Method for deducing traffic transfer amount during advanced tail number traffic restriction in heavy pollution weather

Abstract

The invention discloses a method for deducing the traffic transfer amount during advanced tail number traffic restriction in heavy pollution weather, and the method specifically comprises the steps: dividing time domains: dividing an early peak into three time domains: a preparation time domain, a starting time domain and a peak time domain; traffic data in non-heavy pollution weather and heavy pollution weather are collected and preprocessed; setting a transfer rule aiming at the starting time domain and the peak time domain; and comprehensively calculating the transfer amount from the car travelers to the public traffic and the transfer amount from the slow traffic to the public traffic, which are jointly influenced by the heavily polluted weather and the P-LPR, according to the flow conservation. According to the invention, the car transfer amount caused by the emergency advanced tail number traffic control measure can be obtained, the calculation of the car transfer amount in the specific time period is realized, and the effect evaluation of the emergency traffic management measure can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of traffic management, and particularly relates to a method for inferring traffic transfer volume when implementing early tail number restrictions under heavy pollution weather. Background Art

[0003] Most of the existing studies focus on analyzing the impact of emergency traffic management measures on the overall traffic flow, or estimating the change of traffic flow by combining trajectory data and complex theoretical models. There are relatively few quantitative inference methods for the transfer of private cars to public transportation under polluted weather, and there is a lack of calculation of the transfer volume of traffic modes within a specific time period. Analyzing the overall traffic flow is difficult to reflect the transfer situation of different traffic modes within traffic trips. In addition, the initial trajectory data of residents' trips is difficult to obtain and has low accuracy. At the same time, complex theoretical models also have problems such as large solution difficulty and large calculation scale.

[0004] Meanwhile, with the wide application of private car checkpoint data and public transportation card swiping data in studying travel behavior, how to use these data for effective traffic flow conservation calculation has become an urgent problem to be solved in current traffic management. The existing technologies lack the comprehensive utilization of the traffic flow conservation principle and cannot quickly calculate the traffic mode transfer volume after the implementation of emergency traffic measures in heavy pollution weather in the actual environment, resulting in inaccurate evaluation of the effect of emergency traffic management measures and making it difficult to provide timely and effective support for decision-making. Summary of the Invention

[0005] The present invention aims to evaluate the transfer volume of private cars affected by the early tail number restriction measures to public transportation and the transfer volume of slow traffic to public transportation during the morning rush hour under heavy pollution weather, so as to provide data support and decision-making basis for formulating more scientific and effective traffic management measures. For this purpose, the present invention provides a method for inferring traffic transfer volume when implementing early tail number restrictions under heavy pollution weather.

[0006] A method for inferring traffic transfer volume when implementing early tail number restrictions under heavy pollution weather according to the present invention includes the following steps:

[0007] Step 1: Divide the time domain, and divide the morning rush hour into three time domains: preparation time domain (WE), start time domain (PE), and peak time domain (RE).

[0008] Step 2: Acquisition and processing of traffic data.

[0009] By collecting hourly private car checkpoint data and hourly public transportation card swiping data under non-heavy pollution weather and heavy pollution weather, the average number of private cars, the average number of bus card swipes, and the average number of subway card swiping data within each time domain in these two weather conditions are obtained respectively.

[0010] Step 3: Set transfer rules.

[0011] For the starting time domain PE, set 5 virtual transfer nodes including the time domain and transportation modes, namely ① starting time domain car, ② starting time domain public transportation, ③ trip cancellation, ④ slow traffic, and ⑤ ready time domain car; travelers transfer among these 5 virtual nodes: ① to ⑤ means that the car originally departing in the starting time domain is affected and transferred to the ready time domain for departure; ① to ② means that the car originally departing in the starting time domain is affected and transferred to the starting time domain to use public transportation; ④ to ② means that slow traffic is affected and transferred to the starting time domain to use public transportation for travel; ① to ③ and ② to ③ respectively represent the situations where car and public transportation travelers in the starting time domain cancel their trips after being affected.

[0012] For the peak time domain RE, set 4 virtual transfer nodes including the time domain and transportation modes, namely ① peak time domain car, ② peak time domain public transportation, ③ trip cancellation, and ④ slow traffic; travelers transfer among these 4 virtual nodes: ① to ② means that the car originally departing in the peak time domain is affected and transferred to the peak time domain to use public transportation; ④ to ② means that slow traffic is affected and transferred to the peak time domain to use public transportation for travel; ① to ③ and ② to ③ respectively represent the situations where car and public transportation travelers in the peak time domain cancel their trips after being affected.

