Performance evaluation of dissemination protocols over vehicular networks for an automatic speed fine system

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Vehicular accidents cause severe problems in our society including economic, material, and even life losses. The cause of those situations relies on several factors such as traffic density, vehicular flow, lack of traffic signaling and speed limit violations. Some of these problems cannot completely be eliminated but could be mitigated by proposing solutions such as people's awareness or intelligent radars to monitor speed limit violations. This work proposes a system to automatically generate fines in case of speed limit infractions. Our approach uses vehicular networks to monitor the vehicles' speed. We also propose a dissemination protocol to ensure the propagation and delivery of the generated fines at the road-side units, achieving a 94.99% and 99.91% fine delivery rate in urban scenarios with vehicles' densities of 30 and 200 vehicles per km 2 , respectively.This work was supported by the Spanish Government through Research Project ‘‘sMArt Grid using Open Source Intelligence (MAGOS)’’ under Grant TEC2017-84197-C4-3-R.Peer ReviewedPostprint (published version

Intelligent Advisory Speed Limit Dedication in Highway Using VANET

Variable speed limits (VSLs) as a mean for enhancing road traffic safety are studied for decades to modify the speed limit based on the prevailing road circumstances. In this study the pros and cons of VSL systems and their effects on traffic controlling efficiency are summarized. Despite the potential effectiveness of utilizing VSLs, we have witnessed that the effectiveness of this system is impacted by factors such as VSL control strategy used and the level of driver compliance. Hence, the proposed approach called Intelligent Advisory Speed Limit Dedication (IASLD) as the novel VSL control strategy which considers the driver compliance aims to improve the traffic flow and occupancy of vehicles in addition to amelioration of vehicle’s travel times. The IASLD provides the advisory speed limit for each vehicle exclusively based on the vehicle’s characteristics including the vehicle type, size, and safety capabilities as well as traffic and weather conditions. The proposed approach takes advantage of vehicular ad hoc network (VANET) to accelerate its performance, in the way that simulation results demonstrate the reduction of incident detection time up to 31.2% in comparison with traditional VSL strategy. The simulation results similarly indicate the improvement of traffic flow efficiency, occupancy, and travel time in different conditions

A Review of Quality of Service Issues in Internet of Vehicles (IoV)

Recent years compared to the current scenario, the explosive growth of devices connected and controlled by internet is the major significance of internet of things (iot). One such big example is automotive industry. This industry has the potential to become an iot champion among other industries and fuel the iot cloud services adoption among car owners and walkers alike. Vehicles are progressively being associated with the internet of things which empower them to give universal access to data to drivers and travellers while moving. As the connectivity of vehicles keeps increasing in numbers, traditional concepts of vehicles has to be introduced with new layers which includes versatile data transfer among vehicles, consistency, security, toughness, humans and roadside frameworks of vehicular systems shall be taken into consideration. In this unique situation, the first idea of vehicular ad-hoc networks(vanets) is being changed into another idea called internet of vehicles(iov).the advent of iot has changed the traditional vehicular networks in to intelligent vehicular networks called internet of vehicles. Each entity in iov is connected to the internet. In iov, each vehicle is responsible for transmitting and receiving the information. In an environment where vehicles are mobile and at the same time exchanging information to ensure safe driving on the roads and to minimize road accidents. It is very necessary to provide better services within the limited accessibilities. Hence qos plays a vital role in iov. This paper addresses the qos challenges and its significance in iov. Also, this paper also discusses the measurement parameters that could deliberately effect the performance of iov

Development of collision avoidance application using internet of things (loT) technology for vehicle-to-vehicle (v2v) and vehicle-to-infrastructure (v21) communication system

