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Lane-changing prewarning is an active management measure used to mitigate the impact of incidents on expressways. This measure affects the operational efficiency and safety of traffic flow upstream of the incident location. To evaluate the effectiveness of this measure in mitigating incident-induced congestion and enhancing traffic safety, it is essential to model and simulate traffic flow on expressways, followed by analyzing the simulation results. In this study, we use the lane-changing selection cell transmission model (ls-CTM) to investigate the impact of setting lane-changing prewarning information on expressway traffic flow. By enhancing the composition of flow within the diverging cell in CTM, traffic can determine its downstream direction based on lane-changing probability rather than the path. A case study was conducted to investigate the impact of prewarning distances of 50, 100, and 150 m on road operational efficiency and traffic safety under different traffic volumes. The effectiveness and applicability of three measures to improve traffic safety were also summarized. The research results can provide more rigorous and effective decision-making support for expressway traffic management and control. [ABSTRACT FROM AUTHOR]

Freeway control has been increasingly used as an innovative approach to ease traffic congestion, improve traffic safety and reduce exhaust emissions. As an important predictive model involved in freeway control, the predictive performance of METANET greatly influences the effect of freeway control. This paper focuses on modifying the METANET model by modeling the critical density. First, the critical density model is deduced based on the catastrophe theory. Then, the perturbation wave and traveling wave that are obtained using the macro and micro data, respectively, have been developed to modify the above proposed critical density model. Finally, the numerical simulation is established to evaluate the effectiveness of the modified METANET model based on the field data from the realistic motorway network. The results show that overall, the predicted data from the modified METANET model are closer to the field data than those obtained from the original model. [ABSTRACT FROM AUTHOR]

On-ramp control is an effective way to mitigate traffic congestion in freeways. In this study, a traffic control approach is developed based on the OD data of a regional freeway to alleviate the traffic overload in freeway bottlenecks. We first locate the major driver sources of the freeway bottlenecks and identify the on-ramps for implementing the traffic control schemes. Next, the differential evolution algorithm is employed to calculate the optimal control time at each traffic control on-ramp. The results indicate that the major driver sources of the freeway bottlenecks are limited. Traffic congestion in the freeway bottlenecks can be effectively mitigated by adaptively controlling the waiting time of vehicles at the on-ramps of their major driver sources. [ABSTRACT FROM AUTHOR]

Urban expressways take on rapid and external transport in the city due to their fast, safe, and large capacity. Implementing intelligent and active traffic control can effectively improve the performance of urban traffic and mitigate the urban traffic congestion problem. Real-time traffic guidance is one critical way of intelligent active traffic control, and travel time is the most important input for real-time traffic guidance. We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. The ePL model has a promising nonlinear mapping potential, which is well suitable for the traffic prediction. We used generalized recursive least square (GRLS) to improve the estimation accuracy of the model's parameters. This model is a fuzzy control model. Its output is the forecasting result which is also the fuzzy reasoning result. We tested this model by comparing it to other travel time prediction approaches, with the freeway data from the Caltrans Performance Measurement System. The results from the improved ePL model showed mean absolute error of 5.941 seconds, mean absolute percentage error of 1.316%, and root mean square error of 10.923 s. The performances are better than those of the baseline models including ARIMA and BPN. This model can be used to predict the travel time in the field to be used for active traffic control and traffic guidance. [ABSTRACT FROM AUTHOR]

A road construction project has a significant effect on traffic congestion and operational performance of the project corridor and connecting street links. However, not many researchers focused on travel time performance-based measures to assess the effect of a road construction project on the project corridor and connecting street links. Therefore, travel time data for a freeway road resurfacing project on 100 links within its vicinity were gathered and processed to assess the effect on freeway and connecting arterial street links. A paired t-test was used to examine the differences in means between the before and during road resurfacing project period travel time performance-based measures. There is a significant difference in travel time performance-based measures before and during the road resurfacing project period on freeway links and connecting arterial links. Their significance varied by the time-of-the-day and day-of-the-week on the freeway and connecting arterial links. The average travel time, planning time, and travel time index can consistently explain the effect of a road resurfacing (construction) project on transportation system performance when compared to buffer time and buffer time index. The findings can be used to enhance temporary traffic control practices in the vicinity of road construction projects. [ABSTRACT FROM AUTHOR]

