Efficient Context-Aware Vehicular Traffic Re-Routing Based on Pareto-Optimality: A Safe-Fast Use Case

Vehicular traffic re-routing is key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System, which intends to improve not only mobility but also driving experience and safety of drivers and passengers. Context-aware and multi-objective re-routing approaches will play an important role in traffic management. However, most of these approaches are deterministic and can not support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, and, thus, degrade the overall traffic efficiency. In this work, we introduce Safe and Sound (SNS), a non-deterministic multi-objective re-routing approach for improving traffic efficiency and reduce public safety risks (based on criminal events) for drivers and passengers. SNS employs a hybrid architecture and a cooperative re-routing approach for improving system scalability and computation efforts. SNS uses a recurrent neural network to both predict future safety risks dynamics and enable a personalized re-routing in which each vehicle decides the risks it wants to avoid. Simulation results revealed that when compared to state-of-the-art approaches, SNS reduces the CPU time of the re-routing algorithm in approximately 99% and decreases the average safety risk for drivers and passengers in at least 30% while keeping efficient traffic mobility.

Vehicular traffic re-routing is the key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System (TMS), which intends to improve mobility, driving experience, and safety of drivers and passengers. In this scenario, context-aware and multi-objective re-routing approaches will play an important role in traffic management, considering different urban aspects that might affect path planning decisions such as mobility, distance, fuel consumption, scenery, and safety. There are at least three issues that need to be handled to provide an efficient TMS, including: (i) scalability; (ii) re-routing efficiency; and (iii) reliability. In this way, this thesis contributes to efficient and reliable solutions to meet future TMSs. The proposed solutions were widely compared with other related works on different performance evaluation metrics. The evaluation results show that the proposed solutions are efficient, scalable, and cost-effective, pushing forward state-of-the-art traffic management systems.

Vehicular traffic re-routing is the key to provide better vehicular mobility. However, considering just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of a good Traffic Management System (TMS), which intends to improve mobility, driving experience, and safety of drivers and passengers. In this scenario, context-aware and multi-objective re-routing approaches will play an important role in traffic management, considering different urban aspects that might affect path planning decisions such as mobility, distance, fuel consumption, scenery, and safety. There are at least three issues that need to be handled to provide an efficient TMS, including: (i) scalability; (ii) re-routing efficiency; and (iii) reliability. In this way, this thesis contributes to efficient and reliable solutions to meet future TMSs. The proposed solutions were widely compared with other related works on different performance evaluation metrics. The evaluation results show that the proposed solutions are efficient, scalable, and cost-effective, pushing forward state-of-the-art traffic management systems.

A two-context-aware approach for navigation: A case study for vehicular route recommendation

Vehicular traffic re-routing is the key to provide better traffic mobility. However, taking into account just traffic-related information to recommend better routes for each vehicle is far from achieving the desired requirements of proper transportation management. In this way, context-aware and multi-objective re-routing approaches will play an important role in traffic management. Yet, most procedures are deterministic and cannot support the strict requirements of traffic management applications, since many vehicles potentially will take the same route, consequently degrading overall traffic efficiency. So, we propose an efficient algorithm named as Better Safe Than Sorry (BSTS), based on Pareto-efficiency. Simulation results have shown that our proposal provides a better trade-off between mobility and safety than state-of-the-art approaches and also avoids the problem of potentially creating different congestion spots.

Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment

Purpose–The study aims to develop an innovative approach for monitoring traffic and disaster incidents in the Public and Safety Office of Urdaneta City. This involves implementing image-based processing algorithms through strategically positioned CCTV cameras on city streets. Specific research objectives include identifying user requirements for the monitoring system, determining the suitable image-processing framework, and assessing the acceptance of the developed system. Method–The research primarily focuses on designing and developing an image processing-based traffic and disaster monitoring system. Adopting Extreme Programming (XP) as the software development methodology, the researchers prioritize rapid deliverable production and a collaborative environment between developers and clients. The study employs a descriptive research approach, utilizing quantitative analyses for data collection. Various instruments, such as interviews, survey questionnaires, observations, and literature reviews, were employed to gather user requirements and feedback. ISO 9126 was utilized for assessing user acceptability, offering a structured approach to evaluating software quality. Results–The study aimed to streamline traffic management for the management team by developing a digital system. The focus was on the role of CCTV cameras in reducing crime and traffic violations. Findings highlighted the effectiveness of CCTV installations, particularly at red lights and intersections. Interviews with the Public Order and Safety Office in Urdaneta City emphasized the challenges in manual monitoring and the importance of adhering to safety rules. Collaboration with the PNP Urdaneta highlighted the need for timely responses to incidents. The study underscores the role of technology, collaboration, and efficient reporting in enhancing traffic management and public safety. Conclusion–In this study, our focus was on creating an image recognition-based traffic and incident monitoring system utilizing video surveillance cameras for implementation in Urdaneta City. The following conclusions have been derived: The project requirements were meticulously analyzed by examining the existing business rules and policies of POSO Urdaneta City in incident monitoring implementation, influencing the design and development of the proposed system. While YOLOv3 proved efficient with its AI-based features for achieving research goals, its resource-intensive nature and limited small object detection capacity suggest considering alternative versions for enhanced performance in similar algorithm development. User acceptability testing results reveal a high acceptance level (GWM of 4.5), signifying satisfaction among system implementers. However, the researchers recommend additional technical testing on the CCTV devices for further refinement. Recommendations–The research work has provided means of traffic monitoring through the use of technological innovations. Thus, to support the successful implementation of these technologies, the organization should maintain a sufficient working environment for these tools.Research Implications–This undertaking provides insights as an administrative strategy to enhance traffic management and monitoring procedures using image-based detection. This study can be used to minimize errors and provide comprehensive and evidence-based documentation for traffic and disaster management that will be used in the future. Social Implications–This research endeavor aimed to be part of the mechanism to provide a safer and more secure environment for the community enhancing their safety and security. Keywords–cross capture cam, image detection, disaster management, traffic management

Recently, many cities are facing challenging mobility and safety issues. The former is commonly related to traffic congestion, as a consequence of uncontrolled population growth and accelerated urbanization. The latter regards to elevated number of city-wide criminal incidents. Several Intelligent Transportation Systems (ITS) were proposed to overcome mobility issues; meanwhile, some safety- based systems were proposed to guide pedestrians and drivers toward safest paths. However, most of these systems tackle only one of the issues. Hence, an ITS can guide vehicles toward risky areas, in order to avoid traffic congestion, while a safety-based system can guide them toward congested roads, focusing on the safety of drivers and passengers. This paper introduces itsSAFE (Intelligent Transportation Systems for improving SAfety and traFfic Efficiency), an ITS which employs accurate knowledge about traffic conditions and unsafety levels on roads for improving the safety of drivers and passengers at the same time it deals with traffic congestion. Simulation results under a realistic scenario have shown that itsSAFE outperformed state-of-the-art approaches that deal with mobility or safety issues, by effectively dealing with traffic efficiency and safety.

Aim. In today’s major cities, increased utilization and capacity of the rapid transit systems (metro, light rail, commuter trains with stops within the city limits) – under condi[1]tions of positive traffic safety – is achieved through smart automatic train traffic management. The aim of this paper is to choose and substantiate the design principles and architecture of such system.Methods. Using systems analysis, the design principles and architecture of the system are substantiated. Genetic algorithms allow automating train traffic planning. Methods of the optimal control theory allow managing energy-efficient train movement patterns along open lines, assigning individual station-to-station running times following the principle of mini[1]mal energy consumption, developing energy-efficient target traffic schedules. Methods of the automatic control theory are used for selecting and substantiating the train traffic algorithms at various functional levels, for constructing random disturbance extrapolators that minimize the number of train stops between stations.Results. Development and substantiation of the design principles and architecture of a centralized intelligent hierarchical system for automatic rapid transit traffic management. The distribution of functions between the hierarchy levels is described, the set of subsystems is shown that implement the purpose of management, i.e., ensuring traffic safety and comfort of passengers. The criteria are defined and substantiated of management quality under compensated and non-compensated disturbances. Traffic management and target scheduling automation algorithms are examined. The application of decision algorithms is demonstrated in the context of uncertainty, use of disturbance prediction and genetic algorithms for the purpose of train traffic planning automation. The design principles of the algorithms of traffic planning and management are shown that ensure reduced traction energy consumption. The efficiency of centralized intelligent rapid transit management system is demonstrated; the fundamental role of the system in the digitalization of the transport system is noted.Conclusion. The examined design principles and operating algorithms of a centralized intelligent rapid transit management system showed the efficiency of such systems that ensured by the following: increased capacity of the rapid transit system; improved energy efficiency of train traffic planning and management; improved train traffic safety; assurance of operational traffic management during emergencies and major traffic disruptions; improved passenger comfort.

