧段Pi上参与计算的车辆数目。 [0079] wherein, Vi is the average speed of the arc segment Pi, Ii is the length of the arc segment Pi, tj is the number of vehicles involved in the calculation of arc segments Pi in the path of travel time on the j-th arc Pi Ai vehicle. 这里,当Hi等于0,即该路段上没有数据覆盖时,我们用历史积累的一周不同时间段的历史平均速度进行补充;当Iii不等于O时,路段平均行程速度则是多个样本的调和平均速度。 Here, when Hi is equal to 0, i.e. no data on the road covered, we use the historical average speed history accumulated time period of one week to supplement different; when O Iii not equal, the average link travel speed of the plurality of samples is to reconcile average speed. [0080] 第三步,计算周期交通拥堵指数TCI。 [0080] The third step is the calculation period traffic congestion index TCI.

[0081] 周期交通拥堵指数是指一个统计周期内(通常是5分钟),用一个O~10的数值来描述当前区域路网的拥堵水平,是描述拥堵程度的量化指标。 [0081] The traffic congestion cycle index is a statistical period (typically 5 minutes), with a value of O ~ 10 will be described with the current level of congestion of the road network area, a quantization index of the degree of congestion described. 计算步骤如下: Step calculated as follows:

[0082] 首先,基于路段平均行程速度Vi进行拥堵状态识别,判断出拥堵路段。 [0082] First, based on an average link travel speed Vi for the congestion status identification, determines that the congested road.

[0083] 根据表1中的速度区间表,判断出当前路段是否属于拥堵状态,将为拥堵状态的路段数据提出来以供下一步处理。 [0083] According to Table 1, Table speed range, it is determined whether the current section congested state, the congestion state will be presented to the data link for further processing. 表1中的阈值表数据来源是根据2010年公布的《城市道路交通管理评价指标体系》中规定,城市主干路上机动车平均行程速度的相应的阈值,并根据合肥市实际交通特性进行了微调得到的。 Table threshold table data source 1 is based on the 2010 publication of "urban road traffic management evaluation system" provides the appropriate threshold urban arterial road vehicle average travel speed and fine-tuning to get the actual traffic characteristics Hefei of. 在不同城市使用时,可以根据当地的实际交通特性进行适应微调即可。 When using a different city, you can fine-tune the adaptation to local realities and traffic characteristics.

[0084] 表1基于路段平均旅行速度的道路状态划分速度区间表 [0084] Table 1 is based on an average travel speed of the road section of the state division of the speed range table

[0085] [0085]

Figure CN103761430AD00111

[0086] 其次,计算路段拥堵里程比例RCR。 [0086] Next, calculation of road congestion mileage ratio RCR.

[0087] 分别计算快速路拥堵里程比例RCRf、主干路拥堵里程比例RCRa、次干路拥堵里程比例RCRm和支路拥堵里程比例RCRl,计算公式如下: [0087] calculate the proportion of fast road congestion mileage RCRf, trunk road congestion on mileage RCRa, secondary road congestion and mileage branch ratio RCRm congestion on mileage RCRl, calculated as follows:

[0088] [0088]

Figure CN103761430AD00112

[0091] [0091]

Figure CN103761430AD00121

[0092] RCR=RCRf* ω j+RCRa* ω 2+RCRm* ω 3+RCRl* ω 4 ; [0092] RCR = RCRf * ω j + RCRa * ω 2 + RCRm * ω 3 + RCRl * ω 4;

[0093]其中, [0093] wherein,

[0094] L⑴为路段i的长度,Lc⑴为发生拥堵的路段i的长度, [0094] L⑴ length of link i, Lc⑴ length i of the road congestion occurs,

[0095] nf:快速路路段总个数, [0095] nf: total number of sections Expressway,

[0096] na:主干路路段总个数, [0096] na: total number of sections of trunk road,

[0097] nffl:次干路路段总个数, [0097] nffl: total number of secondary road sections,

[0098] H1:支路路段总个数, [0098] H1: total number of branch sections,

[0099] wl, w2, w3, w4分别代表各个等级道路的权重, Right [0099] wl, w2, w3, w4 represent various levels of heavy road,

[0100] [0100]

Figure CN103761430AD00122

[0101] Wl、W2、《3、w4分别代表各个等级道路的权重,从路网车辆各个等级道路的总通行里程历史数据统计分析得出。 Right [0101] Wl, W2, "3, w4 represent various levels of heavy road from each grade road network total traffic mileage of vehicles statistical analysis of historical data. 在表2中,分别有工作日权值推荐表和节假日权值推荐表,在实际中,也可以参照地方标准来进行wl、w2、w3、w4的指定计算。 In Table 2, respectively, weekday and holiday tables weights recommended weights recommended list, in practice, may be performed with reference to local standards wl, w2, w3, w4 specified calculation.

