Traffic Control Policies Research Articles

Brazilian truckers’ strike and particulate matter (PM10) concentration: Temporal trend and time series models

High particulate matter (PM) emissions from vehicular traffic impact air quality in urban areas. In 2018, a truckers’ strike interrupted some of the services in Brazil, leading to a fuel outage in several cities that significantly reduced the flow of vehicles. This study evaluated air quality during the strike in two cities (Limeira and Campinas) in Southeastern Brazil. PM10 concentration was analyzed in the periods before (BTS — 05/01/2018 to 05/22/2018), during (DTS — 05/23/2018 to 05/30/2018), and after (ATS — 05/31/2018 to 06/30/2018) the strike using the Theil-Sen method and the Autoregressive Integrated Moving Average model with Exogenous Variables (ARIMAX). A reduction in the PM daily mean concentration in both cities occurred during the strike. Considering the daily peak time of vehicular flow (6:00 p.m.), the PM10 concentration was 20% higher in the BTS period compared to the DTS period for both cities. In comparison, the ATS period showed concentrations 17% (Limeira) and 7% (Campinas) higher when compared with the DTS period. The variations were statistically significant based on the time series models, and the influences of wind speed, rainfall on the sampling day and the day before sampling, and weekends were also evaluated. It was also possible to verify the contribution of the truckers’ strike to the PM10concentration in the two cities evaluated. In Limeira, truck traffic had a greater influence on the concentration of PM10, while in Campinas, the contribution of trucks was like that of light vehicles. Based on the variation of the PM10 concentration, the influence of changes in vehicle emission dynamics, one of the main sources of emission in the regions studied, was observed. The results indicate that restricting vehicular traffic had an immediate impact on improving air quality. Therefore, public investment in other types of transport and traffic control policies are suggested.

Designing Traffic Management Strategies for a Heterogeneous Traffic Network

This paper focuses on modeling and controlling a non-signalized heterogeneous traffic network consisting of Human-Driven Vehicles (HDVs) and Autonomous Vehicles (AVs) at a macroscopic level. To describe the traffic network's behavior, we introduce an extended heterogeneous METANET model wherein each vehicle class's density and velocity dynamics are described. To develop traffic control policies, we propose a two-level control structure. At the higher level, a decentralized Extremum-Seeking (ES) control approach is designed to determine an optimal density of human and autonomous vehicles so that the average flow within the cell is maximized. At the lower level, a filtered feedback linearization control approach is designed to determine the velocity of autonomous and human-driven vehicles such that the density of each type of vehicle reaches the values set by the upper-level macroscopic controller. Numerical simulation demonstrates the effectiveness of the proposed approach in managing the traffic flow of a heterogeneous system.

A topological approach for identifying pricing controller locations to ensure controllability of transportation networks

To use efficiently the infrastructure of transportation networks, control strategies have been developed with the aim to reduce negative externalities, such as congestion and pollutant emissions. Previous works demonstrated that the maximum performance achievable by traffic control policies depends on the number and location of controllers employed, which implies the need to determine a set of controllers capable of fully controlling the underlying transportation network. Various approaches have been explored in the literature to locate controllers on networks, however a gap remains in terms of scalability as the methods proposed often exhibit heavy computational complexity. In this paper we aim to propose an approach capable of locating pricing controllers on transportation networks that is scalable, such that it can be applied on large instances, such as city-sized or regional networks. For this purpose, we propose a topology-based approach, adapted from the sensor location problem, as both problems share similar characteristics. We validate our proposed approach by analyzing the performance of controller sets produced on a wide range of artificially generated network ensembles. The analysis we provide reveals that the method proposed, while being easily applicable on large instances, is capable to locate an efficient controller set, and to redirect flows on the network so as to reduce the total time spent by road users. • Identify pricing controller locations on transportation networks. • Topology-based approach with a low computation complexity. • Scalable approach applicable on large networks such as city-sized networks.