Rising number of road accidents have been a common issue that needs to be given attention where most of it causes fatal injury and death. 30% of accidents are involving rear-to-end crashes meanwhile more than 900,000 cases in a year are related to rearblind-spots. Even though safety improvements have been upgraded such as introduction of Assistance Driving Assistance System (ADAS), yet the numbers are still on its endangering path. To solve this issue, Vehicle Ad-hoc Networks (VANET) system is invented to ensure a safer environment for drivers and pedestrians. Vehicle-toInfrastructure (V2I) and Vehicle-to-Vehicle (V2V) Communication System is one of the technologies created under VANET . This dissertation presented the new V2V and V2I system that is applicable to avoid collisions with development of On-Board Unit (OBU) and Roadside Unit (RSU) prototype using Internet of Things (IoT) technology. Single-Board Computers (SBC) is integrated with sensors such as GPS, LiDAR and ultrasonic for OBU while DHT22, CO gas sensor, PM sensor and rain sensor for RSU. Both OBU and RSU connected to internet via 4G module integrated on the SBC which also function as Apache-MySQL-PHP (AMP) server. Location Tracker, Forward Collision Warning (FCW) and Blind Spot Warning (BSW) application is embedded into OBU located in a vehicle known as a Subject Vehicle (SV). All testing involved with obstacle vehicle known as Host Vehicle (HV) executed at Universiti Malaysia Pahang (UMP) Pekan campus. Finding shows that OBU‟s location is as accurate as 0.0124% in latitude while 0.0084% in longitude in real-time at 60 km/h. Such GPS accuracy allow FCW application to generate alert at CP of 80% to the driver. FCW developed is tested at different speed of SV and HV and findings shows that alert is generated at a safe distance and sufficient time for the driver to react. Throughout the field testing, the new TTC has been successfully formulated and verified where the real time distance has been subtracted to 1 meter over current speed. Collision percentage (CP) of 80% is still generated even though the average lagging time (LT) delay of SBC is recorded at 1.3 seconds. The new formulated TTC and CP proven that the driver has ample time to respond to the generated alert, e.g., for the case of HV is at 0 km/h and SV is at 60 km/h, alert is generated at CP of 84.04% with TTC recorded at 2.4s, which is almost aligned with recommendation of International Organizations of Standardization 2013 stating 2.6s is the best time for driver to react. Even though there was a slight delay with the alerts, with consideration of 1m safe distance and 1.3s LT, driver was able to pull off a safe braking after the alert to slow down SV thus to avoid collision from happening. For BSW application, promising results by having 1 second delay in detecting blinded HV at the constant span of 40km/h speed limit between SV and HV which is an enabler to the safe lane changing operation. The presence of host vehicle (HV) or any obstacles is detected in the blinded area of SV. In contrast to OBU, RSU is developed to monitor the weather which in turn influenced the road conditions and eventually lead to the traffic status monitoring. The RSU‟s sensors are sensitively detected the haze, rain, temperature and humidity accurately. Therefore, this system is potentially to produce Variable Speed Limit (VSL) based on the environment conditions. Speed Limit information from the RSU can be accessed through the OBU inside the vehicles using internet from the 4G technology. Implementation of IoT technology has proven to assist the drivers in avoiding collisions thuspotential to reduce the road accidents

Simulation Exploration of the Potential of Connected Vehicles in Mitigating Secondary Crashes

Secondary crashes (SCs) on freeways are a major concern for traffic incident management systems. Studies have shown that their occurrence is significant and can lead to deterioration of traffic flow conditions on freeways in addition to injury and fatalities, albeit their magnitudes are relatively low when compared to primary crashes. Due to the limited nature of crash data in analyzing freeway SCs, surrogate measures provide an alternative for safety analysis for freeway analysis using conflict analysis. Connected Vehicles (CVs) have seen compelling technological advancements since the concept was introduced in the 1990s. In recent years, CVs have emerged as a feasible application with many safety benefits especially in the urban areas, that can be deployed in masses imminently. This study used a freeway model of a road segment in Florida’s Turnpike system in VISSIM microscopic simulation software to generate trajectory files for conflict analysis in SSAM software, to analyze potential benefits of CVs in mitigating SCs. The results showed how SCs could potentially be reduced with traffic conflicts being decreased by up to 90% at full 100% composition of CVs in the traffic stream. The results also portrayed how at only 25% CV composition, there was a significant reduction of conflicts up to 70% in low traffic volumes and up to 50% in higher traffic volumes. The statistical analysis showed that the difference in average time-to-collision surrogate measure used in deriving conflicts was significant at all levels of CV composition

