CN109243174B - Hybrid bicycle traffic wave calculation method based on spatial perception - Google Patents

Hybrid bicycle traffic wave calculation method based on spatial perception Download PDF

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CN109243174B
CN109243174B CN201811029169.4A CN201811029169A CN109243174B CN 109243174 B CN109243174 B CN 109243174B CN 201811029169 A CN201811029169 A CN 201811029169A CN 109243174 B CN109243174 B CN 109243174B
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李冰
成卫
肖海承
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Kunming University of Science and Technology
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Abstract

The invention relates to a hybrid bicycle traffic wave calculation method based on spatial perception, and belongs to the technical field of traffic. The method is based on a mixed traffic flow model of space perception, analyzes space ratio of different mixed bicycle traffic states, determines density and flow of the mixed bicycles, and further calculates the traffic wave speed of the mixed bicycles through a cellular transmission model. The method overcomes the limitation that the conventional method has too ideal assumption when simulating the multi-strand bicycle flow and cannot well simulate the phenomenon of isotropic flow, and improves the calculation accuracy of the traffic wave of the hybrid bicycle.

Description

Hybrid bicycle traffic wave calculation method based on spatial perception
Technical Field
The invention relates to a hybrid bicycle traffic wave calculation method based on spatial perception, and belongs to the technical field of traffic.
Background
With the development of cities and the demand of transportation, the non-motor vehicle travel mode which takes the traditional bicycle as the leading factor in the past in China is evolved into a hybrid non-motor vehicle travel mode which coexists the traditional bicycle and the electric bicycle. And the characteristics of the mixed non-motor vehicle flow and the characteristics of the single non-motor vehicle flow have larger difference due to the characteristic difference of the two types of vehicles and mutual operation interference. The existing research aims at that the assumption is too ideal when the non-motor vehicle lane multi-strand traffic flow runs, and the isotropic traffic flow phenomenon cannot be well simulated; in addition, when three parameters of the hybrid non-motor vehicle flow traffic are analyzed, the heterogeneity of the non-motor vehicle flow cannot be reflected ideally on the basis of the traffic characteristics of the homogeneous motor vehicle flow, and the cellular transmission characteristics of the heterogeneous non-motor vehicle flow are not researched, so that the traffic waves of the hybrid bicycle cannot be calculated accurately. The accurate traffic wave calculation is important for the queue length and delay analysis of the hybrid bicycles, and has important significance for further optimizing the signal timing of the bicycles to reduce the mechanical conflict and the non-conflict and relieve the intersection congestion.
Disclosure of Invention
The invention aims to provide a hybrid bicycle traffic wave calculation method based on spatial perception, further improves the calculation accuracy and robustness of the hybrid bicycle traffic wave, and provides a theoretical basis for bicycle signal timing optimization and non-motor vehicle lane width design.