[0102] 表2《城市道路交通拥堵评价指标体系》北京地方标准 [0102] Table 2 "urban road traffic congestion evaluation system" Beijing local standards

[0103] [0103]

Figure CN103761430AD00123

[0104] 最后,计算路网交通拥堵指数TCI。 [0104] Finally, the road network traffic congestion index TCI.

[0105] 计算公式如下: [0105] calculated as follows:

<img/ >[0107] 其中:a=RCR*100,根据a的值不同,选择相应的计算公式。 <Img /> [0107] where: a = RCR * 100, according to a different value, select the appropriate calculation formula.

[0108] 第四步,提取早晚高峰小时起止时间点。 [0108] The fourth step, extracting start and end time point of morning and evening rush hours.

[0109] 包括如下步骤: [0109] comprising the steps of:

[0110] (I)如图2所示,先判断一天24小时内TCI曲线是否服从正态分布,如果服从正态分布进入下一步计算,如果不服从正态分布,则表示当天交通异常,有突发状况发生。 [0110] (I) shown in Figure 2, first determine whether TCI 24 hours a day within a normal distribution curve, the next step if the calculated normal distribution, normal distribution if not, it indicates abnormal traffic day, there unexpected situations occur. 数据不易作为参考计算数据,因此剔除数据并重新选择数据。 Easy calculation data as the reference data, excluding data and so re-select data. 判别TCI曲线是否服从正态分布采用的方法,是已有的统计学里面正态分布检验法之一,是用样本中位数M与算术平均值的比值和算术平均值与标准差的关系进行判断,反映峰形和峰态,公式如下: The method of determining the normal distribution curve whether to obey TCI employed is one which has a statistical normal distribution test method is performed with the ratio of the median sample and the arithmetic mean M and the relationship between the arithmetic mean and standard deviation determination, reflecting the peak shape and kurtosis, the following formula:

[0111] [0111]

Figure CN103761430AD00131

[0112] 其中:X是算术平均值,M是中位数,s是标准差。 [0112] wherein: X is the arithmetic mean, M is the median, s is the standard deviation.

[0113] (2)设定置信度值c,c为估计值与总体参数允许的误差范围。 [0113] (2) Set the confidence value c, c is the estimated value of the parameter allows the overall error range. 置信度值为判断的估算值,可以根据城市和决策者的实际需要来进行指定,一般来说为了保证较大的可信度,一般取置信度值大于90。 Determination of the confidence value estimates may be specified according to the actual needs of urban and decision-makers, in order to ensure greater reliability in general, and generally the confidence value is greater than 90.

[0114] (3)依据24小时TCI变化值,取TCI的最大值与最小值。 [0114] (3) based on the change value TCI 24 hours, taking the maximum and minimum TCI.

[0115] 以O点至12点为划分,TCI最大值为max_a,其中a为1-288的周期个数,周期为5分钟,288则是根据24小时以5分钟为一周期而划分得来。 [0115] In point O is divided to 12, a maximum value of TCI max_a, wherein a is a number of 1-288 cycles with a period of 5 minutes, 288 for 24 hours is obtained by dividing a 5 minute period according to . TCI前部最小值为其中tl是最小值对应的周期数。 TCI minimum front portion where tl is the number corresponding to the minimum period. TCI后部最小值min_t2,其中t2是最小值对应的周期数。 TCI minimum rear min_t2, where t2 is a period corresponding to the minimum number.

