Influence Of Traffic Congestion Research Articles

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.

REGULARITIES OF CHANGING THE FUNCTIONAL STATE OF THE DRIVER IN THE TRANSPORT SYSTEM IN CASE OF TRAFFIC CONGESTION

The object of this study is the process of driver’s labor activity on road sections in the city's transport system in the process of transporting goods and passengers. The influence of traffic congestion on the level of the functional state of a choleric driver and a phlegmatic driver, which are opposite types of temperament, is considered. The hypothesis of the study is that the level of driver fatigue, determined by a change in his functional state in a traffic jam, affects the driver's reaction time and road safety and depends on the driver's condition and the parameters of the traffic jam. The degree of fatigue, which is determined by a change in the functional state of the driver, is determined based on the concept of the cardiovascular system as an indicator of the adaptive activity of the driver's body by measuring the electrocardiogram. To assess the level of driver fatigue, the irregularities of the electrocardiogram cardio intervals are analyzed, which are a universal response to any type of load: physical or mental. The level of fatigue is assessed in arbitrary units using a special algorithm that takes into account statistical indicators, histogram indicators and data from the spectral analysis of cardio intervals. Regularities of changes in the level of fatigue of a choleric driver and a phlegmatic driver, depending on various conditions of stay in a traffic jam, were obtained using nonlinear models of changes in their functional states. It was revealed that the most significant factor influencing the final level of fatigue of a choleric driver in a congestion is the duration of traffic congestion, the effect of which on the output function is manifested only in conjunction with the initial level of fatigue. The next most important parameter influencing the change in the level of fatigue is the initial value of the level of fatigue before the mash. The influence of the age of the choleric driver on the level of his fatigue in the congestion was less pronounced. As a result of the studies carried out and the revealed patterns, it was found that the duration of the congestion does not significantly affect the condition of the phlegmatic driver. The most important factor influencing his condition is the initial level of fatigue before entering the congestion. It was also found that the conditions of being in a traffic jam most significantly affect older choleric drivers (fifty or more years old) compared to young drivers thirty years old. Analysis of the research results showed that congestion lasting more than ten minutes leads to a significant increase in the level of fatigue of a choleric driver. Such situations can lead to an increased probability of a road traffic accident by a choleric driver. The obtained patterns of changes in the functional state of a choleric driver and a phlegmatic driver in a traffic jam allow predicting the driver's behavior after a traffic jam and assessing various options for the development of the road traffic situation that affect road safety.

Visual Cause Analytics for Traffic Congestion.

Urban traffic congestion has become an important issue not only affecting our daily lives, but also limiting economic development. The primary cause of urban traffic congestion is that the number of vehicles is higher than the permissible limit of the road. Previous studies have focused on dispersing traffic volume by detecting urban traffic congestion zones and predicting future trends. However, to solve the fundamental problem, it is necessary to discover the cause of traffic congestion. Nevertheless, it is difficult to find a research which presents an approach to identify the causes of traffic congestion. In this paper, we propose a technique to analyze the cause of traffic congestion based on the traffic flow theory. We extract vehicle flows from traffic data, such as GPS trajectory and Vehicle Detector data. We detect vehicle flow changes utilizing the entropy from the information theory. Then, we build cumulative vehicle count curves (N-curve) that can quantify the flow of the vehicles in the traffic congestion area. The N-curves are classified into four different traffic congestion patterns by a convolutional neural network. Analyzing the causes and influence of traffic congestion is difficult and requires considerable experience and knowledge. Therefore, we present a visual analytics system that can efficiently perform a series of processes to analyze the cause and influence of traffic congestion. Through case studies, we have evaluated that our system can classify the causes of traffic congestion and can be used efficiently in road planning.

Traffic Congestion and Physical Health of Commuters: Perspective of Dhaka City

The daily commute to the office is the most common scenario for office workers in urban areas. On the other hand, traffic congestion has become a growing concern while commuting. Traffic congestion hurts the environment and the countries economy. Some research found an adverse effect on the mental and physical health of daily commuters. However, the influence of traffic congestion when traveling is not identified yet. This review suggested that the association between road traffic delays and physical health are needed to be explored.

Risk Assessment of an Electrical Power System Considering the Influence of Traffic Congestion on a Hypothetical Scenario of Electrified Transportation System in New York State

With the increasing penetration of electric vehicles (EVs), more and more interactions appear between the transportation system and the power system, which might provide new hazards and channels for the proliferation of failures across the boundaries of the individual systems. In this context, this paper proposes an integrated risk assessment framework for an electric power system, considering scenarios that involve the electrified transportation system enabled by EVs charging technology in New York (NY) State. Firstly, scenarios in the transportation network of NY State, e.g. of reduced capacity and incident, are generated by a Monte Carlo non-sequential algorithm. Then, the cell transmission model (CTM) is used to simulate the evolution of the traffic flows under such scenarios. This allows evaluating the spatial-temporal EV charging loads in different areas of the electrified transportation system of NY State. Correspondingly, the running parameters in the studied power system are updated by the alternative current (AC) power flow model. Finally, the risk for the power system coming from the transportation system scenarios is assessed within a probabilistic risk analysis framework. The proposed integrated risk assessment framework is able to model the propagation of the effects of scenarios in the transportation system onto the power system of NY State and quantify the consequences. A real test case is used to illustrate the proposed framework.

