Road Users Research Articles

ROAD TRAFFIC ACCIDENTS FACTOR ON RURAL ARTERIAL ROADS

In Indonesia, traffic accidents occur on 23.55% of national roads and 75.08% of rural roads. The rural roads were identified as accident-prone areas. As a result, unsafe roads hinder population mobility, disrupt daily life, and reduce access to education, employment, and essential services, thereby affecting the well-being and development of rural communities. To identify the dominant factors that influence the frequency of collisions, a more thorough analysis of the multifactorial causes of traffic accidents in accident-prone areas is necessary. This study focused on analyzing the factors that contribute to road traffic accidents. Cross-tabulation was done using chi-square analysis. The chi-square test does not assume a normal distribution of data, which is beneficial when dealing with real-world data that may not follow normal distribution patterns. This flexibility makes it a robust choice for analyzing traffic accident data, which can be highly variable and not normally distributed. The results of the chi-square analysis for significance values below 0.05 indicate that there is a correlation between accident-causing factors (the independent variable) and collision frequency (the dependent variable). The results indicate that human factors, such as carelessness and high speed, as well as road and environmental factors, such as horizontal alignment, road width, clear zone, road signs, road markings, and land use, influence traffic accidents. Infrastructure factors such as horizontal alignment, road width, clear zones, shoulders, signs, and markings influence traffic accidents because they directly impact road user safety. Non-standard road geometry (horizontal alignment, road width, shoulder width, and clear zone) combined with incomplete road safety facilities (signs and markings) have the potential to cause traffic accidents. Therefore, harmonizing road geometry and equipment is necessary to improve arterial road safety, especially in accident-prone areas. Furthermore, speed management across a variety of land uses is required to reduce traffic accidents.

Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving

Learning contextual and spatial environmental representations enhances autonomous vehicle’s hazard anticipation and decision-making in complex scenarios. Recent perception systems enhance spatial understanding with sensor fusion but often lack global environmental context. Humans, when driving, naturally employ neural maps that integrate various factors such as historical data, situational subtleties, and behavioral predictions of other road users to form a rich contextual understanding of their surroundings. This neural map-based comprehension is integral to making informed decisions on the road. In contrast, even with their significant advancements, autonomous systems have yet to fully harness this depth of human-like contextual understanding. Motivated by this, our work draws inspiration from human driving patterns and seeks to formalize the sensor fusion approach within an end-to-end autonomous driving framework. We introduce a framework that integrates three cameras (left, right, and center) to emulate the human field of view, coupled with top-down bird-eye-view semantic data to enhance contextual representation. The sensor data is fused and encoded using a self-attention mechanism, leading to an auto-regressive waypoint prediction module. We treat feature representation as a sequential problem, employing a vision transformer to distill the contextual interplay between sensor modalities. The efficacy of the proposed method is experimentally evaluated in both open and closed-loop settings. Our method achieves displacement error by 0.67m in open-loop settings, surpassing current methods by 6.9% on the nuScenes dataset. In closed-loop evaluations on CARLA’s Town05 Long and Longest6 benchmarks, the proposed method enhances driving performance, route completion, and reduces infractions.

The safety effect of increased pedestrian protection, autonomous emergency braking for pedestrians and bicyclists on passenger cars, and speed management

