Transport Simulation Framework Research Articles

Machine learning-enhanced model-based scenario optimization for DIII-D

Scenario development in tokamaks is an open area of investigation that can be approached in a variety of different ways. Experimental trial and error has been the traditional method, but this required a massive amount of experimental time and resources. As high fidelity predictive models have become available, offline development and testing of proposed scenarios has become an option to reduce the required experimental resources. The use of predictive models also offers the possibility of using a numerical optimization process to find the controllable inputs that most closely achieve the desired plasma state. However, this type of optimization can require as many as hundreds or thousands of predictive simulation cases to converge to a solution; many of the commonly used high fidelity models have high computational burdens, so it is only reasonable to run a handful of predictive simulations. In order to make use of numerical optimization approaches, a compromise needs to be found between model fidelity and computational burden. This compromise can be achieved using neural networks surrogates of high fidelity models that retain nearly the same level of accuracy as the models they are trained to replicate while reducing the computation time by orders of magnitude. In this work, a model-based numerical optimization tool for scenario development is described. The predictive model used by the optimizer includes neural network surrogate models integrated into the fast Control-Oriented Transport simulation framework. This optimization scheme is able to converge to the optimal values of the controllable inputs that produce the target plasma scenario by running thousands of predictive simulations in under an hour without sacrificing too much prediction accuracy.

Efficient operation of demand-responsive transport (DRT) systems:Active requests rejection

Demand-responsive transport (DRT) is getting more attention in the public sectors. In DRT systems operated by public sectors, providing a good and equal service to the public is more important than maximizing the revenue. This brings additional challenges to the operation of DRT systems. In this study, we explore the idea of actively rejecting requests in DRT systems based on the alternative travel options, such as conventional public transport. By rejecting requests that have good alternatives, we can focus on the passengers without suitable alternative travel options. This can reduce the fleet size of DRT systems while maintaining a good and fair service. To explore the impact of this idea, we develop and implement an active request rejection mechanism in an agent-based transport simulation framework and carry out simulations on a real-world scenario. Results have shown that a fleet size reduction of around 20% can be achieved, without compromising the overall mobility level in terms of travel time, if the active request rejection mechanism with dynamic threshold is used. Furthermore, results have also shown that when a DRT system is overloaded, it may break down, and the service quality will suffer a significant drop. The active request rejection mechanism can prevent this from happening in some cases.

Demand-responsive transport for students in rural areas: A case study in Vulkaneifel, Germany

In rural areas with low population density, demand-responsive transport (DRT) is considered a promising alternative to conventional public transport (PT). With a fleet of smaller vehicles, DRT provides a much more flexible and convenient service. This characteristic makes DRT also a potential mode of transport to serve students in rural areas. If DRT vehicles are used to serve students, then the funding for conventional school buses (or adapted public transport schedules) can be reinvested in the DRT system. This may help to relieve the financial burden experienced by DRT operators and enable the operation of a large-scale DRT service in rural areas. In this study, a demand model for school commutes based on real-world, open-source data for Landkreis Vulkaneifel, a rural region in Germany, is built. Then a feasibility study is carried out using an agent-based transport simulation framework. In the feasibility study, various setups and operational schemes are explored, which are followed by a systematic cost analysis. Based on a conservative estimation, an annual budget of around 1600 Euro per student will be needed to maintain and operate a fleet of DRT vehicles that can transport all the students in the region from home to school on time in the morning. During the remaining time of the day and on school holidays, the vehicles can be used for conventional DRT service for the public.

PATRIC: A high performance parallel urban transport simulation framework based on traffic clustering

