Large-scale Traffic Simulation Research Articles

Large scale multi-GPU based parallel traffic simulation for accelerated traffic assignment and propagation

Traffic simulation is a critical tool for congestion analysis, travel time estimation, and route optimization in urban planning, benefiting navigation apps, transportation network companies, and state agencies. Traditionally, traffic micro-simulation frameworks are based on road segments and can only support a limited number of main roads. Efficient traffic simulation on a regional scale remains a significant challenge due to the complexity of urban mobility and the large scale of spatiotemporal data. This paper introduces a Large Scale Multi-GPU Parallel Computing based Regional Scale Traffic Simulation Framework (LPSim), which leverages graphical processing unit (GPU) parallel computing to address these challenges. LPSim utilizes a multi-GPU architecture to simulate extensive and dynamic traffic networks with high fidelity and reduced computation time. Using the parallel processing capabilities of GPUs, LPSim can perform tens of millions of individual vehicle dynamics simulations simultaneously, significantly outperforming traditional CPU-based approaches. The framework is designed to be scalable and can easily accommodate the increasing complexity of traffic simulations. We present the theory behind GPU-based traffic simulation, the architecture of single- and multi-GPU based simulations, and the graph partition strategies that enhance computation resource load balance. Our experimental results demonstrate the effectiveness of LPSim in simulating large-scale traffic scenarios. LPSim is capable of completing simulations of 2.82 million trips in just 6.28 min on a single GPU machine equipped with 5120 CUDA cores (Tesla V100-SXM2). Furthermore, utilizing a Google Cloud instance with two NVIDIA V100 GPUs, which collectively offer 10240 CUDA cores, LPSim successfully simulates 9.01 million trips within 21.16 min. We further tested our simulator with the same demand on dual NVIDIA A100-PCIE-40GB GPUs, which finished the simulation in 0.0398 h, approximately 113 times faster than the same simulation scenario running on an Intel(R) Xeon(R) Gold 6326 CPU @ 2.90 GHz, which takes 4.49 h to complete. This performance not only demonstrates its speed and scalability advantages over traditional simulation techniques but also highlights LPSim’s unique position as the first traffic simulation framework that is scalable for both single- and multiple-GPU configurations. Consequently, LPSim provides an invaluable tool for individuals and extensive research teams alike, enabling the acquisition of large-scale traffic simulation results in a time-efficient manner. LPSim code is available at: https://github.com/Xuan-1998/LPSim

Close-Range Coordination to Enhance Constant Distance Spacing Policies in Oversaturated Traffic Systems.

In the pursuit of string stability within CACC (cooperative adaptive cruise control) platoons, prevalent research has favored constant time gap (CTG) spacing policies; namely, vehicle interspacing increases linearly with the speed. Although constant distance gap (CDG) spacing policies have greater potential to enhance traffic capacity, they suffer from notable limitations regarding string stability and diminished safety margins at high velocities. In our previous work, we proposed applying CDG in specific scenarios, such as starting platoons at signalized intersections, where traffic throughput is critical and safety requirements can be met due to relatively low speeds. We demonstrated the substantial potential of CDG to increase the capacity of signalized intersections under oversaturated conditions. However, our study also revealed potential performance drops of CDG in dense traffic networks. To address these issues, we propose close-range coordination between vehicles to (1) limit platoon length, (2) create gaps for merging, and (3) avoid entering intersections when there is a high likelihood of stopping within the intersection area. In this paper, we extend our previous work by implementing these three measures. We successfully evaluate their positive impact on CDG's performance in entire traffic systems through large-scale traffic simulations involving several thousand vehicles, thereby affirming our earlier hypothesis.

