A list,

With the development of urban construction, urban roads are getting wider and wider, and the road traffic network is getting more and more complex

There are also more diversified ways for people to travel, from walking, cycling, bus, taxi, to today’s private

Car, subway, high-speed rail and so on. However, the increase in means of travel has not improved road traffic

Traffic congestion problem is increasingly prominent. Therefore, how to optimize the road network structure and improve the density of road network urgently

To solve. Due to the immobilization of urban roads, improving urban roads to relieve traffic pressure has little effect.

And people’s understanding of open community provides an effective means for improving road capacity. Research on small

The influence of district opening on road capacity is a research direction with both theoretical significance and practical value.

For the first problem, it is necessary to establish a reasonable index system to analyze the opening capacity of the community to the surrounding roads

Through the selection principle of indicators, we selected 5 secondary indicators and 18 tertiary indicators, and

According to the nature of the index, the index is divided into qualitative index and quantitative index. First of all, we qualitative the indicators

It is concluded that indicators such as road network density have a role in promoting road capacity, and the number of intersections refers to

The standard acts as a depressant; Secondly, we carry on quantitative analysis to the index, and use entropy weight method to advance the three-level index

A simple weighted average operator is used to fuse the data, so as to establish the open road access of the community

A quantitative evaluation model for the impact of performance ability. Finally, the synthetic qualitative and quantitative analysis shows that the established index system can

It can effectively evaluate the influence of community opening on the traffic capacity of surrounding roads.

For the second problem, in order to obtain the optimal mathematical model of vehicle passage and the influence of community opening on the surrounding roads

Ring. Based on problem 1, this paper constructs the optimization model of the influence index of the neighborhood opening to the surrounding road capacity

At the same time, considering that the opening of the community will affect the cost of the road network, we choose the system manager and the traveler

The user balance (UE) model and system optimization (SO) model after cell opening are proposed based on this

For these models, we build a multi-objective optimization model of vehicle passage, considering that UE model only considers static bars

There are some limitations in the network traffic distribution under the component. Therefore, we construct the traffic based on UE model

Finally, a detailed flow chart is given to solve the model.

For the third problem, the road structure inside the community, the road structure around the community, and the traffic flow of the community

Will have different impacts on the opening effect of the community. In this case, we are based on the road inside the community

Structure will be divided into a font, cross and ring type of the community, according to the neighborhood of the road structure will be small

Is the network, tree, ring and strip shaped community, and considering the different traffic flow near the community in different time periods,

For different types of residential areas, we set the total traffic flow in advance, and then according to the MSA algorithm to get each

Finally, according to the model in question two, the comprehensive evaluation of road capacity is calculated

The number. In the morning rush hour of every day, the circular community has the largest composite index and the best traffic capacity, followed by ten

In the same way, the opening effect of circular community is the most obvious, followed by

Mesh cell, tree cell and strip cell.

For question 4, construct the road design and planning model in the community, and make every road to be built in the community

It is assumed that the roads in the open cell in the model in question 2 are all marked as 0-1 variables and pass

By solving the optimization model, we can get which road in the community has the most influence on improving road capacity

Large, finally, the synthesis of our previous research results, to provide urban planning and traffic management departments about small

Detailed proposals for the impact of district opening on the capacity of surrounding roads.

