Impact Of Traffic Congestion Research Articles

Estrategias de Gestión para la Optimización del Tránsito Urbano en el Cantón La Concordia: Desafíos y Oportunidades

This article analyzes the impact of traffic congestion in La Concordia, Ecuador, from an administrative perspective, and proposes solutions based on a review of existing literature. The study is based on the growing problem of urbanization and insufficient transportation planning, which has caused significant problems in urban traffic, affecting both the quality of life of citizens and the economic productivity of the region. The objective of the study is to propose viable administrative strategies that can reduce vehicular congestion, based on previous experiences in Latin American cities with similar characteristics. In methodological terms, a qualitative literature review was carried out to analyze successful cases in the implementation of sustainable mobility policies, prioritizing those studies that present solutions for medium-sized developing cities. The results of the analysis highlight the importance of implementing intelligent traffic management systems, improving public transport and encouraging the use of non-motorized means of transport, such as bicycles and pedestrian routes. The discussion highlights that, despite technological and administrative advances, there are significant challenges such as lack of funding and social resistance to the use of public transport. The conclusions suggest that the implementation of these solutions must be accompanied by a cultural change and a greater commitment from local authorities to ensure their long-term effectiveness.

Optimizing electric vehicle routing under traffic congestion: A comprehensive energy consumption model considering drivetrain losses

Energy consumption is a critical concern within the electric vehicle (EV) industry. Despite its substantial impact, drivetrain losses have been inadequately addressed in some studies. This paper introduces drivetrain losses as a nonlinear function of velocity, incorporating them into the comprehensive energy consumption model. Considering the influence of traffic congestion on vehicle speed, our proposed model integrates drivetrain losses, aerodynamic losses, tire losses, kinetic energy losses, and potential energy losses. The objective is to optimize routing with the aim of minimizing energy consumption. To tackle this problem, we present a dedicated adaptive large neighborhood search algorithm. Our numerical experiments reveal key findings: (1) Drivetrain losses represent a higher percentage of total energy consumption compared to aerodynamic and tire losses; (2) Drivetrain losses influenced by traffic congestion constitute a significant share, ranging between 63.82% and 72.08% of total energy consumption; (3) Energy consumption experiences notable increases under the impact of traffic congestion.

ТЕНДЕНЦИИ ИЗМЕНЕНИЯ ФУНКЦИОНАЛЬНОГО СОСТОЯНИЯ ВОДИТЕЛЯ-ХОЛЕРИКА В ДОРОЖНОМ ЗАТОРЕ

In this article, as an object of research, the process of a driver's work activity is considered when performing his functions in the urban transport system. Studies have been conducted to assess the impact of traffic congestion on the functional state of a choleric driver, which is one of the widespread and well-known types of temperament. The hypothesis of the study is that a change in the functional state of a driver in a traffic jam affects the degree of fatigue and depends on the condition of the driver and the parameters of the congestion. To assess the level of fatigue of the driver, the concept of the cardiovascular system is used as an appropriate mechanism of adaptive activity of the body. At the same time, an electrocardiogram is measured and the irregularities of cardiac intervals are analyzed, which are a universal response to all types of loads. The degree of fatigue level of the driver is measured in conventional units and is determined by a special technique taking into account statistical indicators, histogram indicators and spectral analysis of cardiac intervals. Using a nonlinear regression model of changes in the functional state of a choleric driver, the corresponding patterns of changes in the level of fatigue for traffic jams of different duration have been determined. As a result of the analysis of the constructed graphs of changes in the final level of fatigue of choleric drivers of different age categories in congestion, it was found that the most significant factor is the value of the initial level of fatigue of the driver. The second most important factor influencing the change in the final level of fatigue of a choleric driver is the duration of traffic congestion. It was revealed that this factor affects the degree of fatigue of the driver at the end of the jam only together with the initial level of fatigue. The age of the choleric driver was in third place in terms of the importance of influencing his final level of fatigue in congestion. It was also found that older drivers, compared with younger drivers, experience the negative impact of traffic congestion with an increase in its duration. Studies have shown that traffic jams lasting more than twelve minutes lead to a significant increase in the final level of fatigue of the choleric driver. This may increase the likelihood of a traffic accident. The established trends in the functional state of the choleric driver in traffic congestion allow us to predict his behavior after congestion and evaluate possible patterns of development of the road traffic situation.

