CN110599760A - Travel behavior simulation method under multi-mode traffic network - Google Patents
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Abstract
The invention discloses a travel behavior simulation method under a multi-mode traffic network, which is characterized in that a multi-mode super traffic network is constructed, virtual nodes and virtual road sections are added in a basic network, the single-layer traffic network is expanded into a bus layer, a network car booking layer, a subway layer, a shared single-car layer and a transfer layer network which are connected with one another, a single geographical position node is expanded into a composite node containing travel time, travel modes and travel states, the complex multi-mode traffic network is converted into a set of single travel mode networks, and the efficiency of shortest path search and a flow distribution algorithm can be effectively improved. Based on the network traffic flow changing along with the change of the trip time period, the distribution change condition of the trip mode in the half-day trip time period can be identified.
Description
Technical Field
The invention relates to the field of traffic network modeling, in particular to a travel behavior simulation method under a multi-mode traffic network.
Background
With the rapid advance of urban construction, the space-time distribution and the mode structure of urban traffic are profoundly changed. Under the multi-mode combined travel environment, the planning and construction of urban traffic infrastructure are directly determined by the change of urban traffic demands. The method has the advantages that the traffic demands of different travel modes are objectively mastered, and the method is the key for scientifically evaluating the construction level and the operation effect of the urban traffic system. The structure of the urban trip mode determines the input gravity center of related departments in facility construction and maintenance, and provides important data basis and evaluation reference for urban traffic management and traffic planning.
With the increase of the number of private cars and the aggravation of the problems of traffic jam, environmental problems, energy shortage and the like, public transportation services and shared transportation modes enter the Chinese market, and more environment-friendly travel choices are provided for travelers. At present, shared traffic becomes a complementary traffic mode of public traffic or a replacement choice of private cars due to the characteristics of high accessibility, low cost, simple and convenient operation and the like. Shared traffic is generally used to complete the final kilometer transit between two traffic stops, and net appointment vehicles are more commonly used for trips with long distance or limited travel time and high alignment requirements.
Since the sharing service is an emerging travel mode, the related research is focused more on vehicle scheduling management and improvement of system operation efficiency. Very few studies discuss the combination of shared transportation means and other transportation means, taking a network appointment as an example: the passengers can get on or off the vehicle on the highly congested road, and the shared single vehicle or subway mode is adopted to pass through so as to avoid traffic jam, and then the network taxi reservation or other traffic modes are continuously used to save the whole travel time.
Disclosure of Invention
The invention aims to solve the technical problem of providing a travel behavior simulation method under a multi-mode traffic network, which expands a single-layer traffic network into a bus layer, a network appointment layer, a subway layer, a shared single-layer traffic network and a transfer layer network which are connected with each other, expands a single geographical position node into a composite node containing travel time, travel modes and travel states, converts the complex multi-mode traffic network into a set of single travel mode networks, and can effectively improve the efficiency of a shortest path search and flow distribution algorithm.
In order to solve the technical problem, the invention provides a travel behavior simulation method under a multi-mode traffic network, which comprises the following steps:
(1) expanding the input basic traffic network through a preset node expansion rule and a node link rule to construct a super traffic network under a multi-traffic mode;
(2) by the generation of the super network, the multi-mode composite node in the basic traffic network is converted into a single travel mode node containing travel time, a travel mode and a travel state in the super network, so that subsequent operation is facilitated;
(3) according to the characteristics of each traffic travel mode, corresponding travel utilities are given to the paths of the traffic travel modes including a subway mode, a public transport mode, a network car booking mode, a shared single car mode and a transfer mode for searching the shortest path;
(4) based on understanding and predefining of a traffic travel scheme, deleting feasible path data with excessive transfer times, and constructing an effective path set;
(5) based on the change characteristic of the traffic volume along with the travel time, the traffic volume at each travel time interval is distributed to a multi-mode traffic network, and the proportion distribution condition of corresponding travel modes under different traffic conditions is obtained.
