CN115457771A - Urban traffic management system and method based on big data - Google Patents
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Abstract
The invention discloses an urban traffic management system and method based on big data, and belongs to the technical field of urban traffic management. The system comprises a multi-source data acquisition module, a prediction model construction module, a prediction model analysis module and an urban traffic management module; the output end of the multi-source data acquisition module is connected with the input end of the prediction model construction module; the output end of the prediction model construction module is connected with the input end of the prediction model analysis module; the output end of the prediction model analysis module is connected with the input end of the urban traffic management module; the invention also provides a big data-based urban traffic management method, which can judge the road traffic jam condition through predicting road parking and road traffic flow, and schedule buses according to the traffic jam condition, thereby solving the problem of traffic jam caused by road parking.
Description
Technical Field
The invention relates to the technical field of urban traffic management, in particular to an urban traffic management system and method based on big data.
Background
The method is advanced in the twenty-first century, the urbanization process of China is increasingly accelerated, the urban scale is increased, and the income and the domestic water of residents in local cities are greatly improved. The following is faster and faster pace of life, stronger and stronger concept of time, farther and farther travel distance, more and more private cars, more frequent economic connections and personnel exchanges between cities and among cities, between cities and abdominal areas, and the great increase in traffic demand has generated great pressure on the existing traffic facilities in cities. At present, the development of traffic facilities generally cannot keep up with the increase speed of traffic demand, so that the problem of traffic jam is caused, and the traffic jam is increasingly serious, which not only brings inconvenience to normal work, study and life of people, but also causes resource waste, and influences the living environment, air quality and the healthy and rapid development of cities.
The urbanization process of China is accelerated, the quantity of private cars is rapidly increased, the total quantity of residents going out is steadily increased, and the distance of going out is rapidly increased. The main causes of traffic congestion are: (1) The urban parking lot has the serious defect problem commonly existing in cities of China, and many cities have no standard parking lots, so that the phenomenon that motor vehicles are randomly parked and randomly placed is common, and traffic jam is caused to a certain extent. (2) The number of vehicle paths is small, the road network structure is unreasonable, and the overall efficiency is low. For a long time, the urban pedestrian road area in China is always in a low level state, the proportion of road land occupied by urban land is small, the road network density is low, the total road capacity supply is seriously insufficient, and in nearly ten years, although the road quantity is rapidly developed, the urban public transport superiority of China cannot reach the increasing speed of 20% of urban traffic volume every year (3), the traffic sharing rate is low, and meanwhile, the number of cars is increased suddenly and the utilization rate is extremely high. The urban rail transit development lags behind, the implementation of the bus priority has problems, the public transit service quality cannot meet the requirements of people, the public transit sharing rate is reduced, and meanwhile, the utilization rate of private cars in China is extremely high.
Lack the parking area of standard, the road network structure is unreasonable to cause the motor vehicle to stop in disorder to put in road both sides to make in early peak and late peak period, traffic flow increases suddenly, appears serious traffic jam, causes huge pressure to urban traffic, and this is the problem that awaits the solution urgently, in present technical means, has not proposed effective solution and has alleviated the traffic jam problem that road parking leads to.
Disclosure of Invention
The invention aims to provide an urban traffic management system and method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a city traffic management method based on big data comprises the following steps:
acquiring historical road parking data, constructing a road parking prediction model, and predicting a road parking prediction value of the next period of the road;
acquiring historical road vehicle flow data, constructing a road vehicle flow prediction model, and predicting a predicted road vehicle flow value of the next period of the road;
obtaining a road traffic jam prediction value according to a road parking prediction value and a road traffic flow prediction value of the next period of the road;
and constructing an urban traffic management simulation platform, setting a road traffic jam threshold value, and scheduling the bus when the road traffic jam prediction value exceeds the road traffic jam threshold value.
On one hand, the public transport priority can control the total amount of urban traffic to a certain extent, reduce the proportion of individual traffic modes and improve the position of public transport in the whole urban traffic system; on the other hand, the bus trip is selected, so that road resources can be more effectively utilized, the congestion of road traffic is reduced, and the purposes of saving energy and protecting environment are achieved.
