CN115907266A - Customized bus route planning method based on passenger flow travel characteristics - Google Patents
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Abstract
The invention discloses a customized bus route planning method based on passenger flow travel characteristics, which comprises the following steps: s1, determining a type and a coverage area of a customized bus route; s2, selecting a target area and an analysis time period; s3, obtaining a high-frequency travel OD pair of the target area; s4, determining that the passenger flow is intensively used as a bus stop of the customized bus route; and S5, determining the customized bus route from the starting area to the ending area. According to the technical process of 'scene selection, OD analysis and route planning', aiming at demand analysis, passenger flow study and point selection of the customized bus, on the basis of commuting passenger flow OD analysis, existing bus track service resource supply evaluation and passenger flow travel service supply and demand matching degree evaluation, high-latency mining of the customized bus is combed, designed and developed, and intelligent planning of a customized bus organization mode is achieved.
Description
Technical Field
The invention relates to the technical field of bus route planning, in particular to a customized bus route planning method based on passenger flow travel characteristics.
Background
The clear operation and management of urban traffic is a necessary trend of future development, and the promotion of the change of the operation management mode of the public traffic system is to comply with the new-age development requirement driven by data as a key element and a core. The conventional public transport is used as a capillary vessel in a public transport system, and the customized public transport is used as an upgraded version and beneficial supplement of the traditional public transport, and is an important means for promoting urban public transport reform and meeting the increasing and good travel demand of people. The customized public transport, also called commercial regular bus, is a one-stop direct-reaching regular bus from community to unit and from unit to community. Citizens can put forward their own demands through special websites, and public transport groups design public transport routes according to the demands and the passenger flow conditions.
However, most of the current customized public transportation services are initiated by passengers actively, the departments in charge of the public transportation group collect the demands of the passengers, and the traditional organization mode of 'demand collection, site survey, line demonstration and trial run' of lines is opened according to conditions, so that the defects that the travel demands of vulnerable groups are difficult to obtain, the demand collection channels are few, the line opening period is long and the like exist, an effective means for mining the potential passenger flow demands is lacked, the passenger flow demands cannot be fed back, and the loss of the customized public transportation users is caused.
Certainly, with the continuous progress of the technology, the intelligent planning of the public transportation has also been applied to a certain extent, taking "a public transportation network planning method" disclosed in chinese patent application No. CN201611209172.5 as an example, the solution aims to minimize the travel time and the number of transfers of passengers, maximize the demand density of the network, comprehensively consider the benefits of the passengers and the operation efficiency of the network, and effectively improve the utilization efficiency of the line by searching the line with the largest passenger flow volume between the OD pairs. The defect that the traditional model is only limited to the travel time of passengers or the target of direct demand density is overcome, so that the lines and the passenger flow are more consistent, and the service level of a public traffic network is improved. However, the constraint conditions in this solution are expressed as follows: "in step S2, the constraint conditions are specifically as follows: 1) The wire nets are communicated; 2) No loop; 3) The number of the lines in the line network meets a preset value; 4) The transfer times do not exceed n times; 5) The number of the line nodes is smaller than a preset maximum value and larger than a preset minimum value; 6) The number of bus lines arranged on the road section is less than a preset value; 7) The wire mesh density is greater than a preset value; 8) Obviously, in the technical scheme, all constraint conditions are determined and invariable, and since invariable constraint conditions are often adopted for the constraint conditions, the space property of the constraint conditions is considered, and when influence factors are changed, part of high-quality feasible schemes are necessarily sacrificed.
The same situation also occurs in the method of comprehensive integrated optimization design of pure electric public transportation network disclosed in chinese patent application No. CN202010581155.4, and the technical solution also discloses a method for setting constraint conditions, but in such technical solution, for example, "the constraint conditions include a line constraint condition, a charging station constraint condition, a vehicle operation constraint condition, and a charging plan constraint condition", but in the technical solution, there is no expression that the constraint conditions are variable with influence factors, so there still exists a problem that the basis of considering the constraint conditions is still present, and when the influence factors change, a part of high-quality feasible solution is inevitably sacrificed.
Disclosure of Invention
In order to solve the technical problems mentioned in the background art, the method for customizing the bus route planning based on the passenger flow travel characteristics is provided.
