Incorporation of more sophisticated human factors (HF) in mathematical models for driving behavior has become an increasingly popular and important research direction in the last few years. Such models enable us to simulate under which conditions perception errors and risk-taking lead to interactions that result in unsafe traffic conditions and ultimately accidents. In this paper, we present a generic multi-level microscopic traffic modelling and simulation framework that supports this important line of research. In this framework, the driving task is modeled in a multi-layered fashion. At the highest level, we have idealized (collision-free) models for car following and other driving tasks. These models typically contain HF parameters that exogenously “govern the human factor”, such as reaction time, sensitivities to stimuli, desired speed, etc. At the lowest level, we define HF variables (task demand and capacity, awareness) with which we maintain what the information processing costs are of performing driving tasks as well as non-driving related tasks such as distractions. We model these costs using so-called fundamental diagrams of task demand. In between, we define functions that govern the dynamics of the high-level HF parameters with these HF variables as inputs. When total task demand increases beyond task capacity, first awareness may deteriorate, where we use Endsley's three-level awareness construct to differentiate between effects on perception, comprehension, anticipation and reaction time. Secondly, drivers may adapt their response in line with Fullers risk allostasis theory to reduce risk to acceptable levels. This framework can be viewed as a meta model, that provides the analyst possibilities to combine and mix a wide variety of microscopic models for driving behavior at different levels of sophistication, depending on which HF are studied, and which phenomena need to be reproduced. We illustrate the framework with a distraction (rubbernecking) case. Our results show that the framework results in endogenous mechanisms for inter- and intra-driver differences in driving behavior and can generate multiple plausible HF mechanisms to explain the same observable traffic phenomena and congestion patterns that arise due to the distraction. We believe our framework can serve as a valuable tool in testing hypotheses related to the effects of HF on traffic efficiency and traffic safety in a systematic way for both the traffic flow and HF community.

Within the AutoDrive project, openPASS is used to develop a cognitive stochastic traffic flow simulation for urban intersections and highway scenarios, which are described in deliverable D1.14. The deliverable D2.16 includes the customizations of the framework openPASS that are required to provide a basis for the development and implementation of the driver behavior model and the evaluated safety function. The trial design for the evaluation of the safety functions is described. Furthermore, the design of the driver behavior study is introduced to parameterize and validate the underlying driver behavior model.

As part of the AutoDrive project, OpenPASS is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. The deliverable D4.20 is about the design of the modules for the stochastic traffic simulation. This initially includes an examination of the existing traffic simulations described in chapter 2. Subsequently, the underlying tasks of the driver when crossing an intersection are explained. The main part contains the design of the cognitive structure of the road user (chapter 4.2) and the development of the cognitive behaviour modules (chapter 4.3).

As part of the AutoDrive project, the OpenPASS framework is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. This framework was adapted and further developed. The deliverable D5.13 deals with the construction of the stochastic traffic simulation. At this point of the process, the theoretical design aspects of D4.20 are implemented. D5.13 explains the operating principles of the different modules. This includes the foundations, boundary conditions, and mathematical theory of the traffic simulation.