According to the technical scheme, in the step S1, the step of constructing the road parking prediction model comprises the following steps:
acquiring historical road parking data, wherein the historical road parking data comprises historical parking data of domestic roads and historical parking data of monitored roads;
the historical parking data of the road comprises parking starting time, parking ending time, parking quantity, parking duration and parking position;
the period is in the unit of hours, one hour is a period, and the time t is the period number;
acquisition time series X = (X) t ，X t-1 ，...，X t-N+1 ) As model input;
the primary moving average is:
wherein S is t Is a moving average of period t, X t The observed value of the t stage is N, and N is the average period number of movement;
and performing second moving average on the first moving average of the time sequence to obtain a second moving average which is:
the road parking prediction model is as follows:
S t+M ＝A t +N t *M
A t ＝2S t -H t
wherein A is t Is intercept, i.e. the basal level of the t-th phenomenon, B t Is the slope, i.e. the variation of the t-th phenomenon per unit time, M is the number of future predicted periods, S t Is a moving average of the t-th period, H t Is the quadratic moving average of the t-th period, S t+M The predicted value is the predicted value of the t + M period road parking.
The quadratic moving average method solves the contradiction that the predicted value lags behind the actual value, is suitable for predicting the time series of the market phenomenon with obvious trend change, and can accurately predict the road parking in real life to achieve an ideal prediction result.
According to the above technical solution, in step S2, constructing a road traffic flow prediction model includes:
acquiring historical traffic flow data of a road, wherein the historical traffic flow data of the road comprises historical traffic flow data of domestic roads and historical traffic flow data of monitored roads;
the historical traffic flow data of the road comprises vehicle entering time, vehicle exiting time, vehicle quantity, main traffic flow and shunt traffic flow;
the period is in the unit of hours, one hour is a period, and the time t is the period number;
acquisition time series Y = (Y) 1 ，y 2 ，...，y n ) As model input;
the first order exponential smoothing formula is:
S t (1) ＝ay t +(1-a)S t-1 (1)
wherein S is t (1) Is a primary exponential smoothing value of t-th period, y t Is the observed value of the t-th stage, S t-1 (1) Is the first exponential smoothing value of the t-1 stage, a is the smoothing exponent;
the quadratic exponential smoothing formula is:
S t (2) ＝S t (1) +(1-a)S t-1 (2)
wherein S is t (2) Is the second exponential smoothing value of the t-th stage, S t-1 (2) The second exponential smoothing value of the t-1 stage, a is a smoothing exponent;
the road traffic flow prediction model is as follows:
Y t+T ＝a t +b t *T
a t ＝2S t (1) -S t (2)
wherein, a t As intercept, i.e. base level of period t, b t Is the slope, i.e. the amount of change in time unit during period t, Y t+T The predicted value of the road traffic flow with the time of T + T period is obtained.
The secondary index smoothing method further strengthens the effect of recent observation values on predicted values, and endows the observation values at different times with different weights, so that the weights of the recent observation values are increased, the predicted values can rapidly reflect actual changes of the market, and the predicted values can be rapidly obtained according to the recent observation values by using the secondary index smoothing method under the actual urban traffic environment, particularly in early and late peak periods, so that the effect of accurate prediction is achieved.
According to the technical scheme, in the step S3, a road traffic jam prediction value is obtained according to a road parking prediction value and a road traffic flow prediction value of the next period of the road;
road traffic congestion prediction value Z:
Z＝p*S t+M +q*Y t+T
wherein, the weight proportion of road parking is represented as p, and the weight proportion of traffic flow is represented as q, S t+M Is a predicted value of t + M period road parking, Y t+T The predicted value of the road traffic flow in the period T + T is obtained.
