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Large and expanding cities suffer from a traffic congestion problem that harms the environment, travelers, and the economy. This paper aims to predict short term traffic congestion on a road section of expressway in Delhi city. For this purpose, we first propose a traffic congestion index based on traffic speed and flow. Clustering techniques and the Greenshield's model were used for the derivation of the congestion index. Using this congestion index, congested time intervals of each day and each location of a weekday were identified. This study also introduces a feature series long short-term memory neural network (FSLSTMNN), which links a long short-term memory (LSTM) layer to each feature. It is trained using the many heterogeneous traffic features data collected in Delhi city for the next five minutes of traffic flow and speed prediction. FSLSTMNN achieved the good capability to learn feature series data. We also trained several traditional and deep-learning models using the same traffic data. The FSLSTMNN reduces mean absolute error 12.90% and 17.13%, respectively, in speed and traffic flow prediction compared to the second good-performance long short-term memory neural network (LSTMNN). Finally, traffic congestion is predicted classwise (light, medium, and congested) using the developed congestion index and traffic speed and flow predicted by the FSLSTMNN. Predicted results are consistent with the measured field data. Study results confirm that the developed congestion index and FSLSTMNN can be used successfully to predict traffic congestion. [ABSTRACT FROM AUTHOR]

The exponential growth of vehicles has led to increased traffic congestion and pollutants emission in cities. Intelligent transportation systems (ITS) shall be built by integrating Artificial Intelligence(AI). Planning optimal route with minimum travel time is a great challenge for the traffic management system. A hybrid model integrating long-short-term memory(LSTM) deep-learning model and Dijkstra's algorithm for recommending the fastest route is proposed in this work. The proposed hybrid approach reduces the travel time to a greater extent. Vehicle speed greatly influences eco-driving factors such as fuel consumption and exhaust emission. Thus, for each road links, the fuel consumption and emission of pollutants such as carbon monoxide(CO), nitrogen oxides(NOx), and hydrocarbon(HC) are estimated for different type of vehicles. The results showed the proposed approach decreases fuel consumption, CO emission, NOx emission, and HC emission up to 54.5%, 61.5%, 64.2%, and 81.9%, respectively, compared to the traditional shortest path. [ABSTRACT FROM AUTHOR]

In Asian megacities undergoing rapid urbanization such as Bangkok, heavy rainfall exacerbates traffic congestion owing to inadequate drainage systems. This study statistically analyzed the extent to which rainfall affects urban traffic speed and how this impact varies depending on regional environmental factors and traffic demand trends, utilizing probe vehicles and rainfall data from 2018 to 2020 in Bangkok. The results clearly indicate that both the intensity of rainfall during driving and previous cumulative rainfall significantly reduce traffic speed. This impact is particularly pronounced during morning and evening rush hours, and in areas with a high proportion of narrow roads or in low-lying areas. On the other hand, areas with rich urban green space, which naturally absorb and retain water, tend to mitigate the speed reduction due to rainfall. This study highlights the fact that the impact of rainfall on traffic varies with time and location, suggesting that the exacerbation of rain-induced congestion can be more effectively mitigated by coordinated improvements in drainage facilities, traffic management and land use. [ABSTRACT FROM AUTHOR]

Detecting road accident impacts as promptly as possible is essential for intelligent traffic management systems. This paper presents a sequential two-stage framework for predicting the most congested traffic level that appears after an accident and the recovery time required for returning to the level of service that existed at the accident report time. As fewer accident characteristics are available at the report time, stage one models rely on real-time traffic and weather variables. With the arrival of the responders at the accident scene, more information is gained; therefore, the second stage model is activated, which updates the remaining accident duration time. We used eXtreme Gradient Boosting (XGBoost), a machine learning algorithm, and Shapley Additive exPlanations (SHAP) for making predictions and interpreting results, respectively. The results show that our framework predicts traffic levels with overall accuracies of around 80%, and duration models have high forecast accuracy with mean absolute percentage errors ranging between 7.26% and 21.59%. Overall, in the absence of accident information, SHAP values identified that weather factors, the traffic speed difference before and after an accident, traffic volume, and the percentage of heavy vehicles before the accident are the most important variables. However, accident variables, including the occurrence of injury or fatal accidents, rear-end collisions, and the number of involved vehicles, are among the most important variables in the second stage of the framework. The findings have practical implications for real-time traffic management of accident events. Road operators could manage post-accident traffic conditions more effectively, and road users could be alerted to take another route or manage their trip. [ABSTRACT FROM AUTHOR]

The key to solving traffic congestion is the accurate traffic speed forecasting. However, this is difficult owing to the intricate spatial-temporal correlation of traffic networks. Most existing studies either ignore the correlations among distant sensors, or ignore the time-varying spatial features, resulting in the inability to extract accurate and reliable spatial-temporal features. To overcome these shortcomings, this study proposes a new deep learning framework named spatial-temporal gated graph convolutional network for long-term traffic speed forecasting. Firstly, a new spatial graph generation method is proposed, which uses the adjacency matrix to generate a global spatial graph with more comprehensive spatial features. Then, a new spatial-temporal gated recurrent unit is proposed to extract the comprehensive spatial-temporal features from traffic data by embedding a new graph convolution operation into gated recurrent unit. Finally, a new self-attention block is proposed to extract global features from the traffic data. The evaluation on two real-world traffic speed datasets demonstrates the proposed model can accurately forecast the long-term traffic speed, and outperforms the baseline models in most evaluation metrics. [ABSTRACT FROM AUTHOR]