Using voice recognition to measure trust during interactions with automated vehicles

Recently ride-sharing platforms have struggled with a decreased supply of drivers, which has negatively impacted their passengers, by subjecting them to long delays and extremely high surge prices. An approach for mitigating these problems is for service providers to facilitate and coordinate carpooling via the recommendation of individually curated paths, not necessarily the shortest, for drivers towards completing their chosen rides. In this paper, we redesign the Weight Evolving Temporal graph structure to efficiently encode large dynamic road networks with temporal ride availability. Leveraging that graph structure, we efficiently define a polynomial-time optimal route recommendation algorithm that increases carpooling opportunities, taking into consideration the spatio-temporal constraints of both drivers and rides in such a highly-dynamic setting. Finally, we use simulations to demonstrate the effectiveness of these route recommendations, on both the driver and passenger experience.

Autonomous driving has many positive impacts, such as improving driver and passenger safety, comfort, and traffic efficiency, but all these advantages are based on people’s trust and acceptance of this mode of driving. Anthropomorphic driving can enhance the trust and comfort of drivers and passengers and is seen as a feasible measure to increase people’s acceptance of autonomous driving. This paper reports the microscopic traffic simulation of three scenarios around a frequently congested intersection, using non-automated vehicles, autonomous driving vehicles, and anthropomorphic driving vehicles to explore their impact on traffic efficiency. The result shows that, compared to non-automated vehicles, both autonomous vehicles and anthropomorphic driving vehicles can improve traffic efficiency during congested periods, increase traffic volume per unit time, reduce the total travel time and time loss, and have a higher average speed. Compared to autonomous vehicles, anthropomorphic driving has a shorter total travel time and a similar time loss. In terms of average speed, anthropomorphic driving performed better than autonomous driving in terms of both congested and non-congested times. In summary, compared to non-automated vehicles, autonomous driving vehicles have a positive effect on improving traffic efficiency, while, compared to autonomous driving, anthropomorphic driving has more advantages in increasing traffic efficiency and reducing traffic congestion.

Intelligent transportation systems (ITS) have attracted much interest among researchers and practitioners in the past decade. The main objectives of ITS are to improve the efficiency of transportation networks, enhance traffic safety, and reduce delays and negative environmental effects by utilizing real-time or predicted traffic information. To this end, six system functions have been identified: advanced traveler information systems (ATIS), advanced traffic management systems (ATMS), commercial vehicle operations (CVO), advanced vehicle control systems (AVCS), advanced public transportation systems (APTS) and advanced rural transportation systems (ARTS). (In 1996, US DOT reclassified 29 ITS user services into six function groups, i.e., travel and traffic management, commercial vehicle operations, public transportation management, electronic payment, emergency management, and advanced vehicle control and safety systems. However, use of the previous acronyms persist.) The application of ITS technologies is characterized by the exchange of vast amounts of data among transportation system users, vehicles, transportation operators, and transportation infrastructure, which makes possible the warning and avoidance of congestion or hazardous conditions, automatic collection of tolls, efficient dispatching of trucks and buses, dramatic improvements in traffic safety and many other benefits. In order to advance the development of such technologies, both hardware and software components must become more sophisticated; otherwise, the full benefit of ITS cannot be attained.

The development of road transport requires cities to have a multi-faceted traffic management system. It is particularly true in city centers, where public transport modes (buses and trams) are also being added in addition to individual vehicles. The article is based on field research carried out, for which the case study was the city center of Szczecin. The article proposes a three-stage research methodology, consisting of an inventory of the current state of the selected infrastructure section, surveys of traffic density and travel time, and analysis of the data obtained (field research), which are the key to selecting locally appropriate ITS-class devices. The actions taken resulted in selecting ITS systems according to criteria such as improvement of traffic safety and fluidity, improvement of the overall traffic management and information system, and improvement in the functioning of public transport. As part of the work, many devices and systems were identified that can significantly improve the collective and individual mobility of the studied street section. These include intersection monitoring, accommodating signaling, a tram priority system, and passenger and parking information boards. This article aims to present the possibilities of implementing ITS systems in city centers, taking into account the specific individual road conditions and the need to improve vehicle flow management, using the example of a ready-made proposal for the city of Szczecin, using the minimum necessary equipment.

Privacy-preserving Authentication Scheme for VANETs using Blockchain Technology