Development, Calibration, and Validation of a Large-Scale Traffic Simulation Model: Belgium Road Network

Development of large-scale traffic simulation models have always been challenging for transportation researchers. One of the essential steps in developing traffic simulation models, which needs lots of resources, is travel demand modeling. Therefore, proposing travel demand models that require less data than classical travel demand models is highly important, especially in large-scale networks. This paper first presents a travel demand model named as probabilistic travel demand model, then it reports the process of development, calibration and validation of Belgium traffic simulation model. The probabilistic travel demand model takes cities' population, distances between the cities, yearly vehicle-kilometer traveled, and yearly truck trips as inputs. The extracted origin-destination matrices are imported into the SUMO traffic simulator. Mesoscopic traffic simulation and the dynamic user equilibrium traffic assignment are used to build the base case model. This base case model is calibrated using the traffic count data. Al-so, the validation of the model is performed by comparing the real (extracted from Google Map API) and simulated travel times between the cities. The validation results ensure that the model is a superior representation of reality with a high level of accuracy. The model will be helpful for road authorities, planners, and decision-makers to test different scenarios, such as the im-pact of abnormal conditions or the impact of connected and autonomous vehicles on the Belgium road network.

Are we getting vehicle emissions estimation right?

Simulation is a valuable prediction tool to evaluate the impact of climate change interventions prior to disruptive implementations that may yield unintended and unwanted consequences. Advances in agent-based simulation allow us to estimate the dynamic emissions produced by vehicles at a fine resolution, with each vehicle modelled individually. The scalability of these models allow decision-makers to evaluate large-scale scenarios. But how good are these models? In this paper, the authors apply a state-of-the-art emissions model in the Multi Agent Transport Simulation (MATSim) framework to simulate individual vehicle emissions. Simulation results are compared with real driving emissions tests, using a portable emissions measurement device on a light and heavy vehicle. Quantifying the gap between macro-level simulation and actual emissions, the paper contributes by showing that pollutants are significantly underestimated, especially for heavy vehicles. This confirms prior micro-scale studies and magnifies the detrimental impact of using uncorrected aggregated models to inform environmental policy affecting transport. • Quantifying the gap in emission estimates on large-scale traffic simulations. • City-scale activity-based transport model coupled with vehicle-specific emissions. • Variation in real driving emissions is much larger than in randomised simulations. • Macro-level models underestimate heavy vehicle emissions more than light vehicles.

Forecasting the spatial and temporal charging demand of fully electrified urban private car transportation based on large-scale traffic simulation

To support power grid operators to detect and evaluate potential power grid congestions due to the electrification of urban private cars, accurate models are needed to determine the charging energy and power demand of battery electric vehicles (BEVs) with high spatial and temporal resolution. Typically, e-mobility traffic simulations are used for this purpose. In particular, activity-based mobility models are used because they individually model the activity and travel patterns of each person in the considered geographical area. In addition to inaccuracies in determining the spatial distribution of BEV charging demand, one main limitation of the activity-based models proposed in the literature is that they rely on data describing traffic flow in the considered area. However, these data are not available for most places in the world. Therefore, this paper proposes a novel approach to develop an activity-based model that overcomes the spatial limitations and does not require traffic flow data as an input parameter. Instead, a route assignment procedure assigns a destination to each BEV trip based on the evaluation of all possible destinations. The basis of this evaluation is the travel distance and speed between the origin of the trip and the destination, as well as the car-access attractiveness and the availability of parking spots at the destinations.The applicability of this model is demonstrated for the urban area of Berlin, Germany, and its 448 sub-districts. For each district in Berlin, both the required daily BEV charging energy demand and the power demand are determined. In addition, the load shifting potential is investigated for an exemplary district. The results show that peak power demand can be reduced by up to 31.7% in comparison to uncontrolled charging.

Large-scale traffic flow simulation based on intelligent PSO

With the rapid development of urban traffic, a large number of vehicles in cities not only bring convenience to people, but also bring a series of traffic problems, including traffic congestion and high traffic accident rates. Driving speed and waiting time of vehicles are two important factors of traffic problems. To simulate the real urban road traffic flow, a one-dimensional traffic flow grid model was proposed, which considered the nearest and next neighbour car at the same time, and connected the front and rear neighbour cars to optimize the traffic flow. The experiment results showed that our traffic flow grid model can simulate the real urban road traffic flow. In addition, we tried to optimize the urban traffic network model and improved the traffic speed of vehicles and reduced the waiting time.