Expressway, as the main artery of urban traffic, realizes the smooth operation of the whole urban road network through reasonably balancing the traffic flow. However, due to the lack of reasonable and effective traffic control, the safety and congestion of expressways are becoming more and more serious. The development of intelligent network technology provides a new idea to solve the control problem of expressways. In this paper, a data-driven ramp control model of urban expressway is constructed. The interaction of traffic information is realized through intelligent network connection technology. The cooperative control strategy of VSL and RM is adopted. The mutual feedback of VSL and RM is realized based on the improved METANET model. The simulation experiment based on VISSIM secondary development shows that the collaborative control strategy under the intelligent network environment could make the vehicle travel time reduced by 20.59% and the speed difference between adjacent sections of the expressway mainline by 34.07%, which realized the coordinated control of the mainline and the on-ramp under the intelligent network environment, alleviate the expressway traffic congestion, reduce the traffic pressure, and improve the efficiency of the road network. [ABSTRACT FROM AUTHOR]

The urban expressway network is the main part of the urban traffic network carrying most of the city's traffic pressure for its continuity and rapidity, but the control method of the traffic flow was too simple to other control methods in application in addition to the ramp control and the fixed speed control. In this paper, the theory of variable speed limit (VSL) was used to develop an optimal control model based on the improved traffic flow simulation model according to the characteristics of urban expressway traffic flow. The objective of the proposed model is to minimize the delay and maximize the traffic flow. It can adjust the traffic flow on the network in space time so that the whole network is in a state of equilibrium which not only is conducive to the control of the local traffic congestion and avoids the spread of congestion but also improves the traffic safety. The SPSA-based solution algorithm was proposed by taking into account the needs of real-time online applications. It can not only ensure the accuracy of the solution but also meet the requirements of the simulation time. The simulation results show that the variable speed limit can be optimized in moderate demand, and the proposed model and algorithm are effective and feasible in this paper. The conclusions are useful to help the traffic management department to formulate reasonable traffic control strategies. [ABSTRACT FROM AUTHOR]

The trend of increasing traffic demand is causing congestion on existing urban roads, including urban motorways, resulting in a decrease in Level of Service (LoS) and safety, and an increase in fuel consumption. Lack of space and non-compliance with cities' sustainable urban plans prevent the expansion of new transport infrastructure in some urban areas. To alleviate the aforementioned problems, appropriate solutions come from the domain of Intelligent Transportation Systems by implementing traffic control services. Those services include Variable Speed Limit (VSL) and Ramp Metering (RM) for urban motorways. VSL reduces the speed of incoming vehicles to a bottleneck area, and RM limits the inflow through on-ramps. In addition, with the increasing development of Autonomous Vehicles (AVs) and Connected AVs (CAVs), new opportunities for traffic control are emerging. VSL and RM can reduce traffic congestion on urban motorways, especially so in the case of mixed traffic flows where AVs and CAVs can fully comply with the control system output. Currently, there is no existing overview of control algorithms and applications for VSL and RM in mixed traffic flows. Therefore, we present a comprehensive survey of VSL and RM control algorithms including the most recent reinforcement learning-based approaches. Best practices for mixed traffic flow control are summarized and new viewpoints and future research directions are presented, including an overview of the currently open research questions. [ABSTRACT FROM AUTHOR]

