Eine realistische Simulation von Verkehrsabläufen kann einen wichtigen Beitrag zur Verkehrsplanung leisten. In dieser Arbeit wird dafür in den bestehenden mikroskopischen Simulator MovSim ein Kreisverkehr implementiert. Anschließende Analysen, die unter Verwendung des Intelligent-Driver-Modells und des Spurwechselmodells MOBIL durchgeführt wurden, legen nahe, dass die relativen Ergebnisse, die sich durch Veränderung des Verkehrsflusses und der Modellparameter ergeben, realitätsnah sind. Zudem konnte gezeigt werden, dass der Einfluss des Verkehrsstromes in der Kreisfahrbahn im Handbuch für die Bemessung von Straßenverkehrsanlagen deutlich stärker ist als in der Simulation.

Automated shuttles are designed to provide a clean transportation and improve access to areas such as where travelers have to walk long distances to/from bus stops. The introduction of automated shuttles in the road network might affect the safety of pedestrians and cyclists as well as traffic performance of motorized vehicles. Several demonstration trials are being conducted to study how automated shuttles operate in real traffic conditions, but they are limited to few vehicles and evaluations of traffic effects at higher penetration rates are not possible. Traffic simulation is a tool that can be used to study effects on traffic performances at different penetration rates of e.g., automated shuttles. However, automated shuttles have not yet been modeled, calibrated, and validated in microscopic traffic simulation tools. Therefore, the objective of this thesis is to model, calibrate and validate automated shuttle’s behavior using the simulation tool SUMO and data collected from the demonstration trial on the area of campus Valla Linköping University, Sweden. The pilot study consists of two automated shuttles, and they operate on a 2.1 km fixed route. The collected data by one of the automated shuttles is analyzed with a focus on the free driving behavior. The analysis shows that the automated shuttle has different maximum operation speeds at different locations and defining one value for the maximum speed when setting up the simulation is not enough. Therefore, virtual speed limits are derived by mimicking the maximum operation speed of the shuttle from the data and used to define segment specific speed limits in the simulation. Additionally, the data is used to calibrate the acceleration and deceleration parameters. The Krauss and the IDM car-following models have been investigated by calibrating the acceleration and deceleration parameters for the free driving situation. The results indicate that both the Krauss and IDM car-following models follows the general trend of the speed and acceleration profiles. The speed profiles produced with the IDM model have smoother profiles at the start and end of acceleration and deceleration phases while in Krauss model the transition of the speed change is more direct and there are in principle no delays for reaction. Although the IDM model performs slightly better for the free driving situation, it can be of interest to consider both models for the calibration of interactions with other roads users since both models are able to capture the general trend of the speed and acceleration profiles. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

The inputs to a microscopic traffic simulation model generally include quantitative, but immeasurable data describing driver behavior and vehicle performance characteristics. Engineers often use default values for parameters such as car-following sensitivity and gap acceptance because it can be difficult to obtain accurate estimates for these parameters. While recent research has indicated that the accuracy of a simulation model can significantly improve if driver behavior parameters are calibrated to local data, this is not a typical practice. Manual calibration of these parameters is often too time-consuming to be cost-effective. Researchers have applied automated calibration procedures using genetic algorithms to these problems with some success, but many engineers lack the tools or the skill set necessary to easily program and implement such a procedure. A graphical user interface (GUI) for a genetic algorithm would make automated calibration much more accessible to students and professional engineers. Another barrier that limits the practicality of calibrating driver behavior parameters is the number of available calibration parameters. The CORSIM (short for CORridor SIMulation) model developed by the Federal Highway Administration contains dozens of optional calibration parameters controlling various aspects of driver behavior. Determining the sensitivity of the model to these parameters is an important step toward finding the appropriate parameter values. The purpose of this thesis is to develop a GUI for a genetic algorithm and perform needed sensitivity analyses to aid in model development and calibration. This thesis tests a simple, automated procedure utilizing a genetic algorithm for the calibration of driver behavior and vehicle performance parameters that can easily be applied by engineers in the field. An existing genetic algorithm script that has been applied to other research has been adapted to fit the purposes of this thesis. As part of this procedure, a sensitivity analysis was performed to recommend which parameters should be included in model calibration. The results of the research suggest that fewer than half of the available driver behavior parameters are necessary to calibrate a model of a linear freeway network. The calibration tests also demonstrate the importance of calibrating to at least two measures of effectiveness in order to better match existing conditions and provide an acceptable level of error for the simulation model.

