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.

Prediction of Vehicular Traffic Flow using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System

2022 ◽  
Vol 24 (2) ◽  
pp. E74-E86
Author(s):  
Isaac Oyeyemi Olayode ◽  
Alessandro Severino ◽  
Tiziana Campisi ◽  
Lagouge Kwanda Tartibu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Time Of Day ◽  
Vehicle Speed ◽  
Road Transportation ◽  
Artificial Neural

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

scholarly journals Transport modeling: An artificial immune system approach

2006 ◽  
Vol 16 (1) ◽  
pp. 3-30
Author(s):  
Dusan Teodorovic ◽  
Jovan Popovic ◽  
Panta Lucic
Keyword(s):  
Immune System ◽  
Real Time ◽  
Traffic Control ◽  
System Approach ◽  
Artificial Immune System ◽  
Control Strategies ◽  
Transportation Systems ◽  
Artificial Immune ◽  
Real Time Traffic ◽  
Wide Range

This paper describes an artificial immune system approach (AIS) to modeling time-dependent (dynamic, real time) transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions) that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies) for different antigens (different traffic "scenarios"). This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.

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%.

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 Designing Maps for Bicycling Simulator Studies – three practical Approaches

2021 ◽  
Vol 4 ◽  
pp. 1-4
Author(s):  
Andreas Keler ◽  
Patrick Malcolm ◽  
Georgios Grigoropoulos ◽  
Klaus Bogenberger
Keyword(s):  
Traffic Control ◽  
Traffic Engineering ◽  
Autonomous Vehicles ◽  
Control Strategies ◽  
Traffic Efficiency ◽  
Road Users ◽  
Key Factor ◽  
Bicycle Infrastructure ◽  
Practical Experiences ◽  
Transportation Research

Abstract. Bicycle simulator studies result from attempts of solving various novel problem statements of modern transportation-related research questions. Examples imply the evaluation of novel traffic control strategies for prioritizing urban bicycle traffic, novel bicycle infrastructure (such as bicycle highways) and the interaction and communication of vulnerable road users with automated or autonomous vehicles. As one of classical disciplines of transportation research, namely traffic engineering, and less related to human factors research, automotive research, geography, urban planning or citizen science, we want to point out those bicycle simulator studies design approaches, which are more related to testing novel traffic control strategies for cyclists, experiencing changing traffic-efficiency and –safety-related parameters in ongoing interfaced microscopic traffic flow simulations. We believe that this is a key factor in experiencing various traffic situations and the evaluation of thereof. In this research, we introduce three practical approaches of how to design maps for bicycling simulator studies. This is mainly resulting from manifold practical experiences from already conducted simulator studies beginning from the year 2018.

Remote Simulation to Evaluate Real-Time Traffic Control Strategies

2000 ◽  
Vol 1727 (1) ◽  
pp. 95-100 ◽  
Author(s):  
David E. Lucas ◽  
Pitu B. Mirchandani ◽  
K. Larry Head
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Control Systems ◽  
Real Time ◽  
Traffic Control ◽  
Control Strategies ◽  
Remote Access ◽  
Hardware In The Loop ◽  
Simulation Environment ◽  
Real Time Traffic

Simulation is a valuable tool for evaluating the effects of various changes in a transportation system. This is especially true in the case of real-time traffic-adaptive control systems, which must undergo extensive testing in a laboratory setting before being implemented in a field environment. Various types of simulation environments are available, from software-only to hardware-in-the-loop simulations, each of which has a role to play in the implementation of a traffic control system. The RHODES (real-time hierarchical optimized distributed effective system) real-time traffic-adaptive control system was followed as it progressed from a laboratory project toward actual field implementation. The traditional software-only simulation environment and extensions to a hardware-in-the-loop simulation are presented in describing the migration of RHODES onto the traffic controller hardware itself. In addition, a new enhancement to the standard software-only simulation that allows remote access is described. The enhancement removes the requirement that both the simulation and the traffic control scheme reside locally. This architecture is capable of supporting any traffic simulation package that satisfies specific input-output data requirements. This remote simulation environment was tested with several different types of networks and was found to perform in the same manner as its local counterpart. Remote simulation has all of the advantages of its local counterpart, such as control and flexibility, with the added benefit of distribution. This remote environment could be used in many different ways and by different groups or individuals, including state or local transportation agencies interested in performing their own evaluations of alternative traffic control systems.

