CN104915731A - Vehicle travel path reconstruction macro/micro integrated new method based on automatic vehicle identification data - Google Patents
Vehicle travel path reconstruction macro/micro integrated new method based on automatic vehicle identification data Download PDFInfo
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Abstract
The invention belongs to the field of traffic planning and management and specifically relates to a vehicle travel path reconstruction macro/micro integrated new method based on automatic vehicle identification (AVI) data. The method constructs a macro/micro integrated reconstructed frame only by utilizing the automatic vehicle identification data and is characterized by, in micro level, reconstructing vehicle paths based on individual path selection principle and with particle filter theory being integrated, and increasing a reconstruction path traffic and path set constraint for a path traffic estimator in macro level; and in the macro level, realizing path traffic estimation based on random user equalization principle and with the path traffic estimator being integrated, and updating travel time consistency of particle filter and a path gravitation observation equation. Through the method, high-precision and complete vehicle travel paths and path traffic and road section traffic can be obtained under the conditions that only the AVI data is available and the AVI coverage rate is low.
Description
Technical field
The invention belongs to traffic programme and management domain, be specifically related to the grand microcosmic of a kind of vehicle driving reconstructing path based on automatic vehicle identification data and integrate new method.
Background technology
In recent years, traffic system information acquisition has presented " mobile model " detection detecting, with Floating Car be representative from " section type " that take inductive coil as representative and has detected to " wide area " automatic vehicle identification being representative with video license auto-recognition system (Automatic Vehicle Identification, AVI) trend changed fast.AVI technology comprises video licence plate automatic identification technology, REID and drive-in toll technology etc.AVI system can collection vehicle ID, vehicle is by the information such as time and vehicle location.AVI technology particularly video licence plate automatic identification technology obtains swift and violent development in China, and the city such as Beijing, Shanghai large-scale promotion is implemented.At present, AVI data are applied to OD(Origin-Destination) Matrix Estimation, path flow estimation, travel time estimation, the field such as traffic circulation evaluation of risk and reconstructing path.But, also do not utilize pure AVI data to carry out the application of reconstructing path at present.
The invention of China Patent Publication No. CN103440764A, discloses a kind of vehicle driving path reconstruction method based on automatic vehicle identification data, and the method input information detects data based on AVI, but the data that need gather in conjunction with traditional flow checkout facility.In addition, the method is only from microscopic individual vehicle driving aspect reconstruct vehicle driving path, not from the angle analysis vehicle driving path of macro network equilibrium, more do not realize the integration of both macro and micro routing information, thus AVI detect vehicle driving effective information and underuse.
Vehicle driving reconstructing path is to the research traffic congestion origin cause of formation, and city OD structure, the research of driver's optimizing paths scheduling theory has important value.In addition, vehicle driving path also will manage in urban dynamic traffic, the traffic analysis that becomes more meticulous, major policy assessment and management the field such as (bus management, vehicle odd-and-even license plate rule, Congestion Toll etc.) and management of public safety (vehicle tracking, VIP security etc.) play a great role.
Summary of the invention
The object of the invention is to for the reconstructing path problem based on pure AVI data, propose the grand microcosmic new method of a kind of vehicle driving reconstructing path based on automatic vehicle identification data.
This method has following two features: one, grand microcosmic mixing reconstruct framework combines based on the individual vehicle routing of microcosmic point particle filter and the stochastic user equilibrium principle based on macroscopic aspect path flow estimator; Two, AVI data are being only had and under the limited condition of AVI coverage rate, the high precision that can realize the complete trip route of fairly large road network vehicle obtains.
