Attitudes toward Applying Facial Recognition Technology for Red-Light Running by E-Bikers: A Case Study in Fuzhou, China

The application of facial recognition technology (FRT) can effectively reduce the red-light running behavior of e-bikers. However, the privacy issues involved in FRT have also attracted widespread attention from society. This research aims to explore the public and traffic police’s attitudes toward FRT to optimize the use and implementation of FRT. A structured questionnaire survey of 270 people and 94 traffic police in Fuzhou, China, was used. In the research, we use several methods to analyze the investigation data, including Mann–Whitney U test, Kruskal–Wallis test, and multiple correspondence analysis. The survey results indicate that the application of FRT has a significant effect on reducing red-light running behavior. The public’s educational level and driving license status are the most influential factors related to their attitudes to FRT (p < 0.001). Public members with these attributes show more supportive attitudes to FRT and more concerns about privacy invasion. There are significant differences between the public and traffic police in attitudes toward FRT (p < 0.001). Compared with the public, traffic police officers showed more supportive attitudes to FRT. This research contributes to promoting the application of FRT legitimately and alleviating people’s concerns about the technology.

Mining the Coopetition Relationship of Urban Public Traffic Lines Based on Time Series Correlation

Along with the evolution of passenger flows within cities, the coordination between public traffic lines should be sustainably optimized with respect to the spatial distribution of the flow, though the lines were planned well at the beginning of the construction. It is critical to determine the coopetition between bus lines to optimize a transit network continuously. A method of mining coopetition relationship (MCBTC, Mining Coopetition relationship between Bus lines based on a Time series Correlation) based on passenger flow is proposed in this study. First, noisy, inconsistent or missing data are eliminated to obtain a passenger flow time series, and the proposed merging algorithm is used to extract the line passenger flow time series (LPFTS, Line Passenger Flow Time Series) by merging the passenger flow of adjacent buses from the same line. Then, to calculate the positive and negative correlation sequence sets, a clustering algorithm is proposed. The two sequence sets represent the competition and cooperation relationships, respectively. The MCBTC method has been tested with a practical data set, and the results show that it is very promising.

Active Safety Planning Method for Transportation Network Design

Traffic accidents are frequent although various countermeasures are introduced. Traffic safety cannot be fundamentally improved if it is not considered in the transportation network design stage. Although it is well known that traffic safety is one of the most important concerns of the public, traffic safety is not adequately accommodated in transportation planning. This paper considers traffic safety as a major criterion in designing a transportation network. It is a kind of proactive measure rather than reactive measure. A bilevel programming model system is proposed where the upper level is the urban planners’ decision to minimize the estimated total number of traffic accidents, and the lower level is the travelers’ response behaviors to achieve transportation system equilibrium. A genetic algorithm (GA) with elite strategy is proposed to solve the bilevel model. The method of successive averages (MSA) is embedded for the lower level model, which is a feedback procedure between destination choice and traffic assignment. To demonstrate the effectiveness of the proposed method and algorithm, an experimental study is carried out. The results show that these methods can be a valuable tool to design a safer transportation network although efficiency, in terms of system total travel time, is slightly sacrificed.

Global Optimization Algorithm Based on Kriging Using Multi-Point Infill Sampling Criterion and Its Application in Transportation System

Public traffic has a great influence, especially with the background of COVID-19. Solving simulation-based optimization (SO) problem is efficient to study how to improve the performance of public traffic. Global optimization based on Kriging (KGO) is an efficient method for SO; to this end, this paper proposes a Kriging-based global optimization using multi-point infill sampling criterion. This method uses an infill sampling criterion which obtains multiple new design points to update the Kriging model through solving the constructed multi-objective optimization problem in each iteration. Then, the typical low-dimensional and high-dimensional nonlinear functions, and a SO based on 445 bus line in Beijing city, are employed to test the performance of our algorithm. Moreover, compared with the KGO based on the famous single-point expected improvement (EI) criterion and the particle swarm algorithm (PSO), our method can obtain better solutions in the same amount or less time. Therefore, the proposed algorithm expresses better optimization performance, and may be more suitable for solving the tricky and expensive simulation problems in real-world traffic problems.

CITY2NAVIGATION (C2N) – A NEW SERVICE FOR DIGITAL, FUTURE-PROOF URBAN TRAFFIC MANAGEMENT

Digital traffic management solutions are essential for the effective management of the continuing growth of road traffic. However, technical possibilities for implementing traffic management strategies by Traffic Control Centres are limited to a strategic network. At the same time, modern mobility apps from routing services offer road users many options to get informed and guided. The latter usually work independently from traffic management strategies of public authorities. Thus, different route recommendations not only lead to uncertainty of road users, but ultimately to a reduction in the effectiveness of the public traffic management strategies. A direct information exchange between both systems does not yet exist. This however would be a key to more efficient traffic management.City2Navigation therefore developed a technical concept for a nationwide implementation of a digital data exchange service (C2N service) to link public traffic management with routing services of private vendors. This service fills the gap between both group of actors, thereby serving as a crucial building block for digital traffic management in response to the goals of European and national frameworks for Intelligent Transport Systems (ITS).The C2N service not only promotes the cooperation of public authorities with private routing services, it also offers a variety of opportunities to develop new business models. It is a complement to C2C and C2X communication solutions in road transport, ultimately also enabling future possibilities for municipalities to conduct efficient and sustainable traffic management.

