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Integrating demand-responsive services into public transport networks – Results from agent-based simulation of demand-responsive transport scenarios for the city of Aachen

Niklas Höing et al.

Journal of Public Transportation2025https://doi.org/10.1016/j.jpubtr.2025.100143article
ABDC B
Weight
0.44

What the paper says

Enabled by the emergence of new technologies, demand-responsive transport (DRT) offers a flexible alternative to fixed-schedule public transport. In order to improve their public transport networks, cities are attempting to integrate new DRT services into their networks without reducing fixed-schedule public transport ridership. This paper addresses the question of how DRT integration into an existing public transport network affects mobility behaviour and network load in a medium-sized city. To this end, we ran two scenarios in an agent-based model (MATSim). First, we set up a ‘Feeder Scenario’, where the DRT represents a feeder to a reduced fixed-schedule public transport network; second, we developed a ‘Replacement Scenario’ in which the DRT completely replaced fixed-schedule public transport in the whole study area. The results show that both scenarios generate extra vehicle traffic compared to the ‘Status Quo’ (the unchanged calibration state, before any scenario is implemented) because DRT trips replace walking and cycling trips as well as bus trips with higher capacities and the reduction in car trips does not compensate for this. Overall, our configuration of the scenarios results in the Replacement Scenario being slightly better than the Feeder Scenario in terms of replacing car trips, total motorised mileage and total vehicle load on roads in the city.

3 citations

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https://doi.org/https://doi.org/10.1016/j.jpubtr.2025.100143

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@article{niklas2025,
  title        = {{Integrating demand-responsive services into public transport networks – Results from agent-based simulation of demand-responsive transport scenarios for the city of Aachen}},
  author       = {Niklas Höing et al.},
  journal      = {Journal of Public Transportation},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jpubtr.2025.100143},
}

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.