Freeway Traffic Flow Research Articles

Expressway traffic flow model study based on different traffic rules

In this paper, two cellular automata traffic models are proposed to simulate the operation of expressway. The results show that the flow rate and the average velocity are generally equal in the same density which is different among the lanes. The analysis of lane changing times and the velocity total deviation show some characteristics which are difficult to be explained by the phase transitions under the fundamental diagram theory. Therefore, the concept of lane changing probability is introduced, and it is concluded that the speed-limit rule can reduce the motivation of lane changing effectively.

Adaptive Cruise Control Operation for Improved Motorway Traffic Flow

This study presents an ACC (adaptive cruise control)–based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that motorway traffic flow efficiency is improved. The potential benefits obtained by applying the proposed control concept are demonstrated for different ACC penetration rates by use of validated microscopic simulation applied to a real motorway stretch where recurrent traffic congestion is created under the current manual driving conditions because of an on-ramp bottleneck. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average vehicle delay and fuel consumption by reducing the space-time extent of congestion compared with the case of only manually driven or regular ACC vehicles.

Car Following Prediction Based on Support Vector Regression and Multi-adaptive Regression Spline by Considering Instantaneous Reaction Time

Car following modeling is one of the major issues in microscopic traffic simulation, and the accuracy and reliability of microscopic simulation models highly depend on the models. This research used a highly efficient method of regression for car following modeling such as support vector regression and compared it with multi-adaptive regression spline method as a nonparametric method. Meanwhile, the factors of “speed” and “distance to the following car” were used as the model’s inputs and it was proved that the accuracy of support vector regression model exceeded that of multi-adaptive regression spline. Driver’s reaction time is another unavoidable factor in car following modeling, which varies based on driver–vehicle features and traffic conditions. The research determines this value with respect to the time lag between the diagrams of the following and leading cars distance, the speed of the following car, and the execution of support vector regression model. The model was then implemented using the microdata of a highway traffic flow in the USA for 3 lanes. The research shows that the proposed model has a proper validity after entering driver’s instantaneous reaction time, which is based on the proximity of the results to the real condition of car following in simulation. A comparison was made between the simulations carried out using traditional models and the models proposed in this research. The results can be considered as a basis for other studies on car following modeling and microscopic traffic simulation in highway field.

The impact of aggressive driving behaviors on multi-lane highway traffic flow

A modified cellular automata traffic model is proposed to simulate four-lane traffic flow, in which drivers are classified into aggressive drivers and cautious drivers and the anticipative velocity of the adjacent vehicles is considered. Analysis from the vehicles’ evolution pattern indicates that vehicles driven by the aggressive drivers are more powerful in behaviors of lane-changing and car-following. The model is refined by using the small cell of one meter long in order to simulate the traffic flow meticulously and realistically. The results indicate that the lane-changing maneuver exhibits different property as the density varies, and it does have a significant impact on the characteristics of the surrounding traffic flow due to their interfering effects on the following vehicles. Furthermore, the phenomenon of high-speed car-following is exhibited, and the results coincide with the empirical data very well. It is shown that the proposed model is reasonable and can partially reflect the real traffic.

Self-driving tourism induced carbon emission flows and its determinants in well-developed regions: A case study of Jiangsu Province, China

Abstract Carbon emissions from the tourism industry are an important measure of the impact tourism has on the environment. Previous studies are predominantly focused on the static estimation of carbon emission from tourism transport. The effective estimation and analysis of carbon emission flows from self-driving tourism, and it’s related determinants, has become increasingly important. Using expressway traffic flow data at the level of toll-gate across Jiangsu Province in China (2014), this paper has estimated the carbon emission flows from self-driving tourism between counties, analyzed the spatial patterns of its inflow, outflow and net flows, and modelled the determinants of these flows globally and locally using the geographically weighted regression method. The spatial distribution of these flows show high concentration in the South, gradually decreasing to the North. The two geographically weighted regression models demonstrate that the determinants of both inflows (the per capita gross domestic product. and the scenic spot’s score) and outflows (the per capita and total population of permanent residents) indicate spatial non-stationarity across Jiangsu province. The flow perspective and geographically weighted regression methods used in this paper have been proven to be effective in theoretical understanding and methodogical analysis of carbon emission trading. It is concluded that the spatial variation of these determinants has provided important evidence for carbon emission trading at county level. This suggests that local governments should take the variations of per capita gross domestic product, score of attractive spots and total population of permanent residents into the process of estimating carbon emission trading between counties.

