A distributed dynamic route guidance approach based on short-term forecasts in cooperative infrastructure-vehicle systems

Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy

Unknown remaining time of signal phase at a signalized intersection generally results in extra accelerations and decelerations that increase variations of operating conditions and thus emissions. A cooperative vehicle-infrastructure system can reduce unnecessary speed changes by establishing communications between vehicles and the signal infrastructure. However, the environmental benefits largely depend on drivers’ compliance behaviors. To quantify the effects of drivers’ compliance rates on emissions, this study applied VISSIM 5.20 (Planung Transport Verkehr AG, Karlsruhe, Germany) to develop a simulation model for a signalized intersection, in which light duty vehicles were equipped with a cooperative vehicle-infrastructure system. A vehicle-specific power (VSP)-based model was used to estimate emissions. Based on simulation data, the effects of different compliance rates on VSP distributions, emission factors, and total emissions were analyzed. The results show the higher compliance rate decreases the proportion of VSP bin = 0, which means that the frequencies of braking and idling were lower and light duty vehicles ran more smoothly at the intersection if more light duty vehicles complied with the cooperative vehicle-infrastructure system, and emission factors for light duty vehicles decreased significantly as the compliance rate increased. The case study shows higher total emission reductions were observed with higher compliance rate for all of CO2, NOx, HC, and CO emissions. CO2 was reduced most significantly, decreased by 16% and 22% with compliance rates of 0.3 and 0.7, respectively.

Video application is a research hotspot in cooperative vehicle-infrastructure systems (CVIS) which is greatly related to traffic safety and the quality of user experience. Dealing with large datasets of feedback from complex environments is a challenge when using traditional video application approaches. However, the in-depth structure of deep learning has the ability to deal with high-dimensional data sets, which shows better performance in video application problems. Therefore, the research value and significance of video applications over CVIS can be better reflected through deep learning. Firstly, the research status of traditional video application methods and deep learning methods over CVIS were introduced; the existing video application methods based on deep learning were classified according to generative and discriminative deep architecture. Then, we summarized the main methods of deep learning and deep reinforcement learning algorithms for video applications over CVIS, and made a comparative study of their performances. Finally, the challenges and development trends of deep learning in the field were explored and discussed.

This paper mainly proposes the theoretical model and practical method of a novel personalized dynamic route recommendation system under Cooperative Vehicle-Infrastructure System. Considering the complexity of travelers preference factors and the time dependence of route feature vector, we introduce Pearson similarity coefficient to measure the correlation between the travelers preference feature vector and the route feature vector, and the route with the highest similarity is recommended to the traveler. Meanwhile, the quantitative analysis and modeling of the corresponding factors are realized for the negative factors in reality such as weather, accidents and temporary traffic control. In particular, this work also designs the Feasible Route Search Algorithm, which could search all feasible routes between the origins and the destinations. In order to verify the feasibility of the proposed route recommendation model, we apply Baidu map system development tools to develop a dynamic route recommendation simulation system with perfect function and user-friendly interface by using C# language. The system displays various functions in the way of simulating real-time navigation.

Real-time regional path decision method in cooperative vehicle infrastructure system

As one of the most important part of the modern comprehensive transportation system, highway plays a critical role in national traffic system, which bears the most of traffic load. Whereas, ramp areas may become bottleneck areas of highway if the control is inappropriate or ineffective. The cooperative vehicle infrastructure system (CVIS) brings great opportunities for ramp control to address many tricky problems, among which the merging control of on-ramp vehicles is one of the most prominent problem related to traffic efficiency and safety. In this paper, we apply the technology of cooperative vehicle infrastructure control in the highway ramp area to enhance the traffic efficiency and safety, and propose a merging control method for on-ramp vehicles. At last, a simulation is conducted to evaluate this control method.

The ability of edge computing determines the effectiveness of the cooperative vehicle-infrastructure system to improve traffic safety, efficiency and capacity, enabling traditional information release method have backstage processing and distribution. It is converted to vehicle, road and backstage after being processed by edge computing algorithm. Vehicles are timely and effectively controlled and induced. Based on the full analysis of the development status at home and abroad and the analysis of actual engineering needs, this paper proposes the composition of the cooperative vehicle-infrastructure system and the main factors affecting edge computing, studies the existing edge computing methods, and finally combines the current smart highway demonstration projects. The actual needs put forward the cooperative vehicle-infrastructure use case research based on edge computing. The use case analysis has significant guiding role for the design, R&D and engineering implementation of the smart highway cooperative vehicle-infrastructure system in China.

