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fairbus.m
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%%%%% basic idea: load data (processed), enqueue heavy edges as seeds, expand, always choose the optimal
function fairbus(weight, k, number_of_turns, seeding_number)
%%%%%%%%%%%% parameters
%{
weight = 0.5; % the weight to balance each dimension
k = 30; % number of new edges in a path
number_of_turns = 1;% number of turns, candidates
seeding_number = 5000;% number of candidate edges as initial paths
%}
max_len = k; % maximum number of edges in a path
max_iter = 100000;
area_lable = 5;% 1: Brooklyn, 2: Manhattan, 3: Statan, 4: Queens, 5 Bronx
area_indicator = 0;% only for NYC dataset
city_choice = 0:1; % 0:1;% test two cities, nyc (1) or chicago (0)
computation_choice = 0;%0:1 full computation or fast easy bound
objectives = [];%for logs
for test_nyc = city_choice
optimization_choice = 0:2;%0:2; %0 for full optimization, 1 for all neighbors, 2 for not use domination table
%% import the graph and dataset.
stoplocations = load('./nyc_data/nyc_transit_stop_locations.mat');
neighor_stops = load('./nyc_data/new_nyc_transit_neighbors0.5_0.2_pre.mat');
K1_origin = load('./nyc_data/new_nyc_random_K1.mat');%for conn, randomly in the begining, use a fixed one
C = load('./nyc_data/nyc_transit_weighted.mat');
nodes_Number = 12340;
if ~test_nyc
area_indicator = 0;
stoplocations = load('./chi_data/chi_transit_stop_locations.mat');
neighor_stops = load('./chi_data/chi_transit_neighbors0.5_0.2.mat');
K1_origin = load('./chi_data/new_chi_random_K1.mat');%for conn, randomly in the begining, use a fixed one
C = load('./chi_data/chi_transit_weighted.mat');
end
stoplocations = stoplocations.arr;%it stores the location information of stops
%neighor_stops = load('/Users/sw160/Desktop/nyc/nyc_transit_neighbors0.5.mat');
B = neighor_stops.F;%
neighbors_node = containers.Map;%store it in the list
neighbor_number = containers.Map;%store it in the list
A = 0;
cursor = 0;
ranked_weight = [];%
for i = 1:size(B,1)%store the neighbors' index
if B(i)~=A
neighbors_node(int2str(B(i))) = i;
if i>1
neighbor_number(int2str(B(i-1))) = i - cursor - 1;
end
cursor = i;
end
A = B(i);%the first item,
end
neighbor_number(int2str(B(i))) = i - cursor;%
%% construct the transit graph
C = C.arr;
G = graph();
tic % load the graph and insert to query
for i = 1:size(C,1)
if or(C(i,1)>nodes_Number, C(i,2)>nodes_Number)%for new york dataset
continue;
end
G = addedge(G, C(i,1), C(i,2), i); % the weight is the index in D
C(i,7) = 0;
C(i,8) = 0;
C(i,9) = 0;
end
toc
A = adjacency(G);%adjacent matrix
graph_dimension = size(A,1);
%[SortC,idxC] = sort(C(:,6),'descend');%sort existing edges by the edge frequency
D = [B;C];%combine possible edges and new edges,
%% parameters and data structures for connectivity
reps = 50;% number of random vectors
iter = 10;% number of krylov iterations
K1 = K1_origin.K1;%
disp(size(K1))
disp(size(A))
tic
base = natural_connectivity(A, graph_dimension, K1, reps, iter);
toc
%% insert the heaviest unlinked edges into the queue
[SortA,idxA] = sort(D(:,6),'descend');%sort by the edge frequency
bound_weight = SortA(1:max_len);%storing the highest max_len
% disp(size(bound_weight));
lb_weight_max = 0;
for i=1:max_len
lb_weight_max = lb_weight_max + SortA(i);% choose the maximum k edges' sum for normalization
end
%% upper bound with k edges
[SortB,idx] = sort(B(:,7),'descend');%sort the edges by their connectivity increment
bound_conn = SortB(1:k);%storing the highest max_len
bottom_conn = SortB(k);%for bound estimation
connectivity_ub = 0;%
for i=1:k
connectivity_ub = connectivity_ub + SortB(i);% choose the maximum k edges' sum to compute
end
lb_conn_max = connectivity_ub;
tic
musco_bound = path_upper_bound(k, A, graph_dimension, base) - base;
toc
%% integrated equity increment
for i=1:size(D)
D(i,8) = weight*D(i,6)/lb_weight_max + (1-weight)*D(i,7)/lb_conn_max;
end
[SortA,idxA] = sort(D(:,8),'descend');%sort by the edge frequency
bound_weight = SortA(1:max_len);
bottom_weight = SortA(max_len);
estimted_running_time = 0;
time_cost_conn_compute = 0.09;
time_cost_ub_compute = 0.13;
if ~test_nyc
time_cost_conn_compute = 0.05;
time_cost_ub_compute = 0.08;
end
for full_computation = computation_choice
if full_computation==1
optimization_choice = 0;%no need to try other method.