According to the technical scheme, in the step S4, an urban traffic management simulation platform is constructed, and a road traffic jam threshold value Z is set 0 When the road traffic jam prediction value exceeds the road traffic jam threshold value, dispatching the bus;
setting road traffic jam numerical threshold value Z 0 (ii) a When Z > Z 0 Then, the bus needs to be scheduled;
dividing the time of a day into three periods, namely an early peak period, a late peak period and a normal period;
the early peak period is set as follows: seven am to nine am; the late peak period is set as follows: five to seven points late; the normal period is set as follows: removing other times of morning and evening peak;
number of buses a scheduled in morning and evening peak periods 1 Comprises the following steps:
a 1 ＝k 1 b+k 2 (Z-Z 0 )；
number of buses a scheduled in normal period 2 Comprises the following steps:
a 2 ＝k 1 b+k 3 (Z-Z 0 )；
the number of buses under the original scheduling plan is recorded as b, and the scheduling coefficient under the original scheduling plan is recorded as k 1 The scheduling coefficient at the early and late peak periods is recorded as k 2 The scheduling coefficient in the normal period is recorded as k 3 。
The urban traffic management simulation platform integrates modern communication, information, computers, networks, GPS and other high and new technologies and is applied to urban traffic management, can monitor road parking data and road traffic flow in real time, and can schedule buses as required.
According to the technical scheme, the system comprises a multi-source data acquisition module, a prediction model construction module, a prediction model analysis module and an urban traffic management module;
the multi-source data acquisition module is used for acquiring historical road parking data and historical road traffic flow data and constructing a multi-source database; the prediction model construction module is used for constructing a road parking prediction model according to the historical road parking data acquired by the multi-source data acquisition module and constructing a road traffic flow prediction model according to the historical road traffic flow data acquired by the multi-source data acquisition module; the prediction model analysis module is used for predicting a road parking prediction value of the next period of the road according to the road parking prediction model and predicting a road traffic flow prediction value of the next period of the road according to the road traffic flow prediction model; the urban traffic management module is used for constructing an urban traffic management simulation platform and dispatching buses according to the road traffic jam condition to relieve the road traffic jam;
the output end of the multi-source data acquisition module is connected with the input end of the prediction model construction module; the output end of the prediction model construction module is connected with the input end of the prediction model analysis module; the output end of the prediction model analysis module is connected with the input end of the urban traffic management module.
According to the technical scheme, the multi-source data acquisition module comprises a domestic road historical parking data acquisition unit and a domestic road historical traffic flow data acquisition unit;
the domestic road historical parking data acquisition unit is used for acquiring domestic road historical parking data; the historical road parking data comprises parking starting time, parking ending time, parking quantity, parking duration and parking positions;
the domestic road historical traffic flow data acquisition unit is used for acquiring domestic road historical traffic flow data; the historical traffic flow data of the road comprises vehicle entering time, vehicle exiting time, vehicle quantity, main traffic flow and shunt traffic flow;
the output end of the domestic road historical parking data acquisition unit is connected with the input end of the prediction model construction module; and the output end of the domestic road historical traffic flow data acquisition unit is connected with the input end of the prediction model construction module.
According to the technical scheme, the prediction model construction module comprises a road parking prediction model construction unit and a road traffic flow prediction model construction unit;
the road parking prediction model construction unit is used for constructing a road parking prediction model; the road traffic flow prediction model construction unit is used for constructing a road traffic flow prediction model;
the output end of the road parking prediction model building unit is connected with the input end of the road traffic flow prediction model building unit.
According to the technical scheme, the prediction model analysis module comprises a road parking prediction model analysis unit and a road traffic flow prediction model analysis unit;
the road parking prediction model analysis unit is used for analyzing a road parking prediction model; the road traffic flow prediction model analysis unit is used for analyzing a road traffic flow prediction model;
the output end of the road parking prediction model analysis unit is connected with the input end of the road traffic flow prediction model analysis unit.
According to the technical scheme, the urban traffic management module comprises an urban traffic management simulation platform construction unit and an urban traffic management analysis unit;
the urban traffic management simulation platform construction unit is used for constructing an urban traffic management simulation platform and simulating road bus dispatching; the urban traffic management analysis unit is used for analyzing the road traffic jam condition, developing different bus schedules according to the road traffic jam condition and feeding back results to the system;
the output end of the urban traffic management simulation platform construction unit is connected with the input end of the urban traffic management analysis unit.
