Actual Traffic Patterns Research Articles

Visualizing road connectivity under traffic influence: a CiteSpace-based method

ABSTRACT The connectivity of roads under the influence of traffic is determined by the topological connections established by the road network and the transport connections formed by moving vehicles between two roads simultaneously. A thorough examination of this connectivity is crucial for comprehending traffic patterns and providing guidance to address transportation issues such as traffic congestion. However, existing methods ignore the intuitive representation and explanation of this connectivity, limiting the understanding of actual traffic patterns. This study aimed to intuitively visualize the connectivity of roads under the influence of traffic using CiteSpace, a bibliometric tool for analyzing the relationship between literature keywords. Here, the literature was replaced by trajectory, and keywords were replaced by roads. We then presented the connectivity of roads under the influence of traffic in a network, where nodes represented roads and edges represented the topological and transport connections between roads. A case study was conducted in Beijing, demonstrating the dynamic connectivity of roads under the influence of traffic in the visualized network. By comparing the visualized network with the topological road network, we identified three types of road connectivity under the influence of traffic: roads with topological connections but lacking transport connections, roads with transport connections but lacking topological connections, and roads with both topological and transport connections. This finding further revealed the traffic relationship between roads. Additionally, we simultaneously presented the multidimensional traffic characteristics in the visualized network, aiding in predicting traffic states.

Traffic arrival pattern estimation at urban intersection using license plate recognition data

Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications.

Lane-Based Traffic Arrival Pattern Estimation Using License Plate Recognition Data

Understanding the traffic arrival process and its patterns is of vital importance for delay and queue analysis at intersections. The installation of advance loop detectors to sense vehicle arrivals could be costly and biased. Utilizing sampled vehicle trajectory data to reconstruct the traffic arrival flow might suffer from small sample sizes. License plate recognition (LPR) data commonly available at intersections in cities in China are promising for overcoming such limitations. This study aims to estimate a lane-based traffic arrival pattern by using LPR data collected at downstream and upstream intersections. The proposed method develops a probability model with an assumption of a two-stage piecewise arrival process for upstream merge movements. Given observations of vehicle arrivals provided by matched cars and trucks in LPR data, the model estimates second-based mean arrival rates for each lane at the downstream intersection. The proposed method is validated using actual LPR data collected at two adjacent intersections in Kunshan City, China. The results demonstrate that the proposed method can describe the traffic arrival patterns of upstream merge movements with either two-stage or uniform arrival processes in different traffic scenarios. In addition, the proposed method is more robust and reliable than an average-based benchmark method in terms of revealing actual traffic arrival patterns under different match rates.

Implementation of a Long-Range Air Traffic Flow Management for the Asia-Pacific Region

We provide a concept of operations and a corresponding implementation of a long-range air traffic flow management in the Asia-Pacific region. This management will provide an appropriate demand-capacity balancing considering both aircraft sequencing by local arrival management procedures and flow optimization to prevent over-demand in the approach area around the airport. Thus, coordination of long-range international flights demands collaboration between different flight information regions and local regulations. As Singapore Changi Airport is a central element of the Asia-Pacific flow management high share of long-haul air traffic, we use this airport to demonstrate our approach. To derive the operational conditions and actual traffic patterns at the airport, ADS-B messages and flight plan information are processed. The data are cleaned, analyzed, and filtered to provide information about arrival flows within given distances to the airport. We provide an efficacy analysis of the long-range air traffic flow management using two approaches. First, we applied a mixed-integer optimization of time shifts of normal distributed flight times. Here, the regulation of long-range flights by time shifts (e.g., achieved by speed advisories) shows a significant relief from periods of over-demand at the airport approach sector. Second, we implement a reference and a test case scenario in an agent-based simulation environment including the local arrival management procedures. Here, the number of holdings and the associated holding time could be reduced by at least 26%.

Traffic pattern detection using topic modeling for speed cameras based on big data abstraction

ABSTRACT The importance of traffic pattern prediction for traffic management systems has significantly increased in recent years. This paper presents a novel method to find unusual traffic patterns by using topic modeling. We have employed topic models to provide an abstraction of speed camera data from Tehran, the capital of Iran. In this methodology, topic modeling is applied to days of weeks and months in a year and extracts weekly and monthly traffic patterns. Analysis of the abstract descriptions and their adaptation to actual urban traffic patterns prove the effectiveness of the proposed method. The model training convergence is also practically verified. Based on our experiments, our method achieves an accuracy of 99% in detecting abnormal conditions, which indicates the fitness of the topic modeling abstraction. Such a powerful abstraction capability can be exploited as a method for data comparison and search procedures.

