The impact of agent definitions and interactions on multiagent learning for coordination in traffic management domains

2020 ◽  
Vol 34 (1) ◽  
Author(s):  
Jen Jen Chung ◽  
Damjan Miklić ◽  
Lorenzo Sabattini ◽  
Kagan Tumer ◽  
Roland Siegwart
Keyword(s):  
Traffic Management ◽  
Multiagent Learning ◽  
The Impact ◽  
Management Domains

A Simulation Environment for Analyzing the Impact of Volcanic Ash on Trajectory-Based Air Traffic Management

2013 ◽  
Author(s):  
Angela Schmitt ◽  
Ruzica Vujasinovic ◽  
Christiane Edinger ◽  
Julia Zillies ◽  
Vilmar Mollwitz
Keyword(s):  
Traffic Management ◽  
Volcanic Ash ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Simulation Environment ◽  
The Impact

scholarly journals Privileging Electric Vehicles as an Element of Promoting Sustainable Urban Mobility—Effects on the Local Transport System in a Large Metropolis in Poland

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3838
Author(s):  
Marta Borowska-Stefańska ◽  
Michał Kowalski ◽  
Paulina Kurzyk ◽  
Miroslava Mikušová ◽  
Szymon Wiśniewski
Keyword(s):  
Transport System ◽  
Electric Vehicles ◽  
Traffic Management ◽  
Negative Impact ◽  
Software Tool ◽  
Research Area ◽  
Shopping Centre ◽  
Local Transport ◽  
Sustainable Urban Mobility ◽  
The Impact

The main purpose of this article was to determine the impact on the equilibrium of the local transport system from privileging EVs by permitting them to use bus lanes. The study used two sets of data: information on infrastructure and traffic management; and information on the recorded road network loads and traffic volumes generated by a given shopping centre—the E. Leclerc shopping centre (an important traffic generator within the city of Łódź, Poland). These sets were then used to develop a microsimulation traffic model for the shopping centre and the associated effects on the localised transport system. The model was constructed by means of the PTV Vissim software tool. An initial simulation was conducted that formed a basis for subsequent scenarios (in total, 17 simulations were performed). On the basis of the conducted analyses, it was established that—for the researched part of the transport system—privileging the still rather uncommon battery electric vehicles (BEVs) engendered a marginal deterioration of traffic conditions. At the same time, allowing BEVs to use bus lanes within the chosen research area had no negative impact on bus journey times.

scholarly journals Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

2021 ◽  
Vol 13 (12) ◽  
pp. 2329
Author(s):  
Elżbieta Macioszek ◽  
Agata Kurek
Keyword(s):  
Travel Behavior ◽  
Traffic Management ◽  
Road Traffic ◽  
Traffic Volume ◽  
The Road ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
On The Road ◽  
The Impact ◽  
The City

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

scholarly journals Innovative Scheme for Efficient Freight Movement and Sustainable Emissions Management

2019 ◽  
Vol 20 (3) ◽  
pp. 242-250
Author(s):  
Afroditi Anagnostopoulou ◽  
Evangelia Papargyri ◽  
Maria Boile
Keyword(s):  
Traffic Management ◽  
Ghg Emissions ◽  
Pollutant Emissions ◽  
Experimental Simulation ◽  
Public Authorities ◽  
Urban Freight ◽  
Policies And Measures ◽  
Technological Advances ◽  
Urban Freight Transport ◽  
The Impact

Abstract This paper presents and analyses an innovative integrated scheme that aims to rationalize and improve the efficiency of urban freight transport as well as to promote reduced GHG emissions and traffic flows. Emissions management has become critical concern for modern companies and public authorities seeking to reduce carbon emissions and energy consumption from private and commercial heavy vehicle fleets, in line with the political targets of COP21 to COP23. The proposed scheme aims to utilize the current technological advances for efficient transport and logistics operations that regional authorities and companies can use and afford in order to provide competitive traffic management decisions as well as improvements in terms of pollutant emissions reduction. Both public and private stakeholders could interact to monitor and evaluate the impact of traffic policies and measures over time as well as the level of success of their routing strategies. Computational results on different scenarios of an experimental simulation model illustrate the competitiveness of the proposed scheme in an effort to quantify its effect.

