2,894 research outputs found
A Macro-Micro Approach to Reconstructing Vehicle Trajectories on Multi-Lane Freeways with Lane Changing
Vehicle trajectories can offer the most precise and detailed depiction of
traffic flow and serve as a critical component in traffic management and
control applications. Various technologies have been applied to reconstruct
vehicle trajectories from sparse fixed and mobile detection data. However,
existing methods predominantly concentrate on single-lane scenarios and neglect
lane-changing (LC) behaviors that occur across multiple lanes, which limit
their applicability in practical traffic systems. To address this research gap,
we propose a macro-micro approach for reconstructing complete vehicle
trajectories on multi-lane freeways, wherein the macro traffic state
information and micro driving models are integrated to overcome the
restrictions imposed by lane boundary. Particularly, the macroscopic velocity
contour maps are established for each lane to regulate the movement of vehicle
platoons, meanwhile the velocity difference between adjacent lanes provide
valuable criteria for guiding LC behaviors. Simultaneously, the car-following
models are extended from micro perspective to supply lane-based candidate
trajectories and define the plausible range for LC positions. Later, a
two-stage trajectory fusion algorithm is proposed to jointly infer both the
car-following and LC behaviors, in which the optimal LC positions is identified
and candidate trajectories are adjusted according to their weights. The
proposed framework was evaluated using NGSIM dataset, and the results indicated
a remarkable enhancement in both the accuracy and smoothness of reconstructed
trajectories, with performance indicators reduced by over 30% compared to two
representative reconstruction methods. Furthermore, the reconstruction process
effectively reproduced LC behaviors across contiguous lanes, adding to the
framework's comprehensiveness and realism
Heterogeneous urban traffic data and their integration through kernel-based interpolation
This paper presents collection and analysis of heterogeneous urban traffic data, and integration of them through a kernel-based approach. The recent development in sensing and information technology opens up opportunities for researching the use of this vast amount of new urban traffic data. In this paper, the data fusion algorithm is developed by using a kernel based interpolation approach. Our objective is to reconstruct the underlying urban traffic pattern with fine spatial and temporal granularity through processing and integrating data from different sources. The fusion algorithm can work with data collected in different space time resolution, with different level of accuracy, and from different kinds of sensors. The properties and performance of the fusion algorithm is evaluated by using a virtual test-bed produced by VISSIM microscopic simulation. The methodology is demonstrated through a real-world application in Central London. This paper contributes to analysis and management of urban transport facilities
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