Variable speed limit strategies analysis with link transmission model on urban expressway

The variable speed limit (VSL) is a kind of active traffic management method. Most of the strategies are used in the expressway traffic flow control in order to ensure traffic safety. However, the urban expressway system is the main artery, carrying most traffic pressure. It has similar traffic characteristics with the expressways between cities. In this paper, the improved link transmission model (LTM) combined with VSL strategies is proposed, based on the urban expressway network. The model can simulate the movement of the vehicles and the shock wave, and well balance the relationship between the amount of calculation and accuracy. Furthermore, the optimal VSL strategy can be proposed based on the simulation method. It can provide management strategies for managers. Finally, a simple example is given to illustrate the model and method. The selected indexes are the average density, the average speed and the average flow on the traffic network in the simulation. The simulation results show that the proposed model and method are feasible. The VSL strategy can effectively alleviate traffic congestion in some cases, and greatly promote the efficiency of the transportation system.

Traffic State Spatial-Temporal Characteristic Analysis and Short-Term Forecasting Based on Manifold Similarity

The study on the spatial-temporal characteristics of highway traffic flow is helpful to deeply understand the inherent evolution of highway traffic system and provide a theoretical basis for prediction and control of highway traffic flow. This paper makes an empirical analysis on the spatial-temporal characteristics of highway traffic flow using manifold similarity index and manifold learning technology. The time series of highway traffic flow is converted into the distance series containing manifold features to calculate the manifold distance between multi-section traffic flow data points, which are highly similar to spatial-temporal distribution of traffic flow speed parameters, and then, the levels calibration of traffic state is carried out according to the manifold distance, so as to reveal the distribution rule of spatial-temporal characteristics of highway traffic flow. Its prediction error is obviously lower than the traditional distance measurement method, which has higher accuracy. The research of this paper can provide new ideas and methods to reveal the highway traffic flow evolution and traffic state prediction.

Highway Traffic Flow Estimation for Surveillance Scenes Damaged by Rain

In this paper, we propose a traffic flow estimation system for intelligent highway surveillance applications under rainy conditions. Major contributions of the proposed system include flexible feature extraction, robust estimation with adaptive clustering, and effective graph-based traffic flow mapping model. To detect rain-drop tampered scenes, features are extracted via salient region detection and block segmentation. For traffic flow estimation, lane directions are automatically detected for daytime scenes. Foreground moving edges accumulated along the traffic flow direction are used as features. We utilize an adaptive clustering algorithm to estimate vehicle count for each frame. For nighttime scenes, statistical features are extracted from the segmented blocks, and regression models are applied to generate per-frame vehicle count. Finally, an effective graph-based mapping method is incorporated to map the vehicle count sequences to per-minute traffic flow. The accuracy of the traffic flow analysis is satisfying even when the cameras are seriously affected by rain. The experiments demonstrate that the proposed system can effectively analyze traffic flow under rainy conditions for highway surveillance cameras.

Integrated control of highway traffic flow

Several efforts have been made during recent years on the control of traffic flow using ramp metering, variable speed limit and lane change control. Most of these techniques reported significant traffic mobility improvements in macroscopic simulations, which cannot be confirmed in some microscopic simulation cases. We demonstrate that the lack of travel time improvement is due to the forced lane changes at the bottleneck which introduce a capacity drop. In this paper, a lane change controller is proposed which provides lane change recommendations to avoid the capacity drop. Combined with the lane change control, a feedback linearisation variable speed limit controller which maintains the bottleneck flow at the maximum level and coordinates with ramp metering controllers is proposed. The integrated VSL, RM and LC controller can improve system performance, manage the ramp queues and maintain the fairness between mainline and on-ramp flows. Microscopic simulations show consistent improvements under different scenarios.