This dissertation addresses Internet background traffic generation and network intrusion detection. It is organized in two parts. Part one introduces a method to model realistic Internet background traffic and demonstrates how the models are used both in a simulation environment and in a lab environment. Part two introduces two different NID (Network Intrusion Detection) techniques and evaluates them using the modeled background traffic. To demonstrate the approach we modeled five major application layer protocols: HTTP, FTP, SSH, SMTP and POP3. The model of each protocol includes an empirical probability distribution plus estimates of application-specific parameters. Due to the complexity of the traffic, hybrid distributions (called mixture distributions) were sometimes required. The traffic models are demonstrated in two environments: NS-2 (a simulator) and HONEST (a lab environment). The simulation results are compared against the original captured data sets. Users of HONEST have the option of adding network attacks to the background. The dissertation also introduces two new template-based techniques for network intrusion detection. One is based on a template of autocorrelations of the investigated traffic, while the other uses a template of correlation integrals. Detection experiments have been performed on real traffic and attacks; the results show that the two techniques can achieve high detection probability and low false alarm in certain instances.

Traffic safety has become the first concern in the transportation area. Crashes have cause extensive human and economic losses. With the objective of reducing crash occurrence and alleviating crash injury severity, major efforts have been dedicated to reveal the hazardous factors that affect crash occurrence at both the aggregate (targeting crash frequency per segment, intersection, etc.,) and disaggregate levels (analyzing each crash event). The aggregate traffic safety studies, mainly developing safety performance functions (SPFs), are being conducted for the purpose of unveiling crash contributing factors for the interest locations. Results of the aggregate traffic safety studies can be used to identify crash hot spots, calculate crash modification factors (CMF), and improve geometric characteristics. Aggregate analyses mainly focus on discovering the hazardous factors that are related to the frequency of total crashes, of specific crash type, or of each crash severity level. While disaggregate studies benefit from the reliable surveillance systems which provide detailed real-time traffic and weather data. This information could help in capturing microlevel influences of the hazardous factors which might lead to a crash. The disaggregate traffic safety models, also called real-time crash risk evaluation models, can be used in monitoring crash hazardousness with the real-time field data fed in. One potential use of real-time crash risk evaluation models is to develop Variable Speed Limits (VSL) as a part of a freeway management system. Models have been developed to predict crash occurrence to proactively improve traffic safety and prevent crash occurrence. iv In this study, first, aggregate safety performance functions were estimated to unveil the different risk factors affecting crash occurrence for a mountainous freeway section. Then disaggregate real-time crash risk evaluation models have been developed for the total crashes with both the machine learning and hierarchical Bayesian models. Considering the need for analyzing both aggregate and disaggregate aspects of traffic safety, systematic multi-level traffic safety studies have been conducted for single- and multi-vehicle crashes, and weekday and weekend crashes. Finally, the feasibility of utilizing a VSL system to improve traffic safety on freeways has been investigated. This research was conducted based on data obtained from a 15-mile mountainous freeway section on I-70 in Colorado. The data contain historical crash data, roadway geometric characteristics, real-time weather data, and real-time traffic data. Real-time weather data were recorded by 6 weather stations installed along the freeway section, while the real-time traffic data were obtained from the Remote Traffic Microwave Sensor (RTMS) radars and Automatic Vechicle Identification (AVI) systems. Different datasets have been formulated from various data sources, and prepared for the multi-level traffic safety studies. In the aggregate traffic safety investigation, safety performance functions were developed to identify crash occurrence hazardous factors. For the first time real-time weather and traffic data were used in SPFs. Ordinary Poisson model and random effects Poisson models with Bayesian inference approach were employed to reveal the effects of weather and traffic related variables on crash occurrence. Two scenarios were considered: one seasonal based case and one crash type v based case. Deviance Information Criterion (DIC) was utilized as the comparison criterion; and the correlated random effects Poisson models outperform the others. Results indicate that weather condition variables, especially precipitation, play a key role in the safety performance functions. Moreover, in order to compare with the correlated random effects Poisson model, Multivariate Poisson model and Multivariate Poisson-lognormal model have been estimated. Conclusions indicate that, instead of assuming identical random effects for the homogenous segments, considering the correlation effects between two count variables would result in better model fit. Results from the aggregate analyses shed light on the policy implication to reduce crash frequencies. For the studied roadway segment, crash occurrence in the snow season have clear trends associated with adverse weather situations (bad visibility and large amount of precipitation); weather warning systems can be employed to improve road safety during the snow season. Furthermore, different traffic management strategies