Research On Charging Strategy Of Electric Taxi Based On Traffic Trajectory Big Data

Promoting the electrification of public transportation represented by taxis is an important practice in the transformation of urban transportation electrification,and it is also an important way to achieve the goal of "carbon peaking and carbon neutrality".At present,the development of urban charging facilities is lagging behind and the layout or configuration is lack of scientific and rationality.In addition,the anxiety of driving range makes the charging station show low utilization rate or queuing for charging,etc.,and it is difficult to adapt to the full electrification of taxis.How to eliminate the information asymmetry between taxis and charging stations,ensure fair competition in the charging of taxi fleets,and arrange the charging load of electric taxis in an orderly and balanced manner is an effective means to alleviate the current contradiction between supply and demand.This thesis mines the historical trajectory data of taxis in Fuzhou,and uses information technology to study the orderly charging strategy of electric taxis to meet the needs of the transformation and growth of electric taxis and achieve a more reasonable utilization of charging resources.The preparatory work includes:(1)Constructing the road network topology in Fuzhou City,and obtaining the effective operation trajectory data of taxis through modeling;(2)Counting the daily traffic flow in Fuzhou City,using the inverse technology and converting it into an Origin Destination(OD)Travel probability matrix;(3)Construct a power consumption model based on average driving speed,and use Monte Carlo random sampling method to generate electric taxi charging demand distribution combined with OD travel probability matrix.In order to effectively integrate multi-network information,eliminate the asymmetry of vehicle pile information,and realize the orderly guidance of electric taxi charging,this thesis proposes a destination-oriented charging navigation strategy(DNCN).The strategy work includes:(1)Constructing a charging navigation system that integrates the collaborative working mode of the traffic information network,the power network and the charging station network in combination with the charging reservation mechanism to coordinate the information interaction of multiple networks;(2)Combining the charging station queuing rate,A dynamic pricing model is constructed to balance the orderly guidance of charging demand;(3)Considering the charging convenience of electric taxis near the next customer-seeking point,a charging decision function is constructed to reduce the detour distance.Aiming at the phenomenon of demand concentration during the charging peak period,DNCN fails to fully consider the fairness of taxi fleet reservation charging competition.This thesis further proposes a dynamic decision-making charging navigation strategy based on fair game(DNCN-FG).Real-time comprehensive cost re-estimation of electric taxis affected by new reservations,and optimal charging re-decision,so that the charging decision of the taxi fleet with reservation charging can reach the globally optimal Nash balance,so as to ensure fair charging competition for taxi fleets,to realize the optimal decision redistribution of charging demand.In this thesis,the proposed DNCN and DNCN-FG strategies are simulated and verified,and compared with other existing charging strategies.The experimental results show that the strategy proposed in this thesis can effectively eliminate the asymmetry of vehicle pile information,realize the integration of multi-network information,balance the spatial distribution of charging load,and improve urban charging in the charging environment where the development of existing charging facilities is lagging behind and the charging load does not match.Resource utilization,and can effectively reduce the charging waiting time and detour time of rental vehicles.