In order to achieve the purpose, the invention adopts the following technical scheme:
the customized bus route planning method based on passenger flow travel characteristics comprises the following steps of:
s1, determining customized bus route types and coverage areas, wherein the customized bus route types comprise a shopping special line, a commuting special line, a medical special line, a reading special line and a tourism special line, different POIs are configured on different route types, different city travel databases are called, and classified management and cross calculation of a passenger flow source are realized;
s2, selecting a target area and an analysis time period, wherein the target area comprises a starting point area and an end point area which are used as a starting point and an end point of the customized bus line;
s3, aiming at the target area, performing OD analysis on at least four influence factors including transfer times, destination distribution, travel time ratio and walking distance, and sequencing according to OD intensity from high to low to obtain a high-frequency travel OD pair of the target area;
s4, according to the high-frequency travel OD pair, aiming at a target cell in a target area, performing cell OD pair special analysis on at least three influence factors including residential/employment population heat distribution, multidimensional travel heat distribution and travel characteristic distribution, and determining that passenger flow is intensively used as a bus stop of a customized bus route;
s5, based on the bus stop of the step S4, determining an optimal bus route between target cells in a target area through a route generation model for minimizing route cost, and finally determining the customized bus route trend from a starting area to an end area, wherein at least a plurality of constraint conditions which are mutually related and suitable for the condition that calculation parameters are variable parameters are set in the route generation model, and the route generation model is as follows:
minZ=∑n+1i=0∑n+1j=0c ij x ij ——（1）;
equation (1) represents the minimum path cost, where c represents the distance transportation cost between bus stops i, j, x ij Is a variable of 0-1, x when the vehicle goes from station i to station j ij =1, otherwise, x ij =0；
The constraints are as follows:
∑ n+1 j=1，j≠ix ij =1，"i∈C——（2）；
equation (2) indicates that each station must pass once, where the crew set C = {1, · · · n };
∑ n+1 i=0，k≠ix ik =∑ n+1 j=1，k≠ix kj ，"k∈C——（3）；
formula (3) indicates that each station must be stopped once after one pass;
∑n j=1x 0j ≤K——（4）；
the formula (4) shows that at most K arcs appear at the initial station;
y i +x ij +q i -Q（1-x ij ）≤y j ，"i，j∈N——（5）；
q j ≤y j ≤Q，"i∈N——（6）;
x ij ∈{0,1}，"i，j∈N——（7）；
in the formulae (5) and (6), y i Representing cumulative occupant demand, y, at the time the vehicle arrives at station i j Representing cumulative occupant demand, q, when a vehicle arrives at station j j The demand of a passenger j is represented, Q represents the total capacity of the vehicle, and the vertex set N = C {0, N +1}, y represents the total capacity of the vehicle i 、y j And q is j All are variable parameters that are adjusted according to actual influencing factors.
In the prior art, for the constraint condition, a constant constraint condition is often adopted, and then, considering the bottom-in-pocket property of the constraint condition, when the influence factor changes, part of high-quality feasible schemes are necessarily sacrificed, so that the method is improved according to the weak items of the technical scheme, the constraint conditions which are related to each other and suitable for the situation that the calculation parameter is a variable parameter are introduced, and in order to further reduce the excessive influence of the constraint condition, certain linkage property is required to be provided among the constraint conditions. The technical scheme overcomes the technical prejudice that the constraint condition is unchanged, greatly improves the technical rationality, and can provide a high-quality feasible scheme more fitting the reality by combining passenger flow OD time varying data under different influence factors.
As a further description of the above technical solution:
further comprising the steps of:
s6, predicting passenger flow OD time-varying data in the coverage range of the customized bus line in the step S5 based on the big data, acquiring a passenger flow intensity parameter of each unit time period, and determining a scheduling plan of the customized bus;
and S7, evaluating and comparing the traffic modes of the customized bus, the conventional bus and the taxi from a plurality of angles including economy, travel time, walking distance, travel distance, transfer times and the like in the coverage range of the customized bus.
As a further description of the above technical solution:
in step S3, the destination distribution is a plurality of destination areas obtained by OD analysis after determining the start area, the travel time ratio is a ratio of the longest transportation means time consumption to the shortest transportation means time consumption, and the walking distance includes less than 500m, 500m-1km, and more than 1 km.
As a further description of the above technical solution:
in step S4, the multidimensional travel heat distribution includes a full-day travel heat distribution, an early peak travel heat distribution, a late peak travel heat distribution, a bus travel heat distribution, and a rail transit travel heat distribution, and the travel characteristic distribution includes a travel time period proportion distribution, a travel mode proportion distribution, and a travel time characteristic distribution.