Parallel traffic simulation requires partitioning the road network into several components that can be assigned to different computing nodes (CPNs). Existing studies focus more on reducing edge-cuts (message-passing pipes between CPNs) to decrease synchronization message amongst CPNs for efficiency improvement. However, even reducing edge-cuts drastically, the volume of messages transmitted might still be high, which does not significantly improve performance. Based on observation that some traffic clusters (TCs) exist during simulation, i.e., areas with high internal and low external traffic density. For high-performance urban transport simulation, we propose a data-driven parallel approach named PATRIC, which can generate parallel partitions automatically based on traffic clustering. Specifically, the TC-based automatic partitioner (TAP) is designed to automatically identify TCs and then construct partitions in parallel. We present a partition-growing algorithm that prevents traffic-intensive TCs being split across multiple CPNs when distributing computing workloads, resulting in more balanced load and fewer synchronization operations. Unlike prior work using fixed thresholds for load balancing, we develop the adaptive partition updater (APU) to fit the dynamic traffic in the road network, which achieves a better trade-off between balancing workload and lowering communication for higher efficiency. Experiments on real-world datasets demonstrate that our approach outperforms the state-of-the-art methods.

Soil salinity simulation based on electromagnetic induction and deep learning

In pursuit of fine modeling and forecasting of water-salt transport in arid zones, this study uses the rapid electromagnetic induction survey as the main tool to establish an empirical relationship between soil volumetric water content, salinity and electrical conductivity based on on-site measurement data. By using deep learning to set up a surrogate model for electromagnetic induction inverse problem, the vertical distribution of soil salinity during salt leaching at non-growth season is revealed. HYDRUS-1D was used to perform inversions of soil hydraulic parameters to obtain accurate numerical solutions. The results show that the surrogate of electromagnetic induction inversion using a deep convolutional encoding-decoding network (NET) with a learning rate of 0.0006 and 1500 sets of training samples has high accuracy, with an R2 of 0.9930 and RMSE of 0.2585 mg/cm3. The HYDRUS inverse modeling was performed using synthetic ECa data, water content and soil salinity observation data, and the combination of ECa and shallow ground (10 cm deep) water content respectively. It was shown that although the accuracy of the water-salt transport model obtained by using ECa data is slightly lower than that of the inversion model using traditional observation data, it can still accurately simulate the water-salt distribution and has the incomparable advantages of being fast, convenient and low-cost, and the accuracy of the model is improved to some extent by incorporating shallow ground water content data into the inversion based on ECa data. Therefore, the water-salt transport simulation framework incorporating soil apparent electrical conductivity proposed in this study can accurately simulate salt transport and can provide strategies and methods for the simulation and prediction of soil water-salt transport using electromagnetic induction technology.

Optimization of demand-responsive transport: The rolling horizon approach

Demand-responsive Transport (DRT) is a popular topic in the field of transportation. The rolling horizon approach can be used to achieve an efficient operation of the DRT system in the context of trip pre-booking. In this study, a rolling horizon optimizer is implemented and used in an agent-based transport simulation framework. A series of experiments are performed to study the performance of the rolling horizon approach. The results of the experiments provide general guidelines on how to set up the rolling horizon optimizer for a DRT system. With a suitable setup, the rolling horizon approach demonstrates a clear advantage over the other approaches available in the simulation framework.

Resource-constrained replanning in MATSim applied to the simulation of peer-to-peer car sharing services

Peer-to-peer car sharing is a service model in which car owners let others use their vehicle while it is not in use by themselves. To simulate such a service in the agent-based transport simulation framework MATSim, a novel synchronization concept is proposed that considers limited resources in the decision-making of the agents. The concept is introduced in detail and demonstrated for peer-to-peer car sharing on a conceptual use case and a large-scale simulation for Berlin. The concept is generalizable to other constrained resources, such as parking or micromobility.

Mitigating bus bunching with real-time crowding information

A common problem in public transport systems is bus bunching, characterized by a negative feedback loop between service headways, number of boarding passengers and dwell times. In this study, we examine whether providing real-time crowding information (RTCI) at the stop regarding the two next vehicle departures can stimulate passengers to wait for a less-crowded departure, and thus alleviate the bunching effect. To this end, we leverage on results from own stated-preference survey and develop a boarding choice model. The model accounts for the presence of RTCI and is implemented within dynamic public transport simulation framework. Application to the case-study model of a major bus corridor in Warsaw (Poland) reveals that RTCI can induce a significant probability (30–70%) of intentionally skipping an overcrowded bus and waiting for a later departure instead. This behaviour, in turn, results in significantly lower vehicle headway and load variations, without deteriorations in total waiting utility. Overall, journey experience improves by 6%, and crucially—the prevalence of denial-of-boarding and excessive on-board overcrowding is substantially reduced, by ca. 40%. Results of our study indicate that the willingness to wait induced by RTCI can be a potential demand management strategy in counteracting bunching, with benefits already attainable at limited RTCI response rates.