end
%% the main algorithm part.
for optimization_indicator = optimization_choice%test three optimization
disp("running experiments on: "+k+"_"+weight+"_"+number_of_turns+"_"+seeding_number+"_"+optimization_indicator+"_"+full_computation+"_"+test_nyc);
best_path = [];% store the current results
min_equity = 0;% maximum value
n = 1;
q = PriorityQueue(1);% a priority queue to rank the candiates by the first column
checked_edges = containers.Map;% for domination on candidates have same ends
seeding_edges = containers.Map;
lb = 1;%the initial bound
for i=1:seeding_number
ix = idxA(i);
newpath = [D(ix,1) D(ix,2)];
if or(isKey(seeding_edges, D(ix,1)+","+D(ix,2)), isKey(seeding_edges, D(ix,2)+","+D(ix,1)))%avoid repetitive
continue
end
seeding_edges(D(ix,1)+","+D(ix,2)) = 1;
seeding_edges(D(ix,2)+","+D(ix,1)) = 1;
% disp(newpath);
%some edges are repetitive.
bw = max_len;
if D(ix,8) < bottom_weight
bw = bw - 1;%
lb = 1 - (bottom_weight-D(ix,8));
end
[frequency, conn, new_equity] = equity(A, newpath, graph_dimension, weight, connectivity_ub, lb_weight_max, K1, reps, iter, 0, D(ix,6),0, D(ix,7),0,base);
if new_equity > min_equity
min_equity = new_equity;
best_path = newpath;
end
if area_indicator % plan for an area
if or(area_lable ~= stoplocations(D(ix,1),4), area_lable~= stoplocations(D(ix,2),4))
continue;
end
end
q.insert([lb*-1 bw 0 frequency conn D(ix,1) D(ix,2)]);%how to indicate new edges for later update, and the last two edges
end
bad = 0;
tic
while n < max_iter && q.size()>0 % the queue is not empty
candidate = q.remove();
lower_bound = candidate(1)*-1;% the potential
if min_equity > lower_bound% current result already smaller than the rest potential
break
end
size_path = size(candidate);
bw = candidate(2); % cursor for upper bound
bc = candidate(3); % record number of turns
frequency = candidate(4);% current frequency (demand)
conn = candidate(5);% current conn
path = candidate(6:size_path(2));%the path, new bound
% disp(n+" "+min_equity+" " + lower_bound+ " " +length(best_path)+" "+q.size()); %the lower bound
if mod(n,100) == 0
cc = n/100;
objectives(cc,optimization_indicator+1+test_nyc*3) = min_equity;
end
last_node = candidate(size_path(2));
first_node = candidate(6);
N = transpose(neighbors(G,last_node));%get existing neighbor in the graph
ne = [];
for i=1:length(N)
ne = [ne, size(B) + G.Edges.Weight(findedge(G,last_node,N(i)))];%locate the edge
end
TF = isKey(neighbors_node, int2str(last_node));
if TF==1%access the linked possible,
for i = 0: neighbor_number(int2str(last_node))%get possible edge
ne = [ne, neighbors_node(int2str(last_node))+i];%add new the neighbor lists
end
end
max_increment = 0;
best_xx = 0;
array = [];
bcc = bc;
best_neighbor = 0;
for i = 1: length(ne)% check all the neighbors, choose the one with maximum increment
index_neighbor = ne(i);
xx = D(index_neighbor, 2);
if D(index_neighbor, 2) == last_node %avoid the edge from graph
xx = D(index_neighbor, 1);
end
if area_indicator % plan for an area
if area_lable ~= stoplocations(xx,4)
continue;
end
end
if ismember(xx,path)%circle-free, not sure it i
continue
end
new_bc = bc;
last_s_node = candidate(size_path(2)-1);
anlges = abs(angle(stoplocations(last_node, 3), stoplocations(last_node, 2), stoplocations(xx, 3), stoplocations(xx, 2), stoplocations(last_s_node, 3), stoplocations(last_s_node, 2))*180/pi);
if anlges < 90 % big turn, not allow, as it will go back