Towards Realistic Urban Traffic Experiments Using DFROUTER: Heuristic, Validation and Extensions.

Traffic congestion is an important problem faced by Intelligent Transportation Systems (ITS), requiring models that allow predicting the impact of different solutions on urban traffic flow. Such an approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, remain unknown. Thus, the main contribution of this paper is a heuristic for improving traffic congestion modeling. In particular, we propose a procedure that, starting from real induction loop measurements made available by traffic authorities, iteratively refines the output of DFROUTER, which is a module provided by the SUMO (Simulation of Urban MObility) tool. This way, it is able to generate an O-D matrix for traffic that resembles the real traffic distribution and that can be directly imported by SUMO. We apply our technique to the city of Valencia, and we then compare the obtained results against other existing traffic mobility data for the cities of Cologne (Germany) and Bologna (Italy), thereby validating our approach. We also use our technique to determine what degree of congestion is expectable if certain conditions cause additional traffic to circulate in the city, adopting both a uniform pattern and a hotspot-based pattern for traffic injection to demonstrate how to regulate the overall number of vehicles in the city. This study allows evaluating the impact of vehicle flow changes on the overall traffic congestion levels.

Lifetime Deflections of Long-Span Bridges under Dynamic and Growing Traffic Loads

Steady traffic growth may pose a safety hazard to in-service bridges, especially long-span bridges subjected to the simultaneous presence of multiple heavy-duty trucks. This study presents a methodology for evaluating the statistical extrapolation of traffic-load effects on long-span bridges. As part of the contributions advancing the state of the art, this study addresses several challenging issues, including traffic growth, and the resulting dynamic impact, and actual traffic patterns. The nonstationarity of the traffic-load effects due to traffic growth is considered in a series system compounded by several interval traffic models. The dynamic impacts of traffic loads were simulated by a traffic-bridge-coupled vibration system, and the statistical characteristics were captured using a level-crossing model. The actual traffic pattern was simulated by stochastic traffic flows on the basis of the statistics of the weigh-in-motion measurements of a highway bridge. Two numerical examples show the ability of the interval-traffic-growth model to capture the nonstationarity of the growing traffic loads. In addition, a case study of a long-span suspension bridge shows the effectiveness of implementing the proposed methodology for the statistical extrapolation of the maximum deflection. The numerical results of the case study also reveal that the degradation of road-roughness conditions leads to more level crossings but also results in a slight increase in the extrapolation of the deflection. However, traffic growth results in rapid increases in both the extrapolated deflection and the probability of exceedance of the deflection limit.

Strategic Air Traffic Planning with Fréchet Distance Aggregation and Rerouting

An aggregate route model for strategic air traffic flow management is presented. It is an Eulerian model, describing the flow between segments of unidirectional point-to-point routes. Aggregate routes are created from flight trajectory data based on similarity measures. Spatial similarity is determined using the Fréchet distance and temporal similarity by comparing average ground speeds. The aggregate routes approximate actual traffic patterns. By specifying the model resolution, an appropriate balance between model accuracy and model dimension can be achieved. The dynamics of the traffic flow on the network of aggregate routes take the form of a discrete-time linear time-invariant system. The traffic flow controls are ground holding and predeparture rerouting. Strategic planning, to use the controls to modify the future traffic flow when local capacity violations are anticipated, is posed as an integer linear programming problem of minimizing a weighted sum of flight delays subject to capacity constraints. Two examples demonstrate the model formulation and results of strategic planning. First, ground delays are introduced to manage high demand in the Los Angeles center; second, ground holding and predeparture rerouting are used to manage a convective weather scenario in the same center.