Tinie – a software package for electronic transport through two-dimensional cavities in a magnetic field

Quantum transport has far-reaching applications in modern electronics as it enables the control of currents in nanoscale systems such as quantum dots. In this paper we introduce tinie: a state-of-the-art quantum transport simulation framework, which can efficiently perform first-principle calculations based on the Landauer-Büttiker formalism. The computational repertoire of tinie includes calculations of transmission, conductivity, and currents running through arbitrary multi-terminal two-dimensional transport devices, with additional tools that enable the computation of the local density of states. The generality of tinie ranges from wide-band approximation calculations to investigating systems subject to an external magnetic field. The future prospects of tinie include the simulation of, e.g., two-dimensional cavities, quantum dots, or molecular junctions. The package is written in Python 3.6, and its well-documented modular structure is designed with an intent to create a platform suited for continuous expansion and development. With tinie it is possible to obtain specific information about the effects of impurities and imperfections in quantum devices, particularly between ballistic and diffusive transport regimes. Program summaryProgram title:tinieCPC Library link to program files:https://doi.org/10.17632/7487cpj9hm.1Developer's repository link:https://gitlab.com/compphys-public/tinieLicensing provisions: MIT LicenseProgramming language: Python 3.6Nature of problem: Numerical calculation of the properties of a two-dimensional nanoscale electron transport system in a uniform magnetic field (zero or non-zero), specifically the currents running through the reservoirs (leads) coupled to a quantum dot (central region) and the corresponding transmission coefficients.Solution method: The problem solution is split into two stages. The first stage (tinie_prepare stage) prepares the transport system data for the main transport calculation. This data comprise Hamiltonian matrices of the uncoupled reservoirs and quantum dot regions, their respective sets of eigenfunctions and the coupling matrices between the quantum dot and the reservoirs. The second stage (tinie stage) performs the transport calculation for the given system using the embedding self-energy technique.Additional comments including restrictions and unusual features: The code is restricted to the non-interacting equilibrium transport problems.The code is modular in structure, allowing for easy extension and introduction of different reservoir/quantum dot/coupling types. Additionally, tinie is compatible with systems in a non-zero uniform magnetic field.The source code is available at https://gitlab.com/compphys-public/tinie and Python package in https://pypi.org/project/tinie/. An extensive documentation of the code functionality can be found in the README.md file accompanying the code.

Analysis of Electric Moped Scooter Sharing in Berlin: A Technical, Economic and Environmental Perspective

Electric moped scooter sharing services have recently experienced strong growth rates, particularly in Europe. Due to their compactness, environmental-friendliness and convenience, shared e-mopeds are suitable for helping to reduce the environmental impact of urban transport. However, its traffic-related, economic and environmental effects are merely represented in academic research. Therefore, this study investigates the ability of an e-moped sharing system to substitute passenger car trips, and the resulting economic and environmental effects. First, we model fleets of 2500, 10,000 and 50,000 shared e-mopeds in Berlin, based on a passenger car scenario generated by the multi-agent transport simulation framework MATSim. Afterwards, the total cost of ownership and a life cycle assessment are conducted. The results indicate that a substantial part of all passenger car trips in Berlin can be substituted. The larger the fleet, the more and longer trips are replaced. Simultaneously, the efficiency in terms of fleet utilization decreases. The scenario with 10,000 e-mopeds offers the lowest total distance-based costs for sharing operators, whereas a fleet consisting of 2500 vehicles exhibits the lowest environmental emissions per kilometer. Already with today’s grid mix, the use of shared e-mopeds results in a significant reduction in environmental impact compared to conventional and battery-electric passenger cars.

