[go: up one dir, main page]

Zeng et al., 2017 - Google Patents

Application of the support vector machine and heuristic k-shortest path algorithm to determine the most eco-friendly path with a travel time constraint

Zeng et al., 2017

View PDF
Document ID
12716469407715206282
Author
Zeng W
Miwa T
Morikawa T
Publication year
Publication venue
Transportation Research Part D: Transport and Environment

External Links

Snippet

This study aims to determine an eco-friendly path that results in minimum CO 2 emissions while satisfying a specified budget for travel time. First, an aggregated CO 2 emission model for light-duty cars is developed in a link-based level using a support vector machine …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/26—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3469—Fuel consumption; Energy use; Emission aspects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/26—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/26—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
    • G01C21/28—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30—Map- or contour-matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • G06Q10/047—Optimisation of routes, e.g. "travelling salesman problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Zeng et al. Application of the support vector machine and heuristic k-shortest path algorithm to determine the most eco-friendly path with a travel time constraint
Zeng et al. Exploring trip fuel consumption by machine learning from GPS and CAN bus data
Ticha et al. Empirical analysis for the VRPTW with a multigraph representation for the road network
Yao et al. Short‐term traffic speed prediction for an urban corridor
Tadei et al. The multi-path traveling salesman problem with stochastic travel costs
US8682633B2 (en) Cost evaluation and prediction
Rodríguez-Puente et al. Algorithm for shortest path search in Geographic Information Systems by using reduced graphs
Yedavalli et al. Microsimulation analysis for network traffic assignment (MANTA) at metropolitan-scale for agile transportation planning
Ozdemir et al. A hybrid HMM model for travel path inference with sparse GPS samples
De Nunzio et al. Bi-objective eco-routing in large urban road networks
Van de Hoef Fuel-efficient centralized coordination of truck platooning
Sung et al. Speed optimization algorithm with routing to minimize fuel consumption under time-dependent travel conditions
Sun et al. Road network metric learning for estimated time of arrival
Liu et al. Planning bike lanes with data: Ridership, congestion, and path selection
Xiao et al. Green vehicle routing problem with time-varying traffic congestion
US11346677B2 (en) Method to measure road roughness characteristics and pavement induced vehicle fuel consumption
Mądziel et al. Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily.
Deng et al. Communication‐based predictive energy management strategy for a hybrid powertrain
Hunter et al. Energy-aware dynamic data-driven distributed traffic simulation for energy and emissions reduction
Kumar et al. A meta-heuristic-based energy efficient route modeling for EV on non-identical road surfaces
Wilbur et al. Artificial intelligence for smart transportation
Muñoz-Villamizar et al. Measuring environmental impact of collaborative urban transport networks: A case study
Barhoumi et al. Fuel consumption in platoons: A literature review
Cintrano et al. CTPATH: A real world system to enable green transportation by optimizing environmentaly friendly routing paths
Nie et al. Investigating the min‐cost minimum fleet problem through taxi data analysis