should be developed according to the distinct seasonal influence factors. In particular, sites with steep slopes need more attention from the traffic management center and operators especially during snow seasons to control the excess crash occurrence. Moreover, distinct strategy of freeway management should be designed to address the differences between single- and multi-vehicle crash characteristics. In addition to developing safety performance functions with various modeling techniques, this study also investigates four different approaches of developing informative priors for the independent variables. Bayesian inference framework provides a complete and coherent way to balance the empirical data and prior expectations; merits of these informative priors have been tested along with two types of Bayesian hierarchical models (Poisson-gamma and Poisson- vi lognormal models). Deviance Information Criterion, R-square values, and coefficients of variance for the estimations were utilized as evaluation measures to select the best model(s). Comparisons across the models indicate that the Poisson-gamma model is superior with a better model fit and it is much more robust with the informative priors. Moreover, the two-stage Bayesian updating informative priors provided the best goodness-of-fit and coefficient estimation accuracies. In addition to the aggregate analyses, real-time crash risk evaluation models have been developed to identify crash contributing factors at the disaggregate level. Support Vector Machine (SVM), a recently proposed statistical learning model and Hierarchical Bayesian logistic regression models were introduced to evaluate real-time crash risk. Classification and regression tree (CART) model has been developed to select the most important explanatory variables. Based on the variable selection results, Bayesian logistic regression models and SVM models with different kernel functions have been developed. Model comparisons based on receiver operating curves (ROC) demonstrate that the SVM model with Radial basis kernel function outperforms the others. Results from the models demonstrated that crashes are likely to happen during congestion periods (especially when the queuing area has propagated from the downstream segment); high variation of occupancy and/or volume would increase the probability of crash occurrence. Moreover, effects of microscopic traffic, weather, and roadway geometric factors on the occurrence of specific crash types have been investigated. Crashes have been categorized as rear- vii end, sideswipe, and single-vehicle crashes. AVI segment average speed, real-time weather data, and roadway geometric characteristics data were utilized as explanatory variables. Conclusions from this study imply that different active traffic management (ATM) strategies should be designed for three- and two-lane roadway sections and also considering the seasonal effects. Based on the abovementioned results, real-time crash risk evaluation models have been developed separately for multi-vehicle and single-vehicle crashes, and weekday and weekend crashes. Hierarchical Bayesian logistic regression models (random effects and random parameter logistic regression models) have been introduced to address the seasonal variations, crash unit level’s diversities, and unobserved heterogeneity caused by geometric characteristics. For the multi-vehicle crashes: congested conditions at downstream would contribute to an increase in the likelihood of multi-vehicle crashes; multi-vehicle crashes are more likely to occur during poor visibility conditions and if there is a turbulent area that exists downstream. Drivers who are unable to reduce their speeds timely are prone to causing rear-end crashes. While for the singlevehicle crashes: slow moving traffic platoons at the downstream detector of the crash occurrence locations would increase the probability of single-vehicle crashes; large variations of occupancy downstream would also increase the likelihood of single-vehicle crash occurrence. Substantial efforts have been dedicated to revealing the hazardous factors that affect crash occurrence from both the aggregate and disaggregate level in this study, however, findings and conclusions from these research work need to be transferred into applications for roadway design and freeway management. This study further investigates the feasibility of utilizing Variable Speed Limits (VSL) system, one key part of ATM, to improve traffic safety on freeways. A proactive traffic safety improvement VSL control algorithm has been proposed. First, an viii extension of the traffic flow model METANET was employed to predict traffic flow while considering VSL’s impacts on the flow-density diagram; a real-time crash risk evaluation model was then estimated for the purpose of quantifying crash risk; finally, the optimal VSL control strategies were achieved by employing an optimization technique of minimizing the total predicted crash risks along the VSL implementation area. Constraints were set up to limit the increase of the average travel time and differences between posted speed limits temporarily and spatially. The proposed VSL control strategy was tested for a mountainous freeway bottleneck area in the microscopic simulation software VISSIM. Safety impacts of the VSL system were quantified as crash risk improvements and speed homogeneity improvements. Moreover, three different driver compliance levels were modeled in VISSIM to monitor the sensitivity of VSL’s safety impacts on driver compliance levels. Conclusions demonstrate that the proposed VSL system could effectively improve traffic safety by decreasing crash risk, enhancing speed homogeneity, and reducing travel time under both high and moderate driver compliance levels; while the VSL system does not have significant effects on traffic safety enhancement under the low compliance scenario. Future implementations of VSL control strategies and related research topics were also discussed.

