Number Of Lanes Research Articles

Smart Movable Road Divider and Clearance Ambulance Path Management Through IOT

Divider is generically used for dividing the Road for ongoing and incoming traffic. This helps keeping the flow of traffic; generally, there is equal number of lanes for both ongoing and incoming traffic. The problem with Static Road Dividers is that the number of lanes on either side of the road is fixed. Since the resources are limited and population as well as number of cars per family is increasing, there is significant increase in number of cars on roads. The existing Road Dividers consists of equal number of lanes. Usually, in morning and evening peak hours the opposite side of the Road Divider is generally underutilized. To overcome this, Smart Movable Road Divider is implemented where the divider is moved based on the density of the traffic using IR Sensors. If the density of the traffic is high on one side, the divider is moved to the other side. This calls for better utilization of existing resources like number of lanes available. Our aim is to formulate a mechanism of automated road divider that can shift lanes, so that we can have number of lanes in the direction of the rush. Each ambulance will be equipped with GPS and GSM modem which in case of emergency will send its GPS coordinates to the cloud server, which will then mark the shortest distance from its present location to the hospital viathe place from where the emergency call has been raised.

Unravelling the veil of traffic safety: a comprehensive analysis of factors influencing crash frequency across US states

Abstract Traffic crashes are a prominent cause of fatalities around the world. This study focuses on the dynamics of traffic safety by analysing factors influencing crash frequency during both nighttime and daytime conditions. Four years' data derived from the Fatality Analysis Reporting System (FARS) database were analysed statistically based on the negative binomial regression model, to identify the contributions of several factors affecting crash outcomes. The findings reveal significant related factors, including area type, roadway alignment, speeding-related factors, sex of driver, number of lanes, grade, surface type and weather conditions, that contributed to the expected crash frequency during both daytime and nighttime conditions. Through quantitative analysis, the extent to which each factor contributes to the expected crash frequency was determined, offering effective insights for policymaking to boost roadway safety. The findings highlight the necessity of implementing targeted strategies to minimize the risk of crashes by creating safer road environments.

IOT BASED MOVABLE ROAD DIVIDING SYSTEM

Road dividers conventionally serve the purpose of segregating traffic on roads into lanes for ongoing and incoming vehicles, facilitating smooth traffic flow. Typically, an equal number of lanes are designated for each direction of traffic. However, static road dividers present a limitation as they do not adapt to varying traffic demands. Our objective is to devise an automated road divider system capable of dynamically adjusting lane allocation, thereby accommodating the prevailing traffic patterns. The potential benefits of such a system include significant savings in time and fuel consumption, particularly by reallocating lanes to the direction experiencing higher traffic volume. This innovation promises to alleviate traffic congestion effectively and enhance traffic management strategies.

Assessment of automated vehicles’ freeway exit distances in mixed and managed lane traffic environments

Vehicles planning to exit at an upcoming freeway off-ramp require adequate exit distance to execute lane change maneuvers, otherwise, traffic disturbances may be experienced. This research assesses the changes in exit distances for a mixed traffic environment comprising automated vehicles (AVs) and human-driven vehicles (HDVs) on freeways with and without a managed lane (ML). A left-side continuous ML was designed and eligible vehicles were AVs. Traffic microsimulation exercises were conducted on a 3.5 km freeway segment, and scenarios varied based on traffic demand, the number of freeway lanes, and AV adoption rates. Traffic demand was set relative to the queue discharge flow rate ( qQ). The results indicated an increase in qQ as the AV adoption rate increased. Also, the exit distances were influenced by the traffic environment, the freeway configuration, and the traffic demand. The optimal exit distance increased when an ML was implemented at 25% and 50% AV adoption rates.

Effects of mobile phone-related distraction on driving performance at roundabouts: Eye movements tracking perspective

Modern road infrastructures are complex networks featuring various elements such as roads, bridges, intersections, and roundabouts, with advanced control systems. Roundabouts have gained prominence as a safer alternative to traditional intersections promoting smoother traffic flow and fewer collisions by guiding traffic in one direction, encouraging reduced speed, and minimizing conflict points.This study investigated driver behavior within roundabouts, focusing on gaze behavior, particularly the left-side mirror and window, under mobile phone distraction conditions. In addition, the effects of roundabout specifications (i.e., number of lanes and size of the central island) and the drivers’ characteristics (i.e., driving experience) were examined.In total, 43 participants, aged 19–56 years including 30 males and 13 females, held a valid driving license, drove through a virtual simulated urban road containing four roundabouts, implemented in a static driving simulator, under baseline condition (no distraction) as well as mobile-induced distraction. Driving simulator data were collected and drivers’ gaze direction and fixation on nine areas of interest were captured with an eye tracker. Resultsshowed that experienced drivers exhibit a more fixation on the left-side mirror and window and were less distracted. Moreover, the road environment, i.e., the number of cars and the roundabout size, significantly influenced the drivers’ attention. As regards the driving performance, the number of infractions increased when the drivers diverted focus from the left side of the car. The outcomes of the present study might help to improve traffic safety at roundabouts.

