Traffic Authorities Research Articles

Characteristics, Roles and Challenges of Traffic Personnel: Implications toward Efficient Traffic Management System

The purpose of this study was to determine the characteristics, roles and challenges of traffic personnel and their implications toward efficient traffic management system in Bacolod City during the second quarter of calendar year 2018. A mixed methods research design was used which involved the use of both quantitative and qualitative methods by means of survey responded by 150 traffic personnel, key informant interview participated by 3 Barangay Captains and a City Councilor, and focus group discussion participated by 6 traffic personnel which were all selected through a purposive and convenience sampling techniques. Frequency count, percentage, weighted mean, standard deviation, Mann- Whitney U, Kruskal Wallis and IBM SPSS Version 19 were employed to analyze and present the data for quantitative part. While the qualitative part of the study, Thematic Analysis was utilized. The findings showed that traffic personnel who participated in the study were almost equally divided when grouped according to age, while majority were male, attained college level, have less than 7 years of experience and designated as traffic enforcer. Meanwhile, not all completed the required trainings. When it comes to their roles as traffic personnel, it showed that they are mainly managing traffic flow and implementing traffic rules and regulations in the roads. Moreover, it showed that majority of them are highly knowledgeable on City Ordinance 338, and there are no significant differences when they were grouped according to age, sex, educational attainment, and job designation. However, significant differences were found in their level of knowledge on the aforementioned ordinance when they were grouped according to length of service and trainings attended. On the other hand, it was found out that the top most challenge experienced by the participants is the arrogance of drivers. The lack of discipline which includes disregarding of traffic rules and regulations among drivers follows next. Ignorance of the traffic rules and regulations among road users, attitude of drivers, bad weather conditions, high volume of vehicles and road widening projects are also included in the short list of challenges encountered by traffic personnel in the City. Finally, results of this study were used in formulating an enhanced traffic management system program for Bacolod Traffic Authority Office.

Integrating Whale Optimization Algorithm and Machine Learning for Efficient Traffic Management

In order to protect extra retort teams, building labours, and the common community, road needs managing vehicle and pedestrian to manage traffic flows and offer guidance regarding traffic congestion, municipal or state roadway authorities may also utilize CCTV and other methods of traffic surveillance. One optimization framework is used to provide prompt and dependable decision-making by combining the dependable Whale Optimization Algorithms (WOA) and the quick-running Machine Learning (ML) algorithms. First, a standard WOA algorithm is used as a decision variable, with the network’s total journey time serving as the unbiased role. In order to select the good performing model for additional hyper-tuning, multiple regression models are trained to estimate the overall journey interval in the traffic network under study. Finally, a good performance regression model, the extreme-gradient decision tree (XGBT), and WOA algorithm are clustered into a lone optimization outline.

A new Norwegian Injury Registration System as a starting point to assess the burden of injuries in Norway

Abstract Introduction The police register is the main database for monitoring traffic injuries. A study made by Safe Traffic in Norway found that only one of three severe traffic injuries is registered in this register. For giving more complete data, a health-based injury monitoring system was established in 2022 for a period of five years, financed by the traffic safety authorities. Methods An Injury Minimum Data Set (MDS) is collected from all injuries that are treated in ten hospitals and seven municipality AEDs in the primary health care, a representative sample of all medically treated injuries in Norway. By recording the unique birth number of the patient, double registrations are deleted. When fully developed, this system will register about 50 % of all injuries treated in the hospitals as in- and out-patients, and about 10 % of all injuries in the primary health care system in Norway. Max AIS (Abbreviated Injury Scale) and diagnosis (ICD-10 - hospitals, and ICPC-2 - primary health care) are among the registered variables for each injury. Based on the ICD-10 codes, long term disability weights (DW) can be calculated (Gabbe et al 2014). Then the injury burden of injuries in Norway can be shown by: 1) Threat to life: a) minor (AIS 1), b) moderate (AIS 2) and c) serious, (AIS 3+, including AIS 4-6). 2) Threat of disability: a) low, b) medium and c) high probability of long-term disability. Results Data from 2023 seems to be complete enough to enable calculations of the annual injury burden in Norway. Future work There is a need to establish internationally agreed disability weights to enable comparisons across countries. And to establish conversions from ICPC-2 codes to ICD-10 codes for enabling calculations of DWs of injuries treated in the primary health system.