The technical scheme of the invention is as follows: a hybrid bicycle traffic wave calculation method based on spatial perception specifically comprises the following steps:
step 1: determining the traffic state of the mixed bicycle flow in the cell, wherein the traffic state of the mixed bicycle flow comprises a free flow traffic state, a semi-crowded traffic state and a completely crowded traffic state;
step 2: respectively calculating the perception density of the electric bicycle and the perception density of the traditional bicycle in the cellular corresponding mixed bicycle flow traffic state, wherein the step 2 specifically comprises the following steps: according to a heterogeneous traffic flow model based on space perception, calculating the perception density of the electric bicycle and the perception density of the traditional bicycle under the state of the cellular corresponding mixed bicycle flow traffic:
(1) calculating the perceived density of the electric bicycle and the perceived density of the traditional bicycle under the free flow traffic state:
firstly, the road space ratio of the electric bicycle in the nth cell under the free flow traffic state is respectively calculated
Figure GDA0003190879620000011
Road space ratio of traditional bicycle
Figure GDA0003190879620000012
Figure GDA0003190879620000013
Further obtaining the sensed density of the electric bicycle in the nth cell under the free flow traffic state
Figure GDA0003190879620000021
Perceived density of traditional bicycle
Figure GDA0003190879620000022
Comprises the following steps:
Figure GDA0003190879620000023
in the formula (I), the compound is shown in the specification,
Figure GDA0003190879620000024
respectively the critical density of the electric bicycle and the critical density of the traditional bicycle,
Figure GDA0003190879620000025
the density of the electric bicycle in the nth cell and the density of the traditional bicycle in the free flow traffic state are respectively set;
(2) calculating the perceived density of the electric bicycle and the perceived density of the traditional bicycle in a semi-crowded traffic state:
firstly, the road space ratio of the electric bicycle in the nth cell under the condition of semi-crowded traffic is respectively calculated
Figure GDA0003190879620000026
Road space ratio of traditional bicycle
Figure GDA0003190879620000027
Figure GDA0003190879620000028
Further obtaining the sensing density of the electric bicycle in the nth cell under the condition of semi-crowded traffic
Figure GDA0003190879620000029
Perceived density of traditional bicycle
Figure GDA00031908796200000210
Comprises the following steps:
Figure GDA00031908796200000211
in the formula (I), the compound is shown in the specification,
Figure GDA00031908796200000212
the density of the electric bicycles in the nth cell and the density of the traditional bicycles in the nth cell in the semi-crowded traffic state are respectively;
(3) calculating the perceived density of the electric bicycle and the perceived density of the traditional bicycle in a completely crowded traffic state:
firstly, the road space ratio of the electric bicycle in the nth cell in the completely crowded traffic state is respectively calculated
Figure GDA00031908796200000213
Road space ratio of traditional bicycle
Figure GDA00031908796200000214
Figure GDA00031908796200000215
Further, the sensing density of the electric bicycle in the nth cell under the completely crowded traffic state can be obtained
Figure GDA00031908796200000216
Perceived density of traditional bicycle
Figure GDA00031908796200000217
Comprises the following steps:
Figure GDA00031908796200000218
in the formula (I), the compound is shown in the specification,
Figure GDA00031908796200000219
respectively the flow blockage density of the electric bicycle and the blockage density of the traditional bicycle;
Figure GDA00031908796200000220
the density of the electric bicycles in the nth cell and the density of the traditional bicycles in the completely crowded traffic state are respectively; w is a1、w2The starting wave speed of the electric bicycle and the starting wave speed of the traditional bicycle are respectively calculated according to the following formulas:
Figure GDA00031908796200000221
in the formula (I), the compound is shown in the specification,
Figure GDA00031908796200000222
the maximum flow rate of the electric bicycle and the maximum flow rate of the traditional bicycle are respectively;
the density of the electric bicycle in the nth cell and the density of the traditional bicycle under different traffic states are calculated according to the following formula:
Figure GDA0003190879620000031
in the formula (I), the compound is shown in the specification,
Figure GDA0003190879620000032
the density of the electric bicycle in the nth cell and the density of the traditional bicycle under the traffic state theta (n) are respectively,
Figure GDA0003190879620000033
the flow rate of the electric bicycle in the nth cell and the flow rate of the traditional bicycle in the traffic state theta (n) are A, B, C; l isnIs the nth cell length; b is the width of the bicycle lane;
and step 3: determining the total density and the total flow of the intracellular mixed bicycle flow;
and 4, step 4: and calculating the traffic waves according to the cellular transmission model.
In the step 3, the calculation process of the total density and the total flow of the cellular mixed bicycle flow comprises the following steps:
according to a heterogeneous traffic flow model based on space perception, the total density of the mixed bicycle flow in the nth cell is the perceived density of any bicycle flow corresponding to the traffic state, and the total flow in each cell is the sum of the electric bicycle flow and the bicycle flow, namely
Figure GDA0003190879620000034
In the formula (I), the compound is shown in the specification,
Figure GDA0003190879620000035
the total density of the mixed bicycle in the nth cell under the traffic state theta (n),
Figure GDA0003190879620000036
the sensing density of the electric bicycle in the nth cell and the sensing density of the traditional bicycle under the traffic state theta (n) are respectively,
Figure GDA0003190879620000037
the total flow rate of the mixed bicycle in the nth cell under the traffic state theta (n) is A, B, C.