As a further description of the above technical solution:
in step S2, the analysis periods include an early peak period, a late peak period, and a full day continuous period.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that: the invention takes ' demand guidance and digital driving ' as a principle, according to the technical process of ' selecting scene, OD analysis, route planning, scheduling planning and submitting scheme ', aiming at demand analysis, passenger flow research and judgment and point selection of the customized bus, through deep communication and process reduction with a bus operation department and a bus management department, on the basis of commuting passenger flow OD analysis, existing bus track service resource supply evaluation and passenger flow travel service supply and demand matching degree evaluation, the customized bus high-latency mining is carried out on the combing, designing and developing of system functions, research results run through each link of the planning design and operation management of a customized bus system, the customized bus operation service level can be effectively improved, the new crossing of the existing customized bus organization mode from ' passive response ' to ' intelligent planning ' is realized, certain innovation and practicability are realized, and a novel generalized ' data intelligent driving ' bus route customization paradigm ' can be formed in the whole country.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
The embodiment provides a technical scheme: the customized bus route planning method based on passenger flow travel characteristics comprises the following steps:
s1, selecting a scene: determining customized bus line types and coverage areas, wherein the customized bus line types comprise a shopping special line, a commuting special line, a medical special line, a reading special line and a tourism special line, the shopping special line mainly covers residential districts and large commercial districts, the commuting special line mainly covers residential districts and industrial park post concentration areas, the medical special line mainly covers a Hospital hospital, a junction, a residential district and the like, the reading special line mainly covers a primary school and a residential district, the tourism special line mainly covers hot scenic spots, commercial districts, residential districts and junctions, different POIs are configured in different line types, different city trip databases are called, classification management and cross calculation of a passenger flow source are realized, specifically, a user line is customized, and POI attribute configuration is carried out on the line, such as: school, hospital etc. through to internet trip data, regional work and live data etc. discern the passenger flow volume in each region, combine POI attribute to distinguish, if: the method comprises the steps that the passenger flow between each area of a commuting special line with a residential area as a starting point and an office building as an ending point is combined with working days and non-working days to carry out cross analysis to obtain the passenger flow of each characteristic time period, such as working days and non-working days;
s2, selecting a target area and an analysis time period, setting the target area as a starting point or a destination, taking a traffic large area as a target, primarily locking a main research area, realizing primary frame selection of a city database, improving data analysis efficiency, anchoring different analysis time periods such as an early peak time period, a late peak time period and a full-day continuous time period by a self-defined set analysis time period, and performing highly self-defined basic target selection definition;
s3, aiming at the target area, performing OD analysis from four aspects of transfer times, destination distribution, travel time ratio and walking distance, sequencing according to OD intensity from high to low, and obtaining high-frequency travel OD pairs of the target area, specifically, sequencing a list according to OD intensity from high to low, manually selecting passenger flow with high passenger flow intensity, and generally selecting OD pairs of top 10;
the transfer times are important indexes reflecting the conversion intensity of the trip chain of the travelers in the city at present and are important indexes reflecting the current trip level, the transfer times directly reflect the trip service level and the experience level, the transfer times are classified according to the basic rules of the city trip, the transfer times are classified once, many times and all according to no transfer, the dynamic synchronous response of the transfer times and the intensity of the passenger flow OD is realized, the dynamic response type connection is realized between the transfer times and the passenger flow OD, and the more targeted customized analysis can be better assisted;
after the destination distribution is a fixed starting point area, a plurality of destination areas are obtained according to OD analysis, for example, if the passenger flow from the area A reaches five areas, namely the areas B, C, D, E and F, the destination distribution is the five areas;
the travel time ratio reflects the time degree spent by travelers at the beginning and the end by adopting different transportation modes, is the ratio of the longest transportation mode time consumption to the shortest transportation mode time consumption, reflects the path advantages and the channel advantages among different transportation modes, is the centralized embodiment of the comprehensive travel efficiency of different transportation modes, is a core index for measuring the advantages and disadvantages of customized public transport and other transportation modes, and is analyzed by aggregating the whole sample elements;
the walking distance is an important index for reflecting the connection level of urban travelers and traffic infrastructures such as parking lots, rail stations, bus stations and the like, and is an important index for reflecting the current situation trip level, the walking distance directly reflects the overall layout balance and supply level of the facilities, and is the most important link in the construction of a trip chain, and classification analysis is carried out according to three categories of less than 500m, 500m-1km and more than 1km by combining the overall layout distribution of the facilities, so that the dynamic response type connection is realized between the transfer times and the passenger flow OD, and the dynamic response type connection can better assist in customized analysis with more pertinence;
