Matthias Heinrichs

Disaggregated Car Fleets in Microscopic Travel Demand Modelling

Microscopic travel demand models take the characteristics of every individual person of the modeled population into account for computing the travel demand for the modeled region. The real world mobility of individuals strongly depends on the specific available car, if any. However, mode choice models usually take a standard average car as reference. This paper shows an integrated approach to model the travel demand with respect to car specific attributes. The proposed work uses a synthetic population for the German capital of Berlin and simulates the travel demand for different examples that replicate car specific changes in fuel price, fleet distribution and entrance restriction. Some of these car-specific measures influence the travel behavior on a level that cannot be modeled when using an average car at all. Furthermore, the results show significant changes in usage of specific car segments, which would be difficult to model using an averaged car.

Das Elektronische Wegetagebuch – Chancen und Herausforderungen einer Automatisierten Wegeerfassung Intermodaler Wege

Dieses Kapitel widmet sich Praxisberichten und Methodenkonzepten. Dabei erfolgt eine Gegenuberstellung von Wegetagebuchern und Smartphone-Trackern. Ausfuhrlich werden hier die derzeit bestehenden Ansatze diskutiert, Wegetracking fur die Mobilitatsforschung zu nutzen. Insbesondere wird eingegangen auf die Vor- und Nachteile von aktivem, d. h. mit einer starkeren Nutzerbeteiligung, versus passivem Tracking sowie von erganzendem versus substituierendem Einsatz von GPS-Trackern zum Wegetagebuch. Neben technischen Optimierungspotenzialen (z. B. in Bezug auf die Akkubelastung) wird dabei die enorme Bedeutung sowohl der Datenschutz-Aktivitaten (Anonymisierung der Daten, Datenhoheit beim Nutzer etc.) als auch der intensiven Interaktion mit dem Nutzer (Erinnerungs-SMS, Energiespartipps, Validierung etc.) deutlich.

Who gets the key first? Car allocation in activity-based modelling

ABSTRACT Decisions concerning household car ownership and the corresponding usage by the household members have significant implications on vehicle usage, fuel consumption and vehicle emissions. In this context, long-term and short-term choices which are strongly interrelated with one another play an important role. The long-term aspects involve the number of vehicles and their different types owned by a household as well as the assignment of a main driver, acting as the primary user, to each vehicle. The short-term dimension is represented by the vehicle allocation within a household at a daily level. In order to better understand the vehicle allocation process in the household context, the paper at hand investigates the importance of the short-term and long-term aspects in this process and explores several approaches to model them. For this purpose, four different methods for car allocation within a household, which strongly differ in their complexity, are implemented into a microscopic agent-based travel demand model and subsequently evaluated. The respective approaches are the following: (1) random car allocation, (2) car allocation by age, (3) car allocation by main driver assignment, and (4) car allocation by household optimization. Given a population of a bigger region that is described by a set of attributes, these various models determine which person of a household uses one of the available cars within the household for his/her daily trips. The simulations show that all four implementations of car allocation result in good representations (with deviations of less than 10%) of observed travel behaviour, their results being closer to each other than initially expected. Model (4), which optimizes car allocation for the entire household, shows the best results when compared to real-world data, while model (3) allows for the adaptation of changes in car ownership and/or socio-demographic and socio-economic attributes of the population.

Simulation of automated transport offers for the city of Brunswick

Abstract The introduction of automated vehicles in the transport system is widely expected to have significant impact on traffic flow and safety, mobility behavior, car ownership and modal usage. Not only the replacement of conventional passenger cars by automated ones but also other forms of mobility offers are being discussed, such as the introduction of vehicle-on-demand fleets. When investigating the effects of such systems, the changes in the perceived travel time have to be regarded. Within this paper, the effects of introducing automated vehicles as well as vehicle-on-demand and shared vehicle-on-demand offers are presented. Eight different simulation settings with different fleet sizes and shares of private automated vehicles were evaluated using an agent-based demand model. The factors for value of time were obtained from a stated preference user survey. The results show only minor changes in the modal split and the amount of rides for the city of Brunswick due to the relatively small travel distances prevailing.

Just do it! Combining agent-based travel demand models with queue based-traffic flow models

Abstract Proper travel demand models aim to create an equilibrium between expected travel times in the planning phase and simulated travel times after mapping the road traffic on the road network. While agent-based travel demand models (ABM) focus on the trip generation mainly based on pre-calculated travel times, traffic flow models simulate these trips and compute travel times taking into account speed restrictions and road capacities. This leads to deviations between the simulated travel times and the initially expected ones especially during rush hour so that both models are not in equilibrium state. Due to the complexity and limited computational resources, combinations of these two models are often simplified in either one or both parts. In this work we present an iteratively combined simulation model with feedback of travel times. We couple an ABM with a queue-based traffic flow model which simulates the set of trips for each agent. The ABM used adjusts its activity generation, destination choice and mode choice according to the re-calculated travel times resulting in more realistic day plans. The traffic flow model takes the sequential character of the trips into account and propagates the delay to the subsequent trips of each modelled agent, resulting in feasible trips. We show that equilibrium of travel time between these two models can be achieved with a low number of iterations. Our approach is sensitive to new travel times in destination and mode choice and results in trips which are consistent for a whole day for each modelled agent.

Embedding intermodal mobility behavior in an agent-based demand model

Abstract Intermodality is the combination of different modes of transport along a single, seamless trip. It is assumed to reduce the amount of emissions generated by transport and being healthier for the users than conventional monomodal trips using passenger cars due to incorporating active modes of transport. In parallel, it promises to be competitive with passenger cars in terms of travel time. For determining the effects of measures that target at increasing the rate of intermodal trips, demand models that replicate the possibility to combine different modes of transport are needed. Herein, the extension of the agent-based demand model TAPAS for incorporating intermodal trips is presented. The given preliminary results show that the system is capable to compute the correct number of intermodal trips.

Introduction of car sharing into existing car fleets in microscopic travel demand modelling

Microscopic travel demand models take the characteristics of every individual person of the modelled population into account for computing the travel demand for the modelled region. Car sharing is an old concept, but the combination of a car sharing fleet parked in a public space with smartphone services to find available cars nearby offers a new mobility service. It enables people to use a fleet operator’s cars by providing individual mobility on demand. However, integrating this mobility option into microscopic travel demand models still is a difficult task due to a lack of data. This paper shows an integrated approach to model car sharing as a new mode for transport within a travel demand model using disaggregated car fleets with car-specific attributes. The necessary parameters for mode choice are estimated from various surveys and integrated into an existing multinominal logit model. The proposed work is used to simulate the travel demand of a synthetic population for the German capital of Berlin. A comparison with the survey results shows that the proposed integration of car sharing meets the real-world data. Furthermore, it is shown that the mode choice reacts well for access restrictions for specific car segments and local accessibility influencing the trip lengths.