Abstract: A system and method for assigning trip requests to schedules for a fleet of one or more available vehicles in a manner that allows for the comparison of the various possible schedules against a number of global factors. Trip requests are combined into packets and one or more packets that can serve as a root packet or a first packet to be serviced by a partial shift are determined. Schedules are determined for each of the one or more possible root packets. The schedules are ranked according to one or more global factors to select a schedule for the partial shift. By generating combinations of packets that can be assigned to partial shifts, the problem of combinatorial explosion is reduced such that alternative schedules can be compared against one or more quality metrics for the schedule.

Abstract: The technology disclosed relates to a method for demand-response scheduling that resolves the most undesirable issues intrinsic to the paradigm of the prior art, namely constructing schedules by “Trip by Trip Insertion” onto service vehicles called routes. These issues are classified into Customer Service, On-time Performance, and Efficiency categories. The first step of the 2-step process is to globally build packets to patterns in a library that provide good Customer Service, On-time Performance protection, and Positive Ridesharing Efficiency. The packets are “Mini-Manifests” that meet all constraints, and once formed, are locked from modification by any subsequent scheduling action. The second step is to globally build routes by chaining packets together, controlled by configurable heuristic strategy. The routes that are built directly resolve the undesirable issues.

Abstract: The technology disclosed relates to a method for demand-response scheduling that resolves the most undesirable issues intrinsic to the paradigm of the prior art, namely constructing schedules by “Trip by Trip Insertion” onto service vehicles called routes. These issues are classified into Customer Service, On-time Performance, and Efficiency categories. The first step of the 2-step process is to globally build packets to patterns in a library that provide good Customer Service, On-time Performance protection, and Positive Ridesharing Efficiency. The packets are “Mini-Manifests” that meet all constraints, and once formed, are locked from modification by any subsequent scheduling action. The second step is to globally build routes by chaining packets together, controlled by configurable heuristic strategy. The routes that are built directly resolve the undesirable issues.

Abstract: The technology disclosed relates to a method for demand-response scheduling that resolves the most undesirable issues intrinsic to the paradigm of the prior art, namely constructing schedules by “Trip by Trip Insertion” onto service vehicles called routes. These issues are classified into Customer Service, On-time Performance, and Efficiency categories. The first step of the 2-step process is to globally build packets to patterns in a library that provide good Customer Service, On-time Performance protection, and Positive Ridesharing Efficiency. The packets are “Mini-Manifests” that meet all constraints, and once formed, are locked from modification by any subsequent scheduling action. The second step is to globally build routes by chaining packets together, controlled by configurable heuristic strategy. The routes that are built directly resolve the undesirable issues.

Abstract: The technology disclosed relates to a method for demand-response scheduling that resolves the most undesirable issues intrinsic to the paradigm of the prior art, namely constructing schedules by “Trip by Trip Insertion” onto service vehicles called routes. These issues are classified into Customer Service, On-time Performance, and Efficiency categories. The first step of the 2-step process is to globally build packets to patterns in a library that provide good Customer Service, On-time Performance protection, and Positive Ridesharing Efficiency. The packets are “Mini-Manifests” that meet all constraints, and once formed, are locked from modification by any subsequent scheduling action. The second step is to globally build routes by chaining packets together, controlled by configurable heuristic strategy. The routes that are built directly resolve the undesirable issues.

Abstract: A system for representing predicted travel times between pick-up and drop-off addresses for which routes are to be scheduled. In some embodiments, a computer system defines a disk representing a geographic area surrounding each address having a radius that is selected such that a travel time between any two locations in the area of the disk is within a predetermined time limit. If the travel time from an address to a man-made or natural obstacle that limits travel in the area of the disk is within the predetermined time limit then the radius of the disk is reduced to the distance to the obstacle. A memory stores predicted travel times between addresses encompassed by the geographic areas of different disks.

Abstract: A system for representing predicted travel times between pick-up and drop-off addresses for which routes are to be scheduled. In some embodiments, a computer system defines a disk representing a geographic area surrounding each address having a radius that is selected such that a travel time between any two locations in the area of the disk is within a predetermined time limit. If the travel time from an address to a man-made or natural obstacle that limits travel in the area of the disk is within the predetermined time limit then the radius of the disk is reduced to the distance to the obstacle. A memory stores predicted travel times between addresses encompassed by the geographic areas of different disks.

Abstract: The present invention relates to travel time determination, for example, determining a predicted travel time between locations based on historical data. One aspect of the invention is directed toward a method for determining a predicted amount of time required to travel from one location to another that includes dividing a region into multiple grid portions and designating each grid portion that contains at least one address of interest as a departure grid, a destination grid, or both. The method can further include generating a searchable data arrangement that includes travel times associated with traveling from selected departure grids to selected destination grids, and updating the travel times using historical data.

Abstract: The present invention relates to transportation management processes and systems. One aspect of the invention is directed toward a method in a computer system for determining one or more routes for transporting items, including receiving multiple orders to transport items from an origination stop to a destination stop, generating one or more consolidations, and generating one or more shipments wherein each shipment includes a selected consolidation that meets at least one shipment parameter or an order. The method further includes generating one or more routes with an ordered sequence of stops wherein each route is associated with one or more shipments. The method still further includes selecting one or more routes based on one or more route parameters.