[0116] 以12点至24点为划分,TCI最大值max_p,其中P是1-288的周期个数,周期为5分钟。 [0116] at 12 to 24 points of division, the maximum Max_p TCI, where P is the number of 1-288 cycle period of 5 minutes. TCI前部最小值min_t3,其中t3是最小值对应的周期数。 TCI minimum front portion min_t3, wherein t3 is a period corresponding to the minimum number. TCI后部最小值min_t4,其中t4是最小值对应的周期数。 TCI minimum rear min_t4, wherein t4 is a period corresponding to the minimum number.

[0117] (4)计算区域总面积S1、S2、S3、S4,其计算公式如下: [0117] (4) calculating the total area of ​​regions S1, S2, S3, S4, is calculated as follows:

Figure CN103761430AD00132
Figure CN103761430AD00141

[0127] (6)将S1、S2、S3、S4和S1'、S2'、S3'、S4'分别对应的代入公式C=SiVSi求解,通过求解分别得到jl、j2、j3、j4,其中,i=l, 2, 3, 4, S/是方差面积、Si是区间面积。 [0127] (6) S1, S2, S3, S4 and S1 ', S2', S3 ', S4', respectively, corresponding to the equation C = SiVSi solved to give jl, j2, j3 by solving respectively, J4, wherein i = l, 2, 3, 4, S / variance area, Si is the area of ​​section.

[0128] (7)确定早高峰时段为Tl至T2,确定晚高峰时段为T3至T4,其中Tl、T2、T3、T4分别依次对应jl、j2、j3、j4的周期开始时间。 [0128] (7) determining early peak times Tl to T2, T3 is determined evening rush hour to T4, wherein Tl, T2, T3, T4 are sequentially correspond jl, j2, j3, j4 cycle start time. 由于jl、j2、j3、j4在此代表的是,通过24小时以5分钟为周期的288个周期数,通过jl、j2、j3、j4所代表的具体时间点T1、T2、T3、Τ4,从而才能判断出早高峰时段为Τ1-Τ2、晚高峰时段为Τ3-Τ4。 Since jl, j2, j3, j4 represented here, by 24 hours the number of 288 cycles at 5 minute period, by a specific time represent jl, j2, j3, j4 points T1, T2, T3, Τ4, whereby in order to determine the early peak times Τ1-Τ2, evening rush hour as Τ3-Τ4.

[0129] 以上显示和描述了本发明的基本原理、主要特征和本发明的优点。 [0129] The above and described the principles of the invention, the main features and advantages of the present invention. 本行业的技术人员应该了解,本发明不受上述实施例的限制,上述实施例和说明书中描述的只是本发明的原理,在不脱离本发明精神和范围的前提下本发明还会有各种变化和改进,这些变化和改进都落入要求保护的本发明的范围内。 The industry the art will appreciate, the present invention is not limited to the above embodiment, the above-described principles and embodiments described in the specification are only embodiments of the present invention, the present invention without departing from the spirit and scope of the invention will have a variety of changes and modifications, changes and modifications within the scope of the invention claimed fall. 本发明要求的保护范围由所附的权利要求书及其等同物界定。 Scope of the invention claimed by the appended claims and equivalents thereof defined.

Claims (7)