The uneven geography of US air traffic delays: Quantifying the impact of connecting passengers on delay propagation

Sustained airport congestion periods translate into delays, especially in hub-and-spoke networks in which delay propagation is more evident.We examine the impact of connecting passenger arrival delays on network delay propagation by using passenger level data combined with flight delay data that allow us to analyse the correlation between delayed incoming flights and departure delays at the 21 U.S. airports with most delays, in July 2018.Results show that correlation between daily arrival delays and daily carrier induced departure delays are statistically significant only for flights carrying high proportions of connecting passengers. Correlation values are also higher for short-to-moderate arrival delays. In addition, a Neural Network model was trained for six major airports to build a delay prediction model and map the potential delay propagation. The results of the propagation scenarios suggest that the presence of a unique dominant carrier at an airport translates into a stronger correlation between arrival and carrier delays than that at airports where different carriers compete for connecting passengers. Furthermore, airline hubs located near the areas of the network with more traffic density, independently of the hub's volume of traffic, are more likely to propagate the delay than hubs located in the periphery. The results of this study can be relevant for airline, airport, and traffic control policies aimed at mitigating airport and network congestion.

Impacts of COVID-19 Traffic Control Policy on Population Flows in Changsha

To investigate the impact of COVID-19 traffic control policies on population flow in Changsha, this paper divided the prevention and traffic control policies into different stages corresponding to the real-time epidemic situation in Changsha. Based on Baidu migration big data, the difference- in- difference model was used to identify different stages of traffic prevention and control policies and quantify the effect of prevention. With the traffic control policy implemented during COVID-19, the average inflow intensity of Changsha City decreased by 83.68%, the average outflow intensity decreased by 69.24% and the internal travel intensity respectively, decreased by 59.74%. After the end of the traffic control policies, the population flow intensity of Changsha City gradually rebounded, and the urban internal travel intensity basically recovered to the same level as in 2019. The results indicated the effectiveness of the traffic control policies on the limitation of population flow and epidemic spread. The results also provide reference for making effective prevention and control policies for the normalized COVID-19 epidemic situations. Copyright © 2021 by Science Press.

Effects of inhomogeneous ground-level pollutant sources under different wind directions

Effects of source inhomogeneity on pollutant dispersion from a cubic building array are investigated as a function of the external wind direction. Using building-resolving large-eddy simulation, it is found that the results depend strongly on the source location and source uniformity inside a near-field region defined by a radius of homogenisation (RAD) based on the spatial autocorrelation of the pollutant concentration. The sensitivity of the RAD to the source location changes abruptly around 30° and is greatly reduced for wind angles between 30 and 45°, in agreement with velocity statistics and the mean horizontal streamlines. The optimal source allocation, which is a proxy for emissions from time-dependent traffic, also changes around 30°. This work clarifies the relationship between inhomogeneous velocity and pollutant statistics and may be applied to the formulation of traffic control policy.

A numerical study of local traffic volume and air quality within urban street canyons

Although pollutant sources are often assumed to be spatially uniform, traffic in real cities may vary significantly in space. Consequently the local air quality within a street may not be determined solely by the traffic volume of the street. Using building-resolving large-eddy simulation, the relationship between traffic volume and air quality is investigated in the context of two idealised problems: (i) the influence of pollutants emitted from a main road on the surrounding side streets and (ii) the pedestrianisation of a central thoroughfare. It is shown that the spatial variation of traffic volume is of crucial importance within a near-field region defined by a radius of homogenisation (RAD). Furthermore, the actual impact depends strongly on the wind direction. Hence the benefits of pedestrianisation may be limited: for example, after removing 100% of the traffic along a street in a central business district, the annual-averaged local concentration decreases by ~30% when the urban background is neglected. The impact may be significantly lower when the background concentration is considered. This work is relevant to the formulation of effective traffic control policy and the improved understanding of spatially inhomogeneous pollutant sources.

Multivariate time series modelling for urban air quality

We introduce a spatio-temporal model to represent development of atmospheric pollution in an urban area. An important element of this is that recorded measurements are often incomplete which undermines time-series approaches. We identify the multiple imputation by chained equation (MICE) method as effective to complete data sequences synthetically. Following on from this, we develop a vector autoregressive moving average (VARMA) model for the spatio-temporal development of atmospheric pollution in urban areas. This model was fitted to hourly measurements of four pollutants (NO, NO2, NOx and PM10) for the whole of the calendar year 2017 at 30 stations across London, completed by MICE as required. We show by cross-validation that the VARMA model is more effective than other formulations, including the Kriging method of spatial interpolation, and seasonal ARMA models for individual stations with either daily or weekly trends. The resulting model can be used for prediction of air quality in different periods and as the basis for assessment of policy interventions such as increasing vehicle emission standards, and traffic management and control policies such as low and ultra-low emission zones.

Effects of COVID-19 on Urban Population Flow in China.

