On the local fractional LWR model in fractal traffic flows in the entropy condition

2015 ◽  
Vol 40 (17) ◽  
pp. 6127-6132 ◽  
Author(s):  
Yong-Mei Guo ◽  
Yang Zhao ◽  
Yao-Ming Zhou ◽  
Zhong-Bin Xiao ◽  
Xiao-Jun Yang
Keyword(s):  
Traffic Flows ◽  
Entropy Condition ◽  
Lwr Model

Hysteresis and stop-and-go waves in traffic flows

2019 ◽  
Vol 29 (14) ◽  
pp. 2637-2678 ◽  
Author(s):  
Andrea Corli ◽  
Haitao Fan
Keyword(s):  
Sufficient Conditions ◽  
Hysteresis Loops ◽  
Phase Region ◽  
Hysteresis Cycle ◽  
Traffic Flows ◽  
Riemann Solutions ◽  
Lwr Model ◽  
Stop And Go ◽  
Necessary And Sufficient ◽  
Real Traffic

Stop-and-go waves, also called phantom jams, are often observed in real traffic flows but can be produced neither by the classical Lighthill–Whitham–Richards (LWR) model nor by its known variants. To capture stop-and-go waves, we add hysteresis to the LWR model. For the model we propose, all possible viscous waves are found, and necessary and sufficient conditions for their existence are provided. In particular, deceleration and acceleration shocks appear; the latter were never rigorously defined before, in spite of the fact that they were observed in real traffic flows. Stop-and-go waves can be constructed by a pair of deceleration and acceleration shocks that completes a hysteresis cycle, illustrating how hysteresis loops lead to stop-and-go waves. In contrast, in the phase region where anticipation (i.e. negative hysteresis) loops exist, stop-and-go waves are not present, and speed variations decay. Riemann solutions are then found for all possible Riemann data. We explicitly show that, in the phase region where hysteresis loops exist, a sufficient deviation in speed of a few vehicles in an otherwise uniform car platoon can generate stop-and-go waves, confirming observations of real traffic experiments.

On prediction of traffic flows in smart cities: a multitask deep learning based approach

2021 ◽  
Author(s):  
Fucheng Wang ◽  
Jiajie Xu ◽  
Chengfei Liu ◽  
Rui Zhou ◽  
Pengpeng Zhao
Keyword(s):  
Deep Learning ◽  
Smart Cities ◽  
Traffic Flows

scholarly journals Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions

2021 ◽  
Vol 11 (6) ◽  
pp. 2574
Author(s):  
Filip Vrbanić ◽  
Edouard Ivanjko ◽  
Krešimir Kušić ◽  
Dino Čakija
Keyword(s):  
Traffic Control ◽  
Ramp Metering ◽  
Transport Infrastructure ◽  
Control Algorithms ◽  
Speed Limit ◽  
Mixed Traffic ◽  
Traffic Flows ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Traffic Demand

The trend of increasing traffic demand is causing congestion on existing urban roads, including urban motorways, resulting in a decrease in Level of Service (LoS) and safety, and an increase in fuel consumption. Lack of space and non-compliance with cities’ sustainable urban plans prevent the expansion of new transport infrastructure in some urban areas. To alleviate the aforementioned problems, appropriate solutions come from the domain of Intelligent Transportation Systems by implementing traffic control services. Those services include Variable Speed Limit (VSL) and Ramp Metering (RM) for urban motorways. VSL reduces the speed of incoming vehicles to a bottleneck area, and RM limits the inflow through on-ramps. In addition, with the increasing development of Autonomous Vehicles (AVs) and Connected AVs (CAVs), new opportunities for traffic control are emerging. VSL and RM can reduce traffic congestion on urban motorways, especially so in the case of mixed traffic flows where AVs and CAVs can fully comply with the control system output. Currently, there is no existing overview of control algorithms and applications for VSL and RM in mixed traffic flows. Therefore, we present a comprehensive survey of VSL and RM control algorithms including the most recent reinforcement learning-based approaches. Best practices for mixed traffic flow control are summarized and new viewpoints and future research directions are presented, including an overview of the currently open research questions.

Empirical Assessment and Modeling of Traffic-induced Air Pollution

2021 ◽  
pp. 036119812110237
Author(s):  
Zhongyang Lu ◽  
Andy H. F. Chow ◽  
Jacky Leung ◽  
Haydn Kwok ◽  
Sammy Cheung
Keyword(s):  
Air Pollution ◽  
Hong Kong ◽  
Road Network ◽  
Historical Data ◽  
Flow Speed ◽  
Traffic Flows ◽  
Real World Data ◽  
Empirical Assessment ◽  
Air Quality Monitoring Stations ◽  
Transport Management

Congestion and traffic-induced air pollution are associated with population growth and economic development. Compared with congestion, there are relatively few studies on modeling and assessment of traffic-induced pollution. This paper presents an empirical assessment and analysis of traffic-induced air pollution with real-world data collected from the Hong Kong Strategic Road Network. The study employed historical data of traffic flows, speeds, and emission of air pollutants collated by the Hong Kong Transport Department and Environmental Protection Department. This paper first reveals the correlation between traffic flows, speeds, and corresponding induced pollutants including nitrogen oxides (NO2, NOX) and carbon monoxide (CO). To gain further statistical insight, a regression analysis was conducted on the flow–speed–emission relationship at three air quality monitoring stations, which revealed the significance of various factors on this relationship. This study contributes to green transport management and urban sustainability.

scholarly journals A Design of an Integrated Cloud-based Intrusion Detection System with Third Party Cloud Service

2021 ◽  
Vol 11 (1) ◽  
pp. 365-379
Author(s):  
Wisam Elmasry ◽  
Akhan Akbulut ◽  
Abdul Halim Zaim
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Cloud Service ◽  
Third Party ◽  
Traffic Flows ◽  
Cloud Environment ◽  
System Service ◽  
Reporting Service ◽  
Bagging Ensemble

Abstract Although cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature.

TripRes

2021 ◽  
Vol 17 (2) ◽  
pp. 1-21
Author(s):  
Xiaolong Xu ◽  
Zijie Fang ◽  
Lianyong Qi ◽  
Xuyun Zhang ◽  
Qiang He ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Discrete Distribution ◽  
Resource Provisioning ◽  
Path Selection ◽  
Urban Traffic ◽  
Traffic Flows ◽  
Multimedia Services ◽  
Traffic Flow Prediction ◽  
The City ◽  
Multimedia Surveillance

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.

Queuing Theory Applications to Communication Systems: Control of Traffic Flows and Load Balancing

2006 ◽  
pp. 991-1022 ◽  
Author(s):  
Panlop Zeephongsekul ◽  
Anthony Bedford ◽  
James Broberg ◽  
Peter Dimopoulos ◽  
Zahir Tari
Keyword(s):  
Load Balancing ◽  
Communication Systems ◽  
Queuing Theory ◽  
Traffic Flows ◽  
Systems Control
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