The step 4 specifically comprises the following steps:
according to the cellular transmission model in the traffic flow, the calculation formula of the adjacent cellular traffic waves under the mixed bicycle flow can be obtained as follows:
Figure GDA0003190879620000038
the total flow of the mixed bicycle in the n-1 th cell under the traffic state theta (n-1), and theta (n-1) is A1、B1、C1。
The invention has the beneficial effects that:
1. the hybrid bicycle traffic wave calculation method based on spatial perception fully considers the running characteristics and mutual influence of the electric bicycle and the traditional bicycle, overcomes the limitation that the assumption is too ideal when the traditional method is used for simulating the multi-strand traffic flow of the non-motor lane and the isotropic traffic flow phenomenon cannot be well simulated, and improves the calculation accuracy;
2. the mixed bicycle traffic wave calculation method based on spatial perception analyzes different mixed bicycle flow traffic states respectively, reflects the change characteristics of the mixed bicycle traffic waves under different traffic states more pertinently,
the reliability is stronger;
3. the hybrid bicycle traffic wave calculation method based on spatial perception is simple to operate, convenient to calculate and lower in cost.
Drawings
FIG. 1 is a flow chart of the steps of the present invention;
FIG. 2 is a schematic diagram of two traffic state neighboring cells of the present invention;
fig. 3 is a comparison graph of estimated parking wave values and observed values according to the present invention.
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Example 1: a method for calculating a hybrid bicycle traffic wave based on spatial perception, as shown in fig. 1, includes:
step 1: determining a traffic state of a mixed bicycle stream in a cell;
step 2: respectively calculating the perception density of the electric bicycle and the perception density of the traditional bicycle in the cellular corresponding mixed bicycle flow traffic state;
and step 3: determining the total density and the total flow of the intracellular mixed bicycle flow;
and 4, step 4: and calculating the traffic waves according to the cellular transmission model.
Wherein it is determined which traffic state the intracellular mixed bicycle flow belongs to:
(1) free flow traffic conditions. In this case, the two types of bicycle flows are free running, the mutual influence is small, and the electric bicycle can freely surpass the traditional bicycle.
(2) Semi-congested traffic conditions. In this case, the electric bicycle flow does not have sufficient space to maintain a free flow state, but the conventional bicycle flow can be maintained.
(3) Completely crowded traffic conditions. In this case, neither type of bicycle stream finds enough space to maintain the free stream velocity.
The calculation process of the electric bicycle perception density and the traditional bicycle perception density in the cellular corresponding mixed bicycle flow traffic state is as follows:
(1) calculation of perceived density of electric bicycle and perceived density of traditional bicycle under free flow traffic state
Firstly, the road space ratio of the electric bicycle in the nth cell under the free flow traffic state is respectively calculated
Figure GDA0003190879620000041
Road space ratio of traditional bicycle
Figure GDA0003190879620000042
Figure GDA0003190879620000043
Further, the sensed density of the electric bicycle in the nth cell under the free flow traffic state can be obtained
Figure GDA0003190879620000044
Perceived density of traditional bicycle
Figure GDA0003190879620000045
Is composed of
Figure GDA0003190879620000046
In the formula (I), the compound is shown in the specification,
Figure GDA0003190879620000047
respectively the critical density of the electric bicycle and the critical density of the traditional bicycle,
Figure GDA0003190879620000048
respectively the nth element in the free flow traffic stateDensity of the electric bicycle in the cell and density of the traditional bicycle.
