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3524. Online routing for Demand Adaptive Systems
Invited abstract in session TD-54: Demand-responsive public transport 3, stream Public Transport Optimization.
Tuesday, 14:30-16:00Room: S01 (building: 101)
Authors (first author is the speaker)
| 1. | Maximilian Schiffer
|
| School of Management & Munich Data Science Institute, Technical University of Munich | |
| 2. | Benedikt Lienkamp
|
| School of Management, Technical University of Munich |
Abstract
As urban populations continue to grow, traditional static and fixed-route transportation systems face unprecedented challenges to meet societies' dynamic and diverse mobility needs. Demand adaptive systems (DAS), which combine traditional fixed bus lines and demand-responsive systems, have emerged as a promising paradigm in public transportation to address these challenges. DAS provides traditional transit-line service for a set of compulsory stops. These compulsory stops are bound to a schedule with fixed time windows during which the vehicle serving the line has to leave each compulsory stop. Passengers may issue requests at optional stops, inducing detours in the vehicle routes.
Focusing on the operational phase, where we adjust vehicle routes and schedules in response to user requests, we formulate and solve a variant of the dynamic single-vehicle routing problem with time windows and profit. We propose an algorithmic framework based on a set of dynamic user requests containing both optional and compulsory stops. We present a machine learning enriched integer programming framework that enables a DAS operator to select a subset of requests to serve while maximizing its expected profit. Here, we determine a route serving all selected requests, respecting compulsory stops' time windows. With this algorithmic framework, we compare traditional fixed-route transportation systems with DAS in Munich and present managerial insights from the operator's and user's perspectives.
Keywords
- Transportation
- Vehicle Routing
- Mathematical Programming
Status: accepted
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