scholarly journals Agent-Based Simulation to Improve Policy Sensitivity of Trip-Based Models

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Rolf Moeckel ◽  
Nico Kuehnel ◽  
Carlos Llorca ◽  
Ana Tsui Moreno ◽  
Hema Rayaprolu
Keyword(s):  
Travel Demand ◽  
Time Budget ◽  
Time Of Day ◽  
Destination Choice ◽  
Trip Generation ◽  
Demand Model ◽  
Agent Based ◽  
Travel Mode Choice ◽  
Travel Time Budget ◽  
Aggregate Nature

The most common travel demand model type is the trip-based model, despite major shortcomings due to its aggregate nature. Activity-based models overcome many of the limitations of the trip-based model, but implementing and calibrating an activity-based model is labor-intensive and running an activity-based model often takes long runtimes. This paper proposes a hybrid called MITO (Microsimulation Transport Orchestrator) that overcomes some of the limitations of trip-based models, yet is easier to implement than an activity-based model. MITO uses microsimulation to simulate each household and person individually. After trip generation, the travel time budget in minutes is calculated for every household. This budget influences destination choice; i.e., people who spent a lot of time commuting are less likely to do much other travel, while people who telecommute might compensate by additional discretionary travel. Mode choice uses a nested logit model, and time-of-day choice schedules trips in 1-minute intervals. Three case studies demonstrate how individuals may be traced through the entire model system from trip generation to the assignment.

scholarly journals Creating an integrated agent-based travel demand model by combining mobiTopp and MATSim

2019 ◽  
Vol 151 ◽  
pp. 776-781 ◽  
Author(s):  
Lars Briem ◽  
Nicolai Mallig ◽  
Peter Vortisch
Keyword(s):  
Travel Demand ◽  
Demand Model ◽  
Agent Based ◽  
Travel Demand Model

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

2019 ◽  
Vol 37 ◽  
pp. 242-249
Author(s):  
Carlos Llorca ◽  
Sasan Amini ◽  
Ana Tsui Moreno ◽  
Rolf Moeckel
Keyword(s):  
Travel Demand ◽  
Demand Model ◽  
Agent Based ◽  
Floating Car Data ◽  
Travel Demand Model

Travel Demand Modeling and Conformity Determination: Atlanta Regional Commission Case Study

2002 ◽  
Vol 1817 (1) ◽  
pp. 172-176 ◽  
Author(s):  
Guy Rousseau ◽  
Tracy Clymer
Keyword(s):  
Land Use ◽  
Travel Demand ◽  
Mode Choice ◽  
Time Of Day ◽  
Traffic Model ◽  
Demand Model ◽  
Primary Input ◽  
Land Use Model ◽  
Home Based ◽  
Travel Demand Model

The Atlanta Regional Commission (ARC) regional travel demand model is described as it relates to its link-based emissions postprocessor. In addition to conformity determination, an overview of other elements is given. The transit networks include the walk and highway access links. Trip generation addresses trip production, trip attraction, reconciliation of productions and attractions, and special adjustments made for Hartsfield Atlanta International Airport. Trip distribution includes the application of the composite impedance variable. In the mode choice model, home-based work uses a logit function, whereas nonwork uses information from the home-based work to estimate modal shares. Traffic assignment includes preparation of time-of-day assignments. The model assigns single-occupancy vehicles, high-occupancy vehicles, and trucks by using separate trip tables. The procedures can accept or prohibit each of the three types of vehicles from each highway lane. Feedback between the land use model and the traffic model is accounted for via composite impedances generated by the traffic model and is a primary input to the land use model DRAM/EMPAL. The land use model is based on census tract geography, whereas the travel demand model is based on traffic analysis zones that are subareas within census tracts. The ARC model has extended the state of the practice by using the log sum variable from mode choice as the impedance measure rather than the standard highway time. This change means that the model is sensitive not only to highway travel time but also to highway and transit costs.

