Microsimulation Tool Research Articles

Development of an agent-based model to understand the diffusion of value management in construction projects as a sustainability tool

Despite the rich literature about VM in construction, questions about how practitioners decide to adopt VM and what motivates adoption remain unanswered. The paper presents an Agent-Based Model as a microsimulation tool, where contractors and policy makers interact in a shared environment, to help understand the adoption of value management (VM) based on a bottom-up approach, considering contractors’ heterogeneity in terms of the perceived benefits of VM, social influence, and innovativeness. By allowing for various diffusion curves to be evaluated, our study conclude that mass media, and incentives will increase the uptake of VM.

Estimating the future cancer management costs attributable to modifiable risk factors in Canada.

An estimated 33-37% of incident cancers in Canada are attributable to modifiable risk factors. Interventions targeting these risk factors would minimize the substantial health and economic burdens Canadians face due to cancer. We estimate the future health and economic burden of cancer in Canada by incorporating data from the Canadian Population Attributable Risk of Cancer (ComPARe) study into OncoSim, a web-based microsimulation tool. Using the integrated OncoSim population attributable risk and population impact measures, we evaluated risk factor-targeted intervention scenarios implemented in 2020, assuming the targeted risk factor prevalence reduction would be achieved by 2032 with a 12-year latency period. We estimate that smoking will be the largest contributor to cancer-related costs, with a cost of CAD $44.4 billion between 2032 and 2044. An estimated CAD $3.3 billion of the cost could be avoided with a 30% reduction in smoking prevalence by 2022. Following smoking, the next highest cancer management costs are associated with inadequate physical activity and excess body weight, accounting for CAD $10.7 billion ($2.7 billion avoidable) and CAD $9.8 billion ($3.2 billion avoidable), respectively. Avoidable costs for other risk factors range from CAD $90 million to CAD $2.5 billion. Interventions targeting modifiable cancer risk factors could prevent a substantial number of incident cancer cases and billions of dollars in cancer management costs. With limited budgets and rising costs in cancer care in Canada, these simulation models and results are valuable for researchers and policymakers to inform decisions and prioritize and evaluate intervention programs.

Estimates of future cancer mortality attributable to modifiable risk factors in Canada.

Modifiable lifestyle, environmental, and infectious risk factors associated with cancer impact both cancer incidence and mortality at the population level. Most studies estimating this burden focus on cancer incidence. However, because these risk factors are associated with cancers of disparate mortality rates, the burden associated with cancer incidence could differ from cancer mortality. Therefore, estimating the cancer mortality attributable to these risk factors provides additional insight into cancer prevention. Here, we estimated future cancer deaths and the number of avoidable deaths in Canada due to modifiable risk factors. The projected cancer mortality data came from OncoSim, a web-based microsimulation tool. These data were applied to the methodological framework that we previously used to estimate the population attributable risks and the potential impact fractions of modifiable risk factors on Canadian cancer incidence. We estimated that most cancer deaths will be attributed to tobacco smoking with an average of 27,900 deaths annually from 2024 to 2047. If Canada's current trends in excess body weight continue, cancer deaths attributable to excess body weight would double from 2786 deaths in 2024 to 5604 deaths in 2047, becoming the second leading modifiable cause of cancer death. Applying targets to reduce these risk factors, up to 34,600 cancer deaths could be prevented from 2024 to 2047. Our simulated results complement our previous findings on the cancer incidence burden since decreasing the overall burden of cancer will be accelerated through a combination of decreasing cancer incidence and improving survival outcomes through improved treatments.

Simulation of U-turn traffic based on VISSIM and PARAMICS micro simulation

Traffic simulation models are one of the most important tools that are widely used to analyse and manage transport systems. These tools are used to model complex traffic sites and evaluate various traffic alternatives and testing their effect prior to application in real sites. Median U- turn is an example of such complicated sites. There are many microsimulation tools such as VISSIM, ALIMSUN and PARAMICS. The goal of this study is to develop the simulation model of median U-turns and compares between VISSIM and PARAMICS models. The data were collect from two-selected site at Al-Diwaniyah city, Iraq. The data used in verification of the developed model represent the through traffic volumes and turning traffic volumes at both directions as well as the average queue length created at the U-turn site. The calibration and validation process of the developed models show that both VISSIM and PARAMICS models can provides reasonable approximation for real traffic at the selected site. Further research is needed to examine these tools based on additional sites and to apply such tools to examine different scenarios to enhance traffic conditions at U-turn sites.