Located in the heart of Silicon Valley, Downtown San Jose is attracting new residents, visitors, and businesses. Clearly, the mobility of these residents, visitors, and businesses cannot be accommodated by streets that focus on the single-occupancy automobile mode. To increase the potential for individuals to use non-single-occupancy modes of travel, the downtown area must have a cohesive plan to integrate multimodal use and public life. Complete streets are an integral component of the multi-modal transport system and more livable communities. Complete streets refer to roads designed to accommodate multiple modes, users, and activities including walking, cycling, transit, automobile, and nearby businesses and residents. A one-way to two-way street conversion is an example of a complete streets project. Similarly, tactical urbanism can provide cost-effective modifications (e.g., through temporary road closures for events like the farmers’ market) that enrich the public life in an urban environment. The ability to serve current and future transportation needs of residents, businesses and visitors through the creation of pleasant, efficient, and safe multimodal corridors is a guiding principle of a smart city. This research project addressed questions that guide the implementation of this overarching principle. These questions relate to travel patterns and potential network impacts of the conversion of the corridor(s) into complete streets. Towards that end, core network in downtown San Jose is simulated via a validated VISSIM model for 2015 traffic conditions (i.e., the base case or Scenario 0). Three scenarios are then modeled as variations to this model. The relevant model outputs from the base and scenario models provide easily digestible information the City can convey various impacts and trade-offs to partners and stakeholders prior to implementation of these plans. The scenarios modeled are based on stakeholder input. Microsimulation allows for detailed modeling and visualization of the transportation networks including movements of individual vehicles and pedestrians. The results based on 2040 traffic volumes provided by the city based on their long-range travel demand model clearly demonstrate that the existing network cannot support the projected level of travel demand. It indicates that the city needs an aggressive travel demand management program to curb the growth of automobile traffic. The output also includes 3-D animations of the traffic flow that can be used in public forums for community outreach. A discussion for such a campaign based on best practices around using these visualizations for public outreach is also provided. Located in the heart of Silicon Valley, Downtown San Jose is attracting new residents, visitors, and businesses. Clearly, the mobility of these residents, visitors, and businesses cannot be accommodated by streets that focus on the single-occupancy automobile mode. To increase the potential for individuals to use non-single-occupancy modes of travel, the downtown area must have a cohesive plan to integrate multimodal use and public life. Complete streets are an integral component of the multi-modal transport system and more livable communities. Complete streets refer to roads designed to accommodate multiple modes, users, and activities including walking, cycling, transit, automobile, and nearby businesses and residents. A one-way to two-way street conversion is an example of a complete streets project. Similarly, tactical urbanism can provide cost-effective modifications (e.g., through temporary road closures for events like the farmers’ market) that enrich the public life in an urban environment. The ability to serve current and future transportation needs of residents, businesses and visitors through the creation of pleasant, efficient, and safe multimodal corridors is a guiding principle of a smart city. This research project addressed questions that guide the implementation of this overarching principle. These questions relate to travel patterns and potential network impacts of the conversion of the corridor(s) into complete streets. Towards that end, core network in downtown San Jose is simulated via a validated VISSIM model for 2015 traffic conditions (i.e., the base case or Scenario 0). A number o Threef scenarios are then modeled as variations to this model. The relevant model outputs from the base and scenario models provide easily digestible information the City can convey various impacts and trade-offs to partners and stakeholders prior to implementation of these plans. The scenarios modeled are based on stakeholder input. Microsimulation allows for detailed modeling and visualization of the transportation networks including movements of individual vehicles and pedestrians. The results based on 2040 traffic volumes provided by the city based on their long-range travel demand model clearly demonstrate that the existing network cannot support the projected level of travel demand. It indicates that the city needs an aggressive travel demand management program to curb the growth of automobile traffic. The output also includes 3-D animations of the traffic flow that can be used in public forums for community outreach. A discussion for such a campaign based on best practices around using these visualizations for public outreach is also provided.

Driving simulators are used to conduct experiments on for example driver behavior, road design, and vehicle characteristics. The results of the experiments often depend on the traffic conditions. One example is the evaluation of cellular phones and how they affect driving behavior. It is clear that the ability to use phones when driving depends on traffic intensity and composition, and that realistic experiments in driving simulators therefore has to include surrounding traffic. This thesis describes a model that generates and simulates surrounding vehicles for a driving simulator. The proposed model generates a traffic stream, corresponding to a given target flow and simulates realistic interactions between vehicles. The model is built on established techniques for time-driven microscopic simulation of traffic and uses an approach of only simulating the closest neighborhood of the driving simulator vehicle. In our model this closest neighborhood is divided into one inner region and two outer regions. Vehicles in the inner region are simulated according to advanced behavioral models while vehicles in the outer regions are updated according to a less time-consuming model. The presented work includes a new framework for generating and simulating vehicles within a moving area. It also includes the development of enhanced models for car-following and overtaking and a simple mesoscopic traffic model. The developed model has been integrated and tested within the VTI Driving simulator III. A driving simulator experiment has been performed in order to check if the participants observe the behavior of the simulated vehicles as realistic or not. The results were promising but they also indicated that enhancements could be made. The model has also been validated on the number of vehicles that catches up with the driving simulator vehicle and vice versa. The agreement is good for active and passive catch-ups on rural roads and for passive catch-ups on freeways, but less good for active catch-ups on freeways.

No description available.

