Traffic Congestion Model Research Articles

INFRAMARGINAL TRAVELERS AND TRANSPORTATION POLICY

AbstractStructural models of traffic congestion, such as the bottleneck model, are used to answer important, policy‐relevant questions. However, existing models typically assume that no travelers are inframarginal regarding when to travel; that is, given equilibrium travel times, no travelers strictly prefer their ex ante departure time to all others. In this article, I address this shortcoming by incorporating inframarginal travelers into these models. This change significantly improves these models' ability to fit the data and changes policy prescriptions. In the case of congestion pricing, it typically changes the optimal toll by at least 25% and significantly worsens the distributional impacts.

Future Smart City with Less Traffic Congestion Model Enabled by (IoT)

The advancement in wireless telecommunication network has increase the accessibility of more users to wireless connectivity. With the advent of the fifth-generation (5G) wireless network, a seamless connectivity is available for internet users globally. A future smart city is a metropolis that utilizes information and communication technologies (ICT) to grow its functionality effectively to disseminate information among the public and to develop the quality of government facilities and the welfare of the citizen. The Internet of Things (IoT) refer to the interconnection of several systems, devices or physical objects/things which are driven by sensors, software, and other equipment in order to interconnect and interchange data with other devices and systems through the internet. The Internet of things (IoT), is a revolutionary method that allows a diverse number of applications to be interconnected in order to create a single communication architecture. Future smart city has resulted in the increase in population, hence there is need to develop a smart traffic light system to help in managing the problem of future smart city; traffic congestion. The Internet of Things (IoT) a key features necessary for employing a large-scale in IoTS are low-cost sensors, high-speed and error-tolerant data communications, smart computations, and numerous applications which helps in solving these challenges associated with traffic congestion. It enables a smart environment, smart energy, and smart transportation system. In this paper, we shall discuss IoT technology, review some literatures on application area of Internet of Things (IoT), and challenges of IoT. And also discuss the applications of IoT, in smart city development, and traffic congestion management in smart city design, and how it proffers solution to future smart city problem.

Traffic Congestion Modeling and Impact on the Work Environment at Brawijaya University

Traffic Congestion Modeling and Impact on the Work Environment at Brawijaya University

Uncovering the spatiotemporal patterns of traffic congestion from large-scale trajectory data: A complex network approach

Understanding the spatiotemporal characteristics of traffic congestion is the cornerstone of generating traffic management and control strategies. Based on the large-scale taxi trajectory data in Shenzhen, China, this study designs an effective framework to explore the spatiotemporal patterns of traffic congestions. To bridge trajectory data with urban road networks, we develop a two-stage map-matching method from the aspects of distance and angle. Then, the free-flow speed of each road segment is extracted and employed to identify traffic congestion. In this way, a novel complex network method, named chronological network (chronnet), is utilized for traffic congestion modeling, and we employ an overlapping community detection algorithm to identify region-level bottlenecks. According to the network properties, we explore the influence scope of traffic congestions and uncover the role of each road segment in the propagation process. Meanwhile, community detection results indicate that there are typical local clustering structures in traffic congestions, and each community also has its unique traffic characteristics. Overall, these findings reveal that the complex network can effectively mine the consecutive patterns of traffic congestion.

How incidents impact congestion on roadways: A queuing network approach

Motivated by the need for transportation infrastructure and incident management planning, we study traffic density under non-recurrent congestion. This paper provides an analytical solution approximating the stationary distribution of traffic density in roadways where deterioration of service occurs unpredictably. The proposed solution generalizes a queuing model discussed in the literature to long segments that are not space-homogeneous. We compare single and tandem queuing approaches to segments of different lengths and verify whether each model is appropriate. A single-queue approach works sufficiently well in segments with similar traffic behavior across space. In contrast, a tandem-queue approach more appropriately describes the density behavior for long segments with sections having distinct traffic characteristics. These models have a comparable fit to the ones generated using a lognormal distribution. However, they also have interpretable parameters, directly connecting the distribution of congestion to the dynamics of roadway behavior. The proposed models are general, adaptable, and tractable, thus being instrumental in infrastructure and incident management. • Traffic congestion model for highways that may experience unexpected slowdowns. • Intuitive analytical solution with a clear relation between incidents and congestion. • A discussion of situations where tandem queues work better than single queues. • The proposed models are similar to lognormal in fit but have explainable parameters.

