Traffic Flow Problems Research Articles

Investigation of a Promoted You Only Look Once Algorithm and Its Application in Traffic Flow Monitoring

We propose a high-performance algorithm while using a promoted and modified form of the You Only Look Once (YOLO) model, which is based on the TensorFlow framework, to enhance the real-time monitoring of traffic-flow problems by an intelligent transportation system. Real-time detection and traffic-flow statistics were realized by adjusting the network structure, optimizing the loss function, and introducing weight regularization. This model, which we call YOLO-UA, was initialized based on the weight of a YOLO model pre-trained while using the VOC2007 data set. The UA-CAR data set with complex weather conditions was used for training, and better model parameters were selected through tests and subsequent adjustments. The experimental results showed that, for different weather scenarios, the accuracy of the YOLO-UA was ~22% greater than that of the YOLO model before optimization, and the recall rate increased by about 21%. On both cloudy and sunny days, the accuracy, precision, and recall rate of the YOLO-UA model were more than 94% above the floating rate, which suggested that the precision and recall rate achieved a good balance. When used for video testing, the YOLO-UA model yielded traffic statistics with an accuracy of up to 100%; the time to count the vehicles in each frame was less than 30 ms and it was highly robust in response to changes in scenario and weather.

Accident Mitigation and Management Measures for NH-44(India) from Khannabal to Qazigung in Jammu-Kashmir

Accidents are not natural but they are caused is a common cliché in the area of traffic safety. Thus, if accidents are caused, surely the reasons responsible for them could be identified and appropriate remedial measures developed and implemented to the extent feasible. This study lays emphasis on the safety analysis and accident studies of National Highway -1A(NH-44) on the 24 Km long stretch of road from Khanabal to Qazigund which is the main connecting link of Kashmir valley to Jammu and the rest of the country. As such this road is subjected to heavy vehicular traffic carrying passengers and goods and is the main strategic road of the state of Jammu and Kashmir State, it is one of the busiest and dangerous roads of the country. The problem of traffic flow is very acute due to complex flow of vehicular traffic, presence of mixed traffic along with pedestrians. Traffic accidents lead to loss of life and property. Also the stretch under study is subjected to rough weather conditions particularly during the winters. The primary objective of this study is to carry out the safety analysis of National Highway-1A(NH-44) on the 24 Km long stretch of road from Khanabal to Qazigund and provide a solution to the traffic accidents which occur on this 24 Km long stretch of road from Khanabal to Qazigund. The National Highway-44 (formerly known as NH 1A) connecting Kashmir Valley to the rest of the country has always been susceptible to accidents.

Research on Road Traffic Flow Status Based on Multi-source Monitoring Data

This paper mainly solves the traffic flow problem based on multi-source monitoring data. By using the methods of time-space fusion, traffic flow dynamics, in-depth learning and queuing theory model, the multi-monitoring data of Shenzhen's main roads are analyzed. The traffic flow description model and congestion prediction model are constructed, and the optimal plan for traffic police departments to cope with the traffic situation is explored, which provides a new way to solve the urban traffic pressure.

Controlling Chaos Using Superior Feedback Technique with Applications in Discrete Traffic Models

In recent years, discrete chaos has found a celebrated place in various dynamical phenomena of nature and science, such as population dynamics in ecology, laser technology, traffic flow system, image encryption and decryption in cryptography, secure communication, etc. But as a recent discipline, control of chaos an important field of research related to chaotic systems has come into play with many scientific and technological advances. In this article, a superior technique to control chaos in a class of one-dimensional discrete systems is developed and the unstable fixed and periodic states responsible for chaotic behavior are stabilized. Due to an extra degree of freedom of an intrinsic parameter $$\alpha$$ in superior control technique, the stability performance increases rapidly than other techniques. Further, the several theoretical as well as numerical simulation results are studied for the efficiency and effectiveness of the superior control technique followed by theorems, examples, remarks, Lyapunov exponent property, and period-doubling bifurcation representation. Moreover, using this system following discrete traffic flow problem is also discussed, “How to impart an unstable traffic behavior into stable and non-traffic zone?”.

