CN104217086A - Urban public transport network optimization method - Google Patents
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Abstract
The invention relates to an urban public transport network optimization method. The benefits of passengers and operators are comprehensively considered, and the use efficiency of paths is effectively improved by searching the path with the maximum direct traveler density between OD pairs. The urban public transport network optimization method overcomes the shortcomings that only the shortest paths are laid, and the laid paths are long and the like in a traditional model. The longitudinal direction of the paths is more consistent with traveler flow, and optimization and service quality of a public transport network is improved. As the model is an NP-hard problem, the problem is solved by a slime mold heuristic algorithm. By the method, the convergence speed of the algorithm is greatly increased while solution quality is ensured, and fine optimization effects are achieved. Therefore, the method can be widely applied to the field of transport network optimization.
Description
Technical Field
The invention relates to a public traffic network optimization method, in particular to a city public traffic network optimization method.
Background
Along with the increasing of urban population, the number of residents who take buses for travel is also increasing. The arrangement of the urban public transport network is an important component of public transport. The setting of the public traffic network has direct influence on the travel time of residents, the number of times of bus transfer and the operation cost of a public traffic system
At present, a plurality of design models and solving algorithms are provided in the existing research, but the existing research is mostly limited by theory and has weak practical applicability. The traditional direct passenger flow method is a feasible method, however, most of them firstly determine the shortest line between the starting and end point pairs, and then find the route with the maximum direct passenger flow in the shortest line. However, because the passenger flow on the shortest line is not necessarily the maximum, it is unreasonable that the lines are all arranged on the shortest line, and although the complexity and the calculation amount of the model can be greatly simplified, the quality of the optimization scheme is sacrificed; on the other hand, since the traffic volume increases with the length of the bus route, the express traffic method tends to route longer routes, so that even if the traffic on one route is very dense, the accumulated traffic volume may be discarded less than the longer route due to the shorter route. Additionally, if too many wires are in the overall network, the cost of operation is increased, and the efficiency of the line and vehicle is not maximized. The inventor finds that the problem can be effectively solved by laying the line network based on the method for directly achieving the passenger flow density.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a public transportation network layout principle based on the maximum direct passenger flow density, and solve the problem of overlong public transportation lines laid by the traditional method by using an advanced heuristic algorithm, so that the method is an urban public transportation network optimization method for effectively improving the public transportation service level.
In order to achieve the purpose, the invention adopts the following technical scheme: an urban public transport network optimization method comprises the following steps: 1) establishing all public transport passenger flow demand matrixes according to resident trip investigation; 2) selecting starting and ending point pairs in a public transport passenger flow demand matrix according to the actual road network condition; 3) randomly selecting one starting and ending point pair from all feasible starting and ending point pairs in a starting and ending point database, and searching all possible lines between the starting and ending point pairs in a road network according to the condition that the maximum bus passenger flow transported by a bus line with a unit length is a target; 4) evaluating all possible lines between the starting point and the terminal point, and deleting the non-feasible lines in the road network; 5) adding the direct passenger flow density maximum line in all bus feasible lines searched among the selected terminal pairs into an alternative line set based on the maximum direct passenger flow density, and repeating the steps 3) -5) until the direct passenger flow density maximum line among all the starting and terminal pairs is obtained in a starting and terminal point database; 6) selecting a line with the maximum direct passenger flow density from a line set based on the maximum direct passenger flow density, adding the selected line into a final bus line network, and laying bus lines; 7) determining the bus passenger flow demand which can not be served by the current bus network by analyzing the condition of the bus stop covered by the current bus network, and correcting a passenger flow demand matrix; 8) and (5) repeating the steps 1) to 7) until no line meeting the condition exists in the laid network, stopping searching and obtaining the final bus network.
In step 2), the criterion for determining whether the starting point pair is feasible is as follows: if the distance between the starting and ending point pairs is less than 5 kilometers or the nonlinear coefficient is greater than 1.5, the starting and ending point pairs are marked as infeasible starting and ending point pairs.
In the step 3), a slime mold algorithm is adopted to solve feasible lines between starting and ending point pairs.
In the step 4), whether the route is a feasible route is evaluated by the following method: the method comprises the following steps of line length evaluation, nonlinear coefficient evaluation and line minimum passenger flow evaluation.
