Vaisagh Viswanathan

Simulation-assisted exploration of charging infrastructure requirements for electric vehicles in urban environments

Abstract High population densities in todays cities are leading to increasing congestion and air pollution. Sustainable cities of the future will require a large scale transition to electro-mobility. The development of electric vehicle charging infrastructure is necessary to enable this transition. Existing methods for determining charging infrastructure take an optimization approach that ignores existing traffic demands and infrastructure. Moreover, the dynamics of vehicle movement like stop-and-go traffic, congestion and the effect of traffic lights are not considered in determining energy consumption. In this paper, we propose a novel nanoscopic city-scale traffic simulation based method for determining charging infrastructure locations; subsequently, we demonstrate its usefulness in spatio-temporal planning through a case-study of Singapore. Through this method, existing traffic and road network data and the dynamics of individual vehicle movement can be taken into consideration in planning.

Cellular Automata-based Anthropogenic Heat Simulation

Abstract Cellular automata (CA) models have been for several years, employed to describe urban phenomena like growth of human settlements, changes in land use and, more recently, dispersion of air pollutants. We propose to adapt CA to study the dispersion of anthropogenic heat emissions on the micro scale. Three dimensional cubic CA with a constant cell size of 0.15 m have been implemented. Simulations suggest an improvement in processing speed compared to conventional computational fluid dynamics (CFD) models, which are limited in scale and yet incapable of solving simulations on local or larger scale. Instead of solving the Navier- Stokes equations, as in CFD, only temperature and heat differences for the CA are modeled. Radiation, convection and turbulence have been parameterized according to scale. This CA- based approach can be combined with an agent-based traffic simulation to analyse the effect of driving behavior and other microscopic factors on urban heat.

Information impact on transportation systems

With a broader distribution of personal smart devices and with an increasing availability of advanced navigation tools, more drivers can have access to real time information regarding the traffic situation. Our research focuses on determining how using the real time information about a transportation system could influence the system itself. We developed an agent based model to simulate the effect of drivers using real time information to avoid traffic congestion. Experiments reveal that the systems performance is influenced by the number of participants that have access to real time information. We also discover that, in certain circumstances, the system performance when all participants have information is no different from, and perhaps even worse than, when no participant has access to information.

Influence of Charging Behaviour Given Charging Station Placement at Existing Petrol Stations and Residential Car Park Locations in Singapore

Electric Vehicles (EVs) are set to play a crucial role in making transportation systems more sustainable. However, charging infrastructure needs to be built up before EV adoption can increase. A crucial factor that is ignored in most existing studies of optimal charging station (CS) deployment is the role played by the charging behaviour of drivers. In this study, through an agent-based traffic simulation, we analyse the impact of different driver charging behaviour under the assumption that CSs are placed at existing petrol stations and residential car park locations in Singapore. Three models are implemented: a simple model with a charging threshold and two more sophisticated models where the driver takes the current trip distance and existing CS locations into account. We analyse the performance of these three charging behaviours with respect to a number of different measures. Results suggest that charging behaviours do indeed have a significant impact on the simulation outcome. We also discover that the sensitivity of model parameters in each charging behaviour is an important factor to consider as variations in model parameter can lead to significant different results.

The effect of information uncertainty in road transportation systems

Abstract Developments in Intelligent Transportation Systems (ITS), navigation devices and traffic sensors make it possible for traffic participants to not just access real time information regarding the traffic situation but, at the same time, also provide data back to the transportation system. This creates a feedback loop that can have significant consequences on the system performance in terms of total average travel time. In the current paper, the effect that different types of information inaccuracy can have on the system performance is investigated. The different sources of inaccuracy are categorised into there groups: sparsity of data sources, collection and presentation inaccuracy. Subsequently, an agent-based microscopic traffic simulation is used to explore the effects that each type of inaccuracy can have on the transportation system. Experiments reveal certain interesting observations. Firstly, less than 20% of the traffic participants need to be data sources for optimal system performance. It was also discovered that lower precision of information presented to participants is sufficient and, in certain cases, better for system performance. This can have important implications on how information is displayed on navigation devices.