Compared with the prior art, the invention has the following beneficial effects:
the invention can provide a road parking prediction model and a road traffic flow prediction model, comprehensively provides a solution for the current situation of road traffic jam caused by road parking based on road parking data and road traffic flow data, can predict the road parking data and the road traffic flow data, establishes a simulation system platform under the condition that the road parking data reaches a certain threshold value, constructs bus points, plans bus running paths, schedules buses, effectively solves the problem of the current situation of road traffic jam caused by road parking, further improves the trip rate of people, and meets the daily trip requirements of people.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart diagram of a big data-based urban traffic management method according to the invention;
fig. 2 is a schematic structural diagram of a big data-based urban traffic management system according to the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution: a city traffic management method based on big data comprises the following steps:
acquiring historical road parking data, constructing a road parking prediction model, and predicting a road parking prediction value of the next period of the road;
acquiring historical road vehicle flow data, constructing a road vehicle flow prediction model, and predicting a predicted road vehicle flow value of the next period of the road;
obtaining a road traffic jam prediction value according to a road parking prediction value and a road traffic flow prediction value of the next period of the road;
and constructing an urban traffic management simulation platform, setting a road traffic jam threshold value, and scheduling the bus when the road traffic jam prediction value exceeds the road traffic jam threshold value.
In step S1, constructing a road parking prediction model includes:
acquiring historical road parking data, wherein the historical road parking data comprises historical parking data of domestic roads and historical parking data of monitored roads;
the historical parking data of the road comprises parking starting time, parking ending time, parking quantity, parking duration and parking position;
the period is in the unit of hours, one hour is a period, and the time t is the period number;
acquisition time series X = (X) t ，X t-1 ，...，X t-N+1 ) As model input;
the primary moving average is:
wherein S is t Is a moving average of period t, X t The observed value of the t stage is N, and N is the average period number of movement;
and performing second moving average on the first moving average of the time sequence to obtain a second moving average which is:
the road parking prediction model is as follows:
S t+M ＝A t +N t *M
A t ＝2S t -H t
wherein A is t Is intercept, i.e. the basal level of the t-th phenomenon, B t Is the slope, i.e. the variation of the t-th phenomenon per unit time, M is the number of future predicted periods, S t Is a moving average of period t, H t Is the quadratic moving average of the t-th period, S t+M The predicted value is the predicted value of the t + M period road parking.
In step S2, constructing a road traffic flow prediction model includes:
acquiring historical traffic flow data of a road, wherein the historical traffic flow data of the road comprises historical traffic flow data of domestic roads and historical traffic flow data of monitored roads;
the historical traffic flow data of the road comprises vehicle entering time, vehicle exiting time, vehicle quantity, main traffic flow and shunt traffic flow;
the period is in the unit of hours, one hour is a period, and the time t is the period number;
acquisition time series Y = (Y) 1 ，y 2 ，...，y n ) As model input;
the first order exponential smoothing formula is:
S t (1) ＝ay t +(1-a)S t-1 (1)
wherein S is t (1) Is a primary exponential smoothing value of t-th period, y t Is the observed value of the t-th stage, S t-1 (1) Is the first exponential smoothing value of the t-1 stage, a is the smoothing index;
the quadratic exponential smoothing formula is:
S t (2) ＝S t (1) +(1-a)S t-1 (2)
wherein S is t (2) Is the second exponential smoothing value of the t-th stage, S t-1 (2) The second exponential smoothing value of the t-1 stage, a is the smoothing index;
the road traffic flow prediction model is as follows:
Y t+T ＝a t +b t *T
a t ＝2S t (1) -S t (2)
wherein, a t As intercept, i.e. base level of period t, b t Is the slope, i.e. the amount of change in time unit during period t, Y t+T The predicted value of the road traffic flow with the time of T + T period is obtained.