s4, aiming at the high-frequency travel OD pair, aiming at a target cell in a target area, carrying out cell OD pair special analysis from the three aspects of residential/employment population heat distribution, multidimensional travel heat distribution and travel characteristic distribution, determining that passenger flow is intensively taken as a preselected bus stop, specifically, carrying out distribution display of the residential/working population heat on the target area based on a gis map, and taking the distribution of the user set bus stops and the position of the concentrated condition of the passenger flow as the stop of the customized bus;
aiming at the residential and employment population heat distribution, the residential and employment population heat distribution of the community level of a starting and ending region is mainly researched, quantitative support analysis is provided for the determination of bus stops, the residential and employment population heat detailed distribution of the starting and ending region is displayed, and the customized bus line stops are directly guided to be distributed around a high heat distribution region;
the multidimensional travel heat distribution comprises full-day travel heat distribution, early-peak travel heat distribution, late-peak travel heat distribution, bus travel heat distribution and rail-to-rail travel heat distribution, the district-level travel intensity heat distribution of a start-point region and a terminal region is mainly researched, the distribution of residence and employment is converted in a mode, the distribution is matched into a travel chain through a high-density big data algorithm, the different types of travel heat distribution of full-day travel, early-late-peak travel, bus travel and rail-to-rail travel is realized, the detailed distribution of population heat of various travel types of the start-point region and the terminal region is displayed, and customized bus route stations are guided to be distributed around a high-heat distribution region more directly;
the travel characteristic distribution comprises travel time period proportion distribution, travel mode proportion distribution and travel time characteristic distribution, the current month accumulated data is used for analysis, long-term change rule parameters are extracted, the travel time period proportion describes an analogy proportion of travel time consumption and reflects the whole travel time consumption level, the travel mode proportion reflects the proportion among different travel modes, the weight of customizing the detail class of the high potential passenger flow of the bus is reflected, and the travel time characteristic distribution displays the time period accumulated value of the whole month and further reflects the time period change frequency;
specifically, OD analysis is a core function module for high-potential passenger flow mining, multiple unified analysis of urban data and traffic travel data is carried out on the basis of a high-grade multi-source big data base, cross comprehensive comparison analysis is carried out on all dimensions such as transfer times, destination distribution, travel time ratio and walking distance around population and travel from residential population, employment population, all-day travel heat power, early-peak travel heat power, late-peak travel heat power, bus travel heat power, rail travel heat power and the like, urban travel OD distribution rules and space-time two-dimensional characteristics are accurately captured and comprehensively depicted, special analysis of cell OD is a core OD analysis link for customizing bus route planning after high-frequency travel OD pairs are comprehensively researched and judged on the four aspects of transfer times, destination distribution, travel time ratio and walking distance in the early stage;
s5, based on the bus stop of the step S4, determining an optimal bus route between target cells in the target area through a route generation model for minimizing route cost, and finally determining the customized bus route trend from the starting area to the ending area, wherein at least a plurality of constraint conditions which are associated with each other and are suitable for the condition that the calculation parameters are variable parameters are set in the route generation model, and the route generation model is as follows:
minZ=∑n+1i=0∑n+1j=0c ij x ij ——（1）;
equation (1) represents the minimum path cost, where c represents the distance transportation cost between bus stops i, j, x ij Is a variable of 0-1, x when the vehicle goes from station i to station j ij =1, otherwise, x ij =0；
The constraints are as follows:
∑ n+1 j=1，j≠ix ij =1，"i∈C——（2）；
equation (2) indicates that each station must pass once, where the crew set C = {1, · · · n };
∑ n+1 i=0，k≠ix ik =∑ n+1 j=1，k≠ix kj ，"k∈C——（3）；
formula (3) indicates that each station must be stopped once after one pass;
∑n j=1x 0j ≤K——（4）；
the formula (4) shows that the starting station has at most K arcs;
y i +x ij +q i -Q（1-x ij ）≤y j ，"i，j∈N——（5）；
q j ≤y j ≤Q，"i∈N——（6）;
x ij ∈{0,1}，"i，j∈N——（7）；
in the formulae (5) and (6), y i Representing cumulative occupant demand, y, at the time the vehicle arrives at station i j Representing cumulative occupant demand, q, when a vehicle arrives at station j j The demand of a passenger j is represented, Q represents the total capacity of the vehicle, and the vertex set N = C {0, N +1}, y represents the total capacity of the vehicle i 、y j And q is j The parameters are variable parameters which are adjusted according to actual influence factors, specific line trend layout is carried out aiming at the customized bus line layout target cell according to the analysis result of the step S4, stations with high passenger flow coverage are selected from the starting and ending cell for line layout mainly through station passenger flow coverage ranges with different service radiuses, and the customized bus line trend is automatically determined through reasonable path planning by utilizing a Gaode map base map.