교통 혼잡 원인과 영향을 분석하기 위한 시각적 분석 기술

본 논문에서는 교통 흐름 이론을 기반으로 교통 혼잡의 원인을 분석하는 기술을 제시한다. 우리는 GPS 궤적 및 차량 감지기 데이터(VD)와 같은 교통 데이터에서 차량의 흐름을 추출한다. 또한 우리는 교통 데이터에 정보이론의 엔트로피를 사용하여 차량의 흐름 변화를 식별한다. 그런 다음 혼잡 지역의 차량 흐름을 정량화할 수 있는 누적 차량 수 커브(N-curve)를 추출한다. 교통 흐름 이론에 따르면 혼잡유형에 따라 고유한 N-curve 패턴을 관찰할 수 있다. 우리는 N-curve를 네 가지의 혼잡 패턴으로 분류할 수 있는 합성곱 신경망을 설계한다. 교통 혼잡의 원인과 영향을 분석하는 것은 어렵고 상당한 경험과 지식이 필요하다. 따라서 논문에서는 교통 혼잡의 원인과 영향을 분석하기 위한 일련의 프로세스를 효율적으로 수행할 수 있는 시각적 분석 시스템을 제시한다. 논문에서는 두 가지의 사례 연구를 통해 교통 혼잡의 원인을 분석할 수 있는 시스템을 평가한다.

Study of the influence of road congestion on the fatigue level of a sanguine driver

The object of research is the process of the driver’s labor activity on city roads in the city’s transport system during the transportation of goods and passengers. The influence of traffic congestion on the functional state of a sanguine driver is studied, which is one of the most common types of temperament. The main hypothesis of the study is that the level of driver fatigue in traffic congestion, which affects the driver’s reaction time and road safety, depends on the driver’s condition and traffic congestion parameters. The driver’s fatigue level is determined based on the concept of the cardiovascular system, as an indicator of the adaptive and adaptive activity of the body by measuring the electrocardiogram. In this case, the unevenness of the cardio intervals is analyzed, which is a universal response to any kind of load. Fatigue level is calculated in arbitrary units according to a special algorithm that takes into account statistical indicators, histogram indicators and data of spectral analysis of cardio intervals. Using a non-linear model of changing the functional state of a sanguine driver, the patterns of changes in the fatigue level under various conditions of stay in traffic congestion are obtained. It is revealed that the most significant factor that affects the final level of driver fatigue in traffic congestion is its initial value before traffic congestion. The second most important parameter affecting the change in the fatigue level of the sanguine driver is the duration of traffic congestion, which affects the initial function only together with the initial fatigue level. The influence of the age of the sanguine driver on the fatigue level in the traffic congestion is manifested to a lesser extent. However, the conditions of stay in traffic congestion most noticeably affect older drivers (sixty or more years) compared with young drivers of twenty years. Analysis of the research results shows that traffic congestions lasting more than twelve minutes lead to a significant increase in the fatigue level of a sanguine driver. This may increase the likelihood of a traffic accident. The trends in the fatigue level of sanguine driver in traffic congestions identified during the study allow to predict the driver’s behavior after exiting a traffic congestion and evaluate possible patterns of road traffic development that directly affect road safety.

Reliability Assessment of Distribution Network and Electric Vehicle Considering Quasi-Dynamic Traffic Flow and Vehicle-to-Grid

With the increasing number of electric vehicles and the emergence of vehicle-to-grid technology, electric vehicles have become distributed loads and power sources with random movement characteristics in the distribution network. In order to evaluate the reliability of the distribution network incorporating electric vehicles, firstly, this paper uses the trip chain theory to describe the travel of electric vehicles. Based on the static traffic flow distribution, a high efficiency quasi-dynamic travel simulation method is proposed to consider the influence of traffic congestion on the path selection. Then, the simulation time advancement of the Monte Carlo method is improved, which makes the reliability assessment of the distribution network containing a large amount of electric vehicles' charging and discharging behaviors realizable. Finally, the practicality of the method is verified by the modified IEEE-RBTS Bus-6 test system. The effects of electric vehicles penetration, discharging threshold, and battery capacity on reliability of both distribution networks and electric vehicles are studied.

