Sap Se

Jing Liu

Qiwei Zhang

Yinling Ni

Ye Jin

Ke Wang

Jun Liu

Yongxun Li

Zhongcun Wang

Ling Chen

Chao Yu

Junpeng Hu

Daihui Zhu

Siyi Chen

Frank Spruenken

Described herein is a framework for optimal demand-based allocation. In accordance with one aspect of the framework, trip data is extracted from vehicle operation data received from one or more vehicle data devices servicing a region of interest. Vehicle demands for multiple tiles of the region of interest and mutual travel times between neighboring tiles are estimated based at least in part on the trip data. A set of candidate tiles for allocating vehicle stations may then be determined by minimizing total travel time based on the mutual travel times and vehicle demands.

Optimal demand-based allocation

Described herein is a descriptive framework to facilitate error detection in recognition data. In accordance with one aspect of the framework, at least one erroneous record is detected in a first set of recognition data. The framework may determine a correction of a first recognized identifier in the erroneous record by searching a second set of recognition data for a matching record with a second recognized identifier substantially similar to the first recognized identifier. A report may then be generated to present the detected erroneous record and the determined correction.

Error detection in recognition data

Described herein is a framework to monitor traffic congestion. In accordance with one aspect of the framework, the framework receives vehicle data from vehicle data sources located in a region of interest. The framework may determine a sample size and an average speed for an edge of the region of interest based on the vehicle data. Congestion probability may then be determined based on the sample size and average speed. A report may be presented based on the congestion probability.

Monitoring traffic congestion

A framework for identifying parking areas from vehicle trajectory data is described herein. Vehicle trajectory data collected from a sensor network having a plurality of sensor stations for detecting vehicles is provided. The vehicle trajectory data is pre-processed to generate a tracking table of vehicles and analyzed to determine an interstation parking area between first and second sensor stations of interest based on input parameters from a user.

Identification of vehicle parking using data from vehicle sensor network

A framework for origin-destination (OD) analysis of vehicle trajectory data is described herein. In accordance with one aspect, a vehicle trajectory dataset is provided to an OD analyzer. The vehicle trajectory dataset includes vehicle trajectory data collected from a sensor network having a plurality of sensor stations for detecting vehicles. The sensor stations of the sensor network are distributed in a geographical area of interest, where the vehicle trajectory data include trajectories of vehicles captured by the sensor network. The vehicle trajectory dataset may be analyzed by the OD analyzer to determine an origin and a destination of trips for trajectories of the vehicles in the vehicle trajectory dataset. The analysis includes calculating a probability distribution of travel time between sensor pairs of the sensor network of a number of (dropped-out) intermediate stations, and determining a stop probability between a station pair in the trajectories of the vehicles, where a stop is a destination of a previous trip and an origin of a next trip in the trajectories.

Origin destination estimation based on vehicle trajectory data

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Nanjing, China

Jing Liu