US20120239289A1 - Transportation Information Systems and Methods Associated With Generating Multiple User Routes - Google Patents

Transportation Information Systems and Methods Associated With Generating Multiple User Routes Download PDF

Info

Publication number
US20120239289A1
US20120239289A1 US13/228,246 US201113228246A US2012239289A1 US 20120239289 A1 US20120239289 A1 US 20120239289A1 US 201113228246 A US201113228246 A US 201113228246A US 2012239289 A1 US2012239289 A1 US 2012239289A1
Authority
US
United States
Prior art keywords
user
scheduling information
location
data
information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US13/228,246
Other languages
English (en)
Inventor
Alex Gontmakher
Frederick Peter Brewin
Noam Ben Haim
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Google LLC
Original Assignee
Google LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Google LLC filed Critical Google LLC
Priority to US13/228,246 priority Critical patent/US20120239289A1/en
Priority to PCT/US2011/051097 priority patent/WO2012034083A2/en
Priority to EP11824214.8A priority patent/EP2614483A4/de
Assigned to GOOGLE INC. reassignment GOOGLE INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: BREWIN, FREDERICK PETER, BEN HAIM, NOAM, GONTMAKER, ALEX
Assigned to GOOGLE INC. reassignment GOOGLE INC. CORRECTIVE ASSIGNMENT TO CORRECT THE LAST NAME OF THE 1ST INVENTOR FROM GONTMAKER PREVIOUSLY RECORDED ON REEL 027211 FRAME 0152. ASSIGNOR(S) HEREBY CONFIRMS THE CORRECT NAME OF THE 1ST INVENTOR IS GONTMAKHER. Assignors: BREWIN, FREDERICK PETER, BEN HAIM, NOAM, GONTMAKHER, ALEX
Publication of US20120239289A1 publication Critical patent/US20120239289A1/en
Assigned to GOOGLE LLC reassignment GOOGLE LLC CHANGE OF NAME (SEE DOCUMENT FOR DETAILS). Assignors: GOOGLE INC.
Abandoned legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
    • G08G1/127—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams to a central station ; Indicators in a central station
    • 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 groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/3423—Multimodal routing, i.e. combining two or more modes of transportation, where the modes can be any of, e.g. driving, walking, cycling, public transport
    • 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 groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/3438—Rendez-vous, i.e. searching a destination where several users can meet, and the routes to this destination for these users; Ride sharing, i.e. searching a route such that at least two users can share a vehicle for at least part of the route
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/02—Reservations, e.g. for tickets, services or events
    • G06Q10/025—Coordination of plural reservations, e.g. plural trip segments, transportation combined with accommodation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry

Definitions

  • This specification generally relates to transportation information and applications thereof.
  • Transportation information can be used for planning trips.
  • a user can refer to a published route schedule of a transportation system when determining which route to use. Determining which route to take, however, can be challenging for users of transportation systems, for example, when two users try to coordinate with each other to meet using a transportation system. Schedules of transportation systems are often inaccurate compared to actual transportation system performance. Furthermore, transportation systems can fail to alert the user to changes, interruptions, or other situations that affect, a planned trip.
  • Embodiments of this description generally relate to transportation information, and applications thereof.
  • Methods, systems, and techniques for providing transportation information are provided.
  • one aspect of the subject matter described in this specification may be embodied in a method for providing transportation information.
  • An exemplary method for providing transportation information includes the actions of receiving user data associated with public transportation usage.
  • the exemplary method also includes receiving public transportation data
  • the exemplary method further includes generating scheduling information based on the user data and public transportation data.
  • Another exemplary method for providing transportation information includes generating first scheduling information for a first user based on first user data, public transportation data, a first starting location, and a first destination location.
  • the exemplary method also includes determining whether a second user starting from a second starting location different from the first starting location would like to join the first user.
  • the exemplary method further includes generating second scheduling information for the second user to meet the first user based on second user data, the public transportation data, the second starting location, real-time location information for the first user, and a second destination location.
  • the exemplary method also includes generating an alert for the second user following the second scheduling information to depart from the second starting location at a specified time to meet the first user following the first scheduling information.
  • Another exemplary method for providing transportation information includes receiving public transportation data including real-time vehicle location information and at least one of historical vehicle location information and scheduled vehicle location information.
  • the exemplary method also includes generating a schedule degradation mode based on a deviation of real-time vehicle location information from at least one of historical vehicle location information and scheduled vehicle location information.
  • the exemplary method also includes generating scheduling information for a user based on a deviation of real-time vehicle location information from at least one of historical vehicle location information and scheduled vehicle location information.
  • the scheduling information includes an indication of the schedule degradation mode.
  • the scheduling information can be changed substantially based on the schedule degradation mode.
  • Another exemplary method for providing transportation information includes receiving user data associated with public transportation usage including at least one of user locations, user movements, user transportation queries, user routes, and user vehicles.
  • the exemplary method also includes receiving public transportation data including routes available to a user.
  • the exemplary method further includes generating a user profile fore the user based on the user data and the public transportation data.
  • the user profile includes automatically generated route suggestions for the user.
  • FIG. 1 is a diagram of an exemplary transportation information system, in accordance with an embodiment.
  • FIG. 2 is a diagram of an exemplary transportation information system including a profile creator, in accordance with an embodiment.
  • FIG. 3 is a diagram, of an exemplary transportation information system including a public transportation analyzer and a rider analyzer, in accordance with an embodiment.
  • FIG. 4 is a diagram of an exemplary transportation information system including a routing engine and a degradation mode, in accordance with an embodiment.
  • FIG. 5 is a diagram of an exemplary transportation information system including a rider analyzer, a routing engine, and updated scheduling information, in accordance with an embodiment.
  • FIG. 6 is a diagram of an exemplary transportation information system including an alert generator and an alert, in accordance with an embodiment.
  • FIG. 7 is a flowchart of a method for providing transportation information, in accordance with an embodiment.
  • FIG. 8 is a flowchart of another method for providing transportation information, in accordance with an embodiment.
  • FIG. 9 is a flowchart of another method for providing transportation information, in accordance with an embodiment.
  • FIG. 10 is a flowchart of another method for providing transportation information, in accordance with an embodiment.
  • FIG. 11 is a diagram of an application of an exemplary transportation information system, in accordance with an embodiment.
  • FIG. 12 is a diagram of an application of an exemplary transportation information system, in accordance with an embodiment.
  • FIG. 13 is a diagram of an application of an exemplary transportation information system, in accordance with an embodiment.
  • FIG. 14 is a diagram of an application of an exemplary transportation information system, in accordance with an embodiment.
  • Embodiments of this disclosure relate to transportation information systems and methods, and applications thereof. Users may need help navigating transportation systems.
  • user data associated with public transportation usage is received.
  • Public transportation data including real-time public transportation data is received.
  • Scheduling information is generated based on the user data and real-time public transportation data.
  • first scheduling information is generated for a first user based on first user data, public transportation data, a first starting location, and a first destination location of the first user.
  • Second scheduling information is generated for the second user to meet the first user based on second user data, public transportation data, the second starting location, real-time location information for the first user, and a second destination location.
  • an alert can be generated for the second user to depart from the second starting location at a specified time to meet the first user.
  • references to “one embodiment”, “an embodiment”, “an example embodiment”, etc. indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
  • FIG. 1 is a diagram of an exemplary transportation information system 100 , in accordance with an embodiment.
  • Transportation information system 100 includes an analyzer 110 .
  • Analyzer 110 includes a profile creator 112 , public transportation analyzer 114 , rider analyzer 116 , routing engine 118 , alert generator 120 , and report generator 122 .
  • Analyzer 110 can receive user data 130 , public transportation data 140 , and other inputs.
  • Analyzer 110 can provide user profile 150 , scheduling information 160 , degradation mode 170 , and other outputs.
  • Report generator 122 can generate reports of the scheduling information.
  • Analyzer 110 is in communication with network(s) 180 , which is in communication with computing device(s) 190 , third party service(s) 194 , mapping service(s) 198 , and other interfaces.
  • Network(s) 180 can include the Internet and one or more networks including local area networks (LANs), wide area networks (WANs), and wireless networks.
  • Computing device(s) 190 can include personal computers, mobile computers including cellular telephones, tablet computing devices, and other devices capable of exchanging information.
  • Third party service(s) 194 can include developers and aggregators.
  • Analyzer 110 can interface with various services, including but not limited to Google Goggles, Google Latitude, Google Calendar, Google Earth, Android, Google Mobile Maps, Google Voice Search, Google Search, Google Maps, and Google Translate.
  • Analyzer 110 can also interface with various transit systems, including but not limited to bus systems, railway systems, subway systems, airline systems, boating systems, traffic infrastructure systems, and alert systems.
  • Components of transportation information system 100 may be implemented as software, hardware, firmware, or any combination thereof.
  • Components of transportation information system 100 may also be implemented as computer-readable code executed on one or more computing devices capable of carrying out the functionality described herein.
  • a computing device can include, but is not limited to, a personal computer, mobile device such as a mobile phone, workstation, embedded system, game console, television, set-top box, or any other computing device.
  • a computing device can include, but is not limited to, a device having a processor and memory for executing and storing instructions.
  • Software may include one or more applications and an operating system.
  • Hardware can include, but is not limited to, a processor, memory and graphical user interface display.
  • the computing device may also have multiple processors and multiple shared or separate memory components.
  • the computing device may be a clustered computing environment or server farm.
  • any subset of the components shown in FIG. 1 may in fact be embodied as a single component.
  • the functionalities of rider analyzer 116 and routing engine 118 may be combined in a single device or module.
  • Other combinations of the functional components of FIG. 1 are also possible, as would be known to a person of skill in the art.
  • analyzer 110 may have more or less than the components shown in FIG. 1 .
  • FIG. 2 is a diagram of an exemplary transportation information system 200 including a profile creator 112 , in accordance with an embodiment.
  • Profile creator 112 can receive user data 130 as input, and can provide user profile 150 as output.
  • User data 130 can include user locations, user movements, user transportation queries, user routes, user vehicles, and other user data related to transportation. Times and localities for the user data can also be provided to profile creator 112 .
  • User data 130 can also include user location data 210 .
  • User location data 210 enables a user's location to be determined through various techniques.
  • User location data 210 can include browser-based location information, mobile computing device location information, public transportation services location data, user check-in to public transportation vehicles (e.g., swiping an access card at a vehicle-mounted reader), user association with public transportation entry ports (e.g., swiping an access card at a station turnstile), user access to a routing search engine or public transportation information, and user-provided locations such as a home address or business address.
  • public transportation vehicles e.g., swiping an access card at a vehicle-mounted reader
  • public transportation entry ports e.g., swiping an access card at a station turnstile
  • user access to a routing search engine or public transportation information e.g., swiping an access card at a station turnstile
  • user-provided locations such as a home address or business address.
  • User location data 210 and user data 130 can be used by profile creator 112 to create user profile 150 .
  • User profile 150 can be created automatically based on user data 130 and user location data 210 .
  • Profile creator 112 can collect known route segments the rider has taken, and build user profile 150 .
  • profile creator 112 can collect known route segments a rider has taken from GPS tacks, vehicle/trip check-ins, timing of boarding and alighting from public transportation, and usage of other public transportation applications.
  • a transportation information system can learn from user locations, movements, queries, preferred/most used routes, trips, vehicles, and other user data 130 , and can thereby provide users with improved transportation information.
  • FIG. 3 is a diagram of an exemplary transportation information system 300 including public transportation analyzer 114 and rider analyzer 116 , in accordance with an embodiment.
  • Public transportation analyzer 114 can receive public transportation data 140 as input, and provide degradation mode 170 as output.
  • Rider analyzer 116 can receive, as input, public transportation data 140 , user data 130 , user profile 150 , and output from public transportation analyzer 114 .
  • Rider analyzer 116 can provide, as output, updated user profile 310 and relevant public transportation route segments 312 .
  • Relevant public transportation route segments 312 can be based on updated user profile 310 .
  • Public transportation analyzer 114 can analyze historical, current (including real-time), scheduled, and other types of public transportation data 140 .
  • public transportation data 140 includes real-time public transportation vehicle location information, public transportation scheduling information, historical public transportation vehicle location information, public transportation stations, public transportation lines, and public transportation available trips.
  • Public transportation data 140 can include any relevant information that enables public transportation analyzer 114 to prepare an analysis.
  • Public transportation systems can have a schedule which determines at what times public transportation vehicles pass certain stations or time points corresponding to the schedule. As long as the transit network operates according to the schedule, very precise plans for trip requests can be generated. Even if vehicles deviate slightly from the schedule, precise routing can provide useful information to users because the vehicles should deviate only slightly from the precise plans.
  • a service provided by a transit network can become degraded even beyond slight deviations from the schedule.
  • Degradation including even slight deviations, can be detected by public transportation analyzer 114 and indicated by degradation mode 170 .
  • scheduling information is substantially changed based on degradation mode 170 .
  • Degradation of public transportation systems can occur in many situations. For example, during, rush hour, buses may not keep up with their schedule because of general traffic or congestion. Buses serving the same line might overtake each other and affect the times at which they arrive, depart, and service each bus stop or reach a point in the schedule. Unpredictable changes that affect the transportation system can cause the operation of the transportation system to depart significantly from the schedule, so that the schedule can no longer be relied upon. Such unpredictable changes can include accidents, rerouting, cancellations, extra trips, dispatcher intervention, and other unpredictable changes. Furthermore, changes in one portion of the transportation system can affect other portions of the transportation system, sometimes in ways that are not immediately apparent to users or even operators.
