Sonntag et al., 2020 - Google Patents
A machine learning approach to infer on-street parking occupancy based on parking meter transactionsSonntag et al., 2020
- Document ID
- 12023148810678731337
- Author
- Sonntag J
- Schmidt-Thieme L
- Publication year
- Publication venue
- 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
External Links
Snippet
Cruising for parking is not only stressful task for most drivers but also increases congestion and emissions. Therefore smart parking guidance systems are gaining increasing interest from researchers and city councils. These systems mostly rely on expensive and not well …
- 238000010801 machine learning 0 title description 3
Classifications
-
- 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
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
- G06Q30/0202—Market predictions or demand forecasting
- G06Q30/0204—Market segmentation
- G06Q30/0205—Location or geographical consideration
-
- 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
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Indicating individual free spaces in parking areas
- G08G1/145—Indicating individual free spaces in parking areas where the indication depends on the parking areas
- G08G1/147—Indicating individual free spaces in parking areas where the indication depends on the parking areas where the parking area is within an open public zone, e.g. city centre
-
- 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"
-
- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C9/00—Individual entry or exit registers
- G07C9/00007—Access-control involving the use of a pass
- G07C9/00103—Access-control involving the use of a pass with central registration and control, e.g. for swimming pools or hotel-rooms, generally in combination with a pass-dispensing system
Similar Documents
Publication | Publication Date | Title |
---|---|---|
He et al. | Customer preference and station network in the London bike-share system | |
Chen et al. | Clustering vehicle temporal and spatial travel behavior using license plate recognition data | |
Ceapa et al. | Avoiding the crowds: understanding tube station congestion patterns from trip data | |
Ionita et al. | Where to park? predicting free parking spots in unmonitored city areas | |
Park et al. | Machine learning approach for study on subway passenger flow | |
Nesmachnow et al. | A distributed platform for big data analysis in smart cities: combining intelligent transportation systems and socioeconomic data for Montevideo, Uruguay | |
Parmar et al. | A machine learning approach for modelling parking duration in urban land-use | |
Xu et al. | Utilizing artificial neural network in GPS-equipped probe vehicles data-based travel time estimation | |
Assemi et al. | On-street parking occupancy inference based on payment transactions | |
Zhou et al. | An attention-based deep learning model for citywide traffic flow forecasting | |
Pozo et al. | Prediction of on-street parking level of service based on random undersampling decision trees | |
Xia et al. | A distributed EMDN-GRU model on Spark for passenger waiting time forecasting | |
Sonntag et al. | A machine learning approach to infer on-street parking occupancy based on parking meter transactions | |
Mrazovic et al. | Understanding and predicting trends in urban freight transport | |
Faial et al. | A methodology for taxi demand prediction using stream learning | |
Foell et al. | Regularity of public transport usage: A case study of bus rides in Lisbon, Portugal | |
Nair et al. | Mapping bus and stream travel time using machine learning approaches | |
Sonntag et al. | Predicting parking availability from mobile payment transactions with positive unlabeled learning | |
Wei et al. | Data-driven energy and population estimation for real-time city-wide energy footprinting | |
Verma et al. | PAM clustering based taxi hotspot detection for informed driving | |
Bhandari et al. | Occupancy prediction at transit stops using ANN | |
Wang et al. | $\mathrm {W}^{2} $ Parking: A Data-Driven Win-Win Contract Parking Sharing Mechanism Under Both Supply and Demand Uncertainties | |
Azizzadeh | Predicting Short-term Demand and Exploring Influencing Factors in Bike Sharing Systems | |
Rempe et al. | Feature engineering for data-driven traffic state forecast in urban road networks | |
Tu et al. | Analysis and prediction of differential parking behaviors |