CN106530691A - Hybrid vehicle model multilane cellular automaton model considering vehicle occupancy space - Google Patents
Hybrid vehicle model multilane cellular automaton model considering vehicle occupancy space Download PDFInfo
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- CN106530691A CN106530691A CN201610935264.5A CN201610935264A CN106530691A CN 106530691 A CN106530691 A CN 106530691A CN 201610935264 A CN201610935264 A CN 201610935264A CN 106530691 A CN106530691 A CN 106530691A
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Abstract
The invention provides a hybrid vehicle model multilane cellular automaton model considering vehicle occupancy space, which is applicable to simulation on a hybrid vehicle model multilane road traffic flow. Firstly, discretization is carried out on the road space, road cells are built, vehicles on the road are divided into three models of heavy vehicles, cars and motorcycles, and different models of vehicles occupy different numbers of cells; then, discretization is carried out on time, and for every time step forward, vehicles in the models are subjected to evolution according to four steps of an acceleration process, a deceleration process, a randomization deceleration process and a vehicle position updating process; the evolution processes are carried out according to a specific lane changing rule and a boundary condition, and each vehicle has an independent expected speed. The hybrid vehicle model multilane cellular automaton model considering vehicle occupancy space is applied to simulation on the hybrid vehicle model multilane road traffic flow and particularly has unique advantages in simulation influences of the motorcycles in urban traffic, and the simulation result has high rationality and accuracy.
Description
Technical field
The present invention relates to more particularly, to a kind of, Cellular Automata Simulation traffic flow technical field, considers that vehicle occupies
The hybrid multilane cellular Automation Model in space, the model can be used for the friendship of accurate simulation hybrid multiple-lane road
It is through-flow.
Background technology
With the quick increase of China's vehicle guaranteeding organic quantity, urban transport problems is increasingly projected, the research to traffic flow
It is increasingly becoming one important topic of contemporary scientific.Traffic flow computer simulation be study traffic flow character important method, institute
Mainly there are fluid mechanic model, car-following model, cellular Automation Model etc. using there is computation model.Cellular Automation Model will be even
Continuous room and time carries out sliding-model control, simulates real things with limited cellular quantity and simple evolution rule
Operating and evolution.As cellular Automation Model can effectively simulate vehicle microscopic movement state in traffic flow, for describing
Actual traffic behavior has unique superiority, therefore is widely used in single-way traffic, multilane traffic, friendship in recent years
The research of all kinds of traffic problems such as logical congestion, bus stop, bicycle traffic, pedestrian traffic.
In urban road, the various such as large car, compact car, motorcycle, non-motor vehicle are mixed on multiple-lane road
Capable phenomenon is more universal, due to different automobile types vehicle take up room it is different with speed, set up the cellular of this kind of road traffic from
Motivation Model has stronger challenge.Scholar has done numerous studies work in cellular automaton traffic flow field in recent years
Make, domestic some scholars have been presented for multiple-lane road cellular automaton traffic flow, but existing model only considers different cars
Speed difference, does not consider the spacial difference of different automobile types vehicle, and during simulation, all kinds vehicle takes a cellular,
The spatial accuracy of analog result is not high.In addition, the maximal rate of same types of vehicles is invariable in existing model, mould is increased
The difference of type analog result and truth.
The content of the invention
Present invention aim to overcome that the shortcoming of prior art, provides a kind of consideration vehicle spacial hybrid vehicle first
Type multilane cellular Automation Model；Large car, compact car and motorcycle can be overcome to be expert at using the modeling traffic flow
Sail custom, take up room the problems different with speed, can effectively improve the accuracy of analog result.
Foregoing invention purpose is realized, the technical solution used in the present invention is as follows：
A kind of to consider the spacial hybrid multilane cellular Automation Model of vehicle, the automaton model is concrete
For：
1) road is represented with n × L discrete network lattice point, wherein n represents road width, and L represents link length, each
The certain physical length of lattice point correspondence, any instant, each lattice point may be empty or be occupied by vehicle.
2) road vehicle is divided into large car, car and motorcycle three types, and large car occupies 2 × 4 lattice points,
Car occupies 2 × 2 lattice points, and motorcycle occupies 1 × 1 lattice point.
3) each car has different maximal raties vi,max, velocity interval desirable 0,1,2 ..., vi,max, every kind of vehicle car
Maximal rate there are different distribution situations.
