CN109872537B - Bus stop optimal setting method considering quantization grading - Google Patents
Bus stop optimal setting method considering quantization grading Download PDFInfo
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- CN109872537B CN109872537B CN201910287909.2A CN201910287909A CN109872537B CN 109872537 B CN109872537 B CN 109872537B CN 201910287909 A CN201910287909 A CN 201910287909A CN 109872537 B CN109872537 B CN 109872537B
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Abstract
The invention belongs to the technical field of traffic engineering, and relates to a bus stop optimal setting method considering quantitative grading. Comprises the following steps: (1) setting the improvement of the traffic capacity of the bus stop and the adjacent roads as an optimization target; (2) constructing a bus stop comprehensive service level evaluation model and determining a quantitative grading standard; (3) constructing a bus stop optimization measure set; (4) and quantifying the comprehensive service level of the bus stop under different optimization measures in a grading manner, and selecting the optimal optimization measure. The invention systematically quantifies the optimal setting method of the bus stop, integrates the traffic capacity of the bus stop and the traffic capacity of adjacent roads into the comprehensive service level of the bus stop, constructs an evaluation model and a quantitative grading standard, constructs an optimization measure set by taking the berth number and the station type as basic elements, and finally obtains the optimal optimization measure of the bus stop by quantifying the comprehensive service level of the bus stop under different optimization measures in a grading way.
Description
Technical Field
The invention belongs to the technical field of traffic engineering, relates to an optimal setting method of a bus stop in the field of traffic engineering, and particularly relates to an optimal setting method of a bus stop considering quantization grading.
Background
The bus stop station is used as a basic element of a bus system, and the full play of the whole function of a bus network is guaranteed. Due to the fact that the bus stop is not set properly, the traffic capacity of the bus stop and the traffic capacity of the adjacent roads of the bus stop are reduced, and the overall service level of the stop is reduced.
(1) Optimizing research of bus stop. On the basis of comparative analysis and comprehensive optimization, a set of optimization technology and method for bus stop layout, setting and design of a comparative system is researched and developed by the Kudzuvine. On the basis of summarizing and analyzing the problems of bus stop layout, site selection, stop construction and the like in the main urban areas of Zhu and City, Zhang Lei proposes a scheme for optimizing the site selection of the bus stop from the viewpoint level. Zhang establishes a micro cellular automaton mixed traffic flow model for bus stops, and obtains an optimized bus stop scheme through simulation. The Liu optimizes the layout scheme of the bus stop with the aim of reducing the running time or delay time of traffic flow on the artery.
(2) The traffic capacity research of the bus stop. Liuhuili has analyzed the influence factor that public transit vehicle arrived and stopped to and the interrelationship between these influence factors and the bus stop traffic capacity, compare the selection to three kinds of calculation models of bus stop traffic capacity. The Liuwei Ding builds a bus station entering time model on the basis of the stop time and station leaving time model, and further builds a straight-line type and bay type bus stop traffic capacity model by combining the definition of traffic capacity. And the Al-Mudhaffar improves the whole TCQSM model to obtain a traffic capacity model according with the actual situation of Swiss. Arhin adopts a multiple regression analysis method of a common least square method to establish a stop bus on-station time model in a road section and near an intersection.
(3) The traffic capacity of adjacent roads of the bus stop is researched. And introducing the Liu into a queuing model to calculate the road section traffic capacity of stopping the bus station at a fixed point. Luo establishes a traffic capacity model of adjacent lanes influenced by the fact that buses pass in and out of a stop station under 3 conditions of straight line type, bay type no overflow and bay type overflow. Benedicbe calculates the traffic capacity of adjacent roads based on the quadratic relation between flow and density, and proves that the traffic capacity of the roads is greatly reduced by the intra-road bus stations. Yao proposes a model for influencing the traffic capacity of a main road under two conditions of normal and overflow of a bay type bus stop
(4) And (4) evaluating and researching the bus stop. Schroez crystal establishes a model for calculating the service level of the bus stop based on the queuing theory. And calibrating the bus waiting service level model by utilizing SP survey, such as Zhurui and the like, comprehensively influencing factors of the waiting service level, and establishing a standard for evaluating the waiting service level of the bus stop. Liu establishes a BRT parking service level evaluation index system based on a humanized idea, and provides a fuzzy evaluation method for evaluating the service level of the BRT parking station.