scholarly journals Multistage Impacts of the Heavy Rain Process on the Travel Speeds of Urban Roads

2021 ◽  
Vol 10 (8) ◽  
pp. 557
Author(s):  
Qiuping Li ◽  
Haowen Luo ◽  
Xuechen Luan
Keyword(s):  
Traffic Management ◽  
Urban Transportation ◽  
Three Dimensional ◽  
Heavy Rain ◽  
The Road ◽  
Road Surfaces ◽  
Speed Change ◽  
The Impact ◽  
The Relationship ◽  
Personal Travel

Heavy rain causes the highest drop in travel speeds compared with light and moderate rain because it can easily induce flooding on road surfaces, which can continue to hinder urban transportation even after the rainfall is over. However, very few studies have specialized in researching the multistage impacts of the heavy rain process on urban roads, and the cumulative effects of heavy rain in road networks are often overlooked. In this study, the heavy rain process is divided into three consecutive stages, i.e., prepeak, peak, and postpeak. The impact of heavy rain on a road is represented by a three-dimensional traffic speed change ratio vector. Then, the k-means clustering method is implemented to reveal the distinct patterns of speed change ratio vectors. Finally, the characteristics of the links in each cluster are analyzed. An empirical study of Shenzhen, China suggests that there are three major impact patterns in links. The differences among links associated with the three impact patterns are related to the road category, travel speeds in no rain days, and the number of transportation facilities. The findings in this research can contribute to a more in-depth understanding of the relationship between the heavy rain process and the travel speeds of urban roads and provide valuable information for traffic management and personal travel in heavy rain weather.

scholarly journals EFFECTS OF THE RAINSTORM ON URBAN ROAD TRAFFIC SPEED — A CASE STUDY OF SHENZHEN, CHINA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 71-75 ◽  
Author(s):  
Q. Li ◽  
X. Hao ◽  
W. Wang ◽  
A. Wu ◽  
Z. Xie
Keyword(s):  
Traffic Management ◽  
Road Traffic ◽  
Urban Traffic ◽  
Gps Tracking ◽  
Shenzhen City ◽  
Adverse Weather ◽  
Stormy Weather ◽  
Wide Scale ◽  
Traffic Speed ◽  
The Impact

The adverse weather may significantly impact urban traffic speed and travel time. Understanding the influence of the rainstorm to urban traffic speed is of great importance for traffic management under stormy weather. This study aims to investigate the impact of rainfall intensity on traffic speed in the Shenzhen (China) during the period 1 July 2015–31 August 2016. The analysis was carried out for five 1-h periods on weekdays during the morning periods (6:00 AM–11:00 AM). Taxi-enabled GPS tracking data obtained from Shenzhen city are used in the analysis. There are several findings in this study. Firstly, nearly half of the roads are significantly affected by the rainstorm. Secondly, the proportion of positive correlated roads is about 35 %, but there still are some roads with uncorrelated traffic speed variation rates (SVR) and rainfall intensities. Thirdly, the impact of the rainstorm on traffic speed is not homogeneous but with obvious spatial difference. This research provides useful information that can be used in traffic management on a city-wide scale under stormy weather.

scholarly journals Capacity Analysis for Approach Channels Shared by LNG Carriers

2020 ◽  
Vol 8 (9) ◽  
pp. 697
Author(s):  
Xiang Gao ◽  
Linying Chen ◽  
Pengfei Chen ◽  
Yu Luo ◽  
Junmin Mou
Keyword(s):  
Traffic Management ◽  
Time Windows ◽  
Management Strategies ◽  
Capacity Analysis ◽  
Behavioral Characteristics ◽  
Traffic Capacity ◽  
Approach Channel ◽  
General Cargo ◽  
The Impact