Preferably, in the step (2), the step of converting the multi-mode composite node in the basic traffic network into a single travel mode node containing travel time, travel mode and travel state in the super network by the generation of the super network specifically includes:
in the multi-mode traffic network M, a place state i belongs to a U set, a transfer state T belongs to a T set, a possible transfer state xi (T) belongs to a T set, the existing transfer times are n, the getting-on and getting-off state indexes are l, the maximum transfer times are K, and the number of time intervals considered in the model is K;
process 1: expanding nodes;
first, for each node i ∈ U, the node is extended to (2+8 κ) (K +1) points:
((i,0,0, l), k), l ═ 0, 1; k1, 2, K + 1; ((i, t, n, l), k), t ═ 1,2,3, 4; n 1,2,. kappa.; l is 0, 1; k1, 2, K +1. the expanded set of points is named N;
and (2) a process: establishing a driving road section;
secondly, finding all driving road sections in the network M, recording road section basic data such as a starting point i, a terminal point i' and usable traffic ways t of the road sections, and calculating free stream journey time t of the road sections; if t ≦ a time interval, establishing a travel route segment between points ((i, t, n,1), k) and points ((i +1, ξ (t), n +1,0), k); if t > a time interval, a driving route is established between points ((i, t, n,1), k) and points ((i +1, ξ (t), n +1,0), k + 1);
and 3, process: establishing a transfer road section;
finally, establishing transfer relations among different travel time intervals and travel traffic modes; scanning all nodes in the point set N, if xi (t) is carpooling, constructing a transfer road section a between points ((i, xi (t), N +1,0), k) and points ((i +1, xi (t), N +1,1), k +1)i∈Aa(ii) a If xi (t) ≠ carpooling, a transfer road section a is constructed between points ((i, xi (t), n +1,0), k) and points ((i +1, xi (t), n +1,1), k)i∈Aa。
Preferably, in the step (3), according to characteristics of each transportation mode, giving corresponding travel utilities to the route in which each transportation mode including the subway mode, the bus mode, the network appointment mode, the shared bicycle mode, and the transfer mode is located specifically:
a) a network car booking mode;
for the path which is taken by the net car booking mode alone, the utility function of the path is as follows:
whereinFor the time of flight on the path p,in order to be the latency on the path p,for cost on path p, αt/αwThe coefficients are corresponding to the running time and the waiting time;
the travel time on path p may be calculated by the following equation:
whereinIs the travel time of the net appointment mode on the section s,respectively representing the net car appointment flow and the bus flow on the road section, deltaspIt is the status index of whether the section s is on the path p;
the network traffic reservation flow on the road section s can be calculated by the following formula:
whereinAppointing traffic for the network on path p;
according to the BPR equation, the link travel time can be calculated by the following formula:
wherein L issIs the total length of the road section s,for indicating average speed, K, of vehicles approaching the Internet on the section ssIs the traffic capacity of the section s, gamma1/β1Then it is the BPR equation parameter;
the latency on path p can be calculated by the following equation:
whereinRespectively representing the difference between the desired travel time and the actual travel time (early/late); sigma/beta is a punishment coefficient of each time unit when the time arrives early and arrives late respectively; lambda [ alpha ]tThe rate of decrease of the vehicle fixed delay, which generally increases with time;
the cost on path p can be calculated by the following formula:
wherein L ispIs the total length of path p, αcIs the cost factor;
b) bus, subway modes;
for a path which is taken out in a bus or subway mode independently, the utility function of the path is as follows:
whereinRepresenting the time of arrival and departure on the path p,for the purpose of the uncomfortable feeling effect on the path p,for cost on path p, αz/αdThe station entering and exiting time and the coefficient corresponding to the uncomfortable feeling are obtained;
the travel time of the bus on the path p can be calculated by the following formula:
whereinIs the travel time of the bus mode on the road section s,respectively representing the net car appointment flow and the bus flow on the road section, deltaspIt is the status index of whether the section s is on the path p;
the bus flow on the road section s can be calculated by the following formula:
whereinIs the frequency of departure of the bus,is a frequency coefficient;
according to the BPR equation, the bus section travel time on the section s can be calculated by the following formula:
whereinRepresenting the average speed of the bus on the road section s;
for a subway, the travel time on the path p can be calculated by the following formula:
whereinIs a fixed value associated with the subway departure schedule;
the waiting time of the bus and subway modes on the path p can be calculated by the following formula:
waiting time on road section sComprises the following steps:
wherein the content of the first and second substances,representing the departure interval of a bus or subway;
the uncomfortable feeling effect of the bus and subway modes on the path p can be calculated by the following formula:
wherein the content of the first and second substances,is the level of basic discomfort that is experienced,representing passenger flow, k, over a section ssRepresenting the vehicle load capacity, gamma2/β2Then it is the corresponding coefficient;
passenger flow on road section sCan be calculated by the following formula:
whereinThe passenger flow on the path p is the passenger flow taking public transport or subway as a travel mode;
c) shared bicycle mode
For a path that individually shares a single-vehicle mode trip, the utility function of the path is:
the travel time on the path p using the shared bicycle can be calculated by the following formula:
whereinIs the travel time of the shared vehicle on the section s, and can be calculated by the following formula:
the uncomfortable feeling effect of the shared bicycle on the path p can be calculated by the following formula:
wherein tau is1/τ2Is the discomfort perception effect coefficient;
sharing the waiting time of a single vehicle on path pWhen the distance between the stations is within the acceptable range, the distance between the stations is a fixed value, when the distance between the stations is beyond the acceptable range,
d) multi-mode
For the multi-modal path, the travel utility function is as follows:
whereinRespectively the traveling utility, delta, of the net appointment car, the bus (subway) and the shared single car on the road section sspIndicating whether the section s is on the path p, TRmnIt is the transfer utility between different travel modes.
