Modeling Sustainable Traffic Behavior: Avoiding Congestion at a Stationary Bottleneck

Sustainable traffic behaviour is increasing in importance as traffic volume rises due to population growth. In this paper, a model for traffic flow at a stationary bottleneck is developed to determine the parameters that cause congestion. Towards this goal, traffic density, speed, and delay were acquired during peak and off-peak periods in the morning and afternoon at a stationary bottleneck in Peshawar, KPK, Pakistan. The morning and afternoon peak periods have high densities, low speeds, and considerable delays. Regression models are developed using this data. These results indicate that there is a linear relationship between density and time at the stationary bottleneck and a negative linear relationship between density and speed. Thus, an increase in density increases the time delay and reduces the speed. I comprehensive traffic delay model is characterized by a stationary bottleneck. The Kolmogorov-Smirnov (KS) test and P-values were used to identify the best-fit distribution for speed and density. The binomial and generalized extreme values are considered the best fits for density and speed. The results presented can be used to develop accurate simulation models for stationary bottlenecks to reduce congestion. Doi: 10.28991/CEJ-2022-08-11-02 Full Text: PD

Real-time Simulation of Dynamic Vehicle Models using a High-performance Reconfigurable Platform

A purely software-based approach for Real-Time Simulation (RTS) may have difficulties in meeting real-time constraints for complex physical model simulations. In this paper, we present a methodology for the design and im-plementationofRTS algorithms,basedontheuseof Field-ProgrammableGateArray(FPGA) technologytoimprove the response time of these models. Our methodology utilizes traditional hardware/software co-design approaches to generate a heterogeneous architecture for an FPGA-based simulator. The hardware design was optimized such that it efficiently utilizes the parallel nature of FPGAs and pipelines the independent operations. Further enhancement is obtained through the use of custom accelerators for common non-linear functions. Since the systems we examined had relatively low response time requirements, our approach greatly simplifies the software components by porting the computationally complexregionsto hardware.We illustratethe partitioningofa hardware-based simulator design across dual FPGAs, initiateRTS usinga system input froma Hardware-in-the-Loop (HIL) framework, and use these simulation results from our FPGA-based platform to perform response analysis. The total simulation time, which includes the time required to receive the system input over a socket (without HIL), software initialization, hardware computation, and transferof simulation results backovera socket, showsa speedup of 2× as compared to a simi-lar setup with no hardware acceleration. The correctness of the simulation output from the hardware has also been validated with the simulated results from the software-only design

Indirect damage of urban flooding:Investigation of flood-induced traffic congestion using dynamic modeling

In many countries, industrialization has led to rapid urbanization. Increased frequency of urban flooding is one consequence of the expansion of urban areas which can seriously affect the productivity and livelihoods of urban residents. Therefore, it is of vital importance to study the effects of rainfall and urban flooding on traffic congestion and driver behavior. In this study, a comprehensive method to analyze the influence of urban flooding on traffic congestion was developed. First, a flood simulation was conducted to predict the spatiotemporal distribution of flooding based on Storm Water Management Model (SWMM) and TELAMAC-2D. Second, an agent-based model (ABM) was used to simulate driver behavior during a period of urban flooding, and a car-following model was established. Finally, in order to study the mechanisms behind how urban flooding affects traffic congestion, the impact of flooding on urban traffic was investigated based on a case study of the urban area of Lishui, China, covering an area of 4.4 km2. It was found that for most events, two-hour rainfall has a certain impact on traffic congestion over a five-hour period, with the greatest impact during the hour following the cessation of the rain. Furthermore, the effects of rainfall with 10- and 20-year return periods were found to be similar and small, whereas the effects with a 50-year return period were obvious. Based on a combined analysis of hydrology and transportation, the proposed methods and conclusions could help to reduce traffic congestion during flood seasons, to facilitate early warning and risk management of urban flooding, and to assist users in making informed decisions regarding travel

Real-time simulation of dynamic vehicle models using high performance reconfigurable computing platforms

A software-based approach for Real-Time Simulation (RTS) may have difficulties in meeting real-time constraints for complex models. In this thesis, we present a methodology for the design and implementation of RTS algorithms, based on the use of Field-Programmable Gate Array (FPGA) technology to improve the response time of these models. Our methodology utilizes traditional Hardware/Software co-design approaches to generate a heterogeneous architecture for an FPGA-based simulator. We have optimized the hardware design such that it efficiently utilizes the parallel nature of FPGAs and pipelines the independent operations. Further enhancement is obtained through the use of custom accelerators for common non-linear functions. Since the systems we examine have relatively low response time requirements, our approach greatly simplifies the software components by porting the computationally complex regions to hardware. We illustrate the partitioning of a hardware-based simulator design across dual FPGAs, initiate RTS using a system input from a Hardware-in-the-Loop (HIL) framework, and use these simulation results from our FPGA-based platform to perform response analysis. The total simulation time, which includes the time required to receive the system input over a socket (without HIL), software initialization, hardware computation, and transfer of simulation results back over a socket, shows a speedup of 2x as compared to a similar setup with no hardware acceleration. The correctness of the simulation output from the hardware has also been validated with the simulated results from the software-only design