Relevant documents are searched, and the limitations of the research of the conventional bus stop optimal setting method can be summarized as follows:
(1) the optimization of the bus stop mostly focuses on the optimization of the layout and the site selection of the stop, and the optimization of the setting form of the stop is lack of systematic quantitative analysis;
(2) when the bus stop is optimally set, the influence of the traffic capacity of adjacent roads on the optimal setting of the bus stop is not researched, and the influence of lane reduction coefficients is not fully considered in an adjacent road traffic capacity model;
(3) the capacity evaluation research of the bus stop station mainly adopts a fuzzy evaluation method or a single index evaluation method, and has certain subjectivity and unicity.
Disclosure of Invention
The invention aims to overcome the defect that the optimal setting of the bus stop is difficult to quantify in the prior art, and provides the optimal setting method of the bus stop considering quantification grading. The method takes improvement of the traffic capacity of the bus stop and the traffic capacity of adjacent roads as optimization targets, constructs a bus stop comprehensive service level evaluation model and quantitative grading standards by considering the service levels of the bus stop and the adjacent roads, selects basic elements of increasing the number of berths of the bus stop and changing the types of the stations to construct a bus stop optimization measure set, quantifies the bus stop comprehensive service levels under different optimization measures in a grading way, and finally obtains the optimal optimization measure of the bus stop.
A bus stop optimal setting method considering quantization grading is characterized by comprising the following steps:
(1) setting the capacity of improving the bus stop and the traffic capacity of adjacent roads as an optimization target;
(2) constructing a bus stop comprehensive service level evaluation model and determining a quantitative grading standard;
(3) constructing a bus stop optimization measure set;
(4) quantifying the comprehensive service level of the bus stop under different optimization measures in a grading manner, and selecting the optimal optimization measure;
the bus stop and the traffic capacity of the adjacent roads in the step (1) are specifically as follows:
1) bus stop passing capacity
Calculating the traffic capacity of the bus stop according to an American traffic engineering manual model:
in the formula:
CBthe station traffic capacity is represented, and the unit is bus/h;
Nebexpressed as the number of valid berths, in units of ones;
g represents the effective green time of a signal phase in units of s;
c represents the period duration of one signal phase, and the unit is s;
r represents a reduction factor for arrival fluctuations and compensating for residence time;
d represents the average residence time in units of s;
tcrepresents dissipation time in units of s;
2) traffic capacity of adjacent roads of bus stop
If the bus stop is a linear bus stop, the traffic capacity of adjacent roads
In the formula:
CAthe traffic capacity of the adjacent road of the bus stop is represented, and the unit is pcu/h;
Cp1the traffic capacity of the outermost lane of the straight-line type stop is represented and has the unit of pcu/h;
T1the bus influence time of the outermost lane of the linear stop is expressed in the unit of s;
Cp2the traffic capacity of a secondary outer lane of the linear type stop is represented and has the unit of pcu/h;
T2the bus influence time of the secondary outer lane of the linear stop is expressed in the unit of s;
k represents the number of lanes of the adjacent road of the stop;
lambda represents the arrival frequency of buses, and the unit is bus/h;
Ebrepresenting a vehicle conversion coefficient for converting the bus into the car;
Cpithe traffic capacity of the ith lane of the adjacent road of the linear stop is represented by pcu/h; (II) if the station is a bay type bus stop, the capacity of adjacent roads to pass
In the formula:
C'p1the traffic capacity of the outermost lane of the bay type stop is represented and has the unit of pcu/h;
T′1the bus influence time of the outermost lane of the bay type stop is expressed in the unit of s;