Capacity of a freeway lane with platoons of autonomous vehicles mixed with regular traffic

In the near future, autonomous vehicles (AVs) will travel sharing the current freeways with human driven vehicles. The efficiency of this mixed traffic scenario will depend on the ability of AVs to behave cooperatively. Otherwise, the introduction of uncoordinated AVs might lead to capacity reductions. Connected AVs (CAVs) traveling in platoons represents a promising management strategy to get the most from the AVs’ technological revolution. Most of previous research has used traffic microsimulation tools to assess platooning and other CAVs’ cooperative driving strategies, achieving good results. However, the robust macroscopic modeling alternative, which typically yields the necessary insights and fundamental knowledge to set the foundations for the development of management and control strategies, remains almost unexplored. This paper contributes to fill this research gap by providing a generalized macroscopic model to estimate the average CAVs platoon length for a given traffic demand and penetration rate of CAVs. Two different platooning schemes are compared (i.e. cooperative and opportunistic) representing the best and worst case scenarios. The estimation of CAVs platoon length is of much importance as it is the main factor driving capacity improvements on freeways, which under the appropriate conditions could exceed 10.000 vehicles per hour and lane.

A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation

The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.

Microscopic modeling of cyclists on off-street paths: a stochastic imitation learning approach

Accurate modeling of bicycles in microsimulation tools is challenging due to the limited availability of detailed data, complexity of cyclist decision-making, and heterogeneity in cycling behavior. This paper proposes an agent-based bicycle simulation method in which generative adversarial imitation learning (GAIL) is used to infer the uncertain intentions and heterogeneous preferences of cyclists from observational data. The model is tested on video-derived data of cyclists on a unidirectional path in Vancouver, Canada. In cross-validation, multivariate distributions of movement variables such as speed, direction, and spacing are similar between observed and simulated cyclist trajectories. The model also performs well in comparison to two other cyclist simulation models from the literature. The proposed approach to agent-based microsimulation is a significant advancement, with continuous, non-linear, and stochastic representation of cyclist states, decisions, and actions. The enhanced consideration of cyclist diversity is necessary for developing bicycle networks for all ages and abilities of riders.

Agent based simulation for the design of a mobility service in the Paris-Saclay area

Autonomous vehicles promise an important opportunity for a complete paradigm shift for the design of transportation systems. These systems will become more flexible and efficient by adapting to the travelers’ demand. On the other hand, multi agent systems offer an intuitive and powerful way to design complex distributed systems and agent based simulation has proved to be well suited for studying the performance of mobility services and their impact on the users and the traffic in general.In this study, we use an Agent-Based modeling approach to design a mobility system based on a fleet of autonomous vehicles (robo-taxis and shuttles) and connected road side units, each of these elements represented by intelligent collaborative agents. In our model, the control is distributed among the agents and the global behaviour of the system emerges from local decisions. We then used a microsimulation tool to test the model on the real road network of the Paris-Saclay area. Since this area is planned to become a center for technology, innovation and education and is currently under constant development, it offers an opportunity for the design and the implementation of new models of transportation services.We designed our mobility system in an incremental manner by introducing more components and more intelligent behaviour and testing its performance at each step.Our results suggest that for our area of study, a mobility service that relies on autonomous vehicles aided by connected road side units that allow to retrieve information about the traffic would perform better than a regular service.