1.一种基于浮动车的路网高峰时段识别方法,其特征在于,包括以下步骤: 1)利用浮动车GPS数据计算路段单车样本速度; 2)提取路段平均行程速度; 3)计算周期交通拥堵指数TCI ; 4)提取早晚高峰小时起止时间点。 A road network during peak hours floating car based recognition method, characterized by comprising the following steps: 1) using a floating car data calculated GPS speed link cycling samples; 2) the average link travel speed extraction; 3) calculation cycle traffic congestion index TCI; 4) extracting the starting and ending time point morning and evening rush hours.
2.根据权利要求1所述的一种基于浮动车的路网高峰时段识别方法,其特征在于:所述的利用浮动车GPS数据计算路段单车样本速度包括以下步骤: 21)通过浮动车GPS数据得到样本车辆j所经过的前后相邻两点的路径信息{Pi; i =I, 2, L, η}; 22)通过路径长度Δ Clj和时间差Δ tj得到此段路径的平均旅行速度;=.23)若途径路段数只有一个或T>30公里/小时时,将[赋给路段P1 ;否则,按四种交通状态原则结合起点的瞬时速度V1和终点的瞬时速度V2,对途径的每个路段速度分别赋值。 The one of the road network of claim 1 peak hours floating car based recognition method, wherein: calculating the floating car using GPS speed data link bicycle sample comprising the steps of: 21) by floating car data GPS samples obtained before and after the vehicle passes the two adjacent points j path information {Pi; i = I, 2, L, η}; 22) Δ Clj the time difference and the path length through the average travel speed [Delta] tj obtained paragraph path; = .23) If the only way a number of sections or T> 30 km / h, the [assigned sections P1; otherwise, according to the principle of combination of four kinds of traffic conditions starting point of the instantaneous velocity and instantaneous velocity V1 end of V2, each of the pathways speed road sections were assigned.
3.根据权利要求2所述的一种基于浮动车的路网高峰时段识别方法,其特征在于,所述的四种交通状态原则的判断方法如下: 31)减速状态,满足Vt >;>v2时,起始路段速度值赋为(V1 +v)/2,其它路段速度值为总的出行时间减去起始路段的出行时间,然后通过距离除以该时间得到速度;32)加速状态,满足vt玄VS V2时,终止路段速度值赋为(v2 + v》/2,其它路段速度值为总的出行时间减去起始路段的出行时间,然后通过距离除以该时间得到速度; 33)先减速后加速,起始路段速度值赋为V1,终止路段速度值赋为V2,中间路段速度值为总的出行时间减去起始路段的出行时间,然后通过距离除以该时间得到速度; 34)先加速后减速,途径路段速度值赋为;。 The one of the road network of claim 2 peak hours floating car based identification method, wherein the method of determining traffic state four principles as follows: 31) a deceleration state, satisfies Vt>;> v2 as the initial value assigned to the link speed (V1 + v) / 2, the other sections of the overall speed value minus the travel time of the link travel time of starting, and speed obtained by dividing the time distance; 32) acceleration state, VS V2 satisfy vt Hyun, the speed value assigned to the termination sections (v2 + v "/ 2, the other sections of the overall speed value minus the travel time of the link travel time of starting, and speed obtained by dividing the time distance; 33 ) after the first decelerating acceleration, initial value assigned to the link speed V1, the speed value assigned terminating segment is V2, the intermediate link speed travel time by subtracting the value of the total link travel time of starting and speed obtained by dividing the time distance ; 34) after the first acceleration and deceleration, the speed value assigned to route sections;.
4.根据权利要求1所述的一种基于浮动车的路网高峰时段识别方法,其特征在于:所述的提取路段平均行程速度计算公式为 4. According to a road network based on identifying the peak hours of floating cars according to claim 1, wherein: the link average travel speed of extraction is calculated as
Figure CN103761430AC00021
其中,Vi为弧段Pi的平均速度,Ii为弧段Pi的长度,tu为第j辆车在路径中弧段Pi上的出行时间,Hi为弧段Pi上参与计算的车辆数目。 Wherein, Vi is the average speed of the arc segment Pi, Ii is the length of the arc segment Pi, tu j for the first vehicle travel time Pi arcs in the path, Hi Pi is the arc involved in calculating the number of vehicles.
5.根据权利要求1所述的一种基于浮动车的路网高峰时段识别方法,其特征在于,所述的计算周期交通拥堵指数TCI包括以下步骤: 51)基于路段平均行程速度Vi进行拥堵状态识别,判断出拥堵路段; 52)计算路段拥堵里程比例RCR,分别计算快速路拥堵里程比例RCRf、主干路拥堵里程比例RCRa、次干路拥堵里程比例RCRm和支路拥堵里程比例RCRl,计算公式如下: The one of the road network of claim 1 peak hours floating car based identification method, wherein the congestion calculation cycle index TCI comprising the steps of: 51) based on the average link travel speed Vi for the congestion status identified, it is determined that the congested road; 52) calculating the ratio of the RCR mileage road congestion, congestion calculates mileage ratio expressway RCRf, RCRA trunk road congestion on mileage, mileage secondary road congestion and the branch ratio RCRm congestion on mileage RCRl, calculated as :
Figure CN103761430AC00031