C'piindicating the capacity of the i-th lane of the road adjacent to the bay type stop,the unit is pcu/h;
constructing a bus stop comprehensive service level evaluation model in the step (2), and determining a quantitative grading standard, wherein the specific contents are as follows:
1) bus stop comprehensive service level evaluation model
The bus stop comprehensive service level evaluation model is a weighted average model based on the bus stop service level and the saturation of the road adjacent to the bus stop;
in the formula:
s represents a comprehensive evaluation function value of the bus stop;
alpha represents the service level weight of the bus stop;
v represents the maximum traffic volume of the adjacent road of the bus stop, and the unit is pcu/h;
beta represents the saturation weight of the adjacent road of the bus stop;
2) bus stop comprehensive service level quantitative grading standard
Establishing a comprehensive service level quantitative grading standard of the bus stop by adopting a weighted average method according to the grade grading standard of the service level of the bus stop and the grade grading standard of the road saturation;
and (3) constructing a bus stop optimization measure set, which comprises the following specific steps:
the method comprises the following steps of selecting the increased berth number of the bus stop and the changed type of the bus stop as main elements to construct a bus stop optimization measure set, and specifically comprises the following steps:
linear bus stop
In the formula:
aprepresents an increase of p berths;
bmpresentation changesThe type of the stop station is unchanged when m is 0, and the type of the stop station is changed from a straight line type to an estuary type when m is 1;
e represents the existing berth number of the bus stop, and the unit is one;
② bay type bus stop
And (4) quantifying the comprehensive service level of the bus stop under different optimization measures in a grading manner, and selecting the optimal optimization measure, wherein the method specifically comprises the following steps:
1) calculating the comprehensive service level of the bus stop under the condition of adopting different optimization measures;
2) sequencing the optimization measures according to the size of the optimized comprehensive service level of the bus stop;
3) based on the quantitative grading standard of the comprehensive service level of the bus stop, the change of the comprehensive service level grade of the bus stop after different optimization measures are adopted is analyzed, and the optimal optimization measure is selected.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention discloses a bus stop optimal setting method considering quantitative grading, which systematically analyzes the optimal setting of a bus stop, constructs an optimal measure set by taking the berth number and the stop type as basic elements, and selects the optimal measure based on the comprehensive service level quantitative grading standard of the bus stop.
(2) According to the bus stop optimal setting method considering quantitative grading, the traffic capacity model of the adjacent roads of the bus stop is corrected by introducing the lane reduction coefficient.
(3) The invention relates to a bus stop optimal setting method considering quantitative grading, which takes the number of berths as a basic parameter and adopts a weighted average method to provide a bus stop comprehensive service level evaluation model which is universal for various types of bus stops under any number of berths.
Drawings
The invention is further described with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of steps of a bus stop optimal setting method considering quantitative grading according to the invention;
FIG. 2 is a summary of optimized target parameters for a bus stop;
FIG. 3 is a flow chart of the construction of a bus stop comprehensive service level evaluation model and quantitative grading standards;
FIG. 4 is a flow chart of the bus stop optimization measure set construction;
FIG. 5 is a flow chart of bus stop optimization option selection;
fig. 6 is a schematic diagram of the integrated service level hierarchy of the bus stop when α is 0.2 and β is 0.8;
fig. 7 is a schematic diagram of the integrated service level hierarchy of the bus stop when α is 0.8 and β is 0.2.