This was the first retrospective study to estimate the effect of increased pedestrian protection, autonomous emergency braking, and speed management to reduce serious injuries among pedestrians and bicyclists. More specifically, the aim was to estimate the injury mitigating effects of the following interventions: AEB with pedestrian and bicyclist detection, Euro NCAP pedestrian test score, active bonnet, traffic calming at pedestrian and bicycle crossings, and additionally, the combined effect of the above-mentioned treatments. The main source of data was the Swedish traffic data acquisition system (Strada), where information of road traffic crashes between passenger cars and pedestrians or bicyclists for the period 1 January 2003–31 December 2022 was obtained. Cars with optional fitment of AEB systems were identified, and the license registration number was used to access individual car equipment lists to identify whether the vehicle was equipped with AEB with pedestrian and/or cyclist detection. Information about traffic calming at pedestrian and bicycle crossings was obtained from the Swedish Transport Administration. The injury metric used was risk of permanent medical impairment (RPMI) of at least one percent and ten percent. RPMI captures the risk of long-term medical impairment based on a diagnosed injury location and Abbreviated Injury Severity (AIS) score. The relative difference between the mean values of RPMI (mRPMI1%+ and mRPMI10%+) was calculated and tested using an independent two sample t-test which was conducted for unequal sample sizes and variance. Although many results were found to be statistically non-significant, the following results were found to be significant at least at 90% level. Pedestrian mRPMI10%+ was reduced by 44% in speed zones ≤ 50 km/h comparing the group struck by cars equipped with AEB with pedestrian detection compared to the group struck by cars without the system. For cyclists, the mRPMI10%+ was reduced by 35% in speed zones ≤ 50 km/h. For crashes within ± 20 meters from a pedestrian or bicycle crossing, the AEB system reduced 60% of pedestrians mRPMI10%+ at crossings with good safety standard compared to crossings of poor safety standard. The comparison of cars with poor performance (1–9 points) in the NCAP pedestrian test and cars with a high score (28–36 points) showed that pedestrian mRPMI10%+ was reduced by 48% across all speed limits, and by 64% including only those aged ≤ 64 years. For bicyclists, a significant reduction of cyclist mRPMI10%+ was found comparing low scoring cars to high scoring cars in ≤ 30 km/h speed limit (-73%) and across all speed limits (-36%). Including only those aged ≤ 64 years, the reduction was 49%. For the active bonnet, a significant reduction of mRPMI1%+ by 24% was observed but given that the rate of helmet wearing was higher in the group struck by cars with active bonnet, this difference cannot be attributed to an effect of an active bonnet. The STA safety rating of pedestrian and bicycle crossings showed that overall pedestrian mRPMI1%+ was reduced by 15%, while cyclists mRPMI10%+ was reduced by 32% comparing crossings of high safety level to crossings of poor safety level. The analysis of combined interventions showed that the total reduction of pedestrians and cyclists mRPMI10%+ together was 69%, from 6.4% to 2%. This paper demonstrates that a road environment with adapted infrastructure and speed, combined with passenger car technologies that improve the safety for vulnerable road users, can create significant reductions of serious (long-term) injuries among pedestrians and bicyclists.

Comparative analysis of injury identification using KABCO and ISS in linked North Carolina trauma registry and crash data

Objective The purpose of this study was to examine differences between police-reported injury severity and trauma registry data among persons with linked records in North Carolina and quantify the degree of alignment. Methods We analyzed linked North Carolina trauma registry and motor vehicle crash data from 2018. Injury severity identification was assessed using police-reported 5-point scale KABCO from crash data and Injury Severity Score (ISS) from trauma records. The analysis was stratified by age, sex/gender, race, ethnicity, and road users type to examine differences across groups. We calculated sensitivity, specificity, positive predictive values, and negative predictive values between police-reported injury severity and trauma registry data using ISS as the gold standard. Results A higher proportion of patients were classified as suspected minor injuries (39.0%) compared to moderate injuries in trauma registry (25.1%). Police-reported crash data underreported injury severity when compared to trauma registry data. Police-reported KABCO had a higher degree of specificity when classifying minor injuries (79.3%) but substantially underestimated seriously injured patients, with a sensitivity of 49.9%. These findings were also consistent when stratified by subpopulations. Conclusion Hospital-based motor vehicle crash data are a main source of injury severity identification for road safety. Police-reported data were relatively accurate for minor injuries but not seriously injured patients. Understanding the characteristics of each data source both separately and linked will be critical for problem identification and program development to move toward a safe transportation system for all road users.

Lost years, mortality burden: the impact of COVID-19 pandemic on premature death due to road traffic accidents in a northern state in Malaysia