RCR=RCRf* ω JRCRa* ω 2+RCRm* ω 3+RCRl* ω 4 ; 其中, L(i)为路段i的长度,Lc (i)为发生拥堵的路段i的长度, nf:快速路路段总个数, na:主干路路段总个数, nffl:次干路路段总个数, Ii1:支路路段总个数, wl, w2, w3, w4分别代表各个等级道路的权重, r L(i)处于第5级拥堵水平的道路Lc.(z) = { 0 ; 53)计算路网交通拥堵指数TCI,计算公式如下: RCR = RCRf * ω JRCRa * ω 2 + RCRm * ω 3 + RCRl * ω 4; wherein, L (i) is the length of the link i is, Lc (i) is the length of the occurrence of congested sections of i, nf: Expressway sections the total number, na: total number of sections of trunk road, nffl: the total number of secondary road sections, Ii1: total number of sections of the right leg, wl, w2, w3, w4 represent various levels of heavy road, r L ( . i) in the fifth stage of the road congestion level Lc (z) = {0; 53) calculating the road network traffic congestion index TCI, calculated as follows:
Figure CN103761430AC00032
其中:a=RCR*100。 Wherein: a = RCR * 100.
6.根据权利要求1所述的一种基于浮动车的路网高峰时段识别方法,其特征在于,所述的提取早晚高峰小时起止时间点包括以下步骤:61)判断一天24小时内TCI曲线是否服从正态分布,如果服从正态分布进入下一步计算,如果不服从正态分布,则表示当天交通异常,剔除数据并重新选择数据; 62)设定置信度值c,c为估计值与总体参数允许的误差范围; 63)依据24小时TCI变化值,取TCI的最大值与最小值, 以O点至12点为划分,TCI最大值为max_a,其中a为1-288的周期个数,周期为5分钟;TCI前部最小值为min_tl,其中tl是最小值对应的周期数;TCI后部最小值min_t2,其中t2是最小值对应的周期数; 以12点至24点为划分,TCI最大值max_p,其中P是1_288的周期个数,周期为5分钟;TCI前部最小值min_t3,其中t3是最小值对应的周期数;TCI后部最小值min_t4,其中t4是最小值对应的周期数 According to one of the road network of claim 1 peak hours floating car based recognition method, characterized in that said extraction morning and evening peak hours starting and ending time points include the steps of: 61) 24 hours a day is determined whether the curve TCI normal distribution, the next step if normal distribution is calculated, if not normally distributed, then the abnormal traffic that day, excluding the data and re-select the data; 62) set confidence value c, c is the estimated value of the overall parameter allowable error range; 63) based on the change value TCI 24 hours, TCI takes the maximum and minimum number of cycles to the point O is divided 12:00, TCI maximum of max_a, wherein a is 1-288, and period of 5 minutes; TCI front portion of minimum min_tl, where tl is the number of cycles corresponding to the minimum; TCI minimum rear min_t2, where t2 is the cycle number corresponding to the minimum; of 12 to 24 points of division, TCI Max_p maximum value, where P is the number 1_288 cycle period of 5 minutes; the front portion TCI minimum min_t3, wherein t3 is a period corresponding to the minimum number; TCI minimum rear min_t4, wherein t4 is a period corresponding to the minimum number 64)计算区域总面积S1、S2、S3、S4, 64) Calculate the total area of ​​regions S1, S2, S3, S4,
Figure CN103761430AC00041
66)将S1、S2、S3、S4和S1'、S2'、S3'、S4'分别对应的代入公式C=Si' /Si求解,通过求解分别得到jl、j2、j3、j4,其中,i=l, 2, 3, 4, S/是方差面积、Si是区间面积; 67)确定早高峰时段为Tl至T2,确定晚高峰时段为T3至T4,其中Tl、T2、T3、T4分别依次对应jl、j2、j3、j4的周期开始时间。 66) The S1, S2, S3, S4 and S1 ', S2', S3 ', S4', respectively, corresponding to the equation C = Si '/ Si solved to give jl, j2, j3, j4 by solving respectively, where, i = l, 2, 3, 4, S / variance area, Si is an interval in the area; 67) determining early peak times Tl to T2, is determined evening rush hour is T3 to T4, wherein Tl, T2, T3, T4 sequentially, corresponding to jl, j2, j3, j4 cycle start time.
7.根据权利要求6所述的一种基于浮动车的路网高峰时段识别方法,其特征在于,所述的判断TCI曲线是否服从正态分布的公式为: The one of the road network of claim 6 peak hours floating car based recognition method, wherein said equation determines whether to obey a normal distribution curve TCI is:
Figure CN103761430AC00042
其中:X是算术平均值,M是中位数,s是标准差。 Wherein: X is the arithmetic mean, M is the median, s is the standard deviation.
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CN104574967A (en) * 2015-01-14 2015-04-29 合肥革绿信息科技有限公司 City large-area road network traffic sensing method based on plough satellite
CN105070056A (en) * 2015-07-23 2015-11-18 合肥革绿信息科技有限公司 Intersection traffic congestion index calculation method based on floating car
CN105139645A (en) * 2015-07-23 2015-12-09 合肥革绿信息科技有限公司 Urban regional road network operation index assessment method based on floating car technology
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CN105405294A (en) * 2015-12-30 2016-03-16 杭州中奥科技有限公司 Early warning method of traffic congestion roads
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CN105608895A (en) * 2016-03-04 2016-05-25 大连理工大学 Local abnormity factor-based urban heavy-traffic road detection method
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CN106781509A (en) * 2017-03-06 2017-05-31 长安大学 A kind of collaborative urban road congestion detection method based on V2V
CN106846806A (en) * 2017-03-07 2017-06-13 北京工业大学 Urban highway traffic method for detecting abnormality based on Isolation Forest

