Minimum costs paths in intermodal transportation networks with stochastic travel times and overbookings

In intermodal transportation, it is essential to balance the trade-off between the cost and duration of a route. The duration of a path is inherently stochastic because of delays and the possibility of overbooking. We study a problem faced by a company that supports shippers with advice for the route selection. The challenge is to find Pareto-optimal solutions regarding the route's costs and the probability of arriving before a specific deadline. We show how this probability can be calculated in a network with scheduled departure times and the possibility of overbookings. To solve this problem, we give an optimal algorithm, but as its running time becomes too long for larger networks, we also develop a heuristic. The idea of this heuristic is to replace the stochastic variables by deterministic risk measures and solve the resulting deterministic optimization problem. The heuristic produces, in a fraction of the optimal algorithm's running time, solutions of which the costs are only a few percent higher than the optimal costs

Data-driven planning of reliable itineraries in multi-modal transit networks

Multi-modal travel itineraries are based on traversing multiple legs using more than one mode of transportation. The more combinations of legs and modes, the more challenging it is for a traveler to identify a reliable itinerary. Transportation providers collect data that can increase transparency for reliable travel planning. However, this data has not been fully exploited yet, although it will likely form an important piece of future traveler information systems. Our paper takes an important step in this direction by analyzing and aggregating data from the operation of scheduled and unscheduled modes to create a reliability measure for multi-modal travel. We use a network search algorithm to evaluate itineraries that combine schedule-based long-distance travel with airlines with last-mile and first-mile drive times to efficiently identify the one with the highest reliability given a start time and travel-time budget. Our network search considers multiple origin and destination airports which impacts the first and last mile as well as the flight options. We use extensive historical datasets to create reliable itineraries and compare these with deterministic shortest travel-time itineraries. We investigate the amount of data that is required to create reliable multi-modal travel itineraries. Additionally, we highlight the benefits and costs of reliable travel itineraries and analyze their structure