CN111951546B - Method for quantifying safety influence range of congestion charging policy - Google Patents

Method for quantifying safety influence range of congestion charging policy Download PDF

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CN111951546B
CN111951546B CN202010645645.6A CN202010645645A CN111951546B CN 111951546 B CN111951546 B CN 111951546B CN 202010645645 A CN202010645645 A CN 202010645645A CN 111951546 B CN111951546 B CN 111951546B
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charging policy
congestion charging
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safety
safety influence
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CN111951546A (en
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郭延永
丁红亮
刘攀
吴瑶
马景峰
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Southeast 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
    • G07—CHECKING-DEVICES
    • G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
    • G07B15/00—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points
    • G07B15/06—Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems
    • 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

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Abstract

The invention relates to a method for quantifying the safety influence range of a congestion charging policy, which comprises the following steps: (1) determining an initial experimental object and an initial control object; (2) collecting data; (3) selecting a scoring model and calibrating parameters; (4) determining the experimental object participating in the comparison and the matched control object; (5) calculating the safety influence effect; (6) and (4) determining the safety influence range of the congestion charging policy. The unit with similar basic characteristic attributes to the initial experimental object in the congestion charging policy safety influence range is selected as the initial control object, the number of traffic accidents in the same time period of the initial control object and the unit is respectively collected, and the actual influence range of the congestion charging policy is judged based on the proposed causal analysis model. The method provided by the invention can analyze the safety influence range of the congestion charging policy by selecting the control object most similar to the experimental object on the basis of quantifying the influence of all attributes.