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CN105788289A (en) * 2014-12-17 2016-07-20 上海宝康电子控制工程有限公司 Method and system for realizing traffic condition assessment and analysis based on computer software system
CN104574967B (en) * 2015-01-14 2016-08-24 合肥革绿信息科技有限公司 Beidou one kind of large-scale urban road network traffic based on sensing method
CN104574967A (en) * 2015-01-14 2015-04-29 合肥革绿信息科技有限公司 City large-area road network traffic sensing method based on plough satellite
CN105070056A (en) * 2015-07-23 2015-11-18 合肥革绿信息科技有限公司 Intersection traffic congestion index calculation method based on floating car
CN105261210A (en) * 2015-07-23 2016-01-20 合肥革绿信息科技有限公司 Beidou-equipment-based calculating method of traffic congestion index of road
CN105139645A (en) * 2015-07-23 2015-12-09 合肥革绿信息科技有限公司 Urban regional road network operation index assessment method based on floating car technology
CN105139647A (en) * 2015-07-27 2015-12-09 福建工程学院 Real-time road congestion detection method
CN105139647B (en) * 2015-07-27 2017-12-08 福建工程学院 A kind of method that congestion in road detects in real time
CN105405294A (en) * 2015-12-30 2016-03-16 杭州中奥科技有限公司 Early warning method of traffic congestion roads
CN105489016B (en) * 2016-02-01 2018-07-10 北京交通发展研究中心 A kind of urban road operating condition appraisal procedure
CN105489016A (en) * 2016-02-01 2016-04-13 北京交通发展研究中心 Urban road operation condition evaluation method
CN105608895A (en) * 2016-03-04 2016-05-25 大连理工大学 Local abnormity factor-based urban heavy-traffic road detection method
CN105931463A (en) * 2016-06-27 2016-09-07 安徽四创电子股份有限公司 Method for calculating road traffic performance index based on traffic surface radar
CN106448159A (en) * 2016-09-09 2017-02-22 蔡诚昊 Road traffic hierarchical early warning method based on dynamic traffic information
CN106448159B (en) * 2016-09-09 2018-11-02 蔡诚昊 A kind of road traffic grading forewarning system method based on dynamic information
CN106600965A (en) * 2017-01-19 2017-04-26 上海理工大学 Sharpness-based automatic identification method for morning and evening peak periods of traffic flows
CN106781509A (en) * 2017-03-06 2017-05-31 长安大学 A kind of collaborative urban road congestion detection method based on V2V
CN106781509B (en) * 2017-03-06 2019-09-10 长安大学 A kind of collaborative urban road congestion detection method based on V2V
CN106846806A (en) * 2017-03-07 2017-06-13 北京工业大学 Urban highway traffic method for detecting abnormality based on Isolation Forest

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