Single-bicycle crashes in Finland – Characteristics and safety recommendations

Problem: Increasing the role of cycling is necessary to reduce physical inactivity. While promoting cycling, attention should also be given to traffic safety. Hence, a better understanding on the underlying factors and safety recommendations of cyclist crashes is needed. This study aims to increase knowledge on fatal single-bicycle crashes (SBCs), where other road users are not collided with. Method: Data from in-depth investigated fatal cyclist crashes in Finland is analyzed from 2010 to 2019. The study presents descriptive analysis of the characteristics, underlying factors, and safety recommendations of SBCs (n = 82) and other cyclist crashes (n = 151). Logistic regression analysis and chi-squared tests were performed to identify significant characteristics for SBCs. Results: Fatal SBCs commonly involved people aged 60 or older, males, and cyclist not wearing a helmet. Cyclist’s health issues influenced the crash in 62.2% of the SBCs. Compared to other cyclist crashes, health issues, alcohol, males, other crash locations than intersections, and weekends were highlighted in SBCs. Safety recommendations emphasized human factors, such as informing cyclist about underlying factors and the use of safety equipment. Discussion: In addition to human factors, the safety recommendations included suggestions regarding the bicycle, the traffic environment, and traffic regulations. This highlights the need to focus on different safety improvement actions to reduce SBCs. This study identified key characteristics of SBCs, which may help traffic safety authorities address this road safety issue and ultimately help to promote cyclist safety. Practical Applications: Cooperation between the actors including health care providers and the police is also proposed to address cyclists’ health issues that contribute to SBCs.

Regulatory options for vehicle telematics devices: balancing driver safety, data privacy and data security

ABSTRACT This article seeks to address the issue of the regulation of telematics in vehicles. The objective is to navigate the need to protect data privacy and data security while enhancing road safety through telematics. Vehicles telematics devices utilizing analytical and predictive technology can help identify and reduce the risk of dangerous driving. Such devices are a growing tool in the insurance industry and amongst vehicle manufacturers, allowing safe driving to be rewarded whilst dangerous driving can be penalized. Data generated through telematics can also be of use to traffic authorities and governments to help with traffic management and planning. As such, the EU is planning to mandate the use of such devices. The growing use of telematics has, however, faced major data privacy and data security concerns. The article evaluates regulatory responses from the US and EU, highlighting specific European countries. The purpose is to find an effective balance through comparative analysis between driver safety, data privacy and data security.

Safely Controlling Commercial Traffic

Luxembourg’s road traffic authority has opted for Kistler’s WIM technology, which weighs trucks and delivery vans without interrupting motorway traffic, to help it meet new EU commercial vehicle enforcement regulations

Dylematy interpretacyjne stosowania sygnałów drogowych ze szczególnym uwzględnieniem sygnalizatora S-2 w „bezpiecznym” zarządzaniu ruchem drogowym. Część I/III

Interpretative dilemmas of the use of road signals with particular emphasis on the S-2 signal in “safe” traffic management The paper is devoted to the issue of signal traffic control in the context of proper design, opinion, and approval of traffic organization, focusing on the safety principles and practices of road users. Formal and legal aspects derived from the basic provisions of the Act on Road Traffic and its executive acts are explained in depth. Both designers and traffic management authorities have a major dilemma about the interpretation of many signal phase systems. This is confirmed by the results of a study - as many as 71% of the analyzed intersections contained errors in the application of the signal allowing turning in the direction indicated by the “green arrow”. In the paper the possibilities of using individual traffic signals on roads and intersections, and the problems associated with the so-called “green arrow” in particular are explained. Interpretative shortcomings regarding the traffic control of public transport means, traffic control at pedestrian crossings and in the “intersection areas”, including the entrance of a non-public road are thoroughly characterized. The paper is divided into three parts. The first one is devoted to the legal basis of signal traffic control in Poland, and description of the rules of traffic participants’ movement in the conditions of traffic controlled by traffic lights. The three-part paper, as a whole, will be crowned by the authors' conclusions in the final part, along with recommendations for amendments to the Act on Road Traffic and executive acts.