Detecting, analysing, and modelling failed lane-changing attempts in traditional and connected environments

Lane-changing is a routine yet a complex driving task that has several negative impacts on both traffic flow efficiency and road safety, and thus, lane-changing models have become an indispensable part of microsimulation tools. The existing models only consider lane-changing manoeuvres that are successfully completed while failed lane-changing attempts (i.e., a lane-changing manoeuvre that is aborted after its initiation) are by and large ignored during calibration and validation of lane-changing models. This ignorance leads to structural incompleteness of lane-changing models. In addition, compared with successful lane-changing manoeuvres, failed lane-changing attempts are more likely to disrupt traffic flow and create safety hazards in both the current lane and the target lane, and thus, further warranting its consideration during lane-changing modelling. A connected environment can minimise these adverse effects by providing driving messages about surrounding traffic and subsequent gaps available in the target lane that drivers can utilise to make informed and safe lane-changing decisions. As such, this study investigates the impact of a connected environment on failed lane-changing attempts and addresses the issue of structural incompleteness of lane-changing models using three steps. First, a Wavelet Transform (WT)-based method is employed to detect failed lane-changing attempts from the real data. Second, the impact of failed lane-changing attempts is examined on both traffic flow efficiency and safety parameters such as average speed reduction and Deceleration Rate to Avoid a Crash (DRAC). Moreover, how a connected environment influences these parameters is also explored using a random parameters binary logistic model. Finally, failed lane-changing attempts are incorporated into the existing lane-changing models. At the first step, the WT-based method shows a reasonable accuracy in detecting failed lane-changing attempts when applied to NGSIM dataset and the driving simulator data collected in this study whereby drivers drove the CARRS-Q Advanced Driving Simulator and failed to complete a lane-changing manoeuvre in two randomised driving conditions: baseline (without driving messages) and connected environment (with driving messages). At the second step, we find that failed lane-changing attempts cause a higher speed reduction in both the current lane and the target lane compared to the successful ones. Similarly, a higher DRAC rate is required during failed lane-changing attempts compared to the successful lane-changing attempts, implying a higher crash risk during failed lane-changing attempts. Furthermore, the connected environment has shown to reduce not only the frequency of failed lane-changing attempts but also their negative impacts on surrounding traffic. Moreover, although the developed random parameters model reveals a significant heterogeneity at the individual level, suggesting that while the majority of the drivers tend to abort lane-changing manoeuvres with increase in the relative speed, this does not hold for a small portion of drivers. Finally, by incorporating failed lane-changing attempts into the existing lane-changing models, the predictive accuracy and realism of the lane-changing models have been enhanced.

Reducing the Non-Recurrent Freeway Congestion with Detour Operations: Case Study in Florida

To alleviate the impacts of freeway incidents and improve the traffic conditions on the entire transportation network, operational systems of discrete facilities need to be coordinated on a corridor. As such, this study focuses on a new traffic diversion methodology for a better utilization of the available traffic capacity of the corridor. The methodology aims to divert incident-induced freeway congestion to the adjacent arterials using the VISSIM microsimulation tool, which can simulate a freeway incident and measure the performance of detour operations. Within the study, an experiment with a 23 full factorial central composite design is utilized in order to define the optimum diversion rate in different demand levels. Experimental results are also modeled with the hierarchical multilevel regression model. Findings indicate that the resultant regression equation can successfully predict the corridor delay with 7 (s/veh) error and 83.85% accuracy. Traffic agencies can employ the proposed hierarchical model to decide whether or not a detour should be implemented. Furthermore, with the estimate of the incident duration and the current V/C ratio on the freeway, the diversion rate that results in a minimum corridor delay can be identified.

Examining failure rate dynamics to estimate capacity of bus rapid transit system corridor

Bus transit capacities are of two types: operational capacity (OC) and maximum capacity (MC). The OC is estimated based on the empirical failure rate (FR), whereas the MC is estimated considering a 25% maximum FR. The hypothesis of this study is that the maximum FR for a bus rapid transit system (BRTS) station would be more than 25%. The OC for a corridor of the BRTS in Ahmedabad, India was first estimated and then, to test the hypothesis, the Vissim microsimulation tool was used for evaluating two stages. Based on the simulation results, a maximum FR of 29% for a BRTS station with two loading areas is proposed. Apart from proposing the maximum FR, the paper also provides revised level-of-service ranges based on the FR for two loading areas at a BRTS station.

