Congested Traffic Conditions Research Articles

The Traffic Congestion Charging Fee Management Model Based on the System Dynamics Approach

In recent years, the condition of urban traffic congestion in China has become more and more aggravated. Thus, from the social and environmental perspective, this paper has proposed a traffic congestion charging fee (TCCF) management model based on the system dynamics approach, taking Shanghai as a case study. The system dynamics approach applied in this paper focuses on the dynamic interrelations among some major variables in the TCCF management model. Hence, through a detailed discussion on these major variables, which are amount of car trips (ACT), growth of cars (GC), amount of NOx generation (ANG), attraction degree of car trips (ADCT), per vehicle area of roads (PVAR), and degree of traffic congestion (DTC), it is discovered that, when TCCF equals 45 yuan/day ⁎ vehicle, the DTC has declined by 67.84% and the ANG by 33.09%, which is a relatively satisfying solution to the traffic congestion problem.

Allocating Travel Times Recorded from Sparse GPS Probe Vehicles into Individual Road Segments

Abstract Probe-based data collection system, which deploys movable sensors in traffic streams to collect traffic records, is commonly used for area-wide measurement of travel times and conditions of traffics on road networks. However, due to sparse distribution of polling positions from a low probe sampling frequency in practice, the sampled points from probe vehicles do not necessarily match with the end points of each link, and the travel time between two consecutive polling points needs to be positioned into an individual link for link travel time calculation. This paper presents the new technique that addresses the allocation of travel times based on data gathered from sparse GPS probe vehicles. The travel time allocation procedure by applying the relationship among instantaneous speeds, sampled positions and tracked times is proposed. The proposed technique is discussed and compared with a ground truth benchmark of high resolution field data recorded from urban roadway corridor in both congested and uncongested traffic conditions. Results suggest that addressing travel time allocation problems by the proposed method significantly outperforms the benchmark method in both complete link and local levels which are demonstrated by an increasing estimation accuracy up to 60%. It is also observed from the local level analysis that the accuracy figures are lower at the intersections comprising high stopped delays, which normally contain highly fluctuated speed profiles. Nonetheless, the proposed technique outperforms the baseline approach in both intersections with low or high stopped delays. The proposed technique offers superior results to the baseline technique in all scenarios, particularly on the segments with highly fluctuated speed profiles.

Capturing Value of Reliability through Road Pricing in Congested Traffic under Uncertainty

Abstract Empirical studies showed that travel time reliability, usually measured by travel time variance, is strongly correlated with travel time itself. Travel time is highly volatile when the demand approaches or exceeds the capacity. Travel time variability is associated with the level of congestion, and could represent additional costs for travelers who prefer punctual arrivals. Although many studies propose to use road pricing as a tool to capture the value of travel time (VOT) savings and to induce better road usage patterns, the role of the value of reliability (VOR) in designing road pricing schemes has rarely been studied. By using road pricing as a tool to spread out the peak demand, traffic management agencies could improve the utility of travelers who prefer punctual arrivals under traffic congestion and stochastic network conditions. Therefore, we could capture the value of travel time reliability using road pricing, which is rarely discussed in the literature. To quantify the value of travel time reliability (or reliability improvement), we need to integrate trip scheduling, endogenous traffic congestion, travel time uncertainty, and pricing strategies in one modeling framework. This paper developed such a model to capture the impact of pricing on various costs components that affect travel choices, and the role of travel time reliability in shaping departure patterns, queuing process, and the choice of optimal pricing. The model also shows the benefits of improving travel time reliability in various ways. Findings from this paper could help to expand the scope of road pricing, and to develop more comprehensive travel demand management schemes.

Modeling vehicle car-following behavior in congested traffic conditions based on different vehicle combinations

ABSTRACT This paper develops an improved cellular automaton model in congested traffic conditions that differentiates vehicle combinations to consider both vehicle physical performance and driving behavior difference in car-following behavior. Car-following is divided into four groups by the lead and lag vehicle type. The vehicle behavior, including steady space gap, acceleration, and behavior choice, was studied to find out the diversity of car-following behavior in different vehicle combinations. The results show that vehicles in different vehicle combinations have different car-following behaviors. In the proposed model, each vehicle combination has its particular update rules and parameters. The model was evaluated by comparison with the existing car-following models at the microscopic and macroscopic level and the stability was also analyzed. The results show that the improved cellular automata model has a satisfactory performance.