Data Assimilation for Spatial Temporal Simulations Using Localized Particle Filtering

As sensor data becomes more and more available, there is an increasing interest in assimilating real time sensor data into spatial temporal simulations to achieve more accurate simulation or prediction results. Particle Filters (PFs), also known as Sequential Monte Carlo methods, hold great promise in this area as they use Bayesian inference and stochastic sampling techniques to recursively estimate the states of dynamic systems from some given observations. However, PFs face major challenges to work effectively for complex spatial temporal simulations due to the high dimensional state space of the simulation models, which typically cover large areas and have a large number of spatially dependent state variables. As the state space dimension increases, the number of particles must increase exponentially in order to converge to the true system state. The purpose of this dissertation work is to develop localized particle filtering to support PFs-based data assimilation for large-scale spatial temporal simulations. We develop a spatially dependent particle-filtering framework that breaks the system state and observation data into sub-regions and then carries out localized particle filtering based on these spatial regions. The developed framework exploits the spatial locality property of system state and observation data, and employs the divide-and-conquer principle to reduce state dimension and data complexity. Within this framework, we propose a two-level automated spatial partitioning method to provide optimized and balanced spatial partitions with less boundary sensors. We also consider different types of data to effectively support data assimilation for spatial temporal simulations. These data include both hard data, which are measurements from physical devices, and soft data, which are information from messages, reports, and social network. The developed framework and methods are applied to large-scale wildfire spread simulations and achieved improved results. Furthermore, we compare the proposed framework to existing particle filtering based data assimilation frameworks and evaluate the performance for each of them

Uma abordagem de consciência de máquina ao controle de semáforos de tráfego urbano

Orientador: Ricardo Ribeiro GudwinTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Neste trabalho, apresentamos uma arquitetura cognitiva distribuída usada para o controle de tráfego em uma rede urbana. Essa arquitetura se baseia em uma abordagem de consciência de máquina - Teoria do Workspace Global - de forma a usar competição e difusão em broadcast, permitindo que um grupo de controladores de tráfego locais interajam, resultando em melhor desempenho do grupo. A ideia principal é que controladores locais geralmente realizam um comportamento reativo, definindo os tempos de verde e vermelho do semáforo, de acordo com informações locais. Esses controladores locais competem de forma a definir qual deles está experienciando a situação mais crítica. O controlador nas piores condições ganha acesso ao workspace global, e depois realiza uma difusão em broadcast de sua condição (e sua localização) para todos os outros controladores, pedindo sua ajuda para lidar com sua situação. Essa chamada do controlador que acessa o workspace global causará uma interferência no comportamento local reativo, para aqueles controladores locais com alguma chance de ajudar o controlador na situação crítica, contendo o tráfego na sua direção. Esse comportamento do grupo, coordenado pela estratégia do workspace global, transforma o comportamento reativo anterior em uma forma de comportamento deliberativo. Nós mostramos que essa estratégia é capaz de melhorar a média do tempo de viagem de todos os veículos que fluem na rede urbana. Um ganho consistente no desempenho foi conseguido com o controlador "Consciência de Máquina" durante todo o tempo da simulação, em diferentes cenários, indo de 10% até maisde 20%, quando comparado ao controlador "Reativo Paralelo" sem o mecanismo de consciência artificial, produzindo evidência para suportar a hipótese de que um mecanismo de consciência artificial, que difunde serialmente em broadcast conteúdo para processos automáticos, pode trazer vantagens para uma tarefa global realizada por uma sociedade de agentes paralelos que operam juntos por uma meta comumAbstract: In this work, we present a distributed cognitive architecture used to control the traffic in an urban network. This architecture relies on a machine consciousness approach - Global Workspace Theory - in order to use competition and broadcast, allowing a group of local traffic controllers to interact, resulting in a better group performance.The main idea is that the local controllers usually perform a purely reactive behavior, defining the times of red and green lights, according just to local information. These local controllers compete in order to define which of them is experiencing the most critical traffic situation. The controller in the worst condition gains access to the global workspace, further broadcasting its condition (and its location) to all other controllers, asking for their help in dealing with its situation. This call from the controller accessing the global workspace will cause an interference in the reactive local behavior, for those local controllers with some chance in helping the controller in a critical condition, by containing traffic in its direction. This group behavior, coordinated by the global workspace strategy, turns the once reactive behavior into a kind of deliberative one. We show that this strategy is capable of improving the overall mean travel time of vehicles flowing through the urban network. A consistent gain in performance with the "Machine Consciousness" traffic signal controller during all simulation time, throughout different simulated scenarios, could be observed, ranging from around 10% to more than 20%, when compared to the "Parallel Reactive" controller without the artificial consciousness mechanism, producing evidence to support the hypothesis that an artificial consciousness mechanism, which serially broadcasts content to automatic processes, can bring advantages to the global task performed by a society of parallel agents working together for a common goalDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétrica153206/2010-1CNPQCAPESFAPES