[0013] Step 4: Apply flow conservation, combine time domain division and transfer rules to construct a transfer model for cars and slow traffic under influence.

[0014] Step 5: Obtain the transfer volumes of cars and slow traffic.

[0015] Input the data processed in Step 2 into the transfer models of cars and slow traffic to directly obtain the transfer volumes of cars and slow traffic when the tail numbers are restricted in advance under heavy pollution weather.

[0016] Furthermore, in Step 1, the ready time domain WE is 1 hour before the start of the advance tail number restriction P-LPR; the starting time domain PE is the time domain with an earlier restriction compared to the daily tail number restriction N-LPR; the peak time domain RE is the remaining time domain in the morning peak.

[0017] Furthermore, in Step 2, the average number of cars in the ready time domain WE, starting time domain PE, and peak time domain RE obtained under non-heavy pollution weather are respectively represented by ; the average number of cars in each time domain under heavy pollution weather are respectively represented by ; the average subway card swiping volume and average bus card swiping volume obtained under non-heavy pollution and heavy pollution weather are respectively represented by and

[0018] Meanwhile, the variation in car trips within each time domain under heavy pollution weather is further obtained as compared to non-heavy pollution weather. Wherein: And the variation in subway and bus trips under heavy pollution weather And Wherein:

[0019] Furthermore, Step 4 is specifically as follows:

[0020] Combined with time domain division and transfer rules, based on the principle of flow conservation, calculate the net outflow of the "car at start time domain" node.

[0021]

[0022] Where λ is the average number of passengers per car, usually taken as 1.2 persons / car, and can also be obtained according to local travel investigation reports.

[0023] The net inflow of the "car at start time domain" node to the "public transportation at start time domain" node Is the transfer volume of cars affected by the early tail number restriction under heavy pollution weather to public transportation:

[0024]

[0025] Where x represents the trip cancellation rate of car users within the start time domain or peak time domain.

[0026] The net inflow of the "car at peak time domain" node to the "public transportation at peak time domain" node

[0027]

[0028] Calculate the change in public transportation system demand:

[0029] Since there is no additional card - swiping transaction for transfers within the subway system, when all passengers using buses also take the subway, the subway card - swiping volume is the lower limit of public transportation usage. Therefore, considering passengers taking buses but not using the subway, it is obtained that:

[0030]

[0031] Where W N , W Hrespectively represent the lower limits of public transport usage in non-heavy pollution and heavy pollution weather; η ∈ (0, 1) is the conversion coefficient between the number of card swipes and the number of trips, and the larger the value, the fewer times the traveler uses the bus; therefore, compared with non-heavy pollution weather, the change in demand for the public transport system ΔW H is:

[0032]

[0033] According to the principle of flow conservation between relevant nodes of public transport, ΔW H is also expressed as:

[0034]

[0035] where δ represents the transfer volume from slow traffic to public transport during the start time domain and peak time domain in heavy pollution weather;

[0036] After substituting formulas (1)-(5) into formula (6), we get:

[0037]

[0038] Obtain the car transfer volume model affected by the early tail number restriction under heavy pollution weather.

[0039] Since the restriction will force car travelers to switch to public transport, and the early restriction will increase the time range of the restriction, making more car travelers switch to public transport, therefore, during the start time domain and peak time domain, the transfer volume from cars to public transport is non-negative. Thus, from formula (6), we can get:

[0040]

[0041] After substituting it into formula (7), we get:

[0042]

[0043] Finally, combined with formula (2), we get the car transfer volume affected

[0044]

[0045] Obtain the slow traffic transfer volume model affected by the early tail number restriction under heavy pollution weather:

[0046] On the one hand, considering formula (8), so there is δ ≤ ΔW H ; on the other hand, affected by heavy pollution and early restriction, some car users will cancel their trips, that is, the trip cancellation rate x ≥ 0. Combining with formula (7), there will be Therefore, the final affected slow traffic transfer volume δ is as follows:

[0047]

[0048] The beneficial technical effects of the present invention are as follows:

[0049] Through the present invention, the transfer volume of cars caused by the emergency early tail number restriction measure can be obtained, and the estimation of the car transfer volume within a specific time period is realized, which is beneficial to improving the effect evaluation of emergency traffic management measures. The acquisition of the involved checkpoint data and card swiping data is relatively easy. When inferring the transfer volume, only the average number of cars, the average bus card swiping volume, and the average subway card swiping volume under non-heavy pollution weather and heavy pollution weather need to be input. At the same time, the inference method obtained by applying the principle of flow conservation is simple to solve, fast in operation, without complex model assumptions and parameter settings, and only needs to input the above-mentioned data to infer the transfer volume, providing data support and decision-making basis for rapid emergency response and formulating more scientific and effective traffic management measures. Description of the Drawings

[0050] Figure 1 It is a schematic flow chart of the method for inferring the traffic transfer volume during the early tail number restriction in heavy pollution weather according to the present invention.