A transportation system designed to prioritize the mobility of automobiles cannot accommodate the growing number of road users. The Complete Streets policy plays a crucial part in transforming streets to accommodate multiple modes of transportation, especially active modes like biking and walking. Complete streets are referred to as streets designed for everyone and enable safety and mobility to all users. A strategy of complete streets transformation is to connect isolated complete street segments to form a complete network that improves active mobility and public transit ridership. This research assessed the impact of efficiently and equitably connecting and expanding the biking network using dedicated lanes on the safety and operation of the network in Atlanta, Georgia. These connections are aimed at increasing the multimodal use of the streets in midtown and downtown Atlanta and achieving the mobility and public health goals through the integration of various modes of travel. The evaluation was done by modeling a well-calibrated and validated network of Midtown and Downtown Atlanta in VISSIM using existing travel demand and traffic design conditions (i.e., the baseline or Scenario 0). A total of three different conditions: existing, proposed, and alternative conditions, were modeled to see the effectiveness of bike infrastructure design improvement and expansion. Three scenarios were then modeled as variations of modal demand of the different condition models. Scenarios modeled are based on input from the City and Community stakeholders. Using the trajectory data from microsimulation, the surrogate safety assessment model (SSAM) from FHWA was used to analyze the safety effect on the bike infrastructure improvement and expansion. Results of this study showed a positive impact of complete streets transformation on the streets of Midtown and Downtown Atlanta. These impacts are quantified in this thesis.

No description available.

Wireless operators adopting third-generation (3G) technologies and those migrating from second-generation (2G) to 3G face a number of challenges related to traffic modeling, demand characterization, and performance analysis, which are key elements in the processes of designing, dimensioning and optimizing their network infrastructure. Traditional traffic modeling assumptions used for circuit-switched voice traffic no longer hold true with the convergence of voice and data over packet-switched infrastructures. Self-similar models need to be explored to appropriately account for the burstiness that packet traffic is expected to exhibit in all time scales. The task of demand characterization must include an accurate description of the multiple user profiles and service classes the network is expected to support, with their distinct geographical distributions, as well as forecasts of how the market should evolve over near and medium terms. The appropriate assessment of the quality of service becomes a more complex issue as new metrics and more intricate dependencies have to be considered when providing a varying range of services and applications that include voice, real-time, and non-real time data. All those points have to be considered by the operator to obtain a proper dimensioning, resource allocation, and rollout plan for system deployment. Additionally, any practical optimization strategy has to rely on accurate estimates of expected demand and growth in demand. In this research, we propose a practical framework to characterize the traffic offered to multimedia wireless systems that allows proper dimensioning and optimization of the system for a particular demand scenario. The framework proposed includes a methodology to quantitatively and qualitatively describe the traffic offered to multimedia wireless systems, solutions to model that traffic as practical inputs for simulation analysis, and investigation of demand-sensitive techniques for system dimensioning and performance optimization. We consider both theoretical and practical aspects related to the dimensioning of hybrid traffic (voice and data) for mobile wireless networks. We start by discussing wireless systems and traffic theory, with characterization of the main metrics and models that describe the users’ voice and data demand, presenting a review of the most recent developments in the area. The concept of service class is used to specify parameters that depend on the application type, performance requirements and traffic characteristics for a given service. Then we present the concept of “user profile,“ which ties together a given combination of service class, propagation environment and terminal type. Next, we propose a practical approach to explore the dynamics of user geographical distribution in creating multi-service, multi-class traffic layers that serve as input for network traffic simulation algorithms. The concept of quality-of-service (QoS) is also discussed, focusing on the physical layer for 3G systems. We explore system simulation as a way to dimension a system given its traffic demand characterization. In that context, we propose techniques to translate geographical distributions of user profiles into the actual number of active users of each layer, which is the key parameter to be used as input in simulations. System level simulations are executed for UMTS systems, with the purpose of validating the methodology proposed here. We complete the proposed framework by applying all elements together in the process of dimensioning and optimization of 3G wireless networks using the demand characterization for the system as input. We investigate the effects of modifying some elements in the system configuration such as network topology, radio-frequency (RF) configuration, and radio resource management (RRM) parameters, using strategies that are sensitive to traffic geographical distribution. Case study simulations are performed for Universal Mobile Telecommunications System (UMTS) networks, and multiple system variables (such as antenna tilts, pilot powers, and RRM parameters) are optimized using traffic sensitive strategies, which result in significant improvements in the overall system capacity and performance. Results obtained in the case studies, allied to a generic discussion of the trade-offs involved in the proposed framework, demonstrate the close dependence between the processes of system dimensioning and optimization with the accurate modeling of traffic demand offered to the system. / Ph. D.

