Road Travel Research Articles

Family Mobilization With Stunting Case Toward Children Under Five Years Old In Indonesia: Literature Review

Stunting is the inadequacy of nutritional intake from the womb to the child at the age of five, which is indicated by the child having less length or height comparing to the age. In this study, the researcher linked family mobility as one of the factors causing the increasing case of stunting. The purpose of this literature study is to describe family mobilization with the stunting case in children under five in Indonesia. The research design used in this study was a traditional literature review. Articles are tracked using several search engines (Google Scholar, PubMed, Springer link, Sinta, and Plos One). The results of this study indicated that family mobilization in reaching health services can affect the nutritional status of children under five. The affordability of health services is caused by several factors such as road access, distance and travel time. It also relates to the family residence, the pattern of care provided, and the socio-economic status of the family which can cause children under five experiencing stunting. The conclusion in this study is that family mobilization such as a lack of family health attention in reaching health services, inadequate needs and a healthier lifestyle or low economic status, as well as a poor or lack parenting knowledge can cause children under five susceptible to infectious diseases so that the children are prone to stunting.

Assessment and Contrast the Sustainable Growth of Various Road Transport Systems using Intelligent Neutrosophic Multi-Criteria Decision-Making Model

This study analyses the elements and approaches to creating sustainable transport systems, with a focus on road travel. The study examines the environmental and economic aspects of sustainable road transport and stresses the need to curb carbon emissions, boost energy efficiency, clean the air, ensure everyone has easy access to transport, and think about societal goals as a whole. Important considerations including environmental effect, energy efficiency, legislative frameworks, and economic impact are highlighted in the study. The MCDM model is used as a complexity instrument to strike a balance between competing objectives and criteria. This research may help stakeholders use the MCDM method to better comprehend the existing condition of transport networks and to better plan for future sustainability actions. The primary goal of this article is to analyze and contrast how various present road transport systems have progressed toward a more sustainable future. Sustainability in road transport systems is discussed, and a framework procedure is presented based on the integrated single-valued neutrosophic set and DEMATEL approach. The factor relationship was built using the DEMATEL technique. There were 14 secondary criteria employed in addition to the four primary ones.

The effect of match location and travel modality on physical performance in A-League association football matches

ABSTRACT This study investigated the impact of match location and travel modality on physical performance of an Australian A-League association football team. Match location comprised of a home vs away comparison; while travel modality compared home matches, road travel, short-flight travel, and long-flight travel. Both models accounted for match result, opposition quality and total distance covered. Physical performance was defined as average running intensity (m.min−1), low-speed activity (LSA), high-speed activity (HSA), very high-speed activity (VHSA), high-intensity efforts (HIE) and sprint efforts. Statistical significance was accepted at p < 0.05. Match location results demonstrated significantly greater average running intensity and LSA for away matches and significantly greater HSA for home matches. Travel modality results demonstrated significantly greater LSA for road travel compared to home matches and long-flight travel, while HSA was significantly greater for home matches and long-flight travel than for road travel. Additionally, home matches demonstrated significantly greater VHSA than road travel. Assessing the impact of travel modality on physical performance provides more contextual information than solely home vs away. Coaches may use this information to plan travel to mitigate detrimental effects on physical performance, particularly concerning road travel on matchdays.

Network Design for Silent Link User Equilibrium

Connected vehicles (CVs) are anticipated to improve road safety and travel efficiency in a transportation system. However, the deployment of CV technologies in transportation networks can lead to privacy issues, as the communication among CVs can expose vehicles' location information. To address this issue, we introduce a privacy protection method named "silent link" to transportation networks and propose a silent link user equilibrium (SLUE) framework to study the impact of privacy protection countermeasures on network flow. A theoretical analysis regarding existence and uniqueness conditions of SLUE is provided. The proposed SLUE facilitates privacy-oriented network design to achieve optimal levels of privacy for CVs. Accordingly, a bi-level network optimization problem is formulated for the design of silent links in transportation networks. Numerical examples are demonstrated using the Braess and Sioux Falls networks.