Preferably, in the step (4), based on understanding and predefining of the transportation scheme, the feasible path data with excessive transfer times is deleted, and the effective path set is specifically constructed as follows:
the method comprises the following steps that two screening conditions are provided, firstly, transfer times are specified, after a super network is constructed and when feasible paths are searched, search rules are preset, paths with two continuous transfer road sections and paths with more than 4 transfer road sections in total in the paths are deleted, and the paths are not put into a feasible route set; secondly, setting an upper limit of feasible path effectiveness and eliminating paths with high traveling effectiveness when initializing and calculating the flow distribution effectiveness with the total flow of 0; the upper bound for the feasible path travel effect can be calculated by the following formula:
whereinThe minimum travel utility in all the possible roads during the initialization calculation, and sigma is a path utility expansion coefficient.
Preferably, in the step (5), based on a characteristic that the traffic volume changes with the travel time, the traffic volume at each travel time interval is distributed to the multi-mode transportation network, and the obtaining of the proportional distribution of the corresponding travel modes under different traffic conditions specifically includes:
considering the random factors of the passengers in making travel route and plan selection, the route selection follows the hierarchical distribution, and therefore, the traffic on the route p is:
wherein q iswIs the travel demand between OD points to w, upIs the effect of travel on the effective path p, θ1The perception coefficient of the trip utility when the path selection is carried out;
based on the Fisk model, the multi-mode network traffic distribution problem can be abstracted into the following user balanced path selection model:
in the formula:
where equations (24), (25) are the total demand balance constraints and equation (26) is the path flow non-negative constraint.
In order to solve the flow distribution problem under the multi-mode traffic network, an MSA (continuous average method) is adopted to solve the user balance path selection model, and the specific solving scheme comprises the following four steps:
(a) generating a network; according to the implementation methods of the steps (1) and (2), the basic multi-mode network is expanded into a super network;
(b) initializing; setting initial flowCalculating path travel utilityAnd determineCreating valid path setsSetting the counting number to be j-0;
(c) calculating travel utility and flow; path utility based on initialization phaseExecuting logic flow distribution and calculating auxiliary flow of the road section
(d) The traffic update is performed based on the MSA rules,
(e) checking the convergence; if it isStopping the operation; otherwise, the counting number is set to j ═ j +1, and the step (2) is returned to.
The invention has the beneficial effects that: according to the invention, novel travel modes such as network car booking and shared single car are considered in a traditional traffic flow distribution model, a multi-mode super traffic network is constructed, a virtual node and a virtual road section are added in a basic network, the single traffic network is expanded into a bus layer, a network car booking layer, a subway layer, a shared single car layer and a transfer layer network which are connected with one another, a single geographical position node is expanded into a composite node containing travel time, travel modes and travel states, the complex multi-mode traffic network is converted into a set of single travel mode networks, and the efficiency of shortest path search and a flow distribution algorithm can be effectively improved; based on the network traffic flow changing along with the change of the trip time period, the distribution change condition of the trip mode in the half-day trip time period can be identified.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
As shown in fig. 1, a method for simulating a travel behavior under a multi-mode transportation network includes the following steps:
1. a state-based composite super network is a method and platform for simulating complex travel behaviors of travelers in a multi-mode traffic network. The invention takes a basic multi-mode traffic network containing four traffic travel modes as input data, and carries out the expansion of the traffic network and the construction of a super traffic network through three processes of node expansion, the establishment of a driving road section and the establishment of a transfer road section.