Determination of Optimal Traffic Light Period Using a Discrete Traffic Flow Model

The traffic flow problem is spread analyzed by many authors like Liu et al., Nagatani and Kaur et al. This paper investigates the traffic flow problem that occurs due to the incorrect traffic lights control. Traffic lights should be match with the traffic flow and must to optimize it. The rational traffic lights period is set by the analysis of the situation on city’s street. A discrete model of traffic flow is used to obtain the results of traffic flow simulation. The results of modelling allow to analyze the dynamics of transport flows in the context of analyzed road and due to the studied boundary conditions. In this article the discrete model of traffic flow is complemented by new parameters (coefficients), which allows to fit the modelling results with real conditions of traffic flows. Authors argued that the last point of all traffic flows is the main aspect of the investigation of the effect of the changed concentration on the modelling street. Also an amplitude spectrum of traffic flow parameters has been created. This amplitude spectrum helps to determine the frequencies of the road network. DOI: http://dx.doi.org/10.5755/j01.mech.24.6.22477

Junction Conditions for Hamilton–Jacobi Equations for Solving Real-Time Traffic Flow Problems

In this paper, we propose junction conditions for discontinuities due to local perturbation, diverging, merging, and multi-in-multi-out junctions. Traffic flows on junctions can be described by a system of coupled Hamilton-Jacobi equations. At their connection points, it is necessary to propose appropriate junction conditions to close the system. Then, we provide an effective numerical method to compute approximate solutions to these Hamilton-Jacobi equations on junctions. The numerical boundary conditions to close the Hamilton-Jacobi system are also proposed. Numerical tests demonstrate the effectiveness of both the proposed junction conditions and the numerical method.

A measure theoretic approach to traffic flow optimisation on networks

We consider a class of optimal control problems for measure-valued nonlinear transport equations describing traffic flow problems on networks. The objective is to minimise/maximise macroscopic quantities, such as traffic volume or average speed, controlling few agents, e.g. smart traffic lights and automated cars. The measure theoretic approach allows to study in a same setting local and non-local drivers interactions and to consider the control variables as additional measures interacting with the drivers distribution. We also propose a gradient descent adjoint-based optimisation method, obtained by deriving first-order optimality conditions for the control problem, and we provide some numerical experiments in the case of smart traffic lights for a 2–1 junction.

Regular Perturbation of Inviscid Burger Equation in a Traffic Flow Problem

The Inviscid Burger Equation is one of the models which can be applied to one-way traffic flow problem. This model was constructed using transport equation in which the flux density following The Greenshield’s approximation. On its development, distinct flux densities were performed by varying the approximation’s model. This cause changing the velocity value. In this paper, we would like to analyze the perturbation problem in the velocity value for inviscid burger equations using multiple scale method. First, we expanded the solution into polynomial of the perturbation parameter and substitute it into the equation. Next, we obtained new equations based on the identical power of it which are quasilinear and linear equations. The new equations were simply solved using Lagrange-Charpit’s method. Since the exact solution of inviscid burger equation can be found using the characteristic method, therefore we also compared both methods. The results show that both solutions are similar.

Regulation of Traffic Flow in Small Cities with High Number of Vehicles: Case of Malisheva City – Kosovo

In this paper, the problem of traffic flow in the city of Malisheva, located in centre of Kosovo is analyzed. This study consists of the analysis of the current situation of traffic flow of the roads in the city centre, the service level of the current situation and also a proposal for new solution for traffic flow problems. This includes solutions design, a simulation of the proposed solution using computer software and an evaluation of the service levels. The core part of solution research includes data collection, the elimination of conflicting points for the three main crossroads, and it provides the calculation of the service levels of traffic. This will be elaborated also with a comparison between the current situation and the proposed solution as main methodology of analysis. While designing the proposed solution, it has been taken into account to fulfil the following main requirements: minimal investments, low implementation costs, maximal traffic circulation, an increase of service levels, minimal environmental and spatial impacts, and maximum safety for all participants in traffic.

Turning Delay Stochastic User Equilibrium Model based on the Weibull Distribution

With the continuous expansion of urban scales and the constant growth of traffic demands, it has become important to accurately predict the distribution of traffic flow so as to relieve the traffic jams and lower the energy consumption. This research mainly focuses on the distribution problem of traffic flow in the urban traffic network. A minimization program has been provided as an alternative formulation for the turning delay stochastic user equilibrium problem. The paper derives the Weibull distribution-based node-link random loading mechanism of turning delay for direct calculation of link and turning flows that are consistent with thepath flow, thus avoiding the enumeration of turning paths. Numerical examples are provided to illustrate the turning delay stochastic user equilibrium (SUE) model and the nodelink- based algorithm. The experiment demonstrates that the present method can reflect the relative performance of link and turning costs well, while presenting its advantages in the simulation of large-scale turning delay flow ssignment.