The COVID-19 epidemic has become a Public Health Emergency of International Concern. Thus, this sudden health incident has brought great risk and pressure to the city with dense population flow. A deep understanding of the migration characteristics and laws of the urban population in China will play a very positive role in the prevention and control of the epidemic situation. Based on Baidu location-based service (LBS) big data, using complex networks method and geographic visualization tools, this paper explores the spatial structure evolution of population flow network (PFN) in 368 cities of China under different traffic control situations. Effective distance models and linear regression models were established to analyze how the population flow across cities affects the spread of the epidemic. Our findings show that: (1) the scope of population flow is closely related to the administrative level of the city and the traffic control policies in various cities which adjust with the epidemic situation; The PFN mainly presents the hierarchical structure dominated by the urban hierarchy and the regional isolation structure adjacent to the geographical location.(2) through the analysis network topology structure of PFN, it is found that only the first stage has a large clustering coefficient and a relatively short average path length, which conforms to the characteristics of small world network. The epidemic situation has a great impact on the network topology in other stages, and the network structure tends to be centralized. (3) The overall migration scale of the whole country decreased by 36.85% compared with the same period of last year’s lunar calendar, and a further reduction of 78.52% in the nationwide traffic control stage after the festival. (4) Finally, based on the comparison of the effective distance and the spatial distance from the Wuhan to other destination cities, it is demonstrated that there is a higher correlation between the effective distance and the epidemic spread both in Hubei province and the whole country.

Resilient multivariable perimeter control of urban road networks under cyberattacks

The Macroscopic Fundamental Diagram (MFD) concept has become a very valuable tool for modeling the aggregated dynamic of large-scale urban traffic networks. This advance in traffic modeling has fueled the development of many urban traffic control (UTC) policies based on the MFD concept. In particular, perimeter control policies with the aim of maintaining a large-scale urban traffic network operating at its maximum capacity by manipulating the transfer flow of vehicles between adjacent homogeneous regions. In addition to achieve a satisfactory performance level, it is desirable that the control system would maintain a reliable operation under unanticipated events such as cyberattacks. It is important to note that modern UTC systems extensively rely on information technology infrastructures making them vulnerable to cyberattacks. Due to the occurrence of real-world cyberattacks affecting controlled physical systems in different fields, the threat of cyberattacks is becoming a serious and increasing concern for the proper operation of UTC. This paper presents a resilient multivariable control framework for large-scale urban traffic networks subject to several types of cyberattacks such as deception attacks and denial of service (DoS) attacks. Additionally, a feedforward–feedback control scheme combining a time-optimal controller with the resilient multivariable controller is also proposed. The aim of this feedforward–feedback controller is to steer the accumulations to their desired values after a large deviation. Finally, the performance of the proposed resilient controller is demonstrated by means of some realistic simulations of urban traffic networks, including stochastic errors in the traffic demands and in the MFD model, in addition to cyberattacks.

Heuristic methods for minimal controller location set problem in transportation networks

To be used efficiently, transportation networks require control strategies to optimize flow distribution. However, the location and type of controllers employed in a transportation network directly impact the level of performance reachable by traffic control policies. In order to guarantee that the highest level of performance is reachable, fully exploiting the available network capacity and reducing negative externalities, we explore methods to identify relevant controller locations in transportation networks while minimizing the number of controllers used. Previous work provided an exact approach to identify controller locations on complex networks. However, said approach exhibits complications when applied to transportation networks containing bi-directional links. We therefore propose simple heuristic algorithms relying only on topological information to solve this problem, while also avoiding heavy computation, thus being able to determine a solution to the minimal controller location set problem on large networks. Based on the existing framework, we aim to provide an experimental setup, with diverse network sizes and configurations to analyze the performances of different heuristic methods, in order to develop an efficient algorithm.

Roadside Air Quality Forecasting in Shanghai with a Novel Sequence-to-Sequence Model.

The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident’s outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In order to take the periodicity of pollutants into empirical air quality forecasting models, this study evaluates the temporal variations of air pollutants and develops a novel sequence to sequence model with weekly periodicity to forecast air quality. Two-year observation data from Shanghai roadside air quality monitoring stations are employed to support analyzing and modeling. The results conclude that the fine particulate matter (PM2.5) and carbon monoxide (CO) concentrations show obvious daily and weekly variations, and the temporal patterns are nearly consistent with the periodicity of traffic flow in Shanghai. Compared with PM2.5, the CO concentrations are more affected by traffic variation. The proposed model outperforms the baseline model in terms of accuracy, and presents a higher linear consistency in PM2.5 prediction and lower errors in CO prediction. This study could assist environmental researchers to further improve the technologies for urban air quality forecasting, and serve as tools for supporting policymakers to implement related traffic management and emission control policies.