Including heavy vehicles in a car‐following model: modelling, calibrating and validating

SummaryHeavy vehicles influence general traffic in many different ways compared with passenger vehicles, and this may result in different levels of traffic instability. Increases in the number and proportion of heavy vehicles in the traffic stream will therefore result in different traffic flow conditions. This research initially outlines the different car‐following behaviour of drivers in congested heterogeneous traffic conditions indicating the necessity for developing a car‐following model, which includes these differences. A psychophysical car‐following model, similar in form to Weideman's car‐following model, was developed. Due to the complexity of the developed model, the calibration of the model was undertaken using a particle swarm optimisation algorithm with the data recorded under congested traffic conditions. This was then incorporated into a traffic microsimulation model. The results showed that the car‐following perceptual thresholds and thus action points of drivers differ based on their vehicle and the lead vehicle types. The inclusion of the heavy vehicles in the model showed significant impacts on the traffic dynamic and interactions amongst different vehicles. Copyright © 2016 John Wiley & Sons, Ltd.

A distributed framework for network-wide traffic monitoring and platoon information aggregation using V2V communications

This study explores an innovative framework for distributed traffic monitoring and information aggregation using vehicle-to-vehicle (V2V) communications alone. We envision the proposed framework as the foundation to an alternative or supplemental traffic operation and management system, which could be particularly helpful under abnormal traffic conditions caused by unforeseen disasters and special events. Each equipped vehicle, through the distributed protocols developed, keeps track of the average traffic density and speed within a certain range, flags itself as micro-discontinuity in traffic if appropriate, and cross-checks its flag status with its immediate up- and down-stream vehicles. The micro-discontinuity flags define vehicle groups with similar traffic states, for initiating and terminating traffic information aggregation. The framework is validated using a microscopic traffic simulation platform VISSIM and its built-in component object model. Vehicle groups are successfully identified and their average speed and density effectively estimated. The impact of market penetration rate (MPR) is also investigated with a new methodology for performance evaluation under multiple traffic scenarios. Our simulation results show that the proposed framework lends itself better to more congested traffic conditions for any given MPR. With 50% MPR, the framework is able to provide information coverage for at least 37.76% of the simulated roadway facility under various traffic scenarios. This indicates that proposed framework could be useful with an MPR as low as 50%. Even with an MRP of 20%, the coverage ratio, under relatively congested traffic, can still reach around 58.82%. The framework is able to provide accurate speed estimation at high spatial resolution for the simulated roadway facility. The maximum relative error is under 10% for relatively congested traffic even with MPR as low as 20%. When there is a wider range of speed distribution (less congested traffic), the worst-case maximum relative error is still under 15% when MPR=20%. The density estimation is more sensitive to MPR, and is more accurate under low demand and high MPR scenarios. As expected, the accuracy of both speed and density estimation increases with MPR for any given traffic scenario.

Using Link Disconnection Entropy Disorder to Detect Fast Moving Nodes in MANETs.

Mobile ad-hoc networks (MANETs) are dynamic by nature; this dynamism comes from node mobility, traffic congestion, and other transmission conditions. Metrics to evaluate the effects of those conditions shine a light on node’s behavior in an ad-hoc network, helping to identify the node or nodes with better conditions of connection. In this paper, we propose a relative index to evaluate a single node reliability, based on the link disconnection entropy disorder using neighboring nodes as reference. Link disconnection entropy disorder is best used to identify fast moving nodes or nodes with unstable communications, this without the need of specialized sensors such as GPS. Several scenarios were studied to verify the index, measuring the effects of Speed and traffic density on the link disconnection entropy disorder. Packet delivery ratio is associated to the metric detecting a strong relationship, enabling the use of the link disconnection entropy disorder to evaluate the stability of a node to communicate with other nodes. To expand the utilization of the link entropy disorder, we identified nodes with higher speeds in network simulations just by using the link entropy disorder.

A self-regulation traffic-condition-based route guidance strategy with realistic considerations: Overlapping routes, stochastic traffic, and signalized intersections

ABSTRACTVariable message signs that provide various types of route guidance information have been widely deployed in large cities. To release proper information only using easily collected data, a simple traffic-condition-based (TCB) route guidance strategy was recently proposed. The strategy works based on the estimation of free-flow and congested traffic conditions and is capable of approximating user optimal equilibrium stably. Due to little consideration of the complexity of urban road networks, the TCB strategy is still away from field applications in urban areas. To further push the strategy toward field tests, this article improves the TCB strategy in the following aspects: supplementing the strategy with a self-regulation ability by considering existing traffic conditions; decomposing link capacity to solve the problem of overlapping routes by comparing link capacity on alternative routes; coping with stochastic traffic; and the impact of signalized intersections by utilizing aggregated data. A scenario for an urban road network in Beijing, China, is simulated to test the improved strategy, and the simulation results clearly indicate the effectiveness of the proposed improvements. The improvements extend the TCB strategy on moderately complicated urban road networks, and still have the advantages of simple diversion rules, easily obtained input data, and stable and effective diversion processes.