The impact of vehicle moving violations and freeway traffic flow on crash risk: An application of plugin development for microsimulation.

This paper presents the use of the Aimsun microsimulation program to simulate vehicle violating behaviors and observe their impact on road traffic crash risk. Plugins for violations of speeding, slow driving, and abrupt stopping were developed using Aimsun’s API and SDK module. A safety analysis plugin for investigating probability of rear-end collisions was developed, and a method for analyzing collision risk is proposed. A Fuzzy C-mean Clustering algorithm was developed to identify high risk states in different road segments over time. Results of a simulation experiment based on the G15 Expressway in Shanghai showed that abrupt stopping had the greatest impact on increasing collision risk, and the impact of violations increased with traffic volume. The methodology allows for the evaluation and monitoring of risks, alerting of road hazards, and identification of hotspots, and could be applied to the operations of existing facilities or planning of future ones.

Feedback-Based Integrated Motorway Traffic Flow Control With Delay Balancing

The development and deployment of simple, yet efficient, coordinated and integrated control tools for motorway traffic control remains a challenge. A generic integrated feedback-based motorway traffic flow control concept is proposed in this paper. It is based on the combination and suitable extension of control algorithms and tools proposed or deployed in other studies, such as ramp metering or variable speed limit (VSL)-enabled cascade-feedback mainstream traffic flow control, and allows for consideration of multiple bottlenecks. The new controller enables coordination of ramp metering actions at a series of on-ramps, as well as integration with VSL control actions, toward a common control goal, which is bottleneck throughput maximization. While doing this, the approach considers a pre-specified (desired) balancing of the incurred delays upstream of the employed actuators, via a suitably designed knapsack problem. Despite the multitude of the offered configurations, options, and possibilities, the generic control algorithm remains simple, efficient, and suitable for field implementation. The control algorithm is demonstrated and evaluated using a validated macroscopic traffic flow model for a number of scenarios.

Uncovering Spatial Structures of Regional City Networks from Expressway Traffic Flow Data: A Case Study from Jiangsu Province, China

On the basis of the “space of flows” theory, city networks emphasizing factor connectivity and spatial connection have become a core perspective for regional spatial relationships. They provide a context for discussing the spatial structures of city networks and a scientific basis for making regional development policies. Relying on expressway traffic flow data from Jiangsu Province in 2014, this study describes macro-spatial patterns and hierarchical structures of city networks, and uses the Walktrap algorithm of community detection to execute space divisions and elucidate potential spatial connection structures in the city networks. In comparison with other algorithms, the Walktrap algorithm demonstrates significant adaptation and stability to the short expressway traffic flows with the characteristics of strong network density and sparse node distribution. The results indicate that the macro-spatial patterns of Jiangsu’s city networks have clear regional differences. The cities with relatively dense spatial linkages are distributed along the Yangtze River banks, and many different sub-network systems have also developed internally. Cities and linkage axles have clear hierarchical structures. City hierarchies have certain spatial couplings with the cities’ intrinsic social economic attributes and geographical locations. The linkage axles hierarchy has clear spatial interactions and superposition with geographical distance. The community detection algorithm identified six spatially connected “city communities”, the Suzhou-Wuxi-Changzhou Community (SWCC), the Nanjing-Zhenjiang-Yangzhou-Taizhou Community (NZYTC), the Nantong-Yancheng Community (NYC), the Lianyungang Community (LYGC), the Huai’an-Suqian Community (HSC), and the Xuzhou Community (XZC). The community spatial metaphors had four aspects. First, trans-prefectural linkages were formed through spatially integrated effects of metropolis regions. Second, some communities share the same boundaries with their prefecture level administrative units; this reveals that significant administrative regional economies still exist in contemporary Jiangsu. Third, several cities located in the marginal areas of prefectures and captured by the powerful center cities in neighboring prefectures have been absorbed into the communities of neighboring prefectures. Fourth, the two cities Jiangyin and Jingjiang, divided into different administrative districts, have switched status opposite city community divisions.