Detailed Description
The invention is described in detail below with reference to the attached drawing figures:
1. setting the aim of improving the traffic stop and the traffic capacity of adjacent roads as an optimization target
1) Bus stop passing capacity
Calculating the traffic capacity of the bus stop according to an American traffic engineering manual model;
in the formula:
CBthe station traffic capacity is represented, and the unit is bus/h;
Nebexpressed as the number of valid berths, in units of ones;
g represents the effective green time of a signal phase in units of s;
c represents the period duration of one signal phase, and the unit is s;
r represents a reduction factor for arrival fluctuations and compensating for residence time;
d represents the average residence time in units of s;
tcrepresents dissipation time in units of s;
2) traffic capacity of adjacent roads of bus stop
If the bus stop is a linear bus stop, the traffic capacity of adjacent roads
In the formula:
CAthe traffic capacity of the adjacent road of the bus stop is represented, and the unit is pcu/h;
Cp1the traffic capacity of the outermost lane of the straight-line type stop is represented and has the unit of pcu/h;
T1the bus influence time of the outermost lane of the linear stop is expressed in the unit of s;
Cp2the traffic capacity of a secondary outer lane of the linear type stop is represented and has the unit of pcu/h;
T2the bus influence time of the secondary outer lane of the linear stop is expressed in the unit of s;
k represents the number of lanes of the adjacent road of the stop;
lambda represents the arrival frequency of buses, and the unit is bus/h;
Ebrepresenting a vehicle conversion coefficient for converting the bus into the car;
Cpithe traffic capacity of the ith lane of the adjacent road of the linear stop is represented by pcu/h;
(II) if the station is a bay type bus stop, the capacity of adjacent roads to pass
In the formula:
C'p1the traffic capacity of the outermost lane of the bay type stop is represented and has the unit of pcu/h;
T1' means Bay type parking stationThe bus influence time of the outermost lane is s;
C'pithe traffic capacity of the ith lane of the road adjacent to the bay type stop is represented and has the unit of pcu/h;
3) and calibrating parameters of the determined optimization target, wherein the parameters are summarized in an attached figure 2, and the specific contents are as follows:
the bus stop traffic capacity is as follows: and (4) calibrating parameters of the split bus stop station, the effective berth number and the dissipation time by considering factors such as the type, the berth number and the setting position of the bus stop station.
a) The green signal ratio:
for the bus stop station near the signalized intersection, the signal control of the intersection can influence the traffic capacity of the bus stop station, the green signal ratio can be taken as 0.5 at the moment, and when the bus stop station is arranged in a road section, the green signal ratio can be taken as 1 due to small influence of the signal period.
b) Effective number of berths:
when the number of berths is greater than 1, if the berthing positions of each line are not clearly divided, the berths are not used in equal amount, and the actually used berthing number of the berthing station is called as an effective berthing number. The corresponding effective berth numbers of different types of docking stations under different berth numbers are shown in table 1.
TABLE 1 effective berth number of bus stop
c) Dissipation time:
the dissipation time of the bus stopped at the linear bus stop is as follows:
in the formula:
l represents the length of the bus in m;
n represents the number of berths of the bus stop, and the unit is one;
lbrepresents the standard berth length in m;
Vcthe unit of the running speed of the bus is m/s;
tathe unit of the acceleration time of the bus exiting from the stop is s;
tbthe deceleration time of the bus entering the stop is expressed in the unit of s;
the dissipation time of the bus stopped at the bay type bus stop is as follows:
in the formula:
tc2representing the waiting time of the bus remitting into the lane, and the unit is s;
the traffic capacity of adjacent roads of the bus stop: the method comprises the steps of carrying out parameter calibration on bus influence time and lane traffic capacity by considering factors such as bus stop types, berth numbers, the number of adjacent road lanes, road classification conditions and the like.
a) Bus influence time
The bus influence time refers to the sum of the influence time of all buses stopping at a certain stop in a unit hour. And calibrating the bus influence time by combining a dissipation time calibration method.
The bus influence time of the outermost lane of the linear bus stop is the total time of deceleration, stop and acceleration when the lane is occupied in the bus stop process, and specifically comprises the following steps:
the bus influence time of the secondary outer lane of the linear bus stop is the lane change time of social vehicles in the outermost lane when the bus stops at the stop, and the method specifically comprises the following steps:
T2＝λD
when a bus stopping at a bay type bus stop is out of the station, waiting time for remitting into a lane exists. The tendency of buses to come out of stops can have an impact on motor vehicles on the outermost lanes of the stop. Compared with a straight-line bus stop, the influence time of the outermost lane of the bay type bus stop reduces the stop time, and the method comprises the following steps:
b) traffic capacity of lane
According to the actual road condition, a multi-lane road is more common than a single-lane road, and the lane traffic capacity model in the multi-lane road is as follows:
CP＝αcClδKn
in the formula:
CPthe lane traffic capacity of the multi-lane road is represented, and the unit is pcu/h;
αcroad classification coefficients representing the lanes of vehicles, as shown in table 2;
Clthe unit of the possible traffic capacity of a single lane is pcu/h, and the unit is shown in the table 3;
delta represents an intersection influence coefficient;
Knthe reduction factor of each corresponding lane is expressed, and as shown in table 4, the lane near the center line or the center bank is generally taken as the first lane;
TABLE 2 road Classification coefficients for Motor vehicle lanes
TABLE 3 Single lane Per Capacity values
TABLE 4 reduction factor of each lane
2. Constructing a bus stop comprehensive service level evaluation model and determining a quantitative grading standard
The invention discloses a bus stop comprehensive service level evaluation model and a quantitative grading standard construction process, which are shown in the attached figure 3 and specifically comprise the following contents:
1) bus stop comprehensive service level evaluation model
The bus stop comprehensive service level evaluation model is a weighted average model based on the bus stop service level and the saturation of the road adjacent to the bus stop. The invention utilizes the constructed bus stop comprehensive service level evaluation model to determine whether the bus stop needs to be optimally set and optimized.