Impact Of Traffic Congestion on Transportation System: Challenges and Remediations - A review

Impact Of Traffic Congestion on Transportation System: Challenges and Remediations - A review

Impact of traffic congestion on asthma-related hospital visits in major Texas cities.

Asthma is one of the most prevalent chronic conditions in the United States and is particularly sensitive to environmental changes in urban areas. While it is known that traffic congestion contributes to increased vehicle emissions and poorer air quality, its direct association with asthma incidence has not been thoroughly explored. This study aimed to address this void by analyzing 148 city-level observations from 2016 to 2020 in Texas, using data from the Texas A&M Transportation Institute and Definitive Healthcare. We investigated the association between traffic congestion, measured by the travel time index, and annual city-level asthma hospital discharges, while adjusting for refinery productivity, minority groups, and education levels through multivariate regression. Our findings revealed a significant positive correlation between the travel time index and asthma visits, indicating that higher traffic congestion is associated with increased hospital visits for asthma. This finding remains consistent across different models, regardless of whether control variables are included. For the control variables, we found that higher refinery productivity was linked to elevated risks of asthma-related hospitalizations, aligning with previous research findings. Although correlations with Black or African American and Hispanic or Latino populations, as well as those with less than a high school education, were not statistically significant, a positive trend was observed. These results emphasize the impact of traffic congestion on asthma prevalence and the necessity for targeted public health interventions and urban planning strategies.

Traffic flow simulation study under large gathering activities based on VISSIM

Based on the above background, this paper starts from the theory of traffic flow and the macro-basic diagram, and simulates and researches the traffic congestion problems generated in the period of large-scale gathering activities during festivals. First of all, this paper summarizes the time correlation and spatial correlation of traffic flow, analyses the judgement method and rating index of traffic congestion, and completes the macroscopic basic map; after defining the simulation parameters of each road segment and signal intersection in the road network, it is implemented by using the VISSIM simulation software; finally, it carries out the simulation analysis with the example of the New Year's Eve firework display at the Chaotianmen Gate in Chongqing, and calculates and analyses the traffic flow state of the road network with output data, and then compares the output data with that of the road network. Finally, we use the output data to calculate and analyses the traffic flow status of the road network, compare it with the traffic flow data under the smooth period, judge the congestion characteristics, draw the macroscopic basic map of the road network under each period, simulate the changes of the macroscopic basic map, analyses the impact of traffic congestion on the traffic flow of the road network, calculate the degree of improvement of the traffic status of the network, and put forward the reasonable optimization scheme to provide a decision-making basis for the evacuation of the traffic under the festival and large-scale gathering activities.

The perception of drivers and passengers on the socio-economic impacts of traffic congestion in Addis Ababa, Ethiopia

ABSTRACT Ethiopia’s economy has made significant strides forward since the beginning of the 21st century. However, along with this, there is a high rate of urbanization, and internal migration, which lead to an increase in the number of vehicle owners in Addis Ababa city, causing traffic congestion problems. This is adversely impacting the socioeconomic and well-being of the residents. Therefore, this study attempted to identify the perception of drivers and passengers on the socio-economic impact (SEI) of traffic congestion in Addis Ababa city by taking the study corridors in the Kolfe keraniyo sub-city. Nine segments were sampled. Data collection included questionnaires distributed among 3240 respondents. A generalized ordered logit model was used. Accordingly, age, income, household size, gender, education, and respondent type (driver or passenger) were significant variables. The descriptive statistical results also indicated that the first, second, and third significant SEI perceived by the respondents were late arrival to the office, extra time added to the car or bus journey, and the increased risk of being in an accident. The outcomes of this study, including the responses to the research inquiries and the acquisition of extensive scientific knowledge, will contribute to the formulation of comprehensive mitigation strategies and management plans.

Stuck in traffic: Do auditors price traffic congestion?