BackgroundThis study addresses the persistent global burden of road traffic fatalities, particularly in middle-income countries like Malaysia, by exploring the impact of the COVID-19 pandemic on Road Traffic Accident (RTA) fatalities in Perak state, Malaysia, with a secondary focus on applying Years of Life Lost (YLL) to understand the implications of these premature deaths.MethodologyThe cross-sectional study retrospectively reviewed certified RTA fatalities from 2018 to 2021, individually counting fatalities in accidents and excluding cases with incomplete death profiles. Data were collected from all Forensic Departments in the government hospitals in Perak. RTA fatalities were confirmed by medical officers/physicians following established procedures during routine procedures. A total of 2517 fatal accident and victim profiles were transcribed into data collection form after reviewing death registration records and post-mortem reports. Inferential analyses were used for comparison between pre- and during COVID-19 pandemic. The standard expected YLL was calculated by comparing the age of death to the external standard life expectancy curve taking into consideration of age and gender in Malaysia.ResultsThis study included 2207 (87.7%) of the RTA fatalities in Perak State. The analysis revealed a decreasing trend in RTA deaths from 2018 to 2021, with a remarkable Annual Percent Change (APC) of -25.1% in 2020 compared to the pre-pandemic year in 2019 and remained stable with lower APC in 2021. Comparison between pre-pandemic (2018–2019) and pandemic years (2020–2021) revealed a difference in the fatality distribution with a median age rise during the pandemic (37.7 (IQR: 22.96, 58.08) vs. 41.0 (IQR: 25.08, 61.00), p = 0.002). Vehicle profiles remained consistent, yet changes were observed in the involvement of various road users, where more motorcycle riders and pedestrian were killed during pandemic (p = 0.049). During pandemic, there was a decline in vehicle collisions, but slight increase of the non-collision accidents and incidents involving pedestrians/animals (p = 0.015). A shift in accident from noon till midnight were also notable during the pandemic (p = 0.028). YLL revealed differences by age and gender, indicating a higher YLL for females aged 30–34 during the pandemic.ConclusionThe decline in RTA fatalities during COVID-19 pandemic underscores the influence of pandemic-induced restrictions and reduced traffic. However, demographic shifts, increased accident severity due to risky behaviors and gender-specific impacts on YLL, stress the necessity for improved safety interventions amidst evolving dynamics.

Survey of driver's functional state change, considering movement through mountain passes

According to traffic accident statistics, the main causes are violations of traffic rules. Based on scientific research, it is clear that the main reason for these events is the drivers' incorrect assessment of the road situation and their inadequate response. Assessing road conditions depends on drivers' qualifications, skills, work experience, reaction time, and other physiological factors. The functional state of drivers is important to consider. It reflects their ability to work reliably and affects both fatigue levels and how fatigue builds up. Factors that impact decision-making speed and quality are crucial when evaluating the risks of the transportation process, particularly those associated with human behavior. This study investigates the safety of freight transportation for drivers, road users, third parties, and the environment. The study focuses on road traffic characteristics in mountainous conditions at high altitudes. The study aims to analyze how drivers' performance is affected while navigating challenging sections of mountain roads in the Ukrainian Carpathians. Four different routes will be examined. The research will provide graphic analysis of the results. It will show the main patterns of changes in drivers' functional state based on the route's complexity. The research aims to analyze indicators of drivers' functional state and their impact on their performance reliability. It allows for a more detailed evaluation of transport routes and planning of truck drivers' work and rest schedules on suburban routes.

Driver-to-Driver Communication Skills: Contributing Factors to Traffic Accidents on South African Roads?

Road traffic accidents are associated with different factors, such as nonsignalised road networks, inexperienced drivers, a lack of communication skills, distraction, and the visual or cognitive impairment of road users, which have led to this increase in traffic accidents. Understanding these factors in alignment with road user communication skills can provide solutions that can reduce traffic fatalities. Road users’ communication is predominantly formal in nature; informal means of communication also exist but are not given proper attention during the driving training and licence testing processes. Therefore, this study investigated the communication skills between drivers as a factor contributing to traffic accidents in South African cities. Consequently, 16 traffic scenarios of communication between drivers were simulated using PTV Vissim 2022 and Blender 3.0 software, and a semistructured interview questionnaire was used to collect data from South African road users. The questionnaire was further used to evaluate the confidence level of road users concerning informal communication. The data were analysed via regression analysis to establish the relationship between traffic accidents and miscommunication between drivers. The information gathered and analysed from this study helps us to understand the informal communication types between drivers and establish where communication loss exists, which could potentially lead to a traffic accident. The results also show that the misinterpretation of communication signals can present the possibility of a traffic safety risk.