This paper presents a bottom-up methodology to estimate multi-pollutant hourly gridded on-road traffic emission using advanced traffic flow and speed data for Delhi. We have used the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. At first, the traffic volume and congestion (travel time delay) relation is applied to model the 24 h traffic speed and flow for all the major road links of Delhi. The modelled traffic flow and speed shows an anti-correlation behaviour having peak traffic and emissions in morning–evening rush hours. We estimated an annual emission of 1.82 Gg for PM (particulate matter), 0.94 Gg for BC (black carbon), 0.75 Gg for OM (organic matter), 221 Gg for CO (carbon monoxide), 56 Gg for NO x (oxides of nitrogen), 64 Gg for VOC (volatile organic compound), 0.28 Gg for NH 3 (ammonia), 0.26 Gg for N 2 O (nitrous oxide) and 11.38 Gg for CH 4 (methane) for 2018 with an uncertainty of 60 %–68 %. The hourly emission variation shows bimodal peaks corresponding to morning and evening rush hours and congestion. The minimum emission rates are estimated in the early morning hours whereas the maximum emissions occurred during the evening hours. Inner Delhi is found to have higher emission flux because of higher road density and relatively lower average speed. Petrol vehicles dominate emission share (>50 %) across all pollutants except PM, BC and NO x , and within them the 2W (two-wheeler motorcycles) are the major contributors. Diesel-fuelled vehicles contribute most of the PM emission. Diesel and CNG (compressed natural gas) vehicles have a substantial contribution in NO x emission. This study provides very detailed spatiotemporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic-related pollution. Moreover, the developed methodology is a step forward in developing real-time emission with the growing availability of real-time traffic data. The complete dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.6553770 (Singh et al., 2022). [ABSTRACT FROM AUTHOR]

A non‐recurrent road traffic anomaly refers to a sudden change in the capacity of a road segment, which deviates from the general traffic patterns, and is usually caused by abnormal traffic events such as traffic accidents and unexpected road maintenance. Timely and accurate detection of non‐recurrent road traffic anomalies facilitates immediate handling to reduce the wastage of resources and the risk of secondary accidents. Compared with other types of traffic anomaly detection methods, prediction algorithms are suitable for detecting non‐recurrent anomalies for their potential ability to distinguish non‐recurrent anomalies from recurrent congestion (e.g., rush hours). A typical prediction algorithm detects an anomaly when the difference between the predicted traffic parameter (i.e., speed) and the actual one is greater than a threshold. However, the subjective setting of thresholds in many prediction algorithms greatly affects the detection performance. This study proposes a novel framework for non‐recurrent road traffic anomaly detection (NRRTAD). The temporal graph convolutional network (T‐GCN) model acts as the predictor to learn the general traffic patterns of road segments by capturing both the topological effects and temporal patterns of traffic flows, and to predict the "normal" traffic speeds. The hierarchical time memory detector (HTM‐detector) algorithm acts as the detector to evaluate the differences between the predicted speeds and the actual speeds to detect non‐recurrent anomalies without setting a threshold. In the experiments with traffic datasets of Beijing, NRRTAD outperformed other methods, not only achieving the highest detection rates but also exhibiting higher resilience to noise. The main advantages of NRRTAD are as follows: (1) adopting the T‐GCN with a weighted graph to integrate differentiated connection strengths of multiple types of topological relations between road segments as well as temporal traffic patterns improves the prediction performance; and (2) utilizing a flexible mechanism in the HTM‐detector to adapt to changing stream data not only avoids subjective setting of a threshold, but also improves the accuracy and robustness of anomaly detection. [ABSTRACT FROM AUTHOR]

• High resolution traffic speed temporospatial distribution and propagation dynamics prediction • Deep learning framework integrating traffic flow dynamics and topological dependency • Dynamic programming capturing traffic flow interdependency The information of network-wide future traffic speed distribution and its propagation is beneficial to develop proactive traffic congestion management strategies. However, predicting network-wide traffic speed propagation is non-trivial. This study develops a t raffic flow d ependency and d ynamics based deep learning aided approach (TD2-DL), which predict network-wide high resolution traffic speed propagation by explicitly integrating temporal-spatial flow dependency, traffic flow dynamics with deep learning method techniques. Specifically, we first develop a graph theory-based method to identify the local temporal-spatial traffic dependency of each road among neighboring roads adaptive to the prediction horizon and traffic delay. Then, traffic speed propagation on every road is mathematically described by v -CTM based on traffic initial and boundary conditions. Next, the long short-term memory (LSTM) model is employed to predict boundary conditions factoring the traffic temporal-spatial dependency and historical data predicted by v -CTM. In this way, we well couple the physical models (traffic dependency and v -CTM) with the deep learning approach, and further make them coevolution under this framework. Last, an EKF is used to assimilate predicted traffic speed predicted by v -CTM coupled with the LSTMs and the field traffic data; an FNN is introduced to impute missing and corrupted data for improving the traffic speed prediction accuracy. The numerical experiments indicated that the TD2-DL predicted the network-wide traffic speed propagation in 30 minutes with accuracy varying from 85%-98%. It outperformed the tested models recently developed in literature. The ablation experimental results confirmed the significance of factoring traffic dependency and integrating data imputation and assimilation techniques for improving the prediction accuracy. [ABSTRACT FROM AUTHOR]