Sorokh-Poth: A Balanced Bangladeshi Road Vehicle Image Dataset Integrated With a Detection System Based On Deep Convolutional Neural Networks

When it comes to the administration of highways, the identification of intelligent vehicles is becoming an increasingly important component. Applications for vehicle detection are essential for both military and civilian usage, including the monitoring of highway traffic, administration, and the control of urban traffic. The vast amount of vehicle image data available online has the potential to encourage the development of increasingly sophisticated object recognition and classification models and algorithms. But finding an organized, balanced, and meaningful dataset continues to be a major challenge. This study proposes Sorokh-Poth, a new complete balanced image dataset based on Bangladeshi road travel that is compatible with a number of CNN-based architectures. The majority of the photographs in the collection were taken with a smartphone. The dataset includes 9,809 classified and annotated images of ten different types of vehicles, including autorickshaws, bicycles, buses, cars, CNG-powered vehicles, lagoon rickshaws, trucks, and vans. This research work utilizes the residual network ResNet-50 model, a CNN-based architecture. Here, features specific to the type of vehicle were automatically retrieved and grouped. Accuracy, precision, recall, and f1-score were just a few of the metrics used for evaluation during the research. The proposed model exhibited an increasing accuracy despite the vehicles shifting physical characteristics. The purpose of this work is to implement various CNN-based residual networks ResNet-50 and ResNet-150V2. Our proposed ResNet-50 model achieves an accuracy of 98.00% in the detection of native Bangladeshi vehicles, according to result comparisons and evaluations.

Urban road travel speed prediction based on multi-feature data fusion

Urban road travel speed prediction based on multi-feature data fusion

COLOR DETECTION USING OPENCV

The program that displays the user's desired print color is called color recognition technology. It compares the color codes that the program already provides with the image whose colors we want to know. Let's begin by asking, We are aware of a disability that is very common in humans called "color blindness." Though it is very common in humans, most people can live with it because, as long as you don't think too much about the colors, it won't affect your overall performance. The future is self- driving vehicles that require color recognition to make road travel simple. It is true that no one has seen the future, that currently there is no cure for color blindness. How ever is always small). The program compares the R, G, and B values of colors to those in the image. After that, the name of the color is shown to the user in a way that is easy for them to see. This system can help normal people, especially men, understand colors, so it could be very useful. It could also be used to connect this system to goggles or specs and helmets for motorcycle drivers. These things may not seem like they would be of any use right now to most people, but those who suffer from it probably find Because it will help them recognize the color (even if they won't see it, they'll still be able to recognize it). Therefore, it is straightforward to assert that the implementation of this program will benefit a large number of individuals. Asa result, the program is designed with very low sensitivity and is equipped with a large number of colors to provide many comparisons and a very small chance of getting a wrong color or a missing color. This program makes use of OpenCV. Keywords: Color Blindness, RGB Values, OpenCV.

Correlated random parameters model with heterogeneity in means for analysis of factors affecting the perceived value of road accidents and travel time

Road safety funding and management have become important issues in improving the quality of life of road users and there is evidence of a difference in driving behavior and the factor of road use between urban and rural areas, which is, in turn, reflected in different road safety evaluations. The purpose of this study is to assess the financial losses caused by road accidents on Thailand’s highways and the related factors empirically, deploying the willingness-to-pay (WTP) approach. Data were obtained from 640 urban and 960 rural car drivers using a stated choice questionnaire and face-to-face interviews. This study used Correlated Random Parameters Binary Logit with Heterogeneity in Means (CRPBLHM) approach to analyze factors affecting WTP. According to the results, the value of a statistical life and injury for urban drivers was 1.63 times higher than that for rural drivers, and the value of travel time reduction per hour for urban drivers is ∼1.14 times higher than that found for rural drivers. Furthermore, the results of the CRPBLHM model reported that there are significant differences between urban and rural drivers’ safety intentions and WTP. In the urban model, it was found that driving behavior (ticket, seatbelts, and speed) and weekday trips significantly increase the WTP, whereas household size and gender are unobserved characteristics among drivers. The rural model revealed that a driver’s education (bachelor’s and master’s degrees held), and a necessary trip were significantly associated with drivers’ valuation of safety; moreover, it was found that household size, sole earner status, own accident, in possession of a doctoral degree, and being young were significant in acting as unobserved characteristics. The results demonstrated differences in the value of road safety and unobserved heterogeneity among drivers, which influence risk perception and valuation with reference to the area context. Relevant agencies can use the results as a guideline for budget allocation and practical policy-related road safety management.