Interactive Web Application for Traffic Simulation Data Management and Visualization

As traffic simulation software becomes more effective for realistically simulating and analyzing traffic dynamics and vehicle interactions on the mesoscopic and microscopic level, the management, dissemination, and collaborative visualization of traffic simulation results produced by individual transportation planners presents a significant challenge. Existing online content management systems have a very limited capability in allowing users to query specific traffic simulation scenarios and geospatially visualize simulation results through shareable and interactive web interfaces. This paper presents a web-based application for promoting the archiving, sharing, and visualization of large-scale traffic simulation outputs. The application is developed to enhance cyber-physical controls, communications, and public education for collaborative transportation planning. Unique features of the web application include: (a) allowing users to upload their new traffic simulation scenarios (parameters and outputs), as well as search existing scenarios using easily accessible interfaces; (b) optimizing simulation output files with heterogeneous data formats and projected coordinate systems for web-based storage and management using a scalable and searchable data/metadata standard; (c) standardizing user-uploaded simulation outputs using web interfaces and data processing libraries with parallel computing capacity; and (d) providing shareable web visual interfaces for visualizing the traffic flow and signal information stored in simulation outputs (e.g., regional traffic patterns and individual vehicle interactions) and visually comparing multiple simulation outputs both spatially and temporally. The paper presents the conceptual design and implementation of this application, and demonstrates the application’s performance for sharing, comparing, and visualizing simulation outputs from VISSIM and SUMO, two commonly used traffic simulation software programs.

Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

The integration of the Internet of Vehicles (IoV) in future smart cities could help solve many traffic-related challenges, such as reducing traffic congestion and traffic accidents. Various congestion pricing and electric vehicle charging policies have been introduced in recent years. Nonetheless, the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management system. In this paper, we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city environment. The proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging stations. We propose a token management system that serves as a virtual currency, where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging fees. The proposed system is designed for Vehicular Ad-hoc Networks (VANETs) in the context of a smart city environment without the need to set up any expensive toll collection stations. Through large-scale traffic simulation in different smart city scenarios, it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.

Agent- and activity-based large-scale simulation of enroute travel, enroute refuelling and parking behaviours in Beijing, China

This paper develops an agent- and activity-based large-scale simulation model for Beijing, China (MATSim-Beijing) to explicitly simulate enroute travel, enroute refuelling and parking behaviours, as well as the associated vehicular energy consumption and emissions, based on MATSim (Multi-Agent Transport Simulation), which is a typical integrated activity-based model. In order to take into account heterogeneous parking and refuelling behaviours, the MATSim-Beijing model incorporates several Multinomial Logit (MNL) models to predict individual choices about the maximum acceptable times of walking from trip destination to parking lot, of diverting to a refuelling station and of queuing at a station, using the data collected in a paper-based questionnaire survey in Beijing. A Sensitivity Analysis (SA) -based calibration method was used to estimate the model parameters by searching for an optimal parameter combination with the objective of minimize the gap between simulated and observed traffic flow data, exhibiting a relatively good performance of decreasing the Mean Absolute Percentage Error (MAPE) by around 23%. Further, the calibrated model was used to investigate whether and how the population scaling and network simplification, which were two commonly used approaches to speeding up large-scale traffic simulations, might influence model accuracy and computing time. The results indicated that both approaches could to some extent influence model outputs, though they could significantly reduce computing time.

Calibration and validation of large-scale traffic simulation networks: a case study

The availability, accuracy and relevance of real world input data are essential for developing a reliable traffic simulation model. Large-scale traffic simulation models, in particular, require data from many sources and in great detail. Though it is now possible to obtain detailed field data with the advent of new technologies such as GPS, cellular phones, RFIDs, it is still a challenge to gather all available data, especially traffic flow data, in the required spatial and temporal accuracy. The central theme of this paper is the calibration and validation (C&V) development of a large-scale traffic simulation model using data from multiple sources.

