Based on a study of probe vehicle data measured on different highways during different days we have revealed some common empirical microscopic characteristic features of sequences of phase transitions from free flow to synchronized flow and back from synchronized flow to free flow occurring before traffic breakdown at a highway bottleneck predicted theoretically by Kerner. Following common empirical features of sequences of these phase transitions have been found: (i) Sequences of empirical phase transitions from free flow to synchronized flow and back from synchronized flow to free flow are observed on different highways at different bottlenecks. (ii) Empirical sequences of the phase transitions exhibit a random character, in particular, at different days very different sequences of phase transitions from free flow to synchronized flow and back from synchronized flow to free flow have been observed at the same bottleneck. (iii) Time durations between the transition from free flow to synchronized flow and the subsequent transition from synchronized flow to free flow within a sequence of the phase transitions are random values. Time intervals between subsequent sequences of the phase transitions are also random values. (iv) Regions of dissolving synchronized flow that result from sequences of phase transitions from free flow to synchronized flow and back from synchronized flow to free flow exhibit random characteristics. (v) Several following each other sequences of phase transitions from free flow to synchronized flow and back from synchronized flow to free flow can lead to waves of synchronized flow between free flow.

The continuous advancement of connected vehicle (CV) technologies and its increasing market penetration (MP) will change the nature of traffic flow fundamentals by upending the traditional traffic composition. The objective of this paper is to create an agent-based modeling framework to evaluate the impact of the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) technology on the mobility performance (i.e., travel time and its reliability) of a two-lane highway work zone scenario. This paper studies when and at what MP will the mobility benefits of connected vehicles emerge. Results showed that the mobility benefits are highly dependent on the CVs MP levels and traffic flow rates. In addition, the study found that the higher the traffic flow rate is, the higher MP level is required to enable the mobility benefits in the mixed traffic environment. Research findings will inform agency professionals, traffic engineers, and planners the achievable benefits that could accompany the implementation of CV technologies in diverse roadway settings such as work zone. Moreover, the results will help the decision-makers in agencies as state DOTs to develop effective resource allocation strategies and evaluate the cost and benefit ratio of deploying this technology in highway work zone scenario.

The shipments of hazardous materials (hazmat) which are indispensable for economic and social development have increased; accordingly, a rising number of incidents involving hazmat transportation may inflict more dread damages to both people and environment. This severe situation has prompted the need for deep mining trip purposes using trajectory information in order to enhance the hazmat-transportation regulatory. This paper presents an unsupervised two-phase framework for inferring multiple trip purposes (i.e. loading, unloading, in-yard, and other stops) based on the passive global positioning system (GPS) data during the hazmat-transportation process. In detail, a scalable ordering points to identify the clustering-structure mixture algorithm (SOMA) is first developed to group hazmat vehicles trip ends into hotspot places in phase I; In phase II, a two-stage trip-purpose identification approach is proposed with a combination of the fuzzy c-means (FCM) method and the point-of-interest (POI) information. The effectiveness and efficiency of the designed two-phase framework are evaluated through the real-world datasets, which are generated by more than 12,000 vehicles in Liaoning Province, China. The results demonstrate that the method can infer four types of freight trip purposes with an accuracy of 82.1%. The proposed approach framework can help analyze the vehicle trips associated with the loading states, which will provide effective decision-making support for the hazmat-transportation regulatory.

This paper develops the two-lane traffic surveillance function of a smart road stud (SRS), which has the capabilities of high-resolution traffic data collection and transmission, and a light-based traffic guidance. The SRS addresses the limitations of the current systems with the potential for high performance and usage for a variety of applications. SRS nodes are mounted on the road surface along the lane separation lines instead of the intrusive middle of the lane detection sensors. The key innovation in SRS design is its ability to detect vehicles in two adjacent lanes in real time by employing a three-axis geomagnetic sensor mounting on the lane line, which is the basis of advanced applications such as traffic surveillance, driving guidance, and congestion monitoring. Multiple vehicles are recognized based on the superposition of the disturbance of the geomagnetic fields. Magnetic field changes along the axes are simulated and analyzed with vehicles modeled as magnet dipoles to determine the criteria for the detection of vehicles in adjacent lanes, which are verified by actual waveforms. Extended dual-window and two-lane vehicle detection algorithms are developed, enabling to distinguish five detection scenarios and count vehicles in two lanes. Field test results show that one SRS can detect traffic in adjacent lanes with a correct vehicle detection rate of over 98%, the same level achievable by intrusive middle of the lane detectors. Based on the enhanced features, high performance, cost-effectiveness, and value-added services, the SRS is a significant improvement on the conventional studs for intelligent transport system applications.