  • Degradation modes can be used to enable different handling by embodiments of a transportation information system.
  • Degradation modes can apply to an entire public transportation system/transit network, or part of the public transportation system/transit network.
  • a degradation mode can indicate that service is running according to schedule with high precision. Another degradation mode can indicate that service is not running according to schedule, with significant deviations. Yet another degradation mode can indicate that service is not running according to schedule at all, but an approximate frequency of vehicles is still maintained. An approximate frequency of vehicles may correspond to normal operating mode for some agencies. Yet another degradation mode can indicate that service is not running according to either schedule or frequencies (for example, no bus serving a station for 30 minutes where normally a bus serves that station every 10 minutes).
  • various services can be provided depending on the degradation mode.
  • the way routes are computed can be changed, e.g., the way that scheduling information is determined and provided. For example, when routes are computed according to a schedule and the next bus passes the first station in 10 minutes, that 10 minutes is used as a basis for starting the route.
  • the route can be started from the current time, and vehicle frequency used as an expected waiting time.
  • a transportation information system can change the way routes (including requested scheduling information 160 ) are displayed, corresponding to the degradation mode.
  • routes can be displayed to indicate that timing is imprecise corresponding to the degradation mode, and that scheduling information 160 is computed based on approximate, and not precise, scheduling times.
  • a transportation information system can compute and display confidence intervals in the computed timings associated with scheduling information 160 .
  • users can be alerted in association with a degradation mode.
  • a transit network may supply alerts for service disruptions, but such alerts may not enable a user to predict what affect the alert will have on a planned trip.
  • a transportation information system can use such alerts to inform a user of an event, and can also derive and show an alert independently from the transit network alerts (separate from or in addition to showing routes in a different way).
  • Alerts may be generated and shown in the following contexts: i) when the user views the map in public transport mode (or just zoomed in enough), ii) when the user views schedules of a station or a line, iii) when the user views a computed route, and iv) while the user is following a previously computed route. Additional techniques and combinations of generating and showing alerts are possible.
  • public transportation analyzer 114 can compare real-time vehicle positions or arrival estimations with the schedule. In another embodiment, public transportation analyzer 114 can compare real-time vehicle positions or arrival estimations with historical data for a similar period. The historical data can be collected over time from a transit network if available, or can be collected over time using embodiments of the transportation information system. Such comparisons can enable embodiments to compute how unusual is the current situation, e.g., degradation mode 170 .
  • Generating an alert can be dependent on schedule adherence. For example, for Zurich, where schedule adherence is high, alerts regarding degradation mode 170 can be generated when a train is over one minute late (and a severe alert if the train is over 10 minutes late). In contrast, for a bus system somewhere else where schedule adherence is not as high, the corresponding values for generating alerts may be 15 minutes or an, hour, adjustable depending on such factors as the schedule adherence, historical performance, and real-time data for the particular transit network.
  • rider analyzer 116 can receive as input public transportation data 140 , user data 130 , user profile 150 , and output from public transportation analyzer 114 . Rider analyzer 116 can then analyze the inputs and generate an updated user profile 310 , including relevant public transportation route segments 312 .
  • Temporal travel patterns of a user may include the user traveling at the same time of day.
  • Locality travel patterns of a user may include the user traveling with the same service, though not necessarily the same trip every day.
  • rider analyzer 116 can identify a cluster of route segments in a transit network that is relevant to a user, and generate an updated user profile 310 that assigns those route segments to updated user profile 310 .
  • locations and times of day can be assigned to a user profile.
  • a user's relevant route segments can be automatically detected based on the user's user profile 150 .
  • Updated user profile 310 can be generated based on detecting features of the transit network local to the user.
  • the local transit network can be segmented and clustered into used and unused services for that user.
  • Clustering can be done locally (similar routes, as defined by vehicle type, route, etc., up to mild variations, including different times of day).
  • Clustering can also be done temporally (services running along a similar route, or sharing similar start and end points, at similar times of the day over time).
  • the route segment clusters can then be used to identify relevant public transportation route segments 312 and other information that may affect the user's trip.
  • Relevant public transportation route segments 312 and other information can be provided to the user. For example, relevant public transportation route segments 312 may be displayed on a screen of a computing device for the user. Relevant public transportation route segments 312 and other information can be stored in updated user profile 310 .
  • using updated user profile 310 can provide various applications.
  • Personalized nearby public transportation information can be enabled for the user. For example, in one embodiment, nearby stations, lines, and trips can be ranked according to the user's profile.
  • public transportation service alerts can be personalized to a user based on deduced commute routes, regardless of an exact time of travel.
  • Alert severity can be customized based on an importance to the user. For example, a mild delay in a station outside of the user's area may still lead to severe delays on a service relevant to the user. Accordingly, the user is alerted even though it may not have been apparent to the user that the mild delay in a station outside of the user's area would have led to a delay that affected the user.
  • routes that the user is not taking can be suggested to the user by analyzing that such routes fit the user's profile or provide benefits. Users may not always know all the transit systems around them or know the fastest route using a transit system. In one example, a suggested route may be faster than the user's currently chosen route. Other benefits may be based on other criteria, such as providing routes having fewer connections, using a rail transportation system instead of a bus, and so on.
  • an alert is generated when a user is taking a vehicle that does not fit the user's profile, or is otherwise an unexpected selection for the user based on user profile 150 , updated user profile 310 , or both (e.g., going on a bus that differs from the user's usual commute at a time when the user is expected to take another action such as going home).
  • a transit system can be personalized for a user by providing known trips for the user (analyzed from user profile 150 , updated user profile 310 , or both).
  • a transportation, information system can enable the user to compare previously taken routes to newly suggested routes.
  • a transportation information system can provide useful defaults for user routing queries.
  • rider analyzer 116 may take note of queries entered by a user and a trip that the user ultimately decides to take. This data may be used to offer suggestions to other users when searching similar trips.
  • User profile 150 , updated user profile 310 , or both can enable embodiments to find a reasonable set of from and to locations and relevant times of day for the user.
  • user preferences for routing parameters can be learned.
  • rider analyzer 116 can determine appropriate connection time buffers, walking speeds, vehicle type preferences, walking distance preferences, and other user preferences by automatic analysis of various data.
  • user preferences can be manually configured.
  • the use of a user's transportation information may be provided as an opt-in service where a user may indicate in advance his or her approval of use of the user's transportation information.
  • FIG. 4 is a diagram of an exemplary transportation information system 400 including routing engine 118 and degradation mode 170 , in accordance with an embodiment.
  • Routing engine 118 can receive, as input, user data 130 , public transportation data 140 , user profile 150 , and degradation mode 170 . Accordingly, routing engine 118 can provide scheduling information 160 as output, including an indication of degradation mode 170 .
  • scheduling information 160 can include explicit mention of the degradation mode and the level of deviation from the schedule. Scheduling information 160 can also be presented in a way that visually represents the level of degradation or deviation from the schedule. For example, a circle centered on a destination can approximate a range of arrival times using a corresponding diameter, in contrast to a point. Routing engine 118 may display the generated scheduling information to a user (e.g., on the user's computing device). Based on degradation mode 170 , routing engine 118 may alter an appearance of scheduling information 160 .
  • FIG. 5 is a diagram of an exemplary transportation information system 500 including rider analyzer 116 , routing engine 118 , and updated scheduling information 520 , in accordance with an embodiment.
  • Rider analyzer 116 can receive, as input, user behavior 510 and scheduling information 160 . Rider analyzer 116 can detect discrepancies between routing suggestions, provided in scheduling information 160 , and routes users actually take, provided in user behavior 510 . Rider analyzer 116 can provide the analysis results to routing engine 118 . Routing engine 118 can use the output from rider analyzer 116 to generate updated scheduling information 520 .
  • a transportation information system can adapt to user preferences, and provide popular routes that are more consistent with user behavior 510 .
  • a transportation information system can provide an indication that scheduling information has been updated, e.g., when generating updated scheduling information 520 . The user can be asked to verify whether updated scheduling information 520 is desirable.
  • FIG. 6 is a diagram of an exemplary transportation information system 600 including alert generator 120 and alert 610 , in accordance with an embodiment.
  • Alert generator 120 can receive, as input, degradation mode 170 , user behavior 510 , user data 130 , public transportation data 140 , user profile 150 , scheduling information 160 , and vehicle identification 620 .
  • Vehicle identification 620 can be determined using various techniques. For example, determining which vehicle a user (passenger of the identified vehicle) is on can involve matching the user's location with real-time data for the identified vehicle's location.
  • the user's location can be provided by a Global Positioning System (GPS) feature on the user's portable computing device such as, for example, a smartphone. Other location methods are possible, including wireless signal triangulation.
  • GPS Global Positioning System
  • the identified vehicle location can be provided by, for example, the transit agencies.
  • vehicle identification 620 corresponding to the user's location, can be provided to alert generator 120 .
  • Alert generator 120 can generate alert 610 , which can be used in various applications.
  • vehicle identification 620 can be provided to various other components of analyzer 110 , including rider analyzer 116 , routing engine 118 , and other components.
  • alert 610 can be used to notify analyzer 110 to use a different method of determining the user's location.
  • analyzer 110 can determine a user's location based on a position of the vehicle the user is in, according to vehicle identification 620 .
  • Position data for the identified vehicle may be gathered using a GPS that functions in tunnels, odometer readings that give the distance the train has travelled, or other methods.
  • a transit network may use transponders positioned on the tracks to identify train locations. Even during a detour, the system can determine that the vehicle will arrive at a specific station, information which can be used for locating the vehicle and user.
  • Alert 610 can also be used to notify the user that a different method of location detection is being used for the user.
  • a user's GPS can be compared with a vehicle's GPS to determine that the user is on a particular vehicle.
  • the user can be “checked in” to that vehicle.
  • the user may also be given the option of manually “checking in” to a vehicle.
  • the user can send the vehicle information to other users who can be kept informed of the user's progress via that vehicle's progress.
  • alert 610 can be used to provide various information to the user. In another embodiment, alert 610 can be used to provide various information to analyzer 110 . Alert 610 can warn the user that the vehicle the user is on is about to enter an area with no cellular signal or no GPS signal. This is useful, for example, if the user is on the phone during a telephone call or using data provided by the cellular signal or GPS signal. Alert 610 can notify the user that analyzer 110 may not be able to provide trip advice while the user is in the tunnel or otherwise without a data signal, GPS signal, or both.
  • Alert 610 can be used to notify analyzer 110 , the user, or both when the vehicle the user is on is about to reach the user's stop. Alert 610 can be used to alert the user if the user is going to miss a connection on a planned trip.
  • Alert 610 can be used to notify other people regarding the user's status. For example, a user's friend who is going to meet the user can be notified of the user's progress on a trip based on the predictions analyzer 110 has for the identified vehicle the user is traveling on.
  • Alert 610 can provide basic information about the trip the user is currently on. For example, analyzer 110 can provide scheduling information 160 including a list of upcoming stations and estimated times, similar to that which is often displayed on trams. Alert 610 can provide notifications of this information to the user, e.g., on the user's own device such as a mobile computing device.