In the multi-mode traffic network M, a place state i belongs to a U set, a transfer state T belongs to a T set, a possible transfer state xi (T) belongs to a T set, the existing transfer times are n, the getting-on and getting-off state indexes are l, the maximum transfer times are K, and the number of time intervals considered in the model is K;
process 1: expanding nodes;
first, for each node i ∈ U, the node is extended to (2+8 κ) (K +1) points:
((i,0,0, l), k), l ═ 0, 1; k1, 2, K + 1; ((i, t, n, l), k), t ═ 1,2,3, 4; n 1,2,. kappa.; l is 0, 1; k1, 2, K +1. the expanded set of points is named N;
and (2) a process: establishing a driving road section;
secondly, finding all driving road sections in the network M, recording road section basic data such as a starting point i, a terminal point i' and usable traffic ways t of the road sections, and calculating free stream journey time t of the road sections; if t ≦ a time interval, establishing a travel route segment between points ((i, t, n,1), k) and points ((i +1, ξ (t), n +1,0), k); if t > a time interval, a driving route is established between points ((i, t, n,1), k) and points ((i +1, ξ (t), n +1,0), k + 1);
and 3, process: establishing a transfer road section;
finally, establishing transfer relations among different travel time intervals and travel traffic modes; scanning all nodes in the point set N, if xi (t) is carpooling, constructing a transfer road section a between points ((i, xi (t), N +1,0), k) and points ((i +1, xi (t), N +1,1), k +1)i∈Aa(ii) a If xi (t) ≠ carpooling, a transfer road section a is constructed between points ((i, xi (t), n +1,0), k) and points ((i +1, xi (t), n +1,1), k)i∈Aa。
2. And according to the characteristics of each traffic travel mode, giving corresponding travel utilities to the paths of the traffic travel modes including a subway mode, a bus mode, a network car booking mode, a shared single car mode and a transfer mode, and carrying out shortest path search.
a) A network car booking mode;
for the path which is taken by the net car booking mode alone, the utility function of the path is as follows:
whereinFor the time of flight on the path p,in order to be the latency on the path p,for cost on path p, αt/αwThe coefficients are corresponding to the running time and the waiting time;
the travel time on path p may be calculated by the following equation:
whereinIs the travel time of the net appointment mode on the section s,respectively representing the net car appointment flow and the bus flow on the road section, deltaspIt is the status index of whether the section s is on the path p;
the network traffic reservation flow on the road section s can be calculated by the following formula:
whereinAppointing traffic for the network on path p;
according to the BPR equation, the link travel time can be calculated by the following formula:
wherein L issIs the total length of the road section s,for indicating average speed, K, of vehicles approaching the Internet on the section ssIs the traffic capacity of the section s, gamma1/β1Then it is the BPR equation parameter;
the latency on path p can be calculated by the following equation:
whereinRespectively representing the difference between the desired travel time and the actual travel time (early/late); sigma/beta is a punishment coefficient of each time unit when the time arrives early and arrives late respectively; lambda [ alpha ]tThe rate of decrease of the vehicle fixed delay, which generally increases with time;
the cost on path p can be calculated by the following formula:
wherein L ispIs the total length of path p, αcIs the cost factor;
b) bus, subway modes;
for a path which is taken out in a bus or subway mode independently, the utility function of the path is as follows:
whereinRepresenting the time of arrival and departure on the path p,for the purpose of the uncomfortable feeling effect on the path p,for cost on path p, αz/αdThe station entering and exiting time and the coefficient corresponding to the uncomfortable feeling are obtained;
the travel time of the bus on the path p can be calculated by the following formula:
whereinIs the travel time of the bus mode on the road section s,respectively representing the net car appointment flow and the bus flow on the road section, deltaspIt is the status index of whether the section s is on the path p;
the bus flow on the road section s can be calculated by the following formula:
whereinIs the frequency of departure of the bus,is a frequency coefficient;
according to the BPR equation, the bus section travel time on the section s can be calculated by the following formula:
whereinRepresenting the average speed of the bus on the road section s;
for a subway, the travel time on the path p can be calculated by the following formula:
whereinIs a fixed value associated with the subway departure schedule;
the waiting time of the bus and subway modes on the path p can be calculated by the following formula:
waiting time on road section sComprises the following steps:
wherein the content of the first and second substances,representing the departure interval of a bus or subway;
the uncomfortable feeling effect of the bus and subway modes on the path p can be calculated by the following formula:
wherein the content of the first and second substances,is the level of basic discomfort that is experienced,representing passenger flow, k, over a section ssRepresenting the vehicle load capacity, gamma2/β2Then it is the corresponding coefficient;
passenger flow on road section sCan be calculated by the following formula:
whereinThe passenger flow on the path p is the passenger flow taking public transport or subway as a travel mode;
c) shared bicycle mode
For a path that individually shares a single-vehicle mode trip, the utility function of the path is:
the travel time on the path p using the shared bicycle can be calculated by the following formula:
whereinIs the travel time of the shared vehicle on the section s, and can be calculated by the following formula:
the uncomfortable feeling effect of the shared bicycle on the path p can be calculated by the following formula:
wherein tau is1/τ2Is the discomfort perception effect coefficient;
sharing the waiting time of a single vehicle on path pWhen the distance between the stations is within the acceptable range, the distance between the stations is a fixed value, when the distance between the stations is beyond the acceptable range,
d) multi-mode
For the multi-modal path, the travel utility function is as follows:
whereinRespectively the traveling utility, delta, of the net appointment car, the bus (subway) and the shared single car on the road section sspIndicating whether the section s is on the path p, TRmnIt is the transfer utility between different travel modes.