Urban freeway traffic control is of great importance for traffic management and intelligent transportation systems. Various approaches have been proposed to relieve urban freeway traffic jam, among which, ALINEA, a ramp metering strategy, is commonly implemented with fixed triggering threshold and static controller parameter. However, such a strategy may not be able to effectively alleviate the traffic congestion while maintaining certain ramp throughput due to two reasons: i). the congestion threshold can be time-varying due to different factors, such as segment ID, weather condition, time, and etc. ii). The congestion evolution patterns are time-varying even for the same segment. In this paper, based on over 890 million records of vehicles collected on ramps in Hangzhou, China, we established dynamic congestion threshold for each road segment with external factors. Based on such dynamic congestion threshold, we further clustered the congestion evolution patterns, and designed adaptive ramp controller which could switch the controller parameter according to the predicted congestion evolution pattern. Finally, in order to show the performance among different strategies, we introduced three baseline groups, which are ‘without Controller’, ‘ALINEA controller’, and ‘Direct RBF(radial basis function)-neural network controller’, respectively.The evaluation of proposed controller design over real large-scale data indicated that our method achieves 8.4%(7.2%), 4.62%(9.48%) efficiency improvement in terms of average speed in km/h (average traffic flow in veh/h) than the performance with normal ALINEA controller and RBF-neural network controller respectively. [ABSTRACT FROM AUTHOR]

Traffic congestion in freeway network is a prevalent transportation problem and leads to a strong degradation of freeway network facilities. This paper proposes a new coordinated optimization approach for freeway network, aiming to relieve the traffic congestion in freeway network equipped with the coordinated traffic controllers. The challenge is how to solve the problem between the traffic control goals of each path and those of the network. In this paper, the bi-level programming theory is used to address this problem. More exactly, in the path level, the optimal control model is designed to relieve congestion in each path of the test network. Furthermore, in the network level, the optimal model considers the traffic equilibrium and cost optimization. The efficiency of the proposed approach is tested on a well-known traffic network calibrated by the field test data of freeway network. The results show that the proposed approach has positive effects in balancing traffic load and improving the service level of freeway networks. [ABSTRACT FROM AUTHOR]

Over a period spanning more than 30 years, several ramp metering algorithms have been developed to improve the operation of freeways. Many of these algorithms were deployed in several regions of the world, and field evaluations have shown their significance to improve traffic conditions on freeways and ramps. Previous reviews of ramp metering algorithms focused more on the research outcomes and evaluations of traditional metering algorithms developed in the early stage of ramp metering research. The purpose of this paper is to cover the more recent developments in ramp metering in relation to the traditional metering strategies. Several local and coordinated ramp metering algorithms were reviewed. In summary, Asservissement Linéaire d’Entrée Autoroutière (ALINEA) was found to be the most widely deployed local ramp metering strategy. The algorithm is simple and implementation costs less than other strategies. It also guarantees the targeted performance goals provided that the on-ramp has sufficient storage. Several extensions were proposed in the literature to fine-tune its performance. Among the coordinated metering strategies, zone based metering is simple to implement and easy to re-configure. System-wide adaptive ramp metering (SWARM) algorithm is more sensitive to calibrate for accurate prediction of traffic states. Heuristic ramp-metering coordination (HERO) algorithm can be useful if both local and coordinated control are desired particularly if the local control is using ALINEA. Fuzzy logic based algorithms are gaining popularity because of the simplicity and the fast re-configuration capability. Advanced real time metering system (ARMS) seems theoretically promising because of its proactive nature to prevent congestion; however, its performance is highly dependent upon accurate predictions. Finally, some guidelines were proposed for future research to develop new proposals and to extend the existing algorithms for guaranteed performance solutions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

There is an incompatibility between how transport engineers think drivers behave in roadworks and how they actually behave. As a result of this incompatibility we are losing approximately a lane's worth of capacity in addition to those closed by the roadworks themselves. The problem would have little significance were it not for the fact a lane of motorway costs approx. £30 m per mile to construct and £43 k a year to maintain, and that many more roadworks are planned as infrastructure constructed 40 or 50 years previously reaches a critical stage in its lifecycle. Given current traffic volumes, and the sensitivity of road networks to congestion, the effects of roadworks need to be accurately assessed. To do this requires a new ergonomic approach. A large-scale observational study of real traffic conditions was used to identify the issues and impacts, which were then mapped to the ergonomic knowledge-base on driver behaviour, and combined to developed practical guidelines to help in modelling future roadworks scenarios with greater behavioural accuracy. Also stemming from the work are novel directions for the future ergonomic design of roadworks themselves. [ABSTRACT FROM AUTHOR]