  • Alert 610 can provide a warning of problems up ahead along the user's route, so that the user can reroute before getting stuck.
  • FIG. 7 is a flowchart 700 of a method for providing transportation information, in accordance with an embodiment.
  • user data associated with public transportation usage is received.
  • public transportation data is received.
  • scheduling information is generated based on the user data and the public transportation data.
  • FIG. 8 is a flowchart 800 of a method for providing transportation information, in accordance with an embodiment.
  • first scheduling information is generated for a first user.
  • routing engine 118 may generate first scheduling information for a first user based on first user data, public transportation data, a first starting location, and a first destination location.
  • routing engine 118 may determine that a second user starting from a second starting location different from the first starting location would like to join the first user.
  • second scheduling information is generated for the second user.
  • routing engine 118 may generate second scheduling information for a second user to meet the first user based on second user data, public transportation data, the second starting location, real-time location information for the first user, and a second destination location.
  • a stage 835 it is determined whether it is time for the second user to depart. If it is not time for the second user to depart, the system waits at a stage 837 and process flow returns to stage 835 . If it is time for the second user to depart, at a stage 840 , an alert is generated for the second user to depart to join the first user. In one embodiment, alert generator 120 generates an alert for the second user to depart to join the first user. Alert generator 120 may generate an alert for the second user to depart from a second starting location at a specified time to meet the first user.
  • FIG. 9 is a flowchart 900 of a method for providing transportation information, in accordance with an embodiment.
  • public transportation data is received.
  • the public transportation data may include scheduled vehicle location information and actual vehicle location information.
  • scheduling information including an indication of the schedule degradation mode is generated for the user.
  • FIG. 10 is a flowchart 1000 of a method for providing transportation information, in accordance with an embodiment.
  • user data associated with public transportation usage is received.
  • public transportation data including routes available to the user is received.
  • a user profile including automatically generated route suggestions is generated.
  • FIG. 11 is a diagram of an application of an exemplary transportation information system, in accordance with an embodiment.
  • Users A 1110 and B 1120 agree to meet at a destination 1150 , e.g., a restaurant, using a transit system.
  • a 1110 plans his route to destination 1150 .
  • As route from origin 1112 to destination 1150 includes portions 1114 and 1116 .
  • a 1110 shares his itinerary with B 1120 .
  • B chooses to “join” this trip.
  • scheduling information and B's scheduling information can be generated based on different forms of travel used by A and B (e.g., their travel preferences).
  • A begins the trip at origin 1112 , and once he boards the vehicle at origin 1112 , the actual times of the vehicles he will catch are calculated (e.g., based on best approximations of connection times, degradation modes, etc., as determined by analyzer 110 ).
  • B 1120 is informed when to begin his trip so that he will meet with A 1110 at stop 1130 .
  • B's route from origin 1122 to destination 1150 includes portions 1124 and 1126 .
  • B's vehicle makes a stop at stop 1130 .
  • a transportation information system instructs B 1120 such that B 1120 arrives at stop 1130 in time to catch the vehicle that A 1110 is using. Therefore, A 1110 and B 1120 meet on the vehicle, and travel together (for example, along the last leg 1140 of a journey). A 1110 and B 1120 proceed to destination 1150 together, and do not have to ride separately in portions 1116 and 1126 of the trip.
  • a vehicle that A 1110 is riding on that travels along a path of As generated scheduling information is identified. The identified vehicle and path are included in B's generated scheduling information.
  • a 1110 or B 1120 may experience delays or a deviation from the original scheduling information.
  • As scheduling information is updated based on real-time transportation data of B 1120 .
  • B's scheduling information is updated based on real-time transportation data of A 1110 . For example, if A 1110 is delayed and there are multiple stops along ifs path, B 1120 may decide to exit a transportation vehicle that B 1120 is riding on, take a coffee break at a particular stop for a time period, and take a different transportation vehicle at a later point in time to join A 1110 at stop 1130 .
  • a 1110 is alerted when real-time transportation data of B 11120 deviates from the second scheduling information.
  • B 1120 is alerted when real-time transportation data of A 1110 deviates from the first scheduling information.
  • FIG. 12 is a diagram of an application of an exemplary transportation information system, in accordance with an embodiment.
  • a system can choose different routes, and even modify a passenger's route postfactum, in order to maximize a portion 1250 of the trip that users A 1210 and B 1220 spend together.
  • B 1220 if traveling alone or taking a direct trip, would not typically choose to travel away from a destination 1260 .
  • B 1220 might choose to go out of his way if that helped him meet A 1210 sooner and allowed A 1210 and B 1220 to spend more time together on the trip.
  • A's scheduling information may be adjusted to increase a portion of the trip that A 1210 and B 1220 spend together.
  • B's scheduling information may be adjusted to increase a portion of the trip that A 1210 and B 1220 spend together. For example, a distance of B's generated route that includes the path of A's generated route is longer than a distance of another route from B's starting location to B's destination location. In this embodiment, A 1210 and B 1220 spend more time together along the route.
  • a 1210 departs from origin 1212 , proceeds along path 1214 to a stop 1230 , then along path 1216 to stop 1240 , and along path 1218 to stop 1260 .
  • a 1210 shares his itinerary with B 1220 and B 1220 chooses to join A's trip.
  • B 1220 has an option of traveling a more direct route from origin 1222 along path 1223 to stop 1240 .
  • B 1220 chooses to spend more time with A 1210 during the trip.
  • B 1220 can be instructed when to depart from origin 1222 , and the vehicle in which A 1210 is riding can be identified, so that B 1220 can find A 1210 .
  • a 1210 and B 1220 travel together from stop 1230 to stop 1240 and on to destination 1260 .
  • FIG. 13 is a diagram of an application of an exemplary transportation information system, in accordance with an embodiment.
  • Users A 1310 and B 1320 want to meet at a common location, timed to arrive approximately simultaneously at destination 1340 .
  • a 1310 is departing from origin 1312
  • B 1320 is departing from origin 1322 .
  • the system can determine that As path will take more time, and generate scheduling instructions for A 1310 to depart before B 1320 departs.
  • a 1310 travels along path 1314 to stop 1330 , then along path 1316 to destination 1340 .
  • the system determines when B 1320 should depart from origin 1322 .
  • B 1320 then departs and travels along path 1324 .
  • B 1320 arrives at destination 1340 approximately at the same time as A 1310 , meeting A 1310 at destination 1340 .
  • A's trip changes or is rerouted for some reason then the system can notify B 1320 , reroute A 1310 , reroute B 1320 , or a combination of these. For example, if A 1310 misses a connection, B 1320 is notified and can be rerouted to meet A 1310 . The system may determine that B 1320 should leave at a later time. Similarly, A 1310 can be notified of changes in B's trip. For example, A 1310 can be notified that B 1320 has chosen to join A's trip, or that B 1320 is expected to meet A 1310 at a certain point. If B's trip changes, A 1310 can be updated and decide whether to be rerouted.
  • FIG. 14 is a diagram of an application of an exemplary transportation information system, in accordance with an embodiment.
  • Users A 1410 and B 1420 agree to meet at a stop 1430 , e.g., a bus stop, using a transit system, and travel together for a duration of time along their respective routes.
  • A's destination is different from B's destination.
  • a 1410 has a destination 1452 and B 1420 has a destination 1454 .
  • a 1410 plans his route to destination 1452 , staring at origin 1412 .
  • A's route from origin 1412 to destination 1452 includes portions 1414 , 1416 , and 1456 .
  • A's vehicle makes stops, including a stop 1430 .
  • a 1410 shares his itinerary with B 1420 .
  • B 1420 chooses to meet A 1410 along his trip.
  • scheduling information is generated for B 1420 to meet A 1410 at stop 1430 and travel along a path 1440 together.
  • B's route from origin 1422 to destination 1454 includes portions 1424 , 1426 , and 1458 .
  • a 1410 and B 1420 can meet on a vehicle at stop 1430 , and travel together until a stop 1450 .
  • a and B proceed to stop 1450 together, and do not have to ride separately in portions 1416 and 1426 of the trip.
  • a 1410 and B 1420 can separate so that A can proceed to destination 1452 and B can proceed to destination 1454 .
  • a 1410 remains in the vehicle and B 1420 departs from the vehicle to ride a different vehicle.
  • B 1420 remains in the vehicle and A 1410 departs from the vehicle to ride a different vehicle.
  • a 1410 and B 1420 depart from the vehicle to ride a different vehicle.
  • Embodiments illustrated in FIGS. 11-14 can be used between multiple vehicle types, including between public transit and personal transportation. If A 1310 is meeting B 1320 simultaneously in FIG. 13 , the system can track when A 1310 gets on the train at origin 1312 , for example. The system will calculate when B 1320 should get in his car, for example, so that B 1320 arrives at destination 1340 at approximately the same time that A 1310 arrives at destination 1340 . This can be extended to any form of traveling such as walking, bicycling, car driving, bus systems, train systems, and any combination of these for routing or meeting en route.
  • riders can be alerted as to the capacity of various vehicles.
  • the system can notify a user that a bus or train car is full.
  • the user can plan to catch the next bus or train car that is not as crowded.
  • a transportation information system can determine such information based on information from a transit system, user behavior 510 , and other data that can identify passenger load in a vehicle.
  • a transportation information system can notify users of various features of a transit network/system. For example, embodiments can determine that it is raining, and therefore can advise a user which stops have a shelter to avoid the rain. For example, the system can alert the user to get off one stop early, so that the user can wait under a shelter at the earlier stop.
  • Embodiments can be used for real-time information in public transportation systems. Users, developers, aggregators, and others can use the information in various ways. Developers/a developer feed can query a subset of the data. An example query can be “arrivals at station X in the next 10 minutes.” Embodiments can provide information with little need to cross reference, and can synthesize many standards across a variety of transportation systems. A snapshot of all the data can be provided, e.g., for an aggregator/aggregator feed. The data can be provided in a consistent format. Data formats can be optimized for aggregation, including a reference to a static schedule and incremental updates.
  • Real-time data can be useful for alerting users if a vehicle is late or otherwise deviating from a schedule, rerouting a nip, or knowing that an event affects a user's trip.
  • a transportation information system can determine whether parts of a public transit system route lack cellular or GPS signals and offer a user the choice of choosing another route with more reliable wireless signals.
  • Embodiments can determine signal dead-spots where there are no requests coming from mobile networks. In high-usage areas, such as major cities, dead-spots may correspond to areas with no signal and embodiments can build up highly localized coverage maps.
  • information can be collected where wireless signals are available on public transit systems.
  • Real-time location data for a user can be used to convert a statically planned trip (e.g., origin/path/destination) to a precise list of concrete vehicles, times, or both associated with the trip.
  • a statically planned trip e.g., origin/path/destination
  • Scheduled information can be matched with real-time data, and can provide updates in the form of time increments to a schedule on a per-trip basis.
  • Real-time data can include service alerts, schedule updates, location information (e.g., users and transit systems), passenger loads, video camera feeds, user GPS information, and other data.
  • Raw data can be made available to websites and other data distributors.
  • Embodiments can provide integration for otherwise loosely connected internal systems at an agency, and compensate when the relation of actual traffic versus scheduled traffic degrades over time.
  • Morning Commute Marcus lives near Altersheim, Zurich. Route to work: Bus 140 from Altersheim to Langnau then the S4 from Langnau to Zurich HB.
  • Marcus gets up and receives an alert on his phone that the bus to the train station is cancelled. Since it is only a short walk to the train, he can just leave a bit earlier and still catch the train.
  • Marcus's phone knows when his first meeting of the afternoon is and alerts him when he needs to take the train back to work. He doesn't waste time waiting at the stop and arrives on time.
  • Marcus's phone After enjoying the concert they head back to Zurich. It starts raining, so Marcus's phone directs them to a bus stop with a shelter nearby. Both of them are tired after a long day and fall asleep on the train. Fortunately, Marcus's phone knows when they are about to reach the station and an alarm sounds to wake them up.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Automation & Control Theory (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Traffic Control Systems (AREA)
  • Navigation (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
US13/228,246 2010-09-09 2011-09-08 Transportation Information Systems and Methods Associated With Generating Multiple User Routes Abandoned US20120239289A1 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US13/228,246 US20120239289A1 (en) 2010-09-09 2011-09-08 Transportation Information Systems and Methods Associated With Generating Multiple User Routes
PCT/US2011/051097 WO2012034083A2 (en) 2010-09-09 2011-09-09 Transportation information systems and methods
EP11824214.8A EP2614483A4 (de) 2010-09-09 2011-09-09 Transportinformationssysteme und -verfahren