Development of Traffic Congestion Model to Evaluate the Level of Congestion and its Associative Cost for an Urban Arterial Road: a Case Study

This paper consists of a study conducted to quantify the traffic congestion problem. The quantification is made in terms of congestion cost and index. To study the effect of congestion, a road stretch of Thiruvananthapuram city in Kerala state was selected. A traffic congestion index was assessed based on travel time model. Congestion index helps to rank the road segments which will be useful for prioritizing the traffic management measures effectively. Another tool used for quantifying the congestion was the congestion cost. From the study, it was inferred that the congestion cost for the study stretch was about 0.03% of GSDP of Kerala and about 25% of the expenditure allocated for the road transport sector during the Kerala state Budget 2020–21. The economic analysis of traffic delay cost will inform the policymakers in monetary terms the actual impact of the traffic congestion and delays that the society bears and helps to allocate the resources efficiently.

Improving emergency response in the era of ADAS vehicles in the Smart City

Management of emergency vehicles can be fostered within a Smart City, i.e. an urban environment in which many IoT devices are orchestrated by a distributed intelligence able to suggest to road users the best course of action in different traffic situations. By extending MATSim (Multi-Agent Transport Simulation Software), we design and test appropriate mitigation strategies when traffic accidents occur within an existing urban area augmented with V2V (Vehicle-to-Vehicle), V2I (Vehicle-to-Infrastructure) capabilities and Advanced Driving Assisted cars (ADAS). Further, we propose traffic congestion models and related mechanisms for improving the necessary time for emergency vehicles to respond to accidents.

Spatial Model of Traffic Congestion by the Changes on City Transportation Route

Traffic congestion is a problem for every city in Java Island, Indonesia, including Bogor City. Factors causing congestion in Bogor City are thought to come from land use, the geometry and performance of road, and public transport routes (urban transport). The change of urban transportation (angkot) route carried out by Bogor City Government aims to reduce traffic density and congestion, but it is not a guarantee that the main problem will be solved. This study aims to determine the spatial patterns of traffic density and congestion with current angkot routes and to construct a model to predict traffic density and congestion when new angkot routes are used. Variables in this research are land use (number of schools and markets/malls), geometry and performance of road (vehicle volume, road capacity, average velocity, road type, number of lanes, number of signalled and non-signal intersection), and an angkot route passing a road. The method used in modelling is multiple regression using one dummy variable and a stepwise regression method. The result of modelling shows that the variables affecting traffic density are velocity, some signalled and non-signal intersection, and angkot route with R2 value 66.9%. At the same time, the influential variables in the traffic congestion model are vehicle volume, road capacity, number of the signalled intersection, and angkot route with R2 value 81.4%. To see accuracy in predicting model of traffic density and congestion, Mean Absolute Percentage Error (MAPE) validation is used. The results show a value of 12.46% for traffic density, which means that the model has good prediction accuracy and 5.62% for traffic congestion, which means the model has high prediction accuracy. Thus, in this study, the land use of school and market is not a factor causing traffic density and congestion, while the geometry and performance of roads and public transportation routes as a factor causing congestion.