For reaching above target, the grand microcosmic of the vehicle driving reconstructing path based on automatic vehicle identification data that the present invention proposes integrates new method.The present invention considers to only have AVI data, AVI coverage rate limited and the situation that there is error, utilizes grand microcosmic to mix reconstruct framework and carries out vehicle driving reconstructing path, can produce path flow, link flow and OD matrix data simultaneously.Grand microcosmic mixing reconstruct framework based on particle filter and path flow estimator is as follows:
Combination frame is made up of two assemblies: microscopic particle filtering unit reconstructs vehicle route based on AVI part path data and four observation equations (consistency of path equation, AVI measurability equation, journey time consistance equation and path gravity equation), and macroscopical path flow estimator assembly is based on AVI part way data on flows estimated path and link flow.Combination frame combines the estimated capacity of two assemblies simultaneously and makes it exist alternately.On the one hand, path flow estimator assembly initialization particle filter observation equation, and upgrade observation equation to reduce the evaluated error of particle filter assembly; On the other hand, the estimated result of particle filter assembly observation path flow estimator, produces reconstruct path flow and thus the intensive beam path flow estimator in path reduces its evaluated error.By mutual interactive refreshing, the individual routing estimated result based on particle filter can reach unanimity with the path flow estimator estimated result based on multinomial logic spy.And pass through the mutual of two assemblies, combination frame overcomes detecting device error and routing distributional assumption, the estimated result of more accurately reality thus can be produced.
The method comprises two algorithm steps: (1), for each car, utilizes particle filter based on four observation equations for possible path particle assigns weight; (2) upgrade for each path flow estimator and particle filter assembly, utilize path flow estimator to determine optimal path flow, link flow and Link Travel Time.
The grand microcosmic new method of the vehicle driving reconstructing path based on automatic vehicle identification data that the present invention proposes, comprise five large steps: the initialization of (1) path flow estimator, (2) particle filter assembly initialization, (3) particle filter assembly is utilized to carry out reconstructing path, (4) utilize path flow estimator to upgrade estimated result, (5) upgrade particle filter assembly observation equation; Concrete steps are as follows:
(1) path flow estimator initialization
(1.1) according to the AVI data obtained, AVI part way flow is obtained
, road section capacity
freely journey time is flowed with section
; AVI is made to lay section dual variable
, non-A/V I lays section dual variable
, path dual variable
;
(1.2) generation pass collection generates, and makes outermost layer iteration count
n=n+ 1
,use AVI section dual variable
, non-A/V I section dual variable
with path dual variable
upgrade section cost
; Determine the shortest path that all OD matrixes are right, upgrade path flow estimator operating path collection
;
(1)
(1.3) define transportation network (
n, A), wherein
nnode set,
asection set, according to known portions section observed volume
a(
), reconstruct path collection and path flow; Controllability path flow estimator is under the condition meeting link flow restriction, road section capacity and reconstruct path flow restriction, and seek the feasible path flow solution meeting stochastic user equilibrium principle, equation is as follows:
(2a)
(2b)
(2c)
(2d)
(2e)
Wherein:
mwith
urepresent the set of observation section and the set of non-viewing section respectively;
rSwith
represent that OD is to set and OD couple respectively
rspath collection;
represent discrete parameter;
represent section
athe permission observational error of observed volume;
represent section
aestimated flow;
represent section respectively
athe traffic capacity and journey time;
represent section, the path factor: if section
abelong to OD couple
rspath
k, then it equals 1, otherwise equals 0.
represent the reconstructed error that reconstructing path allows;
represent OD couple respectively
rspath
kflow, particle filter reconstruct
noD couple after car
rspath
kflow;
pNrepresent particle filter reconstruct
noD is reconstructed to set after car;
(1.4) path flow estimator convergence test; If twice iterative computation obtains path flow feasible solution maximum difference and be less than a certain threshold value, carry out step (2); Otherwise return step (1.2);
(2) particle filter assembly initialization
Path flow estimator Link Travel Time is read in from step (1)
, based on estimator Link Travel Time and road network topology, use depth-first traversal algorithm for all OD to generation particle filter possible path collection
estimate, (such as estimate link flow according to estimated result
and Link Travel Time
), produce journey time consistance observation equation and the path Observation Gravitational equation of particle filter.Journey time consistance observation equation is as follows:
（3a）
（3b）
Wherein:
?