In the formula:
s represents a comprehensive evaluation function value of the bus stop;
alpha represents the service level weight of the bus stop;
v represents the maximum traffic volume of the adjacent road of the bus stop, and the unit is pcu/h;
beta represents the saturation weight of the adjacent road of the bus stop.
2) Bus stop comprehensive service level quantitative grading standard
The classification of the bus stop comprehensive service level is determined by the bus stop service level and the road saturation. The standard of the service level of the bus stop is shown in table 5, and the standard of the road saturation level is shown in table 6.
TABLE 5 bus stop service level grade Standard
TABLE 6 road service level standards
And (5) combining the tables 5 and 6 to obtain the comprehensive service level grading standard of the bus stop, as shown in the table 7.
TABLE 7 comprehensive service level grading Standard of bus stop
3. Construction of bus stop optimization measure set
The specific construction process of the bus stop optimization measure set is shown in the attached figure 4.
The method comprises the following steps of selecting the bus stop berth number increasing and the bus stop type changing as main elements to carry out cross combination, and representing a bus stop optimization measure set in a matrix mode, wherein the method specifically comprises the following steps:
linear bus stop
In the formula:
aprepresents an increase of p berths;
bmwhen m is 0, the type of the stop station is not changed, and when m is 1, the stop station is changed from a straight line type to a bay type;
e represents the existing berth number of the bus stop, and the unit is one;
② bay type bus stop
4. Quantifying the comprehensive service level of the bus stop under different optimization measures in a grading way, and selecting the optimal optimization measure
The optimization measure selection process based on the optimization degree of the quantitative grading bus stop is shown as the attached figure 5, and comprises the following specific steps:
1) calculating the comprehensive service level of the bus stop under the condition of adopting different optimization measures;
2) sequencing the optimization measures according to the size of the optimized comprehensive service level of the bus stop;
3) and analyzing the change of the comprehensive service level grade of the bus stop after adopting different optimization measures based on the quantitative grading standard of the comprehensive service level of the bus stop, and selecting the optimal optimization measure.
Examples
The embodiment of the bus stop optimal setting method considering quantization grading gives out the implementation process and the solving result, but the protection scope of the invention is not limited to the following embodiment.
1. The method of the invention is applied to the great south station of the great road of the south lake of Changchun city for detailed description
The Changchun city large south gate station is two berth linear bus stop stations near an intersection, two adjacent lane roads and a school near the two lane roads have high traffic flow during a peak period. According to investigation, the average operation speed of buses in the Changchun city is 7.67m/s, the average deceleration time of vehicles at the Jordan station is 7.60s, the average acceleration time is 7.48s, and the arrival rate of the vehicles is 33 bus/h. Taking R as 0.833, EbAnd 2, respectively, and performing parameter calibration on the optimization target of the bus stop under the conditions that alpha is 0.2, beta is 0.8, alpha is 0.8, and beta is 0.2, wherein the calibration results are shown in table 8.
TABLE 8 optimized target parameter calibration value for bus stop
2. Constructing a bus stop comprehensive service level evaluation model and determining a quantitative grading standard
And respectively calculating the comprehensive service level grading standard of the bus stop under two weights of alpha being 0.2, beta being 0.8 and alpha being 0.8, and beta being 0.2. And (5) combining the table 7 to obtain the comprehensive service level grading standard of the bus stop under the two weight conditions, as shown in the table 9.