Using Machine Learning to Determine the Motorist Somnolence

Traffic accidents pose an increasing threat to society, and researchers are dedicated to preventing accidents and reducing fatalities, as highlighted by the World Health Organization. One significant cause of accidents is drowsy driving, which often leads to severe injuries and loss of life. The objective of this research is to create a fatigue detection system that can effectively minimize accidents associated with exhaustion. The system utilizes facial recognition technology to identify drowsy drivers by analyzing eye patterns through video processing. When the level of fatigue surpasses a predetermined threshold, the system alerts the driver and adjusts the vehicle's acceleration accordingly. The implementation of OpenCv libraries, such as Haar-cascade, along with Raspberry Pi facilitates seamless integration of the system. This dissertation evaluates advancements in computational engineering for the development of a fatigue detection system to mitigate accidents caused by drowsiness. It offers valuable insights and recommendations to enhance comprehension and optimize the system's effectiveness, ultimately leading to safer road travel.

Pre-trip reservation enabled route guidance and signal control cooperative method for improving network throughput

With the ever-increasing traffic demand, efficient urban traffic operation is facing more and more pressure and challenges. Route guidance and signal control are effective methods to enhance the existing urban traffic capacity and realize the sustainable development of traffic systems. In this paper, a collaborative optimization method of route guidance and intersection signal controlling is proposed under the background of a pre-trip reservation. Vehicles were divided into reserved and non-reserved vehicles, which corresponds to pre-trip reservation and non-reserved travel, respectively. Firstly, route guidance models considering both road network efficiency and vehicle travel time are proposed for different types of vehicles. Then, based on the impact of different travel modes on traffic flow distribution, a collaborative optimization for route guidance and intersection signal control was realized through the proposed dynamic signal optimization method. The experimental results showed that road network capacity was improved by 16% under the condition of high traffic flow by using the proposed optimization method. The proposed method can also alleviate the congestion of some sections without causing the transfer of congestion. The effectiveness of the proposed method was verified by simulation experiments, which can provide a theoretical reference for traffic control and management in the context of pre-trip reservation.

Evaluation of Effectiveness of On-site Wastewater Treatment Systems Located at Roadside Rest Areas: Recognition and Diagnosis of Existing Problems in the Operation of Selected Facilities

Abstract The effects of a dynamic increase in the total length of expressways in Poland include the issue of wastewater generation and disposal at roadside rest areas, which has been gaining importance, especially regarding the safety of local water ecosystems. Currently, the total number of roadside rest areas in Poland is nearly 400, yet in the future it could double, because of road infrastructure expansion and the need to improve road travel comfort. The most common approaches to wastewater disposal are septic tanks or wastewater treatment plants. In view of Poland’s environmental goals, only the second solution can be regarded as a form of final wastewater disposal. The aim of this study was to analyse available solutions for wastewater treatment at roadside rest areas, analysis of the effectiveness of their performance and identification, and diagnosis of existing problems. The tested facilities contained solutions based on biological wastewater treatment, preceded by a mechanical stage with the use of a two-chamber primary settling tank. Two solutions involved a sequencing batch reactor, and the remaining ones used submerged fixed-bed bioreactors. Finally, the obtained results showed unsatisfactory wastewater treatment effectiveness. Parameters such as chemical oxygen demand (COD) were exceeded at five of the six study sites, biochemical oxygen demand (BOD5) was exceeded at three sites, and total suspended solids was exceeded at two sites, which indicates a disturbance in the operation of the treatment plant. The reason for the poor effluent was primarily inadequate technological supervision of the operation of these facilities.

Geographic accessibility to public and private health facilities in Kenya in 2021: An updated geocoded inventory and spatial analysis.