Neural Networks Applied to Microsimulation: A Prediction Model for Pedestrian Crossing Time

Walking is the original form of transportation, and pedestrians have always made up a significant share of transportation system users. In contrast to motorized traffic, which has to move on precisely defined lanes and follow strict rules, pedestrian traffic is not heavily regulated. Moreover, pedestrians have specific characteristics—in terms of size and protection—which make them much more vulnerable than drivers. In addition, the difference in speed between pedestrians and motorized vehicles increases their vulnerability. All these characteristics, together with the large number of pedestrians on the road, lead to many safety problems that professionals have to deal with. One way to tackle them is to model pedestrian behavior using microsimulation tools. Of course, modeling also raises questions of reliability, and this is also the focus of this paper. The aim of the present research is to contribute to improving the reliability of microsimulation models for pedestrians by testing the possibility of applying neural networks in the model calibration process. Pedestrian behavior is culturally conditioned and the adaptation of the model to local specifics in the calibration process is a prerequisite for realistic modeling results. A neural network is formulated, trained and validated in order to link not-directly measurable model parameters to pedestrian crossing time, which is given as output by the microsimulation tool. The crossing time of pedestrians passing the road on a roundabout entry leg has been both simulated and calculated by the network, and the results were compared. A correlation of 94% was achieved after both training and validation steps. Finally, tests were performed to identify the main parameters that influence the estimated crossing time.

Microsimulation and the Evaluation of Safety Levels in the Presence of Roadside Obstacles

Traffic microsimulation has been used for the analysis of road safety. In particular, some studies have confirmed that the reproduction by simulation of user behaviour under different traffic flows and geometry conditions can identify near crashes events that are a good base to estimate real crash risk. According to recent researches it appears that microsimulation tools can evaluate road safety performance and allow engineers to take appropriate countermeasures at specific points of the road network. The results of these approaches have been promising, though, all ongoing research has overlooked one important issue in the estimation of traffic safety levels: single vehicle crashes. According to statistics, collisions with fixed objects result in above 40% of all vehicle fatal crashes. Common used safety indicators are limited in their application since, in fact, they are based on conflicts techniques that do not consider roadside obstacles and barriers. The objective of this paper is to present a specific application of microsimulation software which is able to consider also potential conflicts with roadside objects. A specific microsimulation model add-on has been developed for the estimation of new road safety indicators that considers also potential crashes with roadside objects. First results are very promising and the developed software was applied on Tritone microsimulation package and can be used as an add-on also for other common used microsimulation packages such as Vissim and Aimsun.

Simulation of fuel poverty in France

The assessment of fuel poverty in mainland France is based mainly on data provided by the French national housing survey. However, the last two surveys date from 2006 and 2014. To understand the change in the number of fuel poverty households, we have developed a microsimulation tool that takes into account the three predominant factors in the notion of fuel poverty, that is, household resources, energy prices and dwelling quality. Our tool includes three multiple linear models for estimating the following: 1. disposable income; 2. energy expenditure; and 3. the probability of performing a thermal renovation. We test our model with real values for variation in energy prices and variation in disposable income and a realistic number of housing renovations. The model is calibrated to the two last French national housing survey, matches the data very well and closely reproduces the number in fuel poverty in the 2012/2014 period. We not only evaluate fuel poverty in France in 2018 but also study the effects of variations in the unemployment rate, energy prices and number of thermal renovations.

Exploring the impact of connected and autonomous vehicles on freeway capacity using a revised Intelligent Driver Model

ABSTRACTConnected and autonomous vehicle (CAV) technologies are expected to change driving/vehicle behavior on freeways. This study investigates the impact of CAVs on freeway capacity using a microsimulation tool. A four-lane basic freeway segment is selected as the case study through the Caltrans Performance Measurement System (PeMS). To obtain valid results, various driving behavior parameters are calibrated to the real traffic conditions for human-driven vehicles. In particular, the calibration is conducted using genetic algorithm. A revised Intelligent Driver Model (IDM) is developed and used as the car-following model for CAVs. The simulation is conducted on the basic freeway segment under different penetration rates of CAVs and different freeway speed limits. The results show that with an increase in the market penetration rate, freeway capacity increases, and will increase significantly as the speed limit increases.