Traffic Flow Density Model and Dynamic Traffic Congestion Model Simulation Based on Practice Case with Vehicle Network and System Traffic Intelligent Communication

The massive increase in the number of vehicles has set a precedent in terms of congestion, being one of the important factors affecting the flow of traffic, but there are also effects on the world economy. The studies carried out so far try to highlight solutions that will streamline the traffic, as society revolves around transportation and its symmetry. Current research highlights that the increased density of vehicles could be remedied by dedicated short-range communications (DSRC) systems through communications of the type vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) or vehicle-to-everything (V2X). We can say that wireless communication technologies have the potential to significantly change the efficiency and road safety, thus improving the efficiency of transport systems. An important factor is to comply with the requirements imposed on the use of vehicle safety and transport applications. Therefore, this paper focuses on several simulations on the basis of symmetry models, implemented in practical cases in order to streamline vehicle density and reduce traffic congestion. The scenarios aim at both the communication of the vehicles with each other and their prioritization by the infrastructure, so we can have a report on the efficiency of the proposed models.

On a general class of solutions for an optimal velocity model on an infinite lane

We consider a general class of car-following models. Such models were studied over the last decades both on a circular and on an infinite road. Many interesting phenomena have been studied, e.g. loss of stability and stop and go waves. Whereas there is good mathematical understanding for the circular road, for the infinite road only partial results are available. In this paper we study a general approach to obtain solutions which are valid for the different settings mentioned above. The well known solutions on the circular road can be seen as a subclass of solutions, the known approaches on the infinite lane can be identified and new solutions on the infinite lane are studied. Beside the classical tools from linear analysis we will employ nonlinear bifurcation techniques. Throughout the paper as a prominent example we will use the so-called optimal velocity model proposed in Bando, et al. [1995. “Dynamical model of traffic congestion and numerical simulation.” Physical Review E 51 (2): 1035–1042].

Can Tolling Help Everyone? Estimating the Aggregate and Distributional Consequences of Congestion Pricing

Abstract Economists have long advocated road pricing as an efficiency-enhancing solution to traffic congestion, yet it has rarely been implemented because it is thought to create losers as well as winners. In theory, a judiciously designed toll applied to a portion of the lanes of a highway can generate a Pareto improvement, even before using the toll revenue. This paper explores the practical relevance of this theoretical possibility by using survey and travel time data, combined with a structural model of traffic congestion, to estimate the joint distribution of agent preferences over three dimensions—value of time, schedule inflexibility, and desired arrival time—and evaluate the effects of adding optimal time-varying tolls. I find that adding tolls on half of the lanes of a highway yields a Pareto improvement. Further, the social welfare gains from doing so are substantial—up to $1,740 per road user per year.

Exact Formulation and Comparison Between the User Optimum and System Optimum Solution for Routing Privately Owned Automated Vehicles

The possibility of having driverless cars on the streets seems to be more real than ever. In this paper, we focus on developing exact methods that can determine the effects of privately owned automated vehicles (AVs) and how switching to those vehicles is going to change mobility in urban environments. The considered problem determines the routes of family owned AVs that minimize the transportation costs of that family while considering the possibility of using public transport as an alternative for some trips. We introduce a novel exact linear formulation for this problem which includes a linearized traffic congestion model and which is able to solve the user and system optimum variant of the problem to optimality. The introduced formulation can easily be adapted to consider the current situation with conventional vehicles and a situation where not only the travel time costs of the driver but also costs of the other passengers are taken into account. The main advantage of our novel formulation is that the optimal results can be obtained to explore potential changes of flows with vehicle automation in small networks. We investigated the behavior of the system, given the described scenarios, by applying our formulation to a case study.

Assessment of traffic congestion with ORESTE method under double hierarchy hesitant fuzzy linguistic environment