ObjectivesWe examined nurse practitioners’ use and opinions of recommended communication techniques for the promotion of oral health as part of a Maryland state-wide oral health literacy assessment. Use of recommended health-literate and patient-centered communication techniques have demonstrated improved health outcomes.MethodsA 27-item self-report survey, containing 17 communication technique items, across 5 domains, was mailed to 1,410 licensed nurse practitioners (NPs) in Maryland in 2010. Use of communication techniques and opinions about their effectiveness were analyzed using descriptive statistics. General linear models explored provider and practice characteristics to predict differences in the total number and the mean number of communication techniques routinely used in a week.ResultsMore than 80% of NPs (N = 194) routinely used 3 of the 7 basic communication techniques: simple language, limiting teaching to 2–3 concepts, and speaking slowly. More than 75% of respondents believed that 6 of the 7 basic communication techniques are effective. Sociodemographic provider characteristics and practice characteristics were not significant predictors of the mean number or the total number of communication techniques routinely used by NPs in a week. Potential predictors for using more of the 7 basic communication techniques, demonstrating significance in one general linear model each, were: assessing the office for user-friendliness and ever taking a communication course in addition to nursing school.ConclusionsNPs in Maryland self-reported routinely using some recommended health-literate communication techniques, with belief in their effectiveness. Our findings suggest that NPs who had assessed the office for patient-friendliness or who had taken a communication course beyond their initial education may be predictors for using more of the 7 basic communication techniques. These self-reported findings should be validated with observational studies. Graduate and continuing education for NPs should increase emphasis on health-literate and patient-centered communication techniques to increase patient understanding of dental caries prevention. Non-dental healthcare providers, such as NPs, are uniquely positioned to contribute to preventing early childhood dental caries through health-literate and patient-centered communication.

Collaboration between infrastructures and connected and autonomous vehicles (CAVs) at unsignalised intersections has emerged as an effective strategy for improving transportation efficiency. To enhance the safety and efficiency of CAVs passing through unsignalised intersections, this paper proposes a cooperative vehicle-infrastructure system (CVIS) based multi-vehicle trajectory planning architecture. The architecture comprises a roadside planner and vehicle planners. The roadside planner formulates the problem of generating multi-vehicle trajectories as an optimisation task and addresses it using an improved particle swarm optimisation (PSO) approach along with a space-time voxels strategy to determine the times for vehicles to reach the intersection and the trajectories passing through it. Then, the vehicle planner plans according to the results of the roadside planner. The results show that our approach enables potentially conflicting CAVs to pass through unsignalised intersections without stopping and waiting, ensuring safety while improving traffic efficiency.

As the trend of the new generation of intelligent transportation systems, the application of the cooperative vehicle-infrastructure system (CVIS) is important to reduce traffic congestion and accidents. For the cooperative vehicle-infrastructure system, the roadside unit (RSU) is a new application without enough deployment experience. Meanwhile the deployment of traditional mechanical and electrical infrastructure is generally based on experience and is easy to change the design. This paper analyzes the key technical parameters of RSU and the Digital Measurable Image (DMI) method in context of the CVIS. After collecting field data from a smart highway pilot project (the Deqing-Fuyang expanded Huzhou section in Chang-Shen Highway) and matching the DMI method with highway mileage pile numbers, an RSU deployment scheme based on DMI data was completed. It provides significant support for smart highway demonstration projects with cooperative vehicle-infrastructure intelligent management and control applications.

The premise of implementing an effective traffic control strategy is the accurate traffic state recognition. In the existing study, traffic state recognition methods were processed by using statistical characteristics and long-term scale detection of field traffic data. Hence, the dynamic characteristics and subtle changes in traffic flow were easy to overlook. At present, more and more advanced traffic detection technology provides reliable and accurate data for measuring and distinguishing the state of urban road traffic, such as the cooperative vehicle-infrastructure system, wide-area radar technology, and 5G technology. This study proposes a novel method called HTSI (High Precision Traffic State Identification Method), which is based on the advanced detection technology in traffic state recognition at the intersection: The raw data used for intersection traffic state recognition is high-precision detection data of tracking characteristics, which make the data look like a picture of the intersection at God’s perspective. To this end, we construct an image model for intersections and implement image feature extraction in a way that is different from traditional image processing. Then, the traffic state recognition problem at the intersection is translated into an image searching problem with tags. The image searching is realized by the hashing algorithm. Finally, the comprehensive experiments prove that the proposed method is more accurate and finer than other methods.

