1 Introduction Traveling time forecasting,the core component in GPS navigation systems and taxi-hailing apps,has attracted widespread attention.Existing research mostly focuses on independent points like traffic flow ...1 Introduction Traveling time forecasting,the core component in GPS navigation systems and taxi-hailing apps,has attracted widespread attention.Existing research mostly focuses on independent points like traffic flow prediction[1,2]or route planning[3,4],which ignore globality and lack satisfactory dynamic progress to adopt sophisticated traffic conditions.To facilitate this line of research,we propose a novel Dynamic Traveling Time forecasting framework based on the Spatial and Temporal Graph convolution(DTT-STG)integrated with map-matching,road speed forecasting,and route planning with full consideration of the dynamic spatial and temporal dependency.DTT-STG designs an angle-based map-matching algorithm to describe the direction of vehicles and explores a self-adaptive adjacency matrix combined with diffusion convolution and attention mechanisms to capture the dynamically changing spatial-temporal dependencies.Afterward,the progressive method is exploited to calculate the traveling time and plan the shortest route dynamically in continuously changing traffic states.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.62002216)the Shanghai Sailing Program(No.20YF1414400)+2 种基金the Collaborative Innovation Platform of Electronic Information Master of Shanghai Polytechnic University(SSPU)(No.A10GY21F015)the Research Projects of Shanghai Polytechnic University(Nos.EGD22QD03,EGD23DS05)the Key Disciplines of Computer Science and Technology of SSPU and the Construction of Electronic Information Master Degree of SSPU.
文摘1 Introduction Traveling time forecasting,the core component in GPS navigation systems and taxi-hailing apps,has attracted widespread attention.Existing research mostly focuses on independent points like traffic flow prediction[1,2]or route planning[3,4],which ignore globality and lack satisfactory dynamic progress to adopt sophisticated traffic conditions.To facilitate this line of research,we propose a novel Dynamic Traveling Time forecasting framework based on the Spatial and Temporal Graph convolution(DTT-STG)integrated with map-matching,road speed forecasting,and route planning with full consideration of the dynamic spatial and temporal dependency.DTT-STG designs an angle-based map-matching algorithm to describe the direction of vehicles and explores a self-adaptive adjacency matrix combined with diffusion convolution and attention mechanisms to capture the dynamically changing spatial-temporal dependencies.Afterward,the progressive method is exploited to calculate the traveling time and plan the shortest route dynamically in continuously changing traffic states.