With the new generation of information technology development and the promotion of the Internet, local governments turn their attention to the construction of intelligent transportation systems. More and more cities began building intelligent transportation which has been widely used to monitor urban traffic. Experts can evaluate urban traffic congestion based on the information collected from the big data of intelligent transportation. In recent two years, double hierarchy hesitant fuzzy linguistic term set has been widely used to depict explicit evaluation information, which is straightforward and broad-spectrum. When evaluating traffic congestion in a city, decision makers can utilize double hierarchy hesitant fuzzy linguistic term sets to express vague information. Moreover, the ORESTE method is an applicative method which can select a reliable alternative by subdividing alternatives and reduce the loss of information in the conversion process. In this paper, we propose a double hierarchy hesitant fuzzy linguistic ORESTE method and a new score function of double hierarchy hesitant fuzzy linguistic term set. The method raises a new perspective to reduce the error from other methods and the new score function derives a robust decision-making result. Then, we apply the double hierarchy hesitant fuzzy linguistic ORESTE method to solve a practical case involving choosing the congested city by evaluating the 5S traffic congestion model. Finally, we compare the double hierarchy hesitant fuzzy linguistic ORESTE method with other methods such as the classical ORESTE method and the double hierarchy hesitant fuzzy linguistic MULTIMOORA to illustrate the advantages of our method.

Review properties solutions the mathematical models of transition time traffic congestion

Mathematical model of transition time traffic congestion is a mathematical model modified from Lorenz system that have three variable i.e. deviation distance of vehicles observed with the optimal distance of vehicles, vehicles speed deviation observed with optimum speed and acceleration/braking time vehicles so that the optimum speed reached. In this article, the behavior or properties solutions of mathematical model of transition time traffic congestion are observed and the result are :1) this model has 1 stable equilibrium point for τ0 ≤ 1, while for τ0 > 1 there are 2 stable equilibrium points and 1 unstable equilibrium point, where τ0 is characteristic time to reached optimum velocity. 2) there is a bifurcation pitchfork with bifurcation value when τ0 varied. 3) solution system in geometric symmetric with one of axis .

Improving Structural Models of Congestion

Half of all travelers have such inflexible schedules that they strictly prefer their chosen arrival times to all others; however, existing models of traffic congestion implicitly assume that no such inframarginal travelers exist. This leads these models to predict travel times nearly seven times greater than those observed or a length of the peak period a seventh that observed (or, more often, a combination of the two). Accounting for these travelers significantly improves these models’ ability to fit the data and changes policy prescriptions. In the case of congestion pricing, it typically changes the optimal toll by at least 25% and significantly worsens the distributional impacts.

Can Tolling Help Everyone? Estimating the Aggregate and Distributional Consequences of Congestion Pricing

Economists have long advocated road pricing as an efficiency-enhancing solution to traffic congestion, yet it has rarely been implemented because it is thought to create losers as well as winners. This paper uses survey and travel time data, combined with a structural model of traffic congestion, to estimate the joint distribution of agent preferences and evaluate the aggregate and distributional effects of road pricing. I find that adding tolls on half of the lanes of a highway yields a Pareto improvement. Further, the social welfare gains from doing so are substantial --- up to $1,740 per road user per year.

Equilibrium between Road Traffic Congestion and Low-Carbon Economy: A Case Study from Beijing, China

China has allocated low-carbon targets into all regions and trades, and road traffic also has its own emission reduction targets. Congestion may increase carbon emissions from road traffic. It is worthwhile to study whether it is possible to achieve the goal of road traffic reduction by controlling congestion; that is, to achieve the equilibrium between traffic congestion and a low-carbon economy. The innovation of this paper is mainly reflected in the innovative topic selection, the introduction of a traffic index, and the establishment of the first traffic congestion and low-carbon economic equilibrium model. First, the relevant calculation method of the traffic index is introduced, and the traffic index is used to quantify the traffic congestion degree. Using the traffic index, GDP, and road passenger traffic volume, a nonlinear regression model of road traffic carbon emissions is constructed. Then, the calculation method of the carbon emission intensity of road traffic in the region is proposed. The equilibrium model of traffic congestion and a low-carbon economy is constructed to look for the degree of road traffic congestion that may occur under the permitted carbon emission intensity. Taking Beijing, where electric vehicles account for less than 3% of the total vehicles, as an example, it is difficult to achieve the equilibrium target between road traffic congestion and a low-carbon economy by alleviating traffic congestion in 2020. If the target of traffic carbon emission reduction in 2020 is adjusted from 40%–45% to 19.7% based on 2005, the equilibrium will be achieved. A negative correlation between road traffic carbon emissions and the reciprocal of the traffic index (1/TI) is found after eliminating the effects of GDP and PTV (road passenger traffic volume). As the traffic index decreases by units, the carbon emission reduction accelerates. The results show that carbon reduction targets cannot be simply allocated to various industries. The results of the research on the degree of the impact of traffic congestion on carbon emissions can be used as a basis for carbon reduction decisions of the traffic sector. The research method of this paper can provide a reference for the study of the equilibrium of traffic congestion and a low-carbon economy in other regions.