Specifically, in the present application, the influence factors are not only reflected in the use of the OD pairs, for example, at least four influence factors including transfer times, destination distribution, travel time ratio, and walking distance, but also influence the constraint conditions, so that consideration and use of various influence factors are more comprehensive, and the design of the scheme better conforms to the actual situation.
As described in the present application, in the route generation model for minimizing the route cost, some constraint conditions may be variable, and the use of the model constraint conditions may have different calculation results according to the variation of parameters, for example, the demand of the passenger may be adjustable and variable according to the travel time, season, travel will, and the like, so that the selection of the constraint conditions in the present application is no longer constant, but at least some constraint conditions that are associated with each other and are applicable to the case where the calculation parameters are variable parameters are set in the route generation model.
The variable parameters can be parameters which change according to environmental influence factors including time, holidays, seasons and temperature, different route schemes can be formed during calculation and parameter calculation through different constraints, and lines which better meet actual requirements can be obtained by combining manual selection according to the difference of the route schemes.
As in the present embodiment, the simple example is set as follows: y is j =（Ka i T i + Kb i S i + KC i D i ）y ej ，Ka i +Kb i + KC i =1, in the above formula, y ej For calculating the parameter values of the passenger demand accumulated when arriving at station i, in a general technical solution, y ej Often equal to y j However, in the present embodiment, y j On the basis of the original data, weighted conversion is performed to meet the requirement that the constraint condition changes according to the environmental influence transformation, in the embodiment, T i For the calculated value of temperature in the influencing factor (at site i), S i For seasonal calculations in influencing factors (when arriving at site i), D i Calculating the value of whether the holiday is in the influencing factors (when reaching site i), converting the influencing factors into required values through calculation, and corresponding Ka i 、Kb i And KC i The weighted value corresponding to the influence factor is set manually and adjusted, and the constraint condition can better meet the actual condition by reasonably adjusting the big data result before fitting. As the influencing factors are added, the corresponding weighting values may also be increased. As for T i The value can be obtained by selecting the corresponding result by adopting a conversion function modeThe formula is corresponded, for example, the temperature calculation value corresponding to the daily average air temperature of 20 degrees centigrade is 0.7, and the temperature calculation value corresponding to the daily average air temperature of 32 degrees centigrade is 0.3, namely, when the temperature is comfortable, the trip demand of the passenger for the bus is far more than the high-temperature weather, the trip demand of the passenger for the bus is as for S i The numerical value can also be obtained by adopting a mode of segmented corresponding results, for example, the calculated value of the summer season is 0.8, and the calculated value of the winter season is 0.6, namely, the time length in summer is longer in the daytime, the total travel demand of passengers on the bus is greater than that in winter, and D i The adoption of the corresponding mode of 0-1 in festivals and holidays shows that the festivals and holidays are busy in some areas and the reverse is true in some areas. Through the above steps, y j Compare original y ej Reasonable variation exists, and the source of the relevant data is statistical data accumulated over the years.
In order to further reduce the excessive influence of the constraint conditions, the constraint conditions need to have certain linkage properties, for example, the constraint conditions that each station needs to stop once after passing once can be adjusted according to the requirements of passengers, and whether the constraint conditions are suitable or not needs to comprehensively consider various influence factors rather than be constant.
The method and the device overcome the technical prejudice that the constraint condition is unchanged, greatly improve the technical rationality, and provide a high-quality feasible scheme more fitting reality by combining passenger flow OD time varying data under different influence factors.
