CN106127328A - Cold district ice and snow phase resident trip method optimizing collocation method - Google Patents

Abstract

The present invention provides a kind of cold district ice and snow phase resident trip method optimizing collocation method, Comprehensive Benefit Evaluation is carried out from traffic safety benefit, Saving in time costs and payment cost effectiveness tripartite in the face of cold district ice and snow phase resident various trip mode such as private car, taxi, bus, track traffic, bicycle and walking etc., and based on operational research theory, it is object function to the maximum with trip benefit, sets up cold district ice and snow phase resident trip model of structural optimization with the key influence factor of different trip modes for constraints.The resident trip different modes ratio after optimization can be drawn in actual applications by solving-optimizing model, compared with original cold district ice and snow phase resident trip structure, propose to consider the cold district ice and snow phase optimum trip mode ratio combination of efficiency, safety and economy, can promote the overall benefit that cold district urban transportation is run simultaneously, objectively reach to alleviate traffic congestion, reduce the effect of driving accident rate by distributing trip mode rationally.

Description

Cold district ice and snow phase resident trip method optimizing collocation method

Technical field

The present invention relates to a kind of cold district ice and snow phase resident trip method optimizing collocation method.

Background technology

Resident trip mode structure in different areas, different seasons had bigger by the combined influence of many factors Difference；China is vast in territory, has the territory that sheet belongs to Han Qu；Wherein with high latitude areas such as the Northeasts as representative, one Having nearly six months among Nian is very long winter, and temperature is on the low side, and vehicle passes through at urban road can be by long-time nival zone The harmful effect come, causes the resident in these areas comprehensively to examine according to the road of trip, weather condition when selecting line mode The factors such as safety, efficiency, accessibility and the economy of worry trip.

Factor owing to being considered when cold district ice and snow district resident selects line mode is more, necessarily causes resident trip structure There are differences with the resident trip structure under normal weather, road conditions, and choosing during in order to guide cold district's ice and snow phase resident trip Select the trip mode that comprehensive benefit is maximum, and then carry High-cold regions ice and snow phase urban transportation overall benefit, need to imitate from traffic safety The aspect various trip modes such as private car, taxi, the public transport to the cold district ice and snow phase such as benefit, Saving in time costs, payment cost effectiveness Car, track traffic, bicycle (containing electric bicycle) and walking etc. carry out Comprehensive Benefit Evaluation.

Summary of the invention

Based on problem above, the purpose of the present invention is that the ice and snow phase resident trip method optimizing configuration of offer one cold district Method.This method can provide foundation for the trip mode guiding cold district ice and snow phase traveler to select benefit maximum, promotes whole city City's traffic transportation efficiency.

The present invention is achieved through the following technical solutions above-mentioned purpose:

A kind of cold district ice and snow phase resident trip method optimizing collocation method, as follows:

Step 1, the foundation of cold district ice and snow phase transport structure Optimized model object function:

$\max B=\underset{i=1}{\overset{6}{\Σ}}{B}_i$

Wherein

B_i=B_Si+B_Ti+B_Ei

$B_{Si}=\frac{PE\·PA\·(P_i-P_{i0})\·P_{ai}\·L_a}{z_i}$

B_Ti=PE PA (P_i-P_i0)·T_i·G_T

B_Ei=PE PA (P_i-P_i0)·C_i

$B_i=PE\·PA\·(P_i-P_0)\·(\frac{P_{ai}\·L_a}{z_i}+T_i\·G_T+C_i)$

B trip total benefit B, Wan Yuan in formula；

B_SiTraffic safety benefit, Wan Yuan；

B_TiSaving in time costs, Wan Yuan；

B_EiPay cost effectiveness, Wan Yuan；

PE trembles with fear district's urbanite's number, people；

PA trembles with fear district's ice and snow phase resident day trip rate per capita, person-time/(man day)；

P_iCold district ice and snow phase difference trip mode ratio, %, 1-private car, 2-taxi, 3-regular public traffic, 4-rail Road traffic, 5-bicycle, containing electric bicycle, 6-walking；

P_i0Present situation difference travel mode split rate, %；

P_aThe probability that the ice and snow phase vehicle accident of cold district occurs；

L_aThe average loss of every vehicle accident, ten thousand yuan/time；

z_iThe average carrying number of different trip modes, p/veh；

T_iThe average travel time of different trip modes, h/p；

G_TCalculate the area total output value per capita in the time limit, ten thousand yuan/hour；

C_iThe travel cost of different mode of transportation per persons, ten thousand yuan/person-time.

