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CN109360418B - Urban expressway service level grading method based on vehicle speed discrete characteristics - Google Patents

Urban expressway service level grading method based on vehicle speed discrete characteristics Download PDF

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CN109360418B
CN109360418B CN201811285927.9A CN201811285927A CN109360418B CN 109360418 B CN109360418 B CN 109360418B CN 201811285927 A CN201811285927 A CN 201811285927A CN 109360418 B CN109360418 B CN 109360418B
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service level
speed
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CN109360418A (en
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李健
刘莹
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Tongji University
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
    • G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed

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Abstract

本发明公开了一种考虑车速离散特征的城市快速路服务水平分级方法，旨在突破现有仅以效率评价指标为主的服务水平分级方法，引入车速离散特征综合考虑安全性、可靠性等其它评价指标。本发明基于快速路车牌照识别数据，选取车速方差系数作为描述车速离散的特征指标，通过建立车速速度方差函数提出“关联车速方差系数”和“关联机动可靠性”两种服务水平分级准则，并通过将“方差系数‑密度”拟合结果与已有传统服务水平分级图以密度坐标对应得到新的服务水平分级标准。

Figure 201811285927

The invention discloses an urban expressway service level grading method considering the discrete characteristics of vehicle speed, aiming to break through the existing service level grading method mainly based on the efficiency evaluation index, and introduce the discrete characteristics of vehicle speed to comprehensively consider safety, reliability and other other factors. evaluation indicators. Based on the identification data of expressway license plates, the invention selects the vehicle speed variance coefficient as a characteristic index to describe the dispersion of the vehicle speed, and proposes two service level classification criteria of "correlated vehicle speed variance coefficient" and "correlated maneuver reliability" by establishing a vehicle speed speed variance function. The new service level classification standard is obtained by corresponding the "variance coefficient-density" fitting result with the existing traditional service level classification map with density coordinates.