According to the technical scheme, it is assumed that target areas A and B are respectively the starting point and the ending point of the customized bus line, wherein target cells in the target area A are X1, X2 and X3 in sequence, target cells in the target area B are Y1, Y2 and Y3 in sequence, and a unique optimal path L exists between the target area A and the target area B A-B ，L A-B I.e. the only route between the target cells X3, Y1, further, according to the above-mentioned customized bus route planning method, the preselected bus stops of X1, X2, X3, Y1, Y2, Y3 of the target cells are determined, as shown in table 1 below, and the optimal route between adjacent target cells, as shown in table 2 belowShown in the figure:
TABLE 1
TABLE 2
Then, as can be seen from table 2, the optimal path of the customized bus route is: "L X1-X2 （x11-x23）——L X2-X3 ：（x22-x31）——L A-B （x31-y13）——L Y1-Y2 （y13-y22）——L Y1-Y2 （y31-y32）”。
Further, in step S5, the following alternatives are also included: taking the bus station determined in the step S4 as an anchor point, taking the peripheral areas of 300m, 500m and 800m as different coverage radiuses as passenger flow gathering circles, and obtaining the road trend condition of the starting and ending area and the city block distribution condition of the peripheral cells based on a data engine of a high-grade map to carry out the layout of the route trend, wherein the cells have corresponding POI points, and the positions are mainly judged whether to be concentrated or not, and whether the values for setting the bus station exist or not, for example, a park is not suitable for setting commuting, wherein the passenger flow gathering circles are the passenger flow distribution conditions of the starting and ending points, namely the distribution of the resident/working population and the passenger flow distribution conditions in the step S4;
s6, scheduling planning is to carry out capacity space-time configuration on a customized bus line, passenger flow OD time-varying data of a primary line coverage range are obtained based on high-grade big data, passenger flow strength parameters of each unit time period are obtained, important auxiliary reference for scheduling planning is provided, multi-dimensional calling and inquiring of historical time periods can be carried out through big data accumulation and combination of different characteristic differences such as seasonal variation, and therefore more comprehensive, meticulous and accurate scheduling planning is carried out, the important data analysis achievement basis of actual operation of the line is provided, specifically, passenger flow in each hour is output according to the time period, the customized bus does not run all day long, only planning and running in a high-frequency trip time period is carried out, and scheduling planning and scheduling are carried out by a user in combination of actual conditions through obtaining passenger flow of each hour;
if the customized bus route scheme is planned, automatically extracting passenger flow covered around the route, primarily fitting according to unit hours, sequencing according to the intensity of the passenger flow, and setting the high-intensity time period of the passenger flow at the top, finding that the route is a typical commuting passenger flow distribution characteristic, wherein the passenger flow intensity is very concentrated in the early peak time period, and the passenger flow intensity in the late peak time period is also most concentrated from the view of reverse analysis, and belongs to the barbell-type passenger flow distribution with obvious characteristics;
s7, in the customized bus coverage range, from the aspects of economy, travel time, walking distance, travel distance and transfer times, the traffic modes of the customized bus, the conventional bus and the taxi are evaluated and compared, the method is the last loop of customized bus route layout, is a summary display of the overall situation of the planned customized bus route, is a centralized expression of relevant basic parameters of the customized bus route, is a comprehensive comparison and evaluation of the customized bus route and other traffic modes, is an important technical means for evaluating the utility and the quality of the customized bus route, and realizes systematic, comprehensive and comprehensive comparison and evaluation of the quality of the customized bus route in a city comprehensive transportation system based on high-grade data.
Further, as shown in the following table 3, the traffic between the start and end target areas a and B and the target cells X1, X2, X3, Y1, Y2, and Y3 covered by the start and end target areas a and B is comprehensively evaluated by three transportation modes of customized public transportation, conventional public transportation, and taxi:
TABLE 3
By way of example, in Table 3, the following conclusions can be drawn:
(1) The in-transit time of the customized bus is about 47.18 minutes, compared with the in-transit time of a conventional bus, which is 101.17 minutes, and the customized bus has certain advantages in travel efficiency;
(2) The travel distance of the customized bus is about 28.74 kilometers, the travel distance of the conventional bus is 35.28 kilometers, and the shorter travel distance of the customized bus can provide faster travel efficiency;
(3) The customized bus does not need to be transferred along the way, the conventional bus needs to be transferred for 1 time, and the customized bus trip experience is better in contrast;
(4) The whole walking distance of the customized bus is about 0.00 m, the walking distance of the conventional bus is about 2476.00 m, and the customized bus is more friendly;
(5) The expected cost of the customized bus is 12 yuan, the price is 84.00% lower than that of the taxi taking trip, and the customized bus is more economic and preferential.