Dynamic Lane Assignment and Signal-Timing Collaborative Optimization Considering the Effect of Lane Switching

Dynamic lane assignment (DLA) is an effective method to improve traffic flow and optimize urban road resources. However, lane switching can disrupt traffic under dynamic control due to clearance time and driving characteristics. Traditional rule-based methods hinder the selection of optimal schemes for DLA systems. On-site data analysis shows that lane switching increases saturated headway by 18.4% and reduces lane group utilization by 17.8% compared to fixed lanes in the same direction. To address these challenges, this study proposes an optimization method that integrates DLA and signal timing, considering the impact of lane switching. The proposed model aims to minimize total delay at intersections by optimizing the number and timing of lane switches within a given interval. Case studies and extensive numerical analyses demonstrate that the proposed method outperforms the rule-based approach, reducing total intersection delay by 1.91%. Additionally, the average number of lane switches decreases by 13.8%, reducing safety risks associated with frequent lane switching. Sensitivity analysis reveals the superiority of the proposed method when the utilization rate of variable lanes is below 0.4 and the saturated headway caused by lane switching exceeds 2.9 seconds. Moreover, the proposed method outperforms inductive and adaptive control schemes when the traffic flow deviation coefficient is less than 0.3. This makes it suitable for implementation in cities where drivers have limited experience with variable lanes during the initial stage, when historical and real-time data are more consistent. Overall, the proposed method offers significant improvements in traffic flow efficiency and safety in DLA systems.

The Attitudes of Pedestrians when Using Crosswalks with Distinct Traffic Lanes: A Case Study on Chaeng Watthana Road, Lat Krabang Road, and Kingkaeo Road

One of the most important modes of transportation in cities and suburbs is walking. In large urban areas, walking reduces traffic congestion. However, data indicates that Bangkok is responsible for 1 in 3 pedestrian accidents per year, with an average of 900 such incidents taking place there. Furthermore, based on data collected from 2013 to 2017, car-pedestrian collisions are categorized by the highest severity index due to the average of 55 fatalities per 100 accidents. These statistics show that there are, on average, 740 pedestrian-vehicle incidents annually. The data indicates that to lower the number of pedestrian fatalities, walking mode needs to be enhanced. Consequently, the investigator was interested in determining how crossing facilities and people's attitudes are related. The goal of this research is to examine significant variables that influence how pedestrians feel about crossing the street. A questionnaire was used to collect data, which was separated into two sections: attitude about crossing the road and attitude regarding safety when crossing the road. The study's sample crosswalks are located on Chaeng Watthana Road, Lat Krabang Road, and King Kaew Road. Descriptive statistics and linear regression were employed in the analysis to consider social characteristics, variables related to past road crossing experience, variables regarding attitudes toward using road crossings, and variables regarding crossing facilities. Results from 412 questionnaires show that the number of traffic lanes, vehicle volume and vehicle speed were determined to be the primary factors significantly reducing the attitude of security that one feels when crossing the street. Furthermore, the outcomes held up when descriptive statistics were used for analysis. It was discovered that push-button traffic lights had the biggest influence on attitudes of safety when it came to attitudes regarding crossing facilities.

MGL2Rank: Learning to rank the importance of nodes in road networks based on multi-graph fusion

The identification of important nodes with strong propagation capabilities in road networks is a vital topic in urban planning. Existing methods for evaluating the importance of nodes in traffic networks only consider topological information and traffic volumes, the diversity of the traffic characteristics in road networks, such as the number of lanes and average speed of road segments, is ignored, thus limiting their performance. To solve this problem, we propose a graph learning-based framework (MGL2Rank) that integrates the rich characteristics of road networks to rank the importance of nodes. This framework comprises an embedding module containing a sampling algorithm (MGWalk) and an encoder network to learn the latent representations for each road segment. MGWalk utilizes multi-graph fusion to capture the topology of road networks and establish associations between road segments based on their attributes. The obtained node representation is then used to learn the importance ranking of the road segments. Finally, a synthetic dataset is constructed for ranking tasks based on the regional road network of Shenyang City, and the ranking results on this dataset demonstrate the effectiveness of our method. The data and source code for MGL2Rank are available at https://github.com/ZJ726.