Step 2, constraints:

(2.1) trip distance constraint

Different trip sides under the conditions of identical resident trip wish, before and after cold district ice and snow phase Urban transport structure optimization The total trip distance of formula should be equal, introduces different trip mode average trip distance L_i, by cold district ice and snow phase urban transportation trip away from From constraints be expressed as follows:

$\underset{i=1}{\overset{6}{\Σ}}{L}_i\·P_i\·PA\·PE=\underset{i=1}{\overset{6}{\Σ}}{L}_i\·P_{i0}\·PA\·PE$

(2.2) travel time constraint

On the premise of trip distance is equal, the different trip modes after cold district ice and snow phase resident trip method optimizing always go out The row time should no more than optimize before total travel time of different trip modes:

$\underset{i=1}{\overset{6}{\Σ}}{T}_i\·P_i\·PA\·PE\≤\underset{i=1}{\overset{6}{\Σ}}{T}_i\·P_{i0}\·PA\·PE$

(2.3) public transport capacity constraint

Including regular public traffic and track traffic two kinds, with the public transport peak hour volume of the flow of passengers setting less than public transport Meter capacity is constraints, obtains cold district ice and snow phase urban public transport peak hour capacity constraints as follows:

PE·PA·P_i·K_i≤C_Oi·m_i

K in formula_iDifferent public transport peak hour car flow of the people ratios, %；

m_iThe i-th kind of vehicle for public transport of unit interval peak hour is dispatched a car number ,/hour；

C_OiI-th kind of public transport appraises and decides handling capacity of passengers, and people/.

(2.4) share of public transportation constraint

For regular public traffic and track traffic both transit trip modes, it is considered to public transport Ying Han district ice and snow The resident trip share rate of phase should step up, and therefore bus trip share rate is more than 32% by the present invention, constructed public friendship It is logical that trip proportion is minimum is constrained to:

$\underset{i=1}{\overset{2}{\Σ}}{P}_i\≥32\%$

When share of public transportation rises, the decline of this trip side of taxi ratio can be caused, taxi is gone out The minimum constraint of row ratio is configured to:

P_i>=3%

(2.5) bicycle trip proportion constraint

Bicycle after cold district ice and snow phase structure of mode share optimizes should be not less than excellent with walking ratio containing electric bicycle Trip proportion before change, constraint builds as follows:

(2.5.1) bicycle, containing electric bicycle:

P₅≥P₅₀

(2.5.2) walking:

P₆≥P₆₀。

Step 3, drawn the resident trip different modes ratio after optimization by solving-optimizing model, with original cold district ice Snow phase resident trip structure is compared, and proposes to consider the cold district ice and snow phase optimum trip mode ratio of efficiency, safety and economy Combination.

The beneficial effects of the present invention is:

The present invention is that one is target to the maximum with higher trip benefit, effectively to cold district ice and snow phase resident for realizing resident Travel components are optimized the method for configuration.Compared with original cold district ice and snow phase resident trip structure, propose to consider effect Rate, the cold district ice and snow phase optimum trip mode ratio combination of safety and economy, can be that communications policy provides science with administration section Reference, provides foundation for it when guiding cold district resident to select the highest trip mode of comprehensive benefit, can promote Han Qu simultaneously The overall benefit that urban transportation is run, objectively by distributing rationally, trip mode reaches to alleviate traffic congestion, thing is driven a vehicle in reduction Therefore the effect of rate.

Detailed description of the invention

Below cold district ice and snow phase resident trip method optimizing collocation method is illustrated.

