Mixed Traffic Flow Guidance Strategy At Signalized Arterial Road In The Presence Of Work Zone

Connected and Autonomous Vehicle(CAV)can effectively alleviate the problems of road efficiency and capacity,frequent stop and go caused by unreasonable driving behaviors.As is known to all,the mixed driving of Connected and Autonomous Vehicle and Human-Driven vehicle(HDV)is a necessary stage in the development process of CAV.However,mixed driving adds additional interference to the motion planning and control of CAV,which restricts the play of the vehicle-road collaboration function of CAV.Therefore,this paper carried out the research on the autonomous lane changing and merging strategy of mixed traffic vehicles in the work zone,the research on the speed optimization guidance strategy of mixed vehicles in the signal control road.Carried out simulation verification on multi-intersection sections of Wenjing Road in Xi ’an City in view of vehicle motion control under different mixed modes.It is shown that the mixed traffic flow control strategy proposed in the paper can effectively improve the road capacity,improve the traffic efficiency of the mixed traffic flow,and reduce the fuel consumption of the road.The main research contents of this paper are as follows:(1)In order to obtain more realistic and reasonable road traffic parameters,this paper collects a large number of data by using UAV in work zone and signalized intersection.The characteristics of the collected data were analyzed to determine the speed distribution,lane change duration distribution and merging point position distribution characteristics of vehicles when changing lanes in the construction section.Besides,different turning traffic volumes,signal phase timing and basic road parameters of signalized intersection sections were obtained.The vehicle speed,number of stops and driving characteristics of vehicles in different driving directions at intersections are analyzed,which provides support for the study of vehicle motion control in different mixed modes.(2)Aiming at the problem of low traffic efficiency in work zones and inability to achieve safe,efficient and environmentally friendly autonomous lane changing and merging,an autonomous lane changing and merging strategy for mixed vehicles in work zones is proposed.Based on the different expected time headway and the beginning lane changing and merging position of mixed vehicles.Considering the actual time headway and safe lane changing and merging conditions.For vehicles that do not meet the conditions for lane changing and merging,by optimizing the speed of ego-vehicle or surrounding vehicles to create a safe lane change merging spacing.An autonomous lane changing and merging strategy for mixed traffic vehicles in work zone combined with lane changing and merging control and speed optimization control is proposed.The simulation results show that with the increase of CAV penetration,the average speed and energy consumption of vehicles in the work zone are obviously improved.Compare with no control,the average speed of vehicles in the work zone of peak traffic flow and normal peak traffic flow increased by 28.96% and 24.85% respectively,and the energy consumption decreased by 23.41% and 21.13%,respectively.It shows that this strategy can effectively improve the lane changing and merging efficiency of vehicles in the work zone,reduce the risk of accidents,and reduce the fuel consumption of vehicles in the work zone.(3)Aiming at the problems of traffic congestion and extra energy waste caused by frequent start-stop at signalized intersections,a speed guidance strategy of mixed traffic vehicles for signal-controlled road is proposed.Based on the position,speed and phase information of the vehicle within the length of the guidance interval,the speed guidance mode of the signal control road section is established,and the recommended speed constraint range of intelligent networked vehicles in the networked environment is studied.Then,the objective function for improving traffic efficiency and energy consumption is established,and the speed optimization guidance strategy is given based on multi-objective genetic algorithm.The simulation results show that with the increase of CAV penetration,the speed,fuel consumption and number of intersection stops of vehicles in the signal intersection are obviously improved.At the same time,compared with no control,the average speed of vehicles at intersections increased by27.82%,fuel consumption decreased by 27.88%,and the average number of stops per second decreased by 91.62%.This strategy effectively improves the traffic efficiency of the intersection,reduces the occurrence of parking phenomenon,and reduces the fuel consumption of the signalized intersection.(4)Based on the SUMO simulation system construction and test analysis.Through a case study,the autonomous lane changing and merging strategy of mixed vehicles on work zone and the speed guidance strategy of mixed vehicles on signalized intersection section are verified by comprehensive simulation.The simulation results show that,when the work zone is closer to the intersection,the average speed of vehicles at the intersection and work zone under different CAV penetration rates decreases by 10%～14% and 8%～10% respectively,the energy consumption increases by 6%～8% and 6%～9%.The applicability and effectiveness of autonomous confluence strategy of mixed vehicles on work zone and speed guidance strategy of mixed vehicles on information control roads are verified in the actual multi-intersection road.