In step S3, a road traffic jam prediction value is obtained according to a road parking prediction value and a road traffic flow prediction value of the next period of the road;
road traffic congestion prediction value Z:
Z＝p*S t+M +q*Y t+T
wherein, the weight proportion of road parking is recorded as p, the weight proportion of traffic flow is recorded as q, S t+M Is a predicted value of t + M period road parking, Y t+T The predicted value of the road traffic flow in the period T + T is obtained.
In step S4, an urban traffic management simulation platform is constructed, and a road traffic jam threshold value Z is set 0 On the roadWhen the traffic jam prediction value exceeds the road traffic jam threshold value, dispatching buses;
setting road traffic jam numerical threshold value Z 0 (ii) a When Z > Z 0 Then, the bus needs to be scheduled;
dividing the time of a day into three periods, namely an early peak period, a late peak period and a normal period;
the early peak period is set as follows: seven am to nine am; the late peak period is set as follows: five to seven points late; the normal period is set as follows: removing other times of morning and evening peak;
number of buses a scheduled in morning and evening peak period 1 Comprises the following steps:
a 1 ＝k 1 b+k 2 (Z-Z 0 )；
number of buses a scheduled in normal period 2 Comprises the following steps:
a 2 ＝k 1 b+k 3 (Z-Z 0 )；
the number of buses under the original scheduling plan is recorded as b, and the scheduling coefficient under the original scheduling plan is recorded as k 1 The scheduling coefficient at the early and late peak times is recorded as k 2 The scheduling coefficient in the normal period is recorded as k 3 。
The system comprises a multi-source data acquisition module, a prediction model construction module, a prediction model analysis module and an urban traffic management module;
the multi-source data acquisition module is used for acquiring historical road parking data and historical road traffic flow data and constructing a multi-source database; the prediction model construction module is used for constructing a road parking prediction model according to the historical road parking data acquired by the multi-source data acquisition module and constructing a road traffic flow prediction model according to the historical road traffic flow data acquired by the multi-source data acquisition module; the prediction model analysis module is used for predicting a road parking prediction value of the next period of the road according to the road parking prediction model and predicting a road traffic flow prediction value of the next period of the road according to the road traffic flow prediction model; the urban traffic management module is used for constructing an urban traffic management simulation platform and dispatching buses according to the road traffic jam condition to relieve the road traffic jam;
the output end of the multi-source data acquisition module is connected with the input end of the prediction model construction module; the output end of the prediction model building module is connected with the input end of the prediction model analysis module; the output end of the prediction model analysis module is connected with the input end of the urban traffic management module.
The multi-source data acquisition module comprises a historical parking data acquisition unit of the domestic road and a historical traffic flow data acquisition unit of the domestic road;
the domestic road historical parking data acquisition unit is used for acquiring domestic road historical parking data; the domestic road historical traffic flow data acquisition unit is used for acquiring domestic road historical traffic flow data;
the output end of the domestic road historical parking data acquisition unit is connected with the input end of the prediction model construction module; and the output end of the domestic road historical traffic flow data acquisition unit is connected with the input end of the prediction model construction module.
The prediction model construction module comprises a road parking prediction model construction unit and a road traffic flow prediction model construction unit;
the road parking prediction model construction unit is used for constructing a road parking prediction model; the road traffic flow prediction model construction unit is used for constructing a road traffic flow prediction model;
and the output end of the road parking prediction model building unit is connected with the input end of the road traffic flow prediction model building unit.
The prediction model analysis module comprises a road parking prediction model analysis unit and a road traffic flow prediction model analysis unit;
the road parking prediction model analysis unit is used for analyzing a road parking prediction model; the road traffic flow prediction model analysis unit is used for analyzing a road traffic flow prediction model;
and the output end of the road parking prediction model analysis unit is connected with the input end of the road traffic flow prediction model analysis unit.