An Integrated, Validated, and Applied Activity-Based Dynamic Traffic Assignment Model for the Baltimore-Washington Region

2018 ◽  
Vol 2672 (51) ◽  
pp. 45-55 ◽  
Author(s):  
Lei Zhang ◽  
Di Yang ◽  
Sepehr Ghader ◽  
Carlos Carrion ◽  
Chenfeng Xiong ◽  
...  
Keyword(s):  
Daily Activity ◽  
Travel Demand ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Integrated Model ◽  
Time Of Day ◽  
Practical Implementation ◽  
Baseline Scenario ◽  
Demand Model ◽  
Dynamic Traffic

The paper discusses the integration process and initial applications of a new model for the Baltimore-Washington region that integrates an activity-based travel demand model (ABM) with a dynamic traffic assignment (DTA) model. Specifically, the integrated model includes InSITE, an ABM developed for the Baltimore Metropolitan Council, and DTALite, a mesoscopic DTA model. The integrated model simulates the complete daily activity choices of individuals residing in the model region, including long-term choices, such as workplace location; daily activity patterns, including joint household activities and school escorting; activity location choices; time-of-day choices; mode choices; and route choices. The paper describes the model development and integration approach, including modeling challenges, such as the need to maintain consistency between the ABM and DTA models in terms of temporal and spatial resolution, and practical implementation issues, such as managing model run time and ensuring sufficient convergence of the model. The integrated model results have been validated against observed daily traffic volumes and vehicle-miles traveled (VMT) for various functional classes. A land-use change scenario that analyzes the redevelopment of the Port Covington area in Baltimore is applied and compared with the baseline scenario. The validation and application results suggest that the integrated model outperforms a static assignment-based ABM and could capture behavioral changes at much finer time resolutions.

scholarly journals What Matters Most in Transportation Demand Model Specifications: A Comparison of Outputs in a Mid-size Network

2015 ◽  
Author(s):  
T. Donna Chen ◽  
Kara Kockelman ◽  
Yong Zhao
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Mode Choice ◽  
Demographic Data ◽  
Time Of Day ◽  
Demand Model ◽  
Land Use Patterns ◽  
Transportation Demand ◽  
Step Model ◽  
The Impact

This paper examines the impact of travel demand modeling (TDM) disaggregation techniques in the context of medium-sized communities. Specific TDM improvement strategies are evaluated for predictive power and flexibility with case studies based on the Tyler, Texas, network. Results suggest that adding time-of-day disaggregation, particularly in conjunction with multi-class assignment, to a basic TDM framework has the most significant impacts on outputs. Other strategies shown to impact outputs include adding a logit mode choice model and incorporating a congestion feedback loop. For resource-constrained communities, these results show how model output and flexibility vary for different settings and scenarios.BACKGROUND Transportation directly provides for the mobility of people and goods, while influencing land use patterns and economic activity, which in turn affect air quality, social equity, and investment decisions. Driven by the need to forecast future transportation demand and system performance, Manheim (1979) and Florian et al. (1988) introduced a transportation analysis framework for traffic forecasting using aggregated data that provide the basis for what is known as the four-step model: a process involving trip generation, then trip distribution and mode choice, followed by route choice. Aggregating demographic data at the zone level, the four-step model generates trip productions based on socioeconomic data (e.g., household counts by income and size) and trip attractions primarily based on jobs counts. The model then proportionally distributes trips between each origin and destination (OD) zone pair based on competing travel attractions and impedances, under the assumption that OD pairings with higher travel costs draw fewer trips. Trips between each OD pair are split among a variety of transportation modes, allocating trips to private vehicle, transit, or other

scholarly journals An agent-based transportation impact sketch planning (TISP) model system

2021 ◽  
Vol 14 (1) ◽  
pp. 219-253
Author(s):  
Ayad Hammadi ◽  
Eric J Miller
Keyword(s):  
Land Use ◽  
Architectural Design ◽  
Travel Demand ◽  
Transportation Network ◽  
Geographic Location ◽  
Model System ◽  
Demand Model ◽  
Control Variables ◽  
Agent Based ◽  
Traffic Impact