3. Through the expansion of the basic network and the construction of the super network, the number of nodes and road sections in the network is exponentially increased, and the number of available travel schemes and travel schemes is countless. Through the definition and screening of the feasible paths, the feasible paths with excessive transfer times and excessive travel utility are deleted, and an effective path set is constructed.
The method comprises the following steps that two screening conditions are provided, firstly, transfer times are specified, after a super network is constructed and when feasible paths are searched, search rules are preset, paths with two continuous transfer road sections and paths with more than 4 transfer road sections in total in the paths are deleted, and the paths are not put into a feasible route set; secondly, setting an upper limit of feasible path effectiveness and eliminating paths with high traveling effectiveness when initializing and calculating the flow distribution effectiveness with the total flow of 0; the upper bound for the feasible path travel effect can be calculated by the following formula:
whereinThe minimum travel utility in all the possible roads during the initialization calculation, and sigma is a path utility expansion coefficient.
4. Based on the change characteristic of the traffic volume along with the travel time, the traffic volume at each travel time interval is distributed to the multi-mode traffic network, and the proportional distribution condition of corresponding travel modes under different traffic conditions can be obtained.
Considering the random factors of the passengers in making travel route and plan selection, the route selection follows the hierarchical distribution, and therefore, the traffic on the route p is:
wherein q iswIs the travel demand between OD points to w, upIs the effect of travel on the effective path p, θ1The perception coefficient of the trip utility when the path selection is carried out;
based on the Fisk model, the multi-mode network traffic distribution problem can be abstracted into the following user balanced path selection model:
in the formula:
where equations (24), (25) are the total demand balance constraints and equation (26) is the path flow non-negative constraint.
In order to solve the flow distribution problem under the multi-mode traffic network, an MSA (continuous average method) is adopted to solve the user balance path selection model, and the specific solving scheme comprises the following four steps:
(a) generating a network; according to the implementation methods of the steps (1) and (2), the basic multi-mode network is expanded into a super network;
(b) initializing; setting initial flowCalculating path travel utilityAnd determineCreating valid path setsSetting the counting number to be j-0;
(c) calculating travel utility and flow; path utility based on initialization phaseExecuting logic flow distribution and calculating auxiliary flow of the road section
(d) The traffic update is performed based on the MSA rules,
(e) checking the convergence; if it isStopping the operation; otherwise, the counting number is set to j ═ j +1, and the step (2) is returned to.
The invention expands the single-layer traffic network into a bus layer, a network appointment layer, a subway layer, a shared single-bus layer and a transfer layer network which are connected with each other, expands the single geographical position node into a composite node containing travel time, travel modes and travel states, converts the complex multi-mode traffic network into a set of single travel mode networks, and can effectively improve the efficiency of shortest path search and flow distribution algorithm.
Claims (5)
1. A travel behavior simulation method under a multi-mode traffic network is characterized by comprising the following steps:
(1) expanding the input basic traffic network through a preset node expansion rule and a node link rule to construct a super traffic network under a multi-traffic mode;
(2) by the generation of the super network, the multi-mode composite node in the basic traffic network is converted into a single travel mode node containing travel time, a travel mode and a travel state in the super network, so that subsequent operation is facilitated;
(3) according to the characteristics of each traffic travel mode, corresponding travel utilities are given to the paths of the traffic travel modes including a subway mode, a public transport mode, a network car booking mode, a shared single car mode and a transfer mode for searching the shortest path;
(4) based on understanding and predefining of a traffic travel scheme, deleting feasible path data with excessive transfer times, and constructing an effective path set;
(5) based on the change characteristic of the traffic volume along with the travel time, the traffic volume at each travel time interval is distributed to a multi-mode traffic network, and the proportion distribution condition of corresponding travel modes under different traffic conditions is obtained.