Ii. Source code

%% The state of each grid has three kinds: % use1To denote normal moving vehicles,- 3Represents the vehicles that turn into the community,0Represents empty space,- 888.%% Initialize the running space. Clear all; %clc; warning off; dbstopif error
W = 0; %% Model main parameter red_light_time =60; % Red light time green_light_time =40; % Green time fresh_frequency =0.1; % Refresh rate num_of_street =4; % The number of roads in the community, i.e. the number of intersections. % defines the global variable lane length pixellength =30; % Length of main channel side_length =25; % Cell side length %% Variable used for statistics global speed_index speed_index=0;
loop_times = 10; % number of cycles; time_step_length = loop_times*(red_light_time+green_light_time)/2;
avr_move_steps = ones(1,time_step_length);
store_num_of_cars = ones(1,time_step_length);
store_num_of_jam_cars = ones(1,time_step_length);
avr_mainroad_move_steps = ones(1,time_step_length); %% generates the variables needed to run the improved N-S model. B = side_length+1;
L = 1; pixel = create_pixel(B,L,side_length); % Generate cell space state matrix pixel = create_street(pixel, num_of_street+1,side_length); Pixel_speed = zeros(size(pixel)); % Speed matrix of the trolley, temp_handle = show_pixel(pixel,B,NaN); % display cell matrix %% cycle refresh every step of the image, statistics.for i = 1:loop_times
    waiting_time = 0;
    output = 0;
    entry = 0;
    traffic_capacity = 0;
    if mod(i,2)~=0
        pixel(end,end- 1) = 0; The red light turns greenfor xx=1:green_light_time [pixel,pixel_speed,move_step,num_of_cars,num_of_jam_cars,avr_mainroad_move_step] = go_forward(pixel,pixel_speed); % forward rule [pixel,pixel_speed] = new_cars(pixel,1,pixel_speed); % Adds the generated vehicle to the cellular space matrix entry = Entry +1; %waiting_time = waiting_time + compute_wait(pixel); % % of total sum o time = = = = = = = = = = = = = = temp_handle = show_pixel (pixel, B, temp_handle); Drawnow refresh image % % % = = = = = = = = = = = = = = pixel = clear_boundary (pixel); % Vehicles leaving the system need to be removed from the system %k = k+1;
            pause(fresh_frequency);
            speed_index=speed_index+1;
            avr_move_steps(speed_index)=move_step;
            store_num_of_cars(speed_index) = num_of_cars;
            store_num_of_jam_cars(speed_index)=num_of_jam_cars;
            avr_mainroad_move_steps(speed_index)=avr_mainroad_move_step;
        end
    elsepixel = red_light_on(pixel); The green light turns redfor xx=1:red_light_time [pixel,pixel_speed,move_step,num_of_cars,num_of_jam_cars,avr_mainroad_move_step] = go_forward(pixel,pixel_speed); % forward rule [pixel,pixel_speed] = new_cars(pixel,1,pixel_speed); % Add the generated vehicle to the cell space matrix temp_handle = show_pixel(pixel,B,temp_handle); % Update imagedrawnow
            pause(fresh_frequency);
            pixel = clear_boundary(pixel);
            speed_index=speed_index+1; avr_move_steps(speed_index)=move_step; store_num_of_cars(speed_index) = num_of_cars; store_num_of_jam_cars(speed_index)=num_of_jam_cars; avr_mainroad_move_steps(speed_index)=avr_mainroad_move_step; End end end %% drawing with statistics hold off; time_series = linspace(1,time_step_length,time_step_length);
show_pixel(pixel,B,temp_handle);
figure(2);
% title('Average speed');
% xlabel('time step')
% ylabel('Average distance travelled per vehicle')
para = robustfit(time_series,avr_move_steps);
xdata = [ones(size(time_series,2),1) time_series'];
regress_avr_move_steps=xdata*para; 
%fitresult=createFit(avr_move_steps);
temp_handle=plot(avr_move_steps);
legend( temp_handle, 'Average distance travelled per vehicle' );
hold on;
%plot(fitresult);
title('Average speed');
xlabel('time step')
ylabel('Average distance travelled per vehicle')
hold off
figure(3);
% title('Number of vehicles located on the map');
% xlabel('time step')
% ylabel('Number of vehicles')
temp_handle=plot(store_num_of_cars);
legend( temp_handle, 'Number of vehicles located on the map' );
title('Number of vehicles located on the map');
xlabel('time step')
ylabel('Number of vehicles')
figure(4);
temp_handle=plot(store_num_of_jam_cars);
legend( temp_handle, 'Blocked vehicle' );
title('Number of vehicles blocked');
xlabel('time step')
ylabel('Number of vehicles')
fprintf('Cell side length: % I \n',side_length);
fprintf('Main road Length: % I \n',pixellength);
fprintf('Number of roads: % I \n',num_of_street);
fprintf('The average speed of a vehicle within a traffic light cycle is: %f grid hour \n',mean(avr_move_steps(end-(red_light_time+green_light_time):end)));
fprintf('The average speed of a car on the main road during a traffic light cycle is: %f grid hour \n',mean(avr_mainroad_move_steps(end-(red_light_time+green_light_time):end)));
fprintf('Stable number of vehicles on the map: %f \n'.floor(mean(store_num_of_cars(end- 30:end))));
fprintf('Stable blocked vehicles in map are: %f \n'.floor(mean(store_num_of_jam_cars(end- 30:end))));
fprintf(Blocking probability of stable main path: %f \n',mean(store_num_of_jam_cars(end- 30:end))/pixellength);
Copy the code

3. Operation results

Fourth, note

Version: 2014 a