4) vehicle in model follows certain traveling rule, lane-change rule and boundary condition.
Further, in step during t → t+1, the vehicle in model presses accelerator, moderating process, random slowing down
Process, renewal vehicle location 4 steps of process are developed.
Further, vehicle changing lane in evolutionary process, concrete lane change rule is：Motorcycle is paid the utmost attention on the right side
Side lanes, pay the utmost attention to lane-change to the left, pay the utmost attention to lane-change to the right during large car lane-change during car lane-change.
Further, after model adopts periodic boundary condition, vehicle to drive to dead end street, by the other end from road
Into system.
Further, car speed is point 7 grades, and each vehicle has independent travel speed, each time step
Interior, vehicle can move forward 0～7 lattice.
Further, the VELOCITY DISTRIBUTION of three kinds of vehicle vehicles can set the speed of every kind of vehicle on demand with difference during simulation
Degree distribution.
Compared with prior art, advantages of the present invention is mainly reflected in：One is compared to existing cellular automata traffic
Flow model, the present invention consider different automobile types vehicle volume size and in the road occupy space, analog result can be improved
Accuracy；Two is that in model of the present invention, each car all has independent maximum compared to existing cellular automaton traffic flow and traffic
Speed, and the VELOCITY DISTRIBUTION of three kinds of vehicle vehicles can make simulation closer to truth with difference.
Description of the drawings
Fig. 1 is to consider the spacial hybrid multilane cellular Automation Model schematic diagram of vehicle.
Specific embodiment
The present invention is further detailed explanation with reference to the accompanying drawings and detailed description.It is described herein concrete
Embodiment is used only for explaining the present invention, is not intended to limit the present invention.
Road is carried out into sliding-model control according to the network shown in accompanying drawing 1, cellular Automation Model is set up.With two cars
As a example by road road, 4 × 1000 grid model is set up (for large car and car are two tracks, for motorcycle is 4 cars
Road), each cellular length is 2.5m, and corresponding real road length is about 2.5km.Road vehicle is divided into large car, little
Automobile and motorcycle three types, large car occupy 2 × 4 lattice points, and car occupies 2 × 2 lattice points, and motorcycle occupies 1 × 1
Individual lattice point.Initial time, random mixed distribution, on two tracks, and presses VELOCITY DISTRIBUTION probability according to a certain percentage for three kinds of vehicles
It is random to generate initial velocity.Time step takes 1s, then 1cell/s is equivalent to 9km/h, when common mode intends 3600 steps.In step t →
During t+1, the vehicle in model is by accelerator, moderating process, random moderating process, 4 steps of renewal vehicle location process
Suddenly developed.
It is as follows that lane change rule is set：
(1) motorcycle：
(2) car：
(3) large car：
In formula, di,LFRepresent the spacing of i cars and left front vehicle；di,LBRepresent the spacing of i cars and left back vehicle；di,RF
Represent the spacing of i cars and right front vehicle；di,RBRepresent the spacing of i cars and right back vehicle；dbFor on lane change car and target track
Safe distance needed for rear car, generally takes db=vmaxAfter vehicle meets above-mentioned lane change condition, by certain Probability ptBecome
Road.
Boundary condition is set to：Using periodic boundary condition, after renewal vehicle location terminates every time, on detection road
The position x of head carleadIf, xlead＞ Lroad, then this car will enter system, and x from the other end of roadlast=
xlead-Lroad, x herelead, xlast, LroadThe length of the position of road top car, the position of trailer and road is represented respectively.
The embodiment of invention described above, does not constitute limiting the scope of the present invention.It is any at this
Done modification, equivalent and improvement etc. within bright spiritual principles, should be included in the claim protection of the present invention
Within the scope of.
Claims (6)
1. it is a kind of to consider the spacial hybrid multilane cellular Automation Model of vehicle, it is characterised in that the automat
Model is specially：
Road is represented with n × L discrete network lattice point, wherein n represents road width, and L represents link length, each lattice point pair
Certain physical length, any instant, each lattice point is answered to be occupied for sky or by vehicle；
Road vehicle is divided into into large car, car and motorcycle three types, and is based on vehicle, large car is set and is occupied
2 × 4 lattice points, car occupy 2 × 2 lattice points, and motorcycle occupies 1 × 1 lattice point；
Each car has different maximal raties vi,max, velocity interval desirable 0,1,2 ..., vi,max, the maximum of every kind of vehicle vehicle
Speed has different distribution situations；
Vehicle in model follows traveling rule, lane-change rule and boundary condition.
