Spectral Analysis of Traffic Functions in Urban Areas

The paper is focused on the Fourier transform application in urban traffic analysis and the use of said transform in traffic decomposition. The traffic function is defined as traffic flow generated by different categories of traffic participants. A Fourier analysis was elaborated in terms of identifying the main traffic function components, called traffic sub-functions. This paper presents the results of the method being applied in a real case situation, that is, an intersection in the city of Bucharest where the effect of a bus line was analysed. The analysis was done using different time scales, while three different traffic functions were defined to demonstrate the theoretical effect of the proposed method of analysis. An extension of the method is proposed to be applied in urban areas, especially in the areas covered by predictive traffic control.

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Pilot Implementation of Public Transport Priority in the City of Zagreb

PROMET - Traffic&Transportation ◽

10.7307/ptt.v27i3.1735 ◽

2015 ◽

Vol 27 (3) ◽

pp. 257-265 ◽

Cited By ~ 9

Author(s):

Miroslav Vujić ◽

Sadko Mandzuka ◽

Martin Greguric

Keyword(s):

Public Transport ◽

Traffic Control ◽

Traffic Congestion ◽

Urban Areas ◽

Urban Traffic ◽

Control Measures ◽

Traffic Demand ◽

Public Transport System ◽

Adaptive Traffic Control ◽

The City

The problem with traffic congestion is particularly expressed in urban areas where possibilities for physical increment of capacity are limited or impossible. Significant in the approach to solving this problem is the usage of Public Transport (PT) and the implementation of various advanced control measures that can improve the quality of overall public transport system. The main objective of this research is to explore the possibilities of implementation of adaptive traffic control on signalized intersections giving priority to public transport vehicles through urban traffic network in the city of Zagreb. The possibilities of implementing public transport priority (PTP) technique in the city of Zagreb are analyzed because of specific traffic situations on defined corridors (location of stops, distance between intersections, etc.). With proper usage of PTP techniques (e.g. adequate detector positions, good estimation of PT vehicle arrival time at intersection) the total tram travel time can be significantly reduced. The Level of Service at intersection may be approximately retained because cross-street traffic demand was not ignored. According to technological level of traffic control system in the city of Zagreb, global implementation of PTP is not possible. So, for each intersection the PTP algorithm was developed separately, but mutual traffic influence of all intersections on the corridor was considered. The cooperative concept application within urban traffic control is considered as well.

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Clustering Mining of Urban Traffic Flow Based on CVAE

Journal of Traffic and Logistics Engineering ◽

10.18178/jtle.8.2.34-44 ◽

2020 ◽

pp. 34-44

Author(s):

Min-Tong Su ◽

◽

Jin Zheng ◽

Zu-Ping Zhang

Keyword(s):

Traffic Flow ◽

Traffic Control ◽

Clustering Algorithm ◽

Control Strategies ◽

Urban Traffic ◽

Living Standards ◽

Temporal Data ◽

Low Dimensional ◽

The City ◽

Ap Clustering

Understanding the urban traffic flow at intersections is helpful to formulate traffic control strategies, so as to ease traffic pressure and improve people's living standards. There are many related researches on traffic flow, and similarity research is one of them. Different from the traditional way, this paper studies the traffic flow from the perspective of image similarity. The Convolutional Variational Auto-Encoder (CVAE) is introduced to extract the low-dimensional features of traffic flow during a day, and Affinity Propagation (AP) clustering algorithm is used to cluster the features without real labels. Combining the clustering results with geographic coordinates reveals the distribution pattern of traffic flow. The experimental data includes about 10 million vehicle records at 650 intersections in Changsha on a certain day. The clustering results show that the traffic flow at the intersection of Changsha City can be divided into three categories according to the time-variant trends, and the distribution of each category basically conforms to the daily traffic laws of the city. Furthermore, the effectiveness of the clustering process is further verified by clustering the open source temporal data of different lengths.

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Forecasting Spatially-Distributed Urban Traffic Volumes via Multi-Target LSTM-Based Neural Network Regressor

Mathematics ◽

10.3390/math8122233 ◽

2020 ◽

Vol 8 (12) ◽

pp. 2233

Author(s):

Alessandro Crivellari ◽

Euro Beinat

Keyword(s):

