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

Integrating urban last-mile package deliveries into an agent-based travel demand model

With the expected increase of e-commerce activity, we can expect the share of delivery vehicles in cities to rise as well. On the one hand, this puts great pressure on cities and surrounding areas as emissions rise and space becomes scarce. On the other hand, people are adjusting their travel behaviour such that the increase in e-commerce affects not only last-mile delivery but also private passenger traffic. This paper presents an integrated approach of modelling last-mile deliveries using an agent-based travel demand model. It is intended to account for reciprocal effects between online shopping behaviour and last-mile deliveries. The package orders are generated by agents in the study area and distributed among the package centres. For each package centre, the tour for each delivery agent is created. The presented model allows for the simultaneous simulation of private trips and last-mile deliveries and thus realistic delivery conditions: the model can detect e.g. if an agent or another household member is at home to receive their order. We have applied the model to the city of Karlsruhe, Germany, and describe first results of that simulation. Application of the model allows for a detailed analysis e.g. of delivery success rates both in terms of time and space. The presented modelling framework provides insight into effects of last-mile deliveries on a transportation system and can be availed to analyse policy measures or alternative delivery strategies

Modeling intermodal travel behavior in an agent-based travel demand model

The topic of intermodal passenger mobility has become more important during the last 20 years. As mobility options increase in number and flexibility, it gets more and more attractive to combine multiple modes on single trips. In addition, intermodal travel behavior is expected to contribute to less car dependent mobility and transport sector’s reduction of greenhouse gas emissions. Creating and improving the conditions for such a behavior requires planning with knowledge about influencing factors and highest resistances. Empirical evidence and behavioral models can support decisions on measures improving intermodal travel supply. This work presents an agent-based model approach containing intermodal travel behavior with regard to its most important decisions. It enables the combination of a multitude of modes and can be extended to even more modes. By combining many decisions and influences it is comprehensible and adaptable to different surveys and circumstances. We show that results are realistic and impacts are valid to be able to forecast effects of potential measures

Developing and Evaluating Intermodal E-Sharing Services – A Multi-method Approach

Different studies assume that travel behavior and mobility patterns of people may change within the next years: multimodal and intermodal usage of transport modes are getting more and more important. We expect a great potential for sharing services especially on intermodal trips. We aim at developing and evaluating intermodal electric mobility management concepts from the customer perspective. Since conventional approaches and singular methods are not appropriate, we adopted a multi-method approach consisting of five parts: (1) supply concepts are developed, (2) vehicle requirements for intermodal sharing are identified, (3) intermodal trip information is collected, (4) an agent based model and a macroscopic demand model are developed further in order to represent intermodal trips and e-vehicles and to evaluate several supply concepts, and (5) the impact and acceptance of modern and flexible mobility services like carsharing, bikesharing or new electric vehicle concepts (e.g. segways or light cars) is assessed and evaluated. The proposed methodology can be used for the development of customer oriented and attractive intermodal sharing services. Hence, the model results are essential for the evaluation and economic appraisal of e-sharing services from the supplier perspective. The proposed methodology can be applied to other cities and regions

Integrating Neighbours into an Agent-Based Travel Demand Model to Analyse Success Rates of Parcel Deliveries

The rapid growth of the e-commerce market leads us to expect a further increase in delivery vehicles in urban areas as well. This growth is expected to be accompanied by an increase in emissions while space becomes scarce. Meanwhile, people are adjusting their travel behaviour; therefore, the growing e-commerce market affects both last-mile delivery and private passenger traffic. Failed home deliveries are an important factor. They produce additional traffic by both ”Courier, Express and Parcel” (CEP) service providers and private passengers in the form of repeated delivery attempts or trips to pick up parcels. In this paper we apply an integrated agent-based model of last-mile deliveries and private travel demand. This allows for analysis of interactions between delivery and private passenger traffic and the status of the recipients during delivery. Furthermore, we present a neighbourship model to account for deliveries accepted by neighbours, which is crucial to reproduce realistic delivery success rates. We applied the presented model to the city of Karlsruhe, Germany, and simulated multiple delivery policy scenarios, which we compare to a static model without interactions between private and delivery agents. Our results show that the agent-based model produces more nuanced success rates with respect to different socio-demographic groups. Differentiating these groups is necessary when assessing measures that target specific groups and analysing effects of demographic changes. Also, we show the necessity of considering neighbours in such a model. This paper provides insight into the effects of e-commerce on a transport system and a framework to analyse policy measures or alternative delivery strategies

Modellbasierte Ermittlung von verkehrlichen Potentialen eines stadtbahnbasierten Gütertransports im Projekt LogIKTram in Karlsruhe

Als Reaktion auf das steigende Paketaufkommen werden neue, nachhaltige Konzepte der City-Logistik gesucht. Eine Lösung kann die Nutzung der bestehenden städtischen Schieneninfrastruktur über Cargo Trams sein. Um die verkehrlichen Wirkungen eines derartigen Konzepts quantifizieren zu können, wird in dieser Arbeit ein Güterverkehrsmodell für den Pakettransport, mit Fokus auf gewerbliche Paketnachfrage, vorgestellt. Die Ergebnisse zeigen, dass durch eine Cargo Tram grundsätzlich positive Effekte auf den Verkehr zu erwarten sind. Das Potenzial hängt jedoch stark von verschiedenen Faktoren wie der Anzahl und Lage der City Hubs ab

Determining service provider and transport system related effects of ridesourcing services by simulation within the travel demand model mobiTopp

Purpose Ridesourcing services have become popular recently and play a crucial role in Mobility as a Service (MaaS) offers. With their increasing importance, the need arises to integrate them into travel demand models to investigate transport system-related effects. As strong interdependencies between different people’s choices exist, microscopic and agent-based model approaches are especially suitable for their simulation. Method This paper presents the integration of shared and non-shared ridesourcing services (i.e., ride-hailing and ride-pooling) into the agent-based travel demand model mobiTopp. We include a simple vehicle allocation and fleet control component and extend the mode choice by the ridesourcing service. Thus, ridesourcing is integrated into the decision-making processes on an agent’s level, based on the system’s specific current performance, considering current waiting times and detours, among other data. Results and Discussion In this paper, we analyze the results concerning provider-related figures such as the number of bookings, trip times, and occupation rates, as well as effects on other travel modes. We performed simulation runs in an exemplary scenario with several variations with up to 1600 vehicles for the city of Stuttgart, Germany. This extension for mobiTopp provides insights into interdependencies between ridesourcing services and other travel modes and may help design and regulate ridesourcing services