Communications in Transportation Research最新文献

Macroscopic Fundamental Diagrams (MFDs) are valuable for designing and evaluating network-wide traffic management schemes. Since obtaining empirical MFDs can be expensive, analytical methodologies are crucial to estimate variations in MFD shapes under different control strategies and predict their efficacy in mitigating congestion. Analyses of urban grid networks' abstractions can provide an inexpensive methodology to obtain a qualitative understanding of impacts of control policies. However, existing abstractions are valid only for simple intersection layouts with unidirectional and single-lane links and two conflicting movement groups. Naturally, the real intersections are more complex, with multiple incoming and outgoing lanes, heterogeneous incoming links' capacities and several conflicting movement groups. To this end, we consider a grid network with differences in capacities of horizontal and vertical directions, allowing us to investigate the characteristics of control policies that can avoid pernicious gridlock in heterogeneous networks. We develop a new, more comprehensive network abstraction of such grid networks to analyze and compare the impacts of two families of decentralized Traffic Signal Controllers (TSCs) on the network's stability. The obtained theoretical insights are verified using microsimulation results of grid networks with multiple signalized intersections. The analyses suggest that considering both upstream and downstream congestion information in deciding signal plans can encourage more evenly distributed traffic in the network, making them more robust and effective at all congestion levels. The study provides a framework to understand general expectations from decentralized control policies when network inhomogeneity arises due to variations in incoming link capacities and turning directions.

Although the available traffic data from navigation systems have increased steadily in recent years, it only reflects average travel time and possibly Origin-Destination information as samples, exclusively. However, the number of vehicles participating in the traffic – in other words, the traffic flows being the basic traffic engineering information for strategic planning or even for real-time management – is still missing or only available sporadically due to the limited number of traditional traffic sensors on the network level. To tackle this gap, an efficient calibration process is introduced to exploit the Floating Car Data combined with the classical macroscopic traffic assignment procedure. By optimally scaling the Origin-Destination matrices of the sample fleet, an appropriate model can be approximated to provide traffic flow data beside average speeds. The iterative tuning method is developed using a genetic algorithm to realize a complete macroscopic traffic model. The method has been tested through two different real-world traffic networks, justifying the viability of the proposed method. Overall, the contribution of the study is a practical solution based on commonly available fleet traffic data, suggested for practitioners in traffic planning and management.

Communication delays within connected and autonomous vehicles (CAVs) pose significant risks. It is imperative to address these issues to ensure the safe and effective operation of CAVs. However, the exploration of communication delays on CAV operations and their energy use remains sparse in the literature. To fill the research gap, this study leverages the facilities at America Center of Mobility (ACM) Smart City Test Center to implement and evaluate a CAV merging control algorithm through vehicle-in-the-loop testing. This study aims at achieving three main objectives: (1) develop and implement a CAV merging control strategy in the experimental test bed through vehicle-in-the-loop testing, (2) propose analytical models to quantify the impacts of communication delay on the variability of CAV speed and energy consumption based on field experiment data, and (3) create a predictive model for energy usage considering various CAV attributes and dynamics, e.g., speed, acceleration, yaw rate, and communication delays. To our knowledge, this is one of the first attempts at evaluating the impacts of communication delays on CAV merging operational control with field data, making critical advancement in the field. The results suggest that communication delay has a more substantial effect on energy consumption under high-speed volatility compared to low-speed volatility. Among all factors examined, acceleration is the dominant characteristic that influences energy usage. It also revealed that even minor improvements in communication delay can yield tangible improvements in energy efficiency. The results provide guidance on CAV field experiments and the influence of communication delays on CAV operation and energy consumption.