Estimation of Link Choice Probabilities Using Monte Carlo Simulation and Maximum Likelihood Estimation Method

Seger, Mundher Ali; Kisgyörgy, Lajos

doi:10.3311/ppci.14366

Cited by 3 publications

(4 citation statements)

References 28 publications

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“…Another study by Myrovali et al [9] connects data collection with innovations by highlighting the importance of collecting feedback from various stakeholders. It can be seen that a clear set of indicators and well-defined parameters is a crucial part of the planning process confirmed by Seger and Kisgyörgy [10].…”

Section: Introductionmentioning

confidence: 68%

Development Process of Workplace Mobility Planning in Three Pilot Locations

Esztergár-Kiss

2023

Period. Polytech. Civil Eng.

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A prospective way to make transportation more sustainable is changing the commuters’ travel behavior, which can be realized by creating mobility plans for institutions. A new approach related to workplace mobility planning is developed in three pilot locations in Hungary. As a first step, a roadmap is established including current situation analysis as well as collecting potential measures, setting up a mobility team, and developing pilot actions. The paper presents the planning process taking several aspects, such as commitment, vision, stakeholders’ involvement, situation analysis, measure implementation, and recommendations, into account. In the process, several stakeholders are involved to provide useful inputs through online surveys, personal interviews, and focus group meetings. The pilot development process covers three institutions in three locations with such specific parameters as the size of the city, the number of employees, location, and accessibility. As a result, it is seen that most measures aim at cycling infrastructure development, bike fleet introduction, and electric charger deployment. The proposed process is easily transferable and applicable in any location and any institution. The results not only support the commuters but show a potential to serve long-term societal goals, as well.

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Section: Introductionmentioning

confidence: 68%

Development Process of Workplace Mobility Planning in Three Pilot Locations

Esztergár-Kiss

2023

Period. Polytech. Civil Eng.

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“…For 2-dimensional examples, i.e., examples 1 to 5, the LSFs have been drawn to reflect the characteristics of the functions schematically (Figs. [7][8][9][10][11].…”

Section: Resultsmentioning

confidence: 99%

“…(1) will be extremely challenging if the problem is complex. Simulation methods such as Monte Carlo simulation (MCS) [7][8][9], or approximation methods [10][11][12] are brought up as the alternatives. The exact solution is accessible in MCS, but at the cost of generating too many samples.…”

Section: Introductionmentioning

confidence: 99%

An Iterative Two-step Lagrangian-based Method for Evaluation of Structural Reliability Index

Roudak

Karamloo²,

Shayanfar³

2022

Period. Polytech. Civil Eng.

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In structural reliability analysis, Hasofer-Lind and Rackwitz-Fiessler (HL-RF) method is a widely used approximation method for evaluating the reliability index. However, by increasing the nonlinearity or complexity in the limit state function of a structure, HL-RF may get in trouble for convergence. This paper represents an iterative algorithm that tries to minimize the Lagrange function, associated with the reliability problem. In each iteration of this method, two steps are followed, to satisfy the minimization condition and the existing constraint. In the first step, a movement for minimization in a descent direction is followed. In the second step, another search direction contributes to approach limit state surface, and therefore the next iteration can start from the vicinity of the surface. Employing Lagrange reliability function and limit state function simultaneously in the proposed two-step Lagrangian-based method (TSLB) can help to control the numerical instability in highly nonlinear problems. The accuracy and robustness of the proposed algorithm are shown in illustrative examples of the literature.

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“…The flow is generated with aggregated variables such as space-mean speed, traffic density, and traffic volume of the road links. Macroscopic flow models have a vast literature, just mentioning a few important ones: [37], [38], [39], [40].…”

Section: Preliminaries a Traffic Modeling And Simulationmentioning

confidence: 99%

EGO-Centric, Multi-Scale Co-Simulation to Tackle Large Urban Traffic Scenarios

2023

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Simulating automotive functions that rely on interaction with other vehicles (e.g., perceptionbased control or algorithms relying on inter-vehicular communication) created a demand for traffic simulation in the automotive field as well. Large-scale traffic simulation can be used to generate long, synthetic drive-cycles for EGO vehicles with realistic traffic. An EGO vehicle is defined as the vehicle the scenario revolves around, presumably running a control algorithm to be tested. On the other hand, simulating an entire district or city with thousands of vehicles present is superfluous and comes with a heavy computational burden while only the vicinity of the EGO vehicle is relevant. On the other hand, major traffic patterns can that could still influence the nearby traffic (e.g., traffic disruptions farther away) but can be simulated with lesser accuracy. Thus, simulation accuracy far from the EGO vehicle can be traded for simulation speed. This paper achieves this trade-off by co-simulating SUMO in microscopic and mesoscopic modes using Libsumo API. Microsimulated traffic is continuously spawned in an EGO-centered sub-network based on traffic states in the mesoscopic simulation. Simulation results in large urban scenarios suggest that the behavior of the EGO vehicle in terms of velocity distribution, headway distribution, and lane changes accurately matches pure microsimulation while simulation speed increased by 3 − 10 times. This result assumes linear time complexity control algorithms with respect to the vehicle number and a single EGO vehicle. Reducing the number of microsimulated vehicles with co-simulation yields even larger simulation speed gains for more computationally complex algorithms. The aggregate (macroscopic) traffic parameters match for both the micro-, meso-, and co-simulated cases. Thus coupling the two simulators does not distort the mesoscopic simulation.

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Estimation of Link Choice Probabilities Using Monte Carlo Simulation and Maximum Likelihood Estimation Method

Cited by 3 publications

References 28 publications

Development Process of Workplace Mobility Planning in Three Pilot Locations

Development Process of Workplace Mobility Planning in Three Pilot Locations

An Iterative Two-step Lagrangian-based Method for Evaluation of Structural Reliability Index

EGO-Centric, Multi-Scale Co-Simulation to Tackle Large Urban Traffic Scenarios

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