Due to the adoption of the technical scheme, the invention has the following advantages: 1. unlike the traditional direct passenger flow method, which routes the shortest route between the starting point and the destination point (shortest route between the ODs), the invention adopts the local shortest route between adjacent stations. Under the condition of not influencing the passenger flow, the problem of line selection between two points is simplified into the problem of the shortest path between the two points. Since the size of the passenger flow between the stops is only related to the stops independent of the road sections, if the sequence of the stops is determined, the bus route is actually determined, and therefore the problem of route optimization is simplified into the problem of determining the stops and the sequence. 2. The invention aims at achieving the density of the direct passenger flow, and comprehensively considers two aspects of the length of the line and the direct passenger flow. Compared with a direct passenger flow method, the method provided by the invention is more consistent with the trend of the maximum passenger flow. 3. The model of the invention comprehensively considers the benefits of both the passenger and the operator, and effectively improves the utilization efficiency of the line by searching the line with the maximized DC passenger flow density between the OD pairs. The defects that the traditional model is only limited to the shortest line for laying lines and the line for laying is too long are overcome, the longitudinal direction of the lines is more consistent with the passenger flow, and the optimization and the service quality of the public traffic network are improved. Because the model is an NP-hard problem, the invention adopts the myxomycete heuristic algorithm to solve the problem, and the method greatly accelerates the convergence speed of the algorithm while ensuring the quality of the solution, thereby obtaining good optimization effect. For the above reasons, the invention can be widely applied to the traffic network optimization field.
Drawings
FIG. 1 is a flow chart of the present invention
FIG. 2 is a network diagram of an embodiment employed by the present invention
FIG. 3 is a schematic view of through traffic between sites
FIG. 4 is a schematic cross-sectional through passenger flow diagram
Detailed Description
The invention is described in detail below with reference to the figures and examples.
As shown in figure 1, the invention discloses a city public transport network optimization method, which comprises the following steps:
1) and establishing all public transport passenger flow demand matrixes according to the travel survey of residents.
As shown in fig. 2, the following is described by way of example, and as shown in table 1, the passenger flow demand matrix is as follows:
TABLE 1 passenger flow demand matrix
2) Selecting starting and ending point pairs in a public transport passenger flow demand matrix according to the actual road network condition;
and establishing a starting and ending point alternative database based on the starting and ending points, sequentially screening each starting and ending point pair in the starting and ending point database, and marking the starting and ending point pair as an infeasible starting and ending point pair if the distance between the starting and ending point pairs is less than 5 kilometers or the nonlinear coefficient is more than 1.5.
3) Randomly selecting one starting and ending point pair from all feasible starting and ending point pairs in a starting and ending point database, searching all possible lines between the starting and ending point pairs in a road network according to the condition that the maximum bus passenger flow transported by a bus line with a unit length is a target, wherein the process comprises the following steps:
and (2) setting that the passenger flow volume flowing in from the starting point can all flow out at the end point, wherein the passenger flow volume in the road network meets the kirchhoff equation set according to the flow conservation principle:
wherein,Dodthe passenger flow density of an OD pair taking (o, d) as a starting point and a finishing point of a road section pair obtained in a road network is also the passenger flow volume of unit length, wherein i and j are end points of the road section; lijIs the road network length of the road segment between the i, j two points; q. q.sodThe size of the passenger flow between OD pairs taking the lines (o, d) as starting and ending points;is the pressure generated at point i at the flow rate of the OD pair ending at (o, d).Is the pressure developed at point j at the flow rate of the OD pair starting and ending at (o, d).
Determining the traffic volume in the requested section by pressure, conductivity, route length and through-line traffic density:
wherein Q isodThe OD starting and ending at (o, d) is positive for the flow rate at the desired link from o to d, and d is negative for o.
