Search results

The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

This paper aims to address the urban mobility and traffic congestion problem under environmental dynamics to improve mobility and reduce traffic congestion using system dynamics (SD) simulation and scenarios.

SD simulation was used to analyze urban mobility and traffic congestion. Data were collected from the Transportation Department of Surabaya City. Several scenarios to improve urban mobility and reduce traffic congestion were developed by modifying the structures and parameters of the model.

Several factors influence urban mobility, including modal split, trip frequency, delay performance and the ratio of public transport supply and demand. Urban mobility, daily traffic and road capacity are some factors that affect traffic congestion. Scenarios can be designed based on the assumptions of the proposed strategy.

The study was conducted at Surabaya City, East Java, Indonesia, which is the fourth most-congested city in the world.

By implementing several strategies (mass rapid transit and bus rapid transit development and public transport delay reduction), mobility performance is projected to be improved by 70.34-92.96%. With this increased mobility, traffic congestion is projected to decline by 52.5-65.8%.

The novel contributions of this research are: formulating relationships between several variables; modeling dynamic behavior of urban mobility and traffic congestion; and building scenario models to improve mobility and reduce traffic congestion in Surabaya. With the increase in urban mobility and the decrease in average daily traffic, traffic congestion could be reduced by a minimum of 57.6% and a maximum of 69%.

Traffic accident on inter-city expressways might cause large-scale traffic congestion. It might increase travel times of many drivers and it produces a large social loss. This study aims to estimate the social loss of travel time of drivers caused by traffic accidents on inter-city expressway using traffic simulation model, and to evaluate the effects of outflow recommendations when an accident occurs on the expressway. The traffic simulation model on Tomei Expressway is constructed to estimate the dynamic traffic congestion. Travel time losses of drivers are estimated by the simulation results with hypothetical traffic accidents. It is understood that the total losses of travel times are depending on the positions of accident spots and the occurrence times of accidents, because it might influence to congestion at the bottlenecks of flow capacity. Moreover, the effect of traffic control in emergency situation is discussed. The influences of outflow recommendations for drivers are estimated using the traffic simulation model.

Describes the events leading up to the imposition of the London congestion charge. Views about the congestion charge, both pro and con, are presented. Also discusses, in general terms, the economics of traffic congestion, pointing out that an unregulated market for driving will not reach the social optimum. Contains sufficient data to estimate the deadweight loss in an unregulated market and the reduction of the deadweight loss due to the imposition of the congestion charge in 2003.

To provide a good illustration of how an unregulated market with negative externalities can lead to an overprovision of a good (in this case driving). Also, to show how an externality tax (in this case, London's congestion charge) can lead to an improvement in social welfare.

This paper aims to use active fine lane management methods to solve the problem of congestion in a weaving area and provide theoretical and technical support for traffic control under the environment of intelligent connected vehicles (ICVs) in the future.

By analyzing the traffic capacities and traffic behaviors of domestic and foreign weaving areas and combining them with field investigation, the paper proposes the active and fine lane management methods for ICVs to optimal driving behavior in a weaving area. The VISSIM simulation of traffic flow vehicle driving behavior in weaving areas of urban expressways was performed using research data. The influence of lane-changing in advance on the weaving area was evaluated and a conflict avoidance area was established in the weaving area. The active fine lane management methods applied to a weaving area were verified for different scenarios.

The results of the study indicate that ICVs complete their lane changes before they reach a weaving area, their time in the weaving area does not exceed the specified time and the delay of vehicles that pass through the weaving area decreases.

Based on the vehicle group behavior, this paper conducts a simulation study on the active traffic management control-oriented to ICVs. The research results can optimize the management of lanes, improve the traffic capacity of a weaving area and mitigate traffic congestion on expressways.

The aim of this paper is to add to understanding of how cities function. Specifically, through the lens of power relationships in political organisations, it seeks to study the manner in which accounting and politics are involved in the development of city transport strategies.

The paper uses a comparative case study approach in which documents and media coverage are key elements of the visualising of the city.

The findings are on a number of levels. First, the study explains the efficacy of congestion charging systems. Second, in the politicised organisation of the city, the context in which policy makers sit is crucial in the elaboration of strategies. Third, the adoption of calculative practices such as congestion charging may reflect political rationality rather than actual need.

The focus of the study has been cities – a neglected field, but one with considerable research potential. Second, the mobilisation of concepts of power, as articulated by Clegg, Flyvbjerg and Clegg, represent a novel contribution to the accounting literature.