The transport of liquefied natural gas (LNG) has significant impact on traffic capacity of waterways, especially the approach channels shared by LNG carriers and other types of ships (general cargo ships, container ships, etc.). Few studies take the behavioral characteristics of LNG carriers and their impacts into consideration. In this paper, we propose a framework for capacity analysis of shared approach channels based on the spatial–temporal consumption method. It consists of three modules: (1) the tide module predicts the tidal height and tidal time for identifying the time windows for LNG carriers; (2) the spatial–temporal consumption module is introduced to calculate the capacity of approach channels; (3) the LNG carrier navigation module is for analyzing the characteristics of LNG carriers and the impact on the capacity of approach channels. A spatial–temporal indexed chart is designed to visualize the utilization of the spatial–temporal resources. A case study on the approach channel of Yueqing Bay near the east coast of China is conducted to verify the effectiveness of the framework. The utilization rates of the approach channel and the impact of LNG carriers are presented using our method. The results of the case study indicate that the proposed traffic capacity analyzing framework can provide support for making traffic management strategies.

scholarly journals Artificial Intelligence-Enabled Traffic Monitoring System

2020 ◽  
Vol 12 (21) ◽  
pp. 9177
Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Deep Convolutional Neural Networks ◽  
Stage Of Development ◽  
Daunting Task ◽  
The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

Simple Models for Airport Delays During Transition to a Trajectory-Based Air Traffic System

2009 ◽  
Vol 62 (4) ◽  
pp. 555-570 ◽  
Author(s):  
Peter Brooker
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Queuing Theory ◽  
New Technologies ◽  
Transition Process ◽  
Air Traffic ◽  
Paradigm Shifts ◽  
New Paradigm ◽  
Operational Feature ◽  
The Impact

It is now widely recognised that a paradigm shift in air traffic control concepts is needed. This requires state-of-the-art innovative technologies, making much better use of the information in the air traffic management (ATM) system. These paradigm shifts go under the names of NextGen in the USA and SESAR in Europe, which inter alia will make dramatic changes to the nature of airport operations. A vital part of moving from an existing system to a new paradigm is the operational implications of the transition process. There would be business incentives for early aircraft fitment, it is generally safer to introduce new technologies gradually, and researchers are already proposing potential transition steps to the new system. Simple queuing theory models are used to establish rough quantitative estimates of the impact of the transition to a more efficient time-based – four-dimensional (4D) – navigational and ATM system. Such models are approximate, but they do offer insight into the broad implications of system change and its significant features. 4D-equipped aircraft in essence have a contract with the airport runway – they would be required to turn up at a very precise time – and, in return, they would get priority over any other aircraft waiting for use of the runway. The main operational feature examined here is the queuing delays affecting non-4D-equipped arrivals. These get a reasonable service if the proportion of 4D-equipped aircraft is low, but this can deteriorate markedly for high proportions, and be economically unviable. Preventative measures would be to limit the additional growth of 4D-equipped flights and/or to modify their contracts to provide sufficient space for the non-4D-equipped flights to operate without excessive delays. There is a potential for non-Poisson models, for which there is little in the literature, and for more complex models, e.g. grouping a succession of 4D-equipped aircraft as a batch.

scholarly journals Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing

2020 ◽  
Vol 12 (22) ◽  
pp. 9662 ◽  
Author(s):  
Disheng Yi ◽  
Yusi Liu ◽  
Jiahui Qin ◽  
Jing Zhang
Keyword(s):  
Traffic Management ◽  
Spatial Clustering ◽  
Spatial Interaction ◽  
The Other ◽  
Temporal Data ◽  
Trajectory Data ◽  
Data Field ◽  
Spatio Temporal ◽  
The Impact

Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management.

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