Demand Calibration of Multimodal Microscopic Traffic Simulation using Weighted Discrete SPSA

Oh, Simon; Seshadri, Ravi; Azevedo, Carlos Lima; Ben‐Akiva, Moshe

doi:10.1177/0361198119842107

Cited by 25 publications

(14 citation statements)

References 39 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: Methodsology 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: Methodsology a Simulation Frameworkmentioning

confidence: 99%

Evaluating Automated Demand Responsive Transit Using Microsimulation

Seshadri

et al. 2020

IEEE Access

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“…Calibration of Preday ABM has been performed in several studies, whereby parameters are estimated by Simultaneous perturbation stochastic approximation (SPSA) method with its variants [28,29,31]. However, all studies considered reduction of the dimensionality of the parameter space either by sensitivity analysis (SA) [34] or principal component analysis (PCA) [19].…”

Section: Preday Abmmentioning

confidence: 99%

“…Application of the Preday ABM in simulating various environments requires systematic adjustments or calibration of a large set of parameters (further referred to as ABM parameters), in order to align the associated outputs more closely to the observed values or true output statistics. For that purpose, various optimisation methods are adopted, including primarily gradient-free metaheuristics [28,29,31]. However, Bayesian inference with the recent developments provides a valuable analytical approach for the calibration process [35,36], a great advantage of which is the elimination of necessity to simulate a large sample set in finding the global optima [14,18,20,36].…”

Section: Introductionmentioning

confidence: 99%

Composite Surrogate for Likelihood-Free Bayesian Optimisation in High-Dimensional Settings of Activity-Based Transportation Models

Kuzmanovski

Hollmén

2021

Advances in Intelligent Data Analysis XIX

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Activity-based transportation models simulate demand and supply as a complex system and therefore large set of parameters need to be adjusted. One such model is Preday activity-based model that requires adjusting a large set of parameters for its calibration on new urban environments. Hence, the calibration process is time demanding, and due to costly simulations, various optimisation methods with dimensionality reduction and stochastic approximation are adopted. This study adopts Bayesian Optimisation for Likelihood-free Inference (BOLFI) method for calibrating the Preday activity-based model to a new urban area. Unlike the traditional variant of the method that uses Gaussian Process as a surrogate model for approximating the likelihood function through modelling discrepancy, we apply a composite surrogate model that encompasses Random Forest surrogate model for modelling the discrepancy and Gaussian Mixture Model for estimating the its density. The results show that the proposed method benefits the extension and improves the general applicability to high-dimensional settings without losing the efficiency of the Bayesian Optimisation in sampling new samples towards the global optima.

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“…The objective function is the ordinary least square (OLS) type that minimizes the difference between the observed traffic volume and predicted traffic volumes (only cars) on links where data is available. The algorithm used is SPSA (simultaneous perturbation stochastic algorithm) to find out the new set of parameters to be used for the next calibration iteration as used in [Oh et al 2019]. It should be noted that within one calibration simulation there are 250 MATSIM simulation runs, and therefore, the process is quite time-consuming.…”

Section: Integration Mechanismmentioning

confidence: 99%

Integrated agent-based microsimulation framework for examining impacts of mobility-oriented policies

Adnan

Outay

Ahmed

et al. 2020

Pers Ubiquit Comput

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Travel demand management measures/policies are important to sustain positive changes among individuals' travel behaviour. An integrated agent-based microsimulation platform provides a rich framework for examining such interventions to assess their impacts using indicators about demand as well as supply side. This paper presents an approach, where individual schedules, derived from a lighter version of an activity-based model, are fed into a MATSIM simulation framework. Simulations are performed for two European cities i.e. Hasselt (Belgium), Bologna (Italy). After calibrating the modelling framework against aggregate traffic counts for the base case, the impacts of a few traffic management policies (restricting car access, increase in bus frequency) are examined. The results indicate that restricting car access is more effective in terms of reducing traffic from the network and also shifting car drivers/passengers to other modes of travel. The enhancement of bus infrastructure in relation to increase in frequency caused shifting of bicyclist towards public transport, which is an undesirable result of the policy if the objective is to improve sustainability and environment. In future research, the framework will be enhanced to integrate emission and air dispersion models to ascertain effects on air quality as a result of such interventions.

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Demand Calibration of Multimodal Microscopic Traffic Simulation using Weighted Discrete SPSA

Cited by 25 publications

References 39 publications

Evaluating Automated Demand Responsive Transit Using Microsimulation

Evaluating Automated Demand Responsive Transit Using Microsimulation

Composite Surrogate for Likelihood-Free Bayesian Optimisation in High-Dimensional Settings of Activity-Based Transportation Models

Integrated agent-based microsimulation framework for examining impacts of mobility-oriented policies

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