(2) Electric bicycle perceived density and traditional bicycle perceived density calculation under semi-crowded traffic state
Firstly, the road space ratio of the electric bicycle in the nth cell under the condition of semi-crowded traffic is respectively calculated
Figure GDA0003190879620000049
Road space ratio of traditional bicycle
Figure GDA00031908796200000410
Figure GDA00031908796200000411
Further, the sensed density of the electric bicycle in the nth cell under the condition of semi-crowded traffic can be obtained
Figure GDA00031908796200000412
Perceived density of traditional bicycle
Figure GDA00031908796200000413
Is composed of
Figure GDA00031908796200000414
In the formula (I), the compound is shown in the specification,
Figure GDA00031908796200000415
the density of the electric bicycles in the nth cell and the density of the traditional bicycles in the nth cell in the semi-crowded traffic state are respectively.
(3) Electric bicycle perceived density and traditional bicycle perceived density calculation under completely crowded traffic state
Firstly, the road space ratio of the electric bicycle in the nth cell in the completely crowded traffic state is respectively calculated
Figure GDA0003190879620000051
Road space ratio of traditional bicycle
Figure GDA0003190879620000052
Figure GDA0003190879620000053
Further, the sensing density of the electric bicycle in the nth cell under the completely crowded traffic state can be obtained
Figure GDA0003190879620000054
Perceived density of traditional bicycle
Figure GDA0003190879620000055
Is composed of
Figure GDA0003190879620000056
In the formula (I), the compound is shown in the specification,
Figure GDA0003190879620000057
respectively the flow blockage density of the electric bicycle and the blockage density of the traditional bicycle;
Figure GDA0003190879620000058
the density of the electric bicycles in the nth cell and the density of the traditional bicycles in the completely crowded traffic state are respectively; w is a1、w2The starting wave speed of the electric bicycle and the starting wave speed of the traditional bicycle are respectively calculated according to the following formulas:
Figure GDA0003190879620000059
in the formula (I), the compound is shown in the specification,
Figure GDA00031908796200000510
the maximum flow rate of the electric bicycle and the maximum flow rate of the traditional bicycle are respectively.
The density of the electric bicycle in the nth cell and the density of the traditional bicycle under different traffic states are calculated according to the following formula:
Figure GDA00031908796200000511
in the formula (I), the compound is shown in the specification,
Figure GDA00031908796200000512
the density of the electric bicycle in the nth cell and the density of the traditional bicycle under the traffic state theta (n) are respectively,
Figure GDA00031908796200000513
the flow rate of the electric bicycle in the nth cell and the flow rate of the traditional bicycle in the traffic state theta (n) are A, B, C; l isnIs the nth cell length; and B is the width of the bicycle lane.
The total density and the total flow of the intracellular mixed bicycle flow are calculated as follows:
corresponding to the perceived density of any bicycle flow in the traffic state, the total flow in each cell is the sum of the flow of the electric bicycle and the flow of the bicycle, namely
Figure GDA00031908796200000514
In the formula (I), the compound is shown in the specification,
Figure GDA00031908796200000515
the total density of the mixed bicycle in the nth cell under the traffic state theta (n),
Figure GDA00031908796200000516
the sensing density of the electric bicycle in the nth cell and the sensing density of the traditional bicycle under the traffic state theta (n) are respectively,
Figure GDA00031908796200000517
the total flow of the hybrid bicycle in the nth cell under the traffic state theta (n),θ(n)＝A、B、C。
the traffic wave calculation process comprises the following steps:
as shown in fig. 2, according to the cellular transmission model in the traffic flow, the calculation formula of the adjacent cellular traffic wave in the mixed bicycle flow can be obtained as follows:
Figure GDA00031908796200000518
in the formula (I), the compound is shown in the specification,
Figure GDA00031908796200000519
the total density of the mixed bicycle in the (n-1) th cell under the traffic state theta (n-1),
Figure GDA00031908796200000520
the total flow of the mixed bicycle in the n-1 th cell under the traffic state theta (n-1), and theta (n-1) is A1、B1、C1。
Corresponding to the perceived density of any bicycle flow in the traffic state, the total flow in each cell is the sum of the flow of the electric bicycle and the flow of the bicycle, namely
Figure GDA0003190879620000061
In the formula (I), the compound is shown in the specification,
Figure GDA0003190879620000062
the total density of the mixed bicycle in the nth cell under the traffic state theta (n),
Figure GDA0003190879620000063
the sensing density of the electric bicycle in the nth cell and the sensing density of the traditional bicycle under the traffic state theta (n) are respectively,
Figure GDA0003190879620000064
the total flow rate of the mixed bicycle in the nth cell under the traffic state theta (n) is A, B, C.
