Advanced Driver Assistance Systems (ADAS) offer the possibility of helping drivers to fulfill their driving tasks. Automated vehicles are capable of communicating with surrounding vehicles (V2V) and infrastructure (V2I) in order to collect and provide essential information about driving environment. Studies have proved that automated vehicles have a potential to decrease traffic congestion on road networks by reducing the time headway, enhancing the traffic capacity and improving the safety margins in car following. Furthermore, vehicle movement and driver’s behavior of conventional vehicles will be affected by the presence of automated vehicles in traffic networks. Despite different encouraging factors, automated driving raises some concerns such as possible loss of situation awareness, overreliance on automation and degrading driving skills in absence of practice. Moreover, coping with complex scenarios, such as merging at ramps and overtaking, in terms of interaction between automated vehicles and conventional vehicles need more research. This thesis work aims to investigate the effects of automated vehicles on driver’s behavior and traffic performance. A broad literature review in the area of driving simulators and psychological studies was performed to examine the automated vehicle effects on driver’s behavior. Findings from the literature survey, which has been served as setup values in the simulation study of the current work, reveal that the conventional vehicles, which are driving close to the platoon of automated vehicles with short time headway, tend to reduce their time headway and spend more time under their critical time headway. Additionally, driving highly automated vehicles is tedious in a long run, reduce situation awareness and can intensify driver drowsiness, exclusively in light traffic. In order to investigate the influences of automated vehicles on traffic performance, a microscopic simulation case study consisting of different penetration rates of automated vehicles (0, 50 and 100 percentages) was conducted in VISSIM software. The scenario network is a three-lane autobahn segment of 2.9 kilometers including an off-ramp, on-ramp and a roundabout with some surrounding urban roads. Outputs of the microscopic simulation in this study reveal that the positive effects of automated vehicles on roads are especially highlighted when the network is crowded (e.g. peak hours). This can definitely count as a constructive point for the future of road networks with higher demands. In details, average density of autobahn segment remarkably decreased by 8.09% during p.m. peak hours in scenario with automated vehicles. Besides, Smoother traffic flow with less queue in the weaving segment was observed. Result of the scenario with 50% share of automated vehicles moreover shows a feasible interaction between conventional vehicles and automated vehicles. Meaningful outputs of this case study, based on the input data from literature review, demonstrate the capability of VISSIM software to simulate the presence of automated vehicles in great extent, not only as an automated vehicle scenario but also a share of them, in traffic network. The validity of the output values nonetheless needs future research work on urban and rural roads with different traffic conditions.

This paper aims to investigate the safety impact of connected vehicles and connected vehicles with the lower level of automation features under vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) communication technologies. Examining the lower level of automation is more realistic in the foreseeable future. This study considered two automated features such as automated braking and lane keeping assistance which are widely available in the market with low penetration rates. Driving behavior of connected vehicles (CV) and connected vehicles lower level automation (CVLLA) were modeled in the C++ programming language with considering realistic car following models in VISSIM. To this end, safety impact on both segment and intersection crash risks were explored through surrogate safety assessment techniques under various market penetration rates (MPRs). Segment crash risk was analyzed based on both time proximity-based and evasive action-based surrogate measures of safety: time exposed time-to-collision (TET), time integrated time-to-collision (TIT), time exposed rear-end crash risk index (TERCRI), lane changing conflicts (LCC), and number of critical jerks (NCJ). However, the intersection crash risk was evaluated through the number of conflicts extracted from microsimulation (VISSIM) using the Surrogate Safety Assessment Model (SSAM). A logistic regression model was also developed to quantify the crash risk in terms of observed conflicts obtained in the intersection influence areas. The results suggest that both CV and CVLLA reduce segment crash risk significantly in terms of the five surrogate measures of safety. Furthermore, the logistic regression results clearly showed that both CV and CVLLA have lower intersection crash risks compared to the base scenario. In terms of both segment and intersection crash risks, CVLLA significantly outperforms CV when MPRs are 60% or higher. Thus, the results indicate a significant safety improvement resulting from implementing CV and CVLLA technologies at both segments and intersections on arterials.

