An experimental approach to traffic flow analysis is presented in which methodology from pattern recognition is applied to a specific dataset to examine its utility in determining traffic patterns. The selected dataset for this work, taken from a 1985 study by JHK and Associates (traffic research) for the Federal Highway Administration, covers an hour long time period over a quarter mile section and includes nine different identifying features for traffic at any given time. The initial step is to select the most pertinent of these features as a target for extraction and local storage during the experiment. The tools created for this approach, a two-level hierarchical group of operators, are used to extract features from the dataset to create a feature space; this is done to minimize the experimental set to a matrix of desirable attributes from the vehicles on the roadway. The application is to identify if this data can be readily parsed into four distinct traffic states; in this case, the state of a vehicle is defined by its velocity and acceleration at a selected timestamp. A three-dimensional plot is used, with color as the third dimension and seen from a top-down perspective, to initially identify vehicle states in a section of roadway over a selected section of time. This is followed by applying k-means clustering, in this case with k=4 to match the four distinct traffic states, to the feature space to examine its viability in determining the states of vehicles in a time section. The method’s accuracy is viewed through silhouette plots. Finally, a group of experiments run through a decision-tree architecture is compared to the kmeans clustering approach. Each decision-tree format uses sets of predefined values for velocity and acceleration to parse the data into the four states; modifications are made to acceleration and deceleration values to examine different results. The three-dimensional plots provide a visual example of congested traffic for use in performing visual comparisons of the clustering results. The silhouette plot results of the k-means experiments show inaccuracy for certain clusters; on the other hand, the decision-tree work shows promise for future work.

With the recent widespread deployment of intelligent transportation systems (ITS) in North America there is an abundance of data on traffic systems and thus an opportunity to use these data in the calibration of microscopic traffic simulation models. Even though ITS data have been utilized to some extent in the calibration of microscopic traffic simulation models, efforts have focused on improving the quality of the calibration based on aggregate form of ITS data rather than disaggregate data. In addition, researchers have focused on identifying the parameters associated with car-following and lane-changing behavior models and their impacts on overall calibration performance. Therefore, the estimation of the Origin-Destination (OD) matrix has been considered as a preliminary step rather than as a stage that can be included in the calibration process. This research develops a methodology to calibrate the OD matrix jointly with model behavior parameters using a bi-level calibration framework. The upper level seeks to identify the best model parameters using a genetic algorithm (GA). In this level, a statistically based calibration objective function is introduced to account for disaggregate form of ITS data in the calibration of microscopic traffic simulation models and, thus, accurately replicate dynamics of observed traffic conditions. Specifically, the Kolmogorov-Smirnov test is used to measure the "consistency" between the observed and simulated travel time distributions. The calibration of the OD matrix is performed in the lower level, where observed and simulated travel times are incorporated into the OD estimator for the calibration of the OD matrix. The interdependent relationship between travel time information and the OD matrix is formulated using a Extended Kalman filter (EKF) algorithm, which is selected to quantify the nonlinear dependence of the simulation results (travel time) on the OD matrix. The two test sites are from an urban arterial and a freeway in Houston, Texas. The VISSIM model was used to evaluate the proposed methodologies. It was found that that the accuracy of the calibration can be improved by using disaggregated data and by considering both driver behavior parameters and demand.

An experimental approach to traffic flow analysis is presented in which methodology from pattern recognition is applied to a specific dataset to examine its utility in determining traffic patterns. The selected dataset for this work, taken from a 1985 study by JHK and Associates (traffic research) for the Federal Highway Administration, covers an hour long time period over a quarter mile section and includes nine different identifying features for traffic at any given time. The initial step is to select the most pertinent of these features as a target for extraction and local storage during the experiment. The tools created for this approach, a two-level hierarchical group of operators, are used to extract features from the dataset to create a feature space; this is done to minimize the experimental set to a matrix of desirable attributes from the vehicles on the roadway. The application is to identify if this data can be readily parsed into four distinct traffic states; in this case, the state of a vehicle is defined by its velocity and acceleration at a selected timestamp. A three-dimensional plot is used, with color as the third dimension and seen from a top-down perspective, to initially identify vehicle states in a section of roadway over a selected section of time. This is followed by applying k-means clustering, in this case with k=4 to match the four distinct traffic states, to the feature space to examine its viability in determining the states of vehicles in a time section. The method's accuracy is viewed through silhouette plots. Finally, a group of experiments run through a decision-tree architecture is compared to the kmeans clustering approach. Each decision-tree format uses sets of predefined values for velocity and acceleration to parse the data into the four states; modifications are made to acceleration and deceleration values to examine different results. The three-dimensional plots provide a visual example of congested traffic for use in performing visual comparisons of the clustering results. The silhouette plot results of the k-means experiments show inaccuracy for certain clusters; on the other hand, the decision-tree work shows promise for future work.