Development of a novel organic luminescent material for inkless writing, and self-erasing application is a remarkable solution to reduce paper waste, recycling cost in the printing industry. These innovative materials also offer to reduce global warming due to the lower consumption of paper made from plants. To this endeavor, herein we report the design, synthesis and simple material fabrication of a donor-acceptor type (Z)-1,2-diarylacrylonitrile (1) compound, which in solid state displayed highly contrast and reversible vapochromism under the visible light as well as UV light. We found a unique multiphase luminescence switching from green (λmax = 535 nm) to yellow (λmax = 566 nm) and to orange-red (λ = 580, 640 nm) in solid state. This multiphase switching is induced through the gradual exclusion of entrapped DMSO molecules from the crystalline rod-like materials. Utilizing this purely organic material, we have demonstrated reversible inkless writing/printing on a cellulose strip by employing photothermal effect of sunlight in which sunlight acts as an "inkless pen". We were able to print complex designs utilizing this technique, which is invisible in ambient light and brighter in UV light. To our delight, the writing is self-erasable on keeping at sunlight for the prolonged period, upon keeping at ambient temperature, or instantly on warming. This remarkably smart function of our material offers cost-effective and environmentally benign technique for security data communication and confidential data printing.

This chapter addresses the growing interest in using screen-based maps on the Internet for collaborative planning and design. Specifically, it introduces six prototype web interfaces that invite users to read maps of their community and then make selections and type in comments in order to provide feedback into a planning process. Each of the six prototypes highlights a different tool or feature for navigating or making selections on online maps. New web technology has made it possible to create map-based surveys to receive feedback from the general public, but it is not yet clear what kind of graphic design alternatives and digital map designs and tools are the most useful for novice map-readers. This chapter describes several different interface designs with the purpose of finding which combinations of online tools and maps are most productive in soliciting feedback in a community planning process. The goal is to explore how web-based maps can be advanced beyond mere information provision to actual two-way interaction with the public. Tools such as free-hand drawing may provide new avenues for people to take a greater role in public decisionmaking. Examples are presented of solutions to the basic problems of creating interactive screen-based maps, which include navigating large geographic area maps and making selections on maps.

The development of a telecontrol system for a ship-mounted two-DoF manipulator is greatly required since it must be controlled from the control room of a ship and subjected to random ocean waves. This paper addresses such a task by developing a human-machine interface (HMI) based graphical user interface (GUI). The features cover control and monitoring of the manipulator system as well as object tracking. The HMI is embedded in a console box via a personal computer (PC) and capable of accessing sensors and actuators through a microcontroller. Data processing and communication techniques are also proposed to avoid overlap or lag for real-time implementation. Experimental results show that the telecontrol system successfully monitors and control the manipulator system as well as track the objects using an image processing algorithm.

Smart city systems are increasingly built with data analytics and machine learning techniques, basing on massive data sets. They have a heavy impact on human behavior and quality of life, and thus need to deliver a controllable and sufficiently transparent experience for the users.The aim of my work is to make smart city systems more interoperable and explainable, involving data visualization and communication techniques, sensor data processing, as well as the associated intelligent data value chain production and consumption. Here, the data and the information are shared employing Knowledge Graphs, that are becoming a key enabler for large-scale processing of massive collections of interrelated facts. Examples include the Google Knowledge Graph with dozens of billion facts, dataCommons, DBPedia, YAGO, and Knowledge Vault, a very large scale probabilistic knowledge graph created with information extraction methods for unstructured or semi-structured information. Specifically, Knowledge Graphs provide the means of development of the newest methods for data management, data fusion, data merging, and graph and network optimization and modeling, serving as a source of high quality data and a base for information integration.In particular, Knowledge Graphs help to infer new relationships out of existing facts, giving context and meaning to the content, and can be used in applications. For example, the data generated by a computer vision system could be semantically represented and shared across numerous systems, taking into account the needs and requirements of these systems, as well as the context, provenance, licensing and consent aspects of the generated data. I demonstrate Knowledge Graphs-based methods in advanced smart city applications from the domains such as automation and construction of buildings, energy efficiency, tourism, transport.