Agent-based Transport Model Research Articles

Spatio-temporal mapping of urban noise exposure: insights from the agent-based transport model of the Nantes metropolitan area

Beyond the estimation of daily exposure profiles of agents, agent-based models can be used to investigate the role of different urban areas in noise exposure. This study explores this potential by proposing a new approach to spatio-temporal mapping of the accessibility to quiet areas in agglomerations, integrating mobility into the analysis of place effects. Using an agent and activity-based framework, composed of EQASim, MATSim and NoiseModelling, the Nantes Metropolitan Area (France) is modeled. The definition of the quiet areas conditions and the accessibility metric are analyzed in order to understand the influence of these parameters on the identification of critical urban areas where agents don't have their access to quiet areas assured. The results show a great variability of accessibility according to its definition and emphasize the need to include new variables in the assessment.

A High-Resolution, Large-Scale Agent-Based Transport Model for Health Outcomes Evaluation from Policy Changes

A High-Resolution, Large-Scale Agent-Based Transport Model for Health Outcomes Evaluation from Policy Changes

Activity-based and agent-based transport model of Melbourne: an open multi-modal transport simulation model for Greater Melbourne

Activity- and agent-based models for simulating transport systems have attracted significant attention in recent years. However, building these types of models at a city-wide level and including motorized (i.e. cars and public transport) and non-motorized (i.e. walk and bicycle) modes of transport is a complicated and involved task. This paper presents an open workflow for creating large-scale multi-modal agent-based transport simulation models. The workflow brings together a number of external tools, for example, an activity-based demand generation tool and a road network generation tool, and a set of tools developed for the agent-based model parameter estimation, calibration, and simulation post-processing. We used this workflow to create an activity- and agent-based model for Melbourne and compared the output of the simulation model with observations from the real world in terms of mode share, road volume, travel time, and travel distance. Through these comparisons, we showed that our model is suitable for studying mode choice and road usage behavior of travelers. The calibrated model could be used to test road network change interventions. In addition, a similar workflow can be applied for building simulation models for other cities or could be expanded to include more complicated travel behaviors.

Integrating health effects within an agent-based land use and transport model

IntroductionTransport is a determinant of urban health, and the past two decades have seen a rise in transport health impact assessment (HIA) tools. However, a common limitation of HIAs is the use of aggregation which ignores variations over space, time, and/or demographics. Furthermore, HIAs generally treat travel behaviour exogenously, meaning they can assess the effects of hypothetical behaviour shifts but not the policies that cause behaviour to change. MethodsThis article presents an open-source agent-based integrated land use, transport, and health impacts model that assesses health exposures and impacts related to physical activity, air pollution, and traffic injury risk. The agent-based framework allows exposures and impacts to be assessed with high spatiotemporal and sociodemographic resolution. To achieve a tight integration, the model adapts (traditionally car-oriented) transport modelling methods to capture health-relevant behaviours such as active travel more realistically. In addition, exposure modelling methods have been adapted to make use of the high spatial and temporal detail available in agent-based transport models. DemonstrationTo demonstrate this integrated model, this article presents an example simulation which evaluates the health impacts of a work from home policy in Munich. Overall, there is on average a small net health detriment for individuals required to work from home and a smaller net health benefit for everybody else. ConclusionsAgent-based modelling enables HIAs to jointly assess transport, environmental, and population health outcomes with high granularity, helping policymakers identify who benefits and suffers from proposed policies. This work demonstrates the feasibility of combining agent-based transport and health modelling methods in a seamless framework.

Effects of Periodic Location Update Polling Interval on the Reconstructed Origin–Destination Matrix: A Dutch Case Study Using a Data-Driven Method

Global System for Mobile Communications (GSM) data provides valuable insights into travel demand patterns by capturing people's consecutive locations. A major challenge, however, is how the polling interval (PI; the time between consecutive location updates) affects the accuracy in reconstructing the spatio-temporal travel patterns. Longer PIs will lead to lower accuracy and may even miss shorter activities or trips when not properly accounted for. In this paper, we analyze the effects of the PI on the ability to reconstruct an origin–destination (OD) matrix. We also propose and validate a new data-driven method that improves accuracy in case of longer PIs. The new method first learns temporal patterns in activities and trips, based on travel diaries, that are then used to infer activity-travel patterns from the (sparse) GSM traces. Both steps are data-driven thus avoiding any a priori (behavioral, temporal) assumptions. To validate the method we use synthetic data generated from a calibrated agent-based transport model. This gives us ground-truth OD patterns and full experimental control. The analysis results show that with our method it is possible to reliably reconstruct OD matrices even from very small data samples (i.e., travel diaries from a small segment of the population) that contain as little as 1% of the population’s movements. This is promising for real-life applications where the amount of empirical data is also limited.