Variable speed limit strategies’ analysis with cell transmission model on freeway

Mainline freeway traffic flow control is one of the primary methods of traffic management, which can present the best network situation. In this paper, we integrate variable speed limit (VSL) strategy into the cell transmission model (CTM). Then the implementation of the integrated model on freeway traffic network is discussed. A novel optimal model of controlling freeway traffic flow is proposed for minimizing the total travelling time in the network. A solution algorithm is designed by using a simulation method. Considering the main purpose of the speed limit strategy is to control the mainstream flow, we compare the case where the VSL is used with the one without VSL. A simulation is implemented to show that the control strategy is efficient in describing system’s dynamic performance and the dynamic speed limit strategy significantly alleviates congestion.

Impact of Heavy Vehicles on Highway Traffic Flows: Case Study in the Seoul Metropolitan Area

AbstractHighway capacity manuals (HCM) have incorporated the effect of heavy vehicles (HVs) in the form of passenger car equivalents (PCEs) for estimating highway traffic conditions, which include ...

Effects of low speed limits on freeway traffic flow

Recent years have seen a renewed interest in Variable Speed Limit (VSL) strategies. New opportunities for VSL as a freeway metering mechanism or a homogenization scheme to reduce speed differences and lane changing maneuvers are being explored. This paper examines both the macroscopic and microscopic effects of different speed limits on a traffic stream, especially when adopting low speed limits. To that end, data from a VSL experiment carried out on a freeway in Spain are used. Data include vehicle counts, speeds and occupancy per lane, as well as lane changing rates for three days, each with a different fixed speed limit (80km/h, 60km/h, and 40km/h). Results reveal some of the mechanisms through which VSL affects traffic performance, specifically the flow and speed distribution across lanes, as well as the ensuing lane changing maneuvers. It is confirmed that the lower the speed limit, the higher the occupancy to achieve a given flow. This result has been observed even for relatively high flows and low speed limits. For instance, a stable flow of 1942veh/h/lane has been measured with the 40km/h speed limit in force. The corresponding occupancy was 33%, doubling the typical occupancy for this flow in the absence of speed limits. This means that VSL strategies aiming to restrict the mainline flow on a freeway by using low speed limits will need to be applied carefully, avoiding conditions as the ones presented here, where speed limits have a reduced ability to limit flows. On the other hand, VSL strategies trying to get the most from the increased vehicle storage capacity of freeways under low speed limits might be rather promising. Additionally, results show that lower speed limits increase the speed differences across lanes for moderate demands. This, in turn, also increases the lane changing rate. This means that VSL strategies aiming to homogenize traffic and reduce lane changing activity might not be successful when adopting such low speed limits. In contrast, lower speed limits widen the range of flows under uniform lane flow distributions, so that, even for moderate to low demands, the under-utilization of any lane is avoided. These findings are useful for the development of better traffic models that are able to emulate these effects. Moreover, they are crucial for the implementation and assessment of VSL strategies and other traffic control algorithms.

Study of Rainfall Impacts on Freeway Traffic Flow Characteristics

Abstract Compared with normal weather, the traffic flow characteristics changed under the condition of adverse weather, which accounted for the difference of driving behavior. The influence of freeway traffic flow characteristics caused by different rainfall intensity was quantified through collecting the Hainan Province Eastern Freeway traffic flow and rainfall data. The speed and density distribution of microscopic parameters about freeway traffic flow under different rains was researched, and the highway two-regime speed density distribution traffic flow model under different rainfall weather conditions was calibrated. Besides, the weather influence coefficient that the rainfall intensity and visibility affect the freeway traffic flow model parameters was determined through regression analysis. The research indicates that it has an greater influence on the maximum highway volume and free flow speed as the rainfall intensity increases. Compared with the normal weather, the maximum flow rate decreases15.7%, 19.1%, 32.5% respectively and the free flow speed decreases 4.4%, 7.3%,10.6% respectively because of light rain, moderate rain and heavy rain weather conditions. In the traffic flow model, the breakpoint density and the speed-intercept of traffic flow model are not obviously relevant with the rainfall intensity.