Abstract: The present invention relates to transportation management processes and systems. One aspect of the invention is directed toward a method in a computer system for determining one or more routes for transporting items, including receiving multiple orders to transport items from an origination stop to a destination stop, generating one or more consolidations, and generating one or more shipments wherein each shipment includes a selected consolidation that meets at least one shipment parameter or an order. The method further includes generating one or more routes with an ordered sequence of stops wherein each route is associated with one or more shipments. The method still further includes selecting one or more routes based on one or more route parameters.

Abstract: The present invention relates to travel time determination, for example, determining a predicted travel time between locations based on historical data. One aspect of the invention is directed toward a method for determining a predicted amount of time required to travel from one location to another that includes dividing a region into multiple grid portions and designating each grid portion that contains at least one address of interest as a departure grid, a destination grid, or both. The method can further include generating a searchable data arrangement that includes travel times associated with traveling from selected departure grids to selected destination grids, and updating the travel times using historical data.

Abstract: The present invention relates to travel time determination, for example, determining a predicted travel time between locations based on historical data. One aspect of the invention is directed toward a method for determining a predicted amount of time required to travel from one location to another that includes dividing a region into multiple grid portions and designating each grid portion that contains at least one address of interest as a departure grid, a destination grid, or both. The method can further include generating a searchable data arrangement that includes travel times associated with traveling from selected departure grids to selected destination grids, and updating the travel times using historical data.

Abstract: The present invention relates to transportation management processes and systems. One aspect of the invention is directed toward a method in a computer system for determining one or more routes for transporting items, including receiving multiple orders to transport items from an origination stop to a destination stop, generating one or more consolidations, and generating one or more shipments wherein each shipment includes a selected consolidation that meets at least one shipment parameter or an order. The method further includes generating one or more routes with an ordered sequence of stops wherein each route is associated with one or more shipments. The method still further includes selecting one or more routes based on one or more route parameters.

Abstract: The present invention relates to transportation management processes and systems. One aspect of the invention is directed toward a method in a computer system for determining one or more routes for transporting items, including receiving multiple orders to transport items from an origination stop to a destination stop, generating one or more consolidations, and generating one or more shipments wherein each shipment includes a selected consolidation that meets at least one shipment parameter or an order. The method further includes generating one or more routes with an ordered sequence of stops wherein each route is associated with one or more shipments. The method still further includes selecting one or more routes based on one or more route parameters.

Abstract: The present invention relates to travel time determination, for example, determining a predicted travel time between locations based on historical data. One aspect of the invention is directed toward a method for determining a predicted amount of time required to travel from one location to another that includes dividing a region into multiple grid portions and designating each grid portion that contains at least one address of interest as a departure grid, a destination grid, or both. The method can further include generating a searchable data arrangement that includes travel times associated with traveling from selected departure grids to selected destination grids, and updating the travel times using historical data.

Abstract: An improved transportation scheduling method for scheduling a plurality of trips T1–Tn using a plurality of vehicles V1–Vn, each of said plurality of vehicles having a trip manifest. The method comprises the steps of: (a) generating a useability matrix that determines which of said plurality of vehicles V1–Vn is useable with each of said plurality of trips T1–Tn; (b) determining a best trip of said plurality of trips T1–Tn to be scheduled next; (c) determining a best vehicle from said plurality of vehicles V1–Vn for accommodating said best trip; (d) determining a best insertion pair into said trip manifest of said best vehicle for said best trip and updating said trip manifest to include said best insertion pair; (e) updating said useability matrix to reflect the scheduling of said best trip on said best vehicle; and (f) repeating steps (a)–(e) until all of the trips in said plurality of trips T1–Tn have been scheduled.

Abstract: An improved transportation scheduling method for scheduling a plurality of trips T1-Tn using a plurality of vehicles V1-Vn, each of said plurality of vehicles having a trip manifest. The method comprises the steps of: (a) generating a useability matrix that determines which of said plurality of vehicles V1-Vn is useable with each of said plurality of trips T1-Tn; (b) determining a best trip of said plurality of trips T1-Tn to be scheduled next; (c) determining a best vehicle from said plurality of vehicles V1-Vn for accommodating said best trip; (d) determining a best insertion pair into said trip manifest of said best vehicle for said best trip and updating said trip manifest to include said best insertion pair; (e) updating said useability matrix to reflect the scheduling of said best trip on said best vehicle; and (f) repeating steps (a)-(e) until all of the trips in said plurality of trips T1-Tn have been scheduled.

Abstract: An improved transportation scheduling method for scheduling trips using multiple vehicles, with each of the vehicles having a trip manifest. The method comprises the steps of first generating a usability matrix that determines which of the vehicles is usable with each of the trips. Next, a determination is made as to a best trip of the trips to be scheduled next. A determination is next made as to the best vehicle from the set of vehicles for accommodating the best trip. Then, a determination is made as to the best insertion pair for the trip manifest for the best vehicle for the best trip and updating the trip manifest to include the best insertion pair. Finally, the usability matrix is updated to reflect the scheduling of the best trip to the best vehicle. The steps are repeated until all of the trips have been scheduled.