Abstract: A method of dynamically allocating frequency settings of a transit service includes utilizing AVL/APC to determine travel time and demand variations within a day. Clusters of time periods are formed based thereon and the day is split up. For each of the time periods for which a new frequency setting will be allocated, frequency allocation ranges are computed within which waiting times at multi-modal transfer stops are reduced and a frequency allocation is selected using criteria including passenger demand coverage and operational costs reduction. A plurality of frequency setting solutions are computed using a Branch and Bound approach with Sequential Quadratic Programming (SQP) or a sequential genetic algorithm with exterior point penalization. Sensitivity of the frequency setting solutions is tested to determine a most operationally reliable frequency setting solution for the new frequency setting and a timetable of the transit service is updated accordingly.

Abstract: A method for automatically allocating a plurality of available vehicles to a plurality of original service lines and virtual service lines of a transportation network includes receiving the virtual lines from a virtual line generator; approximating a constrained fleet allocation problem with an unconstrained fleet allocation problem that utilizes penalty terms to penalize violation of constraints; performing a multi-start sequential genetic search using a first population to identify a first solution; generating, using the first solution, a second population; performing a second multi-start genetic search using the second population to identify a second solution; and dispatching vehicles to different routes and lines according to the second solution.

Abstract: A method for providing a typical load profile of a vehicle includes computing dwell times and maximum load stops, wherein a maximum load stop indicates a stop having a largest load on a route for a trip using route information and vehicle scheduling information based on automatic vehicle location data, computing one or more trip load profiles by identifying a load progression rate by performing a local regression procedure dividing the route into subsections such that the loads between adjacent subsections can be represented by a linear function on input of the computed maximum load, the computed dwell times, and maximum load stops, and constraining the identified load progression rate to an admissible value by evaluating the rate load progression with regard to historical dwell times and the computed maximum load stops. The method further includes computing the typical load profile based on load profiles of computed trips.

Abstract: A method for automatically allocating a plurality of available vehicles to a plurality of original service lines and virtual service lines of a transportation network includes receiving the virtual lines from a virtual line generator; approximating a constrained fleet allocation problem with an unconstrained fleet allocation problem that utilizes penalty terms to penalize violation of constraints; performing a multi-start sequential genetic search using a first population to identify a first solution; generating, using the first solution, a second population; performing a second multi-start genetic search using the second population to identify a second solution; and dispatching vehicles to different routes and lines according to the second solution.

Abstract: A method for providing a typical load profile of a vehicle includes computing dwell times and maximum load stops, wherein a maximum load stop indicates a stop having a largest load on a route for a trip using route information and vehicle scheduling information based on automatic vehicle location data, computing one or more trip load profiles by identifying a load progression rate by performing a local regression procedure dividing the route into subsections such that the loads between adjacent subsections can be represented by a linear function on input of the computed maximum load, the computed dwell times, and maximum load stops, and constraining the identified load progression rate to an admissible value by evaluating the rate load progression with regard to historical dwell times and the computed maximum load stops. The method further includes computing the typical load profile based on load profiles of computed trips.

Abstract: A method of dynamically allocating frequency settings of a transit service includes utilizing AVL/APC to determine travel time and demand variations within a day. Clusters of time periods are formed based thereon and the day is split up. For each of the time periods for which a new frequency setting will be allocated, frequency allocation ranges are computed within which waiting times at multi-modal transfer stops are reduced and a frequency allocation is selected using criteria including passenger demand coverage and operational costs reduction. A plurality of frequency setting solutions are computed using a Branch and Bound approach with Sequential Quadratic Programming (SQP) or a sequential genetic algorithm with exterior point penalization. Sensitivity of the frequency setting solutions is tested to determine a most operationally reliable frequency setting solution for the new frequency setting and a timetable of the transit service is updated accordingly.