Lane-Changing Feedback Control for Efficient Lane Assignment at Motorway Bottlenecks

A feedback control strategy is proposed for lane assignment at bottleneck locations. The strategy assumes that some vehicles equipped with vehicle automation and communication systems are capable of receiving and executing specific lane-changing orders or recommendations. From a previously proposed optimal control strategy based on a simplified multilane motorway traffic flow model and formulated as a linear quadratic regulator, a feedback control problem was designed. The aim was to maximize the throughput at bottleneck locations while distributing the total density at the bottleneck area over the lanes according to a given policy by optimal lane assignment of the vehicles upstream of the bottleneck. The feedback control decisions were based on real-time measurements of the traffic state and inflow. The proposed strategy was tested on a nonlinear first-order macroscopic multilane traffic flow model, which also accounted for the capacity drop phenomenon.

Short‐term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information

SummaryShort‐term traffic flow prediction is fundamental for the intelligent transportation system and is proved to be a challenge. This paper proposed a hybrid strategy that is general and can make use of a large number of underlying machine learning or time‐series prediction models to capture the complex patterns beneath the traffic flow. With the strategy, four different combinations were implemented. To consider the spatial features of traffic phenomenon, several different state vectors including different observations were built. The performance of the proposed strategy was investigated using the traffic flow measurements from the Traffic Operation and Safety Laboratory in Wisconsin, USA. The results show the overall performance of hybrid strategy is better than a single model. Also, incorporating observations from adjacent junctions can improve prediction accuracy. Copyright © 2017 John Wiley & Sons, Ltd.

How long will the traffic flow time series keep efficacious to forecast the future?

Abstract This paper investigate how long will the historical traffic flow time series keep efficacious to forecast the future. In this frame, we collect the traffic flow time series data with different granularity at first. Then, using the modified rescaled range analysis method, we analyze the long memory property of the traffic flow time series by computing the Hurst exponent. We calculate the long-term memory cycle and test its significance. We also compare it with the maximum Lyapunov exponent method result. Our results show that both of the freeway traffic flow time series and the ground way traffic flow time series demonstrate positively correlated trend (have long-term memory property), both of their memory cycle are about 30 h. We think this study is useful for the short-term or long-term traffic flow prediction and management.

Hybrid short‐term prediction of traffic volume at ferry terminal based on data fusion

Ferry traffic prediction is an important consideration for urban transportation authorities. A novel methodology which considers cyclical patterns in ferry on-board vehicle traffic and improves on this method by incorporating traffic data from nearby freeway loop detectors is proposed. Since vehicles arriving in ferry terminals often originate from nearby freeways, the study of freeway traffic flow can improve ferry traffic prediction, especially during peak travel periods. In this study, the authors first identify periodic patterns in ferry traffic through analysis in both the time and frequency domain, and propose a modelling methodology which considers these patterns in short-term traffic prediction. In the proposed model, the ferry traffic volume is divided into periodic and dynamic components, with the periodic component approximated by trigonometric functions. Second, this model is expanded to incorporate measured traffic data on nearby freeways. Specifically, the correlation between freeway traffic and ferry traffic volume is studied, and an enhanced model is proposed which integrates the predictions obtained through historical ferry data and from freeway traffic during peak travel time periods. Validation results demonstrate that consideration of periodic patterns increases prediction accuracy and that the proposed model further improves the ferry traffic prediction accuracy during peak time periods.