Investigation of Analyzable Solutions for Left-Turn-Centered Congestion Problems in Urban Grid Networks

Traffic congestion caused by left-turning vehicles in a coordinated corridor is a multifaceted problem requiring tailored solutions. This study explores the impact of shared left-turn lanes within one-way couplets, particularly during peak hours, where high left-turn volumes, limited side street storage, and the overlapped green time between pedestrians and left-turners contribute to queue spillbacks, coordination interruption, and network congestion. The focus of this paper is on the solutions that can be easily analyzed by practitioners, here called “analyzable solutions”. This approach stands in contrast to solutions derived from “non-transparent” optimization tools, which do not allow for a clear assessment of the solution’s adequacy or the ability to predict its impact in real-world applications. This paper investigates the effects of employing two analyzable signal timing strategies: Lagging Pedestrian (LagPed) phasing and Left-Turn Progression (LTP) offsets. Using high-fidelity microsimulation, the authors evaluated different scenarios, assessing pedestrian delays, queue lengths, travel time index, area average travel time index, and environmental impacts such as Fuel Consumption (FC) and CO2 emissions. The effectiveness of the proposed strategies was comprehensively evaluated against the base case scenario, demonstrating considerable improvements in various performance measures, including approximately a 5% reduction in FC and CO2 emissions. Implementation of the LTP strategy alone yields substantial reductions in delays, the number of stops, the queue length for left-turning vehicles, travel times for all road users, and ultimately FC and CO2 emissions. This study offers innovative approach to addressing the complex and multifaceted problem of left-turn-centered congestion in urban grid networks using efficient and down-to-earth analyzable solutions.

Joint Routing and Pricing Control in Bimodal Mixed Autonomy Networks with Elastic Demand and Three-Dimensional Passenger Macroscopic Fundamental Diagram

The interaction between mixed autonomy traffic and public transport vehicles competing for limited road space is a less explored area of research. To evaluate the traffic dynamics in bimodal networks, a three-dimensional (passenger) network fundamental diagram, known as 3D-NFD (3D-pNFD), can be estimated that relates the accumulation of cars (autonomous and human-driven) and buses to the network vehicular (passenger) travel production. In this study, we propose a 3D-pNFD-based congestion pricing scheme considering three vehicle classes of buses, system-optimal (SO) seeking connected and automated vehicles (CAVs), and user-equilibrium (UE) seeking human-driven vehicles (HVs). We develop an iterative tri-level modeling framework for mode choice, pricing, and route choice in a mixed autonomy network. The lower level generates mixed equilibrium traffic flow through an integrated mixed equilibrium simulation-based dynamic traffic assignment model and a transit assignment model. The mid-level finds the optimal toll rates through a 3D-pNFD feedback-based controller. A nested logit-based mode choice model is also applied to capture travelers’ preferences toward three available modes and incorporates elastic demand. To encourage CAVs to follow the SO routing mode, they are provided with a discount on the congestion toll whereas UE-seeking HVs are subject to full price toll. Buses are also entirely exempt from the toll. We explore three scenarios with different discount rates on SO-seeking CAVs to investigate the effect of the incentive plans on the mode choice behaviors of road users in the pricing zone. The performance of the proposed model is evaluated in a large-scale network in Melbourne, Australia.

The racial composition of road users, traffic citations, and police stops

This paper exploits the potential of Global Positioning System datasets sourced from mobile phones to estimate the racial composition of road users, leveraging data from their respective Census block group. The racial composition data encompasses approximately 46 million trips in the Chicago metropolitan region. The research focuses on the relationship between camera tickets and racial composition of drivers vs. police stops for traffic citations and the racial composition in these locations. Black drivers exhibit a higher likelihood of being ticketed by automated speed cameras and of being stopped for moving violations on roads, irrespective of the proportion of White drivers present. The research observes that this correlation attenuates as the proportion of White drivers on the road increases. The citation rate measured by cameras better matches the racial composition of road users on the links with cameras than do stops by police officers. This study therefore presents an important contribution to understanding racial disparities in moving violation stops, with implications for policy interventions and social justice reforms.