Figure 201811285927

Description

Urban expressway service level grading method based on vehicle speed discrete characteristics
Technical Field
The invention relates to the field of traffic data analysis and state evaluation, in particular to an urban expressway service level grading method based on vehicle speed discrete characteristics.
Background
The discrete phenomenon of the vehicle speed is one of the important characteristics of the non-steady traffic flow and is obviously related to the traffic safety. The vehicle speed discrete characterization indexes comprise speed variance, Standard Deviation (SDS), Coefficient of Variation (CVS) and the like, but are difficult to directly observe due to index values, and the distribution characteristics of the indexes still need model calibration. The correlation study proves the significant correlation between the vehicle speed dispersion and the traffic safety phase, but the dispersion quantification is not introduced into the road service level evaluation method.
The level of service (LOS) is an important indicator of traffic flow operating conditions and the quality of service perceived by drivers and passengers. A scientific and reasonable service level grading method is an important basis for road design, traffic operation efficiency evaluation and real-time accident prediction. Each country holds different grading standards for road service levels. LOS is defined as a measurement standard for describing the quality of a traffic flow running state in the United states road traffic Capacity Manual (HCM), service indexes such as speed, comfort, driving freedom, traffic interference degree and convenience are adopted for classification, and road service levels are divided into six levels from A to F according to a flow-density relation. The degree of congestion (the ratio of the actual traffic volume of a link to the estimated traffic volume on the day) is used as an index for evaluating the level of traffic service for the link in japan. The germany 'road traffic capacity manual' is combined with the self national conditions and takes the road traffic capacity manual (HCM) as a blue book, and a service level grading method for evaluating different traffic facilities from the perspective of users is designed. The service level grading evaluation index is revised by the road engineering technical standard (2014 edition) in China, the road congestion degree is judged by saturation, and the difference between the actual running speed and the free flow speed of the passenger car is used as a secondary evaluation index, so that the method is beneficial exploration for the service level grading research. However, the traffic flow service level division standards have common defects: namely, the classification standard only depends on mobility indexes such as density and flow, neglects safety and reliability indexes such as dispersion and extremum, and cannot comprehensively describe actual characteristics of the road running state.
The vehicle speed dispersion is associated with the traffic flow service Level (LOS), a new LOS judgment standard is established according to the vehicle speed dispersion characteristic index, and the service level is expanded to the dimension of 'safety', so that the maneuverability, reliability and potential safety of the traffic flow are reflected, the fuzzy description of the existing HCM service level grade is effectively avoided, and the method has important application value.
Disclosure of Invention
The invention aims to provide a method for grading the service level of an urban expressway based on a vehicle speed discrete characteristic aiming at the defects in the prior art so as to solve the problems in the prior art.
The technical problem solved by the invention can be realized by adopting the following technical scheme:
a city expressway service level grading method based on vehicle speed discrete characteristics comprises the following steps:
1) data collection and cleaning
1.1 data Collection
Acquiring relevant data of the vehicle, including time, speed and section, by a license plate recognition system;
1.2 data cleansing
Eliminating data of '0' value and abnormal value caused by abnormal detector, and eliminating all related data within one hour of accident occurrence;
2) vehicle speed feature extraction
2.1, extracting individual space vehicle speed, wherein original data is point vehicle speed and needs to be converted into space vehicle speed to simulate the relation between variables; the statistical time interval was set to 5 minutes, and the following calculations were made:
Figure BDA0001849038040000031
Figure BDA0001849038040000032
Figure BDA0001849038040000033
Figure BDA0001849038040000034
Figure BDA0001849038040000035
Figure BDA0001849038040000036
in the above formula, i represents the grouping of i corresponding to the ith 5 minute interval, where 1. ltoreq. i.ltoreq.Ni；
m represents m observations in the i packet; n is a radical ofiRepresenting the total number of velocity observations for the i packets;
Figure BDA0001849038040000037
i time average vehicle speed of the group; v. ofm(ti): the vehicle speed corresponding to the m observation values in the i group;
Figure BDA0001849038040000038
i grouping the space average vehicle speed;
Figure BDA0001849038040000039
i grouping the time-averaged vehicle speed variance;
Figure BDA00018490380400000310
i grouping the space average vehicle speed variance; CVS: i grouping space vehicle speed variance coefficients;
3) associated mobility reliability service level ranking criteria
Associating a mobility reliability service level classification criterion, and describing mobility and reliability of traffic flow; vehicle travel time tt with LOS i defined as k%k％ileNo more than free flow state travel time ttfWhere m is a multiplier greater than 1 and increases with increasing service level LOSi, which is used to improve mobility mttfAnd reliability ttk％ileAssociated therewith, see equation (7) for travel time TT or equation (8) for normalized travel time TT, let TTfSet to 100;
LOS i：ttk％ile≤mttf (7)
LOS i：TTk％ile≤M (8)