Recent advances in the optimization of fixed time traffic signals have demonstrated a move towards the use of genetic algorithm optimization with traffic network performance evaluated via stochastic microscopic simulation models. This dissertation examines methods for improved optimization. Several modified versions of the genetic algorithm and alternative genetic operators were evaluated on test networks. A traffic simulation model was developed for assessment purposes. Application of the CHC search algorithm with real crossover and mutation operators were found to offer improved optimization efficiency over the standard genetic algorithm with binary genetic operators. Computing resources are best utilized by using a single replication of the traffic simulation model with common random numbers for fitness evaluations. Combining the improvements, delay reductions between 13%-32% were obtained over the standard approaches. A coding scheme allowing for complete optimization of signal phasing is proposed and a statistical model for comparing genetic algorithm optimization efficiency on stochastic functions is also introduced. Alternative delay measurements, amendments to genetic operators and modifications to the CHC algorithm are also suggested.

We design an Ordinary Delay Differential Equation model for car to car interaction with switching between four distinct force terms including "free acceleration'', "follow acceleration'', "follow braking'', and aggressive driving''. We calibrate this model by recreating a real experiment on spontaneous formation of traffic jams. Once simulations of our model match those of the experiment we develop a model of both intersections using traffic lights, and intersections using roundabouts. Using our calibrated car interaction model we compare traffic light versus roundabout efficiencies in both flux and fuel consumption. We also use simulation results to extract information relevant to macroscopic traffic models. A relationship between flux and density known as The Fundamental Diagram is derived, and we discuss a technique for comparing microscopic to macroscopic models. / Graduate / 0405 / gmcgrego@uvic.ca

Microscopic traffic simulation has proven to be a useful tool for analysis of varioustraffic systems. This thesis consider microscopic traffic simulation of rural roads andthe use of traffic simulation for evaluation of driver assistance systems. A traffic simulation modelling framework for rural roads, the Rural Traffic Simulator(RuTSim), is developed. RuTSim is designed for simulation of traffic on singlecarriageway two-lane rural roads and on rural roads with separated oncoming trafficlanes. The simulated traffic may be interrupted by vehicles entering and leaving themodelled road at intersections or roundabouts. The RuTSim model is applied for analysis of rural road design alternatives.Quality-of-service effects of three alternatives for oncoming lane separation of anexisting Swedish two-lane road are analysed. In another model application, RuTSimis used to simulate traffic on a Dutch two-lane rural road. This application illustratesthat the high level of model detail of traffic micro-simulation may call for use of differentmodelling assumptions regarding driver behaviour for different applications,e. g. for simulation of traffic in different cultural regions. The use of traffic simulation for studies of driver assistance systems facilitateimpact analyses already at early stages of the system development. New and additionalrequirements are however then placed on the traffic simulation model. It isnecessary to model both the system functionality of the considered driver assistancesystem and the driver behaviour in system equipped vehicles. Such requirements canbe analysed using RuTSim. In this thesis, requirements on a traffic simulation model to be used for analysisof road safety effects of driver assistance systems are formulated and investigatedusing RuTSim. RuTSim is also applied for analyses of centre line rumble stripson two-lane roads, of an overtaking assistant and of adaptive cruise control. Thesestudies establish that the assumptions made regarding driver behaviour are crucialfor traffic simulation based analyses of driver assistance systems.

