Speed Limit Violations Research Articles

Managing the road safety risks of last mile deliveries: Do telematics have a role to play?

Objective The research aimed to understand the impact of telematics in reducing traffic violations and crashes among drivers working in parcel delivery and the perceived utility, effectiveness, and acceptability of telematics among drivers using them. The objective was to carry out an online survey among drivers who have telematics versus drivers who do not and compare their behaviors in terms of risks and violations controlling for key demographics an exposure. Methods An online anonymous survey was conducted of 780 home delivery drivers of which 430 used telematics in 2022. The survey was conducted by a fieldwork company and participants were compensated for their time. Univariate and multivariate analysis was conducted on the data. Results Telematics users did not report less crashes than drivers who did not use telematics. Most drivers, irrespective of telematics use agreed that the time pressure of delivery work increased speed limit violations and unsafe or hazardous parking. Multivariate analysis indicated that damage collisions for all drivers were associated with factors such as, driving medium to large vans, hazardous behavior related to parking, and having penalties for violations, especially related to speed. Delivery drivers, while generally facing pressure to speed and take risks to meet delivery schedules, did perceive telematics as a tool for safety enhancement and information dissemination, as well as for the management of performance. Non-telematics users viewed it with skepticism. Conclusions Telematics alone may not ensure the safety of last-mile deliveries and could potentially increase the pressure of deliveries. This research underscores the need for a holistic approach to road safety in the home delivery sector, combining telematics technology with a proactive safety culture.

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management

The influence of roadway characteristics and built environment on the extent of over-speeding: An exploration using mobile automated traffic camera data

Over-speeding is a pivotal factor in fatal traffic crashes globally, necessitating robust speed management strategies to augment road safety. In 2021, the National Highway Traffic Safety Administration reported over 12,000 speed-related fatalities in the United States alone. Previous studies aggregated over-speeding tendency; however, the extent of over-speeding has a significant implication on the crash outcome. This study delves into the prevalence and magnitude of over-speeding in various scenarios, utilizing data from traffic cameras in Edmonton, Canada, and employing a negative binomial statistical model for analysis. The model elucidates the significance and likelihood of over-speeding tendencies by incorporating temporal and built environment variables—year, month, number of lanes, dwelling unit types, school-related, and open green space. Study results indicated that the aggregation of the over-speeding data tends to underestimate the influence of various factors. Notably, the estimated impact of the posted speed limit is up to over two times for the disaggregated models compared to the aggregated model. Further, the summer months exhibit a roughly 25% uptick in speed limit violations for aggregated models compared to about 40% for disaggregated approaches. Conversely, a discernible decline in over-speeding tendencies is observed with camera enforcement, showcasing a 25% reduction over four years. Built environment variables presented mixed results, with one-unit dwellings associated with a 12% increase in over-speeding, while proximity to schools indicated a 10% decrease. These pivotal findings provide policymakers and practitioners with valuable insights to formulate targeted interventions and countermeasures to curtail speed limit violations and bolster overall road safety conditions.

Speed kills? Quantifying the association between police traffic stops, types of stops, and motor vehicle collisions

BackgroundMotor vehicle crashes (MVCs) are a leading cause of traumatic death and injury. Police traffic stops (PTS) are a common approach to enforcing motor vehicle laws intended to prevent MVCs. However, it is unclear which types of PTS are most effective. This study examined the relationship of PTS subtypes among municipal police patrols on non-interstate roads and MVCs and MVC-related deaths. MethodsPTS subtype data were characterized from six North Carolina cities: Charlotte, Durham, Fayetteville, Greensboro, Raleigh, and Winston-Salem. The primary outcomes of this study were yearly non-interstate MVC and MVC-related death rates per 100 population. The data were analyzed as balanced time-series cross-sectional data. The statistical analysis accounted for time-dependent and city-dependent confounding. We used a two-way fixed effects model to analyze the relationship between PTS and MVC or MVC-related deaths. We also utilized the difference in difference (DID) analysis to analyze if the reduction of PTS following a 2012 policing administrative change in Fayetteville had an association with MVC or MVC-related deaths. ResultsWe found no significant overall association between non-interstate PTS and MVCs (Coeff: -0.00006; p = 0.43) or MVC-related deaths (Coeff: -0.00011; p = 0.15). Panel regression suggested no significant relationship between MVCs and MVC-related deaths and PTS related to driving while impaired (p = 0.36), safe movement violation (p = 0.43), or seatbelt violations (p = 0.17). However, speed limit violations (Coeff: -0.00025; p = 0.032) and stop-light/sign violations (Coeff: -0.00147; p = 0.017) related to PTS significantly reduced MVC-related deaths. The DID regression showed no significant impact on MVCs (p = 0.924) or MVC-related deaths (0.706) before and after the police reform period. ConclusionsThe evidence regarding the absence of an overall association and any association with most PTS subtypes suggest that PTS are not effective for MVC death prevention. Policymakers may proceed with exploring modifications to policing efforts without detriments to public safety as defined by MVC and MVC-related deaths. Level of evidenceRetrospective epidemiological study, level IV