in the above formula: i represents numbers from A to E of 1 to 5; TTk％ileStandard travel time, representing k%; m is equal to 100 times M;
assuming that the vehicle speed distribution follows a normal distribution; from this normality assumption and the fitting relation CVS of the measured data (vehicle speed variance coefficient-average speed) is 0.573exp-0.02237SThe vehicle speed probability density and the cumulative density distribution can be obtained, and are shown in formulas (9) and (10); the accumulated density distribution of the normalized travel time under different average speed S levels is shown in a formula (12), and a formula (13) is obtained by converting a formula (8);
Figure BDA0001849038040000041
Figure BDA0001849038040000042
Figure BDA0001849038040000043
Figure BDA0001849038040000044
Figure BDA0001849038040000045
wherein f (v) represents a probability density function of a vehicle speed v, which follows a normal distribution with a mean S and a standard deviation SDS; p (v) represents the cumulative density at which the vehicle speed v is less than a certain level r; p (tt) (p (tt)) represents the cumulative density for which the travel time is less than a certain level; l represents a travel distance; ffs represents the freestream vehicle speed; z represents a normal distribution normalization unit.
Compared with the prior art, the invention has the beneficial effects that:
and realizing the classification criterion of the associated maneuvering reliability service level by a speed discrete feature extraction algorithm based on the actually measured speed and speed of the single vehicle. The method comprises the steps of firstly selecting a vehicle speed variance coefficient as a characteristic index for describing vehicle speed dispersion based on single-vehicle speed data in field license plate identification data, proposing two service level grading criteria of 'associated vehicle speed variance coefficient' and 'associated maneuvering reliability' by establishing a vehicle speed variance function, and obtaining a new service level grading standard by corresponding a 'variance coefficient-density' fitting result with an existing traditional service level grading graph in a density coordinate manner.
Drawings
FIG. 1 is a graph of service level ranking criteria for an associated vehicle speed variance coefficient according to the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
Referring to fig. 1, the city expressway service level grading method based on the vehicle speed discrete characteristic, provided by the invention, comprises the following steps:
1) data collection and cleaning
1.1 data Collection
Acquiring relevant data of the vehicle, including time, speed and section, by a license plate recognition system;
1.2 data cleansing
Eliminating data of '0' value and abnormal value caused by abnormal detector, and eliminating all related data within one hour of accident occurrence;
2) vehicle speed feature extraction
2.1, extracting individual space vehicle speed, wherein original data is point vehicle speed and needs to be converted into space vehicle speed to simulate the relation between variables; the statistical time interval was set to 5 minutes, and the following calculations were made:
Figure BDA0001849038040000061
Figure BDA0001849038040000062
Figure BDA0001849038040000063
Figure BDA0001849038040000064
Figure BDA0001849038040000065
Figure BDA0001849038040000066
in the above formula, i represents the grouping of i corresponding to the ith 5 minute interval, where 1. ltoreq. i.ltoreq.Ni；
m represents m observations in the i packet; n is a radical ofiRepresenting the total number of velocity observations for the i packets;
Figure BDA0001849038040000067
i time average vehicle speed of the group; v. ofm(ti): the vehicle speed corresponding to the m observation values in the i group;
Figure BDA0001849038040000068
i grouping the space average vehicle speed;
Figure BDA0001849038040000069
i grouping the time-averaged vehicle speed variance;
Figure BDA00018490380400000610
i grouping the space average vehicle speed variance; CVS: i grouping space vehicle speed variance coefficients;
3) associated mobility reliability service level ranking criteria
Associating a mobility reliability service level classification criterion, and describing mobility and reliability of traffic flow; vehicle travel time tt with LOS i defined as k%k％ileNo more than free flow state travel time ttfWhere m is a multiplier greater than 1 and increases with increasing service level LOSi, which is used to improve mobility mttfAnd reliability ttk％ileAssociated therewith, see equation (7) for travel time TT or equation (8) for normalized travel time TT, let TTfSet to 100;
LOS i：ttk％ile≤mttf (7)
LOS i：TTk％ile≤M (8)
in the above formula: i represents numbers from A to E of 1 to 5; TTk％ileStandard travel time, representing k%; m is equal to 100 times M;
assuming that the vehicle speed distribution follows a normal distribution; from this normality assumption and the fitting relation CVS of the measured data (vehicle speed variance coefficient-average speed) is 0.573exp-0.02237SThe vehicle speed probability density and the cumulative density distribution can be obtained, and are shown in formulas (9) and (10); normalized travel timeThe cumulative density distribution at the same average speed S level is shown in a formula (12), and a formula (13) is obtained by converting a formula (8);
Figure BDA0001849038040000071
Figure BDA0001849038040000072
Figure BDA0001849038040000073
Figure BDA0001849038040000074
Figure BDA0001849038040000075
wherein f (v) represents a probability density function of a vehicle speed v, which follows a normal distribution with a mean S and a standard deviation SDS; p (v) represents the cumulative density at which the vehicle speed v is less than a certain level r; p (tt) (p (tt)) represents the cumulative density for which the travel time is less than a certain level; l represents a travel distance; ffs represents the freestream vehicle speed; z represents a normal distribution normalization unit.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (1)