Embodiment 1

Object function:

The foundation of cold district ice and snow phase transport structure Optimized model object function, is angle based on traveler, considers The different safety benefit of trip mode, Saving in time costs, payment cost effectivenesses, by cold district ice and snow phase urban transportation travel components Reasonable disposition, it is achieved full mode trip total benefit B (ten thousand yuan) is maximum；And then promote the high efficiency operation of traffic system and Gao Shui Flat service, the ultimate aim for traffic and transportation system sustainable development realizes laying the foundation.Cold district ice and snow phase urban transportation knot Structure optimization object function is:

$\max B=\underset{i=1}{\overset{6}{\Σ}}{B}_i$

Wherein

B_i=B_Si+B_Ti+B_Ei

$B_{Si}=\frac{PE\·PA\·(P_i-P_{i0})\·P_{ai}\·L_a}{z_i}$

B_Ti=PE PA (P_i-P_i0)·T_i·G_T

B_Ei=PE PA (P_i-P_i0)·C_i

$B_i=PE\·PA\·(P_i-P_{i0})\·(\frac{P_{ai}\·L_a}{z_i}+T_i\·G_T+C_i)$

B in formula_SiTraffic safety benefit, Wan Yuan；

B_TiSaving in time costs, Wan Yuan；

B_EiPay cost effectiveness, Wan Yuan.

PE trembles with fear district's urbanite's number, people；

PA trembles with fear district's ice and snow phase resident day trip rate per capita, person-time/(man day)；

P_iCold district ice and snow phase difference trip mode ratio, %, 1-private car, 2-taxi, 3-regular public traffic, 4-rail Road traffic, 5-bicycle, containing electric bicycle, 6-walking；

P_i0Present situation difference travel mode split rate, %；

P_aThe probability that the ice and snow phase vehicle accident of cold district occurs；

L_aThe average loss of every vehicle accident, ten thousand yuan/time；

z_iThe average carrying number of different trip modes, p/veh；

T_iThe average travel time of different trip modes, h/p；

G_TCalculate the area total output value per capita in the time limit, ten thousand yuan/hour；

C_iThe travel cost of different mode of transportation per persons, ten thousand yuan/person-time.

Constraints:

1) trip distance constraint

Different trip sides under the conditions of identical resident trip wish, before and after cold district ice and snow phase Urban transport structure optimization The total trip distance of formula should be equal.Introduce different trip mode average trip distance L_i, Ke Jianghan district ice and snow phase urban transportation is gone on a journey The constraints of distance is expressed as follows:

$\underset{i=1}{\overset{6}{\Σ}}{L}_i\·P_i\·PA\·PE=\underset{i=1}{\overset{6}{\Σ}}{L}_i\·P_{i0}\·PA\·PE$

2) travel time constraint

Travel time is the considerations occupying critical role in resident's go off daily, different trip modes due to The difference of its speed of service and make its travel time different, i.e. operational efficiency there are differences.In the premise that trip distance is equal Under, the different trip modes total travel time after cold district ice and snow phase resident trip method optimizing should no more than optimize front different trip Total travel time of mode:

$\underset{i=1}{\overset{6}{\Σ}}{T}_i\·P_i\·PA\·PE\≤\underset{i=1}{\overset{6}{\Σ}}{T}_i\·P_{i0}\·PA\·PE$

3) public transport capacity constraint

Public transport is crowded significant for solution urbanite's go off daily, alleviation urban road, Er Qie great Power Development of Urban public transport is also a policy of current city transport development.But owing to public transport capacity is limited, and And in different cities, the area model of mass transportation facilities, classification would also vary from, its capacity is also not quite similar, mainly Consider regular public traffic and track traffic two kinds.With the public transport peak hour volume of the flow of passengers less than the operation capacity designed of public transport it is Constraints, obtains cold district ice and snow phase urban public transport peak hour capacity constraints as follows:

PE·PA·P_i·K_i≤C_Oi·m_i

K in formula_iDifferent public transport peak hour car flow of the people ratios, %；

m_iThe i-th kind of vehicle for public transport of unit interval peak hour is dispatched a car number ,/hour；

C_OiI-th kind of public transport appraises and decides handling capacity of passengers, and people/.