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US38136110P 2010-09-09 2010-09-09
US13/228,246 US20120239289A1 (en) 2010-09-09 2011-09-08 Transportation Information Systems and Methods Associated With Generating Multiple User Routes

Publications (1)

Publication Number Publication Date
US20120239289A1 true US20120239289A1 (en) 2012-09-20

Family

ID=46796826

Family Applications (4)

Application Number Title Priority Date Filing Date
US13/228,199 Abandoned US20120296885A1 (en) 2010-09-09 2011-09-08 Transportation Information Systems and Methods
US13/228,246 Abandoned US20120239289A1 (en) 2010-09-09 2011-09-08 Transportation Information Systems and Methods Associated With Generating Multiple User Routes
US13/228,225 Active 2032-01-09 US8645050B2 (en) 2010-09-09 2011-09-08 Transportation information systems and methods associated with degradation modes
US14/145,370 Active US9552729B2 (en) 2010-09-09 2013-12-31 Transportation information systems and methods

Family Applications Before (1)

Application Number Title Priority Date Filing Date
US13/228,199 Abandoned US20120296885A1 (en) 2010-09-09 2011-09-08 Transportation Information Systems and Methods

Family Applications After (2)

Application Number Title Priority Date Filing Date
US13/228,225 Active 2032-01-09 US8645050B2 (en) 2010-09-09 2011-09-08 Transportation information systems and methods associated with degradation modes
US14/145,370 Active US9552729B2 (en) 2010-09-09 2013-12-31 Transportation information systems and methods

Country Status (3)

Country Link
US (4) US20120296885A1 (de)
EP (1) EP2614483A4 (de)
WO (1) WO2012034083A2 (de)

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015095828A1 (en) * 2013-12-20 2015-06-25 Urban Engines, Inc. Transportation system reconstruction
US9389094B2 (en) * 2014-09-05 2016-07-12 Paypal, Inc. Methods and systems for determining routing
US20170163591A1 (en) * 2015-12-04 2017-06-08 International Business Machines Corporation Live events attendance smart transportation and planning
EP3036507B1 (de) * 2013-08-19 2020-06-17 TomTom Navigation B.V. Verfahren und systeme zur gewinnung einer multimodalen route
US10762462B1 (en) * 2018-08-07 2020-09-01 Amazon Technologies, Inc. Sensor-based customer arrival detection
US10769712B1 (en) * 2018-07-16 2020-09-08 Amazon Technologies, Inc. ETA-based item pick-up and fulfillment alternatives
US10921147B1 (en) 2018-08-29 2021-02-16 Amazon Technologies, Inc. Customer and merchant location-based ETA determination
US11200517B2 (en) 2016-08-01 2021-12-14 International Business Machines Corporation Redistribution based on real time presence data
US11466993B2 (en) * 2014-05-06 2022-10-11 Uber Technologies, Inc. Systems and methods for travel planning that calls for at least one transportation vehicle unit
US11551325B2 (en) 2015-12-10 2023-01-10 Uber Technologies, Inc. Travel coordination system implementing pick-up location optimization
US11582328B2 (en) 2017-08-11 2023-02-14 Uber Technologies, Inc. Dynamic scheduling system for planned service requests
US11669785B2 (en) 2014-05-06 2023-06-06 Uber Technologies, Inc. System and methods for verifying that one or more directives that direct transport of a second end user does not conflict with one or more obligations to transport a first end user
US11674810B2 (en) 2017-11-05 2023-06-13 Uber Technologies, Inc. Network computer system to arrange pooled transport services
US12001975B2 (en) 2022-10-13 2024-06-04 Uber Technologies, Inc. Systems and methods for transporting multiple end users

Families Citing this family (53)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120296885A1 (en) * 2010-09-09 2012-11-22 Google Inc. Transportation Information Systems and Methods
US8911507B1 (en) * 2011-11-22 2014-12-16 Symantec Corporation Systems and methods for mitigating mobile device loss
WO2013133067A1 (ja) * 2012-03-08 2013-09-12 オムロン株式会社 出力装置、出力システム、およびプログラム
US9097535B2 (en) * 2012-05-09 2015-08-04 Google Inc. Public transportation journey planning
AU2013311211B2 (en) * 2012-09-07 2017-05-11 Moovit App Global Ltd Public transportation navigator
JP6043161B2 (ja) * 2012-11-13 2016-12-14 株式会社日立システムズ 情報送信システムおよび方法ならびにプログラム
CA2833542C (en) * 2012-11-20 2020-06-30 Accenture Global Services Limited Situation-aware mobile travel advisory to public transport commuters
US9286801B2 (en) 2013-03-06 2016-03-15 International Business Machines Corporation Leveraging information for use in a traffic prediction scenario
US9020754B2 (en) * 2013-03-22 2015-04-28 Here Global B.V. Vehicle arrival prediction
US9273970B2 (en) 2013-05-14 2016-03-01 Google Inc. Systems and methods for generating a plurality of trip patterns
US9217647B2 (en) 2013-05-14 2015-12-22 Google Inc. Guidebook transit routing
US8972190B1 (en) * 2013-05-14 2015-03-03 Google Inc. Systems and methods for generating transit trips
US20150046602A1 (en) * 2013-08-11 2015-02-12 Staccatotrip Data synchronization systems and methods
US20150066558A1 (en) 2013-08-29 2015-03-05 Thales Canada Inc. Context aware command and control system
US9552560B1 (en) * 2013-12-31 2017-01-24 Google Inc. Facilitating communication between event attendees based on event starting time
WO2015162458A1 (en) 2014-04-24 2015-10-29 Singapore Telecommunications Limited Knowledge model for personalization and location services
CA2896404C (en) 2014-07-08 2018-10-30 The Toronto-Dominion Bank Systems and methods for providing sensor-based location proximity detection and notification
EP2975561B1 (de) * 2014-07-14 2023-12-13 Gerrit Böhm Kapazitätsvorhersage für öffentliche Verkehrsmittel
WO2016042357A1 (en) * 2014-09-16 2016-03-24 Singapore Telecommunications Limited Predicting human movement behaviors using location services model
US10572847B2 (en) * 2014-10-10 2020-02-25 Conduent Business Services, Llc Dynamic space-time diagram for visualization of transportation schedule adherence
US10620010B2 (en) 2015-02-05 2020-04-14 Moovit App Global Ltd Public and ordered transportation trip planning
JP5831917B1 (ja) * 2015-05-01 2015-12-09 コガソフトウェア株式会社 通知サーバ、通知方法および通知プログラム
US10072932B2 (en) * 2015-05-07 2018-09-11 Truemotion, Inc. Motion detection system for transportation mode analysis
WO2017049137A1 (en) 2015-09-17 2017-03-23 Truemotion, Inc. Systems and methods for detecting and assessing distracted drivers
CN108292308A (zh) * 2015-11-27 2018-07-17 宝马股份公司 根据移动习惯为用户推荐车辆/乘客资源
CN105512750A (zh) * 2015-11-27 2016-04-20 清华大学 基于卫星降雨数据的雨量站网布设方法及装置
US10841852B2 (en) 2015-12-09 2020-11-17 DataSpark, PTE. LTD. Transportation network monitoring using cellular radio metadata
US11691565B2 (en) 2016-01-22 2023-07-04 Cambridge Mobile Telematics Inc. Systems and methods for sensor-based detection, alerting and modification of driving behaviors
US20170265040A1 (en) * 2016-03-08 2017-09-14 Google Inc. Verification of pickup times in real-time ride-sharing feeds
US10510006B2 (en) * 2016-03-09 2019-12-17 Uptake Technologies, Inc. Handling of predictive models based on asset location
US10176340B2 (en) 2016-03-13 2019-01-08 DataSpark, PTE. LTD. Abstracted graphs from social relationship graph
US11157520B2 (en) 2016-03-28 2021-10-26 DataSpark, Pte Ltd. Uniqueness level for anonymized datasets
US10337876B2 (en) 2016-05-10 2019-07-02 Microsoft Technology Licensing, Llc Constrained-transportation directions
US10386197B2 (en) 2016-05-17 2019-08-20 Microsoft Technology Licensing, Llc Calculating an optimal route based on specified intermediate stops
US11072339B2 (en) 2016-06-06 2021-07-27 Truemotion, Inc. Systems and methods for scoring driving trips
US11562300B2 (en) 2016-06-10 2023-01-24 Conduent Business Services, Llc System and method for optimal automated booking of on-demand transportation in multi-modal journeys
US10060752B2 (en) 2016-06-23 2018-08-28 Microsoft Technology Licensing, Llc Detecting deviation from planned public transit route
US20180122024A1 (en) * 2016-11-01 2018-05-03 Mastercard International Incorporated Systems and Methods for Use in Providing Offers to Consumers Based on Transit Conditions
US10147325B1 (en) 2017-02-02 2018-12-04 Wells Fargo Bank, N.A. Customization of sharing of rides
WO2018150228A1 (en) 2017-02-17 2018-08-23 Dataspark Pte, Ltd Mobility gene for visit data
WO2018150227A1 (en) 2017-02-17 2018-08-23 Dataspark Pte, Ltd Mobility gene for trajectory data
US20210172759A1 (en) 2017-02-17 2021-06-10 Dataspark Pte Ltd Map Matching and Trajectory Analysis
US10546255B2 (en) * 2017-05-05 2020-01-28 Conduent Business Services, Llc Efficient optimization of schedules in a public transportation system
US10760916B2 (en) * 2017-06-07 2020-09-01 International Business Machines Corporation Methods and systems for determining shared routes
SG10201705159PA (en) * 2017-06-21 2019-01-30 Mastercard International Inc Administration Of An Incentive Program Encouraging Public Transport Usage
CN107424407A (zh) * 2017-08-21 2017-12-01 江苏萌小明智能科技有限公司 一种车辆调度系统及车辆调度方案生成方法
WO2019203805A1 (en) * 2018-04-17 2019-10-24 Ford Global Technologies, Llc Filtering for efficient routing data
WO2019209251A1 (en) * 2018-04-23 2019-10-31 Google Llc Determining vehicle crowdedness using real-time location data
US11593735B2 (en) 2019-05-21 2023-02-28 International Business Machines Corporation Automated and efficient personal transportation vehicle sharing
CN110750611A (zh) * 2019-10-16 2020-02-04 上海掌门科技有限公司 一种用于提供避雨信息的方法与设备
US20210108934A1 (en) * 2020-12-23 2021-04-15 Deepak Dasalukunte Collaborative multimodal trip planning
CN113066304B (zh) * 2021-06-03 2021-09-28 云上(南昌)大数据运营有限公司 一种应用城市大脑云平台的交通容量配置系统
CN113743541B (zh) * 2021-11-04 2022-02-08 华中科技大学 一种基于退化模式预测船用动力系统轴承剩余寿命的方法