Predictive Vehicle Route Optimization in Intelligent Transportation Systems

Through the adoption of dedicated short-range communication (DSRC) wireless communication technology, intelligent transportation systems (ITS) will spur a new revolution in the U.S. transportation system. This paper is structured around providing drivers with the least-congested transportation route choices enabled by the ITS-envisioned vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communication platforms. Recent research in vehicle navigation systems has proposed energy consumption and emission optimized routing methodologies using historical traffic data modeling. More than 50% of congestion in U.S. cities is nonrecurring congestion. Nonrecurring congestion reduces the availability of the traffic network, thus rendering historical traffic data-based systems insufficient in more than 50% of the cases. Real-time traffic data modeling provides an enhanced performance in traffic congestion assessment; however, greater performance is expected with a predictive traffic congestion model with increased certainty. This paper compares the conventional shortest path and fastest path vehicle routing methodologies and establish the improvement for environmentally friendly routing in a dynamic and predictive cost dependent traffic network based on Petri Net Modeling. The proposed routing algorithm is validated using a computer-based tool of choice.

OLYMPUS v1.0: development of an integrated air pollutant and GHG urban emissions model – methodology and calibration over greater Paris

Abstract. Air pollutants and greenhouse gases have many effects on health, the economy, urban climate and atmospheric environment. At the city level, the transport and heating sectors contribute significantly to air pollution. In order to quantify the impact of urban policies on anthropogenic air pollutants, the main processes leading to emissions need to be understood: they principally include mobility for work and leisure as well as household behavior, themselves impacted by a variety of social parameters. In this context, the OLYMPUS modeling platform has been designed for environmental decision support. It generates a synthetic population of individuals and defines the mobility of each individual in the city through an activity-based approach of travel demand. The model then spatializes road traffic by taking into account congestion on the road network. It also includes a module that estimates the energy demand of the territory by calculating the unit energy consumption of households and the commercial–institutional sector. Finally, the emissions associated with all the modeled activities are calculated using the COPERT emission factors for traffic and the European Environmental Agency (EEA) methodology for heating-related combustion. The comparison of emissions with AIRPARIF's regional inventory shows discrepancies that are consistent with differences in assumptions and input data, mainly in the sense of underestimation. The methodological choices and the potential ways of improvement, including the refinement of traffic congestion modeling and of the transport of goods, are discussed.

A Novel Framework to Control and Optimize the Traffic Congestion Issue in VANET

In the recent times due to the increase of vehicular nodes in a vehicular communication network, there is a need of developing efficient systems in order to optimize the vehicular traffic congestion issues in urban areas. The current research trends shows that most of the conventional studies focused on developing fuzzy inference systems based vehicular traffic congestion model which has gained lots of attention on detecting and minimizing the congestion levels.We have proposed a new approach towards detection and controlling of traffic congestion in VANET. The proposed system utilizes the communication channels very efficiently and irrespective of any kind of overload. This proposed system aims to introduce a novel framework for identifying traffic jam on Vehicular Ad-hoc Networks. In order to detect and minimize the level of congestion our approach will use a fuzzy logic based approach to notify the drivers about available routes during the traffic congestion. An experimental prototype will be set up to enable the graphical simulation.