Abstract
This paper presents GreenRoute, a fuel-saving vehicular navigation system whose contribution is motivated by one of the key challenges in the design of autonomic services: Namely, designing the service in a manner that reduces operating cost. GreenRoute achieves this end, in the specific context of fuel-saving vehicular navigation, by significantly improving the generalizability of fuel consumption models it learns (in order to recommend fuel-saving routes to drivers). By learning fuel consumption models that apply seamlessly across vehicles and routes, GreenRoute eliminates one of the key incremental costs unique to fuel-saving navigation: Namely, the cost of upkeep with ever-changing fuel-consumption-specific route and vehicle parameters globally. Unlike shortest or fastest routes (that depend only on map topology and traffic), minimum-fuel routes depend additionally on the vehicle engine. This makes fuel-efficient routes harder to compute in a generic fashion, compared to shortest and fastest routes. The difficulty results in two additional costs. First, more route features need to be collected (and updated) for predicting fuel consumption, such as the nature of traffic regulators. Second, fuel prediction remains specific to the individual vehicle type, which requires continual upkeep with new car types and parameters. The contribution of this paper lies in deriving and implementing a fuel consumption model that avoids both of the above two sources of cost. To measure route recommendation quality, we test the system (using 21 vehicles and over 2400 miles driven in seven US cities) by comparing fuel consumption on our routes against both Google Maps' routes and the shortest routes. Results show that, on average, our routes save 10.8% fuel compared to Google Maps' routes and save 8.4% compared to the shortest routes. This is roughly comparable to services that maintain individualized vehicle models, suggesting that our low-cost models do not come at the expense of quality reduction.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2019 IEEE International Conference on Autonomic Computing, ICAC 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-10 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781728124117 |
| DOIs | |
| State | Published - Jun 2019 |
| Event | 16th IEEE International Conference on Autonomic Computing, ICAC 2019 - Umea, Sweden Duration: Jun 16 2019 → Jun 20 2019 |
Publication series
| Name | Proceedings - 2019 IEEE International Conference on Autonomic Computing, ICAC 2019 |
|---|
Conference
| Conference | 16th IEEE International Conference on Autonomic Computing, ICAC 2019 |
|---|---|
| Country | Sweden |
| City | Umea |
| Period | 6/16/19 → 6/20/19 |
Fingerprint
Keywords
- eco drive
- fuel consumption model
- Fuel saving routing
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Hardware and Architecture
- Control and Optimization
Cite this
GreenRoute : A Generalizable Fuel-Saving Vehicular Navigation Service. / Zhao, Yiran; Yao, Shuochao; Liu, Dongxin; Shao, Huajie; Liu, Shengzhong; Abdelzaher, Tarek.
Proceedings - 2019 IEEE International Conference on Autonomic Computing, ICAC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1-10 8831213 (Proceedings - 2019 IEEE International Conference on Autonomic Computing, ICAC 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - GreenRoute
T2 - A Generalizable Fuel-Saving Vehicular Navigation Service
AU - Zhao, Yiran
AU - Yao, Shuochao
AU - Liu, Dongxin
AU - Shao, Huajie
AU - Liu, Shengzhong
AU - Abdelzaher, Tarek
PY - 2019/6
Y1 - 2019/6
N2 - This paper presents GreenRoute, a fuel-saving vehicular navigation system whose contribution is motivated by one of the key challenges in the design of autonomic services: Namely, designing the service in a manner that reduces operating cost. GreenRoute achieves this end, in the specific context of fuel-saving vehicular navigation, by significantly improving the generalizability of fuel consumption models it learns (in order to recommend fuel-saving routes to drivers). By learning fuel consumption models that apply seamlessly across vehicles and routes, GreenRoute eliminates one of the key incremental costs unique to fuel-saving navigation: Namely, the cost of upkeep with ever-changing fuel-consumption-specific route and vehicle parameters globally. Unlike shortest or fastest routes (that depend only on map topology and traffic), minimum-fuel routes depend additionally on the vehicle engine. This makes fuel-efficient routes harder to compute in a generic fashion, compared to shortest and fastest routes. The difficulty results in two additional costs. First, more route features need to be collected (and updated) for predicting fuel consumption, such as the nature of traffic regulators. Second, fuel prediction remains specific to the individual vehicle type, which requires continual upkeep with new car types and parameters. The contribution of this paper lies in deriving and implementing a fuel consumption model that avoids both of the above two sources of cost. To measure route recommendation quality, we test the system (using 21 vehicles and over 2400 miles driven in seven US cities) by comparing fuel consumption on our routes against both Google Maps' routes and the shortest routes. Results show that, on average, our routes save 10.8% fuel compared to Google Maps' routes and save 8.4% compared to the shortest routes. This is roughly comparable to services that maintain individualized vehicle models, suggesting that our low-cost models do not come at the expense of quality reduction.
AB - This paper presents GreenRoute, a fuel-saving vehicular navigation system whose contribution is motivated by one of the key challenges in the design of autonomic services: Namely, designing the service in a manner that reduces operating cost. GreenRoute achieves this end, in the specific context of fuel-saving vehicular navigation, by significantly improving the generalizability of fuel consumption models it learns (in order to recommend fuel-saving routes to drivers). By learning fuel consumption models that apply seamlessly across vehicles and routes, GreenRoute eliminates one of the key incremental costs unique to fuel-saving navigation: Namely, the cost of upkeep with ever-changing fuel-consumption-specific route and vehicle parameters globally. Unlike shortest or fastest routes (that depend only on map topology and traffic), minimum-fuel routes depend additionally on the vehicle engine. This makes fuel-efficient routes harder to compute in a generic fashion, compared to shortest and fastest routes. The difficulty results in two additional costs. First, more route features need to be collected (and updated) for predicting fuel consumption, such as the nature of traffic regulators. Second, fuel prediction remains specific to the individual vehicle type, which requires continual upkeep with new car types and parameters. The contribution of this paper lies in deriving and implementing a fuel consumption model that avoids both of the above two sources of cost. To measure route recommendation quality, we test the system (using 21 vehicles and over 2400 miles driven in seven US cities) by comparing fuel consumption on our routes against both Google Maps' routes and the shortest routes. Results show that, on average, our routes save 10.8% fuel compared to Google Maps' routes and save 8.4% compared to the shortest routes. This is roughly comparable to services that maintain individualized vehicle models, suggesting that our low-cost models do not come at the expense of quality reduction.
KW - eco drive
KW - fuel consumption model
KW - Fuel saving routing
UR - http://www.scopus.com/inward/record.url?scp=85073146005&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073146005&partnerID=8YFLogxK
U2 - 10.1109/ICAC.2019.00011
DO - 10.1109/ICAC.2019.00011
M3 - Conference contribution
AN - SCOPUS:85073146005
T3 - Proceedings - 2019 IEEE International Conference on Autonomic Computing, ICAC 2019
SP - 1
EP - 10
BT - Proceedings - 2019 IEEE International Conference on Autonomic Computing, ICAC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
ER -