Neural Network ◽

Traffic Control ◽

Urban Areas ◽

Short Term Memory ◽

Urban Traffic ◽

Entry And Exit ◽

Short Term ◽

Spatially Distributed ◽

Traffic Volumes ◽

The City

Monitoring the distribution of vehicles across the city is of great importance for urban traffic control. In particular, information on the number of vehicles entering and leaving a city, or moving between urban areas, gives a valuable estimate on potential bottlenecks and congestions. The possibility of predicting such flows in advance is even more beneficial, allowing for timely traffic management strategies and targeted congestion warnings. Our work is inserted in the context of short-term forecasting, aiming to predict rapid changes and sudden variations in the traffic volume, beyond the general trend. Moreover, it concurrently targets multiple locations in the city, providing an instant prediction outcome comprising the future distribution of vehicles across several urban locations. Specifically, we propose a multi-target deep learning regressor for simultaneous predictions of traffic volumes, in multiple entry and exit points among city neighborhoods. The experiment focuses on an hourly forecasting of the amount of vehicles accessing and moving between New York City neighborhoods through the Metropolitan Transportation Authority (MTA) bridges and tunnels. By leveraging a single training process for all location points, and an instant one-step volume inference for every location at each time update, our sequential modeling approach is able to grasp rapid variations in the time series and process the collective information of all entry and exit points, whose distinct predicted values are outputted at once. The multi-target model, based on long short-term memory (LSTM) recurrent neural network layers, was tested on a real-world dataset, achieving an average prediction error of 7% and demonstrating its feasibility for short-term spatially-distributed urban traffic forecasting.

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TripRes

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3401979 ◽

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.

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Discovering Urban Functional Polycentricity: A Traffic Flow-Embedded and Topic Modeling-Based Methodology Framework

Sustainability ◽

10.3390/su12051897 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1897

Author(s):

Shaodong Wang ◽

Yanbin Liu ◽

Wei Zhi ◽

Xihua Wen ◽

Weihua Zhou

Keyword(s):

Urban Planning ◽

Traffic Flow ◽

Urban Area ◽

Traffic Control ◽

Urban Areas ◽

Topic Modeling ◽

Spatial Organization ◽

Rapid Development ◽

Trajectory Data ◽

Function Distribution

With the rapid development of communication and transportation technologies, the urban area is increasingly becoming an ever more dynamic, comprehensive, and complex system. Meanwhile, functional polycentricity as a distinctive feature has been characterizing urban areas around the world. However, the spatial structure of the urban area has yet to be fully comprehended from a dynamic perspective, and understanding the spatial organization of polycentric urban regions (PUR) is crucial for issues related to urban planning, traffic control, and urban risk management. The analysis of polycentricity strongly depends on the spatial scale. In order to identify functional polycentricity at the intra-unban scale, this paper presents a traffic flow-embedded and topic modeling-based methodology framework. This framework was evaluated on real-world datasets from the Wujiang district, Suzhou, China, which contains 151,419 records of taxi trajectory data and 86,036 records of points of interest (POI) data. This paper provides a novel approach to examining urban functional polycentricity via combining urban function distribution and spatial interactions. This proposed methodology can help urban authorities better understand urban dynamics in terms of function distribution and internal connectedness and facilitate urban development in terms of urban planning and traffic control.

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The Fourier transform and spectral analysis

Digital Signal Processing ◽

10.1007/978-1-349-14944-5_9 ◽

1999 ◽

pp. 240-277

Author(s):

Bernard Mulgrew ◽

Peter Grant ◽

John Thompson

Keyword(s):

Spectral Analysis ◽

Fourier Transform ◽

The Fourier Transform

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An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities

Sensors ◽

10.3390/s20247209 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7209

Author(s):

Janetta Culita ◽

Simona Iuliana Caramihai ◽

Ioan Dumitrache ◽

Mihnea Alexandru Moisescu ◽

Ioan Stefan Sacala

Keyword(s):

Traffic Flow ◽

Traffic Control ◽

Smart City ◽

Smart Cities ◽

Hybrid Approach ◽

Well Being ◽

Problem Definition ◽

Urban Traffic ◽

Integrative Approach ◽

City Management

Smart cities are complex, socio-technological systems built as a strongly connected System of Systems, whose functioning is driven by human–machine interactions and whose ultimate goals are the well-being of their inhabitants. Consequently, controlling a smart city is an objective that may be achieved by using a specific framework that integrates algorithmic control, intelligent control, cognitive control and especially human reasoning and communication. Among the many functions of a smart city, intelligent transportation is one of the most important, with specific restrictions and a high level of dynamics. This paper focuses on the application of a neuro-inspired control framework for urban traffic as a component of a complex system. It is a proof of concept for a systemic integrative approach to the global problem of smart city management and integrates a previously designed urban traffic control architecture (for the city of Bucharest) with the actual purpose of ensuring its proactivity by means of traffic flow prediction. Analyses of requirements and methods for prediction are performed in order to determine the best way for fulfilling the perception function of the architecture with respect to the traffic control problem definition. A parametric method and an AI-based method are discussed in order to predict the traffic flow, both in the short and long term, based on real data. A brief comparative analysis of the prediction performances is also presented.

