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

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

Assessing the effects of a growing electric vehicle fleet using a microscopic travel demand model

The German government seeks to increase the number of electric vehicles (EV) in the German car fleet to one million by 2020. Since some characteristics of EVs differ from conventional cars, there is an increasing need to assess the various impacts of a growing EV fleet. In this work, we have focused on possible effects related to the field of transport. We identified three important aspects and evaluated them over a period of one week using the microscopic travel demand model mobiTopp. First, we modelled the potential EV user groups of the near future by developing an EV user model; this model considers both interest in EVs and suitability for EV usage. Second, we simulated the travel behaviour of EV users; we used an EV usage model to consider the restrictions of EVs in choice decisions and also compared the usage behaviour of EV and conventional cars users. Third, we analysed the power consumption of the simulated EVs and evaluated the load peaks based on the simulated travel patterns. Our results indicate that a growing EV fleet implies a more heterogeneous distribution of EVs among car owners. They also indicate that the trip chain length of battery electric vehicles (BEVs) is much lower than that of extended range electric vehicles (EREVs) and conventional cars on average

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

Comparison of Discrete Choice and Machine Learning Models for Simultaneous Modeling of Mobility Tool Ownership in Agent-Based Travel Demand Models

Individual travel behavior, such as mode choice, is determined to a distinct degree by the respective portfolio of available mobility tools, such as the number of cars, public transit pass ownership, or a carsharing membership. However, the choice of different mobility tools is interdependent, and individuals weigh alternatives against each other. This process of parallel trade-offs is currently not reflected in typically used sequential logit models of agent-based travel demand models. This study fills this research gap by applying discrete choice and neural network models on a synthetic population to model multiple mobility tool ownership simultaneously. Using data from a national household travel survey, both model types approximated the given target distributions of mobility tools more accurately than the sequence of three corresponding logit models. Owing to its greater flexibility, the tested shallow and deep neural network exhibited higher predictive accuracy than simultaneous discrete choice models. The results indicated that neural networks with only one hidden layer were more robust and easier to formulate and interpret than deep networks with three hidden layers. Finally, the flat neural network was applied to a different synthetic population resulting in equally accurate results

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

Agent-based model of last-mile parcel deliveries and travel demand incorporating online shopping behavior

In this paper, we present an extension of the agent-based travel demand model mobiTopp with a last-mile parcel delivery module called logiTopp, in which online shopping choice is modeled explicitly. Online shopping behavior is modeled using logistic and Poisson regression models, which consider both the socio-demographic characteristics of the customer and aspects of their travel behavior. As mobiTopp is a framework that simulates travel demand over one week, we are able to capture interactions between travel behavior and online shopping that do not become apparent in single-day simulations. The results show that the integrated choice model reflects the findings presented in the literature in that male, affluent, young professionals are most likely to (frequently) order parcels online compared to other groups of the population. Application of the agent-based model to a city in Germany shows that socio-demographic and behavioral characteristics are considered realistically within the simulation. The model presented here is a suitable simulation tool for alternative urban last-mile delivery solutions, and the open-source and modular framework allows for transfer to other regions as the underlying choice models are consistent with literature from other spatial contexts. The findings are of interest to transportation planners and policymakers as they contribute to the understanding of how increased e-commerce demand influences the transportation system and solutions to mitigate adverse effects

Implementation of connection scan algorithm in tourism intermodal transportation journey planner: a case study

Accessibility to tourist destinations is an important component in a tourism system, especially for natural tourist destinations located in suburban areas. Good linkage of travel information and physical connections with local transportation services for intercity travel can facilitate more people to travel and promote national tourism destinations. This research takes the popular national tourism destinations and their public transportation service in Taiwan as a research object due to the unavailability of integrated public transport information service. Free Independent Travelers (FIT) demand is growing. This research aims to integrate intermodal public transportation information to support FIT by proposing a seamless way journey planner. In this scenario, the journey planner requires timetable data as input. The Connection Scan Algorithm is used to find the earliest arrival time routes at their destinations. This journey planner is built in PHP language and can complement the official tourism travel information website by Tourism Bureau, MOTC. Hence, the FIT could get the quickest routes to reach the destinations without compiling the public transportation information provided independently