Xerox Corporation

Conduent Business Services, LLC

Luis Rafael Ulloa Paredes

Guillaume M Bouchard

Boris Chidlovskii

Frederic Roulland

Victor Ciriza

Cesar Roberto De Souza

Pascal Valobra

Lionel Cazenave

Vassilissa Lehoux

Arturo Mondragon Cardenas

A computer-implemented system and method identify Pareto optimal candidate paths between an origin and a destination for which no other candidate path is strictly better on one of a predefined set of criteria and at least as good on all the others. A constraint is defined for each of the criteria, based on user input. A set of Pareto optimal candidate paths is identified, from an origin to a destination, which respect these constraints. The identification may include, in a search graph composed of nodes connected by edges, iteratively advancing each of a set of possible paths from an origin node by exactly one exit node and updating labels of the exit nodes reached. The exit node labels each include a value for each of the criteria. Labels of reached exit nodes that are dominated by another label of that reached node are removed. Pareto optimal candidate path(s) is/are identified.

Optimization of multiple criteria in journey planning

Methods and systems for interpretable user behavior profiling in off-street parking applications. To render user profiles easy to interpret by decision makers, the semi-automatic discovery and tagging of user profiles can be implemented. Transaction data from one or more (and geographically close) off-street parking installations can be implemented. An analysis of spatio-temporal behavioral patterns can be implemented based on representation of any parking episode by a set of heterogeneous features, the use of clustering methods for automatic pattern discovery, an assessment of obtained clusters, semi-automatic identification/tagging of space-temporal patterns, and a user-friendly interpretation of obtained patterns.

Methods and systems for interpretable user behavior profiling in off-street parking

A method and system are disclosed for learning a demand model and simulation parameters from validation information. Validation information is received from automatic fare collection systems and trips are reconstructed from the validation information. Origins, destinations, and arrival/departure times are estimated from the reconstructed trips. A demand model is then generated from the origins, destinations, and times. Assignment model parameters are then learned from the received validation information and demand model via iterative simulations. Infrastructure changes are made to a simulated transportation network based on the assignment and demand model using the learned parameters. A simulated response of the transportation network to the infrastructure change is then output.

Learning mobility user choice and demand models from public transport fare collection data

Methods and systems for estimating airport usage demand. Airport parking traffic usage data and flight-time table data can be compiled with respect to an airport (or more than one airport). The airport parking traffic usage data and flight-time table data can be analyzed using an efficient time matching approach (e.g., a time segment matching algorithm). An efficient method to match passengers and flights is introduced. Passenger behavior can be estimated with respect to the airport based on the airport parking traffic usage data and flight-time table data.

Method, system and processor-readable media for estimating airport usage demand

This document discloses a system that selects a path to reach a destination in a transportation network. The system receives information related to a public transportation network with multiple stops. Each stop includes at least one associated link with another stop. Each link represents a mode of transportation between two stops. When the system receives a request for routes between an origin and a destination within the transportation network, it will determine a set of routes between the origin and the destination using an iterative process without reference to a graph representation of the network. The system will select a shortest path that corresponds to the route having the earliest arrival time at the destination. The system will also select an alternative path that corresponds to a route having an arrival time that is no more than a threshold amount later than the earliest arrival time.

Efficient route planning in public transportation networks

In a parallel computing method performed by a parallel computing system comprising a plurality of central processing units (CPUs), a main process executes. Tasks are executed in parallel with the main process on CPUs not used in executing the main process. Results of completed tasks are stored in a cache, from which the main process retrieves completed task results when needed. The initiation of task execution is controlled by a priority ranking of tasks based on at least probabilities that task results will be needed by the main process and time limits for executing the tasks. The priority ranking of tasks is from the vantage point of a current execution point in the main process and is updated as the main process executes. An executing task may be pre-empted by a task having higher priority if no idle CPU is available.

Transportation network micro-simulation with pre-emptive decomposition

A method and system are disclosed for estimating origin and destination locations of users of a transportation system. The origins and destinations of known users are determined during a segment of an analysis period from validation information for all users of the transportation system. The origins and destinations are then mapped to probable locations associated with the transportation network. A destination probability is then computed for each destination location of an individual origin location. A unknown users are then apportioned to each destination, which may be based on the number of unknown users on a vehicle traveling from the origin to the destination, the computed probability, and the validation information, so as to estimate the number of users traveling from an origin location to any corresponding destination location on the transportation system.

System and method for estimating origins and destinations from identified end-point time-location stamps

A method and system are disclosed for trip planning using crowdsourcing. Validation information from automatic ticketing validation systems is collected and used to generate validation sequences, which include an origin, destination, and boarding and alighting timestamps. A path is defined from the validation sequence as a sequence of origins and destinations on a transportation network. The frequency of the path occurring for all travelers on the network is determined, along with the duration of travel time on the path. An expected transfer time between pairs of stations on the network is calculated from the duration, the pairs of stations corresponding to an origin and a destination in the sequence. One or more paths between a pair of stations corresponding to a selected origin and destination are identified and a trip plan is generated using the identified paths which have the least determined transfer time and the highest frequency of occurrences.

System and method for trip plan crowdsourcing using automatic fare collection data

A method and system are disclosed for automatically tagging locations using collected traveler information. Traveler information, including a time/date stamp and a unique identification associated with the traveler are collected and stored in a database with locations corresponding to transportation stops. A location query, which includes a location type, an analysis period, optionally, an analysis approach, and a user selected threshold are received and a number of time/location stamps for each location is determined based upon an interval associated with the selected type. The maximum number of time/location stamps for that location is determined, and using the selected threshold, a minimum number of stamps required to designate a location as the selected type is determined. When the number of time/location stamps within the time interval for the selected type is greater than or equal to the minimum number calculated, the location is tagged as the selected location type.

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Meylan, France

Luis Rafael Ulloa Paredes