Table 9 comprehensive service level grading standard of bus stop under each weight
As shown in table 8, when α is 0.2 and β is 0.8, S is 0.81, when α is 0.8, β is 0.2, and S is 0.45. As can be seen from table 9, when α is 0.2 and β is 0.8, the comprehensive evaluation service level of the G station belongs to class C. When α is 0.8 and β is 0.2, it belongs to class B. Therefore, the large-sized south gate station is optimized, and the grade of the bus stop station is improved.
3. Constructing an optimization measure set according with the practical situation of the embodiment
The Changchun city industrial large south gate station is a two-berth linear bus stop station which is positioned near an intersection, and the optimization measures comprise the following three bus stop optimization measures in a centralized manner:
(1) changed into a bay type bus stop (a)0b1)；
(2) Adding one more berth number (a)1b0)；
(3) Adding a berth (a) while changing into a bay type bus stop1b1)。
4. Quantifying the comprehensive service level of the bus stop under different optimization measures in a grading way, and selecting the optimal optimization measure
1) Calculating the optimized comprehensive service level of the bus stop
The comprehensive service levels after the above three optimization measures are adopted under two weights of α ═ 0.2, β ═ 0.8, and α ═ 0.8, and β ═ 0.2, respectively, as shown in tables 10 and 11.
Table 10 comprehensive evaluation under various optimization measures (α ═ 0.2, β ═ 0.8)
Table 11 comprehensive evaluation under various optimization measures (α ═ 0.8, β ═ 0.2)
2) Bus stop optimization measure sequencing
As can be seen from table 10, in the case of α being 0.2 and β being 0.8, the optimization measures of the bus stop are ranked as: a is1b1＞a0b1＞a1b0. As can be seen from table 11, in the case of α being 0.8 and β being 0.2, the optimization measures of the bus stop are ranked as: a is1b1＞a1b0＞a0b1。
3) Based on grade change after optimization of bus stop, optimal optimization measures are selected
Combining table 9 and table 10, we can find the ranking of the overall evaluation after taking different optimization measures in case of α ═ 0.2 and β ═ 0.8, see fig. 6. Wherein, a0b1And a1b1The service level of the station is increased from C level to B level. And a is1b0The level of service is reduced, allowing sites to be optimized, but the level is unchanged. Therefore, when α is 0.2 and β is 0.8, a is selected0b1As the best optimization measure of the large south gate station.
Combining table 9 and table 11, we can find the ranking of the overall evaluation after taking different optimization measures in case of α ═ 0.8 and β ═ 0.2, see fig. 7. Wherein, a1b0And a1b1The service level of the station is increased from B level to A level. And a is0b1The level of service is reduced and the site is optimized, but the rank of the site is not changed. Therefore, when α is 0.8 and β is 0.2, a is selected1b0As the best optimization measure of the large south gate station.
Claims (3)
1. A bus stop optimal setting method considering quantization grading is characterized by comprising the following steps:
(1) setting the capacity of improving the bus stop and the traffic capacity of adjacent roads as an optimization target;
(2) constructing a bus stop comprehensive service level evaluation model and determining a quantitative grading standard;
constructing a bus stop comprehensive service level evaluation model in the step (2), and determining a quantitative grading standard, wherein the specific contents are as follows:
1) bus stop comprehensive service level evaluation model
The bus stop comprehensive service level evaluation model is a weighted average model based on the bus stop service level and the saturation of the road adjacent to the bus stop;
in the formula:
s represents a comprehensive evaluation function value of the bus stop;
alpha represents the service level weight of the bus stop;
lambda represents the arrival frequency of buses, and the unit is bus/h;
CBthe bus stop traffic capacity is represented, and the unit is bus/h;
beta represents the saturation weight of the adjacent road of the bus stop; v represents the maximum traffic volume of the adjacent road of the bus stop, and the unit is pcu/h;
CAthe traffic capacity of the adjacent road of the bus stop is represented, and the unit is pcu/h;
2) bus stop comprehensive service level quantitative grading standard
Establishing a comprehensive service level quantitative grading standard of the bus stop by adopting a weighted average method according to the grade grading standard of the service level of the bus stop and the grade grading standard of the road saturation;
(3) constructing a bus stop optimization measure set;
and (3) constructing a bus stop optimization measure set, which comprises the following specific steps:
the method comprises the following steps of selecting the increased berth number of the bus stop and the changed type of the bus stop as main elements to construct a bus stop optimization measure set, and specifically comprises the following steps:
linear bus stop
In the formula:
aprepresents an increase of p berths;
bmwhen m is 0, the type of the stop station is not changed, and when m is 1, the stop station is changed from a straight line type to a bay type;
e represents the existing berth number of the bus stop, and the unit is one;
p+e≤3；
② bay type bus stop
In the formula:
p+e≤5；
(4) and quantifying the comprehensive service level of the bus stop under different optimization measures in a grading manner, and selecting the optimal optimization measure.