Have traffic restrictions improved air quality? A shock from COVID-19

With the outbreak of COVID-19 (Corona Virus Disease, 2019), China adopted traffic restrictions to reduce the spread of COVID-19. Using daily data before and after the outbreak of COVID-19, an exogenous shock, this paper analyzes the effects of private vehicle restriction policies on air pollution. We find that the private vehicle restriction policies reduce the degree of air pollution to a certain extent. However, their effect varies with other policies implemented in the same period and the economic development of the city itself. Through the analysis of different categories of restrictions, we find that restriction policy for local fuel vehicles and the restriction policy based on the last digit of license plate numbers have the best effect in reducing air pollution. Under the background of COVID-19 epidemic and the implementation of private vehicle restriction policies and other traffic control policies during this period, we have also obtained other enlightenment on air pollution control.

Mitigating the risk of hazardous materials transportation: A hierarchical approach

Hazardous Materials (hazmat), although dangerous, are an irreplaceable aspect of everyday life. This paper is presenting an integrated traffic control policy for hazmat transportation to alleviate the risks associated with hazmat carriers. The proposed policy is devised based on dual toll pricing (DTP) and network design (ND) policies, where a two-stage simulation-based optimization framework is proposed to enhance public safety in highways. This integrated policy is devised to concurrently restrict hazmat carriers from freeways in densely populated areas via the ND policy, and control regular as well as hazmat traffic in tollways via the DTP policy. In the optimization module, mixed integer linear programming is employed to find the optimum integrated policy, where a linear-relaxation technique based on the Karush-Kuhn-Tucker (KKT) optimality conditions is applied to reduce the mathematical model. The simulation module of the proposed framework uses agent-based simulation (ABS) modeling to evaluate the suggested policies realistically. The proposed framework has been demonstrated with real traffic data of San Antonio, Texas under AnyLogic® ABS platform. The experimental results reveal that the proposed framework is able to efficiently find the optimum integrated policy which in return, effectively reduces the risk of hazmat transportation in highways.

DeepGuard: Efficient Anomaly Detection in SDN With Fine-Grained Traffic Flow Monitoring

Software-Defined Networking (SDN) leverages the implementation of reliable, flexible and efficient network security mechanisms which make use of novel techniques such as artificial intelligence (AI) and machine learning (ML). In particular, these techniques - together with SDN - are the key enablers for the design of anomaly detection methods which are based on efficient traffic flow monitoring. In this paper, we tackle this problem by proposing an efficient anomaly detection framework, denoted as DeepGuard, which improves the detection performance of cyberattacks in SDN based networks by adopting a fine-grained traffic flow monitoring mechanism. Specifically, the proposed framework utilizes a deep reinforcement learning technique, i.e., Double Deep ${Q}$ -Network (DDQN), to learn traffic flow matching strategies maximizing the traffic flow granularity while proactively protecting the SDN data plane from being overloaded. Afterwards, by implementing the learned optimal traffic flow matching control policy, the most beneficial traffic information for anomaly detection is acquired at runtime—thereby improving the cyberattack detection performance. The performance of the proposed framework is validated by extensive experiments, and the results show that DeepGuard yields significant performance improvements compared to existing traffic flow matching mechanisms regarding the level of traffic flow granularity. In the case of distributed denial-of-service (DDoS) attacks, DeepGuard achieves a remarkable attack detection performance while effectively preventing forwarding performance degradation in the SDN data plane.

Investigating the Relationship between Controller Locations and Dynamic Traffic Control in Generic Transportation Networks

Traffic control policies aim at reducing the negative externalities that ever-growing demand is causing on transportation networks, such as congestion and pollutant emissions. To achieve these goals, strategies coordinating and aligning the effects of several individual traffic controllers have received increasing attention in research and development in the past decades. However, a considerable gap still exists between the desired and experienced performance of advanced dynamic traffic management systems, resulting in failure to completely prevent the increasing peak-hour congestion in main urban areas worldwide. In this work we contribute to assessing whether this gap might be tied to inefficient network design, rather than algorithmic prowess. Based upon our earlier work, we investigate whether a trend can be found between determining locations of controllers in a network following control theoretical insights, and try to confirm our earlier intuitions when dealing with dynamic traffic assignment, featuring accurate propagation and spillback dynamics. To achieve these goals, we extend an existing synthetic network generation tool to allow us to test this hypothesis on real-life-like road networks, and extend our previously developed algorithms for the controller location problems allowing for sufficient generalization. Test results are presented on a simpler deterministic scenario and on 240 randomly generated networks, showcasing that placing controllers following controllability-based principles is advantageous from the perspective of model-based dynamic traffic management applications.