The positive feedback relationship between the passenger flow volume and the conductivity of the passenger flow volume to the required road section is as follows:
the method comprises the following steps of continuously iterating the algorithm according to the change of the passenger flow and the positive feedback effect of the passenger flow on the conductivity, enabling the direction of line search by the slime algorithm to face the direction of finding a line with the maximum passenger flow density, and realizing the slime algorithm by the following specific steps:
3-1) initializing, setting the site position of the whole road network and the length of each edge in the road network, assigning an initial value of 1 to the conductivity of each edge in the road network, and assigning an initial passenger flow volume value of 0;
3-2) selecting an OD pair in the OD passenger flow demand matrix obtained by investigation at will, and solving according to the formula (1) to obtain the pressure of each station;
3-3) obtaining the passenger flow of each road section between the OD pairs taking (o, d) as the starting point and the ending point by the pressure of the station and the formula (2);
3-4) calculating the conductivity under the OD requirement of the road section according to the passenger flow of each road section and the formula (3);
3-5) if other OD pairs exist in the road network, returning to the step 3-2);
3-6) calculating the total passenger flow on all road sections;
3-7) if the passenger flow change of each section is close to 0 (generally, the value of the passenger flow change of two adjacent sections is less than 10)-6) If the road network system reaches a stable state, entering the step 3-8); if the road network system does not reach the stable state, returning to the step 3-2) until the road network system reaches the stable state;
3-8) finishing the algorithm;
all possible lines in the road network can be searched through the 8 steps, and then 5 conventional indexes of the road network are respectively calculated: firstly, calculating the passenger flow between adjacent stations; finding out the cross section Q of the maximum passenger flow in all adjacent stationskl(ii) a (iii) Total flow Q of linesum(ii) a Length L of lineod(ii) a Passenger flow density D of the total roadOD。
Calculating the passenger flow between the stations: the service scope of a site is a collection of sites which are closer to each other; for example, service area X of site kkRepresenting the set of all stations that can reach station k by foot, i.e. Xk＝{k,k1,k2}; when the passenger flow between the stations is calculated, the passenger flow between the two stations is not simply calculated, but the passenger flow is calculated by taking the service range of the two stations as a unit; as shown in FIG. 3, we calculate that the traffic from site k to site l is equal to XkTo YlThe passenger volume of (a), namely:wherein,representing a slave site service scope XkService scope to site YlThe passenger flow volume of (1); SPklRepresenting the amount of traffic from site k to site l;representing from site k to site l1The passenger flow volume of (1);representing slave site k1Passenger flow to site l;representing slave site k1To site l1The passenger flow volume of (1);representing slave site k2Passenger flow to site l;representing slave site k2To site l1The passenger flow volume of (1).
② as shown in FIG. 4, finding the cross section of the maximum passenger flow in all the adjacent sites
The cross section flow is the total passenger flow passing through a certain road cross section, namely the number of passengers getting on the station before the cross section and getting off the station after the cross section is calculated. Similar to the inter-site passenger volume calculation, the concept of service scope is also used here. E.g. calculating the passenger flow Q of section (k, l) in fig. 4klWe just need to calculate XkTo YlPassenger flow volume ofAnd XkTo ZmPassenger flow volume ofWithout the necessity of calculating YlTo ZmPassenger flow volumeNamely:
(iii) Total flow Q of linesum: the total passenger flow of the line is equal to the sum of the flow rates of all the sections through which the line passes, i.e.:wherein S isODIs a bus line between the OD of the starting and ending points.
All possible line lengths Lod：Wherein L isodRepresents the length of the line with (o, d) as the starting point and the end point; n represents a site set;
passenger flow density D of the total roadOD: calculating the passenger flow density of the bus line according to the total flow and the length of the line;wherein Q issumIndicating the total passenger flow of the line. Where OD is the OD pair and Ω is the starting point database.
4) Evaluating all the obtained possible lines by the following conventional evaluation method, deleting the non-feasible lines to obtain the feasible lines, wherein the evaluation criteria are as follows:
4-1) line length assessment
The bus route is not suitable to be too long or too short, and the route is too long, so that the waiting time of passengers is prolonged; the lines are too short, increasing the number of passenger transfers. Generally, the line length is limited to 20min, and the longest line length is limited to 45min (medium-small city) and 60min (large city). The shortest limit distance (L) is set as the average operating speed of km/hmin) 5km, the longest limit distance (L)max) 11.25km (medium and small cities) and 15km (large cities).
In this embodiment, the distance limit of the metropolitan area is used as a standard, and the searched line length should be greater than 5km and less than 15 km.
4-2) evaluation of nonlinear coefficients of lines
The nonlinear coefficient of the line refers to the ratio of the actual length of the bus line to the space linear distance, and is calculated by the following formula: a nonlinear coefficient representing a line with (o, d) as a starting point and an end point; lodAnd (d) represents the spatial linear distance of the line with the starting point and the ending point of (o, d). The smaller the nonlinear coefficient of the line is, the better the nonlinear coefficient is, and for a common city, the better the nonlinear coefficient is 1.15-1.20, and the general nonlinear coefficient is less than 1.5.
