Connected and automated vehicle technologies are expected to significantly contribute in improving mobility and safety. As connected and autonomous vehicles have not been used in practice at large scale, there are still some uncertainties in relation to their applications. Therefore, researchers utilize traffic simulation tools to model the presence of these vehicles. There are several studies on the impacts of vehicle connectivity and automation at the segment level. However, only a few studies have investigated these impacts on traffic flow at the network level. Most of these studies consider a uniform distribution of connected or autonomous vehicles over the network. They also fail to consider the interactions between heterogeneous drivers, with and without connectivity, and autonomous vehicles at the network level. Therefore, this study aims to realistically observe the impacts of these emerging technologies on traffic flow at the network level by incorporating adaptive fundamental diagrams in a mesoscopic simulation tool. The adaptive fundamental diagram concept considers spatially and temporally varying distributions of different vehicle types with heterogeneous drivers. Furthermore, this study considers the intersection capacity variations and fundamental diagram adjustments for arterial links resulting from the presence of different vehicle types and driver classes. The proposed methodology is applied to a large-scale network of Chicago. The results compare network fundamental diagrams and hysteresis loop areas for different proportions of connected and autonomous vehicles. In addition to quantifying impacts of connected and autonomous vehicles, the results demonstrate the impacts of various factors associated with these vehicles on traffic flow at the network level.
Limited charging infrastructure for electric vehicles (EVs) is one of the main barriers to adoption of these vehicles. In conjunction with limited battery range, the lack of charging infrastructure leads to range-anxiety, which may discourage many potential users. This problem is especially important for long-distance or intercity trips. Monthly traffic patterns and battery performance variations are two main contributing factors in defining the infrastructure needs of EV users, particularly in states with adverse weather conditions. Knowing this, the current study focuses on Michigan and its future needs to support the intercity trips of EVs across the state in two target years of 2020 and 2030, considering monthly traffic demand and battery performance variations. This study incorporates a recently developed modeling framework to suggest the optimal locations of fast EV chargers to be implemented in Michigan. Considering demand and battery performance variations is the major contribution of the current study to the proposed modeling framework by the same authors in the literature. Furthermore, many stakeholders in Michigan are engaged to estimate the input parameters. Therefore, the research study can be used by authorities as an applied model for optimal allocation of resources to place EV fast chargers. The results show that for charger placement, the reduced battery performance in cold weather is a more critical factor than the increased demand in warm seasons. To support foreseeable annual EV trips in Michigan in 2030, this study suggests 36 charging stations with 490 chargers and an investment cost of $23 million.
Integrating activity-based models (ABMs) with simulation-based dynamic traffic assignment (DTA) have gained attention from transportation planning agencies seeking tools to address the arising planning challenges as well as transportation policies such as road pricing. Optimal paths with least generalized cost are needed to route travelers at the DTA level, while at the ABM level, only the least generalized cost information is needed (without fully specified paths). Thus, rerunning (executing) the least generalized cost path-finding algorithm at each iteration of ABM and DTA does not seem to be efficient, especially for large-scale networks. Furthermore, storing the dynamic travel cost skims for multiclass users as an alternative approach is not efficient either in regard to memory requirements. In this study, the aim was to estimate the least generalized cost so as to be used in destination and mode choice models at the ABM level. A heuristic approach was developed to use the simulated vehicle trajectories that were assigned to the optimal paths in the DTA level to estimate different cost measures, including distance, time, and monetary cost associated with the least generalized cost path for any given combination of the origin, destination, and departure time (ODT) and value of time. The proposed approximation method presented in this study used vehicle trajectories, aligned with the origin–destination direction and located in a specific boundary shaping an ellipse around the origin and destination zones at a certain time window, to estimate travel costs for the given ODT and user class. Numerical results for two real-world networks suggest the applicability of the method in large-scale networks in addition to its lower computational burden, including solution time and memory requirements, relative to other alternative approaches.
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