2. model according to claim 1, it is characterised in that in step during t → t+1, the vehicle in model is followed
Traveling rule be：Accelerator, moderating process, random moderating process, renewal vehicle location 4 steps of process are developed.
3. model according to claim 1, it is characterised in that the vehicle in model in evolutionary process, changing lane,
Specifically shining road rule is：Motorcycle is paid the utmost attention in right lane only, pays the utmost attention to lane-change to the left during car lane-change, large-scale
Lane-change to the right is paid the utmost attention to during car lane-change.
4. model according to claim 1, it is characterised in that model adopts periodic boundary condition, vehicle to drive to
After road head, the other end from road is entered.
5. model according to claim 1, it is characterised in that car speed is point 7 grades, each vehicle has independent
Travel speed, in each time step, vehicle can move forward 0～7 lattice.
6. model according to claim 1, it is characterised in that three kinds of vehicle vehicles are to set every kind of car on demand in simulation
The VELOCITY DISTRIBUTION of type.
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Cited By (8)
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| CN106898143A (en) * | 2017-04-10 | 2017-06-27 | 合肥学院 | A kind of magnitude of traffic flow modeling method of pilotless automobile |
| CN106991251A (en) * | 2017-04-27 | 2017-07-28 | 东南大学 | A kind of freeway traffic flow cellular machine emulation mode |
| CN109326132A (en) * | 2018-10-31 | 2019-02-12 | 惠州市德赛西威汽车电子股份有限公司 | A kind of more vehicles collaboration lane change implementation method and device |
| CN109584541A (en) * | 2019-01-31 | 2019-04-05 | 电子科技大学 | A kind of construction method of the mixing road net model of microcosmic traffic simulation system |
| CN110472271A (en) * | 2019-07-01 | 2019-11-19 | 电子科技大学 | A kind of non-motorized lane Mixed contact construction method of microscopic traffic simulation |
| CN113689696A (en) * | 2021-08-12 | 2021-11-23 | 北京交通大学 | Multi-mode traffic collaborative evacuation method based on lane management |
| CN113920724A (en) * | 2021-09-29 | 2022-01-11 | 南通大学 | Improved traffic flow analysis method based on mixed road switching controller |
| CN115909768A (en) * | 2022-10-31 | 2023-04-04 | 桂林电子科技大学 | Intelligent network-connected hybrid traffic flow intersection signal collaborative optimization method and system |
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106898143A (en) * | 2017-04-10 | 2017-06-27 | 合肥学院 | A kind of magnitude of traffic flow modeling method of pilotless automobile |
| CN106991251A (en) * | 2017-04-27 | 2017-07-28 | 东南大学 | A kind of freeway traffic flow cellular machine emulation mode |
| CN109326132A (en) * | 2018-10-31 | 2019-02-12 | 惠州市德赛西威汽车电子股份有限公司 | A kind of more vehicles collaboration lane change implementation method and device |
| CN109584541A (en) * | 2019-01-31 | 2019-04-05 | 电子科技大学 | A kind of construction method of the mixing road net model of microcosmic traffic simulation system |
| CN110472271A (en) * | 2019-07-01 | 2019-11-19 | 电子科技大学 | A kind of non-motorized lane Mixed contact construction method of microscopic traffic simulation |
| CN113689696A (en) * | 2021-08-12 | 2021-11-23 | 北京交通大学 | Multi-mode traffic collaborative evacuation method based on lane management |
| CN113689696B (en) * | 2021-08-12 | 2022-07-29 | 北京交通大学 | Multi-mode traffic collaborative evacuation method based on lane management |
| CN113920724A (en) * | 2021-09-29 | 2022-01-11 | 南通大学 | Improved traffic flow analysis method based on mixed road switching controller |
| CN113920724B (en) * | 2021-09-29 | 2022-06-03 | 南通大学 | Improved traffic flow analysis method based on mixed road switching controller |
| CN115909768A (en) * | 2022-10-31 | 2023-04-04 | 桂林电子科技大学 | Intelligent network-connected hybrid traffic flow intersection signal collaborative optimization method and system |
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Application publication date: 20170322 |