1.一种基于车速离散特征的城市快速路服务水平分级方法，其特征在于，包括如下步骤：1. an urban expressway service level grading method based on the discrete feature of vehicle speed, is characterized in that, comprises the steps: 1)数据收集和清洗1) Data collection and cleaning 1.1数据收集1.1 Data collection 通过车牌照识别系统，获取车辆的相关数据，包括时间、车速和断面；Through the license plate recognition system, obtain relevant data of the vehicle, including time, speed and section; 1.2数据清洗1.2 Data cleaning 剔除由检测器异常引起的“0”值和异常值数据，并将事故发生一小时内的相关数据全部剔除；Remove the "0" value and abnormal value data caused by the abnormality of the detector, and remove all relevant data within one hour of the accident; 2)车速特征提取2) Vehicle speed feature extraction 2.1提取个体空间车速，原始数据为点车速，需转化为空间车速以模拟变量间关系；设定统计时间间隔为5分钟，各式计算如下：2.1 Extract the individual space speed, the original data is the point speed, which needs to be converted into the space speed to simulate the relationship between variables; the statistical time interval is set to 5 minutes, and the various calculations are as follows:
Figure FDA0002797805910000011
Figure FDA0002797805910000011
Figure FDA0002797805910000012
Figure FDA0002797805910000012
Figure FDA0002797805910000013
Figure FDA0002797805910000013
Figure FDA0002797805910000014
Figure FDA0002797805910000014
Figure FDA0002797805910000015
Figure FDA0002797805910000015
Figure FDA0002797805910000016
Figure FDA0002797805910000016
 上式中，i表示对应于第i个5分钟间隔的i分组，其中1≤i≤Ni；In the above formula, i represents the i group corresponding to the i-th 5-minute interval, where 1≤i≤N i ; m表示i分组中的m观测值；Ni表示i分组的速度观测值总量；m represents m observations in group i ; Ni represents the total amount of velocity observations in group i;
Figure FDA0002797805910000021
i分组的时间平均车速；vm(ti)：i分组中m观测值对应的车速；
Figure FDA0002797805910000021
Time-averaged vehicle speed of group i; v m (t i ): vehicle speed corresponding to m observations in group i;
Figure FDA0002797805910000022
i分组空间平均车速；
Figure FDA0002797805910000023
i分组时间平均车速方差；
Figure FDA0002797805910000022
i group space average speed;
Figure FDA0002797805910000023
i grouped time average speed variance;
Figure FDA0002797805910000024
i分组空间平均车速方差；CVS：i分组空间车速方差系数；
Figure FDA0002797805910000024
i-group space average speed variance; CVS: i-group space speed variance coefficient; 3)关联机动可靠性服务水平分级准则3) Classification criteria for associated maneuver reliability service level 关联机动可靠性服务水平分级准则，同时描述交通流的机动性和可靠性；定义LOSi为k％的车辆行程时间ttk％ile不大于自由流状态行程时间ttf的m倍，其中m为大于1的乘子并随服务水平LOSi的增加而增，其用于将机动性mttf与可靠性ttk％ile相联系，见公式(7)行程时间为tt或公式(8)标准化行程时间TT，将ttf设为100；Relevant mobility reliability service level grading criteria, while describing the mobility and reliability of traffic flow; define LOSi as k% vehicle travel time tt k% ile is not greater than m times the free flow state travel time tt f , where m is greater than A multiplier of 1 and increases with service level LOSi, which is used to relate mobility mtt f to reliability tt k%ile , see equation (7) travel time is tt or equation (8) normalized travel time TT , set tt f to 100; LOSi：ttk％ile≤mttf (7)LOSi: tt k%ile ≤ mtt f (7) LOSi：TTk％ile≤M (8)LOSi: TT k%ile ≤M (8) 上述公式中：i表示从A至E编号为1至5；TTk％ile表示k％的标准行程时间；M等于100倍m；In the above formula: i represents the number of 1 to 5 from A to E; TT k%ile represents the standard travel time of k%; M is equal to 100 times m; 假定车速分布服从正态分布；由此正态性假定及实测数据车速方差系数-平均速度拟合关系CVS＝0.573exp-0.02237S，可得车速概率密度和累计密度分布，详见公式(9)和(10)；标准化行程时间在不同平均速度S水平下的累计密度分布见公式(12)，公式(13)由公式(8)转化得出；It is assumed that the vehicle speed distribution obeys a normal distribution; therefore, the normality assumption and the measured data vehicle speed variance coefficient-average speed fitting relationship CVS=0.573exp -0.02237S , the vehicle speed probability density and cumulative density distribution can be obtained, see formula (9) for details. and (10); the cumulative density distribution of the normalized travel time at different average speeds S levels is shown in formula (12), and formula (13) is transformed from formula (8);
Figure FDA0002797805910000025
Figure FDA0002797805910000025
Figure FDA0002797805910000026
Figure FDA0002797805910000026
Figure FDA0002797805910000031
Figure FDA0002797805910000031
Figure FDA0002797805910000032
Figure FDA0002797805910000032
 LOSi：
Figure FDA0002797805910000033
LOSi:
Figure FDA0002797805910000033
 其中，f(v)表示车速v的概率密度函数，车速v以均值S和标准差SDS服从正态分布；P(v)表示车速v小于特定水平r的累计密度；P(tt)(P(TT))表示行程时间小于特定水平的累计密度；L表示行程距离；ffs表示自由流车速；Z表示正态分布标准化单位。Among them, f(v) represents the probability density function of the vehicle speed v, and the vehicle speed v obeys the normal distribution with the mean S and the standard deviation SDS; P(v) represents the cumulative density of the vehicle speed v less than a certain level r; P(tt)(P( TT)) represents the cumulative density of travel time less than a certain level; L represents the travel distance; ffs represents the free-flow vehicle speed; Z represents the normal distribution normalized unit.
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