The urban traffic management module comprises an urban traffic management simulation platform construction unit and an urban traffic management analysis unit;
the urban traffic management simulation platform construction unit is used for constructing an urban traffic management simulation platform and simulating road bus dispatching; the urban traffic management analysis unit is used for analyzing the road traffic jam condition, developing different bus schedules according to the road traffic jam condition and feeding back results to the system;
the output end of the urban traffic management simulation platform construction unit is connected with the input end of the urban traffic management analysis unit.
In the examples:
a road R is arranged and monitored;
the construction of the road parking prediction model comprises the following steps:
constructing a multi-source database according to the road parking historical data;
acquisition time series X = (X) t ，X t-1 ，...，X t-N+1 ) As model input;
namely, in the embodiment, historical data of 8 hours of the day is selected, and then data of 12 hours of the day is predicted;
the primary moving average is:
wherein S is t Is a moving average of period t, X t The observed value of the t stage, N is the average moving stage number;
and performing second moving average on the first moving average of the time sequence to obtain a second moving average which is:
the road parking prediction model is as follows:
S t+M ＝A t +B t *M
A t ＝2S t -H t
wherein A is t Is intercept, i.e. the base level of the t-th phenomenon, B t Is the slope, i.e. the variation of the t-th phenomenon per unit time, M is the number of future predicted periods, S t Is a moving average of period t, H t Is the quadratic moving average of the t-th period, S t+M The predicted value is the t + M period road parking;
when t =8,M =4, S 12 A predicted value of the 12 th-hour road parking;
the method for constructing the road traffic flow prediction model comprises the following steps:
constructing a multi-source database according to the historical traffic data of the road;
acquisition time series Y = (Y) 1 ，y 2 ，...，y n ) As model input;
the first order exponential smoothing formula is:
S t (1) ＝ay t +(1-a)S t-1 (1)
wherein S is t (1) Is an exponential smoothing value of y t Is the observed value of the t-th stage, S t-1 (1) Is the first exponential smoothing value of the t-1 stage, a is the smoothing exponent;
the quadratic exponential smoothing formula is:
S t (2) ＝S t (1) +(1-a)S t-1 (2)
wherein S is t (2) Is the second exponential smoothing value of the t-th stage, S t-1 (2) The second exponential smoothing value of the t-1 stage, a is a smoothing exponent;
the road traffic flow prediction model is as follows:
Y t+T ＝a t +b t *T
a t ＝2S t (1) -S t (2)
wherein, a t As intercept, i.e. base level of period t, b t Is the slope, i.e. the amount of change in time unit during period t, Y t+T The predicted value of the road traffic flow with the time of T + T period is obtained;
when t =8,t =4, Y 12 The predicted value of the road traffic flow at the 12 th hour is obtained;
obtaining a road traffic jam prediction value according to a road parking prediction value and a road traffic flow prediction value of the next period of the road; road traffic congestion prediction value Z:
Z＝p*S t+M +q*Y t+T
wherein, the weight proportion of road parking is recorded as p, the weight proportion of traffic flow is recorded as q, S t+M Is a predicted value of t + M period road parking, Y t+T The predicted value of the road traffic flow in the period T + T is obtained.
Setting road traffic jam numerical threshold value Z 0 (ii) a When Z > Z 0 Then, the bus needs to be scheduled;
dividing the time of a day into three periods, namely an early peak period, a late peak period and a normal period;
the early peak period is set as follows: seven am to nine am; the late peak period is set as follows: five to seven nights; the normal period is set as follows: removing other times of morning and evening peak;
number of buses a scheduled in morning and evening peak periods 1 Comprises the following steps:
a 1 ＝k 1 b+k 2 (Z-Z 0 )；
number of buses a scheduled in normal period 2 Comprises the following steps:
a 2 ＝k 1 b+k 3 (Z-Z 0 )；
the number of buses under the original scheduling plan is recorded as b, and the scheduling coefficient under the original scheduling plan is recorded as k 1 The scheduling coefficient at the early and late peak periods is recorded as k 2 The scheduling coefficient in the normal period is recorded as k 3 。
Obtaining a road traffic jam prediction value according to the predicted value of road parking and the predicted value of road vehicle flow, and setting a road traffic jam threshold value Z 0 And when the road traffic jam prediction value exceeds the road traffic jam threshold value, dispatching the bus to relieve the traffic jam.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A city traffic management method based on big data is characterized by comprising the following steps:
s1: acquiring historical road parking data, constructing a road parking prediction model, and predicting a road parking prediction value of the next period of the road;
s2: acquiring historical road vehicle flow data, constructing a road vehicle flow prediction model, and predicting a road vehicle flow prediction value of the next period of the road;
s3: obtaining a road traffic jam prediction value according to a road parking prediction value and a road traffic flow prediction value of the next period of the road;
s4: and constructing an urban traffic management simulation platform, setting a road traffic jam threshold value, and scheduling the bus when the road traffic jam prediction value exceeds the road traffic jam threshold value.