Qualitative perspectives of bike share schemes in Melbourne, Australia

BackgroundBike share schemes are operational in many countries and aim to encourage modal shift from sedentary to active modes of transport. Understanding user experiences of bike share schemes can contribute to knowledge of motivations to use the schemes and barriers to use. Qualitative research into user experiences of bike share schemes in Australia has been conducted in Brisbane but not Melbourne, and most bike share scheme research is non-peer reviewed literature. This study describes experiences of bike share schemes in Melbourne. MethodsSemi-structured interviews were conducted with 21 bike riders in Melbourne, Australia some of whom used bike share schemes and some who did not. Interviewees were asked about their experiences using bike share schemes in Melbourne. ResultsBike share schemes provide a way for non-users to try bike riding without needing to purchase a bicycle. E-bike schemes are beneficial to people with health issues. User experiences of bike share schemes are not only related to the service itself, but also interactions with other road users and the overall bike riding environment. Mandatory helmet legislation was a barrier to using bike share schemes due to hygiene and convenience concerns. ConclusionsBike share schemes act as an entry to bike riding for people who do not own a bicycle. E-bike fleets are more accessible to people with health or mobility issues. Mandatory helmet legislation could be limiting bike riding's mode share in Melbourne. These findings are relevant to policy makers aiming to increase bike riding's mode share, introduce or expand bike share schemes or mandatory helmet legislation, or a combination of the two.

A non-stationary bivariate extreme value model to estimate real-time pedestrian crash risk by severity at signalized intersections using artificial intelligence-based video analytics

Vehicle-pedestrian crashes are generally severe due to the vulnerability of pedestrians compared to the occupants of vehicles. However, the estimation of pedestrian crash risk by severity has not been given adequate attention in the field of proactive safety assessments applying traffic conflict techniques. This study proposes a novel analytical framework to estimate real-time pedestrian crash risk by severity at the signal cycle level while incorporating the effect of time-varying exogenous variables. Specifically, the study applies a non-stationary bivariate extreme value model to jointly model the post encroachment time and Delta-V for estimating real-time pedestrian crash risk by severity at individual signal cycles. The proposed framework is tested on 144 h of video data collected from three signalized intersections in Queensland, Australia. The developed bivariate extreme value model has been found to reliably predict severe and non-severe pedestrian crash frequencies compared to the historical crash records of severe and non-severe pedestrian crashes at those signalized intersections. Results suggest that the frequency of pedestrian conflicts per signal cycle and average pedestrian speed in a signal cycle are associated with real-time pedestrian crash risks. In addition, pedestrian conflicts per signal cycle and average vehicle speed per cycle were associated with the interaction severity component of the non-stationary bivariate extreme value model. The proposed proactive estimation of pedestrian crash risk by severity levels can help design time-sensitive countermeasures for vulnerable road users.

Identifying interactions among factors related to death occurred at the scene of traffic accidents: Application of “logic regression” method

AimTraffic accidents are caused by several interacting risk factors. This study aimed to investigate the interactions among risk factors associated with death at the accident scene (DATAS) as an indicator of the crash severity, for pedestrians, passengers, and drivers by adopting “Logic Regression” as a novel approach in the traffic field. MethodA case-control study was designed based on the police data from the Road Traffic Injury Registry in northwest of Iran during 2014–2016. For each of the pedestrians, passengers, and drivers’ datasets, logic regression with “logit” link function was fitted and interactions were identified using Annealing algorithm. Model selection was performed using the cross-validation and the null model randomization procedure. Resultsregarding pedestrians, “The occurrence of the accident outside a city in a situation where there was insufficient light” (OR = 6.87, P-value<0.001) and “the age over 65 years” (OR = 2.97, P-value<0.001) increased the chance of DATAS. “Accidents happening in residential inner-city areas with a light vehicle, and presence of the pedestrians in the safe zone or on the non-separate two-way road” combination lowered the chance of DATAS (OR = 0.14, P-value<0.001). For passengers, “Accidents happening in outside the city or overturn of the vehicle” combination (OR = 8.55, P-value<0.001), and “accidents happening on defective roads” (OR = 2.18, P-value<0.001) increased the odds of DATAS; When “driver was not injured or the vehicle was two-wheeled”, chance of DATAS decreased for passengers (OR = 0.25, p-value<0.001). The odds of DATAS were higher for “drivers who had a head-on accident, or drove a two-wheeler vehicle, or overturned the vehicle” (OR = 4.03, P-value<0.001). “Accident on the roads other than runway or the absence of a multi-car accident or an accident in a non-residential area” (OR = 6.04, P-value<0.001), as well “the accident which occurred outside the city or on defective roads, and the drivers were male” had a higher risk of DATAS for drivers (OR = 5.40, P-value<0.001). ConclusionBy focusing on identifying interaction effects among risk factors associated with DATAS through logic regression, this study contributes to the understanding of the complex nature of traffic accidents and the potential for reducing their occurrence rate or severity. According to the results, the simultaneous presence of some risk factors such as the quality of roads, skill of drivers, physical ability of pedestrians, and compliance with traffic rules play an important role in the severity of the accident. The revealed interactions have practical significance and can play a significant role in the problem-solving process and facilitate breaking the chain of combinations among the risk factors. Therefore, practical suggestions of this study are to control at least one of the risk factors present in each of the identified combinations in order to break the combination to reduce the severity of accidents. This may have, in turn, help the policy-makers, road users, and healthcare professionals to promote road safety through prioritizing interventions focusing on effect size of simultaneous coexistence of crash severity determinants and not just the main effects of single risk factors or their simple two-way interactions.

