01623402

Component

In smart cities, one major challenge that the transportation sector faces is fuel/energy consumption, not only because of its economic impact, but also, and more importantly, because of the associated environmental and health impacts. Eco-routing was developed to minimize network-wide vehicle fuel consumption and emission levels by assigning vehicles the most environmentally friendly route (best route). In this paper, the authors propose a novel eco-routing technique (LPS-ECO) that utilizes fuel consumption cost feedback from vehicles and employs linear programming to assign traffic flows to road links. Subsequently, the system stochastically assigns routes to vehicles. The main idea behind the proposed technique is that, instead of routing all vehicles through the same best routes, the traffic load for each individual flow will be divided among the available network resources toward the flow destination. In this way, the system can avoid potential congestion by routing some vehicles to higher-cost routes. The proposed system is compared to the shortest-path-based eco-routing that is used in the Subpopulation Feedback Assignment eco-routing (SFA-ECO) technique. The comparison shows that for low traffic demands there is no significant difference between the two algorithms. However, as the traffic demand increases, LPS-ECO produces fuel consumption savings that reach 38% for the test network used in this paper. LPS-ECO also produces savings in travel time in most of the cases. However, these savings incur a high computational cost due to the large size of the linear program, especially for large networks. Consequently, future research would entail developing heuristics to reduce the computational load.

This paper was sponsored by TRB committee ADC20 Standing Committee on Transportation and Air Quality.

01618707

17-00912

English

0000-0002-5845-2929

15p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Energy consumption; Environmental impacts; Environmental protection; Linear programming; Routing; Stochastic programming; Traffic assignment

Environment; Highways; Operations and Traffic Management

Transportation Research Board Annual Meeting 2017 Paper #17-00912

TRIS, TRB, ATRI

Dec 8 2016 10:14AM