Analysis of Electric Moped Scooter Sharing in Berlin: A Technical, Economic and Environmental Perspective

Electric moped scooter sharing services have recently experienced strong growth rates, particularly in Europe. Due to their compactness, environmental-friendliness and convenience, shared e-mopeds are suitable for helping to reduce the environmental impact of urban transport. However, its traffic-related, economic and environmental effects are merely represented in academic research. Therefore, this study investigates the ability of an e-moped sharing system to substitute passenger car trips, and the resulting economic and environmental effects. First, we model fleets of 2500, 10,000 and 50,000 shared e-mopeds in Berlin, based on a passenger car scenario generated by the multi-agent transport simulation framework MATSim. Afterwards, the total cost of ownership and a life cycle assessment are conducted. The results indicate that a substantial part of all passenger car trips in Berlin can be substituted. The larger the fleet, the more and longer trips are replaced. Simultaneously, the efficiency in terms of fleet utilization decreases. The scenario with 10,000 e-mopeds offers the lowest total distance-based costs for sharing operators, whereas a fleet consisting of 2500 vehicles exhibits the lowest environmental emissions per kilometer. Already with today’s grid mix, the use of shared e-mopeds results in a significant reduction in environmental impact compared to conventional and battery-electric passenger cars.

The effect of unexpected disruptions and information times on public transport passengers: a simulation study

Abstract This paper deals with the simulation of unexpected disruptions in public transport in the agent-based transport simulation framework of MATSim. A replanning logic for public transport passengers is implemented so passengers can be informed about and react to a disruption and use an alternative route. An unexpected disruption of an underground line in Berlin is simulated to analyze how passengers are affected by the disruption and the information time. It is shown that transport companies can minimize the negative effects of unplanned disruptions by informing their passengers as soon as possible once the disruption has occurred because travel times increase drastically when passengers are informed late.

Ingenious Techniques for Creation of Smart Cities by Big Data Technology & Urban Modelling Simulation by Matsim

Ingenious Techniques for creation of Smart Cities by Big Data Technology & Urban modeling simulation by MATSimas the smart cities are on nascent stage in India. The extension of huge information and the advancement of Internet of Things (IoT) innovations have assumed a significant job in the practicality of keen city activities. Enormous information offer the potential for urban areas to get significant bits of knowledge from a lot of information gathered through different sources, and the IoT permits the joining of sensors, radiofrequency recognizable proof, and Bluetooth in reality condition utilizing exceedingly organized administrations. Thus the job of urban reenactment models and their perception are utilized to help territorial arranging offices assess elective transportation ventures, land use guidelines, and natural insurance arrangements. Typical urban simulations provide spatially distributed data about number of inhabitants, land prices, traffic, and other variables for ex- MATSim is an activity-based transport simulation framework designed to simulate large scale scenarios. Such technologies which have been developed in the past few years have proven to be very effective in smart cities of various countries. This project is an attempt to study the feasibility of such modified system, by understanding the implementation of such technologies to improve the existing smart cities and those which are about to become one. This is done by proposing an idea that is by implementing a big data server in the proposed smart city, the data will be collected through smart sensors which will then be sent to server and the mined data will be converted to simplified data for planners, engineers etc. in order to make a economic, self-sustainable & fully automated smart city.

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.

Bicycle traffic and its interaction with motorized traffic in an agent-based transport simulation framework

Cycling as an inexpensive, healthy, and efficient mode of transport for everyday traveling is becoming increasingly popular. While many cities are promoting cycling, it is rarely included in transport models and systematic policy evaluation procedures. The purpose of this study is to extend the agent-based transport simulation framework MATSim to be able to model bicycle traffic more realistically. The network generation procedure is enriched to include attributes that are relevant for cyclists (e.g. road surfaces, slopes). Travel speed computations, plan scoring, and routing are enhanced to take into account these infrastructure attributes. The scoring, i.e. the evaluation of simulated daily travel plans, is furthermore enhanced to account for traffic events that emerge in the simulation (e.g. passings by cars), which have an additional impact on cyclists’ decisions. Inspired by an evolutionary computing perspective, a randomizing router was implemented to enable cyclists to find realistic routes. It is discussed in detail why this approach is both feasible in practical terms and also conceptually consistent with MATSim’s co-evolutionary simulation approach. It is shown that meaningful simulation results are obtained for an illustrative scenario, which indicates that the developed methods will make real-world scenarios more realistic in terms of the representation of bicycle traffic. Based on the exclusive reliance on open data, the approach is spatially transferable.