The use of real-time connected vehicles and HERE data in developing an automated freeway incident detection algorithm

Traffic incidents cause severe problems on roadways. About 6.3 million highway crashes are reported annually only in the United States, among which more than 32,000 are fatal crashes. Reducing the risk of traffic incidents is key to effective traffic incident management (TIM). Quick detection of unexpected traffic incidents on roadways contribute to quick clearance and hence improve safety. Existing techniques for the detection of freeway incidents are not reliable. This study focuses on exploring the potential of emerging connected vehicles (CV) technology in automated freeway incident detection in the mixed traffic environment. The study aims at developing an automated freeway incident detection algorithm that will take advantage of the CV technology in providing fast and reliable incident detection. Lee Roy Selmon Expressway was chosen for this study because of the THEA CV data availability. The findings of the study show that emerging CV technology generates data that are useful for automated freeway incident detection, although the market penetration rate was low (6.46%). The algorithm performance in terms of detection rate (DR) and false alarm rate (FAR) indicated that CV data resulted into 31.71% DR and zero FAR while HERE yielded a 70.95% DR and 9.02% FAR. Based on Pearson’s correlation analysis, the incidents detected by the CV data were found to be similar to the ones detected by the HERE data. The statistical comparison by ANOVA shows that there is a difference in the algorithm’s detection time when using CV data and HERE data. 17.07% of all incidents were detected quicker when using CV data compared to HERE data, while 7.32% were detected quicker when using HERE data compared to CV data

Analyzing Benefits of Connected Vehicle Technologies During Incidents on Freeways and Diversion Strategies Implementation: A Microsimulation-Based Case Study of Florida\u27s Turnpike

The full utilization of connected vehicles (CVs) is highly anticipated to become a reality soon. As CVs become increasingly prevalent in our roadway network, connected technologies have enormous potential to improve safety. This study conducted a microscopic simulation to quantify the benefits of CVs in improving freeway safety along a 7.8-mile section on Florida’s Turnpike (SR-91) system. The simulation incorporated driver compliance behavior in a CV environment. The simulation was implemented via an existing VISSIM network model partially developed by the Florida Department of Transportation (FDOT). In addition, the study analyzed how CVs would assist in detour operations as a strategy for congestion management during traffic incidents on freeways. The Surrogate Safety Assessment Model (SSAM) software was used to evaluate the benefits of CVs based on time-to-collision (TTC) as the performance measure. The TTC was evaluated at various CV market penetration rates (MPRs) of 0%, 25%, 50%, 75%, and 100%. The results showed a decreasing trend of conflicts for morning and evening peak hours, especially from 25% to 100% CV MPRs. The benefits were statistically significant at a 95% confidence level for high CV MPR (above 25%). Upon an incident on the freeway, at higher CV MPRs simulations, the detour strategy seemed to reduce travel time on the freeway. Besides, the detour strategy was more helpful when the incident clearance duration lasted more than 30 minutes. Findings from this study may help the incident management process prepare for detour strategies based on the severity of the incident at hand and could explain the importance of CVs in supporting warning and management strategies for drivers to improve safety on freeways. Keywords: Conflicts, Connected Vehicles, Driver Compliance Rate, Detour, Incident Modeling, Safety Surrogate Measure

Challenges in artificial socio-cognitive systems: A study based on intelligent vehicles

This record contains the (video) data and source code created in relation to the submitted thesis of the same title.The videos included in this collection have been derived using the 3D view components included in the BSF software framework, during a number of scenarios explained more fully in the related thesis: "Challenges in artificial socio-cognitive systems: A study based on intelligent vehicles" Additional views such as the graph views have been created from the rdfUtilities package. These scenarios can be re-run by using the included version of the BSF framework which is provided as zip file. From the command line, run "ant -p" to see available projects, which includes the traffic simulation, institutions, 3D view, and more

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data