Macroscopic Modeling of Connected Autonomous Vehicle Platoons under Mixed Traffic Conditions

Autonomous vehicles (AV) will be present in freeways, and they will have to share current infrastructure with human driven vehicles. This mixed traffic scenario needs to be planed and managed. Some research points that an AV mismanagement could lead to a capacity decrease. This can be solved with connected AV traveling in platoons. Some research exist to that end, but all of them is done using microsimulation tools. These are very powerful and detailed tools but have the shortcoming of strongly rely on an uncertain calibration and give limited insights to the problem. The more robust and simpler to understand macroscopic tools, have almost no platooning models yet. The research presented in this paper fills the gap, by providing a generalized macroscopic model to estimate the average length in vehicle for platoons. This is done by giving a set of rules for AV to form a platoon, including two different platooning schemes representing the best and worst case scenarios. This is of a key importance as greater platoon length is the main factor to drive capacity improvements on highways, which under the appropriate conditions can exceed 10.000 vehicles per hour and lane.

Use of Life Cycle Cost Analysis and Multiple Criteria Decision Aid Tools for Designing Road Vertical Profiles

The current design practice for the vertical profile of roads in rolling and mountainous terrains is to follow the existing grades in order to minimize earthwork costs. This means that the only criterion considered during the design phase is the initial cost. It would be preferable to include other criteria that are directly related to sustainability, particularly fuel consumption and CO2 emissions. Therefore, this paper describes a proposed design procedure that starts by finding feasible alternatives with different grades. Then, a microsimulation traffic tool is used to simulate the movement of predicted vehicles (volume and type) over the different alternatives. The microsimulation tool provides reliable estimates of travel times, fuel consumption, and CO2 emissions for the different alternatives. With these data, it is possible to use life cycle cost analysis (LCCA) or multiple criteria decision aid (MCDA) tools to select the “optimal” alternative. The proposed procedure was used on a case study involving a 6-km highway section with different proposed grades ranging from 2% to 8%. Using LCCA and an MCDA tool, it was revealed that the current design alternative is not the optimal alternative in most considered scenarios (various fuel values for LCCA and different “Cost” weights for MCDA).

In-Depth Investigation of Contributing Factors to Car-Motorcycle Accidents in Budapest City

Abstract Car-motorcycle accidents have been reported higher in recent years in Hungary due to increasing number of motorbikes on road. Car-motorcycle collisions mostly lead to fatal and seriously injured accidents mainly due to the vulnerability of motorcyclists and other related factors. The crash investigation studies aim to analyze the main contributing factors that cause fatal road accidents and injury outcomes. The main goal of this study is to evaluate and compare the contributing factors to car-motorcycle accidents in Budapest city by using a microsimulation tool. The procedure utilized the statistical analysis and data sampling to categorize car-motorcycle accidents by dominant accident types based on collision configurations. The police report is used as a data source for designated accidents and simulation models are plotted according to scale (M 1:200). The simulation crash study results observed the main contributing factors to car-motorcycle accidents such as driver behavior, rider behavior and view obstruction. The comprehensive in-depth investigation also found that most of the car drivers and riders could not perform collision avoidance manoeuvres before the collision. This study can help the traffic safety authorities to solve road safety issues by considering the main contributing factors to car-motorcycle collisions. The study also proposes safety measures to avoid car-motorcycle accidents in future.

