Travel Time Estimation And Route Recommendation Based On Traffic Surveillance Data

With the development of economy as well as the quality of people’s life,the cars’ number has increased very fast,and the condition of transportation in city is also becoming much more complex.Thus,it’s necessary to come up with an efficient algorithm to estimate the travel time of cars.“Travel time estimation” is an important problem in intelligent transportation field,and is also pretty common in transportation monitoring and routing system.And with the implementation of our country’s smart city concept,more and more surveillance cameras are deployed at the intersections of urban roads,in addition,more and more vehicles are equipped with GPS navigation and positioning systems and other equipment that can record vehicle trajectories.Which makes it possible to use the urban traffic surveillance data to estimate the vehicle travel time and query the route.Aiming at the problem of urban travel travel time estimation,this paper proposes a method of using traffic surveillance data to estimate vehicle travel time.The main research contents are as follows:Aiming at the problem of urban travel time estimation,a travel time estimation method based on the urban traffic surveillance data was proposed,which is called UTSD.Firstly,the traffic surveillance cameras were mapped into the urban road network,and a directed weighted road network graph was constructed based on traffic monitoring data recording.Secondly,a spatio-temporal index and a reverse index structure were built for travel time estimation,the former was used to quick index the camera records of all vehicles,and the latter was used to fast obtain the travel time and the passing camera trajectory of each vehicle.These two indexes significantly improved the efficiency of data query and travel time estimation.Finally,based on the constructed indexing structures,an effective travel time estimation and path query method was given.According to the departure time,origin and destination,the vehicles with the same origin and destination were matched on the spatio-temporal index structure,and then the reverse index was used to quickly obtain the travel time estimate and vehicle route.Using the real traffic monitoring big data of a provincial capital city for experimental evaluation,compared with Dijkstra shortest path algorithm based on directed graph and Baidu algorithm,the minimum average relative error of the proposed method UTSD is lower by65.02% and 40.94%,respectively.In addition,the average query time of UTSD was less than 0.3s when the 7-day monitoring data was used as historical data,which verifies the effectiveness and efficiency of the proposed method.