Impacts of Red-Light Enforcement Cameras on Start-Up Lost Time at Urban Signalized Intersections

Red-light cameras (RLCs) are used extensively in numerous countries as a tool to regulate traffic and enhance safety at signalized urban intersections. In this regard, a case study conducted in the Dammam metropolitan area has uncovered a potential unintended consequence of RLC usage. The presence of RLCs can lead to an increase in first-vehicle reaction time (FVRT), resulting in a higher start-up lost time (SLT) at these intersections. To investigate this phenomenon, data were collected from two distinct types of locations wherein three intersections were equipped with RLCs and three were without RLCs. A total of 311 observations or cycles were recorded, with 72 belonging to the first type and 239 belonging to the second type. The analysis revealed that the average FVRT for the intersections with RLCs was 4.27 s. For those without RLCs, it was slightly lower, at 3.68 s. Similarly, the average SLT for the RLC-equipped intersections was 3.63 s, while it measured 2.73 s at the non-RLC intersections. These findings highlighted a noticeable disparity in SLT between the two types of intersections. Further statistical analysis confirmed the existence of significant differences in the means of SLT and FVRT for the two categories of intersections. Specifically, for every 1-second delay in FVRT per location, the total SLT at intersections with RLCs increased by 7.5 s. These results underscored the potential impact of RLCs on traffic efficiency and supported the recommendation for implementing a red-light countdown system at intersections with RLCs. Such a system has the potential to reduce the FVRT and enhance traffic flow. These findings emphasize a careful consideration of the implications of RLC implementation and provide valuable insights for traffic management authorities.

A quasi-dynamic air traffic assignment model for mitigating air traffic complexity and congestion for high-density UAM operations

In spite of the significant effects of COVID-19, UAM operations are still expected to grow smoothly and healthily in the near future. If such dense UAM traffic relies on tactical planning to resolve conflicts in a decentralized control scheme, urban airspace could soon be heavily congested and airspace complexity could be overwhelming. In this paper, we propose a Quasi-dynamic Air Traffic Assignment (QATA) model, which aims to allocate traffic flows among air routes in the planning horizon in order to organize UAM traffic flows and reduce air traffic congestion and complexity within a centralized strategic planning scheme while meeting the demand and respecting some criteria. Firstly, UAM traffics are modeled as flows that operate on a 3D two-way UAM route network. Next, the QATA problem is formulated as an optimization problem involving network dynamics to minimize the air traffic complexity evaluated by the linear dynamical system and congestion defined by traffic density and energy consumption. A simulation-based rolling horizon framework is introduced to decompose the QATA problem into several modified static air traffic assignment problems in each time interval. In order to overcome the limitations of conventional dynamic traffic assignment algorithms, a simulated annealing algorithm using parallel computing and a novel neighborhood generation strategy is proposed to efficiently optimize the problem. By applying the model to a pre-designed large-scale UAM route network in Singapore’s urban airspace, Experimental studies demonstrate the performance of the proposed framework and its applicability. Parallel computing can achieve up to three times faster than the original algorithm. The proposed algorithm significantly reduces the value of the objective function by (32.20±0.29)% in 143.47±3.74 seconds at the 95% confidence interval of 100 experiments, far better compared to the representative conventional dynamic traffic assignment algorithms. This study could be useful to assist air traffic control authorities and air navigation service providers in addressing various issues in unmanned traffic management.