The perfect match? Assessment of excess commute and transport externalities using an agent-based transport model

Excess commute (EC) refers to the sub-optimal allocation of workers to jobs in an urban region in terms of minimal commute distances. This article investigated EC for the Munich metropolitan area using a microscopic agent-based modeling suite. We first considered the optimization strategy, comparing the traditional zone-based transportation problem to a microscopic routine. Zone-based optimization is computationally efficient, but it underestimates EC due to spatial biases, even when the zone system is highly disaggregate. We then applied the optimized job-worker assignment to a microscopic travel demand model, assigning workers to jobs of the same job sector at a shorter distance from their home, accounting for multi-worker households. With shorter commutes, there was a substantial shift toward active modes. However, we only observed moderate impacts on total travel demand, with the overall effect dampened due to compensatory behavior between mandatory and discretionary travel. Finally, the outputs from the demand model were used by a microscopic assignment model to calculate externalities attributable to EC. Because of the complexity of travel demand, we conclude that the EC indicator alone would overestimate the benefits of policies targeting jobs-housing balance.

MoPeD meets MITO: a hybrid modeling framework for pedestrian travel demand

Transport demand models were initially designed for simulating car trips. Nowadays researchers and planners are considering pedestrian travel and its health and safety impacts in the regional transport models. However, the existing transport models lack the knowledge and experience in pedestrian modeling for health assessment. This paper contributes to the modeling practice by developing an integrated model called the MITO/MoPeD. The model builds upon previous model development and integrates the fine-grained pedestrian modeling tool into the agent-based transport model. The MITO/MoPeD model is applied to the Munich metropolitan area. Model performances are analyzed based on travel measures (e.g., walk share, trip length distribution, and pedestrian flow) and physical activity volumes. Results show that the MITO/MoPeD model can better represent pedestrian travel behavior than the existing Munich Model. It performed better in simulating the spatial distribution of walk shares and the distribution of walk trip lengths. Moreover, it overcomes the issue of overestimating physical activity volumes. These findings suggest that the MITO/MoPeD model can deliver more precise travel outcomes. More importantly, it is valuable for addressing pedestrian planning issues such as transportation infrastructure investments, land use planning, assessment of safety and health outcomes, and evaluation of environmental impacts.

Uncertainty and Variability Analysis of Agent-Based Transport Models

This paper presents an analysis of the output variability of agent-based transport models. We simulated a MATSim model of the city of Hanover multiple times with identical input and evaluated the resulting travel times on different level of aggregation. On a global level, we observed minor variations of travel times. However, the results show an increased variation when examining the output on the level of districts or for individual agents. A recommendation for estimating the required number of simulation runs for a stable output of travel time for the purposed aggregation level is derived from our case study.

Relaxation–discretization algorithm for spatially constrained secondary location assignment

ABSTRACT Agent-based transport models demand that the daily activity patterns of artificial agents are described in great detail. While choice models for residential locations or workplaces exist, only few approaches are available to find locations for highly constrained secondary activities such as grocery shopping or recreation at high resolution. The paper describes a data-driven approach of assigning viable locations to such secondary locations while maintaining consistency with homes, workplaces and other fixed points in an artificial traveler's daily plan. Two use cases for Switzerland and Île-de-France are presented, which show that the algorithm is able to assign locations while providing realistic distance distributions that are consistent with mode-specific travel times.

Delivery to homes or collection points? A sustainability analysis for urban, urbanised and rural areas in Belgium

E-commerce is a rapidly growing and evolving sector. The sector is however struggling with organising its last mile deliveries in order to meet the sustainability requirements, both economically and environmentally. Multiple studies have compared deliveries to collection points with deliveries to residential home addresses in sustainability terms. The variety in results indicates that these studies are bound to local parameters (such as regulations, drop densities, collection point networks). Consequently, their results cannot be generalised or transferred to other contexts. In this research, we compare deliveries to stores serving as collection points and homes for online ordered non-food products in Belgium. The solutions are simulated with agent-based transport model TRABAM. The results are evaluated based on their generated external costs and differentiated according to three area types: urban, urbanised and rural areas. Considering the case-specific density of the collection points, the results indicate that home deliveries are preferred in rural and urbanised areas when considering the sustainability impact solely. In urban areas, collection point deliveries are more sustainable.

