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1. INTRODUCTION A robust wireless communication network is the foundation for connected transportation systems. Reliable and seamless vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) data communication is the critical component of Connected Vehicle Technology (CVT) applications. Though there are several communication technologies/options available, such as Wi-Fi, WiMAX, LTE, and DSRC, not all can support low latency, accuracy, and the reliability of data transmission required for CVT safety applications (RITA, 2015a). While dedicated short-range communication (DSRC) provides low latency, fast network connectivity, highly secure and highspeed communication for safety applications, reliance on DSRC only may prove detrimental to diverse CVT applications. As a result, research efforts for wireless technologies that can enhance V2V and V2I communications for diverse applications have been undertaken. The wireless communication research community has been exploring combinations of DSRC with Wi-Fi, WiMAX and LTE communication technologies to provide a robust next-generation communication network for connected vehicles (Dar et al., 2010). Moreover, it is expected that DSRC roadside units will be installed at key locations such as intersections and interchanges. Thus, the limited coverage of DSRC (approximately 300 meters) and integration of existing Wi-Fi, WiMAX, and LTE network creates a heterogeneous wireless network (Het-Net) for CVT applications. For continuous connectivity, the shift from one communication network to another relies on the successful handoffs between the networks that ensure optimal utilization of available communication options (i.e., eliminate the need of using multiple communication options at the same time) and corresponding backhaul communication infrastructure requirements depending on the connected vehicle application requirements. In this study, the authors investigated the potential of a Het-Net to provide connectivity for V2V and V2I communications with optimal network resource allocation based on the connected vehicle application requirements. The objectives of this research were to evaluate the performance of Het-Net for i) V2I communications for collecting traffic data, and ii) V2V communications for a collision warning application. For field experiments, the authors utilized the Clemson University Wi-Fi network, and the National Science Foundation (NSF) sponsored Science Wireless Network (SciWiNet) infrastructure at Clemson, South Carolina that supports WiMAX, 3G and LTE, and DSRC infrastructure installed in test vehicles and roadsides. SciWiNet project supports a mobile virtual network operator (MVNO) for academic research communities (Martin et al., 2015). In additional, an ns-3 simulation experiment was conducted to evaluate Het-Net performance when there are larger numbers of vehicles within close proximity, and validate field test findings. 2. PREVIOUS STUDIES Various wireless technologies have been used to support the data transfer requirements of diverse intelligent t...

Driver behaviors, particularly lane-changing behaviors, have an important effect on the safety and throughput of the roadway-vehicle-based transportation system. Lane-changing models are a vital component of various microscopic traffic simulation tools, which are extensively used and playing an increasingly important role in Intelligent Transportation Systems studies. The authors conducted a detailed review and systematic comparison of existing microscopic lane-changing models that are related to roadway traffic simulation to provide a better understanding of respective properties, including strengths and weaknesses of the lane-changing models, and to identify potential for model improvement using existing and emerging data collection technologies. Many models have been developed in the last few decades to capture the uncertainty in lane change modeling; however, lane-changing behavior in the real world is very complex due to driver distraction (e.g., texting and cellphone or smartphone use) and environmental (e.g., pavement and lighting conditions) and geometric (e.g., horizontal and vertical curves) factors of the roadway, which have not been adequately considered in existing models. Therefore, large and detailed microscopic vehicle trajectory data sets are needed to develop new lane changing models that address these issues, and to calibrate and validate lane-changing models for representing the real world reliably. Possible measures to improve the accuracy and reliability of lane-changing models are also discussed in this paper.Index Terms-Driver behavior, lane-changing models.

Adverse weather conditions for roads, which cause transportation systems to perform far below capacity, can severely affect society's economic output. As elimination of road weather events is not possible, transportation agencies perform proactive and reactive maintenance activities to minimize adverse impacts to keep roadways in optimum condition. While reactive maintenance activities are conducted to clear roadways after the occurrence of extreme weather events, proactive activities minimize these impacts beforehand. The success of proactive activities solely depends on the availability of accurate road weather information, however. Traditional road weather forecasting techniques rely on governmental weather services, which are not appropriate to predict route-specific road weather conditions. In this paper, the authors reviewed current intelligent transportation systems (ITS)-based solutions for minimizing road weather impacts and possible ITS innovations to incorporate diverse data sources to improve road weather management activities. ITS-based initiatives, such as road weather information system (RWIS), allow transportation agencies obtain accurate road weather assessments. Location-specific infrastructures such as RWIS are cost prohibitive for system-wide deployments. Connected vehicles equipped with weather sensors could enhance mobile road weather data collection. This strategy could improve proactive maintenance programs and reduce adverse effects of weather to the surface transportation system.

The introduction of autonomous vehicles in the surface transportation system could improve traffic safety and reduce traffic congestion and negative environmental effects. Although the continuous evolution in computing, sensing, and communication technologies can improve the performance of autonomous vehicles, the new combination of autonomous automotive and electronic communication technologies will present new challenges, such as interaction with other nonautonomous vehicles, which must be addressed before implementation. The objective of this study was to identify the risks associated with the failure of an autonomous vehicle in mixed traffic streams. To identify the risks, the autonomous vehicle system was first disassembled into vehicular components and transportation infrastructure components, and then a fault tree model was developed for each system. The failure probabilities of each component were estimated by reviewing the published literature and publicly available data sources. This analysis resulted in a failure probability of about 14% resulting from a sequential failure of the autonomous vehicular components alone in the vehicle’s lifetime, particularly the components responsible for automation. After the failure probability of autonomous vehicle components was combined with the failure probability of transportation infrastructure components, an overall failure probability related to vehicular or infrastructure components was found: 158 per 1 million mi of travel. The most critical combination of events that could lead to failure of autonomous vehicles, known as minimal cut-sets, was also identified. Finally, the results of fault tree analysis were compared with real-world data available from the California Department of Motor Vehicles autonomous vehicle testing records.

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