4) share of public transportation constraint

For regular public traffic and track traffic both transit trip modes, it is considered to public transport Ying Han district ice and snow The resident trip share rate of phase should step up, and therefore bus trip share rate is more than 32% by the present invention, constructed public friendship It is logical that trip proportion is minimum is constrained to:

$\underset{i=1}{\overset{2}{\Σ}}{P}_i\≥32\%$

When share of public transportation rises, the decline of this trip side of taxi ratio can be caused unavoidably, it is considered to go out Hire a car the factor such as agility of trip, minimum for trip of taxi ratio constraint be configured to:

P_i>=3%

5) bicycle trip proportion constraint

For bicycle (containing electric bicycle) and walking both travel modals, due to cold district resident trip structure Middle walking occupies larger proportion, bicycle (containing electric bicycle) also due to its easily trip characteristics make short distance go on a journey In have certain proportion formula.Bicycle (containing electric bicycle) after cold district ice and snow phase structure of mode share optimizes should with walking ratio Not less than the trip proportion before optimizing, constraint builds as follows:

(1) bicycle (containing electric bicycle):

P₅≥P₅₀

(2) walking:

P₆≥P₆₀

Draw the resident trip different modes ratio after optimization by solving-optimizing model, occupy with original cold district ice and snow phase People's travel components are compared, and propose to consider the cold district ice and snow phase optimum trip mode ratio combination of efficiency, safety and economy.

Embodiment 2

With reference to related data and survey data, ice and snow phase variable-value in Harbin City's is as follows:

Table 1 Partial Variable value

Different trip mode present situation ratio P of table 2_i0(%)

Table 3 trip mode average seating capacity z_i(p/veh)

Consumption T during table 4 different trip mode average travel_i(h/p)

Different trip mode travel cost C of table 5_i(ten thousand yuan/person-time)

The different public transport parameter of table 6

Above-mentioned data being substituted in Optimized model, gained object function is:

MaxB=961.73 (29.80P₁+43.82P₂+34.20P₃+34.71P₄+16.27P₅+12.74P₆)-21580.31

Constraints is:

$s.t.\left\{\begin{array}{c}11.3P_1+10.86P_2+9.29P_3+11.62P_4+3.97P_5+0.97P_6=5.73\\ 0.61P_1+0.6P_2+0.86P_3+0.83P_4+0.42P_5+0.33P_6\≤0.53\\ 103P_3\≤30.77\\ 106P_4\≤2.43\\ P_3+P_4\≥0.32\\ P_2\≥0.03\\ P_5\≥0.07\\ P_6\≥0.35\\ P_1+P_2+P_3+P_4+P_5+P_6\≤1\\ P_i\≥0,i=1,2,3,4,5,6\end{array}\right.$

MATLAB is used to solve；Show that ice and snow phase difference trip mode in Harbin advises ratio after optimizing in conjunction with practical situation As shown in table 7.Comprehensive benefit after optimization is about 2,270,000 yuan/day, it was demonstrated that ice and snow phase resident trip method optimizing configuration side of cold district Method has feasibility.

Different trip mode Optimizing Suggestions ratio P of table 7_i(%)

The different efficiencies of travel modal, safety and economy are not quite similar, and these indexs are carried out at quantization After reason, based on operational research theory, to go on a journey, benefit is object function to the maximum, is about with the key influence factor of different trip modes Bundle condition sets up cold district ice and snow phase resident trip model of structural optimization.Can be drawn by solving-optimizing model in actual applications Resident trip different modes ratio after optimization, can be that communications policy provides science reference with administration section so that it is cold guiding District resident selects to have during the highest trip mode of comprehensive benefit certain foundation, can reach by distributing trip mode rationally simultaneously Alleviate traffic congestion, the effect of reduction driving accident rate, promote the overall benefit that cold district urban transportation is run.Imitate from traffic safety Benefit, Saving in time costs, payment cost effectiveness three aspect build cold district ice and snow phase travel modal benefit calculation model, object function From traveler angle, being optimized cold district ice and snow phase urban transportation travel components, resident is acceptable to optimum results Higher, there is the strongest practical significance；From resident trip distance and travel time, public transport capacity, each trip mode minimum scale Etc. aspect build constraints, to full mode trip total benefit retrain.By on-site inspection, with private car, taxi, public affairs Handing over car, track traffic, bicycle (containing electric bicycle), walking is optimization object, to road traffic and track traffic composition Composite communications transport system is optimized, and does not increase new traffic and puts into, on existing traffic infrastructure, by existing The integration of road traffic resource, it is achieved distributing rationally of resource.

