Urban Traffic Condition Analysis Based on GPS Floating Car Data

2012 ◽  
Author(s):  
Xingquan Liu ◽  
Sixuan Liu ◽  
Zhiqiang Chen ◽  
Minwen Tang
Keyword(s):  
Urban Traffic ◽  
Traffic Condition ◽  
Floating Car Data

A Fuzzy Logic Method of Traffic State Recognition on Urban Traffic Networks

2012 ◽  
Vol 546-547 ◽  
pp. 1071-1074
Author(s):  
Jian Ling Wang ◽  
Hong Bo Lai
Keyword(s):  
Fuzzy Logic ◽  
Traffic Flow ◽  
Urban Traffic ◽  
Traffic Networks ◽  
Traffic Condition ◽  
Time Length ◽  
Traffic State ◽  
Fuzzy Logic Method ◽  
Logic Method ◽  
Urban Traffic Networks

The study object is traffic flow on main road of urban traffic networks, the traffic condition is recognized by traffic flow theory and fuzzy logic method. The average space speed is a variable of the fact flow function, the road congestion degree is described by the ratio of fact flow and traffic capacity; the ratio of congestion time length and total time length is the congestion frequency. Considering congestion degree and congestion frequency, a fuzzy logic method is used to describe the traffic state by three grades: free, congestion and serious congestion. At last, the numerical example is given to analyze traffic state.

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].

Coordinate Signal Control in Urban Traffic of Two-Direction Green Wave Based on Genetic BP Neural Network

2013 ◽  
Vol 823 ◽  
pp. 665-668 ◽  
Author(s):  
Shao Jiao Lv ◽  
Chun Gui Li ◽  
Zhe Ming Li ◽  
Qing Kai Zang
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Condition ◽  
Real Time Traffic ◽  
Coordinate Control ◽  
Analytical Algorithm ◽  
Trunk Road

To maximize the bandwidth of green wave of trunk road is a main issue in the research of signal control in urban traffic. However, the traditional analytical algorithmcan not be applied in actual traffic widely. A novel dynamic two-direction green wave coordinate control strategy was proposed to overcome the problem. By combining the genetic BP neural network with the traditional analytical algorithm, the urban traffic of two-direction was controlled coordinately online. Finally, an actual example was presented. It shows that not only the green wave bandwidth, the phase difference of each intersection and the critical cycle of trunk road were optimized according to real-time traffic flow, but also our algorithm can be used in different traffic condition by adjusting the parameters of the model.

Nighttime Motion Vehicle Detection Based on Multiple Instance Learning

2014 ◽  
Vol 529 ◽  
pp. 370-374
Author(s):  
Shao Ping Zhu
Keyword(s):  
Vehicle Detection ◽  
Learning Model ◽  
Multiple Instance Learning ◽  
Urban Traffic ◽  
Detection Accuracy ◽  
Road Detection ◽  
Traffic Condition ◽  
Sift Feature ◽  
Moving Vehicles ◽  
Label Information

In this paper, we propose an effective approach for detecting moving vehicles in nighttime traffic scenes. We use Multiple Instance Learning method to automatically detect vehicle from video sequences by constructing the Multiple Instance Learning model at nighttime. At first, we extract SIFT feature using SIFT feature extraction algorithm, which is used to characterize moving vehicles at nighttime. Then Multiple Instance Learning model is used for the on-road detection of vehicles at nighttime, in order to improve the detection accuracy, the class label information was used for the learning of the Multiple Instance Learning model. Final experiments were performed and evaluate the proposed method at nighttime under urban traffic condition, the experiment results show that the average detection accuracy is over 96.2%, which validates that the proposed vehicle detection approach is feasible and effective for the on-road detection of vehicles at nighttime and identification in various nighttime environments.

scholarly journals Detection of urban traffic patterns from Floating Car Data (FCD)

2017 ◽  
Vol 22 ◽  
pp. 382-391 ◽  
Author(s):  
Oruc Altintasi ◽  
Hediye Tuydes-Yaman ◽  
Kagan Tuncay
Keyword(s):  
Urban Traffic ◽  
Traffic Patterns ◽  
Floating Car Data

Entropie analysis of floating car data systems

2005 ◽  
Vol 2 ◽  
pp. 169-174
Author(s):  
F. Gössel ◽  
E. Michler ◽  
B. Wrase
Keyword(s):  
Field Trials ◽  
Vehicle Speed ◽  
Data Systems ◽  
Traffic Condition ◽  
Time Profiles ◽  
Spatial Segmentation ◽  
Floating Car Data ◽  
Original Variable ◽  
Vehicle Velocity ◽  
Basic Prerequisite

Abstract. The knowledge of the actual traffic state is a basic prerequisite of modern traffic telematic systems. Floating Car Data (FCD) systems are becoming more and more important for the provision of actual and reliable traffic data. In these systems the vehicle velocity is the original variable for the evaluation of the current traffic condition. As real FCDsystems are operating under conditions of limited transmission and processing capacity the analysis of the original variable vehicle speed is of special interest. Entropy considerations are especially useful for the deduction of fundamental restrictions and limitations. The paper analyses velocity-time profiles by means of information entropy. It emphasises in quantification of the information content of velocity-time profiles and the discussion of entropy dynamic in velocity-time profiles. Investigations are based on empirical data derived during field trials. The analysis of entropy dynamic is carried out in two different ways. On one hand velocity differences within a certain interval of time are used, on the other hand the transinformation between velocities in certain time distances was evaluated. One important result is an optimal sample-rate for the detection of velocity data in FCD-systems. The influence of spatial segmentation and of different states of traffic was discussed.

scholarly journals Dynamical efficiency for multimodal time-varying transportation networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Leonardo Bellocchi ◽  
Vito Latora ◽  
Nikolas Geroliminis
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Transportation Networks ◽  
Transportation System ◽  
Urban Traffic ◽  
Flow Conditions ◽  
Traffic Condition ◽  
Spatial Systems ◽  
Layered Networks ◽  
Critical Zones

AbstractSpatial systems that experience congestion can be modeled as weighted networks whose weights dynamically change over time with the redistribution of flows. This is particularly true for urban transportation networks. The aim of this work is to find appropriate network measures that are able to detect critical zones for traffic congestion and bottlenecks in a transportation system. We propose for both single and multi-layered networks a path-based measure, called dynamical efficiency, which computes the travel time differences under congested and free-flow conditions. The dynamical efficiency quantifies the reachability of a location embedded in the whole urban traffic condition, in lieu of a myopic description based on the average speed of single road segments. In this way, we are able to detect the formation of congestion seeds and visualize their evolution in time as well-defined clusters. Moreover, the extension to multilayer networks allows us to introduce a novel measure of centrality, which estimates the expected usage of inter-modal junctions between two different transportation means. Finally, we define the so-called dilemma factor in terms of number of alternatives that an interconnected transportation system offers to the travelers in exchange for a small increase in travel time. We find macroscopic relations between the percentage of extra-time, number of alternatives and level of congestion, useful to quantify the richness of trip choices that a city offers. As an illustrative example, we show how our methods work to study the real network of a megacity with probe traffic data.

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