ipaths
m th individual continuous AVI is based on the update probability of journey time consistency model;
it is possible path
iaverage travel time between two continuous AVI;
it is the traveled distance time between two continuous AVI;
?
ipaths is based on the final updated probability of journey time consistency model;
it is journey time coefficient;
it is the number of two continuous AVI on path;
Path Observation Gravitational equation is as follows:
（4）
Wherein:
?
ipaths based on the update probability of path gravity model,
it is possible path
iin section
jestimated flow,
it is possible path
isection number,
it is vehicle
kpossible path number;
(3) particle filter assembly is utilized to carry out reconstructing path
(3.1) initialization particle collection.Select the
acar, makes all possible paths
for primary collection,
represent primary prior probability.If do not have historical data, then primary prior probability is made to be
.
(3.2) importance sampling
For AVI part path data, distribute particle weights and normalization according to node consistency observation equation.Node consistency observation equation is as follows:
（5）
Wherein:
id: the car number of expressed portion sub-path;
path: the numbering representing fullpath;
the topological structure interior joint set of expressed portion sub-path;
represent the topological structure interior joint set of fullpath.
The Link Travel Time data estimated for PFE and AVI individual vehicle travel time data, distribute particle weights and normalization according to journey time consistance observation equation;
For AVI part path data and observation start and end moment, distribute particle weights and normalization according to AVI measurability observation equation.AVI measurability observation equation is that the fact caused according to detecting device metrical error carries out backward inference, reduces detecting device error to the impact of vehicle driving reconstructing path.Detecting device does not detect that target vehicle specifically can be divided into two kinds of situations.The first situation is the section that detecting device place passed by by vehicle, but fails vehicle to be detected due to the reason of metrical error; The second situation is that vehicle have selected the section of not installing detecting device place, and thus detecting device fails vehicle to be detected.
For the link flow data that PFE estimates, distribute particle weights and normalization according to path Observation Gravitational equation;
(3.3) outgoing route optimal estimation, selects maximum a posteriori probability path to be optimal estimation.
(3.4) particle filter convergence test.By vehicle driving path collection meter structure path flow matrix
.If
l=
l max (reconstruct gross vehicle number), then stop iteration outgoing route flow
with vehicle fullpath.
(3.5) assembly refresh test.If
l=
l cycle (update cycle) be a certain particle filter estimated path flow simultaneously
than path flow estimator estimated path flow
greatly, so upgrade particle filter reconstruct path flow, enter step (4); Otherwise, return step (3.1).
(4) path flow estimator upgrades estimated result
(4.1) path collection generates.Use AVI section dual variable
, non-A/V I section dual variable
with path dual variable
upgrade section cost
.Determine the shortest path that all OD are right, upgrade path flow estimator operating path collection
.By the reconstruct path collection newly produced
add operating path collection
.
(4.2) meet link flow restriction, road section capacity restriction and reconstructing path flow restriction condition under carry out link flow estimation.Use iteration equalization frame, linear search and feasible solution update algorithm.
(4.3) path flow estimator convergence test.If twice iterative computation obtains path flow feasible solution maximum difference and be less than a certain threshold value, carry out step (5); Otherwise return step (4.1).
(5) particle filter observation equation upgrades.According to estimated result (such as section estimated flow
and journey time
), upgrade particle filter journey time consistance observation equation and path Observation Gravitational equation, return step (3).
The present invention is directed to pure AVI data and the low reconstructing path problem of AVI coverage rate, without under the condition of historical information, utilize the AVI information detected, utilize particle filter and path flow estimator combination frame and algorithm to obtain the complete trip route of real vehicle, the additional informations such as vehicle driving OD matrix, path flow and link flow can be released further.The method can adapt to fairly large road network, can only have AVI data and obtain the complete trip route of high-precision vehicle under the lower condition of AVI coverage rate.
Accompanying drawing explanation
Fig. 1 is that the grand microcosmic that the present invention proposes integrates reconstructing path framework.