Claims (1)

1. a Zhong Han district ice and snow phase resident trip method optimizing collocation method, it is characterised in that method is as follows: step 1, cold district ice The foundation of snow phase transport structure Optimized model object function:

$\max B=\underset{i=1}{\overset{6}{\Σ}}{B}_i$

Wherein

B_i=B_Si+B_Ti+B_Ei

$B_{\mathrm{Si}}=\frac{\mathrm{PE}\·\mathrm{PA}\·(P_i-P_{i0})\·P_{\mathrm{ai}}\·L_a}{z_i}$

B_Ti=PE PA (P_i-P_i0)·T_i·G_T

B_Ei=PE PA (P_i-P_i0)·C_i

$B_i=PE\·PA\·(P_i-P_{i0})\·(\frac{P_{ai}\·L_a}{z_i}+T_i\·G_T+C_i)$

B trip total benefit B, Wan Yuan in formula；

B_SiTraffic safety benefit, Wan Yuan；

B_TiSaving in time costs, Wan Yuan；

B_EiPay cost effectiveness, Wan Yuan；

PE trembles with fear district's urbanite's number, people；

PA trembles with fear district's ice and snow phase resident day trip rate per capita, person-time/(man day)；

P_iCold district ice and snow phase difference trip mode ratio, %, 1-private car, 2-taxi, 3-regular public traffic, 4-track is handed over Logical, 5-bicycle, containing electric bicycle, 6-walking；

P_i0Present situation difference travel mode split rate, %；

P_aThe probability that the ice and snow phase vehicle accident of cold district occurs；

L_aThe average loss of every vehicle accident, ten thousand yuan/time；

z_iThe average carrying number of different trip modes, p/veh；

T_iThe average travel time of different trip modes, h/p；

G_TCalculate the area total output value per capita in the time limit, ten thousand yuan/hour；

C_iThe travel cost of different mode of transportation per persons, ten thousand yuan/person-time.

Step 2, constraints:

(2.1) trip distance constraint

Under the conditions of identical resident trip wish, the different trip modes before and after cold district ice and snow phase Urban transport structure optimization are total Trip distance should be equal, introduces different trip mode average trip distance L_i, by cold district ice and snow phase urban transportation trip distance Constraints is expressed as follows:

$\underset{i=1}{\overset{6}{\Σ}}{L}_i\·P_i\·PA\·PE=\underset{i=1}{\overset{6}{\Σ}}{L}_i\·P_{i0}\·PA\·PE$

(2.2) travel time constraint

On the premise of trip distance is equal, when the different trip modes after cold district ice and snow phase resident trip method optimizing are always gone on a journey Between should no more than optimize before total travel time of different trip modes:

$\underset{i=1}{\overset{6}{\Σ}}{T}_i\·P_i\·PA\·PE\≤\underset{i=1}{\overset{6}{\Σ}}{T}_i\·P_{i0}\·PA\·PE$

(2.3) public transport capacity constraint

Including regular public traffic and track traffic two kinds, transport less than the design of public transport with the public transport peak hour volume of the flow of passengers Can be constraints, obtain cold district ice and snow phase urban public transport peak hour capacity constraints as follows:

PE·PA·P_i·K_i≤C_Oi·m_i

K in formula_iDifferent public transport peak hour car flow of the people ratios, %；

m_iThe i-th kind of vehicle for public transport of unit interval peak hour is dispatched a car number ,/hour；

C_OiI-th kind of public transport appraises and decides handling capacity of passengers, and people/.

(2.4) share of public transportation constraint

For regular public traffic and track traffic both transit trip modes, it is considered to the public transport Ying Han district ice and snow phase Resident trip share rate should step up, and therefore the present invention is by bus trip share rate more than 32%, and constructed public transport goes out Row ratio is minimum to be constrained to:

$\underset{i=1}{\overset{2}{\Σ}}{P}_i\≥32\%$

When share of public transportation rises, the decline of this trip side of taxi ratio can be caused, by trip of taxi ratio The minimum constraint of example is configured to:

P_i>=3%

(2.5) bicycle trip proportion constraint

Cold district ice and snow phase structure of mode share optimize after bicycle containing electric bicycle and walking ratio should be not less than optimizing before Trip proportion, constraint build as follows:

(2.5.1) bicycle, containing electric bicycle:

P₅≥P₅₀

(2.5.2) walking:

P₆≥P₆₀。

Step 3, drawn the resident trip different modes ratio after optimization by solving-optimizing model, with original cold district ice and snow phase Resident trip structure is compared, and proposes to consider the cold district ice and snow phase optimum trip mode ratio combination of efficiency, safety and economy.