Harnessing the Power of HPC in Simulation and Optimization of Large Transportation Networks: Spatio-Temporal Traffic Management in the Greater Toronto Area

The significant growth of many urban areas comes at a cost of increasing demand for mobility and traffic congestion in large-scale urban environments. As congestion levels soar to unprecedented levels, and researchers and governments are challenged addressing the basic needs for transportation and mobility; solutions are becoming more complex and untraditional, creating a strong potential for optimization and simulation of large-scale transportation networks. In this paper, we present a generic traffic management framework for solving large-scale constraint optimization problems in advanced Intelligent Transportation systems (ITS) applications. The framework employs a distributed computing approach to enable replicating analysis of large-scale traffic simulation networks, providing a practical mechanism for solving complex optimization problems in transportation applications. We discuss employing the framework in two use cases, congestion pricing and emergency evacuation, targeting optimization of spatial and temporal traffic management.

Development of Large-Scale Traffic Simulation for the Tokyo Metropolitan Road Network

Development of Large-Scale Traffic Simulation for the Tokyo Metropolitan Road Network

Large-scale Microscopic Traffic Simulation with AI and HPC

Large-scale Microscopic Traffic Simulation with AI and HPC

Large-scale Microscopic Traffic Simulation with AI and HPC

Large-scale Microscopic Traffic Simulation with AI and HPC

140 領域分割法を用いた大規模交通流シミュレーションの高速化

140 領域分割法を用いた大規模交通流シミュレーションの高速化

大規模微視的交通流シミュレーションの効率化

大規模微視的交通流シミュレーションの効率化

広域性と精緻性をそなえた交通流シミュレータのための経路探索の並列化

広域性と精緻性をそなえた交通流シミュレータのための経路探索の並列化

A full-scale simulation model to reproduce urban traffic in real conditions in driving simulators

This paper describes a model capable of simulating large-scale traffic in an urban environment. The goal is of this work is the realistic and detailed simulation of the traffic, reproducing the behavior of each vehicle involved in the environment individually. This model has been developed in order to be integrated in an immersive driving simulator, where the driving position is the center of the simulation and the traffic model reproduces what happens around them. The general behavior of the traffic model is based on the following theory. Depending on the size of the urban environment to be simulated, the number of vehicles involved, and the traffic density, the environment can be studied as a whole or segmented in adjacent areas. Each vehicle model has two components. First, the behavior of the vehicle is simulated individually, modeling acceleration and braking, depending on the type and characteristics of each vehicle (mass, power, size, etc.). Second, the behavior of the drivers is also modeled, by type (passive, moderate, aggressive), playing various maneuvers also common in urban traffic circulation, such as lane changes, behavior at crossings and intersections, etc. A traffic light regulation model and the complete signposting of the urban environment are also included. As a result, the developed traffic model is applicable to large-scale traffic simulation integrated in an immersive driving simulator and is very useful when investigating complex behaviors of these environments. The model has been validated comparing it with results obtained from various references and very satisfactory results have been obtained.

City Planning Supported by Large-scale Traffic Simulator

City Planning Supported by Large-scale Traffic Simulator

Integrating an Agent-Based Travel Behavior Model with Large-Scale Microscopic Traffic Simulation for Corridor-Level and Subarea Transportation Operations and Planning Applications

Application of microscopic traffic simulation beyond the corridor level analysis is not widely seen in literature. This is partly because of the fact that a simulation model cannot capture behavior responses such as peak spreading. This study develops a framework that integrates agent-based travel behavior models with large-scale traffic simulation to capture the regional impacts of new development. The proposed model is then applied to the I-270/I-495/I-95 corridor in the north Washington, DC metropolitan area in a case study. Findings from this study reveal the potential of the proposed model to capture network dynamics and behavioral reactions. This framework also provides a valuable tool for the evaluation of new transportation infrastructure, such as the intercounty connector (ICC) corridor currently under con- struction, and its operation strategies. DOI: 10.1061/(ASCE)UP.1943-5444.0000139. © 2013 American Society of Civil Engineers. CE Database subject headings: Traffic management; Simulation; Traffic models; Travel patterns. Author keywords: Traffic simulation; Agent-based model; Departure time choice; Intercounty connector.