Driving Frequency Modulates Correlations Between Executive Functions and Driving Performance: A Driving Simulator Study.

Driving a car requires a complex combination of various cognitive functions (e.g., visual perception, motor control, decision making, and others), and deficits in any of these processes may compromise driving safety. Amongst these, executive functions such as inhibitory control, task switching, and decision-making are important, as they enable drivers to process information from their surroundings and respond appropriately to changing road conditions. Although previous research has focused on laboratory measures of individual executive functions, it remains unclear whether performance on such laboratory tests readily translates to actual on-the-road driving performance, especially since drivers' skill levels can vary widely, based on their driving frequency. To this end, we divided 30 participants into two categories based on their driving frequency (i.e., daily commuter vs. weekend only drivers), and we used three well-known executive functioning tasks (the stop signal task, Iowa gambling task or IGT, and a task-switching test) to see whether scores on these tasks predicted such driving performances and behaviors such as braking time, lane-keeping, speed limit violations, and inter-vehicle distance (e.g., in a driving simulator). Participants went through a follow-lead-car scenario in the driving simulator for 20minutes and then completed the three executive tasks. We found that stop signal reaction time (SSRT) best predicted driving performance, and remained predictive against driver distraction, as well as variabilities in driving frequency. The IGT predicted speed limit violations in high-frequency drivers, whereas task-switching cost predicted lane keeping performance in low-frequency drivers. Together, these results highlight the importance of driving frequency when considering correlates between executive functions and driving performance and behavior. They also imply that executive tasks better predict driving performance in low-frequency (or inexperienced) drivers, while driver temperament (i.e., impulsiveness as indicated by IGT) better predicted driving performance in high-frequency (or experienced) drivers.

АНАЛИЗ ДОРОЖНО-ТРАНСПОРТНОГО ТРАВМАТИЗМА НА ОБЩЕСТВЕННОМ ТРАНСПОРТЕ В РЕСПУБЛИКЕ ТАТАРСТАН ЗА 2018-2022 гг.

Report. Introduction. Road traffic accidents involving passenger transport vehicles are characterized by highly severe consequences and usually cause massive public outcry. Major accidents involving public transport occur infrequently, but they are always characterized by many victims, injured and dead. Aim of this paper is to study the injury degree of road traffic accidents involving passenger transport vehicles, such as buses, trolleybuses, or trams. Materials and Methods. The study is based on the data made available via the federal statistical observation over road accidents involving motor and public transport in the Republic of Tatarstan over the years 2018-2022. For this purpose, we used generalization methods and quantitative, qualitative, and comparative analysis techniques. Mathematical simulation techniques were used to analyze the data with the computational tools of the MS Excel electronic package. Необходимо повышение осведомленности пассажиров о правилах безопасности и процедурах эвакуации в случае чрезвычайного проис- шествия. Results and Discussion. An acute problem of modern society is the lack of effectively ensuring passenger transport safety, which leads to more frequent road accidents and injuries on the roads. Transporting passengers by urban public transport is one of the most important industries in the economy of the Republic of Tatarstan. Road safety issues are a priority in many strategic and program documents of the socio-economic development of the Republic. Human factor is one of the key factors in ensuring road safety. The most common causes of such accidents are speed limit violations and drivers’ inattention and negligence. Eventually, drivers’ failure to comply with the work-rest ratio leads to accidents and loss of life. There is a need for raising passengers’ awareness of safety rules and emergency evacuation procedures. Conclusions. There are still serious shortcomings in traffic management, leading to accidents with many dead and injured. Implementing measures aimed at preventing road accidents will help enhance road safety and significantly reduce the number of accidents.

