Abstract: A system for managing fare collection in a public transit network may include a fare collection system and a fare management system. The fare collection may collect fare information from passengers boarding or getting off a vehicle. The fare management system may use the received fare information to determine a fare return series over a period of time, which includes multiple fare returns. The fare management system may use a hierarchical cluster method to cluster multiple fare returns into one or more clusters represented in a dendrogram, apply a “silhouette” method to the dendrogram and obtain an optimal clustering. The system may further output a graphic representation, such as a trend line, for each of the clusters over a time period. Based on the clustering results, the system may also deploy a revised fare class schedule or transit system schedule to the fare collection system.

Abstract: A method for generating a visual representation of transport schedule adherence in a transit system including collecting transport schedule data received by a plurality of sensors deployed in a plurality of transit vehicles of the transit system, organizing and saving the received transport schedule data to a data storage facility, and receiving a user selection for the generation of a visual representation of transport schedule adherence in the transit system.

Abstract: An incident management system in a public transit network receives incident data collected by one or more sensors of the transit network, segments a display screen into multiple cells so that each of the cells corresponds a route segment and a time segment in which one or more vehicles in the transit network traveled, and outputs a graphical representation for each cell. The sensors may be installed on a vehicle to detect various types of incidents such as disturbance, safety and mechanical issues, or any incidents external to the vehicle. The graphical representation may be indicative of the estimated future incident rate for the route segment and the time segment to which each cell corresponds, and displayed by a color scale in a heatmap. The system may estimate the incident rate using a linear model that can be obtained in a training process.

Abstract: An incident management system in a public transit network receives incident data collected by one or more sensors of the transit network, segments a display screen into multiple cells so that each of the cells corresponds a route segment and a time segment in which one or more vehicles in the transit network traveled, and outputs a graphical representation for each cell. The sensors may be installed on a vehicle to detect various types of incidents such as disturbance, safety and mechanical issues, or any incidents external to the vehicle. The graphical representation may be indicative of the estimated future incident rate for the route segment and the time segment to which each cell corresponds, and displayed by a color scale in a heatmap. The system may estimate the incident rate using a linear model that can be obtained in a training process.

Abstract: A method of proactively identifying print device incidents for servicing by a service provider may include receiving print device data for one or more print devices, determining a probability value for each of the one or more of the print devices based on the print device data, where each probability value represents a likelihood that the print device will experience a subsequent incident of an incident type within a timeframe, identifying one or more of the print devices associated with probability values that each exceed a threshold value, and for one or more of the print devices that is identified, automatically initiating a service request for the subsequent incident.

Abstract: A system for managing fare collection in a public transit network may include a fare collection system and a fare management system. The fare collection may collect fare information from passengers boarding or getting off a vehicle. The fare management system may use the received fare information to determine a fare return series over a period of time, which includes multiple fare returns. The fare management system may use a hierarchical cluster method to cluster multiple fare returns into one or more clusters represented in a dendrogram, apply a “silhouette” method to the dendrogram and obtain an optimal clustering. The system may further output a graphic representation, such as a trend line, for each of the clusters over a time period. Based on the clustering results, the system may also deploy a revised fare class schedule or transit system schedule to the fare collection system.

Abstract: A method and system for identifying demand in a transportation system. A boarding count model is determined based upon passenger arrival information, and a geographic and time-specific generalized boarding model is determined based upon the boarding count model as well as information related to a plurality of stops on a route in the transportation system. For each of the plurality of stops, an approximated uniform arrival model is determined based upon the generalized arrival model and a time period between arriving vehicles at a specific stop, an instantaneous demand model is determined based upon the uniform arrival model, and a probability of no demand model is determined based upon the uniform arrival model. A report including the instantaneous demand and the probability of no demand determined can be generated. Based upon the report, various operational parameters for the transportation system can be manually or automatically adjusted.

Abstract: A system in a public transit network may include a fare collection system, a fare management system, and a processing device for assessing the riskiness of fare classes in the transit network and causing the fare management system to take an action based on the assessment. Particularly, the processing device will receive fare information from the fare collection system, such as via a token reader, determine a fare return series over a period of time, apply a Capital Asset Pricing Model to the fare return series and assess the return versus volatility chart for multiple fare classes. Based on the assessment, the processing device will cause the fare management system to take an action such as changing a fare price or a route, deleting a fare class or a route, combining one or more fare classes or routes, or combining one or more fare classes with one or more routes.

Abstract: A method for computing a metric for evaluating reliability of a transportation service includes collecting transportation data for at least a part of a transportation network. For at least one of the stops on at least one route of the network, dimensions of the metric for evaluating reliability are computed. The dimensions are selected from: a) a perceived waiting cost, b) a cost of lateness at a final destination, based on a difference between a scheduled arrival time and an actual arrival time; and c) an annoyance cost due to a missed connection at the stop. A representation of at least one of the computed dimensions is generated for at least one of the stops, for at least one of the passengers and is output.

Abstract: To determine origin-destination (OD) data for a transportation system, a processor may receiving passenger statistics collected from a set of data collection devices deployed throughout the transportation system. The processor developing a model representing passenger usage of a portion of the transportation system. The model comprises nodes that each represent a geographical area of the transportation network, and links that represent transportation routes that connect two nodes. The system determines link utilization rates for each of the links by applying the passenger statistics to the model, and it generates an observation report comprising an origin-destination matrix for at least the portion of the transportation system so that the origin-destination matrix comprises the determined link utilization rates.