In the embodiment, the method is characterized in that the west-imported bicycle lane data (late peak) at the intersection of the east road and the white dragon road of Kunming city in Yunnan province is selected, the method is verified by using the parking wave velocity estimation value, and the parking wave velocity estimation value is compared with the single traffic flow traffic wave model calculation result (the density and the flow of the bicycles in the cells are determined in an equivalent conversion mode) so as to verify the precision of the method.
The verification result is obtained by calculating the Mean Absolute Error (MAE), the mean percentage (MAPE) and the Root Mean Square Error (RMSE) of the traffic flow proportion, and the results are shown in Table 1, wherein the calculation formulas of MAE, MAPE and RMSE are respectively as follows:
Figure GDA0003190879620000065
Figure GDA0003190879620000066
Figure GDA0003190879620000067
where m is the number of samples, which in this example totals 95 samples.
TABLE 1 West import bicycle parking waves MAE, MAPE and RMSE at the intersection of east and white dragon around the city
Figure GDA0003190879620000068
The result shows that the MAE, MAPE and RMSE of the parking wave using the method are all smaller than the error of a single traffic flow traffic wave model, and the accuracy of the method is fully verified; the observation value, the calculated value of the method and the calculated value of the single traffic flow traffic wave model are shown in fig. 3.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit and scope of the present invention.

Claims (3)

1. A hybrid bicycle traffic wave calculation method based on spatial perception is characterized by comprising the following steps:
step 1: determining a cellular hybrid bicycle flow traffic state, the hybrid bicycle flow traffic state
The states include a free flow traffic state, a semi-congested traffic state, a fully congested traffic state;
step 2: respectively calculating the perception density of the electric bicycle and the perception density of the traditional bicycle in the cellular corresponding mixed bicycle flow traffic state, wherein the step 2 specifically comprises the following steps:
according to a heterogeneous traffic flow model based on space perception, calculating the perception density of the electric bicycle and the perception density of the traditional bicycle under the state of the cellular corresponding mixed bicycle flow traffic:
(1) calculating the perceived density of the electric bicycle and the perceived density of the traditional bicycle under the free flow traffic state:
firstly, the road space ratio of the electric bicycle in the nth cell under the free flow traffic state is respectively calculated
Figure FDA0003190879610000011
Road space ratio of traditional bicycle
Figure FDA0003190879610000012
Figure FDA0003190879610000013
Further obtaining the sensed density of the electric bicycle in the nth cell under the free flow traffic state
Figure FDA0003190879610000014
Perceived density of traditional bicycle
Figure FDA0003190879610000015
Comprises the following steps:
Figure FDA0003190879610000016
in the formula (I), the compound is shown in the specification,
Figure FDA0003190879610000017
respectively the critical density of the electric bicycle and the critical density of the traditional bicycle,
Figure FDA0003190879610000018
the density of the electric bicycle in the nth cell and the density of the traditional bicycle in the free flow traffic state are respectively set;
(2) calculating the perceived density of the electric bicycle and the perceived density of the traditional bicycle in a semi-crowded traffic state:
firstly, the road space ratio of the electric bicycle in the nth cell under the condition of semi-crowded traffic is respectively calculated
Figure FDA0003190879610000019
Road space ratio of traditional bicycle
Figure FDA00031908796100000110
Figure FDA00031908796100000111
Further obtaining the sensing density of the electric bicycle in the nth cell under the condition of semi-crowded traffic
Figure FDA00031908796100000112
Perceived density of traditional bicycle
Figure FDA00031908796100000113
Comprises the following steps:
Figure FDA00031908796100000114
in the formula (I), the compound is shown in the specification,
Figure FDA00031908796100000115
