Usage and User Characteristics - Insights from MOIA, Europe’s Largest Ridepooling Service

New, i.e., shared and digitized, mobility services have been entering urban mobility markets around the globe. Among these new offerings is ridepooling, a mobility solution that bundles requests from passengers with similar routes in real-time and matches them with a vehicle. Ridepooling is quite novel in Germany and knowledge about users, changes in travel behavior, and impacts on the urban traffic system is scarce. To address this gap, we conducted an online survey among users and non-users of MOIA, a German ridepooling provider. Over 12,000 respondents completed the survey. The article presents results on ridepooling users’ characteristics and usage patterns. We found that MOIA users cover all age groups and are multimodal travelers—which leads us to assume that ridepooling enriches mobility portfolios and also serves as an alternative to the private car. MOIA is mostly used occasionally and, in particular, during the evening or the night. A specific focus of the article lies on users with mobility impairments as well as how and by whom ridepooling is used on work-related trips. Both topics are particularly relevant in light of changing travel patterns and transforming urban transport systems towards more sustainability

How does the setup of sample collection influence survey results - an example of new mobility services

Conducting surveys in transportation research is becoming more complex. Depending on the survey subject, the survey format and the circumstances of the sample collection the motivation of respondents to participate and consequently the results can vary substantially. Skewness of samples and sample selection bias occur to different degrees. This study compares different surveys which were created to capture the acceptance regarding shared autonomous minibuses in Germany. By analyzing distributions of behavior, perception and intention to use the services, biases in the datasets are worked out. The results show that voluntary on-site surveys lead to more positive perception of minibuses

What do people think about autonomous minibuses in Germany?

Autonomous minibuses gain increasing importance as a mobility option for public transport. In order to realize advantages like increased safety and productivity as well as reduced emissions and congestion user’s needs have to be considered when further developing this technology. So far, there is only a little knowledge about use intentions for autonomous minibuses in public transport and the general perception. This paper presents the results of a Germany-wide online survey and answers the questions: What do people think about autonomous minibuses? When and how would people use them? What are the perceived advantages and disadvantages? Firstly, we analyzed the data descriptively. Secondly, we performed a principal component analysis to reduce data complexity and to identify attitude-based influences on people’s perception. Over 60% liked the minibuses and could imagine to use them in the future. We found one component representing a general attitude towards the minibuses that correlates strongly with the intention to use

Integrating Autonomous Busses as Door-to-Door and First-/Last-Mile Service into Public Transport: Findings from a Stated Choice Experiment

Autonomous busses and on-demand (OD) services have the potential to improve the public transport system. However, research on potential traffic impacts is still ongoing, mainly because of a lack of existing use cases of autonomous driving as part of public transport. The availability of revealed preference data for mode choice decisions is thus very limited. Therefore, we conducted a stated choice experiment to assess mode choice preferences with regard to use cases as the main mode of transport and as the solution for the first and last mile. We also distinguished between OD and schedule-based (sched.) services. The target population of the survey is the population of Baden-Württemberg, a state in southwestern Germany. The responses of 1,434 people were analyzed using a nested logit approach. On this basis, we established exemplary utility functions and descriptively derived recommendations for efficient forms of deploying autonomous busses in addition to already existing well-developed public transport systems. It was found that, under the given conditions, public transport pass owners without a car in their household would be the most interested in using autonomous busses. Car owners without a smartphone see less benefit. It was also shown that the recruiting method of the respondents is crucial. Those reached via social media were significantly more positive than those contacted via an online panel. Further evaluations show that autonomous busses are rated similarly to existing public transport and consequently have particularly high potential on medium distances, especially if their deployment leads to shorter access routes

Mode choice and ride-pooling simulation: A comparison of mobiTopp, Fleetpy, and MATSim

On-demand ride-pooling systems have gained a lot of attraction in the past years as they promise to reduce traffic and vehicle fleets compared to private vehicles. Transport simulations show that automation of vehicles and resulting fare reductions enable large-scale ride-pooling systems to have a high potential to drastically change urban transportation. For a realistic simulation of the new transport mode it is essential to model the interplay of ride-pooling demand and supply. Hence, these simulations should incorporate (1) a mode choice model to measure demand levels and (2) a dynamic model of the on-demand ride-pooling system to measure the service level and fleet performance. We compare two different simulation frameworks that both incorporate both aspects and compare their results with an identical input. It is shown that both systems are capable of generating realistic results and assessing mode choice and ride-pooling schemes. Commonalities and differences are identified and discussed

Analysis and Prediction of Electromobility and Energy Supply by the Example of Stuttgart