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management.

HVD-Net: A Hybrid Vehicle Detection Network for Vision-Based Vehicle Tracking and Speed Estimation

Visual surveillance of on-road vehicles has emerged as a vibrant research topic within the fields of computer vision and Intelligent Transportation Systems (ITSs) to mitigate traffic problems. Road accidents result in traffic congestion, vehicle damage, injuries, and fatalities. The likelihood, severity, and fatality of automobile collisions are all increased by speeding. Autonomous Speed Limit Violation Detection (SLVD) mechanism offers the best way to estimate speed with high precision using Intelligent Transportation Systems (ITSs) technology. Vision-based Vehicle Speed Monitoring (VSM) pipeline is proposed in this work, which includes mechanisms for vehicle detection, tracking, and speed measurement. VSM pipeline is based on a three-tier architecture, exploiting a single RSU-camera. In the first tier, a real-time CNN-based Hybrid Vehicle Detection Network (HVD-Net) is designed for vehicle detection. The HVD-Net utilizes multi-level and multi-scale features to minimize the impact of vehicle scale variation and maximize detection accuracy. Secondly, a Simple Online Real-time Tracker (SORT) along with HVD-Net is adopted to track vehicles' trajectories. The tracker utilizes a Kalman filter for vehicle state estimation and a Hungarian algorithm to solve the multi-vehicle association problem. Finally, a mechanism is presented to estimate the vehicle speed in the motion plane to mitigate speed overestimation. Empirical evaluations on three datasets demonstrate that the proposed vehicle detection, tracking, and speed estimation schemes perform better when compared with the relevant and state-of-the-art schemes. The estimated pipeline vehicle speed is achieved with accuracy of 87.242%.

Evaluation of the driving performance and user acceptance of a predictive eco-driving assistance system for electric vehicles

In this work, a predictive eco-driving assistance system (pEDAS) with the goal to assist drivers in improving their driving style and thereby reducing the energy consumption in battery electric vehicles (BEVs) while enhancing the driving safety and comfort is introduced and evaluated. pEDAS in this work is equipped with two model predictive controllers (MPCs), namely reference-tracking MPC and car-following MPC, that use the information from onboard sensors, signal phase and timing (SPaT) messages from traffic light infrastructure, and geographical information of the driving route to compute an energy-optimal driving speed. An optimal speed suggestion and informative advice are indicated to the driver using a visual feedback. Moreover, the warning alerts during unsafe car-following situations are provided through an auditory feedback. pEDAS provides continuous feedback and encourages the drivers to perform energy-efficient car-following while tracking a preceding vehicle, travel at safe speeds at turns and curved roads, drive at energy-optimal speed determined using dynamic programming in freeway scenarios, and travel with a green-wave optimal speed to cross the signalized intersections at a green phase whenever possible. Furthermore, to evaluate the efficacy of the proposed eco-driving assistance system, user studies were conducted with 41 participants on a dynamic driving simulator. The objective analysis revealed that the drivers achieved mean energy savings up to 10%, reduced the speed limit violations, and avoided unnecessary stops at signalized intersections by using pEDAS. Finally, the user acceptance of the proposed pEDAS was evaluated using the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). The results showed an overall positive attitude of users and that the perceived usefulness and perceived behavioral control were found to be the significant factors in influencing the behavioral intention to use pEDAS.

Evaluating Driver Response to an Advanced Speed Display near Uncontrolled Median Openings

Uncontrolled median openings contribute to more road crashes because drivers do not obey traffic laws such as driving between lanes and following speed limits. This study examined the effectiveness of the different types of signs near an uncontrolled median opening in Mahmoud Abad, Iran. There were Signs 1 and 2 with a single message, while Signs 3 and 4 contained multi-messages. An Internet of Things (IoT) test system was developed to collect and record vehicle speed data before and after each sign installation. Results showed that speeding behavior decreased almost immediately after the road signs were installed and remained almost constant until the beginning of uncontrolled median openings. Moreover, multi-message traffic signs reduce vehicle speeds twice as much as single-message signs. Additionally, only multi-message signs are effective at reducing speed limit violations.

