On-Demand Ride-Pooling services have the potential to increase traffic efficiency compared to private vehicle trips by decreasing parking space needed and increasing vehicle occupancy due to higher vehicle utilization and shared trips, respectively. Thereby, an operator controls a fleet of vehicles that serve requested trips on-demand while trips can be shared. In this highly dynamic and stochastic setting, assymetric spatio-temporal request distributions can drive the system towards an imbalance between demand and supply when vehicles end up in regions with low demand. This imbalance would lead to low fleet utilization and high customer waiting times. This study proposes a novel rebalancing algorithm to predictively reposition idle fleet vehicles to reduce this imbalance. The algorithm first samples artificial requests from a predicted demand distribution and simulates future fleet states to identify supply shortages. An assignment problem is formulated that assigns repositioning trips considering multiple samples and forecast horizons. The algorithm is implemented in an agent-based simulation framework and compared to multiple state-of-the-art rebalancing algorithms. A case study for Chicago, Illinois shows the benefits of applying the repositioning strategy by increasing service rate and vehicle revenue hours by roughly 50% compared to a service without repositioning. It additionally outperforms all comparison algorithms by serving more customers, increasing the pooling efficiency and decreasing customer waiting time regardless of the forecasting method applied. As a trade-off, the computational time increases, but with a termination within a couple of seconds it is still applicable for large-scale real world instances.

On-demand ride-pooling (ODRP) services have the potential to improve traffic conditions in cities and at the same time offer user-centric mobility services. Recently, an analytical model, which investigates the influence of service quality parameters, such as detour, maximum waiting time, and boarding time, on the fraction of trips which could potentially be shared (a quantity called shareability), has been presented. The aim of this study is to test this model with a simulation framework that models an ODRP service in different levels of detail. The results show that by increasing the modeling complexity, in which we consider network topology, trip distribution patterns, optimization objectives, and changing velocity, the theoretical value of shareability and the actual experienced shared rides are decreased. It is observed that the shareability predicted by the mathematical model could be confirmed by a certain simulation setup with the objective to maximize shared rides. Nevertheless, changing the optimization objective to optimizing the total kilometers driven has the highest impact on shareability, decreasing it by up to 50%. By using a fitting procedure within this simulation setup, we can still exploit the analytical model to predict the influence of service quality parameters. This study may be useful for other researchers who plan to model ride-pooling systems and for operators who want to have an estimation of the level of shared rides they can achieve in an operating area.

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.

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Whereas in some cities ropeways already belong to the transit system, in Germany they are better known from skiing in the alps or as tourist attractions that were implemented in relation to expositions as in Koblenz or Berlin. Nonetheless, a ropeway system has several advantages, which make it an interesting alternative in urban public transportation. In this paper, we investigate the varying attitude of residents and commuters towards a ropeway system and its potential on a route in the north of Munich. To get an impression of their opinion, we conducted an online survey focusing on route choice depending on transit mode and travel times. In general, the respondents had a positive attitude towards this novel option and rate it with similar attractiveness to subway. To investigate the demand for the ropeway, the results of the survey were used to add a new transportation mode in the VISUM model for transit in Munich.