This research explores the feasibility of providing innovative and effective solutions for traffic congestion. The design of reservation systems is being considered as an alternative and/or complementary travel demand management (TDM) strategy. A reservation indicates that a user will follow a booking procedure defined by the reservation system before traveling so as to obtain the right to access a facility or resource. In this research, the reservation system is introduced for a cordon-based downtown road network, hereafter called the Downtown Space Reservation System (DSRS). The research is executed in three steps. In the first step, the DSRS is developed using classic optimization techniques in conjunction with an artificial intelligence technology. The development of this system is the foundation of the entire research, and the second and third steps build upon it. In the second step, traffic simulation models are executed so as to assess the impact of the DSRS on a hypothetical transportation road network. A simulation model provides various transportation measures and helps the decision maker analyze the system from a transportation perspective. In this step, multiple simulation runs (demand scenarios) are conducted and performance insights are generated. However, additional performance measurement and system design issues need to be addressed beyond the simulation paradigm. First, it is not the absolute representation of performance that matters, but the concept of relative performance that is important. Moreover, a simulation does not directly demonstrate how key performance measures interact with each other, which is critical when trying to understand a system structure. To address these issues, in the third step, a comprehensive performance measurement framework has been applied. An analytical technique for measuring the relative efficiency of organizational units, or in this case, demand scenarios called network Data Envelopment Analysis (DEA), is used. The network model combines the perspectives of the transportation service provider, the user and the community, who are the major stakeholders in the transportation system. This framework enables the decision maker to gain an in-depth appreciation of the system design and performance measurement issues. / Ph. D.

This dissertation seeks to advance the modeling of network flows over time for the purposes of improving evacuation planning. The devastation created by Hurricanes Katrina and Rita along the Gulf Coast of the United States in 2005 have recently emphasized the need to improve evacuation modeling and planning. The lessons learned from these events, and similar past emergencies, have highlighted the problem of congestion on the interstate and freeways during an evacuation. The intent of this research is to develop evacuation demand management strategies that can reduce congestion, delay, and ultimately save lives during regional evacuations. The primary focus of this research will concern short-notice evacuations, such as hurricane evacuations, conducted by automobiles. Additionally, this dissertation addresses some traffic flow and optimization deficiencies concerning the modeling of congested network flows. This dissertation is a compilation of three manuscripts. Chapters 3 and 4 examine modeling network flows over time with congestion. Chapter 3 demonstrates the effects of congestion on flows using a microscopic traffic simulation software package, INTEGRATION. The flow reductions from the simulation are consistent with those found in several empirical studies. The simulation allows for the examination of the various contributing factors to the flow reductions caused by congestion, including level of demand, roadway geometry and capacity, vehicle dynamics, traffic stream composition, and lane changing behavior. Chapter 4 addresses some of the modeling and implementation issues encountered in evacuation planning and presents an improved modeling framework that reduces network flows due to congestion. The framework uses a cell-based linear traffic flow model within a mixed integer linear program (MILP) to model network flows over time in order to produce sets of decisions for use within an evacuation plan. The traffic flow model is an improvement based upon the Cell Transmission Model (CTM) introduced in Daganzo (1994) and Daganzo (1995) by reducing network flows due to congestion. The flow reductions are calibrated according to the traffic simulation studies conducted in Chapter 3. The MILP is based upon the linear program developed in Ziliaskopoulos (2000); however, it eliminates the "traffic holding" phenomenon where it cannot be implemented realistically within a transportation network. This phenomenon is commonly found in mathematical programs used for dynamic traffic assignment where the traffic is unrealistically held back in order to determine an optimum solution. Lastly, we propose additional constraints for the MILP that improve the computational performance by over 90%. These constraints exploit the relation of the binary variables based on the network topology. Chapter 5 applies the improved modeling framework developed in Chapter 4 to implement a demand management strategy called group-level staging -- the practice of evacuating different groups of evacuees at different times in order to reduce the evacuation duration. This chapter evaluates the benefits of group-level staging, as compared to the current practice of simultaneous evacuation, and explores the behavior of the modeling framework under various objective functions. / Ph. D.