Double Deep Q-Network with a Dual-Agent for Traffic Signal Control

Adaptive traffic signal control (ATSC) based on deep reinforcement learning (DRL) has shown promising prospects to reduce traffic congestion. Most existing methods keeping traffic signal phases fixed adopt two agent actions to match a four-phase suffering unstable performance and undesirable operation in a four-phase signalized intersection. In this paper, a Double Deep Q-Network (DDQN) with a dual-agent algorithm is proposed to obtain a stable traffic signal control policy. Specifically, two agents are denoted by two different states and shift the control of green lights to make the phase sequence fixed and control process stable. State representations and reward functions are presented by improving the observability and reducing the leaning difficulty of two agents. To enhance the feasibility and reliability of two agents in the traffic control of the four-phase signalized intersection, a network structure incorporating DDQN is proposed to map states to rewards. Experiments under Simulation of Urban Mobility (SUMO) are carried out, and results show that the proposed traffic signal control algorithm is effective in improving traffic capacity.

Vehicle emission control on road with temporal traffic information using deep reinforcement learning

The increased vehicle usage significantly aggravate the urban air pollution, which have great impact on the public health. Therefore, it is necessary to make proper traffic control policies and reduce traffic emissions. However, it is difficult to establish control strategies based on modeling methods, and carry out online control based on historical traffic information for the complex time-varying characteristics of emissions. In this paper, we present a deep reinforcement learning emission control strategy, which automatically learns the optimal traffic flow and speed limits to reduce traffic emission on the target road segment based on the temporal traffic information. The proposed approach is evaluated on real world vehicle emission data in Hefei. And the results demonstrate the effectiveness of the proposed approach against baseline methods.

A Game-Based Adaptive Traffic Signal Control Policy Using the Vehicle to Infrastructure (V2I)

In this paper, we propose a game-based policy for the signal control of an isolated intersection together with the rerouting of the vehicles in this intersection using V2I technology. The game starts at the beginning of each signal cycle on the condition that there are vehicles delayed in the intersection at the end of current cycle. Then the delayed vehicles negotiate with the signal controller to decide the signal setting for next cycle while rerouting their next movements. During the game, each delayed vehicle selects one route from their own acceptable route space to reduce the intersection delay time, while the signal controller adjusts the signal setting to meet the traffic demand of the delayed vehicles as well as maximize the throughput. The game can be terminated with end conditions in finite rounds. The vehicles coming after the end of game reroute in a similar way with the delayed vehicles but based on the game results. Numerical experiments on a calibrated network of Caohejing District in Shanghai indicate that our proposed method can effectively mitigate road congestions and improve the network capacity, especially at high traffic loads.

Traffic control which maximises network throughput: Some simple examples

This paper seeks traffic signal control policies which maximise long-run network throughput when demand is within network capacity and also when demand is beyond network capacity. The paper considers smooth versions of two well-known responsive traffic signal control policies: the P0 policy (see Smith, 1979b, 1980 or Smith et al., 2015) and the maxpressure or MP policy (see Varaiya, 2013a). The paper gives two simple example networks where both of these smooth responsive traffic control policies give rise to decreasing throughput for increasing demands; when these demands are beyond the network capacity. The paper then suggests a simple modification of the two responsive traffic control policies and shows that the two modified policies do maximise the long run throughput of the two signal-controlled networks; both when a steady input demand D is within network capacity and also when a steady demand exceeds network capacity. The modified version of P0 is as follows: for a certain positive number K,forD⩽Kuse responsive policyP0,and forD>Kuse the green-times generated byP0whenD=K.Thus in this modification the green-time proportions generated by responsive P0 are frozen as demand passes through K; and remain fixed for all demands D which exceed K. The paper shows that the above modification when applied to a continuous version of MP also maximises throughput in both networks for all values of the steady demand D. Finally the paper considers a signal controlled network with route choice. It is shown that if routes are chosen by travellers in their own best interests then the continuous version of MP does not always maximise throughput, even if the constant input demand D is within the network capacity, but P0 does always maximise throughput, even when the constant input demand D is beyond the network capacity.