Two Dimensions for Determining and Analyzing the Patterns of the Modal Accessibility Gap in Nanjing, China

Measuring the modal accessibility gap (MAG) is useful for evaluating sustainable transportation development and urban land use. Meanwhile, reducing reliance on private transport (e.g., car use) and raising public transport usage (e.g., bus, metro, light rail, bicycle) is frequently a major goal of transport strategies. Against this background, this study measures the MAG in multi-modal transportation networks from two perspectives: mobility (travel modes and traffic conditions) and proximity (the location and attractiveness of the destination) and describes the methodology developed for an application across the metropolitan area of Nanjing, China. The results show substantial differences between public and private transport in a multi-modal urban network environment based on a realistic “door-to-door” model. In addition, we find that the accessibility advantage of cars is always better than the transit and cycling modes but only from a mobility perspective. The analysis of the sustainability of urban transport should also take account of the location and service capability of the destination and the influence of traffic congestion. The patterns of the smallest MAG scores between the transit and car modes are concentrated along metro and bus routes, and thus they change with traffic fluctuations. On the contrary, the patterns of the smallest MAG scores between the cycling and car modes are concentrated in central urban areas and are thereby affected by traffic conditions including road destiny, river obstacles, and the cross-river channel. Finally, the range of the lowest MAG scores shows a broader distribution in central urban areas in peak hours compared with in off-peak hours.

E-commerce and traffic congestion: An economic and policy analysis

E-commerce, due to its ability to re-direct consumers from physical stores to online, can potentially alleviate traffic congestion. In this paper, we set up a theoretic model to analyze interactions between a firm’s distribution strategy and traffic congestion. In an unregulated economy, we first characterize the private firm’s optimal strategy concerning e-commerce under the influence of traffic congestion. We then examine a centralized economy where the firm is publicly owned and derive the distribution strategy that maximizes social welfare. Comparing the two cases, we show that the private firm’s incentives may deviate from the socially optimal decisions, which leads to inefficiency. We identify two effects, i.e., monopoly effect and congestion externality effect, which drive the private firm to deviate from the social optimum. Based on our analysis, we propose a differentiated tolls/rebates policy to achieve maximum social welfare. Under such a policy, the firm will not only adopt the socially optimal distribution strategy but offer the socially optimal quantities.

Method and application of grade division for road traffic congestion based on driver’s feeling

Although road traffic congestion is an objective state of traffic flow, drivers have different feelings about road traffic congestion. First of all, in order to reveal the law that driver’s mental state changes along with congested degree of road traffic, the targeted questionnaire was designed to analyze the driver’s psychological feeling under different state of traffic flow. Then, from the perspectives of the driver’s subjective feeling, the new definition of road traffic congestion was put forward, so the pressure coefficient of traffic congestion, which is written as [Formula: see text], is defined to measure traffic congestion pressure. Furthermore, by adopting statistical methods to associate the driver’s subjective feeling toward traffic congestion with objective parameters of traffic flow, the grading thresholds of the unblocked state, mild congested state, moderate congested state, and severe congested state were calculated, which are [Formula: see text], [Formula: see text], and [Formula: see text], respectively. And in the field of application for the grading thresholds, Fourier Transform theory was introduced to calculate domain frequency indexes of the driver’s heart rate variability in the four states of traffic flow, respectively. The results show that the domain frequency indexes of the driver’s heart rate variability present obvious differences in the four states, which illustrates the solving rationality and applied value of the new grading thresholds for traffic congestion based on driver’s feeling. On one hand, the new grading method for traffic congestion, which combines the subjectivity with objectivity, can reflect the driver’s actual feeling; on the other hand, it lays the foundation for the study about the influence of traffic congestion on the driver’s physiological and psychological characteristics.

Research of the influence of traffic congestion on the functional state of driver

Обґрунтовано необхідність оцінки впливу транспортної пробки на стан водія. Розглянуто чинники, які впливають на функціональний стан водія в транспортному заторі. Складена регресійна модель оцінки психофізіологічного стану водія в транспортній пробці. Оцінені статистичні характеристики отриманого регресійного рівняння. Проведено порівняння впливу умов транспортної пробки на групи водіїв за типом нервової системи.

The Influence of Traffic Congestion, Daily Hassles, and Trait Stress Susceptibility on State Driver Stress: An Interactive Perspective1

State driver stress was measured in both low and high traffic congestion using cellular telephones. The contributions of time urgency, trait driver stress, and hassles were also examined. Drivers showed substantially more state driver stress under high than low congestion. Time urgency made a significant positive contribution to state driver stress at both congestion levels. Trait driver stress also contributed positively under low congestion. There was a significant hassles X trait stress interaction under high congestion. Hassles exposure moderately increased state driver stress for high trait stress drivers, but reduced state driver stress for medium and low trait stress drivers. These findings indicate that state driver stress is influenced by a combination of situational and personal factors, including factors external to the driving context.