LONG-TERM CORRELATIONS AND MULTIFRACTALITY OF TRAFFIC FLOW MEASURED BY GIS FOR CONGESTED AND FREE-FLOW ROADS

In this paper, a GIS-based method was developed to extract the real-time traffic information (RTTI) from the Google Maps system for city roads. The method can be used to quantify both congested and free-flow traffic conditions. The roadway length was defined as congested length (CL) and free-flow length (FFL). Chengdu, the capital of Sichuan Province in the southwest of China, was chosen as a case study site. The RTTI data were extracted from the Google real-time maps in May 12–17, 2013 and were used to derive the CL and FFL for the study areas. The Multifractal Detrended Fluctuation Analysis (MFDFA) was used to characterize the long-term correlations of CL and FFL time series and their corresponding multifractal properties. Analysis showed that CL and FFL had demonstrated time nonlinearity and long-term correlations and both characteristics differed significantly. A shuffling procedure and a phase randomization procedure were further integrated with multifractal detrending moving average (MFDMA) to identify the major sources of multifractality of these two time series. The results showed that a multifractal process analysis could be used to characterize complex traffic data. Traffic data collected and methods developed in this paper will help better understand the complex traffic systems.

Comparative Analysis of Traffic Performance of Urban Transportation Systems

The network structure of an urban transportation system has a significant impact on its traffic performance. This study used network indicators along with several traffic performance measures, including speed, trip length, travel time, and traffic volume, to compare a selection of seven transportation networks with a variety of structures under different travel demand conditions. The selected network structures were modified linear, branch, grid, three-directional grid, one-ring web, two-ring web, and radial. Chosen for the analysis was a base origin–destination matrix to which different multiplier factors were applied to simulate various travel demand conditions. Results show that overall the two-ring web network offered the most efficient traffic performance, followed by the grid and the one-ring networks. A policy application of this study is that the branch, three-directional grid, and radial networks are suited mostly for small cities with uncongested traffic conditions. In contrast, the two-ring web, grid, and one-ring web networks are better choices for large urban areas because they offer more connectivity and thus allow them to perform efficiently under congested traffic conditions.

A Traffic Congestion Assessment Method for Urban Road Networks Based on Speed Performance Index

This study aimed to analyze traffic congestion in urban road networks. The speed performance index was adopted to evaluate the existing road network conditions of congestion, then road segment and network congestion indexes were introduced to respectively measure the congestion levels of urban road segment and network. This study also carried out a traffic congestion analysis for Beijing expressway network, based on the speed performance data collected from January 1 to November 1, 2012, by Beijing Traffic Management Bureau (BTMB). Based on these analyses the proposed congestion indexes can well assess the traffic congestion conditions of urban road networks, more importantly, such an assessment study provides traffic control and management agencies an accurate and clear understanding of operation status of traffic networks.

EFFECT OF CONGESTION ON FUEL COST AND TRAVEL TIME COST ON MULTI-LANE HIGHWAYS IN INDIA

The vehicles normally move at their free speeds when it is least impeded due to traffic flow under lean traffic (free flow) conditions. As traffic flow increases, the vehicles cannot sustain their free speeds due to interactions from other vehicles in the traffic stream. In addition to that the vehicles that are operating in the congested traffic conditions will consume more fuel than those operating in steady state traffic conditions for the same average speed. This leads to increase in travel time and fuel consumption of the vehicles and thereby adding to total road user cost (RUC). On the contrary, fuel consumption is also high at very high speeds under free flow traffic flow conditions leading to increase RUC. Considering these scenarios, the travel time and fuel cost of the vehicle due to the congestion and free flow conditions (uncongested) has to be necessarily modelled in order to estimate realistic assessment of RUC on Indian highways. In the present study, the congestion cost relationships have been developed between Congestion Factor, a ratio of cost under congestion and steady state conditions and Volume-Capacity Ratio by considering various vehicle types plying on varying widths of multi-lane highways (four, six and eight lane divided carriageways) through the collection of exhaustive time related and fuel related data. Time related data was collected through questionnaire survey method whereas fuel consumption data was collected using advanced sophisticated fuel flow measuring equipment (V-Box). The developed equations have been successfully applied to demonstrate their applicability in terms of estimating realistic effect of congestion on time and fuel cost by considering a section on NH-2 in Delhi. The analysis shows that the congestion effect is more significant on fuel cost for heavy commercial vehicles whereas it is more prominent on time cost for passenger vehicles. However, the congestion effect on combined fuel and time cost is more significant on multi-axle trucks followed by cars, two wheelers and buses.