A first look at bridging discrete choice modeling and agent-based microsimulation in MATSim

The agent-based transport simulation framework MATSim allows for the simulation of dynamic transport scenarios with agents, that adaptively make travel choices. Regarding mode choice, a heavily randomized process is used to date, which allows for very unrealistic mode decisions in the short run to arrive at consistent mode shares after a large number of iterations. The authors show that implementing a discrete mode choice model may drastically increase the convergence speed of the simulation, but point out that considerable future research is necessary to make travel decisions consistent and to back the process with a strong theoretical foundation.

Modeling bicycle traffic in an agent-based transport simulation

Abstract: Cycling as an inexpensive, healthy, and efficient mode of transport for everyday traveling is becoming increasingly popular. While many cities are promoting cycling, it is rarely included in transport models and systematic policy evaluation procedures. The purpose of this study is to extend the agent-based transport simulation framework MATSim to take into account attributes of the infrastructure that are relevant for cycling and the decisions that cyclists take. It is shown that meaningful simulation results are obtained for both an illustrative test scenario and a Berlin scenario. Further attributes (e.g. personal or bicycle-related attributes) that have an effect on the behavior of cyclists can be included into the simulation and, by this, into policy evaluation. Based on the exclusive reliance on open data, the approach is transferable to other spatial contexts.

Quadrupole magnet field mapping for FRIB

Extensive magnetic field map measurements have been done on a newly built superconducting quadrupole triplet with sextupole and octupole coils nested within every quadrupole. The magnetic field multipole composition and fringe field distributions have been analyzed and an improved parameterization of the field has been developed within the beam transport simulation framework. Parameter fits yielding standard deviations as low as 0.3% between measured and modeled values are reported here.

Within-Day Replanning of Exceptional Events

Typical software used in the simulation of traffic behavior focuses on scenarios describing common situations, such as an ordinary working day without remarkable incidents. To simulate such scenarios, iterative approaches are used. They assume that the people simulated adapt to previous iterations’ results. Such iterative approaches produce meaningful results for various scenarios only when typical, repetitive situations are modeled. However, a scenario may also contain incidents that occur randomly, and thus substantially increase a model's complexity. In such scenarios, an iterative approach would produce illogical and even erroneous results. Within-day replanning is an attempt to handle such scenarios. This paper describes problems that arise when an iterative simulation approach is applied to a scenario with exceptional events. The within-day replanning technique is introduced and implemented in the multiagent transport simulation framework, allowing simulated agents to replan the routes between their activities while they are traveling. By doing this, agents can take current traffic conditions into account, an important requirement for scenarios containing unpredictable incidents such as road accidents. The implementation capability is demonstrated by conducting experiments in which capacities of several arterial roads in the city center of Zurich, Switzerland, are reduced as the result of an exceptional event. It is demonstrated that agents affected by those events are able to reduce their travel times if they replan their routes by using within-day replanning.

Computational study of the Seebeck coefficient of one-dimensional composite nano-structures

The Seebeck coefficient (S) of composite nano-structures is theoretically explored within a self-consistent electro-thermal transport simulation framework using the non-equilibrium Green’s function method and a heat diffusion equation. Seebeck coefficients are determined using numerical techniques that mimic experimental measurements. Simulation results show that, without energy relaxing scattering, the overall S of a composite structure is determined by the highest barrier within the device. For a diffusive, composite structure with energy relaxation due to electron-phonon scattering, however, the measured S is an average of the position-dependent values with the weighting factor being the lattice temperature gradient. The results stress the importance of self-consistent solutions of phonon heat transport and the resulting lattice temperature distribution in understanding the thermoelectric properties of a composite structure. It is also clarified that the measured S of a composite structure reflects its power generation performance rather than its cooling performance. The results suggest that the lattice thermal conductivity within the composite structure might be engineered to improve the power factor over the bulk by avoiding the conventional trade-off between S and the electrical conductivity.