Description

Method for quantifying safety influence range of congestion charging policy
Technical Field
The invention relates to the field of road traffic accident safety, in particular to a method for quantifying the safety influence range of a congestion charging policy.
Background
With the continuous development of traffic patterns, the number of private cars rises year by year, and traffic jam occurs in many cities. In order to reduce the traffic congestion and improve the road use efficiency, many countries and cities begin to implement a traffic congestion charging policy in a certain area. The traffic congestion policy has made a significant effort to solve the road congestion, but as vehicles are reduced, the speed of the road is significantly increased, and the number of bicycles is also significantly increased, thereby causing a significant increase in traffic safety accidents within a certain range. In this context, it is particularly important to reasonably evaluate the safety impact effect and the actual impact range of the congestion charging policy.
In the research and research field and the patent application field, the safety influence range of the congestion charging policy is not discussed. Most scholars are concerned with the geographical boundaries of policy enforcement and discuss the safety impact effect of the congestion charging policy based on the geographical boundaries. The invention provides a method for quantifying the safety influence range of a congestion charging policy, which not only can improve the knowledge structure framework of the related field, but also can provide a powerful support result for the benefit evaluation and analysis of government departments and policy making departments.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the above problems, the present invention provides a method for quantifying a safety influence range of a congestion charging policy, which can discuss an actual influence range of traffic safety of the congestion charging policy on the basis of quantifying influences of other covariates.
The technical scheme is as follows: the invention provides a method for quantifying a congestion charging policy security influence range, which specifically comprises the following steps:
(1) selecting a cell in the congestion charging policy as an initial experimental object and a cell outside the congestion charging policy as an initial control object by taking the geographical boundary of the congestion charging policy as a reference line;
(2) selecting covariates participating in model analysis as the number Q of accidents in a region, the total number N of population in the region, the economic development GDP of the region, the road network density K of the region, the road grade M of the region, the annual average daily traffic of motor vehicles AADT of the region and the annual average daily traffic of bicycles AADB of the region;
(3) calculating the scoring conditions of the initial experimental object and the control object by introducing covariates influencing the traffic accident safety by adopting a scoring model;
(4) determining the control object matched with each experimental object according to the closest score principle based on the comprehensive score condition of the initial experimental object and the control object obtained in the step (3);
(5) calculating the traffic accident safety influence effect ATT of the congestion charging policy on the experimental object:
Figure GDA0003192129170000021
wherein, ATT represents the effect of traffic accident safety influence on experimental objects by charging policy, tiN, t represents the number of accidents per subject, i 1,2jN, representing the number of accidents per control object, j 1,2.. n;
(6) and determining the safety influence range of the congestion charging policy.
Further, the sample ratio of the initial experimental object to the control object in the step (1) is 1: 10.
Further, the step (3) is realized by the following formula:
Figure GDA0003192129170000022
wherein N represents the population count of the region; GDP represents the economic development of a region; k is the road network density of the region; m represents each road grade, the main road is marked as 2, the secondary main road is marked as 1, and the branch road is marked as 0; AADT is the annual average daily traffic volume of the motor vehicle in the area; AADB is the annual average daily traffic volume of the region; alpha is a constant term; beta is anAre regression vector coefficients.
Further, the score closest principle calculation model in the step (4) is as follows:
μimin { | subject score-initial control subject score | }.
Further, the step (6) is realized as follows:
if the safety influence effect ATT calculated in the step (5) is obvious on the level of 95%, judging that the congestion charging policy has influence on traffic safety in the range, continuing to expand the number of control objects, repeating the steps (1) to (6) until the ATT is not obvious on the level of 95%, and determining the safety influence range of the congestion charging policy; and (3) if the safety influence effect ATT calculated in the step (5) is not significant at a 95% level, and the influence range of the congestion charging policy is smaller than the geographical boundary for implementing the policy, reducing the geographical boundary of the congestion charging policy, repeating the steps (1) to (6) until the ATT is found to be significant at a 95% level, and determining the safety influence range of the congestion charging policy.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: 1. considering that traffic is a dynamic system, respectively collecting the number of traffic safety accidents and other covariates influencing traffic safety in the same time period of a potential experimental object and a potential control object by continuously selecting the potential experimental object and the potential control object, and judging the actual influence range of the traffic safety of a congestion charging policy by adopting a causal analysis model; 2. the method can not only perfect the knowledge structure framework of the related field, but also provide a powerful support result for the benefit evaluation analysis of government departments and policy making departments.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
the invention provides a method for quantifying a congestion charging policy safety influence range, which can discuss the actual influence range of the congestion charging policy traffic safety on the basis of quantifying the influence of other covariates.
As shown in fig. 1, a method for quantifying the safety influence range of a congestion charging policy includes the following steps:
(1) determining an initial subject and an initial control subject
The geographical boundary of the congestion charging policy is used as a reference line, a cell in the congestion charging policy is selected as an initial experimental object, and a cell outside the congestion charging policy is selected as an initial control object. The proportion of the samples of the initial experimental object and the control object of the causal analysis model adopted in the method recommended by literature data is 1: 1.5-1: 30, and the proportion of the samples adopted in the research is 1: 10.
(2) Data acquisition
The covariates participating in the model analysis are selected as the number Q (start/year) of accidents in the area, the total number N (unit: people) of the area, the economic development GDP (unit: hundred yuan) of the area, the road network density K (unit: kilometer per square kilometer) of the area, the road grade M of the area, the annual average daily traffic of motor vehicles AADT (unit: vehicles/day) of the area and the annual average daily traffic of bicycles AADB (unit: vehicles/day) of the area. The variable data can be obtained by field investigation and local related transportation departments (traffic police departments).
(3) Score model selection and parameter calibration
The adopted scoring model is a logit model, and scoring conditions of an initial experimental object and a control object are calculated by introducing covariates influencing traffic accident safety.
Figure GDA0003192129170000031
Wherein N represents the population number of the region, GDP represents the economic development of the region, K represents the road network density of the region, M represents each road grade (the main road is marked as 2, the secondary road is marked as 1, and the branch road is marked as 0), AADT is the annual average daily traffic volume of the motor vehicles of the region, and AADB is the annual average daily traffic volume of the bicycles of the region; alpha is a constant term, betanIs a regression vector coefficient;
(4) determining the experimental object participating in the comparison and the matched control object
Determining the control object matched with each experimental object according to the score closest principle based on the comprehensive score condition of the initial experimental object and the control object in the step (3); the calculation model with the closest score to the principle is as follows:
μimin { | subject score-initial control subject score | };
(5) security impact effect calculation
Calculating the safety influence effect ATT of the congestion charging policy based on the experimental object and the matched control object in the step (4):
Figure GDA0003192129170000041
wherein, ATT represents the effect of traffic accident safety influence on experimental objects by charging policy, tiN, t represents the number of accidents per subject, i 1,2jJ represents the number of accidents per control object, and j is 1,2.
(6) Determination of the safe impact area of a congestion charging policy
The method is divided into two cases: if the safety influence effect ATT of the step (5) is found to be significant on the level of 95%, the congestion charging policy can be judged to have an influence on traffic safety in the range, the number of control objects needs to be expanded continuously, the research adopts a unit gradient of 100 meters to expand the geographical boundary of the congestion charging policy, the steps (1) - (6) are repeated until the ATT is found to be insignificant on the level of 95%, and the safety influence range of the congestion charging policy is finally determined. If the ATT of the safety influence effect in the step (5) is not significant on the 95% level in the calculation, and the influence range of the congestion charging policy is smaller than the geographical boundary for implementing the policy, the geographical boundary of the congestion charging policy needs to be gradually narrowed by taking 100 meters as a unit, and the steps (1) to (6) are repeated until the ATT is significant on the 95% level, and finally the safety influence range of the congestion charging policy is determined.
The present invention will be described with reference to specific examples.
1) Determining initial experimental subjects and initial control subjects:
selecting the ratio of the experimental object to the reference object as 1:10, assume the subject (in the congestion charging area) is labeled b1～b10The control object (outside the congestion charging area) is marked with b11～b200。
2) Acquiring variable data:
the data on each experimental section and the reference section obtained by the field survey and the research by the transportation section and the land resource management section are shown in table 1.
TABLE 1 statistics of sample data
Sample numbering Q N GDP Mi AADB AADT
b1 Q1 N1 GDP1 2 AADB1 AADT1
b2 Q2 N2 GDP2 1 AADB2 AADT2
～ ～ ～ ～ ～ ～ ～
b200 Q200 N200 GDP200 1 AADB200 AADT200
3) Calculating a fraction value:
respectively substituting the data acquired in the step 2) into a logit model to obtain the corresponding score condition of each group, wherein the score of the experimental object 1 is P1。
Figure GDA0003192129170000051
4) Determining the experimental subject participating in the comparison and the matched control subject:
and 3) determining the experimental objects and the matched control objects which participate in the comparison according to the score values of the experimental objects and the reference objects obtained in the step 3), wherein the score of each object is shown in the following table 2. Then based on the scoring condition of each object, respectively finding out respective matched objects for all experimental objects, namely scoring the nearest objects:
μimin { | subject score-pre-reference subject score | }.
TABLE 2 score difference result statistics table
Object b1 b2 ～ b200
Score of γ1 γ2 ～ γ200
5) And (3) calculating the safety influence effect: after the experimental object and the control object matched with each experimental object are determined, calculating the traffic safety influence ATT of the congestion charging policy based on the causal analysis model, and counting whether the ATT is significant at a 95% level.
6) Determination of the safety influence range of the congestion charging policy: since this case was performed under simulation data, assuming that the initial result of the present invention, i.e., the ATT of the first time (5), is significant at the 95% level, the experimental subject was scaled up with a gradient of 100 meters, assuming b1～b20The control object may be randomly selected from a range of 100 m outside the congestion charging area, and b is assumed to be11～b300. And repeating the steps 1) -6), finding that ATT is not significant at a 95% level, and determining that the range is the final influence range of the congestion charging traffic safety, namely the congestion charging policy is influenced by the traffic safety brought by the congestion charging policy within a range of 100 meters outside the geographical boundary.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.