Exploring determinants of freeway service area usage in the context of sustainable and collaborated development for transport and tourism

Freeway service areas (FSA) provide a variety of services for road users, but these services in some cases do not guarantee the satisfaction of road users. Understanding the factors that affect the satisfaction of FSA is crucial for transportation and tourism-related departments. This paper uses a structural equation model (SEM) to examine the relationships between geographical location, routine function and satisfaction with FSA, in the context of six representative FSAs in Shaanxi province, China. The samples are divided into three clusters to investigate the sensitivity of tourists with different travel demands to the above relationship. In addition, the ordered logit model is used to verify the results of SEM and analyze the influencing factors of FSA satisfaction. Results indicate that increasing attraction levels of transport and tourism are considered to be the key determinants in improving the satisfaction with FSA, and policies and suggestions should meet tourists’ different demands. Undifferentiated policies, while useful to help improve satisfaction with FSA, may have a negative impact on some certain groups, such as those on an official business trip. This study provides theoretical and practical insights into how relevant departments and organizations with respect to transport and tourism formulate strategies for the construction and sustainable development of FSA.

Privacy-preserving authentication approach for vehicular networks

Vehicle AdHoc networks have an important role in intelligent transport systems that enhance safety in road usage by transmitting real traffic updates in terms of congestion and road accidents. The dynamic nature of the vehicular AdHoc networks make them susceptible to attacks because once malicious users gain access to the network they can transform traffic data. It is essential to protect the vehicular ad hoc network because any attack can cause unwanted harm, to solve this it is important to have an approach that detects malicious vehicles and not give them access to the network. The proposed approach is a privacy preserving authentication approach that authenticates vehicles before they have access to the vehicular network thereby identifying malicious vehicles. The model was executed in docker container that simulates the network in a Linux environment running Ubuntu 20.04. The model enhances privacy by assigning Pseudo IDs to authenticated vehicles and the results demonstrate effectiveness of the solution in that unlike other models it boasts faster authentication and lower computational overhead which is necessary in a vehicular network scenario.

Comprehensive risk assessment of Pakistan railway network: A semi-quantitative risk matrix approach

Pakistan Railway (PR) is vital to the country's transportation infrastructure, facilitating passenger and freight transportation. However, the growing number of accidents associated with PR has raised concerns about its overall safety. In addition, there have been limited research efforts to investigate PR accidents and their underlying causes The present study conducts a comprehensive risk assessment and safety management of PR using a semi-quantitative risk matrix approach. The study combines historical accident data and expert evaluations to assess the likelihood and consequences of different railway accident types and potential contributing factors. The descriptive statistics analysis has revealed varying degrees of severity for different types of railway accidents in Pakistan. For instance, accidents like passenger and goods train derailments and collisions at unmanned level crossings were identified as extreme and intolerable, whereas train fire accidents were categorized as high and undesirable. Moreover, accidents attributed to human error are classified as extreme and intolerable, while those caused by negligence of road users and track defects are classified as high and undesirable. The study utilized the risk matrix approach and identified critical risk areas that can help the decision-makers prioritize effective risk mitigation strategies. In light of the present study's findings, policy implications, such as investment in infrastructure to mitigate risks associated with aging or deteriorating tracks, bridges, and tunnels, and human resource development for railway personnel to improve their skills, decision-making abilities, and awareness of safety protocols, are recommended.