An efficient approach to create agent-based transport simulation scenarios based on ubiquitous Big Data and a new, aspatial activity-scheduling model

Agent-based transport simulation models are a particularly useful tool to analyze demand-oriented transport policies and new mobility services, which have both gained significant attention lately. Since travel diaries, a traditional source to create the transport demand in agent-based transport models, are often hard to procure and not policy-sensitive, alternative approaches to creating travel demand representations for simulation scenarios are sought. In this study, a particularly efficient approach based on Big Data and a new, aspatial activity-based demand model with comparatively low input data requirements is established. Home, work, and education locations are informed based on mobile-phone-based origin-destination matrices. Other activity locations are modeled within the scope of the coevolutionary algorithm of the agent-based transport model, which is also responsible for finding suitable travel options of the modeled individuals. As a result, a comparatively lightweight process chain to create an agent-based transport simulation scenario is established, which is transferable to other regions. A basic quality evaluation of the created tool chain is carried out against a well-validated transport simulation model of the same region.

Long-term application potential of urban air mobility complementing public transport: an upper Bavaria example.

In this paper, the required models and methods to analyze and quantify the potential demand for urban air mobility (UAM) complementing public transport and possible impacts were defined and applied to the Munich Metropolitan region. An existing agent-based transport model of the study area were used and extended to cover socio-demographic changes up to the year 2030 and intermodal UAM services. An incremental logit model for UAM was derived to simulate demand for this new mode. An airport access model was developed as well. Three different UAM networks with different numbers of vertiports were defined. Sensitivity studies of ticket fare and structure, flying vehicle cruise speed, passenger process times at vertiports and different Urban Air Mobility networks sizes were performed. For the reference case, UAM accounts for a modal share of 0.5%. The absolute UAM demand is concentrated on very short routes; hence, UAM vehicle flight speed variation shows low UAM demand impacts. Kilometer-based fare, number of UAM vehicles per vertiport and passenger process times at vertiports show a significant impact on UAM demand.

A novel population-based occupancy modeling approach for district-scale simulations compared to standard-based methods

District-scale building energy models can be a powerful tool for the integration of renewable energy sources and efficiency measures in urban areas. One key limitation of these models, however, has been their rather simplified treatment of building occupants. Since it is their activities which create the needs for energy in an area, an improved analysis of the effects of occupants on demand at the district scale is needed.This paper presents a novel population-based approach (PopAp) inspired by agent-based transportation models, in which a population of occupants was defined based on class and employee registers and each was given an individual daily schedule. This approach was then used to assess the effect of occupant presence modeling on district-scale energy demand simulations by comparing the data-centric PopAp method to standard-based deterministic and stochastic approaches.The maximum number of occupants in the area was found to be 33% higher for the deterministic model compared to the data-centric PopAp results, a deviation that was especially pronounced in education buildings. The results for space heating, space cooling and electricity demand for lighting and appliances show that while the mean deviation between models on a yearly basis is within 10% for all demands, on an hourly scale the deviation for space cooling and electricity exceeded 15%. Given the importance of the hourly scale for peak demand prediction for technology sizing, more detailed occupant modeling approaches should be considered when planning energy systems.

An improved replanning strategy for congested traffic conditions in MATSim

Abstract Agent-based transport models such as MATSim have been developed and used for the past few decades to simulate transportation systems in different cities and are capable of accurately reproducing travel patterns at different levels of aggregation. Given their disaggregate representation of both travel demand and supply as well as the high level of spatial and temporal detail, agent-based transport models also have the potential to allow for unprecedentedly detailed and disaggregated analysis. When simulating large study areas, it is often common to increase the replanning rate to achieve a quicker convergence. However, this comes at the cost of causing substantial oscillations in link-level dynamics which render disaggregate analysis difficult, if not impossible. This paper tries to reconcile both objectives: favour quicker convergence as with higher replanning rates while mitigating the strong oscillations of the link-level dynamics.