To achieve universal health coverage, adequate geographic access to quality healthcare services is vital and should be characterized periodically to support planning. However, in Kenya, previous assessments of geographic accessibility have relied on public health facility lists only, assembled several years ago. Here, for the first time we assemble a geocoded list of public and private health facilities in 2021 and make use of this updated list to interrogate geographical accessibility to all health providers. Existing health provider lists in Kenya were accessed, merged, cleaned, harmonized, and assigned a unique geospatial location. The resultant master list was combined with road network, land use, topography, travel barriers and healthcare-seeking behavior within a geospatial framework to estimate travel time to the nearest (i) private, (ii) public, and (iii) both (public and private-PP) health facilities through a travel scenario involving walking, bicycling and motorized transport. The proportion of the population within 1 h and outside 2-h was computed at 300 × 300 spatial resolution and aggregated at subnational units used for decision-making. Areas with a high disease prevalence for common infections that were outside 1-h catchment (dual burden) were also identified to guide prioritization. The combined database contained 13,579 health facilities, both in the public (55.5%) and private-for-profit sector (44.5%) in 2021. The private health facilities' distribution was skewed toward the urban counties. Nationally, average travel time to the nearest health facility was 130, 254, and 128 min while the population within 1-h was 89.4, 80.5, and 89.6% for the public, private and PP health facility, respectively. The population outside 2-h were 6% for public and PP and 11% for the private sector. Mean travel time across counties was heterogeneous, while the population within 1-h ranged between 38 and 100% in both the public sector and PP. Counties in northwest and southeast Kenya had a dual burden. Continuous updating and geocoding of health facilities will facilitate an improved understanding of healthcare gaps for planning. Heterogeneities in geographical access continue to persist, with some areas having a dual burden and should be prioritized toward reducing health inequities and attaining universal health coverage.

COVID-19 Restriction Movement Control Order (MCO) Impacted Emissions of Peninsular Malaysia Using Sentinel-2a and Sentinel-5p Satellite.

The unprecedented outbreak of Coronavirus Disease 2019 (COVID-19) has impacted the whole world in every aspect including health, social life, economic activity, education, and the environment. The pandemic has led to an improvement in air quality all around the world, including in Malaysia. Lockdowns have resulted in industry shutting down and road travel decreasing which can reduce the emission of Greenhouse Gases (GHG) and air pollution. This research assesses the impact of the COVID-19 lockdown on emissions using the Air Pollution Index (API), aerosols, and GHG which is Nitrogen Dioxide (NO2) in Malaysia. The data used is from Sentinel-5p and Sentinel-2A which monitor the air quality based on Ozone (O3) and NO2 concentration. Using an interpolated API Index Map comparing 2019, before the implementation of a Movement Control Order (MCO), and 2020, after the MCO period we examine the impact on pollution during and after the COVID-19 lockdown. Data used Sentinel-5p, Sentinel-2A, and Air Pollution Index of Malaysia (APIMS) to monitor the air quality that contains NO2 concentration. The result has shown the recovery in air quality during the MCO implementation which indirectly shows anthropogenic activities towards the environmental condition. The study will help to enhance and support the policy and scope for air pollution management strategies as well as raise public awareness of the main causes that contribute to air pollution.

Investigation and analysis of implementation challenges for autonomous vehicles in developing countries using hybrid structural modeling

The advent of autonomous vehicles (AV) heralds yet another turning point in the evolution of the urban landscape, our daily lives, and the advancement of human civilization as a whole. A very disruptive technology, whose implementation will have ramifications for road safety, air pollution, travel behavior, parking issues, commute time, and other aspects of daily life. The introduction of autonomous vehicles for regular transportation in developed countries is expected to take place over the next decade. Its implementation would be impossible for developing countries for another five decades, at the very least. However, there is potential for its use in selected applications soon. This paper investigates and analyzes the challenges that will be faced in the widespread adoption AV in developing countries. The literature is used to identify a variety of challenges which are then modeled using a hybrid approach that incorporates both ISM (Interpretive Structural Modeling) and MICMAC (Matrice d'impacts croisés multiplication appliquée á un classment) techniques. The results of this analysis would help policy makers and AV manufacturers to understand the level of significance of each challenge and its interrelations. This would help them to focus on critical challenges and initiate remedial actions.

