Year: 2020
Type: article
Abstract: Given a spatial graph, an origin and a destination, and on-board diagnostics (OBD) data, the energy-efficient path selection problem aims to find the path with the least expected energy consumption (EEC). Two main objectives of smart cities are sustainability and prosperity, both of which benefit from reducing the energy consumption of transportation. The challenges of the problem include the dependence of EEC on the physical parameters of vehicles, the autocorrelation of the EEC on segments of paths, the high computational cost of EEC estimation, and potential negative EEC. However, the current cost estimation models for the path selection problem do not consider vehicles’ physical parameters. Moreover, the current path selection algorithms follow the “path + edge” pattern when exploring candidate paths, resulting in redundant computation. Our preliminary work introduced a physics-guided energy consumption model and proposed a maximal-frequented-path-graph shortest-path algorithm using the model. In this work, we propose an informed algorithm using an admissible heuristic and propose an algorithm to handle negative EEC. We analyze the proposed algorithms theoretically and evaluate the proposed algorithms via experiments with real-world and synthetic data. We also conduct two case studies using real-world data and a road test to validate the proposed method.
DOI: 10.1145/3406596
Institution: University of Minnesota
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
Awards: DataNet Full Proposal: Terra Populus: A Global Population/Environment Data Network, FY13 NARI: IDENTIFYING AND ANALYZING PATTERNS OF EVASION, DYNAMIC PURPOSE-AWARE GRAPH MODELS FOR COMPOSTIE NETWORKS, III: Small: Towards Spatial Database Management Systems for Flash Memory Storage, G2510179285, III: Small: Investigating Spatial Big Data for Next Generation Routing Services, University of Minnesota Clinical and TranslationalmScience Institute (UMN CTSI), THE UNIVERSITY OF MINNESOTA-TWIN CITIES: NEW AWARD.
CONTROL NUMBER: 1564-1563
TITLE: ''CLOUD CONNECTED DELIVERY VEHICLES: BOOSTING FUEL ECONOMY USING PHYSICS-AWARE SPATIOTEMPORAL DATA ANALYTICS AND REAL-TIME POWERTRAIN CONTROL'', G4982169844, III: Medium: Investigating Spatial-Temporal Informatics for Transportation Science, FEW: A Workshop to Identify Interdisciplinary Data Science Approaches and Challenges to Enhance Understanding of Interactions of Food Systems and Water Systems, G7112848026, S&CC-IRG Track 1: Connecting the Smart-City Paradigm with a Sustainable Urban Infrastructure Systems Framework to Advance Equity in Communities, Collaborative Research: Understanding Climate Change: A Data Driven Approach
OpenAlex ID: W3088992249