Although the detrimental impact of traffic congestion on firm operations and human health is widely acknowledged, it is unclear whether auditors perceive traffic congestion as a risk factor. We posit and find that the traffic congestion levels in audit clients' domicile cities are positively associated with audit fees. Using a structured equation model, we identify and illustrate several channels that explain the effect of traffic congestion on audit fees. We also find evidence of audit fee increases after events that expose clients to higher levels of traffic congestion. Finally, the effect of traffic congestion on audit fees is more pronounced when clients have higher operation concentration near their headquarters or when auditors' offices are located near clients’ headquarters. Collectively, our findings suggest that auditors view traffic congestion as a risk factor that is priced into audit fees.

Negative Impact of Traffic Congestion on Air Pollution and Estimation Delay Mode for Signalized Intersections in Babil City Utilizing SIDRA8.0 Plus Software

Traffic congestion is one of the most important factors contributing to air pollution. Vehicle emissions account for a significant portion of this pollution. Estimate the negative impact of traffic congestion by empirical model for predicting total delay time as a function of traffic conditions and geometric features and calculating the quantities of gases emitted during the traffic congestion at the signalled intersection is the goal of this paper. Two signalized intersections in Babil City with different characteristics are select. video recording is used to measure traffic volumes during peak and off-peak times. Afield survey is used to measure geometric design elements, signal timings, phasing, and selected emissions of HC, CO, and CO2 in (kg/hr) are based on delay. with 95% confidence, a linear regression model predicted the delay model. according to the statistical analysis, both geometry and traffic characteristics have a substantial influence on delay time. The constructed regression model for estimating delay has a high degree of correlation with actual values. The proportion of the total width of the exit route of an intersection of straight-forward departing traffic to the total width of groups of lanes departing on the same exit route at the same phase at the stop line it is observed that although the SIDRA 8.0 PLUS delay overestimates the delay at the high (v/c) range, there is no noticeable difference with the field delay and the study will offer suggestions to transportation and public health officials on how to try eco-routing and deco-signal timing to lower the risk of air pollutants.

DETERMINING TRANS JOGJA BUS STOP LOCATIONS USING A COMBINATION OF SPATIAL ANALYSIS AND FUZZY LOGIC

<p><em>The increase in population from migration coupled with natural growth resulted in a high population density in Yogyakarta City, with a negative impact of traffic congestion due to the number of vehicles that were not proportional to road capacity. One of the efforts to lessen congestion was through efficient public transportation. Trans Jogja can be one of the solutions to this problem. However, despite having up to 90 stops in Yogyakarta City, the existing stop locations are not evenly distributed. Thus, in order to propose appropriate bus stop locations, this study aimed to utilize the weighting and utilization of GIS, based on a number of factors usithe ng fuzzy logic method. The result showed that the weight of eh parameter also influenced locations with a high suitability. Values with a high match were pixel values ranging from 6.824 to 9.49. The location of high suitability was close to the road around the location with a high level of crowds, such as office areas, shopping centers, hotels, educational facilities, and tourism. This study proved that fuzzy logic could be used as a tool in spatial analysis to obtain criteria for a location by considering the probability of correctness of each selected parameter.</em></p>

Traffic congestion and air pollution: Empirical evidence before/after COVID-19 in Seoul, Korea

Severe traffic congestion leads to toxic levels of air pollution. We attempt to explore how traffic congestion affects air pollution, especially PM10 and PM2.5, by focusing on the city of Seoul, Korea, which is one of the largest cities in the world and is plagued by severe traffic congestion. We employ a fixed-effect panel model with various datasets, including air pollution, travel speed, and weather conditions. The findings show that traffic congestion adversely affects air quality by increasing the concentration of PM10 and PM2.5. In particular, the impact of traffic congestion on PM10 and PM2.5 was 1.2 times and 1.7 times greater during the COVID-19 period than during the non-COVID-19 period, respectively. In addition, an increase in one unit of RCI is associated with 0.4 increase in PM10 during the peak hours, but 6.7 increase in PM10 during the non-peak hour because of the increased traffic of trucks and motorcycles induced by increased online shopping. To mitigate air pollution, it is suggested that not only reducing vehicle use, but also minimizing traffic congestion is important.