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.

Factors Affecting the Impact of Autonomous Vehicles on Freeway Operations – An Exploratory Analysis Using PCEs

Autonomous vehicles (AVs) and human-driven vehicles (HDVs) will share the roads for a long time, hence the need to study traffic flows mixing AVs and HDVs, especially during the AV introduction period. This paper aims to investigate the roadway and traffic characteristics that affect the impact of AVs on freeway traffic operations, using an adapted version of the HCM6 truck passenger-car equivalent (PCE) methodology. A large number of scenarios comprising different roadway characteristics, AV types and traffic flow compositions were simulated using Vissim to obtain AV PCEs. The results indicated that, for all scenarios considered, an AV has a 20% lower impact on the quality of service and operation than an HDV. A CART decision tree indicated that the most important factors affecting the AVs’ impact on traffic operations are vehicle-to-vehicle connectivity level and the capability of travelling in platoons. Maximum platoon length did not matter, and the increase in the number of traffic lanes reduced the positive impact of AVs on service quality.

Study of medium and long-term free flow capacity and queue discharge rates on roads.

With the rise in vehicle ownership, traffic congestion has emerged as a major barrier to urban progress, making the study and optimization of urban road capacity exceedingly crucial. The research on the medium and long-term free-flowing capacity and queue emission rate of roads takes an in-depth exploration of this issue from a cutting-edge perspective, aiming to find solutions adaptable to the progression of the times. The purpose of this study is to understand and predict the road capacity and queue emission rate more accurately, thus improving the urban traffic condition. Existing literature primarily focuses on short-term forecasts of road capacity, leaving a notable void in the research of medium and long-term road capacity and queue emission rate. This gap often results in a lack of sufficient foresight when urban traffic planning faces practical issues. To fill this void, this study undertook an in-depth examination of the road capacity and queue emission rate over the medium and long term (10 years) based on big data analysis and artificial intelligence theories. This paper employs a Radial Basis Function (RBF) neural network, combined with twelve other parameters that could potentially impact road capacity, such as traffic volume, road width, number of lanes, traffic signal control methods, etc., to analyze the relationship between each parameter and free-flow traffic and queue emission rate. These analyses are grounded in extensive road data, encompassing not only the city's main roads but also secondary roads and community roads. The study results show a continuous downward trend in the free-flowing capacity of roads and a slight upward trend in the queue emission rate over the past decade. Further analysis reveals the extent of impact each factor has on the free-flow traffic and queue emission rate, providing a scientific basis for future urban traffic planning.

Analisis Hubungan Geometrik Jalan dengan Potensi Kecelakaan di Jalan Imogiri - Dlingo, Bantul, Indonesia

Imogiri - Dlingo road section is a primary collector road located in the Mangunan tourist area, Bantul, Indonesia. This road is a provincial road with hilly terrain, lots of sharp turns, and long ascents and descents, which are potential for traffic accidents. Police data records that in February 2022 there was an accident on this road with 13 deaths and 33 injuries. Therefore, this study aims to evaluate road geometric, both horizontal and vertical alignments on Imogiri - Dlingo road based on Guidelines for Road Geometric Design number 13/P/BM /2021. To do so, road geometric analysis is conducted by employing road safety inspection data, which is then modeled using AutoCAD Civil 3D software. Based on the survey on road geometric, it is known that the number and width of lanes and shoulders are in accordance with Guidelines. Meanwhile, the analysis results using computer program show that this road has 24 horizontal and 6 vertical alignments. Nevertheless, 87% of bend radius, 67% of curve length, 58% of bend angle, 43% of slope, 57% of critical length, and 67% of control design (K) are not fulfilled the requirements. Therefore, Imogiri - Dlingo road is dangerous road and is potentially causing fatal accidents.