Computation and Assessment of Environmental Emissions Resulting from Traffic Operations at Roundabouts

Now recognised as a dangerous side effect of increasing human activity and particularly the use of fossil fuels, is the impact on air quality, which has deteriorated considerably in recent times. In particular, the dramatic rise in road traffic has driven up the levels of air pollution in our towns and cities, leading to serious problems for both public health and the environment. To redress this, Environment Policies developed by the World Health Organisation (WHO) and European Commission (EC) have identified clean air as an essential requirement to human health and for the environment – a human right. Therefore, the need for managing emissions resulting from road traffic has seen significant attention. Coincidentally, the increases in the last decades of computational ability has enabled rapid development of transport modelling tools that can be integrated with environmental emissions packages. This has led to the increased ability to test the impact of road emissions on public health and environment. The computational ability to analyse the impact of traffic conditions for both current and forecast operations has enabled engineers to identify improved management and operation techniques for the road intersections with a view to reduce the emissions’ impact. This study presents a new and innovative approach to inform such forecast emissions, representing an approach to develop designs that can reduce emissions by linking various software packages as demonstrated at a trial site at a road intersection at Bath, United Kingdom. The technique linked the transport micro-simulation tool VISSIM with the environmental software EnViVer for emissions analysis. The models are also linked to Geographic Information System software, QGIS, to display changing levels of NOx, CO 2 and PM (Particulate Matter) under alternative intersection operational management regimes. This approach has enabled three important outcomes: 1) development of a methodology to directly test emissions and inform air quality thresholds using a series of software tools in an integrated manner, 2) enable the investigation of the air quality outcomes for alternative intersection designs and 3) to produce functions for intersection operational parameters that can predict NO X , CO 2 and PM savings. The results have demonstrated that the linking of the computational ability of micro-simulation modelling of road intersections with environmental package has been successful in analysis of operations. The ‘combined’ modelling approach has enabled design development of future intersection operation to with a view to minimising NO X , CO 2 and PM. This approach has brought together the crucial traffic parameters of road speed, delay and queuing and related them directly to change in emissions formulation, with the evidenced based prospect of adoption for similar studies in future. To put numbers to the achievement, the results have shown significant achievements for both the NO X level, which is projected to reduce by 14-34%, CO 2 by over 13-32% and PM by over 14-26% compared with the base conditions.

A cross-study on video data gathering and microsimulation techniques to estimate pedestrian safety level in a confined space

Nowadays, pedestrian safety is a growing problem: on the one hand pedestrian movement is increasing, on the other hand measures to improve walkers’ safety are still scarce. During the years, various behavioural models have been proposed, which consider several parameters characterizing both the environment and the road users. Many studies define accident risk as a combination of three main factors, which are the accident occurrence probability, the vulnerability of the involved users and the economic and social damages due to the accident. The approaches spotted in literature about pedestrian safety assessment are various and they mainly deal with vehicle-pedestrian accidents. These studies have been developed in order to foresee and reduce possible losses in human lives. Also pedestrian-pedestrian encroachments can lead to dangerous consequences, but researches about them are still limited. This study aims to highlight the correspondence and/or the differences between different analysis methods applied to pedestrian field, focusing on situations where no interactions with vehicular traffic exist. This is one step of a wider research, which has been carried out on a confined space, selected because of easy replicability of its general geometrical features and of the effects on pedestrian trajectories induced by some local peculiarities of the facility. The study focuses on the evaluation of a real case, which has been monitored through the use of cameras and analysed via a dedicated tool. The achieved results have been then compared to the outputs obtained by reconstructing the same situation in a micro-simulation model. The first step - video acquisition and elaboration - allowed to observe how people behave in the examined area and how the environment influences their trajectories, while the second phase permitted to understand if a microsimulation tool can reliably reply pedestrian movement in the analysed scenario and therefore provide surrogate safety values comparable to the ones obtained from real data. In the model, a simplified but still accurate environment has been set up: homogeneous geometric features have been drawn and no obstacles have been considered. The modelled pedestrian flow is a bi-directional, 2400 ped/h flux, characterized by heterogeneity of agents: both male and female adults. In order to be able to compare the data obtained by video footages and elaborated through an ad hoc tracking tool with outputs of the microsimulation model, from the whole flow on the ramp some intersecting pedestrians have been selected in both directions of walking. This cross-study of two different techniques has allowed to inspect the effects of the environment on pedestrian dynamics and to precautionary estimate the level of safety via calculation of surrogate safety parameters.