Research On Key Technologies For Sharing Bicycles

Nowadays,urban air pollution is becoming more and more serious.The weather such as smog and sandstorms is increasing year by year.The environment around the world is not optimistic.Secondly,in many first-tier cities with convenient transportation,there is still a problem of the last mile.But with the advent of shared bikes,these problems are solved.Shared bicycles have effectively improved environmental and transportation problems,thereby enhancing the city's image.As a green transportation method,the environmental benefits of bicycles are obvious.Secondly,shared bicycles are an effective complement to an increasingly perfect urban transportation network.Its free and flexible rental model can provide services for short-distance travel in the last few kilometers.And sharing bicycles also enhances the health of the public and resists various diseases.The emergence of shared bicycles has a great role in promoting individuals and society,but the problems it brings can not be ignored.Some users are not strong in public awareness and contract spirit,and they are free to destroy and occupy bicycles.Secondly,the illegal and illegal behavior is serious,occupying public areas such as sidewalks and motor vehicles,causing traffic accidents.There is also that the company has not yet formed a sound management and service,over-delivery,resulting in waste of resources,failure of the bicycle can not be processed in time.The following research has been mainly carried out on the above problems:(1)As people say that sharing bicycles is the gold stone of human quality,its appearance has also triggered the dark side of some people,private use of buses,taking over for themselves,and even the phenomenon of buying and selling bicycles.These behaviors seriously affect society.Order is detrimental to the image of the city and the country.In order to solve the problem of bicycle loss,this paper adds an anti-theft monitoring module to the system to detect whether the bicycle is stolen,and use the positioning information on the electronic map to find the stolen vehicle to ensure the safety of the company and others.(2)With the increasing number of vehicles,urban traffic has become more and more complicated,and traffic speeds are very prone to traffic accidents.In addition,in order to satisfy curiosity,some children often have a brake,which causes many traffic accidents and even threatens life safety.Therefore,this paper adds a speed measurement and speed limit module to the system.Through the map,analyze the traffic conditions in the city and limit the speed according to the urban traffic flow.For example,when a certain road condition is crowded or repaired,if the user's speed is too fast,the automatic speed is reduced to a suitable speed,which can reduce the occurrence of traffic accidents.(3)The random parking of shared bicycles is also one of the causes of frequent traffic accidents.Due to the number of traffic accidents caused by sharing bicycles,an effective bicycle parking management system is particularly important.Firstly,the k-means clustering method is used to divide the whole urban area into small sub-areas,then plan parking spots for a single area,use NSGAII algorithm for multi-objective optimization,and compare the advantages and disadvantages of traditional algorithms to NSGA.The ? algorithm has been upgraded to make it more stable and powerful,and is suitable for our entire system.(4)Many companies have over-supplied bicycles in order to seize the market,resulting in oversupply in many cities,resulting in waste of resources and a great load on urban transportation.Aiming at this problem,this paper proposes a delivery volume prediction model,combined with the influencing factors of bicycle delivery,using the popular neural network algorithm at present,can accurately determine the number of reasonable bicycles at each parking point,and ensure the reasonable number of cities.Sex.And for the problem of the imbalance of the number of bicycles at the parking point caused by the time change,the Markov chain probability model is used for dynamic optimization.