Agent-based integrated land use/transport models: a study on scale factors and transport model simulation intervals

Abstract Modern agent-based models often suffer from long runtimes (computation times). While quite a few studies deal with runtime issues in transport models, there is less research with respect of agent-based, integrated land-use transport (ILUT) models. This paper examines how the transport model interval in an ILUT model suite affects runtime and results. In addition, the possibility of scaling the synthetic population, which is a common approach in agent-based transport models, is tested on the land-use side. Results suggest that transport models do not necessarily need to run every one or two years and larger intervals like five years lead to similar results. However, if intervals are too long, there error of estimated travel times in the years between transport model updates become larger. Scaling the land-use population does not result in large reductions of runtimes if the transport model population is kept uniform. Thus, scaling the land-use model population can be used to actually model every agent individually in the transport model. On the other hand, if the transport model population is scaled down with respect of the land use model, runtimes can be improved. In both cases, the results of the land use model appear to be stable in when using smaller population sub-samples, as far as the resolution of the analysis is not too small.

Light duty vehicle choice models examining alternative fuel technology preferences among commercial fleet owners

Agent-based transportation models are valuable tools for evaluating energy use, emissions and mobility impacts of transportation. In this context, vehicle ownership models are critical for linking personal and commercial activities to their impacts on the transportation system. Further, predicting the fuel type that is chosen by an establishment when purchasing a vehicle is important for modeling the impacts of commercial transportation on energy consumption and related outputs. This study develops an econometric model that can be used to predict the fuel technology that is chosen by an establishment when purchasing a Light Duty Vehicle (LDV) for business purposes. Factor analysis was also used to gain insights into the underlying preferences and needs of establishments, which drive the fuel choice decision. The results can be used in agent-based models or other models in simulations of the transportation system and energy consumption. The outcomes are also useful for modeling the adoption of alternative fuels among commercial fleet owners over time, which is important from both a policy perspective as well as an automobile manufacturer perspective.

Development of an Agent-Based Transport Model for the City of Hanover Using Empirical Mobility Data and Data Fusion

The model presented in this work is based on the agent-based simulation framework MATSim. We describe a new approach for collecting and processing the underlying data required for the development of a MATSim scenario. In cooperation with the administration of Hanover, available data was centrally collected, analysed and clustered. Using data fusion, the dataset was combined and enriched with additional information from open data sources in order to improve the model’s level of detail. In combination with the German mobility survey Mobilität in Deutschland 2017, the developed model is specifically adapted for the regional characteristics of Hanover, but yet transferable to other scenarios.

The MATSim Open Berlin Scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data

With more diverse transport policies being proposed and the advent of novel transport services and technologies, the transport system is becoming more individualized in many aspects. Transport models, the most important tool to assess policies and schemes, need to be sufficiently expressive to address these developments. Agent-based transport models, where travelers with individual properties and the ability to act and decide autonomously are resolved individually, allow to appropriately model and analyze such policies. This paper describes the MATSim Open Berlin Scenario, a transport simulation scenario for the Berlin metropolitan area implemented in the agent-based transport simulation framework MATSim. The scenario is solely based on open data and the demand for transport is created based on a fully synthetic procedure. Contrary to most transport simulation scenarios, no information from a travel diary survey is required as input. As such, the scenario generation procedure described in this study is spatially transferable and facilitates the creation of agent-based transport simulation scenarios for arbitrary regions.

Validation of an agent-based travel demand model with floating car data

Abstract This paper compares the results of the agent-based travel demand model MITO (Microscopic Travel Demand Orchestrator) with Floating Car Data. MITO is developed using household travel survey data, and uses the traffic assignment model MATSim. The model estimates the travel demand for an average working day and is applied to the metropolitan area of Munich. In contrast to traditional approaches where travel demand models are validated using the local traffic counts, average travel speed from Floating Car Data (FCD) are used in this study. The main advantage of using FCD is that they cover extremely large parts of the network, whereas the local traffic counts are sparse and limited to a few major streets. The average link travel time and average speed between the model estimation and the FCD were compared with the goal of validating travel time calculations within an agent-based transport model.

Noise Shielding in an Agent-Based Transport Model Using Volunteered Geographic Data

This paper describes an improved noise modeling approach for the agent-based transport simulation MATSim. In contrast to previous versions, the new implementation takes into account the shielding of noise at building facades. The simplified approach is based on German noise modeling guidelines. As a proof of concept, a comparative calculation of noise immissions for a use case in the city of Munich with and without the consideration of buildings reveals more realistic immission values when shielding is taken into account. While uncovered areas are not affected by the updated calculation, backyards and areas behind larger building blocks show a major reduction in immissions. When looking at noise exposure costs in a dense area, ignoring the effect of shielding seems to significantly overestimate costs by up to 20%. The presented approach is a step forwards incorporating environmental aspects in an agent-based integrated land use/transport modeling suite.