Abstract: To determine origin-destination (OD) data for a transportation system, a processor may receiving passenger statistics collected from a set of data collection devices deployed throughout the transportation system. The processor developing a model representing passenger usage of a portion of the transportation system. The model comprises nodes that each represent a geographical area of the transportation network, and links that represent transportation routes that connect two nodes. The system determines link utilization rates for each of the links by applying the passenger statistics to the model, and it generates an observation report comprising an origin-destination matrix for at least the portion of the transportation system so that the origin-destination matrix comprises the determined link utilization rates.

Abstract: A method for generating a visual representation of transport schedule adherence in a transit system including collecting transport schedule data received by a plurality of sensors deployed in a plurality of transit vehicles of the transit system, organizing and saving the received transport schedule data to a data storage facility, and receiving a user selection for the generation of a visual representation of transport schedule adherence in the transit system.

Abstract: A transportation service data assessment system includes a data set holding f performance metrics for various route components of a transportation service. When the system receives a selected set of operational data parameter labels, as well one or more route components, it develops a matrix of performance metrics corresponding to the operational data and route components, determines a distance between each row of the performance metric matrix to yield a multi-dimensional matrix, and maps the distance data to a 2-D or 3-D coordinate set so that to yield a coordinate matrix. The system groups the data of the third matrix into clusters and presents the clusters on a display so that outliers are visually distinguished from clustered items, and so that redundant items are also visually apparent in the clusters.

Abstract: A method and system for identifying demand in a transportation system. A boarding count model is determined based upon passenger arrival information, and a geographic and time-specific generalized boarding model is determined based upon the boarding count model as well as information related to a plurality of stops on a route in the transportation system. For each of the plurality of stops, an approximated uniform arrival model is determined based upon the generalized arrival model and a time period between arriving vehicles at a specific stop, an instantaneous demand model is determined based upon the uniform arrival model, and a probability of no demand model is determined based upon the uniform arrival model. A report including the instantaneous demand and the probability of no demand determined can be generated. Based upon the report, various operational parameters for the transportation system can be manually or automatically adjusted.

Abstract: Methods and systems of determining a failure copula for a consumable are disclosed. Multiple consumable levels for a consumable and multiple timestamps are received from a device. Each timestamp is associated with one of the consumable levels. The consumable levels are ordered into a time-ordered sequence based on the associated timestamps. For at least one consumable level in the time-ordered sequence, a computing device determines a value associated with the consumable level based on at least one earlier and at least one later consumable level in the time-ordered sequence. The computing device identifies historical consumable replacement(s) based on the values associated with the consumable levels in the time-ordered sequence and determines one or more consumable lifetimes and one or more consumable yields based on the historical consumable replacement(s). The computing device determines a failure copula based on the one or more consumable lifetimes and consumable yields.

Abstract: In a transportation system, identifying factors that contribute to schedule deviation provides for improving the operation of the system. A processing device collects operational information related to the operation of at least one vehicle along a transportation route. The device determines a plurality of actual scheduled arrivals for the transportation route and compare the operating information with the actual scheduled arrivals to determine mean delay data for each driver. The device determines schedule deviation for each driver based upon the operating information and fits the mean delay data for each driver, standard deviation delay data for each driver, and the schedule deviation information for each driver into a results set. The device fits the data using a maximum likelihood modeling technique and/or a Bayesian modeling technique. The results set are presented to a manager or another similar authority role in the transportation system for further action.

Abstract: Methods and systems of filtering non-actionable alerts are disclosed. A processing device receives an alert from a printing device and determines a page count for the printing device. The processing device compares the page count with at least one threshold value associated with the alert and the printing device. In response to the page count satisfying the at least one threshold value, the processing device identifies the alert as a valid alert.

Abstract: A method embodiment herein begins by capturing a source image. The source image is segmented into first planes. The first planes can each comprise a mask plane and foreground plane combination. The binary images in the first planes are structurally analyzed to identify different regions of text, tables, handwriting, line art, equations, etc., using a document model that has information of size, shape, and spatial arrangement of possible regions. Then, the method extracts (crops out) these regions from the foreground plane to create second mask/foreground plane pairs. Thus, the method creates “second” planes from the first planes, so that a separate second plane is created for each of the regions. Next, tags are associated with each of the second planes (to create tagged mask/foreground plane pairs) and the second planes and associated tags are combined into a mixed raster content (MRC) document.

Abstract: A method and a device for predicting a future occurrence of a transportation system incident are disclosed. The method includes receiving transportation incident data comprising information collected during and related to operation of at least one transportation vehicle, the information comprising at least a time stamp, constructing at least one count regression model based upon the transportation incident data, producing a results set based upon the at least one count regression model, predicting at least one future occurrence of an incident based upon the results set, and presenting the at least one predicted future occurrence. The device includes at least a processing device and computer readable medium containing a set of instructions configured to cause the device to perform the method.

Abstract: There is provided a print demand forecasting system and method for use with a print production system in which print demand data is collected for each print job processed during a selected time interval. The collected print demand data is stored in memory and then processed to obtain a first time series component and a second time series component. The first and second time series components are corresponded respectively with first and second forecast models. One of the first and second forecast models is selected with an S process, and the selected one of the first and second forecast models is used to obtain forecast data.