An Efficient Computational Technique for Fractal Vehicular Traffic Flow.

In this work, we examine a fractal vehicular traffic flow problem. The partial differential equations describing a fractal vehicular traffic flow are solved with the aid of the local fractional homotopy perturbation Sumudu transform scheme and the local fractional reduced differential transform method. Some illustrative examples are taken to describe the success of the suggested techniques. The results derived with the aid of the suggested schemes reveal that the present schemes are very efficient for obtaining the non-differentiable solution to fractal vehicular traffic flow problem.

Neutrosophic Linear Equations and Application in Traffic Flow Problems

A neutrosophic number (NN) presented by Smarandache can express determinate and/or indeterminate information in real life. NN (z = a + uI) consists of the determinate part a and the indeterminate part uI for a, u 2 R (R is all real numbers) and indeterminacy I, and is very suitable for representing and handling problems with both determinate and indeterminate information.

Big data analysis in a geoinformatic problem of short-term traffic flow forecasting based on a k nearest neighbors method

Accurate and timely information on the current and predicted traffic flows is important for the successful deployment of intelligent transport systems. These data play an essential role in traffic management and control. Using traffic flow information, travelers could plan their routes to avoid traffic congestion, reduce travel time and environmental pollution, as well as improving traffic operation efficiency in general. In this paper, we propose a distributed model for short-term traffic flow prediction based on a k nearest neighbors method, that takes into account spatial and temporal traffic flow distributions. The proposed model is implemented as a MapReduce based algorithm in an Apache Spark framework. An experimental study of the proposed model is carried out on a traffic flow data in the transportation network of Samara, Russia. The results demonstrate that the proposed model has high predictive accuracy and an execution time sufficient for real-time prediction.

Traffic Flow Optimization Using a Quantum Annealer

Quantum annealing algorithms belong to the class of meta-heuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum processing units (QPUs) produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks. In this paper, we present a real-world application that uses quantum technologies. Specifically, we show how to map certain parts of a real-world traffic flow optimization problem to be suitable for quantum annealing. We show that time-critical optimization tasks, such as continuous redistribution of position data for cars in dense road networks, are suitable candidates for quantum computing. Due to the limited size and connectivity of current-generation D-Wave QPUs, we use a hybrid quantum and classical approach to solve the traffic flow problem.

Where do bike lanes work best? A Bayesian spatial model of bicycle lanes and bicycle crashes

US municipalities are increasingly introducing bicycle lanes to promote bicycle use, increase roadway safety and improve public health. The aim of this study was to identify specific locations where bicycle lanes, if created, could most effectively reduce crash rates. Previous research has found that bike lanes reduce crash incidence, but a lack of comprehensive bicycle traffic flow data has limited researchers’ ability to assess relationships at high spatial resolution. We used Bayesian conditional autoregressive logit models to relate the odds that a bicycle injury crash occurred on a street segment in Philadelphia, PA (n = 37,673) between 2011 and 2014 to characteristics of the street and adjacent intersections. Statistical models included interaction terms to address the problem of unknown bicycle traffic flows, and found bicycle lanes were associated with reduced crash odds of 48% in streets segments adjacent to 4-exit intersections, of 40% in streets with one- or two-way stop intersections, and of 43% in high traffic volume streets. Presence of bicycle lanes was not associated with change in crash odds at intersections with less or more than 4 exits, at 4-way stop and signalized intersections, on one-way streets and streets with trolley tracks, and on streets with low-moderate traffic volume. The effectiveness of bicycle lanes appears to depend most on the configuration of the adjacent intersections and on the volume of vehicular traffic. Our approach can be used to predict specific street segments on which the greatest absolute reduction in bicycle crash odds could occur by installing new bicycle lanes.

Neutrosophic Linear Equations and Application in Traffic Flow Problems

A neutrosophic number (NN) presented by Smarandache can express determinate and/or indeterminate information in real life. NN (z = a + uI) consists of the determinate part a and the indeterminate part uI for a, u ∈ R (R is all real numbers) and indeterminacy I, and is very suitable for representing and handling problems with both determinate and indeterminate information. Based on the concept of NNs, this paper presents for first time the concepts of neutrosophic linear equations and the neutrosophic matrix, and introduces the neutrosophic matrix operations. Then, we propose some solving methods, including the substitution method, the addition method, and the inverse matrix method, for the system of neutrosophic linear equations or the neutrosophic matrix equation. Finally, an applied example about a traffic flow problem is provided to illustrate the application and effectiveness of handling the indeterminate traffic flow problem by using the system of neutrosophic linear equations.