Microscopic traffic simulation software have gained significant popularity and are widely used both in industry and research mainly because of the ability of these tools to reflect the dynamic nature of the transportation system in a stochastic fashion. To better utilize these software, it is necessary to understand the underlying logic and differences between them. A Car-following model is the core of every microscopic traffic simulation software. In the context of this research, the thesis develops procedures for calibrating the steady-state car-following models in a number of well known microscopic traffic simulation software including: CORSIM, AIMSUN, VISSIM, PARAMICS and INTEGRATION and then compares the VISSIM and INTEGRATION software for the modeling of traffic signalized approaches. The thesis presents two papers. The first paper develops procedures for calibrating the steady-state component of various car-following models using macroscopic loop detector data. The calibration procedures are developed for a number of commercially available microscopic traffic simulation software, including: CORSIM, AIMSUN2, VISSIM, Paramics, and INTEGRATION. The procedures are then applied to a sample dataset for illustration purposes. The paper then compares the various steady-state car-following formulations and concludes that the Gipps and Van Aerde steady-state car-following models provide the highest level of flexibility in capturing different driver and roadway characteristics. However, the Van Aerde model, unlike the Gipps model, is a single-regime model and thus is easier to calibrate given that it does not require the segmentation of data into two regimes. The paper finally proposes that the car-following parameters within traffic simulation software be link-specific as opposed to the current practice of coding network-wide parameters. The use of link-specific parameters will offer the opportunity to capture unique roadway characteristics and reflect roadway capacity differences across different roadways. Second, the study compares the logic used in both the VISSIM and INTEGRATION software, applies the software to some simple networks to highlight some of the differences/similarities in modeling traffic, and compares the various measures of effectiveness derived from the models. The study demonstrates that both the VISSIM and INTEGRATION software incorporate a psycho-physical car-following model which accounts for vehicle acceleration constraints. The INTEGRATION software, however uses a physical vehicle dynamics model while the VISSIM software requires the user to input a vehicle-specific speed-acceleration kinematics model. The use of a vehicle dynamics model has the advantage of allowing the model to account for the impact of roadway grades, pavement surface type, pavement surface condition, and type of vehicle tires on vehicle acceleration behavior. Both models capture a driver's willingness to run a yellow light if conditions warrant it. The VISSIM software incorporates a statistical stop/go probability model while current development of the INTEGRATION software includes a behavioral model as opposed to a statistical model for modeling driver stop/go decisions. Both software capture the loss in capacity associated with queue discharge using acceleration constraints. The losses produced by the INTEGRATION model are more consistent with field data (7% reduction in capacity). Both software demonstrate that the capacity loss is recovered as vehicles move downstream of the capacity bottleneck. With regards to fuel consumption and emission estimation the INTEGRATION software, unlike the VISSIM software, incorporates a microscopic model that captures transient vehicle effects on fuel consumption and emission rates. / Master of Science