Simulating the bi-directional pedestrian flow under high densities by a floor field cellular automaton model

In this paper we propose one floor field cellular automaton model, which can simulate the bi-directional pedestrian flow at high densities. Based on the model rules proposed by Nowak and Schadschneider, we make some modifications, including changes of cell size, realistic velocity configurations and extended lateral movement. The best parameters are determined by the results of sensitivity analysis. Some data extracted from pedestrian flow experiments, including the fundamental diagrams and the lane numbers after lane formation, are used for model validations. When the corridor width is 2m, the simulation results show that the fundamental diagrams of bi-directional flow are quantitatively similar to the experimental results, and the lane formation could be successful at high densities. By comparing with the statistical results in the experiments, we find the probabilities of the occurrence of different lanes are similar to the experimental results: two-lane and three-lane states usually emerge, while the probability for more lanes is low. The inhomogeneous distributions of pedestrians also could be observed. We think our model could be a good choice for simulating the lane formation process at high densities.

Comparative Evaluation of Crash Hotspot Identification Methods: Empirical Bayes vs. Potential for Safety Improvement Using Variants of Negative Binomial Models

The empirical Bayes (EB) method is widely acclaimed for crash hotspot identification (HSID), which integrates crash prediction model estimates and observed crash frequency to compute the expected crash frequency of a site. The traditional negative binomial (NB) models, often used to estimate crash predictive models, typically struggle with accounting for the unobserved heterogeneity in crash data. Complex extensions of the NB models are applied to overcome these shortcomings. These techniques also present new challenges, for instance, applying the EB procedures, especially for out-of-sample data. This study applies a random parameter negative binomial (RPNB) model within the EB framework for HSID using out-of-sample data, comparing its performance with a varying dispersion parameter NB model (VDPNB). The research also evaluates the potential for safety improvement (PSI) scores for both models and compares them with EB estimates using three generalised criteria: high crashes consistency test (HCCT), common sites consistency test (CSCT), and absolute rank differences test (ARDT). The results yield dual insights. Firstly, the study highlights associations between crash covariates and frequency, emphasising the significance of roadway geometric design characteristics (e.g., lane width, number of lanes, and parking type) and traffic volume. Some variables also influenced overdispersion parameters in the VDPNB model. In the RPNB model, annual average daily traffic (AADT) and lane width emerged as random parameters. Secondly, the HSID performance assessment revealed the superiority of the EB method over PSI. Notably, the RPNB model, compared to the VDPNB, demonstrates superior performance in EB estimates for HSID with out-of-sample data. This research recommends adopting the EB method with RPNB models for robust HSID.

Demodulation and Vibration Signal Systems for Photonic Fiber Optic Pressure Sensor

The article describes the optical elements of signal demodulation and polling systems from photonic pressure sensors on inclined fiber Bragg gratings, which are often used to measure the refractive index (RI). A new design of a photonic fiber-optic Bragg pressure sensor with an inclined lattice has been developed, which is connected to standard multimode fibers with an inclined Bragg lattice connected to a metal diaphragm, which is a deformed inclined cantilever. The light source is polarized using the first polarizer and directed to the photonic crystal fiber in such a way as to excite multimode fibers. In this work, a method was developed for determining the optical elements of the spectral contour length system, which consists of setting the cut-off wavelength and then determining the accompanying refractive index. An experimental study determined the curve of the chain length change in the set. To process random signals, the spatial correlation method is used in combination with an approach to digital images based on the number of lanes and the direction of movement. The experimental results differ from the theoretical ones by about 4%. The developed correlation method reflects frequency as well as randomness, it is used in the photographic process together with the image correction given in this document.

Capacity Adjustment of Lane Number for Mixed Autonomous Vehicles Flow Considering Stochastic Lateral Interactions

Capacity Adjustment of Lane Number for Mixed Autonomous Vehicles Flow Considering Stochastic Lateral Interactions

Urban Environment’s Contributory Factors for the Adoption of Cargo Bike Usage: A Systematic Literature Review

The supply chain sector plays a crucial role in driving economic development and globalization. However, the environmental repercussions of logistics and freight transport have become more pronounced. Nowadays, there is an ever-increasing acceptance regarding the opinion that the use of more sustainable urban freight transport has the potential to offer great social, economic, and environmental benefits. This study examines and highlights, via a systematic literature review, the urban environment’s factors that can essentially influence the promotion and usage of cargo bikes for last-mile deliveries in the urban environment. The aforementioned literature review revealed the importance of the quality of the urban environment’s components for the perceived and objective safety of people who make use of cargo bikes. In particular, the most essential factors for the increased use of cargo bikes were found to be traffic load, speed limits, and heavy vehicle traffic. Bicycle infrastructure is also an important factor in bicycling acceptance, as it provides the backbone for a comfortable and safe bicycle ride. Two other factors that can seriously affect cyclists’ sense of safety are street intersections and the width and number of road lanes, as the interaction between cargo bikes and motorized vehicles increases the possibility of traffic accidents. All the above factors need to be considered via various public policies that are not isolated countermeasures but form part of Sustainable Urban Mobility Plans that are currently being implemented in many European cities to ensure continuity and create a sustainable future.

