Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

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

mcboost: Multi-Calibration Boosting for R

Implements 'Multi-Calibration Boosting' (2018) and 'Multi-Accuracy Boosting' (2019) for the multi-calibration of a machine learning model's prediction. 'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups. Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are. This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations

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

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

Booking Processes in Autonomous Carsharing and Taxi Systems

With the introduction of autonomous vehicles, ride-hailing, taxi, and carsharing might all become on-demand door-to-door mobility services. We assume that the carsharing mode will develop into a system with more flexibility for users while ride-hailing and taxi will focus on the efficiency of the system. The flexibility of the autonomous carsharing system can present itself in multiple ways, e.g. the option to drive manually, withhold the information of the final stop, freely choosing routes and stops, or reserving a vehicle for an unspecified amount of time. The more efficient autonomous taxi system requires user requests to contain the information of the destination and the operator has full control over the vehicles. We discuss some implications (vehicle-search and assignment, scaling, ridepooling and relocations) of the different systems qualitatively and show a numerical study of a small and large scale scenario reflecting the efficiency advantage of the autonomous taxi system

Integrating demand forecasts into the operational strategies of shared automated vehicle mobility services: spatial resolution impacts

This study aims to evaluate and quantify the impact of demand forecast spatial resolution on the operational performance of a shared-use automated vehicle (AV) mobility service (SAMS) fleet. To perform the evaluation, this study employs an agent-based modeling framework that includes user requests, AVs, and an SAMS fleet controller. In the simulation, an SAMS fleet controller dynamically assigns AVs to on-demand user requests and repositions empty AVs throughout the service region to serve expected future demand requests. The fleet controller uses an offline demand forecast model and an online optimization model that jointly assigns AVs to users and repositioning trips. Results indicate that despite demand forecast quality decreasing at higher spatial resolutions, the operational efficiency of the SAMS fleet increases with higher spatial resolution forecasts (i.e. smaller subareas). Results also indicate that there is a significant operational value associated with improving short-term demand forecasts at high spatial resolutions