Ground transportation systems are essential for the mobility of people, goods and services. Thus, making sure these systems are resilient to the impact of natural and man-made disasters has become a top priority for engineers and policy makers. One of the major obstacles for increasing the resilience of ground transportation systems is the lack of a measuring framework. Such measuring framework is critical for identifying needs, monitoring changes, assessing improvements, and performing cost-benefit analysis. This research addresses this problem by developing a traffic-based framework for measuring the resilience of ground transportation systems under normal and extreme conditions. The research methodology consisted of: (1) creating a microscopic traffic model of the road under study, (2) simulating different intrusions and interventions, and (3) measuring the resilience of the system under the different scenarios using the framework developed. This research expanded the current definition of infrastructure resilience, which includes the assessment of system performance versus time, to add a third dimension of resilience for ground transportation system's applications, namely: location. This third dimension considers how the system changes along the different locations in the network, which reflects more accurately the continuous behavior of a ground transportation network. The framework was tested in a 24 km segment of Interstate 95 in Virginia, near Washington, D.C. Four hazard conditions were simulated: inadequate base capacity, traffic incidents, work zones, and weather events. Intervention strategies tested include ramp meters and the use of the shoulder lane during extreme events. Public policy was also considered as a powerful intervention strategy. The findings of this research shed light over the current and future resilience of ground transportation systems when subject to multiple hazards, and the effects of implementing potential interventions. / Ph. D. / Ground transportation systems are essential for the mobility of people, goods and services. Thus, making sure these systems are <i>resilient</i> to the impact of natural and manmade disasters has become a top priority for engineers and policy makers. Disaster resilience is defined as the ability of a system to withstand the impact of a disaster and recover as quickly as possible. One of the major obstacles for increasing the resilience of ground transportation systems is the lack of a measuring framework. Such measuring framework is critical for identifying needs, monitoring changes, assessing improvements, and performing cost-benefit analysis. This research addresses this problem by developing a traffic-based framework for measuring the resilience of ground transportation systems under normal and extreme conditions. The research methodology consisted of: (1) creating a microscopic traffic model of the road under study, (2) simulating multiple hazards and mitigation strategies, and (3) measuring the resilience of the system under the different scenarios using the framework developed. This research expanded the current definition of infrastructure resilience, which includes the assessment of system performance versus time, to add a third dimension of resilience for ground transportation system’s applications, namely: location. This third dimension considers how the system changes along the different locations in the network, which reflects the continuous behavior of a ground transportation network. The framework was tested in a 24 km segment of Interstate 95 in Virginia, near Washington, D.C. Four hazard conditions were simulated: inadequate base capacity, traffic incidents, work zones, and weather events. Intervention strategies tested include ramp meters and the use of shoulder lanes. Public policy was also considered as a powerful intervention strategy. The findings of this research shed light over the current and future resilience of ground transportation systems when subject to multiple hazards, and the effects of implementing potential interventions.

Everyone has had the experience of sitting in a traffic jam, or of seeing cars bunch up on a road for no apparent good reason. Nowadays the quantity of vehicles in the Lithuanian cities are growing very fast. The necessity of circumstantial territory analysis emerged for increasing Transportation Systems Control demand. Microscopic traffic flow models are important for analyzing individual crossroads and assessing interaction between some vehicles. In this paper there are analyzed microscopic traffic flow model. In this paper there answered what effect is produced by small differences in the velocities of the cars. And we have that microscopic traffic flow is in stability when kT < 0.5 and not in stability when kT > 0.5. Its very important for modeling microscopic traffic flow model. In this paper there are shown that microscopic traffic flow in K. Mindaugo – Birštono streets has Puasson distribution. There are made microscopic traffic flow simulation model of K. Mindaugo – Birštno streets crossroad by Arena 3.0 program and performed regression analysis for prognosticating microscopic traffic flow queues.

Vehicles that today are searching for a place to stop impacts other vehicles in cities. It can also be seen that the number of vehicles that desire to conduct a pick-up or drop-off increases with an increased number of ride-hailing services. New technology routing advises for such vehicles could improve the overall performance of a traffic network. This thesis analyses therefore how a routing strategy, that incorporates curbside availability information, can impact the performance. To analyse the effects of how curbside availability information can impact a network, a microscopic traffic simulation model was constructed in PTV Vissim and two different routing strategies were developed and implemented in the model. One strategy that represents the scenario of today where vehicles searches the traffic network while attempting to make a stop at a pick-up and drop-off slot. The second strategy routes vehicles to a slot based on curbside availability information. This strategy directs vehicles to an available slot and therefore reduces the time a vehicle is cruising before a stop has been made. A simulation experiment was set-up to compare the strategies that were developed with different penetration rates of vehicles that desired to stop. The results shows that the average travel time can be reduced with up to 25.2\% when vehicles have information compared to the scenario with no information. Similar findings is identified for average delay per vehicle which is reduced with up to 49.0\% and average traveled distance decreased with up to 15.5\%. The results of this thesis needs however to be studied in a wider context in order to draw reliable conclusions. The thesis propose further investigations whether a strategy that incorporates availability information can be implemented in a real world scenario and further investigations whether an implementation of a strategy like this would be socioeconomic beneficial.