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.

One of the core problems in the strategic planning of railway operations revolves around developing an optimal line plan. The line plan optimisation problem aims to build a workable line system that achieves specific objectives. Many models presented in existing literature typically focus on either maximising direct traveller numbers or minimising costs. In contrast, this paper introduces a model with diverse objectives for addressing line plan optimisation problems, allowing for variations in stopping patterns across different lines. Our model examines how setting different objectives can result in different line plan designs. This will be valuable for railway operators, offering diverse perspectives when selecting the most suitable design, particularly in the context of new railway service development, such as the introduction of a high-speed train. A case study of future semi high-speed rail in Indonesia is presented to test the model.

Vehicle trajectory data have become essential for many research fields, such as traffic flow, traffic safety, and automated driving. To make trajectory data useable for researchers, an overview of the included road section and traffic situation as well as a description of the data processing methodology is necessary. In this paper, we present a trajectory dataset from a German highway with two lanes per direction, an off-ramp and congested traffic in one direction, and an on-ramp in the other direction. The dataset contains 8,648 trajectories and covers 87 ​min and an ∼1,200 ​m long section of the road. The trajectories were extracted from drone videos using a posttrained YOLOv5 object detection model and projected onto the road surface via three-dimensional (3D) camera calibration. The postprocessing methodology can compensate for most false detections and yield accurate speeds and accelerations. The trajectory data are also compared with induction loop data and vehicle-based smartphone sensor data to evaluate the plausibility and quality of the trajectory data. The deviations of the speeds and accelerations are estimated at 0.45 ​m/s and 0.3 ​m/s², respectively. We also present some applications of the data, including traffic flow analysis and accident risk analysis.