Autonomous vehicles are predicted to disrupt the current landscape of urban mobility.Many studies have investigated how autonomous vehicles, either operated as a serviceor as private cars, could compete against public transport and even replace it. Fewerstudies have investigated how autonomous vehicles could actually be an opportunityfor the public transport sector, as a new type of offer that would cover specific needsalong traditional modes such as buses or metros.The aim of this project is to quantify the effect of replacing part of the public transportnetwork of Uppsala by demand-responsive autonomous fleets. This is achieved bybuilding a transport model based on the traditional four-step transport model andcalculating the total cost of the network both from the passenger and the operator’sperspective.The study shows that autonomous vehicles can slightly improve the performance of thenetwork and work best when combined with traditional bus lines. However, they alsoincrease the traffic and have a risk to cause congestion.

Enabling vehicles to preempt the normal operation of traffic signals has the potential to improve the safety and efficiency of both the requesting vehicle and all of the other vehicles. Little is known about which strategy is the most effective to exit from preemption control and transition back to the traffic signals normal timing plan. Common among these traffic signal transition strategies is the method of either increasing or decreasing the cycle length of the signal timing plan, as the process followed to return to the coordination point of the effected signal timing plan, to coordinate its operation with adjacent traffic signals. This research evaluates commonly available transition strategies: best way, long, short, and hold strategies. The major contribution of this research is enhancing the methodology to evaluate the impacts of using these alternative transition strategies. Part of this methodology consists of the "software-in-the-loop" simulation tool which replicates the stochastic characteristics of traffic flow under different traffic volume levels. This tool combines the software from a traffic signal controller (Gardner NextPhase Suitcase Tester, version 1.4B) with a microscopic traffic simulation model (CORSIM, TSIS 5.2 beta version). The research concludes that a statistically significant interaction exists between traffic volume levels and traffic signal transition strategies. This interaction eliminates the ability to determine the isolated effects of either the transition strategies on average travel delay and average travel time, or the effects of changes in traffic volume levels on average travel delay and average travel time. Conclusions, however, could be drawn on the performance of different transition strategies for specific traffic volume levels. As a result, selecting the most effective transition strategy needs to be based on the traffic volume levels and conditions specific to each traffic signal or series of coordinated traffic signals. The research also concludes that for the base traffic volume and a 40% increase in traffic volume, the most effective transition strategies are the best way, long or hold alternatives. The best way was the most effective transition strategy for a 20% increase in traffic volume. The least effective strategy is the short transition strategy for both the base and 40% increase in traffic volume, and the long and short for a 20% increase in traffic volume. Further research needs to be conducted to assess the performance of different transition strategies in returning to coordinated operation under higher levels of traffic volume (e.g., approaching or exceeding congested flow regime), with varying cycle lengths, with different signal timing plans, and when different roadway geometric configurations (e.g., turn lanes, length of turn lanes, number of lanes, spacing between intersections) are present. / Ph. D.

With an increasing population of super heavy aircraft operating in the National Airspace System and with the introduction of NextGen technologies, the wake vortex problem has become more important for airport capacity and the en-route air traffic operations. The vortices generated by heavy and super heavy aircraft can generate potential hazards to other aircraft on nearby flight paths. Moreover, the design of new airport procedures needs to consider the interactions between aircraft in closer paths. New methods and models are required to examine these effects before new operations are conducted in the National Airspace System (NAS). Reducing wake vortex separations to safe levels between successive aircraft is essential for NextGen operations. One approach taken recently by ICAO and the FAA is to introduce a re-categorization (ReCat) of wake vortex separations to six groups from the existing five groups employed by the FAA in the United States. Reduced aircraft separations can increase capacity in the NAS with corresponding savings in delay times at busy airports. Future NextGen operations are likely to introduce smaller aircraft separations in the en-route and in the terminal area. Such operations would require better methods to identify potential wake hazards from reduced separation operations. This dissertation describes a model to identify potential wake encounters in the future NAS. The goal of the dissertation is to describe the Enhanced Wake Encounter Model (EWEM), a model that employs a detailed NASA-developed wake model to generate wake zones for different aircraft categories under different flight conditions that can be used with aircraft flight path data to identify potential wake encounters. The main contribution of this model is to gain an understanding of potential wake encounters under future NAS operations. / Ph. D.