A Distributed Routing Algorithm for Data Collection in Low-Duty-Cycle Wireless Sensor Networks

In order to prolong the lifetime of wireless sensor networks (WSNs), a low-duty-cycle mode is widely used to save the energy for sensor nodes. Under this mode, sensor nodes switch between active and dormant states, which incurs a high latency for traditional routing algorithms. To mitigate this, in this paper, the data collection problem in low-duty-cycle WSNs is formulated as a delay optimization problem of traffic flow with consideration of both congestion and collision, which is solved by a distributed algorithm based on network utility maximization. Our proposed distributed routing algorithm achieves a better tradeoff between latency and energy conservation than existing schemes, and our schemes can find a nearly global-optimal-path to achieve almost minimum average end-to-end (E2E) delay with less energy consumption. The computation complexity and energy consumption of the distributed algorithm are analyzed and evaluated in detail. The simulation results show that the proposed algorithm can achieve almost the same average E2E delay performance as the global optimal algorithm with less energy, and reduce the average E2E delay by about 30% than the shortest path algorithm when the data generation rate is high.

Cumulative Dual Foreground Differences for Illegally Parked Vehicles Detection

Illegally parked vehicles on the urban road may create a traffic flow problem as well as a potential traffic accident, such as crashing between parked and other vehicles. Thus, the intelligent traffic monitoring system should be able to prevent this situation by integrating an illegally parked vehicle detection module. However, implementing such a module becomes more challenging due to road environments, such as weather conditions, occlusion, and illumination changing. Hence, this work addresses a method to implement an illegally parked vehicle detection based on the cumulative dual foreground differences from the short- and long-term background models, temporal analysis, vehicle detector, and tracking. The extensive experiments were conducted using both iLIDS and our proposed datasets to evaluate the effectiveness of the proposed method by comparing with other methods. The results showed that the method is effective in detecting illegally parked vehicles and can be considered as part of the intelligent traffic monitoring system.

Local Wave Activity and the Onset of Blocking along a Potential Vorticity Front

Abstract Interaction between a train of transient waves and a diffluent westerly jet is examined using a regional quasigeostrophic equivalent barotropic model with a (nearly) binary potential vorticity (PV) distribution. Unlike most previous studies, but consistent with the observed extratropical tropopause, cross-stream variation in the layer thickness is allowed to contribute to the discontinuity in PV. In all cases examined, short (i.e., barotropic) edge waves are continuously forced in the upstream, then migrate downstream, and eventually exit the domain. A quasilinear 1D theory based on the conservation of local wave activity predicts that no steady wave train can be maintained where the westerly zonal flow is decelerated below one-half of the initial value, at which point the wave envelope develops a migratory shock analogous to the Lighthill–Whitham–Richards traffic flow problem. Fully nonlinear high-resolution 2D calculations show that the wave train indeed undergoes a significant transformation once the zonal flow along the jet axis is decelerated below the threshold. The subsequent flow evolution depends on the nature of the discontinuity in the basic-state PV. When the discontinuity is entirely due to the vorticity profile, waves are compressed and partially deflected sideways but no complete blocking occurs. When the discontinuity in PV is augmented by the layer thickness variation, the incident wave train is blocked and split into two tracks at the stagnation point, eventually leading to a formation of a modon-like vortex pair, reminiscent of an atmospheric blocking. Implications for low-frequency variability of the atmosphere are discussed.

Uncertainty quantification for linear hyperbolic equations with stochastic process or random field coefficients

In this paper hyperbolic partial differential equations with random coefficients are discussed. Such random partial differential equations appear for instance in traffic flow problems as well as in many physical processes in random media. Two types of models are presented: The first has a time-dependent coefficient modeled by the Ornstein–Uhlenbeck process. The second has a random field coefficient with a given covariance in space. For the former a formula for the exact solution in terms of moments is derived. In both cases stable numerical schemes are introduced to solve these random partial differential equations. Simulation results including convergence studies conclude the theoretical findings.