The planning of on-demand services requires the formation of vehicle schedules consisting of service trips and empty trips. This paper presents an algorithm for building vehicle schedules that uses time-dependent demand matrices (= service trips) as input and determines time-dependent empty trip matrices and the number of required vehicles as a result. The presented approach is intended for long-term, strategic transport planning. For this purpose, it provides planners with an estimate of vehicle fleet size and distance travelled by on-demand services. The algorithm can be applied to integer and non-integer demand matrices and is therefore particularly suitable for macroscopic travel demand models. Two case studies illustrate potential applications of the algorithm and feature that on-demand services can be considered in macroscopic travel demand models.
As the introduction of fully automated vehicles enhances the attractiveness of carsharing and ridesharing systems, cities and regions may want to examine the effects of this development. This paper presents a framework for how to integrate those services in traditional macroscopic travel demand models, which are commonly used to evaluate the impacts of changed transport supply. Addressed topics are (1) the implementation of direct and intermodal ridesharing into the demand modeling process, presenting two approaches for the latter, (2) the pooling of ridesharing trips and (3) the scheduling of automated and shared vehicles. The first approach for integrating intermodal ridesharing includes ridesharing as an additional transport system, which uses the road network and which is integrated in the timetable-based public transport assignment. The second approach uses direct-link connections between traffic zones and suitable public transport transfer stops for the ridesharing feeder trips instead. Using the second approach, preliminary results of a test scenario for the Stuttgart region are presented.
We compare different evaluation functions that are all designed to measure the quality of a timetable from passengers' perspective. Already in small examples fundamentally different timetables can be preferred by evaluation functions that seem to be similar. To investigate this effect in practice, we design a set of evaluation functions as representatives for a wide range of commonly used evaluation functions in optimization models, evaluation applications, or choice models. These functions are compared by analyzing their evaluation values of multiple timetables in three case studies. To investigate to what extent these evaluation functions agree on a good or a bad timetable, we apply cluster analysis as well as a novel methodology to quantify the similarity of pairs of evaluation functions based on the values they yield on different timetables. We empirically show that the choice of the evaluation function can have a significant impact on the assessed quality of timetables, and thus also on which timetable is considered optimal, even though all evaluation functions are meant to evaluate the same-the quality of a timetable from passengers' perspective. Due to the structure of the designed evaluation functions, it is further possible to identify which components of the functions influence the results of an evaluation and under which conditions they this is most pronounced. This can be very beneficial when designing timetable evaluation functions for passengers.
Timetabling for railway services often aims at optimizing travel times for passengers. At the same time, restricting assumptions on passenger behavior and passenger modeling are made. While research has shown that passenger distribution on routes can be modeled with a discrete choice model, this has not been considered in timetabling yet. We investigate how a passenger distribution can be integrated into an optimization framework for timetabling and present two mixed-integer linear programs for this problem. Both approaches design timetables and simultaneously find a corresponding passenger distribution on available routes. One model uses a linear distribution model to estimate passenger route choices, the other model uses an integrated simulation framework to approximate a passenger distribution according to the logit model, a commonly used route choice model. We compare both new approaches with three state-of-the-art timetabling methods and a heuristic approach on a set of artificial instances and a partial network of Netherlands Railways (NS). Highlights• We propose a novel timetabling approach with integrated passenger distribution model • Two mixed integer linear programs for this problem are developed • One uses a linear distribution model, the other a simulation of passenger distribution • One integrates a linear distribution, the other a simulation of passenger distribution • We compare our models/programs in experiments to state-of-the-art timetabling methods • Integrating a passenger distribution model can help to find better timetables Declarations of interest none Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 2
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