To identify network bottlenecks of urban expressway effectively is a foundational work for improving network traffic condition and preventing traffic congestion. This study proposes a methodology to estimate the impact of traffic incident on urban expressway on the basis of modified cell transmission model. The metastable state was taken into account in the proposed method to reflect the actual operating state of traffic flow on urban expressway as much as possible. Regarding the location of traffic incident, the method of cell restructuring settings was discussed. We then proceed to introduce a new concept of the effected length in a given time period as the evaluation indicator to directly depict the influence of traffic incident. The proposed method was tested on a 6516-meter urban expressway section of west second ring road in Beijing. The simulation results indicated that the proposed methodology performs well to predict the impact of traffic incident on urban expressway. [ABSTRACT FROM AUTHOR]

The objective of this article is to propose an analytical traffic model adapted to freeway weaving sections. To this aim, a theoretical weaving section is considered at the macroscopic level as the superposition of two merges and two diverges. The model endogenously incorporates the capacity drop related to the weaving lane changes. It only depends on six parameters: three parameters of the fundamental diagram (the free-flow speed, the jam density, and the wave speed in congestion), the vehicles' acceleration, the relaxation factor, and the length of the anticipation zone. A sensitivity analysis proves that the acceleration rate and the relaxation parameter most influence the capacity drop. The analytical model agrees well with microsimulation results and empirical data collected on a weaving section in France. [ABSTRACT FROM AUTHOR]

The prevailing variable speed limit (VSL) systems as an effective strategy for traffic control on motorways have the disadvantage that they only work with static VSL zones. Under changing traffic conditions, VSL systems with static VSL zones may perform suboptimally. Therefore, the adaptive design of VSL zones is required in traffic scenarios where congestion characteristics vary widely over space and time. To address this problem, we propose a novel distributed spatial-temporal multi-agent VSL (DWL-ST-VSL) approach capable of dynamically adjusting the length and position of VSL zones to complement the adjustment of speed limits in current VSL control systems. To model DWL-ST-VSL, distributed W-learning (DWL), a reinforcement learning (RL)-based algorithm for collaborative agent-based self-optimization toward multiple policies, is used. Each agent uses RL to learn local policies, thereby maximizing travel speed and eliminating congestion. In addition to local policies, through the concept of remote policies, agents learn how their actions affect their immediate neighbours and which policy or action is preferred in a given situation. To assess the impact of deploying additional agents in the control loop and the different cooperation levels on the control process, DWL-ST-VSL is evaluated in a four-agent configuration (DWL4-ST-VSL). This evaluation is done via SUMO microscopic simulations using collaborative agents controlling four segments upstream of the congestion in traffic scenarios with medium and high traffic loads. DWL also allows for heterogeneity in agents' policies; cooperating agents in DWL4-ST-VSL implement two speed limit sets with different granularity. DWL4-ST-VSL outperforms all baselines (W-learning-based VSL and simple proportional speed control), which use static VSL zones. Finally, our experiments yield insights into the new concept of VSL control. This may trigger further research on using advanced learning-based technology to design a new generation of adaptive traffic control systems to meet the requirements of operating in a nonstationary environment and at the leading edge of emerging connected and autonomous vehicles in general. [ABSTRACT FROM AUTHOR]

An approach for predicting incident durations that are susceptible to severe congestion, the occurrence of secondary incidents, and their joint effect is proposed. First, a fuzzy entropy feature selection methodology is applied to determine redundant factors and rank factor importance with respect to their contribution on the predictability of incident duration. Second, neural network models for incident duration prediction with single and competing uncertainties are developed. The results indicate that alignment, collision type, and downstream geometry may be considered as redundant when modeling incident duration. Rainfall intensity is a highly contributing feature, while lane volume, number of blocked lanes, as well as number of vehicles involved in the incident are among the top ranking factors for determining the extent of duration. Finally, the joint consideration of severe congestion and secondary incident occurrence may improve the generalization power of the prediction models. [ABSTRACT FROM AUTHOR]