Highway Driving Speed Limiting System With Wi-Fi Module Based on Nodemcu Esp8266

High speed while driving is a factor that causes increased fatalities when traffic accidents happen; accidents due to exceeding the speed limit often occur on freeways or toll roads and often cause fatalities. In order to prevent speed limit violations and reduce fatality rates in the event of an accident, in this research, a device that can limit the speed when driving on the toll road is made by relying on WiFi and the NodeMCU ESP8266 microcontroller. The research method used is the Research and Development (RnD) method. The product trial determines the success rate of the designed tool. This study carried out three stages of testing, namely, the initial trial, the first implementation test, and the second implementation test, for implementation test was carried out using a dyno test as a simulation of driving speed on toll roads and using two variables laying. The results of this study, the speed limiter can be implemented on the vehicle and can function to limit the speed of ± 90 km / h when placed on the APP sensor. The tool can read the sensor voltage signal and send a voltage signal to the ECU according to the programming concept that has been designed.

A Fuzzy Logic-Based Smart Traffic Management Systems

Traffic congestion is a serious problem in many developing countrieslike Egypt. In the presence of thetraditional traffic light system, it creates health risks, wastes time andfuel, and pollutes the air. Therefore, there is a big need for a smart trafficmanagement system. This study contributes in solving this problem byintroducing an artificial intelligence-based smart traffic light system usingfuzzy logic to ensure the smooth flow of traffic in cities. Research resultsindicate that smart traffic light management is very crucial in smart cities toreduce their carbon footprint to save the world and save energy. Other possibleapplication could be implemented with minor system upgrades such as Detectionand Management of traffic Congestion, Automatic Billing of Toll Charges,Automatic detection of speed limit Violation, Route planning, IntelligentInternet of Vehicles, and Prevention of Road Accidents. Four traffic lightapproaches (fixed-time, smart-time, fixed-time-fuzzy logic, andsmart-time-fuzzy-logic) are developed with different eight traffic scenarios.The four approaches are applied with two tools, the first tool is a realMarquette which consists of 4 roads with only one or two vehicles. The secondis a simulation software which consists of 4 roads with more than 100 vehiclesthrough 10 min. Many sensors such as (IR sensors, rain sensors, and LEDs tocontrol traffic light) are used to collect data and then the fuzzy algorithm isused to ensure the smooth flow of traffic in cities. The traffic data acquiredfrom the vehicles is fed into the proposed model to maximize the duration ofthe green light based on the road state. According to experimental results, theproposed smart fuzzy technique simulation software reduced the average waitingtime of the vehicles from 769 to 289 sec in heavy rain condition. It also reducedthe total time for all vehicles from 2490 to 1154 sec.

A multi-country survey for collecting and analyzing facts related to road traffic safety: Legislation, enforcement, and education for safer drivers

Road traffic safety is a crucial global objective. It is important for every country to review and improve road safety policies and strategies, based on an objective understanding of its own road traffic safety situation in the world. With the aim of contributing to such a review and understanding, this study conducted a multi-country survey to gather information about various facts related to legislation, enforcement, and education, which are designed to achieve safer drivers in nine countries, by using a common format. This paper introduces the results of the survey with a particular focus on legislation and enforcement regarding speed limit violations and red-light-running as well as on education within the driving licensing systems. It highlights variations, in terms of penalty settings and enforcement levels, in different countries; it also examines the training durations necessary for acquiring a driving license and license renewal procedures. In this way, it demonstrates the potential and significance of understanding the relative position of each country in terms of road traffic safety through an international comparison despite its limited information.

