scholarly journals Clustering Mining of Urban Traffic Flow Based on CVAE

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.

scholarly journals Spectral Analysis of Traffic Functions in Urban Areas

2015 ◽  
Vol 27 (6) ◽  
pp. 477-484 ◽  
Author(s):  
Florin Nemtanu ◽  
Ilona Madalina Costea ◽  
Catalin Dumitrescu
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Fourier Analysis ◽  
Traffic Flow ◽  
Traffic Control ◽  
Urban Areas ◽  
Urban Traffic ◽  
The Fourier Transform ◽  
The City ◽  
Different Time Scales

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.

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.

Proactive Traffic Merging Strategies for Sensor-Enabled Cars

2010 ◽  
pp. 180-199 ◽  
Author(s):  
Ziyuan Wang ◽  
Lars Kulik ◽  
Kotagiri Ramamohanarao
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Road Traffic ◽  
Control Strategies ◽  
The Road ◽  
Lack Of Information ◽  
Travel Delay ◽  
Hoc Networks

Congestion is a major challenge in today’s road traffic. The primary cause is bottlenecks such as ramps leading onto highways, or lane blockage due to obstacles. In these situations, the road capacity reduces because several traffic streams merge to fewer streams. Another important factor is the non-coordinated driving behavior resulting from the lack of information or the intention to minimize the travel time of a single car. This chapter surveys traffic control strategies for optimizing traffic flow on highways, with a focus on more adaptive and flexible strategies facilitated by current advancements in sensor-enabled cars and vehicular ad hoc networks (VANETs). The authors investigate proactive merging strategies assuming that sensor-enabled cars can detect the distance to neighboring cars and communicate their velocity and acceleration among each other. Proactive merging strategies can significantly improve traffic flow by increasing it up to 100% and reduce the overall travel delay by 30%.

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

Sensors ◽  
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.

The Analysis of Traffic Flow Model

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].

scholarly journals Performance Evaluation of Heuristics and Meta-Heuristics Traffic Control Strategies Using the UTNSim Traffic Simulator

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Ng Kok Mun ◽  
Mamun Ibne Reaz
Keyword(s):  
Traffic Control ◽  
Control Strategies ◽  
Research Question ◽  
Computation Time ◽  
Urban Traffic ◽  
Superior Performance ◽  
Computational Time ◽  
Performance Indices ◽  
Different Types ◽  
Real Time Application

In the past few decades, intelligent traffic controllers have been developed to responsively cope with the increasing traffic demands and congestions in urban traffic networks. Various studies to compare and evaluate the performance of traffic controllers have been conducted to investigate its effect on traffic performances such as its ability to reduce delay time, stops, throughputs and queues within a traffic network. In this paper, the authors aim to present another comparative study on heuristics versus meta-heuristics traffic control methods. To our knowledge, such comparison has not been conducted and could provide insights into a purely heuristic controller compared to meta-heuristics. The study aims to answer the research question “Can heuristics traffic control strategies outperformed meta-heuristics in terms of performance and computational costs?” For this purpose, a heuristics model-based control strategy (MCS) which was previously developed by the authors is compared to genetic algorithms (GA) and evolution strategy (ES) respectively on a nine intersections symmetric network. These control strategies were implemented via simulations on a traffic simulator called UTNSim for three different types of traffic scenarios. Performance indices such as average delays, vehicle throughputs and the computational time of these controllers were evaluated. The results revealed that the heuristic MCS outperformed GA and ES with superior performance in average delays whereas vehicle throughputs were in close agreement. The computation time of the MCS is also feasible for real-time application compared to GA and ES that has longer convergent time.

scholarly journals Traffic Flow Variables Estimation: An Automated Procedure Based on Moving Observer Method. Potential Application for Autonomous Vehicles

2019 ◽  
Vol 20 (3) ◽  
pp. 205-214
Author(s):  
Marco Guerrieri ◽  
Giuseppe Parla ◽  
Raffaele Mauro
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Autonomous Vehicles ◽  
Control Strategies ◽  
Digital Cameras ◽  
Mean Flow ◽  
Real Time Traffic ◽  
First Results ◽  
Technical Advances ◽  
Flow Variables

Abstract The estimation of traffic flow variables (flow, space mean speed and density) plays a fundamental role in highways planning and designing, as well as in traffic control strategies. Moving Observer Method (MOM) allows traffic surveys in a road, or in a road network. This paper proposes a novel automated procedure, called MOM-AP based on Moving Observer Method and Digital Image Processing (DIP) Technique able to automatically detect (without human observers) and calculate flow q, space mean speed vs and density k in case of stationary and homogeneous traffic conditions. In order to evaluate how reliable is the MOM-AP, an experiment has been carried out in a segment of one two-lane single carriageway road, in Italy. 30 datasets for the segment have been collected (in total 30 round trips). A comparative analysis between MOM-AP and traditional MOM has been carried out. First results show that the current MOM-AP algorithms underestimate the local mean flow variable values of around 10%. Nowadays MOM-AP may be implemented in smartphone apps. Instead, in the near future, it is realistic expecting the increase in the use of automated procedures for calculating the traffic flow variables (based on the “moving observer method”), due to the amount of sensors and digital cameras employed in the new autonomous vehicles (AVs). Considering such technical advances, the MOM-AP is a feasible model for real-time traffic analyses of road networks.

scholarly journals Pilot Implementation of Public Transport Priority in the City of Zagreb

2015 ◽  
Vol 27 (3) ◽  
pp. 257-265 ◽  
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.

scholarly journals Deployment Strategy for Car-Sharing Depots by Clustering Urban Traffic Big Data Based on Affinity Propagation

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zhihan Liu ◽  
Yi Jia ◽  
Xiaolu Zhu
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Optimization Method ◽  
Urban Traffic ◽  
Affinity Propagation ◽  
Superior Performance ◽  
Trajectory Data ◽  
Car Sharing ◽  
Gps Trajectory Data ◽  
Ap Clustering

Car sharing is a type of car rental service, by which consumers rent cars for short periods of time, often charged by hours. The analysis of urban traffic big data is full of importance and significance to determine locations of depots for car-sharing system. Taxi OD (Origin-Destination) is a typical dataset of urban traffic. The volume of the data is extremely large so that traditional data processing applications do not work well. In this paper, an optimization method to determine the depot locations by clustering taxi OD points with AP (Affinity Propagation) clustering algorithm has been presented. By analyzing the characteristics of AP clustering algorithm, AP clustering has been optimized hierarchically based on administrative region segmentation. Considering sparse similarity matrix of taxi OD points, the input parameters of AP clustering have been adapted. In the case study, we choose the OD pairs information from Beijing’s taxi GPS trajectory data. The number and locations of depots are determined by clustering the OD points based on the optimization AP clustering. We describe experimental results of our approach and compare it with standard K-means method using quantitative and stationarity index. Experiments on the real datasets show that the proposed method for determining car-sharing depots has a superior performance.

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