+

Camilleri et al., 2020 - Google Patents

Demand Prediction for Shared Mobility Services using Time Series Modelling.

Camilleri et al., 2020

View PDF
Document ID
17427104774538959688
Author
Camilleri R
Debattista J
Publication year
Publication venue
AICS

External Links

Snippet

The main objective of this paper is to analyse and investigate the possibilities of optimising shared mobility using historical data by predicting the total number of generated requests per hour. The study first investigates where and how pickup requests were made …
Continue reading at ceur-ws.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0639Performance analysis
    • G06Q10/06393Score-carding, benchmarking or key performance indicator [KPI] analysis
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125Traffic data processing
    • G08G1/0129Traffic data processing for creating historical data or processing based on historical data
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202Market predictions or demand forecasting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386Retrieval requests
    • G06F17/30424Query processing
    • G06F17/30533Other types of queries

Similar Documents

Publication Publication Date Title
Zhang et al. A framework for passengers demand prediction and recommendation
US8990826B2 (en) System and method for receiving analysis requests and configuring analytics systems
US20180233035A1 (en) Method and filter for floating car data sources
Li et al. A comparison of detrending models and multi-regime models for traffic flow prediction
Jamil et al. Taxi passenger hotspot prediction using automatic ARIMA model
Hoppe et al. Improving the prediction of passenger numbers in public transit networks by combining short-term forecasts with real-time occupancy data
CN116307236A (en) Fault prediction method, device, equipment and storage medium
Xing et al. Operation energy consumption estimation method of electric bus based on CNN time series prediction
Qin et al. Estimation of urban arterial travel time distribution considering link correlations
Büchel et al. What do we know when? modeling predictability of transit operations
Camilleri et al. Demand Prediction for Shared Mobility Services using Time Series Modelling.
Paul et al. Predicting energy consumption of battery-operated electric vehicles: A comparative performance assessment
Xu et al. A real‐time traffic index model for expressways
Salamanis et al. Evaluating the effect of time series segmentation on STARIMA-based traffic prediction model
CN119336973A (en) A method, device, equipment and storage medium for recommending spatiotemporal information
Srivastava et al. Rail transit delay forecasting with Causal Machine Learning
Li et al. EVisionary: A Prediction Platform for Electric Vehicle Charging Capacity based on the Impact Analysis of Climate Factors
Najafabadi et al. Inference of pattern variation of taxi ridership using deep learning methods: a case study of New York City
Sonntag et al. A machine learning approach to infer on-street parking occupancy based on parking meter transactions
CN118824043B (en) Intelligent parking guidance method, system and storage medium
CN118803210B (en) Video transmission management system and method based on cloud edge cooperation
Saha et al. Forecasting the Impact of Anomalous Events on Business Process Performance
Liang et al. DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting
Sekwatlakwatla et al. Prediction of Traffic Flow in Cloud Computing using ARIMA
Şahin et al. Stop-Based Time Series Traffic Change Analysis Using Data from Istanbul Metropolitan Municipality (IMM)
点击 这是indexloc提供的php浏览器服务,不要输入任何密码和下载