United States Department of Transportation
i
Speed Trajectory Data from Adaptive Eco-Driving Applications [supporting datasets]
-
2019-10-17
-
[PDF-191.77 KB]
Details:
-
Creators:
-
Corporate Creators:
-
Corporate Contributors:
-
Subject/TRT Terms:
-
Publication/ Report Number:
-
DOI:
-
Resource Type:
-
Geographical Coverage:
-
Corporate Publisher:
-
Abstract:The eco-approach and departure (EAD) application for signalized intersections has been proved to be environmentally efficient in a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, the traffic-related information received from sensing or communication devices is highly uncertain due to the limited sensing range and varying driving behaviors of other vehicles. This uncertainty increases the difficulty to predict the actual queue length of the downstream intersection. It further brings great challenge to derive an energy efficient speed profile for vehicles to follow. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions including uncertain traffic condition. A graph-based model is created with nodes representing dynamic states of the host vehicle (distance to intersection and current speed) and indicator of queue status and directed edges with weight representing expected energy consumption between two connected states. Then a dynamic programing approach is applied to identify the optimal speed for each vehicle-queue-signal state iteratively from downstream to the upstream. The uncertainty can be addressed by formulating stochastic models when describing the transition of queue-signal state. For uncertain traffic conditions, numerical simulation results show an average energy saving of 9%. It also indicates that energy consumption of a vehicle equipped with adaptive EAD strategy and a 100m-range sensor is equivalent to a vehicle with conventional EAD strategy and a 190m-range sensor. To some extent, the proposed strategy could double the effective detection range in eco-driving.
-
Format:
-
Funding:
-
Collection(s):
-
Main Document Checksum:
-
Download URL:
-
File Type:
