Abstract:
Systems and methods for pattern based transit routing are provided. One exemplary method includes determining a plurality of trip patterns between an origin and a destination responsive to a user request. Trips for each of the plurality of trip patterns are generated, each trip corresponding to an instance of transportation. The trips are filtered. Each trip in the filtered trips is annotated with transit data. Responsive to a user request, one or more trips are selected from the annotated trips based upon the transit data associated with each respective trip, the user request, or combinations thereof.
Abstract:
Systems and methods for generating a plurality of trip patterns are provided. One exemplary method includes receiving transit graph data describing a plurality of nodes respectively corresponding to a plurality of transit stations and a plurality of arcs respectively connecting the plurality of nodes. The method also includes performing a plurality of identification iterations. Each identification iteration includes determining an optimal transit trip connecting an origin node to a destination node based on a cost model. Each identification iteration also includes revising the cost model based on the determined optimal transit trip, such that the arc costs associated with one or more arcs associated with the optimal transit trip are increased. Each optimal transit trip can have an associated trip pattern describing a sequence of nodes traversed by such optimal transit trip. One exemplary system can include a transit planning platform that includes a trip pattern identification module.
Abstract:
Systems and methods for generating transit trips between an origin and a destination are provided. Searches can be undertaken to identify optimal departure times from a source station and/or one or more intermediate stations while maintaining a lowest cost arrival time at a destination station. In this manner, public transportation journey schedules for routes can be determined and recommendations can be provided even if different journey schedules show identical costs.
Abstract:
Systems and methods for generating a plurality of trip patterns are provided. One exemplary method includes receiving transit graph data describing a plurality of nodes respectively corresponding to a plurality of transit stations and a plurality of arcs respectively connecting the plurality of nodes. The method also includes performing a plurality of identification iterations. Each identification iteration includes determining an optimal transit trip connecting an origin node to a destination node based on a cost model. Each identification iteration also includes revising the cost model based on the determined optimal transit trip, such that the arc costs associated with one or more arcs associated with the optimal transit trip are increased. Each optimal transit trip can have an associated trip pattern describing a sequence of nodes traversed by such optimal transit trip. One exemplary system can include a transit planning platform that includes a trip pattern identification module.
Abstract:
An interactive user interface for displaying available trips is provided. The user interface includes a calendar overview tool. An exemplary computing system has a processor and a memory. The computing system is configured to perform operations including obtaining data describing a plurality of trips between an origin and a destination. Each trip has a departure time and an arrival time. The operations also include respectively representing the plurality of trips with a plurality of trip identifiers at different positions on a first axis of a graph. The graph has units of time on a second axis. Each trip identifier extends from the arrival time to the departure time of the trip such trip identifier represents. An interval of time depicted by the graph can be adjusted by a user.
Abstract:
Systems and methods for recommending time independent or “guidebook” transit routes between an origin and a destination are provided. A score is generated for each transit route. The score can be used to evaluate and prioritize the transit routes so that one or more transit routes can be recommended to a user. The score can be computed based on characteristics of a trip duration function generated for the transit route. The trip duration function specifies a trip cost (e.g. a trip duration) for the transit route as a function of time (such as departure time or arrival time) over the time interval. The trip duration function can be a piecewise linear function with one or more linear trip segments. Each linear trip segment models the trip cost, including waiting time, of a transit trip associated with the transit route over at least a portion of the time interval.
Abstract:
Systems and methods for recommending time independent or “guidebook” transit routes between an origin and a destination are provided. Inputs of a trip pattern, an interval of times, and the timetables of the trips of the trip pattern, can result in output of a list of all considered trips (including departure time and duration thereof) and a list of lines for each transit step.
Abstract:
Systems and methods for filtering an initial set of transit trips between an origin and a destination to identify a subset of reasonable transit trips between the origin and the destination are provided. Once identified, the subset of reasonable transit trips can be further analyzed based on various criteria (e.g. earliest arrival, lowest price, least amount of waking, least number of transfers, etc.) to recommend one or more transit trips to a user. The subset of reasonable transit trips can be identified by obtaining transit trip data suitable for identifying a trip duration (e.g. departure time and arrival time) for each trip in the initial set of transit trips. This transit trip data can be used to filter out trips from the set that do not provide an optimal trip duration at some specific time over a time interval.
Abstract:
Systems and methods for identifying a direction name associated a transit trip along a transit line for use in, for instance, grouping departure times or arrival times associated with transit trips at a transit stop are provided. Data indicative or an ordering ranking the plurality of transit stops in terms of importance can be accessed. Important transit stops between the reference transit stop and an ending transit stop associated with a transit trip can be identified using the ordering of the plurality of transit stops. The station names associated with the identified important transit stops can be used as a basis for the direction name associated with the transit trip at the particular reference transit stop. Departure times can be grouped by the identified direction names and using other parameters, such as by headsign.
Abstract:
Systems and methods for summarizing a guidebook result are provided. An exemplary system includes a transit trip identification module, a cost function generation module, a service window identification module, a daytime interval conversion module, and a guidebook summarization module. An exemplary method includes identifying a plurality of service windows comprising intervals of time over which the available transit trips satisfy a quality criterion. The exemplary method also includes selecting as a guidebook summary a set of days of the week and a continuous interval of time which include only portions of service windows, such that a total number of hours included in the guidebook summary is maximized