A traffic impact sketch planning (TISP) model is presented for the estimation of the likely travel demand generated by a major land-use development or redevelopment project. The proposed approach overcomes the problems with the non-behavioral transportation-related studies used in practice for assessing the development design impacts on the local transportation system. The architectural design of the development, in terms of the number and type of dwellings, by number of bedrooms per unit, and the land-use categories of the non-residential floorspace, are reflected in the TISP model through an integrated population and employment synthesis approach. The population synthesis enables the feasible deployment of an agent-based microsimulation (ABM) model system of daily activity and travel demand for a quick, efficient, and detailed assessment of the transportation impacts of a proposed neighborhood or development. The approach is not restricted to a certain type of dataset of the control variables for the geographic location of the development. Datasets for different geographic dimensions of the study area, with some common control variables, are merged and cascaded into a synthesized, disaggregate population of resident persons, households and jobs. The prototype implementation of the TISP model is for Waterfront Toronto’s Bayside Development Phase 2, using the operational TASHA-based GTAModel V4.1 ABM travel demand model system. While the conventional transportation studies focus on the assessment of the local traffic impacts in the immediate surroundings of the development, the TISP model investigates and assesses many transportation related impacts in the district, city, and region, for both residents and non-residents of the development. TISP model analysis includes the overall spatiotemporal trips distribution generated by the residents and non-residents of the development for the auto and non-auto mobility systems and the simulated agents diurnal peaking travel times. The model results are compared with the trips estimates by a prior project traffic impact study and the Institute of Transportation Engineers (ITE) Trip Generation Manual (TGM) rates of weekday trips for the relevant land uses. Future extensions and improvements of the model including the generalization and full automation of the model, and the bi-level macro-micro representation of the transportation network are also discussed.

scholarly journals Modeling Framework and Implementation of Activity- and Agent-Based Simulation: An Application to the Greater Boston Area

2018 ◽  
Vol 2672 (49) ◽  
pp. 146-157 ◽  
Author(s):  
Isabel Viegas de Lima ◽  
Mazen Danaf ◽  
Arun Akkinepally ◽  
Carlos Lima De Azevedo ◽  
Moshe Ben-Akiva
Keyword(s):  
Travel Demand ◽  
Time Of Day ◽  
Agent Based Modeling ◽  
Travel Patterns ◽  
Activity Schedules ◽  
Modeling Framework ◽  
Agent Based ◽  
Similar Activity ◽  
Home Based ◽  
Wide Range

This paper presents a utility-maximizing approach to agent-based modeling with an application to the Greater Boston Area (GBA). It leverages day activity schedules (DAS) to create a framework for representing travel demand in an individual’s day. DAS are composed of a sequence of stops that make up home-based tours with activity purposes, intermediate stops, and subtours. The framework introduced in this paper includes three levels: (1) the Day Pattern Level, which determines if an individual will travel and, if so, what types of primary activities and intermediate stops they will do; (2) the Tour Level, which models the mode, destination, and time-of-day of the different primary activities; and (3) the Intermediate Stop Level, which generates intermediate stops. The models are estimated for the GBA using the 2010 Massachusetts Travel Survey (MTS). They are then implemented in SimMobility, the agent-based, activity-based, multimodal simulator. It run in a microsimulation using a Synthetic Population. Produced results are consistent with the MTS. Compared with similar activity-based approaches, the proposed framework allows for more flexibility in modeling a wide range of activity and travel patterns.

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

2021 ◽  
Vol 184 ◽  
pp. 202-209
Author(s):  
Tim Wörle ◽  
Lars Briem ◽  
Michael Heilig ◽  
Martin Kagerbauer ◽  
Peter Vortisch
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Demand Model ◽  
Agent Based ◽  
Travel Demand Model
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