Increasing the efficiency of investigation methods for rail transport accidents

Increasing volume and speed of rail traffic bring a rise in strain of both rolling stock and rail track. Identifying causes of rail accidents and developing adequate responding guidelines play a large part in ensuring traffic safety. Another key factor is analyzing and estimating the involvement of various deviations in the locomotive-car-track system parameters in a derailment or other catastrophic events. Increased stress on car and rail track elements has led to higher frequency of failures in operation and shorter mileage between overhauls. This in turn has deteriorated the technical condition of the locomotive-car-track system and affected the ongoing tendency of growing derailments of freight cars in curves and on mountainous terrains. In particular, the data obtained from rail accident investigations has shown a two-fold increase in the number of car derailments in the aforementioned conditions as the outcome of deviations in the locomotive-car-track system parameters between 2012 and 2020. For this reason, ensuring traffic safety is increasingly dependent on computational-and-empirical evaluation and danger classification of different deviations in system parameters, as well as development mathematical models of concerning processes and situations. The issue has been partially resolved due to investigations in causes of defects and accidents in the car-railway system, followed by identification of the most dangerous defects and the development of appropriate recommendations aimed at improving traffic safety. However, this approach does not fully consider the effect of violations of speed limit in combination with other deviations, nor the danger level of various deviations of the locomotive-car-track system’s elements when moving through mountain-pass sections of the track. In particular, the influence of the uncanceled lateral acceleration that occurs when a train moves in such sections (track slope between 8 and 25 ‰), including when coupled with extra track subsidence conditions, speed limit violation and abnormal elevation of the outer rail inside a curve, has not been fully assessed. Finally, the influence of wear of wheel rolling surfaces and ridges when moving through curves and mountain-pass sections has also yet to be fully researched. Therefore, it remains a pressing task to advance the computational-and-empirical approach as well as mathematical models for assessing danger levels of individual failures and their combinations in the locomotive-car-track system, and to develop recommendations for identifying accident causes and improving traffic safety in curves and on mountainous terrains.

Comparative analysis of drive-cycles, speed limit violations, and emissions in two cities: Toronto and Beijing

Driving behavior and speed enforcement are both important to road safety and affect vehicle exhaust emissions. Relationships between driving characteristics and safety or emissions have been assessed in multiple studies. However, there is scant information on whether safe driving also reduces emissions and how this relationship changes across urban areas. This study makes use of two similar GPS datasets collected in the metropolitan areas of Toronto and Beijing to conduct a comparative analysis of driving characteristics, speed limit violations, and emissions. Emissions for all trips were computed using the same emission rate database derived from a Portable Emissions Monitoring System (PEMS). We observe that the average speeds in the two cities are close to 25 km/h. In Toronto, the fraction of time spent at speeds over 80 km/h on expressways is 40% higher than in Beijing. We also note a higher level of accelerations in Toronto. The trips in Beijing have approximately 14%, 57%, 14%, and 21% lower emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), and particle number (PN), respectively. Drivers in Toronto violate speed limits in 93% of their trips for 21% of trip travel time while the numbers for Beijing are 43% and 4%. These differences are not necessarily due to driving behavior, but rather to driving characteristics, which encompass the effects of behavior, road network design, traffic congestion, trip patterns, and speed enforcement. A scenario was evaluated by reconstructing drive-cycles to assess the effects of speed limit enforcement for trips where violations were detected. In Toronto, if obeying the speed limit, the mean trip travel time was estimated to increase by 1.8 min. In contrast, trip emissions of CO2, CO, NOx, and PN were found to decrease, on average, by 5.2%, 19.1%, 5.2%, and 2.9%, respectively. Speed limit enforcement can result in lower emissions, by reducing aggressive accelerations.

Alcohol-impaired driving and road safety: Examining the impact of alcohol on driving errors of Indian drivers