Investment Feasibility and Strategic Development Scenarios of Yogyakarta-Bawen Toll Road

This study examines the feasibility of the investment in the Yogyakarta-Bawen toll road project by analyzing traffic conditions, expenditure costs, toll revenues, and investment value. Simulation using PTV Visum shows a significant increase in the volume-to-capacity ratio (VCR) on the national road sections around the toll road until 2061. The modeling result also indicates that the operation of the toll road reduces traffic loads on several surrounding national road sections with a decrease in VCR. The analysis results reveal a negative net present value (NPV) amounting to -Rp.506,560,648,100, and an internal rate of return (IRR) of 11.49% is under the discount rate (12.03%). In the tariff adjustment scenario, the project shows an IRR of 12.48% with an NPV of Rp 575,501 million, and the payback period (PBP) is achieved after 16 years. In the concession adjustment scenario, the IRR is 12.12% with an NPV of Rp 123,461 million, and the PBP is achieved after 17 years. Meanwhile, the tariff and concession adjustment scenario show an IRR of 12.48% with an NPV of Rp 652,033 million, and the PBP is achieved after 16 years. Based on the investment feasibility evaluation, the recommended best strategy is tariff adjustment. This strategy allows for an optimal increase in toll revenue and investment returns. It is expected that the Yogyakarta-Bawen toll road project can achieve better investment feasibility and provide long-term benefits to investors, road users, and the overall community.

Detecting road signs and deformations for road safety in Senegal

Nowadays, road safety is a major concern for public authorities on national, secondary and classified roads in developing countries. This paper looks at the problem of non-compliance with road signs and the deteriorating state of roads, which are the cause of many accidents. Road users sometimes miss roadside signs because their attention is focused on the road. At other times, they regularly miss certain road markings or road deformations in unfamiliar areas, which is dangerous for travelers. Ideally, therefore, drivers should be able to be alerted to road signs or deformations from a distance without having to divert their attention. This would make it possible to avoid accidents, especially on national roads between regions of a country and between countries in sub-Saharan Africa.In this paper, we propose a model for the detection and recognition of road signs and deformations using deep learning. The dataset for this model will consist of a hybrid dataset that includes Kaggle data and data from AGEROUTE (Autonomous Agency for Works and Road Management) in Senegal. In addition, the proposed model is trained on two datasets; one for training 80% and the other for testing 20%. Experimental results showed that the proposed model achieved an accuracy of 95.34% for the datasets.

The drive of a patient's life

Lenses specially optimised for driving can increase road safety and user comfort. Optician presents some of the best available to UK practices

Analisis Dinding Penahan Tanah Tebing Sei Seruyan Desa Pembuang Hulu Kecamatan Hanau

River buffers are an important part of a road network system because they have a significant effect if the river collapses or if it does not function properly. Because riverbanks are structures that cross roads and other traffic, the collapse of riverbanks will reduce or withstand traffic and also cause buildings that stand alongside eroded rivers and landslides, which in turn will disrupt the comfort of community traffic and live and disturb economic relations. So it is very important to calculate the structure of the cliff building in Pembuang Hulu Village, Hanau District, so that it does not endanger the road users and community comfort activities. This study uses secondary data collection methods, then calculated in three models of variations in the condition of the retaining wall to obtain stability and structural analysis which can then calculate the value of bolster safety and sliding safety in buildings. Based on the calculation results obtained as follows, the capacity value of the soil behind the building of the retaining wall is qa = qu / 3 = 17.933 t/m^2 where the safety factor = 3.00 is normal qae = qu / 2 = 26,900 t/m^2 where the safety factor = 2.00 earthquake conditions. The value of stability and analysis of the structure of the retaining wall against rolling is Fg = 3.027&gt;1.5, the shear is Fs = 1.587&gt; 25.25 and foundation reinforcement 221.5 and the minimum stress value of qmin = 3.8006 tons/m² and the maximum stress value of qmax = 9.6454 tons/m² . The reinforcement design of the walls and the foundation of the soil retaining building to be safe against landslides is to reinforce 1925 the walls using 2225. guide