TC-GCN: Triple cross-attention and graph convolutional network for traffic forecasting

With the rapid development of urbanization, increasingly more data are being acquired by intelligent transportation systems (ITSs), which is of great significance for traffic flow forecasting. Efficient intelligent traffic management systems (TMSs) depend on accurately forecasting traffic flows as well as reasonably researching and judging traffic states. However, the current prediction models used in transportation forecasting tasks are traditional temporal- and spatial-dimensional prediction approaches. Especially when faced with road channelization, the numbers of lanes and lane types increase significantly, causing significant increases in the dimensionality and complexity of traffic data, and research models utilizing the cross-interaction relationships among multiple dimensions have not been considered. This has led to unsatisfactory prediction results in complex traffic environments. Therefore, it is very important to explore the exchange–correlation of complex dimensional data and the mining of hidden attributes. This paper presents a method for performing multidimensional cross-attention and spatiotemporal graph convolution. This method fully considers cross-information and constructs an attention cross-view between every pair of dimensions among the channel, time and space domains to model the cross-dimensional dependencies of traffic data. We innovatively propose a triple cross-attention and graph convolutional network (TC-GCN), which can achieve further improved traffic forecasting performance. The TC-GCN is verified on two real-world traffic datasets, namely, METR-LA and PEMS-BAY, and the experimental results are compared with those of multiple advanced baselines, showing that the proposed approach outperforms most of these baselines, which proves the effectiveness of the method proposed in this paper.

Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques

Objective Motorcycle crashes often result in severe injuries on roads that affect people’s lives physically, financially, and psychologically. These injuries could be notably harmful to drivers of all age groups. The main objective of this study is to investigate the risk factors contributing to the severity of crash injuries in different age groups. Methods This Objective is achieved by developing accurate machine learning (ML) based prediction models. This research examines the relationship between potential risk factors of motorcycle-associated crashes using (ML) and Shapley Additive explanations (SHAP) technique. The SHAP technique further helped interpreting ML methods for traffic injury severity prediction. It indicates the significant non-linear interactions between dependent and independent variables. The data for this study was collected from the Provincial Emergency Response Service RESCUE 1122 for the Rawalpindi region (Pakistan) over three years (from 2017 to 2020). The Synthetic Minority Oversampling Technique (SMOTE) is employed to balance injury severity classes in the pre-processing phase. Results The results demonstrate that age, gender, posted speed limit, the number of lanes, and month of the year are positively associated with severe and fatal injuries. This research also assesses how the modeling framework varies between the ML and classical statistical methods. The predictive performance of proposed ML models was assessed using several evaluation metrics, and it is found that Catboost outperformed the XGBoost, Random Forest (RF) and Multinomial Logit (MNL) model. Conclusion The findings of this study will assist road users, road safety authorities, stakeholders, policymakers, and decision-makers in obtaining substantial and essential guidance for reducing the severity of crash injuries in Pakistan and other countries with prevailing conditions.

Lane numbers and their impact on commercial motor vehicle crash safety: An econometric perspective

This research investigates the intricate relationship between the number of lanes on highways and injury severities sustained by commercial motor vehicle (CMV) drivers. Many studies have addressed crash determinants, but the safety implications of differing numbers of lanes remain insufficiently examined, especially during the highway planning stages. Our study fills this knowledge gap by analyzing injury severity crash factors for a varied number of lane scenarios. Employing a random parameters logit modeling framework, we differentiated injury levels for 2–4 lanes and 6–10 lanes. Key factors were identified for each number of lanes, with older, loss of vehicle control, non-collision crashes, and crashes on locations where grade or hill existed being more perilous and increasing the risk of sustaining severe injuries on 2-lane highways. For 4-lane highways, factors such as non-Oregonian drivers, older drivers, crashes that occurred during the spring season, and crashes that occurred beyond shoulders were associated with an elevated probability of being involved in severe injury crashes. Regarding highways with 6 lanes and higher, driving too fast for conditions and driver error (drowsy, fatigued, inattentive, or reckless) increases the odds of being involved in higher levels of injury crashes. To enhance truck driver safety, we recommend the implementation of electronic stability control in CMVs, moderated speeds on graded sections, improved curve markers, and robust public safety campaigns.