[0051] Figure 2 It is a schematic diagram of setting the time domain transfer rule according to the present invention.

[0052] Figure 3 It is a schematic diagram of setting the peak time domain transfer rule according to the present invention. Detailed Embodiment

[0053] The present invention will be further described in detail below with reference to the drawings and specific implementation methods.

[0054] A method for inferring the traffic transfer volume during the early tail number restriction in heavy pollution weather according to the present invention is as Figure 1 shown, including the following steps:

[0055] Step 1: Divide the time domain. The morning peak is divided into three time domains: the preparation time domain (WE), the start time domain (PE), and the peak time domain (RE).

[0056] The preparation time domain WE is 1 hour before the start of the early tail number restriction P-LPR; the start time domain PE is the time domain with early restriction compared to the daily tail number restriction N-LPR; the peak time domain RE is the remaining time domain in the morning peak.

[0057] For example, during the morning rush hour on days with non-severe pollution, the traffic restriction time is from 7:00 to 9:00. However, during heavy pollution days, the traffic restriction starts 1 hour earlier, so the restriction time becomes from 6:00 to 9:00. Therefore, 5:00 - 5:59 is set as the preparation time domain (WE), 6:00 - 6:59 is the starting time domain (PE), and 7:00 - 9:00 is the peak time domain (RE).

[0058] Step 2: Acquisition and processing of traffic data.

[0059] By collecting hourly car checkpoint data and hourly public transportation card-swipe data under non-severe pollution weather (N) and heavy pollution weather (H), the average number of cars, as well as the average number of bus card swipes and the average number of subway card-swipe data, within each time domain for these two types of weather are obtained respectively.

[0060] The average number of cars in the preparation time domain WE, starting time domain PE, and peak time domain RE under non-severe pollution weather are represented by respectively. The average number of cars in each time domain under heavy pollution weather are represented by respectively; the average subway card-swipe volume and the average bus card-swipe volume under non-severe pollution and heavy pollution weather are represented by and

[0061] Meanwhile, further obtain the change in the number of car trips within each time domain under heavy pollution weather compared to non-severe pollution weather wherein: as well as the change in subway and bus trips under heavy pollution weather and wherein:

[0062] Step 3: Set transfer rules.

[0063] For the starting time domain PE, set 5 virtual transfer nodes including time domain and transportation mode, including ① cars in the starting time domain, ② public transportation in the starting time domain, ③ cancel trip, ④ slow traffic, ⑤ cars in the preparation time domain; travelers transfer among these 5 virtual nodes: ① to ⑤ means that cars originally departing in the starting time domain are affected and transferred to depart in the preparation time domain; ① to ② means that cars originally departing in the starting time domain are affected and transferred to use public transportation in the starting time domain; ④ to ② means that slow traffic is affected and transferred to use public transportation in the starting time domain; ① to ③ and ② to ③ respectively represent the situations where car and public transportation travelers in the starting time domain cancel their trips after being affected. As Figure 2 shown.

[0064] For the peak time domain RE, 4 virtual transfer nodes including the time domain and transportation modes are set, including ① cars in the peak time domain, ② public transportation in the peak time domain, ③ trip cancellation, and ④ slow traffic; travelers transfer among these 4 virtual nodes: ① to ② means that cars originally departing in the peak time domain are affected and transferred to public transportation in the peak time domain; ④ to ② means that slow traffic is affected and transferred to public transportation for travel in the peak time domain; ① to ③ and ② to ③ respectively represent the situations where car and public transportation travelers in the peak time domain cancel their trips after being affected. As Figure 3 shown.

[0065] Step 4: Apply the flow conservation, combine the time domain division and transfer rules, and construct the transfer models of cars and slow traffic under the influence.

[0066] Calculate the net outflow of the "cars in the start time domain" node

[0067]

[0068] Among them, λ is the average number of passengers per car, usually taken as 1.2 persons / car, and can also be obtained according to the local travel survey report.