The method takes 'demand guidance and digital driving' as principles, and carries out system function combing, designing and developing on the customized public transport high-potential passenger flow mining on the basis of commuting passenger flow OD analysis, existing public transport track service resource supply evaluation and passenger flow outgoing service supply and demand matching degree evaluation by carrying out deep communication and flow reduction with a public transport operation department and a public transport management department according to the technical flow of 'selection scene-OD analysis-line planning-scheduling planning-submitting scheme'. The research result runs through each link of the planning design and the operation management of the customized bus system, the customized bus operation service level can be effectively improved, the new crossing of the change of the existing customized bus organization mode from the passive response to the intelligent planning is realized, certain innovativeness and practicability are realized, and a novel generalized data intelligent-driven bus route customization mode can be formed in the whole country.
Firstly, the bus is different from the traditional bus in that: the main difference between the customized bus and the traditional bus lies in accurate response to passenger demands, and the starting and ending points and the starting time of a route are completely customized according to the passenger flow scale and the travel demands. Therefore, the bus customization system is different from the universality and fixity (fixed line, fixed station and fixed departure time) of the traditional bus, and is more flexible and autonomous in bus customization and can meet the individual requirements of passengers. In the aspect of popularization and operation, compared with the traditional public transport, the popularization and the operation of the customized public transport depend on the mutual transmission between users, the user experience feeling is more concerned, and the promotion and the operation win with better service. Compared with the traditional public transport, the customized public transport has the following characteristics:
(1) The circuit design is more flexible: the traditional bus route is used for booking fare, setting the route and setting the station position, the service area is limited, and meanwhile, the setting of stations along the route and the parking of the first station and the last station have higher requirements. In addition, the customized buses have few stop stations, generally direct buses with few or no intermediate stations are arranged along the way from a fixed starting point to a fixed terminal point, and the customized buses have the advantage of good line direct performance, can effectively increase the service coverage area of public transportation and provide convenient, fast and efficient service for more citizens;
(2) The operation service is more efficient: by collecting the travel requirements of passengers with similar travel starting and ending points, the customized bus can realize the direct transportation service of the starting and ending points, has the right to use a bus priority lane, reduces the interweaving conflict with social vehicles, and ensures that the operation efficiency of the bus is higher than that of the conventional buses, subways and other transportation modes which need to stop for multiple times to get on and off the bus;
(3) The trip demand is more accurate: the customized public transport often utilizes the internet platform, accords with the accurate demand of passenger, collects, arranges in order, the analysis passenger's trip demand, and the service scene relates to commute, tour, scenic spot etc. satisfies the accurate demand of the point-to-point directness of passenger.
Secondly, the existing legacy mode: if the citizen makes an appointment for buying tickets 10 minutes in advance through a Beijing customized public transport APP, the customized public transport platform selects a concentrated travel direction, region and time period according to the appointment conditions on the citizen line of the peripheral region, and a directional travel service is designed. If no line meeting the requirement of the user is available, line customization can be initiated, and the bus is organized to provide customized service for the shifts with the scale of the number of travelers. The public transportation group can regularly summarize background data, match the demand of the same direction and perform line evaluation planning. Generally, the number of passengers reaches 50% of the number of seats of the vehicle, and the line can be driven. Taking the most common commuting shift as an example, the commuting shift generally uses 54 vehicles, and the passengers can drive the vehicle when the number of the passengers reaches 27;
in this embodiment: on the basis of urban traffic travel data, a high-frequency travel demand is extracted by adopting an analysis method of regularly and dynamically accumulated data observation according to multi-element dimensions influencing travel quality, and traffic travel pain points inside and outside a key research area are found out. Aiming at trip pain points, various comprehensive transportation advantages such as 'intensification, high quality and rapid transportation' of the customized bus are fully combined, the customized bus and the city trip pain points are organically matched through big data correlation analysis mainly based on city POI, and a brand-new solution is found out. According to the planning trend of the customized bus route, the highly intelligent customized bus service of dynamic scheduling, dynamic connection and dynamic response is realized by combining the space-time two-dimensional distribution characteristics of the urban trip passenger flow, the comprehensive service capability of an urban public transport system is comprehensively improved, a more diversified, efficient and layered urban public transport system is created, high-quality trip is served, and high-quality life is promoted;
compared with the traditional mode, the method changes the situation that citizens report passive discovery problems into the situation that management departments actively excavate the travel areas of all areas, guides the investment of public transportation, and can better improve the willingness of citizens to travel. Meanwhile, demand analysis, passenger flow research and judgment and point selection of the customized bus are carried out, deep communication and flow restoration are carried out between the demand analysis, passenger flow research and judgment and the point selection and are carried out with a bus operation department and a bus management department, and the basis is based on commuting passenger flow OD analysis, existing bus track service resource supply evaluation and passenger flow travel service supply and demand matching degree evaluation; the bus route can be planned and customized more comprehensively.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (10)
1. The customized bus route planning method based on passenger flow travel characteristics is characterized by comprising the following steps of:
s1, determining a type and a coverage area of a customized bus route;
s2, selecting a target area and an analysis time period;
s3, obtaining a high-frequency travel OD pair of the target area;
s4, according to the high-frequency travel OD pair, carrying out cell OD pair special analysis on a target cell in the target region, and determining a bus stop of the customized bus line;
s5, determining an optimal bus route between target cells in a target area through a route generation model for minimizing route cost, and then determining the trend of a customized bus route, wherein at least a plurality of constraint conditions which are related to each other and are suitable for the condition that calculation parameters are variable parameters are set in the route generation model;
and S6, determining the scheduling plan of the customized bus according to the OD time-varying data of the passenger flow in the coverage area of the customized bus line.