Fig. 2 is the reconstructing path process flow diagram based on automatic vehicle identification data that the present invention proposes.
Fig. 3 is the fairly large road network figure that the embodiment of the present invention 1 adopts.
Fig. 4 is the embodiment of the present invention 1 path flow estimated result under different AVI coverage rate condition.
Fig. 5 is the embodiment of the present invention 1 link flow estimated result under different AVI coverage rate condition.
Fig. 6 is the embodiment of the present invention 1 vehicle trip route reconstruct accuracy under different AVI coverage rate condition.
Embodiment
Elaborate below in conjunction with accompanying drawing 2 pairs of embodiments of the invention: the present embodiment is implemented under premised on technical solution of the present invention, give detailed embodiment and concrete operating process, but protection scope of the present invention is not limited to following embodiment.
Embodiment 1: this path reconstruction method is used for certain the fairly large road network shown in Fig. 2.Comprise 127 nodes in this road network, 151 sections, and 42, traffic zone is set.Vehicle license video detector is laid at road section.Required acquisition input information: the vehicle license information after identification, vehicle due in, AVI detecting device is numbered.
After the above-mentioned input information of acquisition, with vehicle
acomplete trip route be reconstructed into example, known vehicle
apart path, step is as follows:
(1) path flow estimator initialization
(1.1) path collection generates.Use AVI section dual variable
, non-A/V I section dual variable
with path dual variable
upgrade section cost
.Determine the shortest path that all OD are right, upgrade path flow estimator operating path collection
.
(1.2) path flow estimator is under the condition meeting link flow restriction, road section capacity and reconstruct path flow restriction, seeks the feasible path flow solution meeting stochastic user equilibrium principle.Use iteration equalization frame, linear search and feasible solution update algorithm.
(1.3) path flow estimator convergence test.If twice iterative computation obtains path flow feasible solution maximum difference and be less than a certain threshold value, carry out step (2); Otherwise return step (1.2).
(2) particle filter initialization
(2.1) possible path collection generates.Based on estimation journey time and road network topology, use depth-first traversal algorithm for all OD to generation particle filter possible path collection
.
(2.2) observation equation generates.(such as link flow is estimated according to estimated result
and Link Travel Time
), produce journey time consistance observation equation and the path Observation Gravitational equation of particle filter.
(3) particle filter is used to carry out reconstructing path
(3.1) initialization particle collection.Select the
acar, makes all possible paths
for primary collection,
represent primary prior probability.If do not have historical data, then primary prior probability is made to be
.
(3.2) importance sampling.Input AVI part path data, distribute particle weights and normalization according to node consistency observation equation.The Link Travel Time data that input PFE estimates and AVI individual vehicle travel time data, distribute particle weights and normalization according to journey time consistance observation equation.Input AVI part path data and observation start and end moment, distribute particle weights and normalization according to AVI measurability observation equation.The link flow data that input PFE estimates, distribute particle weights and normalization according to path Observation Gravitational equation.
(3.3) outgoing route optimal estimation.Maximum a posteriori probability path is selected to be optimal estimation.
(3.4) particle filter convergence test.By vehicle driving path collection meter structure path flow matrix
.If
l=
l max (reconstruct gross vehicle number), then stop iteration outgoing route flow
with vehicle fullpath.
(3.5) assembly refresh test.If
l=
l cycle (update cycle) be a certain particle filter estimated path flow simultaneously
than path flow estimator estimated path flow
greatly, so upgrade particle filter reconstruct path flow, enter step (4); Otherwise, return step (3.1).
(4) path flow estimator upgrades estimated result
(4.1) path collection generates.Use AVI section dual variable
, non-A/V I section dual variable
with path dual variable
upgrade section cost
.Determine the shortest path that all OD are right, upgrade path flow estimator operating path collection
.By the reconstruct path collection newly produced
add operating path collection
.