A Novel Explanatory Tabular Neural Network to Predicting Traffic Incident Duration Using Traffic Safety Big Data

Traffic incidents pose substantial hazards to public safety and wellbeing, and accurately estimating their duration is pivotal for efficient resource allocation, emergency response, and traffic management. However, existing research often faces limitations in terms of limited datasets, and struggles to achieve satisfactory results in both prediction accuracy and interpretability. This paper established a novel prediction model of traffic incident duration by utilizing a tabular network-TabNet model, while also investigating its interpretability. The study incorporates various novel aspects. It encompasses an extensive temporal and spatial scope by incorporating six years of traffic safety big data from Tianjin, China. The TabNet model aligns well with the tabular incident data, and exhibits a robust predictive performance. The model achieves a mean absolute error (MAE) of 17.04 min and root mean squared error (RMSE) of 22.01 min, which outperforms other alternative models. Furthermore, by leveraging the interpretability of TabNet, the paper ranks the key factors that significantly influence incident duration and conducts further analysis. The findings emphasize that road type, casualties, weather conditions (particularly overcast), and the number of motor and non-motor vehicles are the most influential factors. The result provides valuable insights for traffic authorities, thus improving the efficiency and effectiveness of traffic management strategies.

Smart Assistance for Safe Driving

An accident is a life changing, shocking, abrupt, and unexpected external action which occurs at an instance and at any place, with no apparent fault or deliberate reason but with major impacts. Carelessness of the driving force is the major factor of such accidents. The traffic authorities provide a lot of instructions to the vehicle operators. But many of them don't obey the principles. In today’s day of age ensuring that riders follow safety protocols is much more difficult than anticipated for the government to track and regulate. But still the principles are being violated by the users. so as to beat this we introduce an intelligent system, Smart Helmet, which automatically checks whether the person is wearing the helmet and has nonalcoholic breath while driving. Here we've a transmitter at the helmet and therefore the receiver at the bike. there's a switch wont to sure the wearing of helmet on the top. The switch is in ON condition ensures that the helmet is placed and safely secured by the rider. An alcohol detecting sensor is placed near to the mouth piece of the rider at the inside of helmet to detect the presence of alcohol. The data to be transferred is sent using LoRa Module from transmitter on helmet. The receiver at the bike also receives the data through LoRa. The engine should not be able to turn ON if any of the two conditions is violated. Main control unit has complete control over the function of relay and thus control over the ignition, it is able to control the engine via a relay and an interfacing circuit.

Real-Time Traffic Forecast System for the Accident-Prone Large-Scale Transportation Network in the Seoul Metropolitan Area

This study proposes a reliable and efficient real-time forecasting platform for use in an accident-prone large-scale transportation network. We showed the method could be applied to various roadway sections without any loss of performance or efficiency. Due to its robustness, efficiency, and versatility, the method could be implemented in the Seoul Metropolitan Area to provide traffic authorities and road users with future traffic information even under accident conditions. This is the major contribution of this research and contrasts with state-of-the-art techniques proposed by prior studies, which rely heavily on parameter tuning with large historical datasets and produce only site-specific forecasts with limited prediction horizons under recurrent traffic conditions. The proposed method makes no assumptions about the physical structure of the transportation network and can be applied to different roads under different traffic conditions without time constraints.

Redesign of the communications network based on high availability of traffic management technologies to improve the communication

Rapid urbanisation and rising population mobility have resulted in an increase in the number of vehicles on the road, providing considerable challenges for road traffic management authorities in the form of increased congestion, accidents, and pollution. Researchers in both industry and academia have been attempting to improve the performance of present road Traffic Management Systems in light of the issues that future "smart cities" would face by leveraging advances in sensing, communication, and dynamic adaptive technologies. The interaction between the development of society and the development of technologies does not escape these two factors, which impact each other more profoundly each time. This research is being conducted in response to challenges in Baghdad, Iraq, such as infrastructure, supply, and equipment, which have an impact on the proper performance of work by employees and employees who operate in the business. Fifty individuals from the Iraqi city of Baghdad were hired for the study project. Following that, the communications device research was carried out. Based on the study's findings, a strategy for upgrading Baghdad's communications network using high-availability technology is given. Its execution would help the organisation. We believe that the redesign work provided in this work can also help to improve organisational communicative performance by providing subsidies for network communications and contemplation on the most appropriate internal communication forms for public profile and organisational culture.