continue;
end
if anlges < 135 % small turn, can have some
new_bc = bc+1;
end
if new_bc > number_of_turns
continue
end
% optional
newpath = [path, xx];% add to the last end
K1 = K1_origin.K1;%
[newfre, newconn, new_equity] = equity(A, newpath, graph_dimension, weight, connectivity_ub, lb_weight_max, K1, reps, iter, frequency, D(index_neighbor,6),conn, D(index_neighbor, 7), full_computation,base);
estimted_running_time = estimted_running_time + time_cost_conn_compute;
if new_equity > min_equity
min_equity = new_equity;
best_path = newpath;
best_array = candidate;
best_array(4) = newfre;
best_array(5) = newconn;
end
%choose the one with maximum increment on equity
incrementcc = D(index_neighbor,8);
if full_computation
incrementcc = new_equity;
end
if incrementcc > max_increment
max_increment = incrementcc;
best_neighbor = index_neighbor;
best_xx = xx;
bcc = new_bc;
% array = [lb*-1 new_bw bc start_angle D(index_neighbor,9) newfre newconn, newpath];
end
end
if max_increment>0
path = [path, best_xx];
bottom_weight = bound_weight(bw);
if D(best_neighbor,8) < bottom_weight
bw = bw - 1;%
lower_bound = lower_bound - (bottom_weight - D(best_neighbor,8));
end
frequency = frequency + D(best_neighbor,6)/lb_weight_max;
conn = conn + D(best_neighbor,7)/lb_conn_max;
bc = bcc;
else
bad = bad+1;
end
if bw<1 % no more edges to be added, as it aready has k edges
continue
end
N = transpose(neighbors(G,first_node));%get existing neighbors in the graph
ne = [];
for i=1:length(N)
ne = [ne, size(B)+G.Edges.Weight(findedge(G,first_node,N(i)))];%locate the edge in D
end
TF = isKey(neighbors_node, int2str(first_node));
if TF==1%access the linked possible,
for i = 0: neighbor_number(int2str(first_node))%get possible edge
ne = [ne, neighbors_node(int2str(first_node))+i];%add new the neighbor lists
end
end
max_increment = 0;
array = [];
for i = 1: length(ne)
index_neighbor = ne(i);
xx = D(index_neighbor, 2);
if D(index_neighbor, 2) == last_node %avoid the edge from graph
xx = D(index_neighbor, 1);
end
if area_indicator % plan for an area
if area_lable ~= stoplocations(xx,4)
continue;
end
end
if ismember(xx,path)%circle-free
continue
end
new_bc = bc;
last_s_node = candidate(7);
anlges = abs(angle(stoplocations(first_node, 3), stoplocations(first_node, 2), stoplocations(xx, 3), stoplocations(xx, 2), stoplocations(last_s_node, 3), stoplocations(last_s_node, 2))*180/pi);
if anlges < 90 % not going back, pruned
continue;
end
if anlges < 135 % one more turn in the route,
new_bc = bc+1;
end
if new_bc > number_of_turns
continue
end
newpath = [xx, path];%add to the start position
K1 = K1_origin.K1;%
[newfre, newconn, new_equity] = equity(A, newpath, graph_dimension, weight, connectivity_ub, lb_weight_max, K1, reps, iter, frequency, D(index_neighbor,6), conn, D(index_neighbor, 7),full_computation,base);
estimted_running_time = estimted_running_time + time_cost_conn_compute;
if optimization_indicator ~= 2 % use domination table
if xx < last_node
checking = xx +"_"+last_node; %% adding to checking lists
else
checking = last_node + "_"+ xx; %% adding to checking lists
end
if isKey(checked_edges, checking)==1
if checked_edges(checking) > new_equity
continue;
else
checked_edges(checking) = new_equity;
end
else
checked_edges(checking) = new_equity;
end
end
if new_equity > min_equity
min_equity = new_equity;
best_path = newpath;