[0069] The net inflow of the "cars in the start time domain" node to the "public transportation in the start time domain" node is the transfer volume of cars affected by the early tail number restriction under heavy pollution weather to public transportation:

[0070]

[0071] Among them, x represents the trip cancellation rate of car users within the start time domain or peak time domain.

[0072] The net inflow of the "cars in the peak time domain" node to the "public transportation in the peak time domain" node

[0073]

[0074] Calculate the change in the demand of the public transportation system:

[0075] Since there is no additional card - swiping transaction for transfers within the subway system, when all passengers using buses also take the subway, the subway card - swiping volume is the lower limit of the public transportation usage. Therefore, considering the passengers taking buses but not using the subway, it is obtained that:

[0076]

[0077] Among them, W N ,W Hrespectively represent the lower limits of public transportation usage in non-heavily polluted and heavily polluted weather; η ∈ (0, 1) is the conversion coefficient between the number of card swipes and the number of trips, and the larger the value, the fewer times the traveler uses the bus; therefore, compared with non-heavily polluted weather, the change in demand for the public transportation system ΔW H is:

[0078]

[0079] According to the principle of flow conservation between relevant nodes of public transportation, ΔW H is also expressed as:

[0080]

[0081] where δ represents the transfer volume from slow traffic to public transportation during the start time domain and peak time domain in heavily polluted weather;

[0082] After substituting formulas (1)-(5) into formula (6), we get:

[0083]

[0084] Obtain the car transfer volume model affected by the early tail number restriction in heavily polluted weather:

[0085] Since the restriction will force car travelers to switch to public transportation, and the early restriction will increase the time range of the restriction, making more car travelers switch to public transportation. Therefore, during the start time domain and peak time domain, the transfer volume from cars to public transportation is non-negative. Thus, from formula (6), we can get:

[0086]

[0087] After substituting it into formula (7), we get:

[0088]

[0089] Finally, combined with formula (2), we get the car transfer volume affected

[0090]

[0091] Obtain the slow traffic transfer volume model affected by the early tail number restriction in heavily polluted weather:

[0092] On the one hand, considering formula (8), so there is δ ≤ ΔW H ; on the other hand, affected by heavy pollution and early restriction, some car users will cancel their trips, that is, the trip cancellation rate x ≥ 0. Combining formula (7), there will be Therefore, the final affected slow traffic transfer volume δ is as follows:

[0093]

[0094] Step 5: Obtain the car and slow traffic transfer volumes.

[0095] Input the data processed in Step 2 into Equations (10) and (11) to directly obtain the car and slow traffic transfer volumes during the early restricted tail number period in heavy pollution weather.

[0096] Obtaining the transfer volume is beneficial in two aspects. On the one hand, it helps ensure the emergency services of public transportation. The emergency plan for public transportation can be prepared according to the scale of historical transfer volumes. For example, when the car and slow traffic transfer volumes exceed the existing public transportation service capacity, the number of departure trips should be appropriately increased and the headway should be shortened. On the other hand, it can be used to evaluate the effect of the early tail number restriction in heavy pollution weather on reducing car use, providing support for optimizing the design of the restricted tail number policy in heavy pollution weather. Finally, considering that heavy pollution weather increases the health risks of travelers and the health exposures faced by different transportation modes are different, usually cars

Claims

1. A method for inferring traffic diversion volume during early tail number restrictions under heavy pollution weather, characterized in that, It includes the following steps: Step 1: Divide the time domain. The morning rush hour is divided into three time domains: the preparation time domain WE, the start time domain PE, and the peak time domain RE; Step 2: Acquisition and processing of traffic data; By collecting the hourly car checkpoint data and hourly public transportation card swiping data under non-heavy pollution weather and heavy pollution weather, the average number of cars in each time domain, as well as the average number of bus card swipes and the average number of subway card swiping data in these two weather conditions are obtained respectively; Step 3: Set transfer rules; For the start time domain PE, set 5 virtual transfer nodes including the time domain and transportation mode, including ① cars in the start time domain, ② public transportation in the start time domain, ③ trip cancellation, ④ slow traffic, ⑤ cars in the preparation time domain; Travelers transfer among these 5 virtual nodes: ① to ⑤ means that the cars originally departing in the start time domain are affected and transferred to the preparation time domain for departure; ① to ② means that the cars originally departing in the start time domain are affected and transferred to use public transportation in the start time domain; ④ to ② means that slow traffic is affected and transferred to use public transportation for travel in the start time domain; ① to ③ and ② to ③ respectively represent the situations where car and public transportation travelers in the start time domain cancel their trips after being affected; For the peak time domain RE, set 4 virtual transfer nodes including the time domain and transportation mode, including ① cars in the peak time domain, ② public transportation in the peak time domain, ③ trip cancellation, and ④ slow traffic; Travelers transfer among these 4 virtual nodes: ① to ② means that the cars originally departing in the peak time domain are affected and transferred to use public transportation in the peak time domain; ④ to ② means that slow traffic is affected and transferred to use public transportation for travel in the peak time domain; ① to ③ and ② to ③ respectively represent the situations where car and public transportation travelers in the peak time domain cancel their trips after being affected; Step 4: Using flow conservation, combined with time domain division and transfer rules, construct a transfer model for cars and slow traffic under the influence; Step 5: Obtain the transfer volumes of cars and slow traffic; Input the data processed in Step 2 into the transfer model of cars and slow traffic to directly obtain the transfer volumes of cars and slow traffic when the tail numbers are restricted in advance under heavy pollution weather.