Analysis of Accident Sites from Motorcycles among High School Students Using Geographic Information Systems, Sukhothai Province

Road accidents are a major global problem, especially accidents from riding a motorcycle, as these affect both human life and property. Therefore, identifying accident sites is important for accident prevention. This study aimed to analyze the density of accident sites involving motorcycles among high school students in 2019 by using Geographic Information System data (GIS) in Sukhothai Province. In the study, in-depth interviews were used with respondents, including high school students who had accidents on motorcycles, and traffic police officers who were responsible for investigating accidents in schools. In addition, reports of accident sites were used to arrange GIS data layers and analyze the density of the accident sites using Kernel Density Estimation (KDE). The study results revealed that accidents occurred at 217 accident sites in the study area. The map of accident sites and density was created by using GIS data. The areas with accidents in heavy traffic were the roads in the three main districts: Mueang, Si Samrong, and Sawankhalok. Regarding the analysis, accidents were caused by fast cut-off riding, narrow road shoulders, and road users’ non-compliance with traffic regulations. The study results were submitted to traffic authorities, schools, departments responsible for rural roads, and local government organizations, and used for planning and developing models to prevent traffic accidents involving motorcycles among high school students through the student council.

Public transport demand estimation by frequency adjustments

This article addresses the problem of estimating the demand for public transport from two approaches. First, we propose a bilevel optimization problem that allows estimating the demand using historical data and the observed bus frequencies. This model has been applied to small theoretical networks and the transit network of Tandil (a medium-sized city in Buenos Aires, Argentina), showing good results. However, from a practical point of view, the computation time of the algorithm used to solve the bilevel problem is long, reducing its applicability by traffic authorities. To solve this, we propose to use an artificial neural network module that allows to quickly detect if the change in demand is significant enough (for example, beyond a predefined threshold). If it is substantial, the operator can decide to run the algorithm to estimate the demand and take action to adapt the system to the new reality, for example, adapting vehicle frequencies or incorporating more vehicles into the system so that the current demand can be served. The machine learning approach allows it to be used as a fast change detection tool, avoiding running the expensive algorithm for false positives.

Driving status and attitudes of patients with epilepsy holding a driving license in eastern China

PurposePatients with epilepsy (PWE) remain completely and permanently banned from driving under the latest changes to China's laws. The twoaims of this study were, firstly, to evaluate the driving status of PWE with a driving license and the determinants of them continuing to drive; secondly, to investigate awareness and perceptions of epilepsy driving restrictions among PWE and the general population. MethodsPatients with epilepsy with a driver’s license who attended the Fourth Affiliated Hospital and the Second Affiliated Hospital of Zhejiang University for treatment of their condition were invited to participate in a questionnaire survey between June 2021 and June 2022. During the same period, age-matched people with driver's licenses without epilepsy living in the cities of Hangzhou and Yiwu, Zhejiang province were invited to participate in the questionnaire study. ResultsA total of 291 PWE with a driver’s license and 289 age-matcheddrivers among the general public participated in the survey. Of the sample, 41.6% of PWE and 26.0% of general drivers stated that they were aware of legal driving restrictions for PWE in China. In the past year, 54% of PWE had driven and 42.5% drove a vehicle daily. Logistic regression revealed that male sex (95% confidence interval [CI]: 1.36–3.61, P = 0.001), age (95% CI: 1.12–3.27, P ≤ 0.036), and the number of antiseizure medications taken (95% CI: 0.24–0.25, P ≤ 0.001) were independently associated with illegally driving with epilepsy. In terms of legalissues, 71.1% of PWE did not support a lifetime ban on driving and 50.2% disagreedwith physician reporting of PWE to the traffic authorities. ConclusionIllegal driving is highly prevalent among PWE who hold a driving license, and male gender, age, and number of ASMs had an independent association with illegal driving in patients with epilepsy. There are highly varying opinions on the current driving laws with respect to PWE. Detailed national standards for medical fitness for driving that are easy to implement and enforce are urgently required for China.