2. A method for optimal setting of bus stops taking into account the quantization step as described in claim 1, characterized in that:
the bus stop and the traffic capacity of the adjacent roads in the step (1) are specifically as follows:
1) bus stop passing capacity
Calculating the traffic capacity of the bus stop according to an American traffic engineering manual model:
in the formula:
Nebexpressed as the number of valid berths, in units of ones;
g represents the effective green time of a signal phase in units of s;
c represents the period duration of one signal phase, and the unit is s;
r represents a reduction factor for arrival fluctuations and compensating for residence time;
d represents the average residence time in units of s;
tcrepresents dissipation time in units of s;
2) traffic capacity of adjacent roads of bus stop
If the bus stop is a linear bus stop, the traffic capacity of adjacent roads
In the formula:
Cp1the traffic capacity of the outermost lane of the straight-line type stop is represented and has the unit of pcu/h;
T1the bus influence time of the outermost lane of the linear stop is expressed in the unit of s;
Cp2the traffic capacity of a secondary outer lane of the linear type stop is represented and has the unit of pcu/h;
T2the bus influence time of the secondary outer lane of the linear stop is expressed in the unit of s;
k represents the number of lanes of the adjacent road of the stop;
Ebrepresenting a vehicle conversion coefficient for converting the bus into the car;
Cpithe traffic capacity of the ith lane of the adjacent road of the linear stop is represented by pcu/h;
(II) if the station is a bay type bus stop, the capacity of adjacent roads to pass
In the formula:
C'p1the traffic capacity of the outermost lane of the bay type stop is represented and has the unit of pcu/h;
T1' represents the bus influence time of the outermost lane of the bay type stop, and the unit is s;
C'pithe traffic capacity of the ith lane of the adjacent road of the bay type stop is represented by pcu/h.
3. A method for optimal setting of bus stops taking into account the quantization step as described in claim 1, characterized in that:
and (4) quantifying the comprehensive service level of the bus stop under different optimization measures in a grading manner, and selecting the optimal optimization measure, wherein the method specifically comprises the following steps:
1) calculating the comprehensive service level of the bus stop under the condition of adopting different optimization measures;
2) sequencing the optimization measures according to the size of the optimized comprehensive service level of the bus stop;
3) based on the quantitative grading standard of the comprehensive service level of the bus stop, the change of the comprehensive service level grade of the bus stop after different optimization measures are adopted is analyzed, and the optimal optimization measure is selected.
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| CN110288829B (en) * | 2019-06-19 | 2020-07-17 | 河海大学 | Method for setting and judging parking berth number of bay type bus stop |
| CN112185109B (en) * | 2020-09-14 | 2021-04-09 | 河海大学 | Bearing capacity probability reduction representation method for intelligent network connection road |
| CN114023065A (en) * | 2021-11-03 | 2022-02-08 | 沈阳世纪高通科技有限公司 | Algorithm for intelligently diagnosing intersection service level by utilizing video analysis data |
| CN114780892B (en) * | 2022-03-31 | 2022-12-13 | 深圳微盐红创设计院有限公司 | Online exhibition and display intelligent interaction management system based on artificial intelligence |
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| CN104992555A (en) * | 2015-07-22 | 2015-10-21 | 济南市市政工程设计研究院（集团）有限责任公司 | Rail station linkage facility bus stop scale prediction method |
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