The expressways in Beijing are confronted with more serious traffic congestions. Based on the survey data obtained fromthe typical sections at the expressways, the time dependent characteristics of traffic flow parameters were analyzed in detail and the data gap was found in this paper. The Fast Fourier Transform (FFT) method is proposed to transfer the data of traffic flow parameters for describing the fluctuation characteristics of traffic flow. Two methods of identification, the graph method and the control line method, were proposed as to the change time of traffic bottleneck forming and dissipating. The findings in this paper have already been applied in traffic management and ramp control at the expressways in Beijing. [ABSTRACT FROM AUTHOR]

We present an advanced interpolation method for estimating smooth spatiotemporal profiles for local highway traffic variables such as flow, speed and density. The method is based on the 'adaptive smoothing method' which takes as input stationary detector data as typically collected by traffic control centers. We generalize this method to allow for fusion with floating car data or other traffic information. The resulting profiles display transitions between free and congested traffic in great detail, as well as fine structures such as stop-and-go waves. We establish the accuracy and robustness of the method and demonstrate three potential applications: (1) compensation for gaps in data caused by detector failure; (2) separation of noise from dynamic traffic information; and (3) the fusion of floating car data with stationary detector data. [ABSTRACT FROM AUTHOR]

Abstract: Recent studies have shown clear contributions of non-exhaust emissions to the traffic related PM10 load of the ambient air. These emissions consist of particles produced by abrasion from brakes, road wear, tire wear, as well as vehicle induced resuspension of deposited road dust. The main scope of the presented work was to identify and quantify the non-exhaust fraction of traffic related PM10 for two roadside locations in Switzerland with different traffic regimes. The two investigated locations, an urban street canyon with heavily congested traffic and an interurban freeway, are considered as being typical for Central Europe. Mass-relevant contributions from abrasion particles and resuspended road dust mainly originated from particles in the size range 1–10 μm. The results showed a major influence of vehicle induced resuspension of road dust. In the street canyon, the traffic related PM10 emissions (LDV: 24 ± 8 mg km−1 vehicle−1, HDV: 498 ± 86 mg km−1 vehicle−1) were assigned to 21% brake wear, 38% resuspended road dust and 41% exhaust emissions. Along the freeway (LDV: 50 ± 13 mg km−1 vehicle−1, HDV: 288 ± 72 mg km−1 vehicle−1), respective contributions were 3% brake wear, 56% resuspended road dust and 41% exhaust emissions. There was no indication for relevant contributions from tire wear and abrasion from undamaged pavements. [Copyright &y& Elsevier]

The effect of an upgrade and its length is very significant for traffic flow characteristics. Road traffic in developing countries like India is highly heterogeneous comprising vehicles of wide ranging physical dimensions, weight and dynamic characteristics such as engine power, acceleration rate, etc. Due to these variations, the effect of grade on vehicles in heterogeneous traffic may vary significantly among vehicle categories. Variation in the level of the interaction between vehicles on upgrades may result in different sets of traffic flow characteristics. Hence, it is necessary to model traffic flow on upgrades and study, in depth, changes in traffic flow characteristics with alteration in the magnitude of an upgrade and its length. Computer simulation has emerged as an effective technique for modelling traffic flow due to its capability to account for randomness related to traffic. This study is concerned with applying a simulation model of heterogeneous traffic flow, named HETEROSIM, to study the traffic flow characteristics and performance of different vehicle types on upgrades of different magnitudes. [ABSTRACT FROM AUTHOR]

The article discusses the circulation and the lyrical literature of traffic. It mentions that traffic jams are said to represent the crucial points of theoretical or poetic exchange. The author explores the infrastructure of Canada while tracing the operation of the expressway and the traffic itself, and the engagement of traffic with genre, language, and representation.

