Marcel Rieser

Capturing Human Activity Spaces: New Geometries

“Activity space,” defined as the local areas within which people move or travel during the course of their activities during a specified time period, is a measure of an individuals spatial behavior that captures individual and environmental differences and offers an alternative approach to studying the spatial reach of travelers. The shape and the area of activity space are a product of how it is conceptualized and measured. This paper enlarges the set of geometries that can be used to describe activity space. It tests four parametric geometries (ellipse, superellipse, Cassini oval, and bean curve), which are identified as those capturing a specific share of all locations visited (i.e., 95%) while minimizing the area covered. They are estimated for a number of long-duration data sets while distinguishing among trip purposes. This paper presents a flexible, easily adaptable method for calculating activity spaces of different shapes and a qualitative comparison of the four shape types on the basis of the given surveys. The choice of an appropriate shape representing an individuals activity space is highly dependent on the spatial distributions and frequencies of the locations visited by the person in the given time period.

Large Scale Microscopic Evacuation Simulation

The evacuation of whole cities or even regions is an important problem, as demonstrated by recent events such as evacuation of Houston in the case of Hurricane Rita or the evacuation of coastal cities in the case of Tsunamis. A robust and flexible simulation framework for such large-scale disasters helps to predict the evacuation process. Existing methods are either geared towards smaller problems (e.g. Cellular Automata techniques or methods based on differential equations) or are not microscopic (e.g. methods based on dynamic traffic assignment). This paper presents a technique that is both microscopic and capable to process large problems.

Emergency Preparedness in the Case of a Tsunami—Evacuation Analysis and Traffic Optimization for the Indonesian City of Padang

The “Last-Mile Evacuation” research project develops a numerical last mile tsunami early warning and evacuation information system on the basis of detailed earth observation data and techniques as well as unsteady, hydraulic numerical modeling of small-scale flooding and inundation dynamics of the tsunami including evacuation simulations in the urban coastal hinterland for the city of Padang, West Sumatra, Indonesia. It is well documented that Sumatra’s third largest city with almost one million inhabitants is located directly on the coast and partially sited beneath the sea level, and thus, is located in a zone of extreme risk due to severe earthquakes and potential triggered tsunamis. “Last-Mile” takes the inundation dynamics into account and additionally assesses the physical-technical susceptibility and the socio-economic vulnerability of the population with the objective to mitigate human and material losses due to possible tsunamis. By means of discrete multi-agent techniques risk-based, time- and site-dependent forecasts of the evacuation behavior of the population and the flow of traffic in large parts of the road system in the urban coastal strip are simulated and concurrently linked with the other components.

Effects of a Simple Mode Choice Model in a Large-Scale Agent-Based Transport Simulation

The traditional transportation planning forecasting process is the four-step process, consisting of the following four steps (for example, Ortuzar and Willumsen 1995):1. Trip generation, where sources and sinks of travel are computed 2. Destination choice, where sources and sinks are connected to trips. This results in the so-called origin–destination (OD) matrix 3. Mode choice, where the trips are differentiated by mode 4. Assignment, where routes are found for the trips, taking into account that much-used streets become slower (“congested assignment”).

Multi-Agent Transport Simulations and Economic Evaluation

Tolls are frequently discussed policies to reduce traffic in cities. However, road pricing measures are seldom implemented due to high investments and unpopularity. Transportation planning tools can support planning authorities by solving those problems if they take into account the following aspects:

PRELIMINARY RESULTS OF A MULTI-AGENT TRAFFIC SIMULATION FOR BERLIN

This paper provides an introduction to multi-agent traffic simulation. Metropolitan regions can consist of several million inhabitants, implying the simulation of several million travelers, which represents a considerable computational challenge. We reports on our recent case study of a real-world Berlin scenario. The paper explains computational techniques necessary to achieve results. It turns out that the difficulties there, because of data availability and because of the special situation of Berlin after the reunification, are considerably larger than in previous scenarios that we have treated.

Adding Mode Choice to Multiagent Transport Simulation

It had been shown previously that so-called agent-based traffic micro-simulations could be used for dynamic traffic assignment, that is, iterative route adjustment, until either a Nash equilibrium or some steady state distribution between alternatives had been found. It was also shown that the same approach could be extended to (departure) time adjustment; that is, time adjustment and route adjustment could exist in the same iterative approach. In this paper it is shown that the approach can be extended to mode choice by forcing every synthetic traveler to consider every available mode. The implementation is verified with a test case for which an approximate solution can be analytically derived and for which it is shown that simulation and theory are consistent. It is then applied to a large-scale real-world example, the metropolitan Zurich, Switzerland, area, with about 1 million inhabitants. For this example, it is shown that the adaptive scheme, albeit seemingly simple, can outperform a more traditional approach that first computes mode choice on the basis of aggregate data and then runs the assignment for car traffic only. Sensitivity tests show that the model reacts in meaningful ways, in particular concerning the interaction between the time structure of activities and mode choice.

Preliminary Results Of A Multiagent Traffic Simulation For Berlin

This paper provides an introduction to multiagent traffic simulation. It includes some description of where we are with respect to the implementation of a real-world Berlin scenario. It turns out that the difficulties there, because of data availability and because of the special situation of Berlin after the reunification, are considerably larger than in previous scenarios that we have treated.