GARCH and ANN-based DDoS detection and filtering in cloud computing environment

Nowadays, distributed denial-of-service (DDoS) attack is one of the major security threats in cloud computing environment as it compromises the availability of the services and risks everything including financial loss, reputation and losing faith of the customers. In this paper, we have proposed a novel solution, which can detect DDoS attack traffic in cloud environment using chaos theory. To predict the network traffic state, nonlinear time series model [i.e., generalised autoregressive conditional heteroskedasticity (GARCH) model] is used as it can capture the long-range dependence (LRD) and long-tail distribution which is an important property of network traffic. Prediction error is calculated using the prediction made by GARCH model and actual traffic pattern. Filtering is carried out using back propagation artificial neural network (ANN) on the traffic that exceeds the certain limit specified by some threshold. In our proposed approach, threshold is calculated dynamically, which makes our approach platform independent.

GARCH and ANN-based DDoS detection and filtering in cloud computing environment

Nowadays, distributed denial-of-service (DDoS) attack is one of the major security threats in cloud computing environment as it compromises the availability of the services and risks everything including financial loss, reputation and losing faith of the customers. In this paper, we have proposed a novel solution, which can detect DDoS attack traffic in cloud environment using chaos theory. To predict the network traffic state, nonlinear time series model [i.e., generalised autoregressive conditional heteroskedasticity (GARCH) model] is used as it can capture the long-range dependence (LRD) and long-tail distribution which is an important property of network traffic. Prediction error is calculated using the prediction made by GARCH model and actual traffic pattern. Filtering is carried out using back propagation artificial neural network (ANN) on the traffic that exceeds the certain limit specified by some threshold. In our proposed approach, threshold is calculated dynamically, which makes our approach platform independent.

Price Uncertainty and Market Power in Retail Gasoline: The Role of Price Signs on an Italian Highway

We investigate the effect of consumers' price uncertainty and search costs on gasoline price levels. The introduction of a mandatory price disclosure policy on the Italian highway enables us to observe market prices when (a) consumers have no price information; and (b) some information about the prices of competing stations is available to consumers. To characterize the observed pricing outcomes in the marketplace we then propose a model of consumers' sequential search and purchase of gasoline and combine it with a model of gas station pricing. Using data on actual prices and traffic patterns on a particular segment of the highway, we obtain estimates for consumer search costs and other model parameters. Via a counterfactual analysis, we then predict the levels of prices that would prevail in a scenario where consumers have full price knowledge. We also use our model and estimates to measure the stations' market power as a consequence of consumers' lack of price information. We find that when consumers do not have price information, gas stations are able to charge 35.8% more, in terms of higher price-cost margins, than when prices are known. We provide welfare implications of the current mandatory disclosure policy, and quantify the additional benefit of making price information available to all consumers.

Racial formation, inequality and the political economy of web traffic

ABSTRACTFew studies attempt to demonstrate whether and how systemic racial inequality might form on the web. I use racial formation theory to conceptualize how race is represented, and systematically reproduced on the web, and how both may reveal forms of racial inequality. Using an original dataset and network graph, I document the architecture of web traffic, and the actual traffic patterns among and between race-based websites. Results demonstrate that web producers create hyperlink networks that steer audiences to websites without respect to racial or nonracial content. However, user navigation reflects a racially segregated traffic pattern; users navigate to racialized versus nonracialized websites (and vice versa) more than what would be expected by chance. These results, along with disparities in website traffic rankings, provide evidence of, and demonstrates how a race-based hierarchy might systematically emerge on the web in ways that exemplify disparate forms of value, influence and power that exist within the web environment.