(4.2) meet link flow restriction, road section capacity restriction and reconstructing path flow restriction condition under carry out link flow estimation.Use iteration equalization frame, linear search and feasible solution update algorithm.
(4.3) path flow estimator convergence test.If twice iterative computation obtains path flow feasible solution maximum difference and be less than a certain threshold value, carry out step (5); Otherwise return step (4.1).
(5) particle filter observation equation upgrades.According to estimated result (such as section estimated flow
and journey time
), upgrade particle filter journey time consistance observation equation and path Observation Gravitational equation, return step (3).
What wherein Fig. 4 represented is path flow estimated result under different AVI coverage rate condition, and what Fig. 5 represented is link flow estimated result under different AVI coverage rate condition, and what Fig. 6 represented is vehicle trip route reconstruct accuracy under different AVI coverage rate condition.For embodiment 1 road network, under the AVI coverage rate condition of 30%-80%, path flow estimate and link flow estimated result good.Under the AVI coverage rate condition of 30%-80%, the accuracy of vehicle driving reconstructing path rises to 99% from 68%.
Claims (1)
1. the grand microcosmic new method of the vehicle driving reconstructing path based on automatic vehicle identification data proposed, it is characterized in that comprising five large steps: the initialization of (1) path flow estimator, (2) particle filter assembly initialization, (3) particle filter assembly is utilized to carry out reconstructing path, (4) utilize path flow estimator to upgrade estimated result, (5) upgrade particle filter assembly observation equation; Concrete steps are as follows:
(1) path flow estimator initialization
(1.1) according to the AVI data obtained, AVI part way flow is obtained
, road section capacity
freely journey time is flowed with section
; AVI is made to lay section dual variable
, non-A/V I lays section dual variable
, path dual variable
;
(1.2) generation pass collection generates, and makes outermost layer iteration count
n=n+ 1
,use AVI section dual variable
, non-A/V I section dual variable
with path dual variable
upgrade section cost
; Determine the shortest path that all OD matrixes are right, upgrade path flow estimator operating path collection
;
(1)
(1.3) define transportation network (
n, A), wherein
nnode set,
asection set, according to known portions section observed volume
a(
), reconstruct path collection and path flow; Controllability path flow estimator is under the condition meeting link flow restriction, road section capacity and reconstruct path flow restriction, and seek the feasible path flow solution meeting stochastic user equilibrium principle, equation is as follows:
(2a)
(2b)
(2c)
(2d)
(2e)
Wherein:
mwith
urepresent the set of observation section and the set of non-viewing section respectively;
rSwith
represent that OD is to set and OD couple respectively
rspath collection;
represent discrete parameter;
represent section
athe permission observational error of observed volume;
represent section
aestimated flow;
represent section respectively
athe traffic capacity and journey time;
represent section, the path factor: if section
abelong to OD couple
rspath
k, then it equals 1, otherwise equals 0;
represent the reconstructed error that reconstructing path allows;
represent OD couple respectively
rspath
kflow, particle filter reconstruct
noD couple after car
rspath
kflow;
pNrepresent particle filter reconstruct
noD is reconstructed to set after car;
(1.4) path flow estimator convergence test; If twice iterative computation obtains path flow feasible solution maximum difference and be less than a certain threshold value, carry out step (2); Otherwise return step (1.2);
(2) particle filter assembly initialization
Path flow estimator Link Travel Time is read in from step (1)
, based on estimator Link Travel Time and road network topology, use depth-first traversal algorithm for all OD to generation particle filter possible path collection
estimate, (such as estimate link flow according to estimated result
and Link Travel Time
), produce journey time consistance observation equation and the path Observation Gravitational equation of particle filter; Journey time consistance observation equation is as follows:
（3a）
（3b）
Wherein:
?
ipaths
m th individual continuous AVI is based on the update probability of journey time consistency model;
it is possible path
iaverage travel time between two continuous AVI;
it is the traveled distance time between two continuous AVI;
?