No-notice evacuations of metropolitan areas can place significant demands on transportation infrastructure. Connected vehicle (CV) technology, with real-time vehicle to vehicle and vehicle to infrastructure communications, can help emergency managers to develop efficient and cost-effective traffic management plans for such events. The objectives of this research were to evaluate the impacts of CVs on no-notice evacuations using a case study of a downtown metropolitan area. The microsimulation software VISSIM was used to model the roadway network and the evacuation traffic. The model was built, calibrated, and validated for studying the performance of traffic during the evacuation. The researchers evaluated system performance with different CV penetration rates (from 0 to 30 percent CVs) and measured average speed, average delays, and total delays. The findings suggest significant reductions in total delays when CVs reached a penetration rate of 30 percent, albeit increases in delays...

Understanding the effects of having automated vehicles in the future traffic scenarios is an important research topic that attracts a great deal of attention currently. The difficulty in studying this problem is the fact that real life measurement and testing of these scenarios can not be made as there are still a very small fraction of automated vehicles in the traffic. So analyzing and understanding the effects of mixed traffic requires extensive simulative analysis. In this paper we analyze this problem using real traffic data in combination with the open-source SUMO traffic simulation software. The traffic flow is modeled based on the measurement data from a section of the Austrian A2 motorway, while the effects of automated vehicles at various penetration rates is simulated and consequently some observation are made.

This paper provides quantitative evaluation of safety implications of aggressive driving (speeding, following closely and weaving through traffic) by using microscopic traffic simulation approach. Combination of VISSIM and Surrogate Safety Assessment Model (SSAM) were used to model motorway and assess safety of the simulated vehicle. The use of vehicle conflicts was validated by correlating it to historic crashes. Crash risk, severity levels and the magnitude of the perceived benefits of aggressive driving were quantified relative to normal drivers under two scenarios: (1) congested, and (2) non-congested traffic conditions. Involvement in vehicle conflicts is used to determine crash-risk while reductions in Post Encroachment Time (PET) and travel time were used to determine the severity levels of the expected crashes and the magnitude of the perceived benefits. The results indicated that the crash risk of aggressive drivers was found to be in the range 3.10–5.8 depending on traffic conditions and type of road aggression. PET of the conflicts involving aggressive drivers reduced by 7–61% indicating high severity levels of the expected crashes. Moreover, the magnitude of the perceived benefit in terms of reduction in travel time was found to be as little as 1–2%. The study concluded that aggressive driving is entailed with a massive risk while its benefits are actually very little.

Connected vehicles (CV) technology has recently drawn an increasing attention from governments, vehicle manufacturers, and researchers. One of the biggest issues facing CVs popularization associates it with the market penetration rate (MPR). The full market penetration of CVs might not be accomplished recently. Therefore, traffic flow will likely be composed of a mixture of conventional vehicles and CVs. In this context, the study of CV MPR is worthwhile in the CV transition period. The overarching goal of this study was to evaluate longitudinal safety of CV platoons by comparing the implementation of managed-lane CV platoons and all lanes CV platoons (with same MPR) over non-CV scenario. This study applied the CV concept on a congested expressway (SR408) in Florida to improve traffic safety. The Intelligent Driver Model (IDM) along with the platooning concept were used to regulate the driving behavior of CV platoons with an assumption that the CVs would follow this behavior in real-world. A high-level control algorithm of CVs in a managed-lane was proposed in order to form platoons with three joining strategies: rear join, front join, and cut-in joint. Five surrogate safety measures, standard deviation of speed, time exposed time-to-collision (TET), time integrated time-to-collision (TIT), time exposed rear-end crash risk index (TERCRI), and sideswipe crash risk (SSCR) were utilized as indicators for safety evaluation. The results showed that both CV approaches (i.e., managed-lane CV platoons, and all lanes CV platoons) significantly improved the longitudinal safety in the studied expressway compared to the non-CV scenario. In terms of surrogate safety measures, the managed-lane CV platoons significantly outperformed all lanes CV platoons with the same MPR. Different time-to-collision (TTC) thresholds were also tested and showed similar results on traffic safety. Results of this study provide useful insight for the management of CV MPR as managed-lane CV platoons.