Investigating the Accuracy of Forecasting Travel Duration for Uber Trips and Analyzing the Influence of Weather on Traffic Flow

Accurately predicting the travel time between two destinations is an essential aspect of traffic monitoring and facilitating ridesharing services. However, this is a highly complex and challenging task, which involves a multitude of variables that cannot be resolved straightforwardly. Previous studies on travel time prediction have focused on evaluating the duration of individual road segments or specific sub-paths before integrating the necessary time for each sub-path. While this method may provide some insight, it may result in an incorrect or imprecise time estimate. To address this issue, this research aims to utilize machine learning techniques to predict the duration of trips in ride-sharing networks, by utilizing the Uber movement dataset. The proposed system employs Python programming to calculate the distance between the pickup and drop-off locations. Furthermore, the study explores the various factors that affect travel time in a descriptive analysis. This includes examining the impact of traffic congestion, weather conditions, and road construction on travel time. The suggested approach incorporates a robust regression model known as Huber regression to enhance the accuracy of trip duration prediction and increase the precision of the algorithm. The Huber regression model is robust to outliers, making it suitable for the Uber movement dataset, which may contain unexpected and extreme values. The dataset is processed using k-fold cross-validation, which splits the dataset into k subsets, with each subset used for validation once while the remaining subsets used for training the model. However, this approach presents several challenges that need to be addressed, including the difficulties with tracking variables, the need for extensive data transformation due to the diverse data types contained in the dataset, and the challenge of handling unlabeled places during the segmentation of geographical data. Additionally, outliers in the dataset can lead to substantial data differences and affect the model's accuracy. Data normalization is slow due to the time-consuming nature of reading duplicated information. To mitigate these issues, additional study is required to improve the model's layout and address the challenges of working with the Uber movement dataset.

Impact of traffic congestion on health (Physical and psychological)- A study of Udaipur city, Rajasthan

Impact of traffic congestion on health (Physical and psychological)- A study of Udaipur city, Rajasthan

Impact of traffic congestion on spatial access to healthcare services in Nairobi.

Geographic accessibility is an important determinant of healthcare utilization and is critical for achievement of universal health coverage. Despite the high disease burden and severe traffic congestion in many African cities, few studies have assessed how traffic congestion impacts geographical access to healthcare facilities and to health professionals in these settings. In this study, we assessed the impact of traffic congestion on access to healthcare facilities, and to the healthcare professionals across the healthcare facilities. Using data on health facilities obtained from the Ministry of Health in Kenya, we mapped 944 primary, 94 secondary and four tertiary healthcare facilities in Nairobi County. We then used traffic probe data to identify areas within a 15-, 30- and 45-min drive from each health facility during peak and off-peak hours and calculated the proportion of the population with access to healthcare in the County. We employed a 2-step floating catchment area model to calculate the ratio of healthcare and healthcare professionals to population during these times. During peak hours, <70% of Nairobi's 4.1 million population was within a 30-min drive from a health facility. This increased to >75% during off-peak hours. In 45 min, the majority of the population had an accessibility index of one health facility accessible to more than 100 people (<0.01) for primary health care facilities, one to 10,000 people for secondary facilities, and two health facilities per 100,000 people for tertiary health facilities. Of people with access to health facilities, a sub-optimal ratio of <4.45 healthcare professionals per 1,000 people was observed in facilities offering primary and secondary healthcare during peak and off-peak hours. Our study shows access to healthcare being negatively impacted by traffic congestion, highlighting the need for multisectoral collaborations between urban planners, health sector and policymakers to optimize health access for the city residents. Additionally, growing availability of traffic probe data in African cities should enable similar analysis and understanding of healthcare access for city residents in other countries on the continent.