Unsettled Issues Regarding Visual Communication Between Automated Vehicles and Other Road Users

As automated road vehicles begin their deployment into public traffic, and they will need to interact with human driven vehicles, pedestrians, bicyclists, etc. This requires some form of communication between those automated vehicles (AVs) and other road users. Some of these communication modes (e.g., auditory, motion) were discussed in “Unsettled Issues Regarding Communication of Automated Vehicles with Other Road Users.” Unsettled Issues Regarding Visual Communication Between Automated Vehicles and Other Road Users focuses on sisual communication and its balance of reach, clarity, and intuitiveness. This report discusses the different modes of visual communication (such a simple lights and rich text) and how they can be used for communication between AVs and other road users. A particular emphasis is put on standardization to highlight how uniformity and mass adoption increases efficacy of communications means.

The Effects of Residential Built Environment on Supporting Physical Activity Diversity in High-Density Cities: A Case Study in Shenzhen, China

In high-density cities, physical activity (PA) diversity is an essential indicator of public health and urban vitality, and how to meet the demands of diverse PA in a limited residential built environment is critical for promoting public health. This study selected Shenzhen, China, as a representative case; combined the diversity of PA participants, types, and occurrence times to generate a comprehensive understanding of PA diversity; fully used data from multiple sources to measure and analyze PA diversity and residential built environment; analyzed the relationships between the built environment and PA diversity; and explored the different effects in clustered and sprawled high-density urban forms. PAs in clustered areas were two times more diverse than those in sprawled areas. Accessibility, inclusiveness, and landscape attractiveness of residential built environment jointly improved PA diversity. Clustered areas had significant advantages in supporting PA diversity since they could keep the balance between dense residence and landscape reservation with an accessible and inclusive public space system. The residential built environment with dense street networks, public traffic and service, multi-functional public space system, and attractive landscapes is crucial to improve the diverse PA to achieve more public health outputs in high-density cities. To promote health-oriented urban development, clustered urban form is advocated, and step-forward strategies should be carried out.

Loss to follow-up after surgery in patients with hip fractures

Background: Non-attendance with scheduled postoperative follow-up visits remains a common problem in orthopaedic clinical researches. The goal of this study was to determine the risk factors for loss to follow-up of hip-fracture patients postoperatively. Methods: A 1-year postoperatively retrospective analysis was conducted on patients who underwent surgery for hip-fractures between January 2017 and December 2018. According to whether they finished the appointed follow-up schedule, the patients were divided into two groups: LTFU Group (Follow-up loss group) and FU Group (Follow-up group). Electronic Medical Records (EMR) was examined to identify the patients’ variables of interest and telephone or text message interviews were attempted on those who didn’t return for follow-up to determine the reasons for loss to follow-up. The baseline characteristics between the 2 groups were compared and the statistical differences were analyzed by logistic regression. Results: 1041 patients met the inclusion criteria were included in this study, of which 212 (20.37%) were lost to follow-up at 1 year postoperatively. The logistic regression analysis showed that old age at surgery, fracture type, distance to hospital, HA surgery and patients transport to hospital by urban-rural public traffic or bus were found to be risk factors for noncompliance with the follow-up visit. As for the reasons for loss to follow-up in in LTFU Group, 75 patients (35.4%) claimed symptoms improvement, 43 (20.3%) cited difficulties of transportation to hospital, 23 (10.8%) chose other health care institutions, and 57 (26.9%) couldn’t travel to hospital alone. Other reasons including thinking follow-up was unnecessary (n=4, 1.8%), no spare time (n=5, 2.4%) and financial problems (n=5, 2.4%). Conclusion: Loss to follow-up was common in patients with hip-fracture postoperatively. Our study suggested advanced age, difficult transport, long distance, fracture type and surgical procedures were risk factors for noncompliance.

Improvement of methods for assessing the effectiveness of dedicated lanes for public transport

There are different configurations of street and road networks in cities, which is why those transportation models that determine how effectively a public transport network is operated are different. Along with this, some transport areas may have characteristic features predetermined by the density of a street network, the intensity of individual and public traffic. The special feature of the current study is determining the operational effectiveness of dedicated lanes for public transport given a significant density of the main street and road network. Significant density is characterized by its value for the distance between adjacent intersections in the range of 150‒200 m. With such planning patterns, there is a mutual influence of the conditions of individual and public transport between adjacent intersections. An increase in the distance between intersections disrupts the stability of traffic flow through its disintegration into separate groups based on the dynamic characteristics of vehicles. A characteristic feature of the proposed procedure for evaluating the operational effectiveness of dedicated lanes is that the use of a GPS monitoring system makes it possible to relatively quickly determine the areas of the network where there are the greatest delays in movement in real time. After that, attention is focused on investigating the main factors of influence and their parameters followed by modeling. The reported results would in the future contribute to devising a clear sequence of transport-related research based on a set of their methods in order to acquire representative data and define adequate patterns. An important practical result is the use of not only established normative approaches to the design of dedicated lanes, which are common for all types of street and road networks but taking into consideration the peculiarities characteristic of their individual sections.

Building Urban Public Traffic Dynamic Network Based on CPSS: An Integrated Approach of Big Data and AI

The extensive proliferation of urban transit cards and smartphones has witnessed the feasibility of the collection of citywide travel behaviors and the estimation of traffic status in real-time. In this paper, an urban public traffic dynamic network based on the cyber-physical-social system (CPSS-UPTDN) is proposed as a universal framework for advanced public transportation systems, which can optimize the urban public transportation based on big data and AI methods. Firstly, we introduce three modules and two loops which composes of the novel framework. Then, the key technologies in CPSS-UPTDN are studied, especially collecting and analyzing traffic information by big data and AI methods, and a particular implementation of CPSS-UPTDN is discussed, namely the artificial system, computational experiments, and parallel execution (ACP) method. Finally, a case study is performed. The data sources include both traffic congestion data from physical space and cellular data from social space, which can improve the prediction performance for traffic status. Furthermore, the service quality of urban public transportation can be promoted by optimizing the bus dispatching based on the parallel execution in our framework.