Jensen et al., 2019 - Google Patents
Using crowd source data in bicycle route choice modelingJensen et al., 2019
View PDF- Document ID
- 8800011007194408509
- Author
- Jensen A
- Paolo A
- Rasmussen T
- Nielsen O
- Publication year
- Publication venue
- Proceedings from the Annual Transport Conference at Aalborg University
External Links
Snippet
We present a bicycle route choice model modelled in Value of Distance space based on revealed GPS data and an improved network with a very detailed representation of the bicycle infrastructure and detailed calculations of the related attributes. Beside common …
- 230000000694 effects 0 abstract description 8
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
- G01C21/3492—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
- G01C21/34—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
- 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
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/012—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from other sources than vehicle or roadside beacons, e.g. mobile networks
-
- 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
- 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
- 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
- G08G1/096844—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route where the complete route is dynamically recomputed based on new data
-
- 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
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Bernardi et al. | Modelling route choice of Dutch cyclists using smartphone data | |
Hood et al. | A GPS-based bicycle route choice model for San Francisco, California | |
Casello et al. | Modeling cyclists’ route choice based on GPS data | |
Ton et al. | Evaluating a data-driven approach for choice set identification using GPS bicycle route choice data from Amsterdam | |
Strauss et al. | Speed, travel time and delay for intersections and road segments in the Montreal network using cyclist Smartphone GPS data | |
Scott et al. | Route choice of bike share users: Leveraging GPS data to derive choice sets | |
Conrow et al. | Comparing spatial patterns of crowdsourced and conventional bicycling datasets | |
Khatri et al. | Modeling route choice of utilitarian bikeshare users with GPS data | |
Sanders et al. | Ballpark method for estimating pedestrian and bicyclist exposure in Seattle, Washington: Potential option for resource-constrained cities in an age of big data | |
Dane et al. | Route choice decisions of E-bike users: Analysis of GPS tracking data in the Netherlands | |
Halldórsdóttir et al. | Efficiency of choice set generation methods for bicycle routes | |
Wysling et al. | Where to improve cycling infrastructure? Assessing bicycle suitability and bikeability with open data in the city of Paris | |
CN110288205B (en) | Traffic influence evaluation method and device | |
KR20150072471A (en) | Traffic flow prediction system using spatiotemporal stochastic model | |
Meister et al. | Route choice modeling for cyclists on urban networks | |
Beheshtitabar et al. | ROUTE CHOICE MODELLING FOR BICYCLE TRIPS. | |
Huber et al. | Disaggregation of aggregate GPS-based cycling data–How to enrich commercial cycling data sets for detailed cycling behaviour analysis | |
Koch et al. | Taste variation in environmental features of bicycle routes | |
Raffler et al. | Cycling investment expedience: Energy expenditure based Cost-Path Analysis of national census bicycle commuting data | |
Huber et al. | Modelling bicycle route choice in German cities using open data, MNL and the bikeSim web-app | |
Grond | Route choice modeling of cyclists in Toronto | |
D’Apuzzo et al. | An introductory step to develop Distance Decay Functions in the Italian context to assess the modal split to e-bike and e-scooter | |
Jensen et al. | Using crowd source data in bicycle route choice modeling | |
Meister et al. | Route choice modelling for cyclists on dense urban networks | |
Halefom et al. | How much traffic stress can cyclists endure? |