SimMobility Short-Term: An Integrated Microscopic Mobility Simulator

Azevedo, Carlos Lima; Deshmukh, Neeraj; Marimuthu, Balakumar; Oh, Simon; Marczuk, Katarzyna; Soh, Harold; Basak, Kakali; Toledo, Tomer; Peh, Li-Shiuan; Ben‐Akiva, Moshe

doi:10.3141/2622-02

Cited by 47 publications

(20 citation statements)

References 18 publications

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“…SimMobility is a high-fidelity agent-based microsimulation laboratory, which coherently integrates individuals' choices and travel behavior emphasizing the principle of activitybased accessibility across different timescales represented by three core models: the Long-term model that captures yearto-year evolution of land use and agents' socio-economic activities [35], the Mid-term model, which predicts day-today individual activity patterns and travel behavior, and network dynamics at a mesoscopic level [36], and the Short-term model that simulates agents' movements at the microscopic granularity [37], [38]. The overall structure and information flow across models are depicted in Figure 1.…”

Section: Methodology a Simulation Frameworkmentioning

confidence: 99%

Evaluating Automated Demand Responsive Transit Using Microsimulation

Seshadri

et al. 2020

IEEE Access

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Recent advancements in automated vehicle technology and the concurrent emergence of ride-hailing services have focused increasing attention on Automated Mobility-on-Demand (AMOD; a system of shared driverless taxis) as a potential solution for sustainable future urban mobility. However, the impacts of an unrestricted deployment of AMOD are as yet uncertain and likely to be contextspecific; evidence with existing on-demand services suggests that they may lead to the cannibalization of mass-transit and increased traffic congestion. In this context, automated demand-responsive transit (also termed microtransit), which provides similar on-demand services (stop-to-stop or curbside) through higher capacity vehicles, may prove to be a promising substitute and/or complement. In this study, we evaluate the performance of such an automated demand response transit system (hereafter AMOD minibus) through agent-based simulations of the Singapore network. Towards this end, we extend SimMobility (an agentand activity-based microsimulation laboratory) with the capability of modeling an AMOD minibus service including demand, supply and their interactions. On the demand side, we use an activity-based model system that draws on data from a stated-preferences survey conducted in Singapore. On the supply side, an insertion heuristic is applied to dynamically perform both the assignment of requests to vehicles and vehicle routing. Scenario simulations on the Singapore network (with an area-wide deployment of the AMOD services) indicate the potential benefits of an automated demand responsive transit service for local circulation, which can result in a reduction of Vehicle Kilometres Traveled of up to 50% (compared to the AMOD shared taxis) whilst satisfying the same demand, with a modest increase in average travel times. INDEX TERMS Agent-based simulation, automated mobility-on-demand (AMOD), high-capacity ridesharing, shareability.

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Section: Methodology a Simulation Frameworkmentioning

confidence: 99%

Evaluating Automated Demand Responsive Transit Using Microsimulation

Seshadri

et al. 2020

IEEE Access

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“…The freight models are currently estimated for Singapore. Further details of different components of SimMobility are available in the literature (Adnan et al 2016;Zhu et al 2018;Lu et al 2015;Azevedo et al 2017). The models incorporated in SimMobility were developed using a variety of datasets, including those obtained from FMS.…”

Section: Fig 125 Simmobility Frameworkmentioning

confidence: 99%

Laboratories for Research on Freight Systems and Planning

Alho¹,

Sakai²,

Zhao³

et al. 2021

Urban Informatics

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Advancements in information and communication technologies (ICT) and the advent of novel mobility solutions have brought about drastic changes in the urban mobility environment. Pervasive ICT devices acquire new sources of data that can inform detailed transportation simulation models, and are useful in analyzing new policies and technologies. In this context, we developed software laboratories that leverage the latest technological developments and enhance freight research. Future mobility sensing (FMS) is a data-collection platform that integrates tracking devices and mobile apps, a backend with machine-learning technologies and user interfaces to deliver highly accurate and detailed mobility data. The second platform, SimMobility, is an open-source, agent-based urban simulation platform which replicates urban passenger and goods movements in a fully disaggregated manner. The two platforms have been used jointly to advance the state of the art in behavioral modeling for passenger and goods movements. In this chapter, we review recent developments in freight-transportation data-collection techniques, including contributions to transportation modeling, and state-of-the-art transportation models. We then introduce FMS and SimMobility and demonstrate a coordinated application using three examples. Lastly, we highlight potential innovations and future challenges in these research domains.

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“…Daily travel decisions like mode choice, route choice, activity-travel patterns, and incident-sensitive (re)scheduling are considered at the temporal scale of minutes to hours, up to a single day. • Short-Term (ST): is microscopic traffic simulator involves lane-changing, gap acceptance, route choice, and acceleration-braking behavior at the temporal scale of seconds to minutes (Azevedo et al 2017). e housing market model, implemented through a daily bid-auction approach, forms the crux of the LT modeling framework.…”

Section: Simmobilitymentioning

confidence: 99%

framework to generate virtual cities as sandboxes for land use-transport interaction models

Basu¹,

Ponce‐Lopez²,

Ferreira³

2021

JTLU

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One of the major critiques of land use-transport interaction (LUTI) models over the ages has been their over-dependence on individualized software and context. In an effort to address some of these concerns, this study proposes a framework to construct "virtual cities" that can act as sandboxes for testing different features of a LUTI model, as well as provide the capability to compare different LUTI models. We develop an approach to translate any prototypical transportation infrastructure network into a plausible land use zoning plan and synthetic population that are suitable for spatially detailed LUTI microsimulation of the virtual city. Disaggregate units of spatial geometry, like parcels and post codes, are generated using geospatial techniques applied to the transportation network. Households and jobs are randomly sampled from an actual city, and allocated in the virtual city based on matching density gradients. Students are matched with schools and workers are matched with jobs to complete the calibration of a synthetic population for the virtual city. Following the adjustment of behavioral models to complement the reduced scale of the virtual city, we demonstrate the integration between the land use and transportation simulation components in our LUTI model, SimMobility. The benefits of faster convergence times and shorter simulation times are clearly demonstrated through this exercise. We hope that this study, and the open-source releases of the SimMobility software with the virtual city database, can accelerate experimentation with LUTI models and aid the transition from individualized LUTI models to a common shared integrated urban modeling platform.

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SimMobility Short-Term: An Integrated Microscopic Mobility Simulator

Cited by 47 publications

References 18 publications

Evaluating Automated Demand Responsive Transit Using Microsimulation

Evaluating Automated Demand Responsive Transit Using Microsimulation

Laboratories for Research on Freight Systems and Planning

framework to generate virtual cities as sandboxes for land use-transport interaction models

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