the density of the electric bicycles in the nth cell and the density of the traditional bicycles in the nth cell in the semi-crowded traffic state are respectively;
(3) calculating the perceived density of the electric bicycle and the perceived density of the traditional bicycle in a completely crowded traffic state:
firstly, the road space ratio of the electric bicycle in the nth cell in the completely crowded traffic state is respectively calculated
Figure FDA0003190879610000021
Road space ratio of traditional bicycle
Figure FDA0003190879610000022
Figure FDA0003190879610000023
Further, the sensing density of the electric bicycle in the nth cell under the completely crowded traffic state can be obtained
Figure FDA0003190879610000024
Perceived density of traditional bicycle
Figure FDA0003190879610000025
Comprises the following steps:
Figure FDA0003190879610000026
in the formula (I), the compound is shown in the specification,
Figure FDA0003190879610000027
respectively being flow resistance of electric bicyclePlug density, traditional bicycle plug density;
Figure FDA0003190879610000028
the density of the electric bicycles in the nth cell and the density of the traditional bicycles in the completely crowded traffic state are respectively; w is a1、w2The starting wave speed of the electric bicycle and the starting wave speed of the traditional bicycle are respectively calculated according to the following formulas:
Figure FDA0003190879610000029
in the formula (I), the compound is shown in the specification,
Figure FDA00031908796100000210
the maximum flow rate of the electric bicycle and the maximum flow rate of the traditional bicycle are respectively;
the density of the electric bicycle in the nth cell and the density of the traditional bicycle under different traffic states are calculated according to the following formula:
Figure FDA00031908796100000211
in the formula (I), the compound is shown in the specification,
Figure FDA00031908796100000212
the density of the electric bicycle in the nth cell and the density of the traditional bicycle under the traffic state theta (n) respectively
Figure FDA00031908796100000213
The flow rate of the electric bicycle in the nth cell and the flow rate of the traditional bicycle in the traffic state theta (n) are A, B, C; l isnIs the nth cell length; b is the width of the bicycle lane;
Figure FDA00031908796100000219
step 3: determining intracellular mixingTotal density and total flow of the bicycle flow;
and 4, step 4: and calculating the traffic waves according to the cellular transmission model.
2. The spatial perception-based hybrid bicycle traffic wave calculation method according to claim 1, wherein: in the step 3, the calculation process of the total density and the total flow of the cellular mixed bicycle flow comprises the following steps:
according to a heterogeneous traffic flow model based on space perception, the total density of the mixed bicycle flow in the nth cell is the perceived density of any bicycle flow corresponding to the traffic state, and the total flow in each cell is the sum of the electric bicycle flow and the bicycle flow, namely
Figure FDA00031908796100000214
In the formula (I), the compound is shown in the specification,
Figure FDA00031908796100000215
the total density of the mixed bicycle in the nth cell under the traffic state theta (n),
Figure FDA00031908796100000216
the sensing density of the electric bicycle in the nth cell and the sensing density of the traditional bicycle under the traffic state theta (n) are respectively,
Figure FDA00031908796100000217
the total flow rate of the mixed bicycle in the nth cell under the traffic state theta (n) is A, B, C.
3. The spatial perception-based hybrid bicycle traffic wave calculation method according to claim 1, wherein: the step 4 specifically comprises the following steps:
according to the cellular transmission model in the traffic flow, the calculation formula of the adjacent cellular traffic waves under the mixed bicycle flow can be obtained as follows:
Figure FDA00031908796100000218
in the formula (I), the compound is shown in the specification,
Figure FDA0003190879610000031
the total density of the mixed bicycle in the (n-1) th cell under the traffic state theta (n-1),
Figure FDA0003190879610000032
the total flow of the mixed bicycle in the n-1 th cell under the traffic state theta (n-1), and theta (n-1) is A1、B1、C1。
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