2. The big data-based urban traffic management method according to claim 1, wherein: in step S1, constructing a road parking prediction model includes:
acquiring historical road parking data, wherein the historical road parking data comprises historical parking data of domestic roads and historical parking data of monitored roads;
the historical parking data of the road comprises parking starting time, parking ending time, parking quantity, parking duration and parking position;
the period is in the unit of hours, one hour is a period, and the time t is the period number;
acquisition time series X = (X) t ，X t-1 ，...，X t-N+1 ) As model input;
the primary moving average is:
wherein S is t Is a moving average of period t, X t The observed value of the t stage is N, and N is the average period number of movement;
and performing second moving average on the first moving average of the time sequence to obtain a second moving average which is:
the road parking prediction model is as follows:
S t+M ＝A t +B t *M
A t ＝2S t -H t
wherein A is t Is intercept, i.e. the base level of the t-th phenomenon, B t Is the slope, i.e. the variation of the t-th phenomenon per unit time, M is the number of future predicted periods, S t Is a moving average of the t-th period, H t Is the quadratic moving average of the t-th period, S t+M The predicted value is the predicted value of the t + M period road parking.
3. The big data-based urban traffic management method according to claim 1, wherein: in step S2, constructing a road traffic flow prediction model includes:
acquiring historical traffic flow data of a road, wherein the historical traffic flow data of the road comprises historical traffic flow data of domestic roads and historical traffic flow data of monitored roads;
the period is in the unit of hours, one hour is a period, and the time t is the period number;
acquisition time series Y = (Y) 1 ，y 2 ，...，y n ) As model input;
the first order exponential smoothing formula is:
S t (1) ＝ay t +(1-a)S t-1 (1)
wherein S is t (1) Is a first exponential smoothing value of t period, y t Is the observed value of the t-th stage, S t-1 (1) Is the first exponential smoothing value of the t-1 stage, a is the smoothing exponent;
the quadratic exponential smoothing formula is:
S t (2) ＝S t (1) +(1-a)S t-1 (2)
wherein S is t (2) Is the second exponential smoothing value of the t-th stage, S t-1 (2) The second exponential smoothing value of the t-1 stage, a is a smoothing exponent;
the road traffic flow prediction model is as follows:
Y t+T ＝a t +b t *T
a t ＝2S t (1) -S t (2)
wherein, a t As intercept, i.e. base level of period t, b t Is the slope, i.e. the variation of the T-th period in unit time, and T is the expected number of lead periods; y is t+T The predicted value of the traffic flow of the road with the time of the T + T period is obtained.
4. The big data-based urban traffic management method according to claim 1, wherein: in step S3, obtaining a road traffic jam prediction value according to a road parking prediction value and a road traffic flow prediction value of the next period of the road;
road traffic congestion prediction value Z:
Z＝p*S t+M +q*Y t+T
wherein, the weight proportion of road parking is represented as p, and the weight proportion of traffic flow is represented as q, S t+M Is a predicted value of t + M period road parking, Y t+T The predicted value of the road traffic flow in the period T + T is obtained.