An Information Processing Based Model of Pre-evacuation Behavior for Agent Based Egress Simulation

During a fire evacuation, evacuees do not start evacuating immediately on hearing a fire alarm. This delay in reaction is often the cause of unnecessary deaths. However, it is hardly ever considered in computational models of egress. In this paper, an agent based computational model for pre-evacuation behavior is proposed and implemented by modeling cue perception and communication. Through experiments, the significant impact that pre-evacuation behavior modeling can have is also demonstrated.

Influence of charging behaviour given charging infrastructure specification: A case study of Singapore☆

Abstract Electric vehicles (EVs) are set to play a crucial role in making transportation systems more sustainable. However, charging infrastructure needs to be built up before EV adoption can increase. A crucial factor that is ignored in most existing studies of optimal charging station (CS) deployment applying agent-based nanoscopic traffic simulation is the role played by the charging behaviour of drivers. In this study, through an agent-based traffic simulation, we analyse the impact of different driver charging behaviour under the assumption that CSs are placed at existing petrol stations and residential car park locations in Singapore. Three models are implemented: a simple model with a charging threshold and two more sophisticated models where the driver takes the current trip distance and existing CS locations into account. We analyse the effect of these three charging behaviour models on the performance of the charging infrastructure with respect to a number of different measures. Results suggest that charging behaviours do indeed have a significant impact on the simulation outcome. We also discover that the sensitivity of model parameters in each charging behaviour and initialisation parameters of the agents are an important factor to consider. Variations in model and initialisation parameters can lead to significant different results. In addition, we investigate into a different charging infrastructure distribution using a grid-based approach for Singapore. Results propose that a more evenly distributed charging infrastructure with the grid-based approach is less effective than the one with charging station placement at existing petrol stations and residential car park locations.

BISOS: Backwards Incremental System Optimum Search algorithm for fast socially optimal traffic assignment

This paper presents an algorithm, called the Backwards Incremental System Optimum Search (BISOS) for achieving system near-optimum traffic assignment by incrementally limiting accessibility of roads for a chosen set of agents. The described algorithm redistributes traffic volumes homogeneously around the city and converges significantly faster than existing methods for system optimum computation in current literature. Furthermore, as previous methods have mainly been developed for theoretical purposes, the solutions provided by them do not contain all the necessary information for a practical implementation such as explicit paths for the commuting population. In contrast, the BISOS algorithm preserves the information about the exact paths of all commuters, throughout the whole process of computing the system optimum assignment. Furthermore, a realistic traffic scenario is simulated using Singapore as a case study by utilizing survey and GPS traffic data. The BISOS routing method needs 15 times less routing computations to get within 1% of the optimal solution for a simulated scenario compared to conventional methods for system optimum computation.

Online Data Extraction for Large-Scale Agent-Based Simulations

Cloud-based simulation systems reduce the upfront hardware costs of running high-performance experiments and increases the ease with which simulation experiments can be repeated. The data being generated by simulations can be large. Commonly used data storage systems such as relational databases can handle large amounts of data, but the analysis is a challenging problem. Moreover, handling this amount of data in cloud services can be both expensive (bandwidth and storage costs) and time-consuming. However, a lot of the data that is generated by agent-based simulations does not contribute directly to the purpose of the experiment being conducted. We propose an extension to cloud-based simulation systems that rather than storing raw simulation output data, uses stream data processing to generate the result dataset while the simulation is running. This can then be used to store only the data required for later use, this saving both time and money.

Traffic State Estimation Using Floating Car Data

There is an increasing availability of floating car data both historic, in the form of trajectory datasets and real-time, in the form of continuous data streams. This paves the way for several advanced traffic management services such as current traffic state estimation, congestion and incident detection and prediction of the short-term evolution of traffic flow. In this paper, we present an analysis of using probe vehicles for reconstructing traffic state. We employ detailed agent-based microscopic simulations of a real world expressway to estimate the state from floating car data. The probe penetration required for accurate traffic state estimation is also determined.