A Traffic Management System for Minimization of Intersection Traffic Congestion: Case Bengkong Junction, Batam

Purpose: An increase in the number of vehicles that are not matched by the growth of road area is one of the causes of road Traffic congestion in Batam City. Traffic congestion occurs, especially in business and densely populated residential areas. The government has carried out various engineering and traffic management to keep the road service level under control. This study aims to analyze traffic management's efficiency in reducing Traffic congestion on roads in Batam City.&#x0D; Design/methodology/approach: This study uses a case study at the Bengkong red light intersection, Batam City. The analysis was carried out using The Indonesian Highway Capacity Manual-1997.&#x0D; Findings: Traffic management and engineering must be implemented gradually to get a suitable implementation model. Actual conditions in the field become essential in determining the right choice of traffic management and engineering.&#x0D; Research limitations/implications: Traffic management and engineering analyzes were conducted locally at the case study site.&#x0D; Practical implications: The analysis of traffic management reduces traffic congestion's impact.&#x0D; Originality/value: Original paper&#x0D; Paper type: A case study

Impact of traffic congestion on road users in Tangerang City

One of the negative impacts of transportation problems is congestion. Traffic congestion in urban areas is gradually becoming an important social and economic problem that needs to be addressed. Tangerang City is one of the supporting cities for the Jakarta metropolitan area as well as an area with a significant increase in population and vehicles, resulting in an increase in the number of people's movements or mobility in order to meet the needs of life. Meanwhile, the length of roads in Tangerang City during 2018-2019 did not increase and it is possible that there will be an imbalance between movement and available road capacity or minimal accessibility. The research approach used is quantitative with primary data sources obtained from closed questionnaire instruments from 180 road user respondents at the TangCity Mall intersection, Tangerang City. Analysis of the data used is a structural equation model with the AMOS 22.00 program to determine the factors that influence the impact of congestion. The results of the analysis show that the factors that most influence the impact of congestion are decreased concentration and loss of energy during traffic jams, which are more common in men who are married at the age of over 35 years, with a diploma level of education, as well as the type of work as a Civil Servant.

Impact of traffic congestion on near-road black carbon particulate matter and nitric oxide levels

Impact of traffic congestion on near-road black carbon particulate matter and nitric oxide levels

Dynamic accessibility by car to tertiary care emergency services in Cali, Colombia, in 2020: cross-sectional equity analyses using travel time big data from a Google API

ObjectivesTo test a new approach to characterise accessibility to tertiary care emergency health services in urban Cali and assess the links between accessibility and sociodemographic factors relevant to health equity.DesignThe...

Insights into the socio-economic impacts of traffic congestion in the port and industrial areas of Chittagong city, Bangladesh

Traffic congestion is one of the major barriers to the economic development of developing economies, resulting in severe social and economic impacts. The severity of traffic congestion in port and industrial areas is more thought-provoking than destructive barriers. The purpose of this research is to better understand the social, economic, and environmental effects of road traffic congestion in a developing city's port and industrial areas. This study adopted an on-site survey strategy to collect data from regular road-users by administering questionnaires, performing volume count surveys, and measuring travel time, delay time, and vehicle speed throughout the day. Along with documenting numerous social impacts, this assessment also assessed the impacts on stress through a four-point Likert-type scale. The congestion level in the four divided road sections was identified using the Level of Service index and based on respondents’ opinions. The delayed costs, fuel loss costs, pollution costs, and loss of vehicle operators due to traffic congestion have been estimated. The study found that excessive vehicles, narrow roads, ineffective public transport modes, and bus operators’ attitudes are causing congestion, resulting in high to extreme stress levels for road users, with a total economic loss of $2.01 million per day. The overall stress score of 3.23 ± 0.71 indicates the impact of traffic congestion on the respondents was high to an extreme level and had significant social impacts on different sociodemographic groups. This study can guide future work regarding sustainable transportation systems in emerging cities as well as traffic planning and policy adaptation for sustainable transportation systems, focusing on the reduction of adverse socioeconomic impacts.

Adaptive Approaches for Tidal-Flow Lanes in Urban-Road Networks

Every year, traffic congestion costs the global economy billions of dollars in lost productivity, particularly in urban areas. Traffic congestion is a complex problem, as traffic conditions may change at any time. Tidal-flow lanes can be utilised as a feasible traffic-congestion-mitigation strategy to balance the fluctuating traffic demands throughout the day. This paper proposes an adaptive-lane-reversal approach for tidal-flow lanes, to decrease the impact of traffic congestion in urban areas. In order to evaluate the adaptive approach under various traffic conditions, several algorithms and parameter sets are examined, using various network models and traffic demands. As a result, the total travel time of the vehicles in the various networks was decreased by up to 81%.