How to efficiently identify traffic bottlenecks in the expressway network and implement targeted measures is an important issue to mitigate traffic congestion. In recent years, with the proliferation of smartphones, smartphone-based navigation applications are receiving the worldwide popularity, which are being treated as an extremely rich source of traffic data. In this paper, we are devoted to proposing a novel approach to investigate traffic state and identify recurrent bottlenecks quickly and accurately from macroscopic network perspective, through fusing the data sources of fixed detectors and mobile navigation apps. First of all, this paper plots flow-speed fundamental diagram to derive critical speed, which is treated as the criterion to determine traffic state. Once the criterion has been established, typical bottlenecks in the entire expressway network can be efficiently identified through only utilizing smartphone-based probe speed data. Secondly, three pioneering indicators are put forward to quantify oversaturated traffic state and classify bottleneck patterns on urban expressway network. Applying this methodology, we take Beijing expressway network as a case to identify different patterns of bottlenecks, which are validated to comply with the reality. Moreover, the relationship between critical speed and the associated road segment features is explored and shows the possibility of predicting the critical speed even without flow data. As an end, some recommendations are proposed for improving different types of traffic bottlenecks.

In the era of information and communications technology and big data, roadway traffic is being monitored by various sensors. Notably, sensors equipped in vehicles, as shown in Figure 1, are providi...

According to the evolution tendency of the control decision process from a trackside to a train-borne system, various autonomous localization units for railway vehicles were developed. As recommended in railway standards, the design process of each system, here the autonomous localization units (LU), follows the V-model whose first step is to define its availability requirement in order to satisfy the global ETCS system requirements. The classical approach for assigning the subsystem availability is based on the assumption that failure parameters of other units are precisely known. This assumption is too restricted in reality due to the lack of information. In this paper, we propose a new approach that allows taking into account uncertainties in the dependability parameters of the ETCS components for identifying the upper threshold of the LU unavailability to reach ETCS availability requirements. Using fuzzy fault trees, the fuzzy unavailability of the ETCS without the autonomous LU is evaluated. Then, based on its membership function, we assess the satisfaction rate that an advanced ETCS with the autonomous LU can satisfy the ETCS availability target.

As traffic congestion intensifies, identification and control of critical areas to balance road network traffic flow are of paramount importance to mitigate and prevent traffic congestion, based on the road network traffic realities. Most previous traffic sub-region division methods of static characteristics are not suitable for dynamic traffic management. This article proposes the division model of traffic sub-regions and identification method of critical control areas based on macroscopic fundamental diagram (MFD) theory closely integrated with dynamic characteristics of road network traffic. To be specific, the existence of small-scale road network MFD is confirmed at first through the traffic data transmitted from Jiangbei district in Chongqing, China. The initial control sub-regions of the road network are formulated with each small-scale road network as a control area. Then, the traffic state value of sub-regions is calculated by means of regression analysis. A set of rules are designed to aggregate the initial sub-regions, which are associated with the traffic state and geographically adjacent, and the adjusted traffic sub-regions are obtained by means of correlation coefficient analysis. Finally, the identification method of traffic critical control sub-regions is proposed based on clustering analysis. In addition, the identification model of critical control sub-regions is applied and analyzed in the experimental road network of Jiangbei district in Chongqing, China. The results demonstrate that the proposed model is flexible and efficient enough to improve the control over road networks, promoting application of traffic sub-region division based on MFD in dynamic traffic control over actual road networks.

Model-based condition monitoring is an increasingly important area for rail transportation. The key elements of such condition monitoring methodologies are low-cost vehicle sensors and intelligent algorithms. In this study, a swarm intelligence-based multiple models approach is proposed to detect different friction conditions by using velocity measurements of a railway vehicle. In this case of application, estimated parameter is the maximum friction coefficient. Additionally, proposed methodology is tested experimentally by using the measurements taken from a tram wheel test stand. Multiple mathematical models of the test stand are created with different maximum friction coefficients, whereas all initial conditions and other system parameters are same for each model. Therefore, comparison of the output of each model with measurements is considered to interpret the parameter value of the model, which best represents the system, is selected as parameter estimate. Unlike the traditional multiple models approach, a swarm intelligence-based evolution of the models is proposed. Experiments carried out on the test stand reveal that the proposed methodology is promising to be used as an on-board friction condition monitoring tool for railway vehicles with traction. Furthermore, it can be considered to detect weather conditions since friction conditions change due to the weather events such as rain, ice, snowfall, condensation of water droplets, and leaves on the line and it can be used as an auxiliary system for intelligent traction and high adhesion control systems.

This article presents a novel intersection traffic management system for automated vehicles and quantifies its impact on fuel consumption and greenhouse gas emissions of CO2 relative to traditional traffic signal and roundabout intersection control. The developed intelligent traffic management (ITM) techniques, which are based on a spatiotemporal reservation scheme, ensure that vehicles proceed through the intersection without colliding with other vehicles while at the same time reducing the intersection delay and environmental impacts. Specifically, the spatiotemporal reservation scheme provides each vehicle a collision-free path that is decomposed into a speed profile along with navigational instructions. The integration of the developed microscopic traffic simulator with instantaneous emission model, provides improved assessments of the environmental impact of traffic control strategies at intersections. The simulator architecture integrates several ITM algorithms, vehicle sensors, V2V/V2I communications, and emission and fuel consumption models. Each vehicle is modeled by an agent and each agent provides information depending on the specific vehicle sensors. The ITM system is supported by V2V and V2I communications, allowing the exchange of information among vehicles and infrastructure. The data include the estimated vehicle position and speed. Compared with traditional traffic management techniques, the simulation results prove that the proposed ITM system reduces CO2 emissions significantly. The research also shows that these reductions are more significant when the traffic flow increases.