5. The big data-based urban traffic management method according to claim 1, wherein: in step S4, an urban traffic management simulation platform is constructed, and a road traffic jam threshold value Z is set 0 When the road traffic jam prediction value exceeds the roadWhen the road traffic jam threshold value is reached, a bus is scheduled;
setting road traffic jam numerical threshold value Z 0 (ii) a When Z > Z 0 Then, the bus needs to be scheduled;
dividing the time of a day into three periods, namely an early peak period, a late peak period and a normal period;
the early peak period is set as follows: seven am to nine am; the late peak period is set as follows: five to seven nights; the normal period is set as follows: removing other times of morning and evening peak;
number of buses a scheduled in morning and evening peak periods 1 Comprises the following steps:
a 1 ＝k 1 b+k 2 (Z-Z 0 )；
number of buses a scheduled in normal period 2 Comprises the following steps:
a 2 ＝k 1 b+k 3 (Z-Z 0 )；
the number of buses under the original scheduling plan is recorded as b, and the scheduling coefficient under the original scheduling plan is recorded as k 1 The scheduling coefficient at the early and late peak periods is recorded as k 2 The scheduling coefficient in the normal period is recorded as k 3 。
6. A urban traffic management system based on big data is characterized in that: the system comprises a multi-source data acquisition module, a prediction model construction module, a prediction model analysis module and an urban traffic management module;
the multi-source data acquisition module is used for acquiring historical road parking data and historical road traffic flow data and constructing a multi-source database; the prediction model construction module is used for constructing a road parking prediction model according to the historical road parking data acquired by the multi-source data acquisition module and constructing a road traffic flow prediction model according to the historical road traffic flow data acquired by the multi-source data acquisition module; the prediction model analysis module is used for predicting a road parking prediction value of the next period of the road according to the road parking prediction model and predicting a road traffic flow prediction value of the next period of the road according to the road traffic flow prediction model; the urban traffic management module is used for constructing an urban traffic management simulation platform and dispatching buses according to the road traffic jam condition to relieve the road traffic jam;
the output end of the multi-source data acquisition module is connected with the input end of the prediction model construction module; the output end of the prediction model building module is connected with the input end of the prediction model analysis module; the output end of the prediction model analysis module is connected with the input end of the urban traffic management module.
7. The big data based urban traffic management system according to claim 6, wherein: the multi-source data acquisition module comprises a historical parking data acquisition unit of a domestic road and a historical traffic data acquisition unit of the domestic road;
the domestic road historical parking data acquisition unit is used for acquiring domestic road historical parking data; the domestic road historical traffic flow data acquisition unit is used for acquiring domestic road historical traffic flow data;
the output end of the domestic road historical parking data acquisition unit is connected with the input end of the prediction model construction module; and the output end of the domestic road historical traffic flow data acquisition unit is connected with the input end of the prediction model construction module.
8. The big data based urban traffic management system according to claim 6, wherein: the prediction model construction module comprises a road parking prediction model construction unit and a road traffic flow prediction model construction unit;
the road parking prediction model construction unit is used for constructing a road parking prediction model; the road traffic flow prediction model construction unit is used for constructing a road traffic flow prediction model;
the output end of the road parking prediction model building unit is connected with the input end of the road traffic flow prediction model building unit.
9. The big data based urban traffic management system according to claim 6, wherein: the prediction model analysis module comprises a road parking prediction model analysis unit and a road traffic flow prediction model analysis unit;
the road parking prediction model analysis unit is used for analyzing a road parking prediction model; the road traffic flow prediction model analysis unit is used for analyzing a road traffic flow prediction model;
the output end of the road parking prediction model analysis unit is connected with the input end of the road traffic flow prediction model analysis unit.
10. The big data based urban traffic management system according to claim 6, wherein: the urban traffic management module comprises an urban traffic management simulation platform construction unit and an urban traffic management analysis unit;
the urban traffic management simulation platform construction unit is used for constructing an urban traffic management simulation platform and simulating road bus dispatching; the urban traffic management analysis unit is used for analyzing the road traffic jam condition, expanding different bus schedules according to the road traffic jam condition and feeding back results to the system;
the output end of the urban traffic management simulation platform construction unit is connected with the input end of the urban traffic management analysis unit.
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