This paper seeks to identify bottlenecks in the energy grid supply regarding different market penetration of battery electric vehicles in Stuttgart, Germany. First, medium-term forecasts of electric and hybrid vehicles and the corresponding charging infrastructure are issued from 2017 to 2030, resulting in a share of 27% electric vehicles by 2030 in the Stuttgart region. Next, interactions between electric vehicles and the local energy system in Stuttgart were examined, comparing dif-ferent development scenarios in the mobility sector. Further, a travel demand model was used to generate charging profiles of electric vehicles under consideration of mobility patterns. The charg-ing demand was combined with standard household load profiles and a load flow analysis of the peak hour was carried out for a quarter comprising 349 households. The simulation shows that a higher charging capacity can lead to a lower transformer utilization, as charging and household peak load may fall temporally apart. Finally, it was examined whether the existing infrastructure is suitable to meet future demand focusing on the transformer reserve capacity. Overall, the need for action is limited; only 10% of the approximately 560 sub-grids were identified as potential weak points

Ridepooling in der Modellierung des Gesamtverkehrs - Methodenbericht zur MOIA Begleitforschung

In der MOIA Begleitforschung wurden über zwei Jahre Effekte von Ridepooling auf das Hamburger Verkehrssystem untersucht. Die Studie liefert auf Basis umfassender empirischer Daten und einer Modellierung in hohem Detailgrad Erkenntnisse zu der noch neuen Verkehrsform und trägt dazu bei, die Potenziale von Ridepooling künftig noch zielgerichteter zu erschließen. Das im Rahmen der Begleitforschung entwickelte Verkehrsmodell besteht aus dem agentenbasierten Verkehrsnachfragemodell mobiTopp sowie dem Flottensimulationsmodell FleetPy und berücksichtigt die Angebots- und Nachfrageseite. mobiTopp bildet die Mobilität der gesamten Bevölkerung von Hamburg und Umland sowie der Privat- und Geschäftsreisenden im Wochenverlauf ab. Die Implementierung aktueller empirischer Erkenntnisse zur Nutzung neuer Mobilitätsangebote wie Ridepooling aber auch Car- , Bike- oder E-Scootersharing resultiert in besonders belastbaren Ergebnissen. Die Kopplung mit dem Flottenmodell sorgt für eine realitätsnahe Abbildung des Ridepooling-Dienstes, der Angebotsqualität und der verkehrlichen Wirkungen. Im Rahmen der Simulationsstudie wurden vier Szenarien entwickelt, die zeigen, wie sich die Mobilität in der Hansestadt zukünftig entwickeln kann

Mode choice and ride-pooling simulation: A comparison of mobiTopp, Fleetpy, and MATSim

On-demand ride-pooling systems have gained a lot of attraction in the past years as they promise to reduce traffic and vehicle fleets compared to private vehicles. Transport simulations show that automation of vehicles and resulting fare reductions enable large-scale ride-pooling systems to have a high potential to drastically change urban transportation. For a realistic simulation of the new transport mode it is essential to model the interplay of ride-pooling demand and supply. Hence, these simulations should incorporate (1) a mode choice model to measure demand levels and (2) a dynamic model of the on-demand ride-pooling system to measure the service level and fleet performance. We compare two different simulation frameworks that both incorporate both aspects and compare their results with an identical input. It is shown that both systems are capable of generating realistic results and assessing mode choice and ride-pooling schemes. Commonalities and differences are identified and discussed.ISSN:1877-050

Analysis and Prediction of Electromobility and Energy Supply by the Example of Stuttgart

This paper seeks to identify bottlenecks in the energy grid supply regarding different market penetration of battery electric vehicles in Stuttgart, Germany. First, medium-term forecasts of electric and hybrid vehicles and the corresponding charging infrastructure are issued from 2017 to 2030, resulting in a share of 27% electric vehicles by 2030 in the Stuttgart region. Next, interactions between electric vehicles and the local energy system in Stuttgart were examined, comparing different development scenarios in the mobility sector. Further, a travel demand model was used to generate charging profiles of electric vehicles under consideration of mobility patterns. The charging demand was combined with standard household load profiles and a load flow analysis of the peak hour was carried out for a quarter comprising 349 households. The simulation shows that a higher charging capacity can lead to a lower transformer utilization, as charging and household peak load may fall temporally apart. Finally, it was examined whether the existing infrastructure is suitable to meet future demand focusing on the transformer reserve capacity. Overall, the need for action is limited; only 10% of the approximately 560 sub-grids were identified as potential weak points