Rome vehicle accidents analysis and city riskiness prediction by Artificial Intelligence

Rome municipality publishes geo-referenced vehicle accidents reports and detailed road traffic counters that, in conjunction with Open Street Map roads details, enable deep analyses of the tragic phenomenon. This study focuses on the full year 2021, during which a total of 62,081 vehicles casualties were registered by Rome traffic authorities. The broad Rome territory, overcoming a bunch of Italian chief towns, the wide and capillary set of arteries and the municipality roads extension greater with respect to many European capitals, enhance the statistical relevance of the analyzed data. Artificial intelligence method takes into account three potential accidents predictors as the level of road traffic, the event timing, and the driving complexity of the different zones. The proposed method has the capability of linking time/traffic/complexity indexes of a specific zone of a city with the accident riskiness, moreover, is applicable to whatever urban complex scenario and takes care of the result format in order to facilitate road safety decision makers in smoothing the tragic sequence of accidents. Further possible improvements directions are discussed.

Original and Low-Cost ADS-B System to Fulfill Air Traffic Safety Obligations during High Power LIDAR Operation

LIDAR is an atmospheric sounding instrument based on the use of high-power lasers. The use of these lasers involves fulfilling obligations with respect to air safety. In this article, we present a low-cost air traffic surveillance solution integrated into an automated operating system for the Rayleigh-Mie-Raman LIDAR of Clermont Ferrand and the statistical elements of its application over more than two years of operation from September 2019 to March 2022. Air traffic surveillance that includes the possibility of shutting off lasers is required by international regulations because LIDAR is equipped with a class four laser that presents potential dangers to aircraft flying overhead. The original system presented in this article is based on software-defined radio. ADS-B transponder frames are analyzed in real-time, and laser emission is stopped during LIDAR operation when an aircraft is detected within a 2 km radius around the LIDAR. The system was accredited in 2019 by the French air traffic authorities. Laser shutdowns due to the detection of aircraft near the Clermont Ferrand LIDAR caused a data loss rate of less than 2% during the period of application.

Reposition optimization in the free-floating bike-sharing system considering transferring travels from urban rail transit

The popular free-floating bike-sharing (FFBS) system serves as a first- and last-mile solution to connect urban rail transit (URT). Recently, a number of studies have investigated how to enhance the integration between FFBS and URT. Nevertheless, how to reposition the FFBS system to promote URT ridership is still a challenge. This study proposes a bike-sharing repositioning framework to balance the demand and supply of FFBS and promote URT ridership. First, a repositioning quantity determination method based on the geographically weighted regression model is applied to determine the number of bikes to be delivered to or collected from each transfer area. Second, we establish an optimization model to minimize the total repositioning cost considering carbon price. Both the repositioning of normal bikes and recycling of broken bikes are included in this model. Subsequently, a hybrid Cuckoo Search algorithm is designed to solve the problem. The extended 2-opt, random removal and insertion algorithms are introduced to generate new solutions, which enhance the search ability. Finally, several numerical studies are conducted using data collected in Shenzhen, China, to verify the effectiveness of the proposed methods. The results demonstrate that the methodology proposed in this study is reasonable and effective. The findings are expected to guide traffic authorities to get environmental and highly utilized scheduling schemes for the integration between FFBS and URT systems.

Self-Organized Neural Network Method to Identify Crash Hotspots

Crash hotspot identification (HSID) is an essential component of traffic management authorities’ efforts to improve safety and allocate limited resources. This paper presents a method for identifying hotspots using self-organizing maps (SOM). The SOM method was used to identify high-risk areas based on five commonly used HSID methods: crash frequency, equivalent property damage only, crash rate, empirical Bayes, and the societal risk-based method. Crashes on a major road in Iran were examined using the proposed method. Based on these criteria, high-risk locations were grouped into six clusters, which provided appropriate criteria for each location depending on the importance of the cluster. The findings show that the identification of crash hotspots tends to focus on areas with more crashes and deaths, demonstrating that the research methodology was appropriate.