Truck–drone hybrid routing problem with time-dependent road travel time

Combining trucks and drones in package delivery provides a promising venue for a future logistics system that is more efficient and sustainable than the existing one. However, how to coordinate trucks and drones, particularly under uncertain traffic conditions (thus, travel time), remains a critical question in this field. To address this challenge, this study proposes and solves a truck–drone hybrid routing problem with time-dependent road travel time (TDHRP-TDRTT) to address the truck–drone cooperation issue. TDHRP-TDRTT is formulated as a cost minimization problem with constraints associated with logistics demand and supply. An iterative local search heuristic algorithm based on intra-pair and inter-pair customer exchanges and link re-optimization is developed to solve TDHRP-TDRTT. Our results on small-scale and benchmark instances show that the proposed algorithm has better computational performance than CPLEX solver, the adaptive large neighborhood search, hybrid genetic-sweep algorithm, and variable neighborhood search. A case study using traffic data from Chongqing, China shows that the truck–drone solution improves the timeliness of delivery, undertakes sensitivity analysis considering four road congestion states, significantly reduces trucking mileage, and facilitates overcoming terrain limitations. Therefore, the proposed model and algorithm are of practical significance in reducing operating cost, improving transportation efficiency, and facilitating a smart and sustainable urban logistics distribution system.

Estimation and Reliability Research of Post-Earthquake Traffic Travel Time Distribution Based on Floating Car Data

To carry out the estimation and reliability research of post-earthquake traffic travel time, which has the great influence for efficient allocation of relief materials. By analyzing the relationship among floating vehicle trajectory, target path and road network path, the intermediate parameters of converting floating vehicle trajectory data into target path travel time were defined and improved. In addition, the road damage identification method relying on lane detection is applied for evaluating the damage of road after the earthquake through the image information. Then, Bayesian average adaptive kernel density estimation method was used to estimate the distribution of post-earthquake road travel time, and a new formula for calculating the reliability of road travel time after earthquake was proposed. According to the example simulation and analysis, the proposed post-earthquake road travel time distribution estimation and its reliability are verified. The results show that when the threshold value is determined, the travel time of the path before the earthquake is the most dependable, and with the increase in the earthquake damage index, the travel time of this road section becomes increasingly unreliable. However, after the earthquake, the peak probability density of road travel time distribution weakens, and the overall probability shifts to the direction of long time.

Application of Different Data Analytics for Evaluation of Heavy Vehicle Vulnerability in Cold-Region Rural Highways

Understanding the impacts of different driving conditions on truck speed is critical to the development and maintenance of resilient highway freight transportation systems. This study attempts to evaluate the combined impact of road weather, travel lane, vehicle type, and truck payload conditions on drivers’ speed choice by modelling speed distributions as normal distributions. Two data analytics, a regression-based approach (RBA) and a central limit theorem (CLT)-based approach (CBA), are adapted to model context-specific speed distributions. The regression-based approach models population-level speed distributions by considering samples of individual speed data, whereas the CBA uses sampling distributions produced according to the CLT. A holistic approach is proposed to identify overall vehicle-specific collision risks imposed by different road-weather conditions, based on the speed distribution parameters estimated. Implications of the study results pertaining to the trucking industry are threefold. First, adapting different data analytics leads to different study results; yet, the CBA is recommended to model speed distributions. Second, truck speeds are significantly affected by the presence of adverse road-weather conditions, yet marginally varied under different loading conditions. Third, overall, tractor–trailer combinations (TTCs) entail high collision risks, particularly when transporting a freight load under adverse road-weather conditions. The study results would be useful to policymakers, particularly for effective speed management in extremely cold regions. Trucking companies may use the study results to identify the least risk-posing road-weather conditions to deploy safe freight transport operations. The resulting speed distribution models are also useful as input to calibrate traffic micro-simulation models.