2. A travel behavior simulation method under the multi-mode transportation network according to claim 1, wherein in the step (2), the multi-mode composite node in the base transportation network is converted into a single travel mode node containing travel time, travel mode and travel state in the super network by the generation of the super network, specifically:
in the multi-mode traffic network M, a place state i belongs to a U set, a transfer state T belongs to a T set, a possible transfer state xi (T) belongs to a T set, the existing transfer times are n, the getting-on and getting-off state indexes are l, the maximum transfer times are K, and the number of time intervals considered in the model is K;
process 1: expanding nodes;
first, for each node i ∈ U, the node is extended to (2+8 κ) (K +1) points: ((i,0,0, l), k), l ═ 0, 1; k1, 2, K + 1; ((i, t, n, l), k), t ═ 1,2,3, 4; n 1,2,. kappa.; l is 0, 1; k1, 2, K +1. the expanded set of points is named N;
and (2) a process: establishing a driving road section;
secondly, finding all driving road sections in the network M, recording road section basic data such as a starting point i, a terminal point i' and usable traffic ways t of the road sections, and calculating free stream journey time t of the road sections; if t ≦ a time interval, establishing a travel route segment between points ((i, t, n,1), k) and points ((i +1, ξ (t), n +1,0), k); if t > a time interval, a driving route is established between points ((i, t, n,1), k) and points ((i +1, ξ (t), n +1,0), k + 1);
and 3, process: establishing a transfer road section;
finally, establishing transfer relations among different travel time intervals and travel traffic modes; scanning all nodes in the point set N, if xi (t) is carpooling, constructing a transfer road section a between points ((i, xi (t), N +1,0), k) and points ((i +1, xi (t), N +1,1), k +1)i∈Aa(ii) a If xi (t) ≠ carpooling, a transfer road section a is constructed between points ((i, xi (t), n +1,0), k) and points ((i +1, xi (t), n +1,1), k)i∈Aa。
3. A travel behavior simulation method under the multi-mode transportation network according to claim 1, wherein in the step (3), according to characteristics of each travel mode, corresponding travel utilities are given to paths where each travel mode including a subway mode, a bus mode, a network car booking mode, a shared bicycle mode and a transfer mode is located, specifically:
a) a network car booking mode;
for the path which is taken by the net car booking mode alone, the utility function of the path is as follows:
whereinFor the time of flight on the path p,in order to be the latency on the path p,for cost on path p, αt/αwThe coefficients are corresponding to the running time and the waiting time;
the travel time on path p may be calculated by the following equation:
whereinIs the travel time of the net appointment mode on the section s,respectively representing the net car appointment flow and the bus flow on the road section, deltaspIt is the status index of whether the section s is on the path p;
the network traffic reservation flow on the road section s can be calculated by the following formula:
whereinAppointing traffic for the network on path p;
according to the BPR equation, the link travel time can be calculated by the following formula:
wherein L issIs the total length of the road section s,for indicating average speed, K, of vehicles approaching the Internet on the section ssIs the traffic capacity of the section s, gamma1/β1Then it is the BPR equation parameter;
the latency on path p can be calculated by the following equation:
whereinRespectively representing the difference between the desired travel time and the actual travel time (early/late); sigma/beta is a punishment coefficient of each time unit when the time arrives early and arrives late respectively; lambda [ alpha ]tThe rate of decrease of the vehicle fixed delay, which generally increases with time;
the cost on path p can be calculated by the following formula:
wherein L ispIs the total length of path pDegree, alphacIs the cost factor;
b) bus, subway modes;
for a path which is taken out in a bus or subway mode independently, the utility function of the path is as follows:
whereinRepresenting the time of arrival and departure on the path p,for the purpose of the uncomfortable feeling effect on the path p,for cost on path p, αz/αdThe station entering and exiting time and the coefficient corresponding to the uncomfortable feeling are obtained;
the travel time of the bus on the path p can be calculated by the following formula:
whereinIs the travel time of the bus mode on the road section s,respectively representing the net car appointment flow and the bus flow on the road section, deltaspIt is the status index of whether the section s is on the path p;
the bus flow on the road section s can be calculated by the following formula:
wherein f iss t1Is the frequency of departure of the bus,is a frequency coefficient;
according to the BPR equation, the bus section travel time on the section s can be calculated by the following formula:
whereinRepresenting the average speed of the bus on the road section s;
for a subway, the travel time on the path p can be calculated by the following formula:
whereinIs a fixed value associated with the subway departure schedule;
the waiting time of the bus and subway modes on the path p can be calculated by the following formula:
waiting time W on road section ss tComprises the following steps:
wherein the content of the first and second substances,representing the departure interval of a bus or subway;
the uncomfortable feeling effect of the bus and subway modes on the path p can be calculated by the following formula:
wherein the content of the first and second substances,is the level of basic discomfort that is experienced,representing passenger flow, k, over a section ssRepresenting the vehicle load capacity, gamma2/β2Then it is the corresponding coefficient;
passenger flow on road section sCan be calculated by the following formula:
whereinThe passenger flow on the path p is the passenger flow taking public transport or subway as a travel mode;
c) shared bicycle mode
For a path that individually shares a single-vehicle mode trip, the utility function of the path is:
the travel time on the path p using the shared bicycle can be calculated by the following formula:
whereinIs the travel time of the shared vehicle on the section s, and can be calculated by the following formula:
the uncomfortable feeling effect of the shared bicycle on the path p can be calculated by the following formula:
wherein tau is1/τ2Is the discomfort perception effect coefficient;
sharing the waiting time of a single vehicle on path pWhen the distance between the stations is within the acceptable range, the distance between the stations is a fixed value, when the distance between the stations is beyond the acceptable range,
d) multi-mode
For the multi-modal path, the travel utility function is as follows:
whereinRespectively the traveling utility, delta, of the net appointment car, the bus (subway) and the shared single car on the road section sspIndicating whether or not the section s isOn path p, TRmnIt is the transfer utility between different travel modes.