Research on Traffic Flow Mathematical Model in Urban Traffic

In this paper, we take the urban road network as the object, a further study on the method of traffic signal self organization under the condition of traffic state identification and local congestion. In the urban road traffic state identification study, according to the need of traffic signal control, respectively from qualitative and quantitative point of view, the urban road traffic state is defined. In order to be able to obtain real-time traffic data as the foundation, design urban road traffic state evaluation index system. Based on the current mainstream micro traffic simulation software VB and VISSIM as the tool to build the experimental platform, and set up the UTC-CI (Urban Traffic Control for Congested Intersections) experimental environment, the above method is verified by simulation. The results show that the traffic state identification method and the related signal control method have the expected effect in reducing congestion duration.

무선 멀티미디어 센서 네트워크에서 경로간 간섭회피를 위한 부분 다중경로 라우팅 기법

Efficient routing algorithm is required to transmit data from source to destination by multi-hop transmission in wireless sensor networks. In the multi-hop transmission, multipath routing can be one of the solutions to cope with the traffic congestion and unstable link condition. In this paper, we propose partial multipath routing which does not establish a secondary full routing path but a partial multipath to complement some poor links, and it can enable stable data transmission and reduce the number of nodes in routing path and the required total power compared with conventional multipath routing.

A108 Transport Systems and Public Health: the Case of Traffic Congestion and Signal Control

Abstract Background In the context of transport systems, traffic congestion is always presented as an operational issue with negative socio-economic impacts. Rarely in engineering research and curricula is congestion portrayed as a public health issue-- a stress causing factor-- as in fact it is. It is accepted in the public health domain that repeated and prolonged driving in congested traffic conditions is a form of chronic stress and a serious health risk. Managing stress in difficult traffic conditions is a multifaceted activity in which proper traffic control can play a significant part. Methods This papers presents the human health side of traffic congestion and connect that to known and quantifiable traffic phenomena that, for modeling purposes, may be used as stress markers. Traffic control can thus be optimized with explicit consideration of stress. A case of a health-sensitive control algorithm for congested signalized systems is presented where control parameters are optimized to explicitly reduce the occurrence and intensity of unhealthy stress-inducing traffic conditions. The traffic signal control algorithm is formulated as a dynamic signal dynamic speed controller that directly and explicitly reduces the known stress-inducing operational traffic conditions. Results The resulting controller is combinatorial optimization problem. The control decision variables are traffic signal control parameters and network link speeds. Genetic Algorithms were used to solve the optimization. The results show that the signal and speed control generated markedly improved traffic flow where stress-inducing conditions were either eliminated or significantly reduced. Conclusions As extended and long-term exposure to congestion and difficult traffic conditions is a documented stressor with negative long term health impacts, current methods of signal control need to be modified to explicitly consider and minimize the impact of known stress-inducing traffic conditions.

Mathematic Modeling and Performance Analysis of an Adaptive Congestion Control in Intelligent Transportation Systems

In this paper, we develop a preventive congestion control mechanism applied at highway entrances and devised for Intelligent Transportation Systems (ITS). The proposed mechanism provides a vehicular admission control, regulates input traffic and performs vehicular traffic shaping. Our congestion control mechanism includes two classes of vehicles and is based on a specific priority ticket pool scheme with queue-length threshold scheduling policy, tailored to vehicular networks. In an attempt to cope with vehicular traffic fluctuation, we integrate an adaptation ticket generation module. The enhanced mechanism is then able to detect road congestion and provide performance metrics to road site units at the highway entrance. Mathematical modeling based on the embedded Markov chain method highlights the benefits of the proposed scheme. Performance analysis demonstrates the mechanism capability to accurately characterize road traffic congestion conditions, shape vehicular traffic and reduce travel time.