Characteristics Analysis and Equilibrium Optimization of Mixed Traffic Flow considering Connected Automated and Human-Driven Vehicles

Considering the impact of informatization condition, vehicles on the road network are divided into connected automated vehicles (CAVs) and human-driven vehicles (HDVs), which follow the principle of system optimization and stochastic user equilibrium, respectively. Taking the road network reserve capacity maximization model under the condition of road capacity constraint as the upper-level programming and the traffic assignment model under heterogeneous flow environment as the lower level programming, then a bilevel programming model is constructed. Among them, the nonuniform demand growth multiplier is adopted for each OD pair to reflect the inconsistency of traffic demand structure growth, and the calculation of link capacity is related to the market penetration of CAVs. The incremental method, method of successive averages, and simulated annealing algorithm are used to solve the model, and the effects of different market penetration on road network capacity, travel time, and saturation are analyzed through a numerical example. The relevant data under different weights are normalized and the optimal deployment scheme of CAVs and HDVs in different periods is obtained by comprehensive evaluation. Meanwhile, the mixed equilibrium flow state is explored under the premise of given market penetration to verify the feasibility of the model and algorithm.

Smart Card-Based Identity Management Protocols for V2V and V2I Communications in CCAM: A Systematic Literature Review

Besides developing new Cooperative, Connected and Automated Mobility (CCAM) services for the improvement of road safety and travel experience, researchers are considering protection mechanisms to ensure the security of these services and the safety of involved users (drivers but also, e.g., cyclists and pedestrians). In particular, several Identity Management (IDM) protocols have been designed as the first line of defence against external attackers. Among these protocols, a promising trend in research consists in the use of a Smart Card (SC) as a technical enabler for strong authentication. Indeed, many SC-based IDM protocols for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications in real-time CCAM services have been proposed in the literature which present interesting features and promising usability experimental results. However, this research line is far from being exhausted, especially considering the recent spread of SC technologies and use cases. For this reason, in this paper we propose a systematic literature review on SC-based IDM protocols for real-time CCAM services. In particular, we identify characterising assumptions of CCAM scenarios and extrapolate a unified high-level view of the steps composing a SC-based IDM protocol. Then, we present a detailed survey of several SC-based IDM protocols. Finally, we identify trends in research and formulate guidelines and useful recommendations to provide a solid base which researchers can use as a starting point for the design of new and improved SC-based IDM protocols for CCAM scenarios.

Traffic ahead: Navigating the road to carbon neutrality

The journey to carbon neutrality will require trillions of dollars of capital investment over many years. Analytical models based on foresight offer guidance, but an overreliance on them can lead to a myopic focus on a single pathway. Some argue that the solution is to develop higher-resolution models, fed with increasingly granular data. Here, we note that real-time feedback is an important and underappreciated complement to this approach.We discuss how nations and corporations are currently embarking on the energy transition with the equivalent of a high-level map. The ambition is clear, but there is a lack of confidence in the best route. Routing options and conditions are changing rapidly. We outline the features of a navigation app for the energy transition that offers real-time, actionable guidance for the energy transition would be hugely beneficial. The essential function of such an app would be to collect, secure, process, and present data in a way that can support decisions by relevant actors. Such a capability would require three essential elements: route topology awareness with high-resolution details of the benefits and costs of different decarbonization options; data platforms that provide insight into the real-time situation; and trade-off tools that readily convey accurate, up-to-date, and easily-interpretable estimates of switching costs. These elements exist at varying levels of maturity, and improvement and integration is needed to deliver the utility and convenience of a road travel app.This Perspective draws on our collective experience mapping paths to carbon neutrality for the Princeton Net-Zero America study and one of China's largest energy companies (China Energy) to develop the idea of navigation apps for the energy transition. Starting with a resolved accounting of CO2 emissions, we illustrate the importance of accounting for differing motivations and leverage among actors. We discuss the essential features of a navigation app and show how progress in these areas can help address bottlenecks in large-scale clean energy deployment. We conclude with a call for increased collaboration with tech companies to accelerate development of these capabilities.