Research on the Real-Time Prediction Model of the Traffic Flow Based on Wavelet Neural Network

2012 ◽  
Vol 241-244 ◽  
pp. 2088-2094
Author(s):  
Hui Ying Wen ◽  
Gui Feng Yang ◽  
Wei Tiao Wu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Traffic Flow ◽  
Traffic Safety ◽  
Traffic Control ◽  
Wavelet Neural Network ◽  
Mountain Area ◽  
Analog Simulation ◽  
Traffic Flow Prediction ◽  
Real Time Traffic

Real-time traffic flow prediction is the core of traffic control and management, which is the basis of traffic safety in mountain area. Traffic flow, which is highly time-relevant, with the features of high non-linear and non-determinism, can be treated as the time sequence forecast. Considering these features, this paper deals specially with this issue based on Wavelet neural network. Besides, by taking a road in mountain area for example, the paper realizes the analog simulation through the Matlab software programming. And the simulation results show that the traffic flow can be precisely forecast using Wavelet neural network, and its value is close to the expectations. The MAE of the Wavelet neural network is 20.1074 and the MSE is 2.5254.

scholarly journals Deep Learning and YOLOv3 Systems for Automatic Traffic Data Measurement by Moving Car Observer Technique

2021 ◽  
Vol 6 (9) ◽  
pp. 134
Author(s):  
Marco Guerrieri ◽  
Giuseppe Parla
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Traffic Data ◽  
Fundamental Interest ◽  
Traffic Conditions ◽  
Traffic Stream ◽  
First Results ◽  
Highway Facilities ◽  
Flow Variables ◽  
Good Agreement

Macroscopic traffic flow variables estimation is of fundamental interest in the planning, designing and controlling of highway facilities. This article presents a novel automatic traffic data acquirement method, called MOM-DL, based on the moving observer method (MOM), deep learning and YOLOv3 algorithm. The proposed method is able to automatically detect vehicles in a traffic stream and estimate the traffic variables flow q, space mean speed vs. and vehicle density k for highways in stationary and homogeneous traffic conditions. The first application of the MOM-DL technique concerns a segment of an Italian highway. In the experiments, a survey vehicle equipped with a camera has been used. Using deep learning and YOLOv3 the vehicles detection and the counting processes have been carried out for the analyzed highway segment. The traffic flow variables have been calculated by the Wardrop relationships. The first results demonstrate that the MOM and MOM-DL methods are in good agreement with each other despite some errors arising with MOM-DL during the vehicle detection step due to a variety of reasons. However, the values of macroscopic traffic variables estimated by means of the Drakes’ traffic flow model together with the proposed method (MOM-DL) are very close to those obtained by the traditional one (MOM), being the maximum percentage variation less than 3%.

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.

Empirical Assessment of a Markovian Traffic Flow Model

1997 ◽  
Vol 1591 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Chang-Jen Lan ◽  
Gary A. Davis
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Field Data ◽  
High Order ◽  
Flow Dynamics ◽  
Model Parameters ◽  
Flow Models ◽  
Real Time Traffic ◽  
Flow Parameters ◽  
Traffic Flow Models

Previous research efforts on developing traffic flow models to account for traffic flow dynamics over transportation networks have centered on macroscopic high-order models. It is unclear whether traffic flow dynamics can be well described using a high-order model formulation, but for real-time traffic control, it is important to have tractable yet accurate models. Described here is a set of tractable traffic flow models based on the Markovian compartment concept. The basic models can be further modified to produce effects analogous to high-order models in capturing unstable traffic behavior during congestion. Special treatments are also made to account for the effects of conflicting flow on the predicted turning exit flow at intersection approaches. The proposed models are evaluated using field data. The results indicate that all the model parameters, including traffic flow parameters and gap acceptance parameters, are reasonably estimated, and the underlying models provide good fits to the field data.

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