An improved correlation for pressure drop in a tunnel under traffic jam using CFD

Undoubtedly, an accurate estimation of pressure drop due to vehicles can contribute to correctly predicting the number of jet fans which ultimately leads to choosing the optimum ventilation system in a tunnel. The main objective of the present study is to improve the 1D (one-dimensional) model, which currently is unable to consider the effects of combination of cars in traffic jam on the total drag force. The CFD technique has been applied to model several tunnels in different lengths under severe traffic jam conditions; this way, the difference between pressure drop predicted by CFD simulation and 1D model can be investigated. The CFD simulation results were used to develop a new correlation for pressure drop due to cars inside the tunnel as a function of tunnel average air velocity and tunnel length. Additionally, the differences between the pressure drop due to the cars predicted by the CFD and the 1D model for different traffic conditions were investigated. It was shown that the existing 1D model underestimates or overestimates the pressure drop depending on the traffic condition and the drag-area value used in analysis. Finally, 1D model is modified by a simple equation for the pressure drop in a tunnel in congested traffic conditions using an equivalent friction factor resembling the stopped vehicles.

IJARCCE - Computer and Communication Engineering

Importance of the road infrastructure for the society could be the same as importance of blood vessels for humans. Road surface quality should be monitored and repaired on a regular basis. It is very difficult to design a optimal system which gathers the road condition data and processes it. Participatory sensing approach can be mostly used for such data collection.The paper is describes a mobile sensing system (android application) for road irregularity detection using Android OS based smart phone sensors. Selected data processing algorithms are discussed and their evaluation presented with true positive rate as high as 90% using real world data. The optimal parameters for the algorithms are determined as well as recommendations for their application.Continuously keeping track on road and traffic conditions in a city is a problem widely studied. Many methods have available towards addressing this problem. But this methods proposed require dedicated hardware such as GPS devices and accelerometers in vehicles or cameras on roadside and near traffic signals. All such proposed are unaffordable tothe common man regarding of monetary cost and human effort required. We extend a prior study to improve the algorithm based on using accelerometer, GPS and magnetometer sensor readings for trafficand road conditions detection. We are specifically made research in identifying braking events - frequent braking indicates congested traffic conditions - and bumps on the roads to characterize the type of road.

Characterizing heavy metal build-up on urban road surfaces: Implication for stormwater reuse

Stormwater reuse is increasingly popular in the worldwide. In terms of urban road stormwater, it commonly contains toxic pollutants such as heavy metals, which could undermine the reuse safety. The research study investigated heavy metal build-up characteristics on urban roads in a typical megacity of South China. The research outcomes show the high variability in heavy metal build-up loads among different urban road sites. The degree of traffic congestion and road surface roughness was found to exert a more significant influence on heavy metal build-up rather than traffic volume. Due to relatively higher heavy metal loads, stormwater from roads with more congested traffic conditions or rougher surfaces might be suitable for low-water-quality required activities while the stormwater from by-pass road sections could be appropriate for relatively high-water-quality required purposes since the stormwater could be relatively less polluted. Based on the research outcomes, a decision-making process for heavy metals based urban road stormwater reuse was proposed. The new finding highlights the importance to undertaking a “fit-for-purpose” road stormwater reuse strategy. Additionally, the research results can also contribute to enhancing stormwater reuse safety.

Study of unique merging behavior under mixed traffic conditions

Roads in developing countries carry mixed traffic with wide variations in static and dynamic characteristics of vehicles. The traffic flow is also generally devoid of lane discipline, with vehicles occupying any available road space ahead. In such a regime of traffic flow, the phenomena of merging of vehicles at intersections of two roads is complex, warranting further study. The merging maneuvers at T-intersections under congested traffic conditions were studied microscopically through video-recording. In congested situations, the merging vehicle attempts a complex merging maneuver to enter the main traffic stream. Two unique merging processes are commonly observed in mixed traffic: group and vehicle cover merging (these are generally not observed in countries such as US). The author is using these words first time in this study. These reflect the different types of driver behavior – merging in groups, and by taking cover of another vehicle. Probabilistic models for group and vehicle cover merging are developed that capture this unique merging behavior. Comprehensive microscopic data collection and extraction were carried out to study the merging process at T-intersection under congested conditions. Merging models were then estimated using maximum likelihood method with disaggregate data that was collected for a case study T-intersection in Chennai city, India. Such models can find applications in simulation of highly congested traffic flow in a realistic manner under mixed traffic conditions. They can also give insights on devising better traffic control measures at such intersections.