Citations (31)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4360875A (en) * 1981-02-23 1982-11-23 Behnke Robert W Automated, door-to-door, demand-responsive public transportation system
US5559707A (en) * 1994-06-24 1996-09-24 Delorme Publishing Company Computer aided routing system
US6253148B1 (en) * 1997-07-04 2001-06-26 Jean-Claude Decaux Information system for informing users of a public transport network about waiting times at stops in the network
US20010056363A1 (en) * 2000-06-26 2001-12-27 Gantz Donald T. System for providing ride matching services using e-mail and the internet
US20020062192A1 (en) * 2000-11-17 2002-05-23 Koninklijke Philips Electronics N.V. Method, and related system and apparatus, for providing travel-related information to a mobile communications device
US6424910B1 (en) * 2000-11-22 2002-07-23 Navigation Technologies Corp. Method and system for providing related navigation features for two or more end users
US20030100993A1 (en) * 2001-11-27 2003-05-29 Kirshenbaum Evan R. Automatic gathering and analysis of data on commute paths
US20030109266A1 (en) * 2000-02-09 2003-06-12 Moshe Rafiah Integrated journey planner
US20030177020A1 (en) * 2002-03-14 2003-09-18 Fujitsu Limited Method and apparatus for realizing sharing of taxi, and computer product
US20040049424A1 (en) * 2002-06-21 2004-03-11 Murray Thomas A. System and method for facilitating ridesharing
US20050033614A1 (en) * 2003-08-05 2005-02-10 Sabre Inc. System and method for coordinating travel itineraries
US6975997B1 (en) * 1999-07-07 2005-12-13 Honda Giken Kogyo Kabushiki Kaisha Method for efficient vehicle allocation in vehicle sharing system
US7027995B2 (en) * 2001-06-01 2006-04-11 International Business Machines Corporation Dynamic resource scheduling to optimize location of meeting participants
US20060155460A1 (en) * 2005-01-08 2006-07-13 Stephen Raney Method for GPS carpool rendezvous tracking and personal safety verification
US7080019B1 (en) * 2001-03-04 2006-07-18 Ducktrip, Llc Ride share contact system
US7082364B2 (en) * 2003-06-24 2006-07-25 Bellsouth Intellectual Property Corporation Methods, systems and computer program products for ride matching based on current location information
US20060276960A1 (en) * 2003-06-24 2006-12-07 Maria Adamczyk Methods, systems and computer program products for ride matching based on selection criteria and driver characteristic information
US20070106468A1 (en) * 2005-11-07 2007-05-10 France Telecom Product, service and activity based interactive trip mapping system, method, and computer program product
US20080054072A1 (en) * 2005-11-17 2008-03-06 Lalitesh Katragadda Vehicle information systems and methods
US7353034B2 (en) * 2005-04-04 2008-04-01 X One, Inc. Location sharing and tracking using mobile phones or other wireless devices
US20080091342A1 (en) * 2006-10-11 2008-04-17 Jeffrey Assael System and method for ride matching
US20100121662A1 (en) * 2003-06-26 2010-05-13 Becker Ian S System and Method of Booking Transportation
US20100274569A1 (en) * 2009-04-23 2010-10-28 Douglas Reudink Real-time location sharing
US7856300B2 (en) * 2006-11-21 2010-12-21 Aisin Seiki Kabushiki Kaisha Seat with side support control for vehicle responsive to distance and position in curve roads
US8090707B1 (en) * 2005-07-07 2012-01-03 Rearden Commerce Inc. Chance meeting addition to trip planner or meeting planner
US8140256B1 (en) * 2006-08-16 2012-03-20 University Of South Florida Dynamic ride matching system
US8145417B1 (en) * 2008-12-31 2012-03-27 Cellco Partnership Enabling a first mobile device to navigate to a location associated with a second mobile device
US8150608B2 (en) * 2006-09-22 2012-04-03 Rockstar Bidco, LP Method and apparatus for enabling commuter groups
US8285570B2 (en) * 2009-08-28 2012-10-09 Rideamigos Corp. Matching system for ride reservation platforms
US20120290652A1 (en) * 2011-05-13 2012-11-15 Zeljko BOSKOVIC Arrangement and method for transport sharing
US20130041941A1 (en) * 2010-04-09 2013-02-14 Carnegie Mellon University Crowd-Sourcing of Information for Shared Transportation Vehicles

Family Cites Families (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5400020A (en) * 1993-05-18 1995-03-21 Global Research Systems, Inc. Advance notification system and method
US20020069017A1 (en) * 1995-08-14 2002-06-06 Schmier Kenneth J. Public transit vehicle arrival information system
US7983836B2 (en) * 1997-10-22 2011-07-19 Intelligent Technologies International, Inc. Vehicle-traffic control device communication techniques
US7119696B2 (en) * 2001-11-19 2006-10-10 Volvo Trucks North America, Inc. System for ensuring driver competency
US7394403B2 (en) * 2002-02-14 2008-07-01 Everyday Wireless, Llc Everyday wireless vehicle notification and vehicle location systems and methods related thereto
US6845316B2 (en) * 2002-10-14 2005-01-18 Mytrafficnews.Com, Inc. Distribution of traffic and transit information
US20040107028A1 (en) * 2002-12-03 2004-06-03 Catalano Anthony B. Override protocol system for affording vehicle safety and for preventing hijacking
US7174153B2 (en) * 2003-12-23 2007-02-06 Gregory A Ehlers System and method for providing information to an operator of an emergency response vehicle
KR20050093210A (ko) 2004-03-18 2005-09-23 에스케이 텔레콤주식회사 개인 일정 관리 서비스 제어 시스템 및 그 방법
KR100617808B1 (ko) 2005-02-22 2006-08-28 삼성전자주식회사 대중교통 정보 안내 시스템 및 그 방법
US8138907B2 (en) * 2005-08-11 2012-03-20 University Of South Florida Travel assistant device
US7920967B1 (en) * 2005-09-08 2011-04-05 TransLoc, Inc. Methods and devices for providing route information
US8229657B2 (en) * 2006-06-08 2012-07-24 Lg Electronics Inc. Method and apparatus for providing and using public transportation information
KR20090042573A (ko) 2007-10-26 2009-04-30 (주)케이티에프테크놀로지스 위치 정보를 이용한 교통 정보 알림 단말기와 교통 정보제공 서버 및 그 방법
ES2436105T3 (es) * 2007-11-15 2013-12-27 Vodafone Holding Gmbh Procedimiento para proporcionar información de horario de transporte, terminal móvil y unidad de proceso central
EP2881712A1 (de) 2007-11-24 2015-06-10 Routerank Ltd Optimierte Routenplanung
US9159238B2 (en) 2008-10-02 2015-10-13 Microsoft Technology Licensing, LLP Location-aware selection of public transportation
US8566016B2 (en) * 2010-05-13 2013-10-22 Saab Sensis Corporation Surface hold advisor using critical sections
US20120023033A1 (en) * 2010-07-21 2012-01-26 Martin Tomasz Transport information system
US20120296885A1 (en) * 2010-09-09 2012-11-22 Google Inc. Transportation Information Systems and Methods
US8880490B2 (en) * 2010-09-10 2014-11-04 Google Inc. Correlating transportation data
US9267806B2 (en) * 2011-08-29 2016-02-23 Bayerische Motoren Werke Aktiengesellschaft System and method for automatically receiving geo-relevant information in a vehicle

Patent Citations (33)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4360875A (en) * 1981-02-23 1982-11-23 Behnke Robert W Automated, door-to-door, demand-responsive public transportation system
US5559707A (en) * 1994-06-24 1996-09-24 Delorme Publishing Company Computer aided routing system
US6253148B1 (en) * 1997-07-04 2001-06-26 Jean-Claude Decaux Information system for informing users of a public transport network about waiting times at stops in the network
US6975997B1 (en) * 1999-07-07 2005-12-13 Honda Giken Kogyo Kabushiki Kaisha Method for efficient vehicle allocation in vehicle sharing system
US20030109266A1 (en) * 2000-02-09 2003-06-12 Moshe Rafiah Integrated journey planner
US20010056363A1 (en) * 2000-06-26 2001-12-27 Gantz Donald T. System for providing ride matching services using e-mail and the internet
US20020062192A1 (en) * 2000-11-17 2002-05-23 Koninklijke Philips Electronics N.V. Method, and related system and apparatus, for providing travel-related information to a mobile communications device
US6424910B1 (en) * 2000-11-22 2002-07-23 Navigation Technologies Corp. Method and system for providing related navigation features for two or more end users
US7080019B1 (en) * 2001-03-04 2006-07-18 Ducktrip, Llc Ride share contact system
US7027995B2 (en) * 2001-06-01 2006-04-11 International Business Machines Corporation Dynamic resource scheduling to optimize location of meeting participants
US20030100993A1 (en) * 2001-11-27 2003-05-29 Kirshenbaum Evan R. Automatic gathering and analysis of data on commute paths
US20030177020A1 (en) * 2002-03-14 2003-09-18 Fujitsu Limited Method and apparatus for realizing sharing of taxi, and computer product
US20040049424A1 (en) * 2002-06-21 2004-03-11 Murray Thomas A. System and method for facilitating ridesharing
US20060276960A1 (en) * 2003-06-24 2006-12-07 Maria Adamczyk Methods, systems and computer program products for ride matching based on selection criteria and driver characteristic information
US7082364B2 (en) * 2003-06-24 2006-07-25 Bellsouth Intellectual Property Corporation Methods, systems and computer program products for ride matching based on current location information
US20100121662A1 (en) * 2003-06-26 2010-05-13 Becker Ian S System and Method of Booking Transportation
US20050033614A1 (en) * 2003-08-05 2005-02-10 Sabre Inc. System and method for coordinating travel itineraries
US7136747B2 (en) * 2005-01-08 2006-11-14 Stephen Raney Method for GPS carpool rendezvous tracking and personal safety verification
US20060155460A1 (en) * 2005-01-08 2006-07-13 Stephen Raney Method for GPS carpool rendezvous tracking and personal safety verification
US7353034B2 (en) * 2005-04-04 2008-04-01 X One, Inc. Location sharing and tracking using mobile phones or other wireless devices
US8090707B1 (en) * 2005-07-07 2012-01-03 Rearden Commerce Inc. Chance meeting addition to trip planner or meeting planner
US20070106468A1 (en) * 2005-11-07 2007-05-10 France Telecom Product, service and activity based interactive trip mapping system, method, and computer program product
US20080054072A1 (en) * 2005-11-17 2008-03-06 Lalitesh Katragadda Vehicle information systems and methods
US8140256B1 (en) * 2006-08-16 2012-03-20 University Of South Florida Dynamic ride matching system
US8150608B2 (en) * 2006-09-22 2012-04-03 Rockstar Bidco, LP Method and apparatus for enabling commuter groups
US8340890B1 (en) * 2006-09-22 2012-12-25 Rockstar Bidco, LP Method and apparatus for enabling commuter groups
US20080091342A1 (en) * 2006-10-11 2008-04-17 Jeffrey Assael System and method for ride matching
US7856300B2 (en) * 2006-11-21 2010-12-21 Aisin Seiki Kabushiki Kaisha Seat with side support control for vehicle responsive to distance and position in curve roads
US8145417B1 (en) * 2008-12-31 2012-03-27 Cellco Partnership Enabling a first mobile device to navigate to a location associated with a second mobile device
US20100274569A1 (en) * 2009-04-23 2010-10-28 Douglas Reudink Real-time location sharing
US8285570B2 (en) * 2009-08-28 2012-10-09 Rideamigos Corp. Matching system for ride reservation platforms
US20130041941A1 (en) * 2010-04-09 2013-02-14 Carnegie Mellon University Crowd-Sourcing of Information for Shared Transportation Vehicles
US20120290652A1 (en) * 2011-05-13 2012-11-15 Zeljko BOSKOVIC Arrangement and method for transport sharing

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Garcia, Ander, et al. "Personalized tourist route generation." Current Trends in Web Engineering. Springer Berlin Heidelberg, 2010. 486-497. *
Gidofalvi, Gyözö, and Torben Bach Pedersen. "Cab-sharing: An effective, door-to-door, on-demand transportation service." Proceedings of the 6th European Congress on Intelligent Transport Systems and Services, 18-20 Jun, 2007, Aalborg, Denmark. ERTICO, 2007. *
Gidófalvi, Gyözö, Gergely Herenyi, and Torben Bach Pedersen. "Instant Social Ride-Sharing." Proceedings of the Fifteenth World Congress on Intelligent Transport Systems, Nov 16-20, 2008, New York, NY, USA. Intelligent Transportation Society of America, 2008. *

Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3036507B1 (de) * 2013-08-19 2020-06-17 TomTom Navigation B.V. Verfahren und systeme zur gewinnung einer multimodalen route
US9915542B2 (en) 2013-12-20 2018-03-13 Google Llc Transportation system reconstruction
WO2015095828A1 (en) * 2013-12-20 2015-06-25 Urban Engines, Inc. Transportation system reconstruction
US11669785B2 (en) 2014-05-06 2023-06-06 Uber Technologies, Inc. System and methods for verifying that one or more directives that direct transport of a second end user does not conflict with one or more obligations to transport a first end user
US11466993B2 (en) * 2014-05-06 2022-10-11 Uber Technologies, Inc. Systems and methods for travel planning that calls for at least one transportation vehicle unit
US10788328B2 (en) 2014-09-05 2020-09-29 Paypal, Inc. Methods and systems for determining routing
US9389094B2 (en) * 2014-09-05 2016-07-12 Paypal, Inc. Methods and systems for determining routing
US9998420B2 (en) * 2015-12-04 2018-06-12 International Business Machines Corporation Live events attendance smart transportation and planning
US20170163591A1 (en) * 2015-12-04 2017-06-08 International Business Machines Corporation Live events attendance smart transportation and planning
US11551325B2 (en) 2015-12-10 2023-01-10 Uber Technologies, Inc. Travel coordination system implementing pick-up location optimization
US11200517B2 (en) 2016-08-01 2021-12-14 International Business Machines Corporation Redistribution based on real time presence data
US11582328B2 (en) 2017-08-11 2023-02-14 Uber Technologies, Inc. Dynamic scheduling system for planned service requests
US11924308B2 (en) 2017-08-11 2024-03-05 Uber Technologies, Inc. Dynamic scheduling system for planned service requests
US11674810B2 (en) 2017-11-05 2023-06-13 Uber Technologies, Inc. Network computer system to arrange pooled transport services
US10769712B1 (en) * 2018-07-16 2020-09-08 Amazon Technologies, Inc. ETA-based item pick-up and fulfillment alternatives
US10762462B1 (en) * 2018-08-07 2020-09-01 Amazon Technologies, Inc. Sensor-based customer arrival detection
US10921147B1 (en) 2018-08-29 2021-02-16 Amazon Technologies, Inc. Customer and merchant location-based ETA determination
US12001975B2 (en) 2022-10-13 2024-06-04 Uber Technologies, Inc. Systems and methods for transporting multiple end users

Also Published As

Publication number Publication date
EP2614483A2 (de) 2013-07-17
EP2614483A4 (de) 2014-05-14
US20120232776A1 (en) 2012-09-13
WO2012034083A2 (en) 2012-03-15
US20140111358A1 (en) 2014-04-24
US8645050B2 (en) 2014-02-04
US20120296885A1 (en) 2012-11-22
US9552729B2 (en) 2017-01-24
WO2012034083A3 (en) 2012-08-23
WO2012034083A8 (en) 2012-04-19

Similar Documents

Publication Publication Date Title
US9552729B2 (en) Transportation information systems and methods
US11908034B2 (en) Computer system arranging transport services for users based on the estimated time of arrival information
US9373259B2 (en) Situation-aware mobile travel advisory to public transport commuters
US11118924B2 (en) Method and system for predicting traffic conditions
US9024752B2 (en) Traveler hurry status monitor
US9659492B2 (en) Real-time vehicle spacing control
JP4097677B2 (ja) ナビゲーションシステム、経路探索サーバおよび端末装置
US9602961B2 (en) Monitoring method and system
US20180087911A1 (en) Time regulated navigation of travel through an airport
JP6058139B2 (ja) 公共輸送機関ナビゲータ
US20140207375A1 (en) Distribution of location and movement information of meeting participants
US20190303806A1 (en) Boarding management system, boarding management method, and system
JP6140312B2 (ja) 複数の経由地を含む最適経路伝達方法及びこのための装置
US9273970B2 (en) Systems and methods for generating a plurality of trip patterns
US9494431B2 (en) Method for acquiring or providing update information for route to third party and apparatus for same
CA2864007C (en) Traveler hurry status monitor
JP7075187B2 (ja) 情報処理端末、情報処理方法及びプログラム
JP2022105212A (ja) 情報処理端末、情報処理方法及びプログラム
EP2648140A1 (de) Monitor zur Überwachung des Eliestatus eines Reisenden

Legal Events

Date Code Title Description
AS Assignment

Owner name: GOOGLE INC., CALIFORNIA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:GONTMAKER, ALEX;BREWIN, FREDERICK PETER;BEN HAIM, NOAM;SIGNING DATES FROM 20110907 TO 20110928;REEL/FRAME:027211/0152

AS Assignment

Owner name: GOOGLE INC., CALIFORNIA

Free format text: CORRECTIVE ASSIGNMENT TO CORRECT THE LAST NAME OF THE 1ST INVENTOR FROM GONTMAKER PREVIOUSLY RECORDED ON REEL 027211 FRAME 0152. ASSIGNOR(S) HEREBY CONFIRMS THE CORRECT NAME OF THE 1ST INVENTOR IS GONTMAKHER;ASSIGNORS:GONTMAKHER, ALEX;BREWIN, FREDERICK PETER;BEN HAIM, NOAM;SIGNING DATES FROM 20110907 TO 20110928;REEL/FRAME:027399/0368

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION

AS Assignment

Owner name: GOOGLE LLC, CALIFORNIA

Free format text: CHANGE OF NAME;ASSIGNOR:GOOGLE INC.;REEL/FRAME:044142/0357

Effective date: 20170929