Traffic congestion

About: Traffic congestion is a research topic. Over the lifetime, 16826 publications have been published within this topic receiving 235654 citations. The topic is also known as: traffic jam & traffic snarl-up.

Automobile Externalities and Policies

Abstract: This paper discusses the nature, and magnitude, of externalities associated with automobile use, including local and global pollution, oil dependence, traffic congestion and traffic accidents. It then discusses current federal policies affecting these externalities, including fuel taxes, fuel-economy and emissions standards, and alternative fuel policies, summarizing, insofar as possible, the welfare effects of those policies. Finally, we discuss emerging pricing policies, including congestion tolls, and insurance reform, and we summarize what appears to be the appropriate combination of policies to address automobile externalities.

The Fundamental Law of Road Congestion: Evidence from US Cities

Abstract: We investigate the effect of lane kilometers of roads on vehicle-kilometers traveled (VKT) in US cities. VKT increases proportionately to roadway lane kilometers for interstate highways and probably slightly less rapidly for other types of roads. The sources for this extra VKT are increases in driving by current residents, increases in commercial traffic, and migration. Increasing lane kilometers for one type of road diverts little traffic from other types of road. We find no evidence that the provision of public transportation affects VKT. We conclude that increased provision of roads or public transit is unlikely to relieve congestion. (JEL R41, R48)

Congestion control and traffic management in ATM networks: recent advances and a survey

Abstract: Congestion control mechanisms for ATM networks as selected by the ATM Forum traffic management group are described. Reasons behind these selections are explained. In particular, selection criterion for selection between rate-based and credit-based approach and the key points of the debate between these two approaches are presented. The approach that was finally selected and several other schemes that were considered are described.

Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

Abstract: Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the traffic congestion. With the increasing popularity of taxi requesting services such as Uber and Didi Chuxing (in China), we are able to collect large-scale taxi demand data continuously. How to utilize such big data to improve the demand prediction is an interesting and critical real-world problem. Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations. Recent advances in deep learning have shown superior performance on traditionally challenging tasks such as image classification by learning the complex features and correlations from large-scale data. This breakthrough has inspired researchers to explore deep learning techniques on traffic prediction problems. However, existing methods on traffic prediction have only considered spatial relation (e.g., using CNN) or temporal relation (e.g., using LSTM) independently. We propose a Deep Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial and temporal relations. Specifically, our proposed model consists of three views: temporal view (modeling correlations between future demand values with near time points via LSTM), spatial view (modeling local spatial correlation via local CNN), and semantic view (modeling correlations among regions sharing similar temporal patterns). Experiments on large-scale real taxi demand data demonstrate effectiveness of our approach over state-of-the-art methods.

Real-World Carbon Dioxide Impacts of Traffic Congestion

Abstract: Transportation plays a significant role in carbon dioxide (CO2) emissions, accounting for approximately a third of the U.S. inventory. To reduce CO2 emissions in the future, transportation policy makers are planning on making vehicles more efficient and increasing the use of carbon-neutral alternative fuels. In addition, CO2 emissions can be lowered by improving traffic operations, specifically through the reduction of traffic congestion. Traffic congestion and its impact on CO2 emissions were examined by using detailed energy and emission models, and they were linked to real-world driving patterns and traffic conditions. With typical traffic conditions in Southern California as an example, it was found that CO2 emissions could be reduced by up to almost 20% through three different strategies: congestion mitigation strategies that reduce severe congestion, allowing traffic to flow at better speeds; speed management techniques that reduce excessively high free-flow speeds to more moderate conditions; and s...