Observations of freeway traffic flow are usually quite scattered about an underlying curve when plotted versus density or occupancy. Although increasing the sampling intervals can reduce the scatter, whenever an experiment encompasses a rush hour with transitions in and out of congestion, some outlying data stubbornly remain beneath the ‘equilibrium’ curve. The existence of these nonequilibrium points is a poorly understood phenomenon that appears to contradict the simple kinematic wave (KW) model of traffic flow. This paper provides a tentative explanation of the phenomenon, based on experimental evidence. The evidence was a FIFO queue that grew and receded over two detector stations, generating typical flow-density scatter plots at both locations. The locations were far from other interacting traffic streams. The data revealed that a transition zone where vehicles decelerated gradually existed immediately behind the queue. The transition zone was quite wide (about 1 km at both locations), moved slowly (approximately with the ‘shock’ velocity of KW theory), and as a result spent many minutes over each detector station. Disequilibrium flow-density points arose only when the transition zone was over the detectors, suggesting that the transition zone explains their occurrence. The disequilibrium points drifted gradually from one branch of the curve to the other, as KW theory would have predicted if ‘shocks’ had a characteristic width equal to the dimension of the transition zone. Nothing was found in the data to contradict this view. This paper also shows that in our case, if one neglects the shocks' physical dimension, the position of every vehicle can be predicted with KW theory to within approximately five vehicle spacings. Thus, it appears that KW theory can predict rather accurately traffic behavior at the back of FTFO queues, i.e., when the lanes are equally attractive to all drivers. We end with a discussion offering some perspective on how the findings of this paper related to the traffic thinking found in the current literature. [ABSTRACT FROM AUTHOR]

This work focuses on developing a variety of strategies for alleviating congestion at freeway merging points as well as improving the safety of these points. On the Tokyo Metropolitan Expressway, traffic congestion frequently occurs at merging bottleneck sections, especially during heavy traffic demand. The Tokyo Metropolitan Expressway public corporation, generally applies different empirical strategies to increase the flow rate and decrease the accident rate at the merging sections. However, these strategies do not rely either on any behavioral characteristic of the merging traffic or on the geometric design of the merging segments. There have been only a few research publications concerned with traffic behavior and characteristics in these situations. Therefore, a three-year extensive study has been undertaken to investigate traffic behavior and characteristics during the merging process under congested situations in order to design safer and less congested merging points as well as to apply more efficient control at these bottleneck sections. Two groups of strategies were investigated in this study. The First group was related to the traffic characteristics, and the second group to the geometric characteristics. In the first group, the control strategies related to closure of freeway and ramp lanes as well as lane-changing maneuver restriction were investigated through a simulation program, detector data, and field experiment. In the second group, the angle of convergence of the ramp with the freeway in relation to merging capacity was analyzed using a simulation program. Results suggested the potential benefits of using proposed strategies developed in this work and can serve as initial guidance for the reduction of delay and improvement of safety under congested traffic conditions. [ABSTRACT FROM AUTHOR]

This article explores the meaning of congestion on highway-type rural and suburban facilities, such as tollways and freeways, and develops congestion thresholds, above which it will be necessary to add lanes to specific sections of these facilities. The study originated from the need for the development of a tender for a major tollway facility in Israel. It was necessary to clearly identify the conditions at which the flow could be termed "congested," in the sense of lower levels of service, dense flows, and tense driving conditions. Several congestion measures for the defined purpose were explored. In the end, density of flow and the resulting flow rate were found to be the most appropriate measures. Based on the measures selected, specific thresholds were developed for four-and six-lane rural and suburban sections. The measures presented are dependent primarily on the percentage of heavy vehicles. It is assumed that the conditions of the facility are "ideal," meaning a high design speed, level terrain, and wide lanes and shoulders. An example is presented and evaluated of the estimation of a nonlinear growth curve for a rural section of a tollway facility and the approximate time at which the congestion threshold will be crossed. [ABSTRACT FROM AUTHOR]

The article presents information on a model implemented to determine traffic signal timing in a province known as Tuah. The model was duly validated for Tuah. The supervising traffic engineers were, however, not impressed and loudly proclaimed their reluctance to engage in further funding until a practical problem could be solved, said practical problem being the congestion at the most heavily congested intersection in Tuah. The problem was that rush-hour traffic could not get off the freeway and through the interchange fast enough to avoid queuing back onto the freeway. Realizing that the two problems were interconnected, the traffic engineers took much data, created the proper input to the computer, and adjusted the signal timing on the simulation model until the traffic showed a near-optimal improvement. One year later, two traffic engineers from the country of Falicornia discovered that the interchange had been modeled incorrectly-that a Markovian assumption in the model of the interchange simply did not hold.