2. The customized bus route planning method based on passenger flow travel characteristics according to claim 1, wherein the route generation model is:
minZ=∑n+1i=0∑n+1j=0c ij x ij ——（1）;
equation (1) represents the minimum path cost, where c represents the distance transportation cost between bus stops i, j, x ij Is a variable of 0-1, x when the vehicle goes from station i to station j ij =1, otherwise, x ij =0。
3. The customized bus route planning method based on passenger flow travel characteristics according to claim 2, wherein the constraint conditions of the formula (1) comprise:
∑ n+1 j=1，j≠ix ij =1，"i∈C——（2）；
equation (2) indicates that each site must pass once, where the crew set C = {1, · · n }.
4. The customized bus route planning method based on passenger flow travel characteristics according to claim 3, wherein the constraint conditions of the formula (1) further include:
∑ n+1 i=0，k≠ix ik =∑ n+1 j=1，k≠ix kj ，"k∈C——（3）；
equation (3) indicates that each station must also be stopped once it passes.
5. The customized bus route planning method based on passenger flow travel characteristics according to claim 4, wherein the constraint conditions of the formula (1) further comprise:
∑n j=1x 0j ≤K——（4）；
equation (4) indicates that there are at most K outgoing arcs at the starting site.
6. The customized bus route planning method based on passenger flow travel characteristics according to claim 5, wherein the constraint conditions of the formula (1) further include:
y i +x ij +q i -Q（1-x ij ）≤y j ，"i，j∈N——（5）；
q j ≤y j ≤Q，"i∈N——（6）;
x ij ∈{0,1}，"i，j∈N——（7）；
in the formulae (5) and (6), y i Representing cumulative occupant demand, y, at the arrival of a vehicle at station i j Representing cumulative occupant demand, q, at vehicle arrival at station j j The demand of a passenger j is represented, Q represents the total capacity of the vehicle, and the vertex set N = C {0, N +1}, y represents the total capacity of the vehicle i 、y j And q is j All are variable parameters that are adjusted according to actual influencing factors.
7. The customized bus route planning method based on passenger flow travel characteristics according to claim 1, wherein the target area comprises a starting area and an end area, and the analysis period comprises an early peak period, a late peak period and a full day continuous period.
8. The customized bus route planning method based on passenger flow travel characteristics according to claim 7, wherein for the target area, OD analysis is performed on at least four influencing factors including transfer times, destination distribution, travel time ratio and walking distance, and high-frequency travel OD pairs of the target area are obtained by sorting according to OD intensity from high to low.
9. The customized bus route planning method based on passenger flow travel characteristics according to claim 8, wherein the destination distribution is a plurality of destination areas obtained by OD analysis after determining a starting area, and the travel time ratio is a ratio of a longest transportation mode time consumption to a shortest transportation mode time consumption.
10. The customized bus route planning method based on passenger flow travel characteristics according to claim 1, wherein according to the high-frequency travel OD pair, aiming at a target cell in a target area, cell OD pair special analysis is performed from at least three influence factors including residential/employment population heat distribution, multidimensional travel heat distribution and travel characteristic distribution, and the passenger flow is determined to be used as a bus stop of the customized bus route in a centralized manner.
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