4. A travel behavior simulation method under the multi-mode transportation network according to claim 1, wherein in the step (4), based on understanding and predefining of the transportation travel plan, the feasible path data with excessive transfer times is deleted, and the effective path set is specifically constructed as follows:
the method comprises the following steps that two screening conditions are provided, firstly, transfer times are specified, after a super network is constructed and when feasible paths are searched, search rules are preset, paths with two continuous transfer road sections and paths with more than 4 transfer road sections in total in the paths are deleted, and the paths are not put into a feasible route set; secondly, setting an upper limit of feasible path effectiveness and eliminating paths with high traveling effectiveness when initializing and calculating the flow distribution effectiveness with the total flow of 0; the upper bound for the feasible path travel effect can be calculated by the following formula:
whereinThe minimum travel utility in all the possible roads during the initialization calculation, and sigma is a path utility expansion coefficient.
5. A travel behavior simulation method under the multi-mode transportation network according to claim 1, wherein in the step (5), based on the variation characteristic of the traffic volume along with the travel time, the traffic volume at each travel time interval is distributed to the multi-mode transportation network, and the obtaining of the proportion distribution of the corresponding transportation travel modes under different transportation conditions specifically comprises:
considering the random factors of the passengers in making travel route and plan selection, the route selection follows the hierarchical distribution, and therefore, the traffic on the route p is:
wherein q iswIs the travel demand between OD points to w, upIs the effect of travel on the effective path p, θ1The perception coefficient of the trip utility when the path selection is carried out;
based on the Fisk model, the multi-mode network traffic distribution problem can be abstracted into the following user balanced path selection model:
in the formula:
where equations (24), (25) are the total demand balance constraints, and equation (26) is the path flow non-negative constraint;
in order to solve the flow distribution problem under the multi-mode traffic network, an MSA (continuous average method) is adopted to solve the user balance path selection model, and the specific solving scheme comprises the following four steps:
(a) generating a network; according to the implementation methods of the steps (1) and (2), the basic multi-mode network is expanded into a super network;
(b) initializing; setting initial flowCalculating path travel utilityAnd determineCreating valid path setsSetting the counting number to be j-0;
(c) calculating travel utility and flow; path utility based on initialization phaseExecuting logic flow distribution and calculating auxiliary flow of the road section
(d) The traffic update is performed based on the MSA rules,
(e) checking the convergence; if it isStopping the operation; otherwise, the counting number is set to j ═ j +1, and the step (2) is returned to.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111508220A (en) * | 2020-04-10 | 2020-08-07 | 重庆交通开投科技发展有限公司 | Method for accurately performing tail end connection based on public transport population distribution |
| CN112129306A (en) * | 2020-09-24 | 2020-12-25 | 腾讯科技（深圳）有限公司 | Route generation method and device, computer equipment and storage medium |
| CN113392488A (en) * | 2021-06-04 | 2021-09-14 | 东南大学 | Method for disassembling hub node of integrated transportation network and constructing virtual network |
| CN114049770A (en) * | 2021-12-01 | 2022-02-15 | 北京航空航天大学 | Flow prediction method and system after circuit break of multi-mode traffic system |
| CN116822762A (en) * | 2023-03-29 | 2023-09-29 | 北京交通大学 | Metro network system stability monitoring control method, system and equipment |