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The Analysis of Traffic Flow Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.380-384.237 ◽

2013 ◽

Vol 380-384 ◽

pp. 237-240

Author(s):

Xiao Wei Wei

Keyword(s):

Traffic Flow ◽

Traffic Control ◽

Complex Analysis ◽

Flow Model ◽

Flow Simulation ◽

Urban Traffic ◽

Traffic Condition ◽

Traffic Flow Model ◽

Real Time Traffic ◽

Dual Channel

With worsening traffic condition in large and medium-sized cities, it has become one of the most important steps for the urban traffic strategy to solve the traffic problems. Since the urban traffic is a complex system in various factors and huge scale, to establish related mathematical model through computer numerical simulation is a significant solution to the comprehensive problems of complex analysis, decision and planning. At present researches on the problems have been achieved in many foreign countries, but domestic research is not enough, especially in the practical application. The macroscopic traffic flow model and microscopic traffic flow model are described and cellular automaton model, dual channel decision model and car-following model are analyzed in this paper, prediction of the ideal traffic flow and trip distribution is consequently concluded, which deepen the understanding to the traffic flow of various phenomenon intrinsic mechanism and predict most closely to the actual situation of traffic flow, which can make fundamental work for traffic flow simulation and for real-time traffic control[1-3].

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Effective Safety Improvements Through Low-Cost Treatments

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1734-01 ◽

2000 ◽

Vol 1734 (1) ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Tapan K. Datta ◽

David Feber ◽

Kerrie Schattler ◽

Sue Datta

Keyword(s):

Traffic Control ◽

Urban Areas ◽

Joint Venture ◽

Low Cost ◽

Evaluation Study ◽

Traffic Crashes ◽

Traffic Crash ◽

Study Results ◽

Before And After ◽

The City

A vast majority of traffic crashes in urban areas occur at signalized intersections. Roadway geometry, traffic control, adjacent land uses, and environmental factors at intersections often contribute to the high incidence of traffic crashes and injuries. A public-private partnership project to identify high-crash and high-risk locations in the city of Detroit was initiated in 1996. Eighteen candidate sites were selected, and an extensive engineering study was conducted to develop countermeasures to help alleviate the traffic crash problem at the selected sites. The Automobile Association of America, Michigan, was the private partner in this joint venture and, in partnership with the city of Detroit, was a major contributor to covering the cost of improvements. The selection of countermeasures was based on state-of-the-art methodology and analysis, and implementation of the selected countermeasures at some of the sites was undertaken as the initial phase of the project. A comprehensive before-and-after evaluation study was performed at three of the improved sites. The study revealed that the safety improvements that were implemented lowered both crash and severity experience. The differences between the before and after crash frequencies proved to be statistically significant. Additionally, a benefit-cost analysis at the study locations indicated extraordinary results. This research presents the evaluation study results and discusses the countermeasures and improvements that were the most successful in mitigating traffic crash problems at the selected study locations.

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Fourier Analysis with Generalized Integration

Mathematics ◽

10.3390/math8071199 ◽

2020 ◽

Vol 8 (7) ◽

pp. 1199

Author(s):

Juan H. Arredondo ◽

Manuel Bernal ◽

María Guadalupe Morales

Keyword(s):

Fourier Transform ◽

Fourier Analysis ◽

Integral Theory ◽

Dense Subspace ◽

Fourier Cosine ◽

Sine Transform ◽

Integrable Functions ◽

Fourier Sine Transform ◽

Fourier Cosine Transform ◽

The Fourier Transform

We generalize the classic Fourier transform operator F p by using the Henstock–Kurzweil integral theory. It is shown that the operator equals the H K -Fourier transform on a dense subspace of L p , 1 < p ≤ 2 . In particular, a theoretical scope of this representation is raised to approximate the Fourier transform of functions on the mentioned subspace numerically. Besides, we show the differentiability of the Fourier transform function F p ( f ) under more general conditions than in Lebesgue’s theory. Additionally, continuity of the Fourier Sine transform operator into the space of Henstock-Kurzweil integrable functions is proved, which is similar in spirit to the already known result for the Fourier Cosine transform operator. Because our results establish a representation of the Fourier transform with more properties than in Lebesgue’s theory, these results might contribute to development of better algorithms of numerical integration, which are very important in applications.

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