Abstract:
Systems, methods, and apparatuses are disclosed for predicting or estimating the value of a variable speed sign (VSS). Probe data is received from multiple vehicles associated with a road segment. Location values are derived from the probe data. Center distance values are calculated based on the location values and the road segment. Clusters are derived from the probe data. Center distance values are grouped according to the respective clusters and a lane is assigned to at least one cluster based on the center distance values. The speed of the cluster predicts or estimates the corresponding lane of the VSS.
Abstract:
Systems, methods, and apparatuses are disclosed for predicting the value of a variable speed sign (VSS) and determining the predicted value's associated confidence level. Highly assisted driving (HAD) vehicles may read or capture images of the VSS. The speed limit values, images, or videos of the VSS are reported and received by a network and database for analysis. A predicted speed limit value is determined for the variable speed sign from at least a portion of the received traffic data. A confidence level is also calculated for the predicted speed limit value for the variable speed sign.
Abstract:
Methods for providing a highly assisted driving (HAD) service include: (a) transmitting telematics sensor data from a vehicle to a remote first server; (b) transmitting at least a portion of the telematics sensor data from the remote first server to a remote second server, wherein the remote second server is configured to execute a HAD application using received telematics sensor data, and wherein the HAD application is configured to output a HAD service result; and (c) transmitting the HAD service result from the remote second server to a client. Apparatuses for providing a HAD service are described.
Abstract:
Precision traffic flow indication may involve receiving device data over a period of time representing a plurality traffic flow readings associated with a road involving a plurality of subsections. Calculating traffic flows and determining road subsections having similar traffic flows may also be involved. Also, indicating a different traffic flow level for a first subsection and a second subsection of road may be involved.
Abstract:
Methods for providing a highly assisted driving (HAD) service include: (a) transmitting telematics sensor data from a vehicle to a remote first server; (b) transmitting at least a portion of the telematics sensor data from the remote first server to a remote second server, wherein the remote second server is configured to execute a HAD application using received telematics sensor data, and wherein the HAD application is configured to output a HAD service result; and (c) transmitting the HAD service result from the remote second server to a client. Apparatuses for providing a HAD service are described.
Abstract:
A first destination and a first route strategy are received from a first device. The first route strategy includes a first rider destination hierarchy for a ride sharing algorithm. A second destination and a second route strategy is received from a second device. The second route strategy includes a second rider destination hierarchy for a ride sharing algorithm. A route is generated to the first destination and the second destination based on the rider destination hierarchies specified by the route strategies.
Abstract:
An approach is provided for creating an origin-destination matrix from probe trajectory data. The approach includes receiving probe trajectory data, wherein the probe trajectory data is associated with at least one subset of a plurality of travel nodes. The approach further includes processing and/or facilitating a processing of the probe trajectory data to construct one or more microscopic origin-destination matrices, wherein the at least one microscopic origin-destination matrix represents one or more preferred travel paths through the subset of the plurality of travel nodes. The approach also involves causing, at least in part, an aggregation of the one or more microscopic origin-destination matrices to construct at least one aggregated origin-destination matrix to represent the plurality of travel nodes.
Abstract:
System and methods for detecting and obtaining lane level insight in unplanned incidents. Probe-based vehicles and lane-level insight using a lane-level map-matcher are used to acquire information. This information is aggregated and used to differentiate lane activity in terms of traffic and safe navigation. With the identification of probes per-lane and probe speeds per-lane, sudden reductions in probe speeds may be obtained at a lane-based level. This is used to verify or detect lane-level incident or hazard warnings and consequently alert a driver to safer navigation paths ahead of time, like maneuver to a different lane or to take an alternative route.
Abstract:
A method is provided for determining and verifying lane closures using lane-level map-matching to monitor all lanes of a road. Methods may include: receiving a plurality of probe data points, where the probe data points; map-matching the probe data points to one or more road segments; determining, for the probe data points, lanes of travel of the one or more road segments; determining a volume of traffic along the lanes of travel of the one or more road segments based on the lanes of travel of the one or more road segments determined for the probe data; determining an expected volume of traffic along the lanes of travel; and generating an indication of a lane closure in response to a volume of traffic along a first lane of travel of the one or more road segments being a predetermined amount below an expected volume of traffic.
Abstract:
A method and apparatus for calculating an estimated travel time and a method and apparatus for defining a model configured to estimate a travel time are provided. The method for estimating a travel time includes obtaining a movement pattern for a train, determining one or more predicted times when the train will be present at a train crossing where a road path and a train path intersect using the movement pattern, obtaining vehicle historical data including an average time for a vehicle to travel one or more road segments adjoining the train crossing, obtaining a start time, and calculating the travel time for the one or more road segments based on the vehicle historical data, the start time, and the predicted time when the train will be present at the train crossing.