| CN117235556A (en) * | 2023-11-13 | 2023-12-15 | 浙江大学城乡规划设计研究院有限公司 | Traffic manager-oriented multi-mode traffic composite network construction method |
| CN117409563A (en) * | 2023-10-12 | 2024-01-16 | 华中科技大学 | Multi-mode dynamic public traffic flow distribution method based on shared bicycle |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101308555A (en) * | 2008-06-27 | 2008-11-19 | 北京工业大学 | Multi-mode optimal path selection method for public transport |
| CN101692271A (en) * | 2009-09-30 | 2010-04-07 | 青岛海信网络科技股份有限公司 | Comprehensive guidance method of multiple means of transportation |
| CN101799981A (en) * | 2010-02-09 | 2010-08-11 | 华南理工大学 | Multi-mode public transport region scheduling control method |
| CN104200650A (en) * | 2014-08-27 | 2014-12-10 | 浙江工业大学 | Multimode public transportation transferring method in urban congestion period |
| CN105844362A (en) * | 2016-03-30 | 2016-08-10 | 西南交通大学 | Urban traffic comprehensive travel decision-making model |
| CN107103142A (en) * | 2017-07-11 | 2017-08-29 | 交通运输部公路科学研究所 | Comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology |
| CN108647802A (en) * | 2018-03-26 | 2018-10-12 | 华东师范大学 | Based on the anti-congestion methods of double-layer traffic network model |
-
2019
- 2019-10-17 CN CN201910986075.4A patent/CN110599760B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101308555A (en) * | 2008-06-27 | 2008-11-19 | 北京工业大学 | Multi-mode optimal path selection method for public transport |
| CN101692271A (en) * | 2009-09-30 | 2010-04-07 | 青岛海信网络科技股份有限公司 | Comprehensive guidance method of multiple means of transportation |
| CN101799981A (en) * | 2010-02-09 | 2010-08-11 | 华南理工大学 | Multi-mode public transport region scheduling control method |
| CN104200650A (en) * | 2014-08-27 | 2014-12-10 | 浙江工业大学 | Multimode public transportation transferring method in urban congestion period |
| CN105844362A (en) * | 2016-03-30 | 2016-08-10 | 西南交通大学 | Urban traffic comprehensive travel decision-making model |
| CN107103142A (en) * | 2017-07-11 | 2017-08-29 | 交通运输部公路科学研究所 | Comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology |
| CN108647802A (en) * | 2018-03-26 | 2018-10-12 | 华东师范大学 | Based on the anti-congestion methods of double-layer traffic network model |
Non-Patent Citations (2)
| Title |
|---|
| 曾曦: "市域多模式交通网络通勤出行选择研究", 《中国优秀硕士学位论文全文数据库（济与管理科学辑）》 * |
| 潘星: "层次策略在多模式公交网络路径最优的应用", 《机电技术》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111508220A (en) * | 2020-04-10 | 2020-08-07 | 重庆交通开投科技发展有限公司 | Method for accurately performing tail end connection based on public transport population distribution |
| CN112129306A (en) * | 2020-09-24 | 2020-12-25 | 腾讯科技（深圳）有限公司 | Route generation method and device, computer equipment and storage medium |
| CN112129306B (en) * | 2020-09-24 | 2024-05-28 | 腾讯科技（深圳）有限公司 | Route generation method, route generation device, computer equipment and storage medium |
| CN113392488A (en) * | 2021-06-04 | 2021-09-14 | 东南大学 | Method for disassembling hub node of integrated transportation network and constructing virtual network |
| CN114049770A (en) * | 2021-12-01 | 2022-02-15 | 北京航空航天大学 | Flow prediction method and system after circuit break of multi-mode traffic system |
| CN116822762A (en) * | 2023-03-29 | 2023-09-29 | 北京交通大学 | Metro network system stability monitoring control method, system and equipment |
| CN117409563A (en) * | 2023-10-12 | 2024-01-16 | 华中科技大学 | Multi-mode dynamic public traffic flow distribution method based on shared bicycle |
| CN117409563B (en) * | 2023-10-12 | 2024-03-26 | 华中科技大学 | Multi-mode dynamic public traffic flow distribution method based on shared bicycle |
| CN117235556A (en) * | 2023-11-13 | 2023-12-15 | 浙江大学城乡规划设计研究院有限公司 | Traffic manager-oriented multi-mode traffic composite network construction method |
| CN117235556B (en) * | 2023-11-13 | 2024-02-27 | 浙江大学城乡规划设计研究院有限公司 | Traffic manager-oriented multi-mode traffic composite network construction method |
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