2. The method for inferring traffic diversion volume during early tail number restrictions in heavy pollution weather according to claim 1, wherein In Step 1, the preparation time domain WE is 1 hour before the start of the advance tail number restriction P-LPR; the start time domain PE is the time domain with an earlier restriction compared to the daily tail number restriction N-LPR; the peak time domain RE is the remaining time domain in the morning rush hour.

3. A method for inferring traffic diversion volume during early tail number restrictions in heavy pollution weather according to claim 1, characterized in that In step 2, for the non-heavy pollution days, the average number of cars in the time domain WE, the starting time domain PE, and the peak time domain RE are respectively represented by ; for the heavy pollution days, the average number of cars in each time domain is respectively represented by ; for the non-heavy pollution and heavy pollution days, the average subway card swiping volume and the average bus card swiping volume are respectively represented by and Meanwhile, the variation in the number of car trips in each time domain under heavy pollution weather is further obtained as compared to non-heavy pollution weather Wherein: And the variation in the number of subway and bus trips under heavy pollution weather And Wherein:

4. A method for inferring the traffic transfer volume during the early tail number restriction under heavy pollution weather according to claim 3, characterized in that The specific content of Step 4 is as follows: Based on the time domain division and transfer rules, and the principle of flow conservation, calculate the net outflow of the "starting time domain car" node Where λ is the average number of passengers per car; Net inflow from the "Starting time domain car" node to the "Starting time domain public transportation" node That is, the transfer volume of cars to public transportation affected by the early tail number restriction under heavy pollution weather: Where x represents the trip cancellation rate of car users in the start time domain or peak time domain; Net inflow from the "Peak-hour Cars" node to the "Peak-hour Public Transport" node Calculate the change in demand for the public transportation system: Since there is no additional card swiping transaction for transfers within the subway system, when all passengers using buses also take the subway, the subway card swiping volume is the lower limit of the public transportation usage. Therefore, considering the passengers taking buses but not using the subway, it is obtained that: Among them, W N , W H respectively represent the lower limits of public transportation usage in non-heavily polluted and heavily polluted weather; η ∈ (0, 1) is the conversion coefficient between the number of card swipes and the number of rides, and the larger the value, the fewer times the traveler uses the bus; therefore, compared with non-heavily polluted weather, the change in demand for the public transportation system ΔW H is as follows: According to the principle of flow conservation between relevant nodes of public transportation, ΔW H is also expressed as: Where δ represents the transfer volume from slow traffic to public transportation in the start time domain and peak time domain under heavy pollution weather; After substituting formulas (1)-(5) into formula (6), it is obtained that: Obtain the car transfer volume model affected by the early tail number restriction under heavy pollution weather; Since the traffic restriction will force car travelers to switch to public transportation, and the early traffic restriction will increase the time range of the traffic restriction, making more car travelers switch to public transportation. Therefore, within the start time domain and peak time domain, the transfer volume from cars to public transportation is not negative. Thus, from equation (6), we can obtain: After substituting it into equation (7), we get: Finally, combining equation (2) gives the car transfer volume under the influence Obtain the slow traffic transfer volume model affected by the early tail number restriction under heavy pollution weather: On the one hand, considering Equation (8), it follows that δ ≤ ΔW H ; on the other hand, affected by heavy pollution and early traffic restrictions, some car users will cancel their trips, that is, the trip cancellation rate x ≥ 0. Combining Equation (7), we will have Therefore, finally, the affected slow traffic transfer volume δ can be obtained as follows:

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