Driving error is an intrinsic component of unsafe driving behaviour. Even though alcohol results in substantial crash fatalities on Indian roads, there is hardly any evidence-based research on alcohol impairment in the driving errors of Indian drivers. The present study examined the effects of differentiated Blood Alcohol Concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%) on the driving errors of Indian drivers. 82 volunteers participated in the driving simulator study and drove in rural and urban environments in a randomized and counterbalanced order. Generalized negative binomial regression models were developed for longitudinal and lateral driving error measures. Compared to the sober state, the increments in speed limit violations were 53%, 138% and 143% in rural driving environment, and 68%, 89%, 95% in urban driving environment at 0.03%, 0.05% and 0.08% BACs. While 0.03% and 0.05% BACs did not significantly influence the lane excursions in rural environment, 0.08% BAC resulted in 107% increment; corresponding increments were 71%, 84%, and 148% in urban environment. In rural environment, 0.03%, 0.05% and 0.08% BACs resulted in 28%, 39% and 58% increments in low RPM frequencies (RPM < 1000), and 76%, 95% and 106% increments in high RPM frequencies (RPM > 5000). In urban environment, the increments were 35%, 56% and 71% for low RPMs at 0.03%, 0.05% and 0.08% BACs; whereas, for high RPMs, the increments at 0.05% and 0.08% BACs were 45% and 67% respectively (no significant influence of 0.03% BAC). The study identified the significant risk factors influencing driving errors, thereby sketching the risky driving profiles.

Autonomous Real-Time Speed-Limit Violation Detection and Reporting Systems Based on the Internet of Vehicles (IoV)

Over-/underspeeding is one of the leading causes of road accidents. Traditional systems of detecting and reporting speed-limit violations are not suitable for smart cities. Even the sophisticated conventional systems that use cameras or RFIDs for automating speed-limit violations have several drawbacks, including cost, complexity, reliability, and maintenance. In this paper, we present two systems based on the Internet of Vehicles (IoV) to automatically detect speed-limit violations and autonomously report the committed violations to the authorities. Our systems require no extra hardware or equipment: only the On-Board Unit (OBU), the Road Side Unit (RSU), and the Cloud Server software have to be updated to have a fully functioning system as long as the IoV infrastructure is deployed. One of the systems will be installed on the OBU. A second alternative system design is to use Cloud Servers (CSs) and the IoV beacons that are sent from the vehicles. Additionally, unlike the existing systems installed in specific locations, all roads in the smart cities and highways will be fully monitored. Adaptive fine calculation according to new dynamic policies can be easily integrated into the proposed system. Furthermore, the proposed system can accurately operate in all weather conditions. Moreover, it allows the dynamic adjustment of the speed limits according to the current weather conditions. We have validated the proposed system by building a prototype system that effectively and accurately detects and reports over-/underspeed traffic violations alongside any road.

Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data

Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speeds. This study aims to explore the different effects of the speed management strategies on the speeding proportions at urban and suburban arterials. Probe speed data was used to calculate the speeding proportions. To overcome the variability of probe speed data caused by the signalized intersections, a new method was suggested to calculate the speeding proportion, and a fractional split model was estimated to adjust the probe speed data. A Beta regression model was developed to analyze the speeding proportion. A grouped random parameter modeling structure was adopted to realize the different effects of speed management strategies and other road attributes on speeding proportions by different road types. Besides, a fixed beta model was developed for the comparison. The results suggested the grouped random parameter model could provide better performance over the counterpart and could realize the different effects of road features and other contributing factors on the speeding of different roads. It is expected that the findings could help inform more appropriate road design in order to reduce speed limit violations on urban and suburban arterials.

The Relationship between the Elapsed Time from the Onset of Red Signal until Its Violation and Traffic Accident Occurrence in Abu Dhabi, UAE

Few studies have been carried out in UAE relating red light violations to a number of factors, such as speed limit violations, geometric design of the intersection, and the elapsed time from the onset of red signal until the time of the violation to the occurrence of the accident. This study bridges this gap by attempting to investigate the relationship between the elapsed time from the onset of red signal and the occurrence of the accident. To achieve this objective, Poisson’s regression, between occurrence of accident and elapsed time from the onset of red signal and the occurrence of the accident at various geometric designs of intersections (3-leg and 4-leg), was carried out. The research found that at 4-leg intersections, almost all red light violation related accidents occur between 1 to 2 s from onset of red light until its violation time. The research also showed that at 3-leg intersections, most of red light violation related accidents occur in less than 1 s from the onset of red light until violation time. It was also found that at lead lag signalized intersections, regardless of the type of the intersection, most accidents tend to occur between 2 to 3 s from the onset of red light.