Distributed model predictive control

Distributed model predictive control

대학 학사정보시스템의 클라우드 컴퓨팅을 위한 최적용량 분석

최근 클라우드 컴퓨팅은 대학의 정보시스템 구축에도 영향을 미치고 있다. 대학 학사정보시스템을 클라우드 컴퓨팅 환경으로 설계할 때 자원낭비와 서비스품질을 고려하여 적정한 처리용량을 산정하는 것은 매우 중요한 일이다. 사용자가 기대하는 서비스품질을 만족시키는 클라우드 컴퓨터의 처리용량은 사용자 트랜잭션의 발생 패턴과 자원요구 특성에 근거하여 예측해야 한다. 본 논문에서는 대학 학사업무에서 발생하는 실제 트래픽을 분석하고 특정한 평균 응답시간을 만족시키는 클라우드 컴퓨터의 처리용량을 산출하는 기법을 제시한다. 이를 위해 학사업무 클라우딩 서비스 모델을 정립하고 대학 학사업무의 실제 운용데이터로부터 도출한 트래픽 패턴과 자원요구 특성을 적정용량 분석 모델에 적용하여 현실적인 값들을 유도한다. 제시된 서비스 모델과 실제 운용 데이터를 바탕으로 한 트래픽과 적정용량의 분석 결과는 유사한 규모의 대학 정보시스템 진화에 충분히 활용될 수 있다. Recently, cloud computing has been affecting academic information system buildup. When university academic affairs information system is designed with cloud computing, it is very important to estimate the adequate processing capacity for the cloud computer taking into account a waste of resources and the quality of service. Cloud computer's processing capacity to meet the quality of service expected by users should be predicted based on users' transaction patterns and resource requirements characteristics. In this paper, we analyze actual traffic patterns occurring in university academic affairs business and propose a method to calculate the optimal processing capacity for cloud computer to satisfy the given average response time. To justify our research, we apply the research results to a real university academic affairs business case and obtain the optimal processing capacity for the integrated academic affairs business.

Energy-Efficient and Bandwidth-Reconfigurable Photonic Networks for High-Performance Computing (HPC) Systems

Optical interconnects are becoming ubiquitous for short-range communication within boards and racks due to higher communication bandwidth at lower power dissipation when compared to metallic interconnects. Efficient multiplexing techniques (wavelengths, time, and space) allow bandwidths to scale; static or predetermined resource allocation of wavelengths can be detrimental to network performance for nonuniform (adversial) workloads. Dynamic bandwidth reallocation (DBR) based on actual traffic pattern can lead to improved network performance by utilizing idle resources. While DBR techniques can alleviate interconnection bottlenecks, power consumption also increases considerably with increase in bit rate and channels. In this paper, we propose to improve the performance of optical interconnects using DBR techniques and simultaneously optimize the power consumption using dynamic power management (DPM) techniques. DBR reallocates idle channels to busy channels (wavelengths) for improving throughput, and DPM regulates the bit rates and supply voltages for the individual channels. A reconfigurable optoelectronic architecture and a performance adaptive algorithm for implementing DBR and DPM are proposed in this paper. Our proposed reconfiguration algorithm achieves a significant reduction in power consumption and considerable improvement in throughput, with a marginal increase in latency for synthetic and real (Splash-2) traffic traces.

Energy-aware on-demand scatternet formation and routing for Bluetooth-based wireless sensor networks

Bluetooth is a promising short-range wireless communication technology with the characteristics of interference resilience and power efficiency, both desirable for wireless sensor networks. The new Intel Mote sensor devices have Bluetooth technology incorporated as the standard wireless communications interface. When using Bluetooth in applications where multihop routing is required, groups of Bluetooth piconets combine together to form a scatternet. However, most of the existing scatternet formation protocols are designed to facilitate communications between any two pairs of devices, regardless of the actual traffic demand pattern. For wireless sensor network applications with low-duty-cycle traffic patterns, an on-demand scatternet formation protocol can achieve significant power saving by avoiding unnecessary network connectivity. To that end, we introduce an on-demand scatternet and route formation protocol designed specifically for Bluetooth-based wireless sensor networks. Our protocol builds a scatternet on demand, and is able to cope with multiple sources initiating traffic simultaneously. In addition, our energy-aware forwarding nodes selection scheme is based on local information only, and results in more uniform network resource utilization and improved network lifetime. Simulation results show that our protocol can provide scatternet formation with reasonable delay and good load balance, which results in prolonged network lifetime for Bluetooth-based wireless sensor networks.

Free-Flight and en Route Air Safety: A First-Order Analysis

Under present arrangements, U.S. commercial planes do not travel “as the crow flies” from origin to destination; rather, they are generally restricted to paths within a grid. New technologies, however, raise the possibility of moving to a “free-flight” regime under which planes could fly directly from point to point. Striving for general insight rather than definitive conclusions, we use geometrical probability to assess how free-flight could affect the safety and efficiency of en route air traffic operations. We work with two air traffic control sectors: one hypothetical and the other based on actual traffic patterns over Albany, New York. Though tentative, the results suggest that—so long as certain operational constraints are retained—the changed geometry of flight paths after a transition to free-flight might tend in itself to diminish mid-air collision risk. Much depends, however, on whether the human/technological capabilities of future air traffic control can match the extraordinary effectiveness of the existing system.