best_array = candidate;
best_array(4) = newfre;
best_array(5) = newconn;
end
%choose the one with maximum increment on equity
incrementcc = D(index_neighbor,8);
if full_computation
incrementcc = new_equity;
end
if or(incrementcc > max_increment, optimization_indicator) % only use maximum, or use every one
max_increment = incrementcc;
lb = lower_bound;
bottom_weight = bound_weight(bw);
if D(index_neighbor,8) < bottom_weight
new_bw = bw - 1;%
lb = lb - (bottom_weight-D(index_neighbor,8));
end
if new_bw >= 1
array = [lb*-1 new_bw new_bc newfre newconn newpath];
end
if optimization_indicator == 1
if length(array) < (5 + max_len) && length(array) >= 7
q.insert(array); % insert every neighbor
end
end
end
end
if max_increment > 0 && optimization_indicator ~= 1
if length(array) < (5 + max_len) && length(array) >= 7
if -array(1) > min_equity
q.insert(array);% only insert the best
end
estimted_running_time = estimted_running_time + time_cost_ub_compute;
end
else
bad = bad+1;
a = 0;% no more edges into the queue, just skip this process
end
n = n+1;
end
disp("#bad:" + bad);
disp("we found a path: ");
disp(best_path);
count_new_edges = 0;
fileIDnew = fopen('./new_edge_b.txt','w');
A_temp = A;
content = 'geometry\n"{""type"": ""LineString"", ""coordinates"": [';
teb = 0;
for i=1:size(best_path,2)-1%count the number of new edges, we can also recompute the connectivity
if A(best_path(i),best_path(i+1)) == 0
id = best_path(i);
count_new_edges = count_new_edges + 1;
fprintf(fileIDnew,'[%3.6f,%3.6f],',stoplocations(id,3), stoplocations(id,2));
end
A_temp(best_path(i),best_path(i+1)) = 1;
A_temp(best_path(i+1),best_path(i)) = 1;
for adsad = 1:size(D,1)
if best_path(i) == D(adsad, 1) && best_path(i+1) == D(adsad, 2)
teb = teb + D(adsad, 7)/connectivity_ub;
end
end
end
% fprintf(fileIDnew,'[%3.6f,%3.6f]]}"\n',stoplocations(length(best_path),3), stoplocations(length(best_path),2));
disp("increased equity: "+min_equity);
disp("#new edges: "+count_new_edges);
base1 = natural_connectivity(A_temp, graph_dimension, K1, reps, iter);
disp("real increase: "+(base1-base)/connectivity_ub);
disp("estimated increase conn: "+best_array(5));
disp("recompute: "+teb);
disp("estimated increase demand: "+best_array(4));
disp("estimated time on conn and ub: "+ estimted_running_time);
%write file for mapv visualization
fileID = fopen('./logs/nyc_fairbus_k501.txt','w');
fileID3 = fopen('./logs/nyc_route_stops.txt','w');
fileID2 = fopen('./logs/nyc_busroutes_new.txt','w');
if ~test_nyc
fileID = fopen('./logs/chi_fairbus_k501.txt','w');
fileID3 = fopen('./logs/chi_route_stops.txt','w');
fileID2 = fopen('./logs/chi_busroutes_new.txt','w');
end
fprintf(fileID,'geometry\n"{""type"": ""LineString"", ""coordinates"": [');
for i=1:length(best_path)
id = best_path(i);
fprintf(fileID2,'%d\n',id);
fprintf(fileID3,'%3.6f,%3.6f\n',stoplocations(id,3), stoplocations(id,2));%for finding related routes in the edge
fprintf(fileID,'[%3.6f,%3.6f]',stoplocations(id,3), stoplocations(id,2));
if i~=length(best_path)
fprintf(fileID,',');
end
end
fprintf(fileID,']}"\n');% \n is essential, it will not work if without it.
fclose(fileID);
fclose(fileID2);
fclose(fileID3);
q.clear();
end
end
end
toc
logs = "./logs/new_exp_logs";%give every d
logs = logs+"_"+k+"_"+weight+"_"+number_of_turns+"_"+seeding_number;
logs = logs+".xls";
delete(logs);
writematrix(objectives, logs);
end