In this study, a new approach was proposed which uses an existing DRT service as a starting point for the investigation of today’s and future on-demand services. In a first step, the existing DRT serviceBerlK¨onig in the Eastern inner-city center area of Berlin is implemented in the model taking into consideration the real-world service characteristics (e.g., service area, service quality, fares). Real-world DRT user data is used to calibrate the transport model which accounts for transport users’ mode choice reactions. In a second step, the model setup is altered, e.g. the DRT service area is extended to the entire city area of Berlin. Furthermore, the DRT service is transferred to other regions in Germany (Gladbeck, Vulkaneifel). In a third step, the calibrated model setup is transferred to a future scenario where the DRT mode is operated by autonomous vehicles and the DRT mode is integrated into the regular (schedule-based) public transit system. Technically, this is done by setting behavioral and cost parameters for the simulated DRT mode equal to those for today’s public transit.
The proposed methodology was successfully applied to three case studies in Germany and allows for a spatially and temporally detailed investigation of today’s and future DRT concepts. The simulated BerlK¨onig service confirms observations made in the real-world, e.g. passenger volumes by origin-destination relation and an increase in relative trip frequency in the afternoon and evening times.
Overall, the study highlights the importance to simulate the entire transport system, including demand which does not directly interact with the DRT mode. DRT is not only influenced by non-DRT users by competing for limited road capacities but it is also important to detect mode shifts and other intended or unintended side effects. That is, the present study goes far beyond most existing studies (see Sec. 1) which only model DRT demand by tracking users of an existing DRT system or by using floating car data of regular taxis. These studies limit the model to academic use cases which are useful for benchmarking different dispatch strategies but not sufficient for the investigation of the system-wide effects in the entire transport system.
One of the key findings is that potentials for the DRT mode are identified for two different perspectives:
• From the operator’s point of view, areas with high population densities (Berlin inner-city center, Gladbeck) are found to be most promising since travel demand can be served more efficiently compared to the low-demand areas. That is, there are more rides per DRT vehicle and fewer vehicle-kilometers per served passenger; however, relative DRT trip shares are rather low.
• The opposite is observed for the users’ point of view: In areas (and times) with rather poor schedule-based public transit provision (Vulkaneifel, Spandau, entire Berlin area including outskirts), the DRT mode is a very attractive alternative to the existing modes of transportation. In these areas, relative DRT trip shares are rather high, however, the DRT service is less efficient (larger relative fleet size, more vehicle-kilometers per DRT ride) which results in higher operational costs.
In the today’s scenario, the increase is traffic volume is rather small and is not expected to yield a significant increase in traffic congestion. The future scenario is found to differ greatly from the today’s scenario. Reduced monetary costs and higher affinities for new
mobility concepts yield a drastic increase in DRT trip shares and vehicle-kilometers. In the future scenario, total traffic volumes are found to dramatically increase in the urban case studies. Depending on the capacity impact of autonomous vehicles, this can lead to a strong increase in congestion. In contrast, for the rural case study of the Vulkaneifel district, the increase in traffic volume is rather low and an increase in traffic congestion is only expected locally at single hot-spots, e.g. schools in the morning period.
In the today’s scenario, the assumptions regarding operator’s costs are found to yield a profitable service in the Berlin inner-city case (BlnCity-DRT-1), Gladbeck case (Gl-DRT-1) and Vulkaneifel case (Vu-DRT-(Gl-DRT-1). In the other cases for the today’s scenario, the costs exceed the revenues and the DRT operator makes losses. In contrast, in the future scenario, a profitable service is found for all DRT service areas.
A further observation is that, spatial DRT usage patterns are very different depending on the specific case study and service area. The simulation outcome for the rural area (Vulkaneifel) differ greatly from the results for urban areas (Berlin, Berlin inner-city, Gladbeck). The spatial patterns also change between the today’s and future scenario, e.g. from rather tangential connections to radial connections in the Berlin case study.
In both the today’s and future scenario, there is a mode shift effect from regular (schedule-based) public transit to the DRT mode. In the today’s scenario, this is the predominant mode shift effect. However, in the future scenario, a significant number of users also change from the private car mode to the DRT mode. From a sustainability perspective, there are also undesired mode shift effects, e.g. from bicycle and walk to DRT.
The most important takeaways for the technical setup of future DRT simulation studies are the following:
• The base case needs to containall relevant modes of transportationfrom which users may switch to innovative mobility concepts, in particular conventional ride-hailing modes (taxis) and the informal ride sharing mode, i.e. car passengers, often also referred to as ”ride”.
• Travel demand should cover the entire population and all trip purposes that are relevant for DRT services, in particular long-distance travelers on their way to airports or railway stations and school traffic demand.
• DRT related parameters for simulated dispatching, pooling and rebalancing should be set based on the (expected) real-world service and may be adjusted/calibrated depending on the specific service area (e.g. different parameters for rural vs. urban areas).
• The option ofpre-booking a DRT ride before departing from an activity location improves the DRT operator’s flexibility which allows to reduce operational costs without reducing user benefits. This feature should be considered by the DRT simulation framework.
• Because of the non-linear relationship between trip density and ride-sharing service parameters, it is recommended to carefully upscale simulation results for population samples and rather uselarge population samples, optimally the full population.
Additional points for future studies include:
• As monetary costs, e.g. the DRT fares, play an important role, an income-de-pendent willingness to pay should be considered for future implementations of such models, e.g. by means of a person-specific income-dependent marginal utility of money. Optimally, this parameter together with further behavioral parameters are validated against real-world demand sensitivities. Also, the general attitude towards new mobility concepts, the ownership of a smartphone, and the willingness to try out new modes instead of using the own car should be reflected by heterogeneous behavioral parameters, e.g. based on socio-demographic attributes.
• Most of today’s DRT services are used especially on Friday and Saturday evenings.
It should therefore be considered to set up a transport model forweekend daysin addition to the average working day.
To further improve DRT simulation studies, spatially and temporally disaggregated survey data is required. Especially for low-demand areas and times of day, the DRT as a system reacts to individual requests. Operational key figures depend on very accurate temporal and spacial demand patterns. The commonly used zonal-based demand is too coarse for DRT and in particular intermodal transport decisions. The same holds true for activity start and end times. Typically, mobility surveys report activity times with a resolution of 15 minutes or more. This needs to be disaggregated in order to not e.g. artificially increase the pooling rate.
Acknowledgements
This research was funded in part by the German Federal Ministry of Transport and Digital Infrastructure (funding number 16AVF2160, project ”AV ¨OV”). The authors wish to thank Stefan Geier and the entire BerlK¨onigteam at BVG for several helpful discussions and providing the insightful data used in this study.
The authors also thank the entire AV ¨OV project team, including Paula Ruppert, Alexander Schmidt, and William Charlton. The authors are also grateful to the participants of the workshops in Gladbeck and Vulkaneifel, in particular the administration of the district Vulkaneifel and the city of Gladbeck.
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