ipaths is based on the final updated probability of journey time consistency model;
it is journey time coefficient;
it is the number of two continuous AVI on path;
Path Observation Gravitational equation is as follows:
（4）
Wherein:
?
ipaths based on the update probability of path gravity model,
it is possible path
iin section
jestimated flow,
it is possible path
isection number,
it is vehicle
kpossible path number;
(3) particle filter assembly is utilized to carry out reconstructing path
(3.1) initialization particle collection; Select the
acar, makes all possible paths
for primary collection,
represent primary prior probability; If do not have historical data, then primary prior probability is made to be
;
(3.2) importance sampling
For AVI part path data, distribute particle weights and normalization according to node consistency observation equation; Node consistency observation equation is as follows:
（5）
Wherein:
id: the car number of expressed portion sub-path;
path: the numbering representing fullpath;
the topological structure interior joint set of expressed portion sub-path;
represent the topological structure interior joint set of fullpath;
The Link Travel Time data estimated for PFE and AVI individual vehicle travel time data, distribute particle weights and normalization according to journey time consistance observation equation;
For AVI part path data and observation start and end moment, distribute particle weights and normalization according to AVI measurability observation equation; AVI measurability observation equation is that the fact caused according to detecting device metrical error carries out backward inference, reduces detecting device error to the impact of vehicle driving reconstructing path; Detecting device does not detect that target vehicle specifically can be divided into two kinds of situations; The first situation is the section that detecting device place passed by by vehicle, but fails vehicle to be detected due to the reason of metrical error; The second situation is that vehicle have selected the section of not installing detecting device place, and thus detecting device fails vehicle to be detected;
For the link flow data that PFE estimates, distribute particle weights and normalization according to path Observation Gravitational equation;
(3.3) outgoing route optimal estimation, selects maximum a posteriori probability path to be optimal estimation;
(3.4) particle filter convergence test; By vehicle driving path collection meter structure path flow matrix
; If
l=
l max (reconstruct gross vehicle number), then stop iteration outgoing route flow
with vehicle fullpath;
(3.5) assembly refresh test; If
l=
l cycle (update cycle) be a certain particle filter estimated path flow simultaneously
than path flow estimator estimated path flow
greatly, so upgrade particle filter reconstruct path flow, enter step (4); Otherwise, return step (3.1);
(4) path flow estimator upgrades estimated result
(4.1) path collection generates; Use AVI section dual variable
, non-A/V I section dual variable
with path dual variable
upgrade section cost
; Determine the shortest path that all OD are right, upgrade path flow estimator operating path collection
; By the reconstruct path collection newly produced
add operating path collection
;
(4.2) meet link flow restriction, road section capacity restriction and reconstructing path flow restriction condition under carry out link flow estimation; Use iteration equalization frame, linear search and feasible solution update algorithm;
(4.3) path flow estimator convergence test; If twice iterative computation obtains path flow feasible solution maximum difference and be less than a certain threshold value, carry out step (5); Otherwise return step (4.1);
(5) particle filter observation equation upgrades; According to estimated result (such as section estimated flow
and journey time
), upgrade particle filter journey time consistance observation equation and path Observation Gravitational equation, return step (3).
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| CN113223293B (en) * | 2021-05-06 | 2023-08-04 | 杭州海康威视数字技术股份有限公司 | Road network simulation model construction method and device and electronic equipment |
| CN113420488A (en) * | 2021-05-18 | 2021-09-21 | 东南大学 | Urban road network OD estimation method based on track reconstruction |
| CN113420488B (en) * | 2021-05-18 | 2024-03-08 | 东南大学 | Urban road network OD estimation method based on track reconstruction |
| CN114333292A (en) * | 2021-11-22 | 2022-04-12 | 上海电科智能系统股份有限公司 | Traffic restoration method based on trajectory reconstruction technology |
| CN114333292B (en) * | 2021-11-22 | 2022-11-18 | 上海电科智能系统股份有限公司 | Traffic restoration method based on trajectory reconstruction technology |
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