Claims (4)

1. A method of quantifying congestion charging policy security impact, comprising the steps of:
(1) selecting a cell in the congestion charging policy as an initial experimental object and a cell outside the congestion charging policy as an initial control object by taking the geographical boundary of the congestion charging policy as a reference line;
(2) selecting covariates participating in model analysis as the number Q of accidents in a region, the total number N of population in the region, the economic development GDP of the region, the road network density K of the region, the road grade M of the region, the annual average daily traffic of motor vehicles AADT of the region and the annual average daily traffic of bicycles AADB of the region;
(3) calculating the scoring conditions of the initial experimental object and the control object by introducing covariates influencing the traffic accident safety by adopting a scoring model;
(4) determining the control object matched with each experimental object according to the closest score principle based on the comprehensive score condition of the initial experimental object and the control object obtained in the step (3);
(5) calculating the traffic accident safety influence effect ATT of the congestion charging policy on the experimental object:
Figure FDA0003192129160000011
wherein, ATT represents the effect of traffic accident safety influence on experimental objects by charging policy, tiN, t represents the number of accidents per subject, i 1,2jN, representing the number of accidents per control object, j 1,2.. n;
(6) determining the safety influence range of a congestion charging policy;
the step (3) is realized by the following formula:
Figure FDA0003192129160000012
wherein N represents the population count of the region; GDP represents the economic development of a region; k is the road network density of the region; m represents each road grade, the main road is marked as 2, the secondary main road is marked as 1, and the branch road is marked as 0; AADT is the annual average daily traffic volume of the motor vehicle in the area; AADB is the annual average daily traffic volume of the region; alpha is a constant term; beta is anAre regression vector coefficients.
2. The method according to claim 1, wherein the sample ratio of the initial subject to the control subject in step (1) is 1: 10.
3. The method for quantifying the safety influence range of the congestion charging policy according to claim 1, wherein the score closest principle calculation model in the step (4) is as follows:
μimin { | subject score-initial control subject score | }.
4. The method for quantifying congestion charging policy security scope of influence according to claim 1, wherein the step (6) is implemented as follows:
if the safety influence effect ATT calculated in the step (5) is obvious on the level of 95%, judging that the congestion charging policy has influence on traffic safety in the range, continuing to expand the number of control objects, repeating the steps (1) to (6) until the ATT is not obvious on the level of 95%, and determining the safety influence range of the congestion charging policy; and (3) if the safety influence effect ATT calculated in the step (5) is not significant at a 95% level, and the influence range of the congestion charging policy is smaller than the geographical boundary for implementing the policy, reducing the geographical boundary of the congestion charging policy, repeating the steps (1) to (6) until the ATT is found to be significant at a 95% level, and determining the safety influence range of the congestion charging policy.
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