4-3) minimum line traffic assessment
According to the design requirements of the bus line, the bus line can be opened only after the number of passengers reaches a certain standard. If the total amount of traffic for the resulting line is below the minimum open line traffic (which is typically set to be no less than 500 people/hour), it is not feasible to route the line and the line is deleted.
And deleting non-feasible lines by the three evaluation methods to obtain feasible lines.
5) Adding the direct passenger flow density maximum line in all bus feasible lines searched among the selected terminal pairs into an alternative line set based on the maximum direct passenger flow density, and repeating the steps 3) -5) until the direct passenger flow density maximum line among all the terminal pairs is obtained in a starting and ending point database;
6) selecting a line with the maximum direct passenger flow density from a line set based on the maximum direct passenger flow density, adding the selected line into a final bus line network, and laying bus lines;
7) determining the bus passenger flow demand which can not be served by the current bus network by analyzing the condition of the bus stop covered by the current bus network, thereby correcting the passenger flow demand matrix and providing a basis for next route search based on the maximization of the direct current passenger flow density;
8) and (4) repeating the steps 1) to 7) until no line meeting the conditions exists in the laid network or a preset cycle number is reached (set according to general experience), stopping searching and obtaining the final public transport network.
In this embodiment, the finally obtained search result is shown in table 2, where MDTD (Maximum direct passenger density), MDT (Maximum direct passengers), and MDTSP (Maximum direct passenger on shortest route) are included, the direct passenger of the route proposed by the present invention is not the Maximum, and the route is not laid on the shortest route, however, the length of the route and the direct passenger are considered comprehensively, a balance point is found between the route and the direct passenger, the transportation network can meet the travel demand of residents, and the service level of the public transportation network can be effectively improved.
TABLE 2 calculation results
The above embodiments are only used for illustrating the present invention, and the structure, connection mode, manufacturing process, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.
Claims (5)
1. An urban public transport network optimization method comprises the following steps:
1) establishing all public transport passenger flow demand matrixes according to resident trip investigation;
2) selecting starting and ending point pairs in a public transport passenger flow demand matrix according to the actual road network condition;
3) randomly selecting one starting and ending point pair from all feasible starting and ending point pairs in a starting and ending point database, and searching all possible lines between the starting and ending point pairs in a road network according to the condition that the maximum bus passenger flow transported by a bus line with a unit length is a target;
4) evaluating all possible lines between the starting point and the terminal point, and deleting the non-feasible lines in the road network;
5) adding the direct passenger flow density maximum line in all bus feasible lines searched among the selected terminal pairs into an alternative line set based on the maximum direct passenger flow density, and repeating the steps 3) -5) until the direct passenger flow density maximum line among all the starting and terminal pairs is obtained in a starting and terminal point database;
6) selecting a line with the maximum direct passenger flow density from a line set based on the maximum direct passenger flow density, adding the selected line into a final bus line network, and laying bus lines;
7) determining the bus passenger flow demand which can not be served by the current bus network by analyzing the condition of the bus stop covered by the current bus network, and correcting a passenger flow demand matrix;
8) and (5) repeating the steps 1) to 7) until no line meeting the condition exists in the laid network, stopping searching and obtaining the final bus network.
2. The method for optimizing the urban public transport network according to claim 1, characterized in that: in step 2), the criterion for determining whether the starting point pair is feasible is as follows: if the distance between the starting and ending point pairs is less than 5 kilometers or the nonlinear coefficient is greater than 1.5, the starting and ending point pairs are marked as infeasible starting and ending point pairs.
3. The method for optimizing the urban public transport network according to claim 1, characterized in that: in the step 3), a slime mold algorithm is adopted to solve feasible lines between starting and ending point pairs.
4. The method for optimizing the urban public transport network according to claim 2, characterized in that: in the step 3), a slime mold algorithm is adopted to solve feasible lines between starting and ending point pairs.
5. The method for optimizing a network of urban buses as claimed in claim 1, 2, 4 or 5, characterized in that: in the step 4), whether the route is a feasible route is evaluated by the following method: the method comprises the following steps of line length evaluation, nonlinear coefficient evaluation and line minimum passenger flow evaluation.
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