• Quantifies the freeway merging conflict risk for an autonomous vehicle (AV). • Develops an AV merging decision strategy based on conflict risk assessment. • Shows safety benefits of the AV merging decision strategy. Introduction: The safe freeway merging operation for fully Autonomous Vehicles (AVs) in mixed traffic (i.e., the presence of AVs and non-AVs in a traffic stream) is a challenging task. Under a mixed traffic environment, an AV merging operation could significantly increase conflict risks and reduce operational efficiency. Method: This study quantifies the freeway merging conflict risk and develops a freeway merging decision strategy based on conflict risk assessment for an AV attempting to merge to a traffic stream with non-AVs on the freeway. The performance of the risk-based merging decision strategy is evaluated in uncongested, near-congested, and congested traffic conditions. Results: The analyses show that the risk-based merging strategy causes less abrupt deceleration of an AV's immediate upstream vehicle in the target lane on the freeway compared to the based models (i.e., two models based on gap acceptance concepts and a safe gap model based on a surrogate measure, 'Time-to-Collision (TTC)'). The risk-based merging strategy meets the minimum safe gap between an AV intending to merge and its immediate downstream vehicle in the target lane. The risk-based merging strategy produces lower conflict risk in terms of 'Time Exposed Time-to-Collision (TET)' and 'Time Integrated Time-to-Collision (TIT)' compared to the base models. Moreover, the risk-based merging strategy has a lower impact on the average speed of traffic in the target lane compared to the base models considered in this study. Conclusions: The risk-based merging strategy shows higher safety benefits for an AV's merging operation compared to base models. Practical Applications: The findings of this research would help design AV controllers for improving the safety of an AV merging operation in a mixed traffic stream. [ABSTRACT FROM AUTHOR]

In order to solve the problem of traffic congestion in a certain area, this paper develops a set of traffic optimization decision system. For analyzing the actual traffic conditions and calculating the traffic volume, density and traffic speed, a traffic prediction model is established and updated iteratively to modify the prediction model parameters. Based on this model, the congestion degree is estimated at the current road section, thus, an intelligent decision-making and the coordinated optimization methods are proposed. Moreover, this paper implements some application experiments on the isometric road of a three-intersection and obtains better prediction results of traffic density and traffic speed on the three-section highway. At the same time, compared with other existing prediction methods, the prediction model presented in this paper not only has higher accuracy, shorter prediction time and stronger anti-interference ability, but also has better effect on vehicle diversion. In addition, it also greatly relieves the traffic pressure on the road, maximizes the complementary advantages between intersections, and balances the good cooperation between each intersection. [ABSTRACT FROM AUTHOR]

This study analyzed traffic congestion scenarios in underground roads diverging and merging sections and underground sections that run parallel to ground roads using a microscopic traffic simulation model. The appropriate ratio of the ramp inflow or outflow of traffic volume to the entry traffic volume at the starting section of the underground road network was analyzed. Furthermore, the effect of traffic operation such as adequate throughput, average traffic speed, occupancy, and volume versus capacity (V/C) ratios before the occurrence of traffic congestion in all three road networks was properly produced. To maintain a minimum level of service of all underground sections at the D–E level, the underground inflow traffic volume should be equal to approximately 70% of the 4000 pcph inflow traffic volume from an open road to the underground. The results of this study are a useful reference for establishing traffic control application standards to minimize underground traffic congestion. They also provide useful insights for underground road design. [ABSTRACT FROM AUTHOR]

Ramp merging is considered to be one of the major causes of traffic accidents and congestion due to its inherent chaotic nature. With the development of the connected and automated vehicle (CAV) technology, CAVs can conduct cooperative merging using communication, and can also handle complicated situations even with legacy vehicles. In this article, a game theory-based ramp merging strategy has been developed for the optimal merging coordination of CAVs in mixed traffic, which can determine the dynamic merging sequence and corresponding longitudinal/lateral control. This strategy improves the safety and efficiency of the merging process by ensuring a safe intervehicle distance and harmonizing the speeds of CAVs in the traffic stream. To verify the proposed strategy, mixed traffic simulation runs under different penetration rates and different congestion levels have been carried out on an innovative Unity-SUMO integrated platform, which connects a game engine-based driving simulator with a state-of-the-art microscopic traffic simulator. The results show that the average speed of traffic flow can be increased up to 210%, while the fuel consumption can be reduced up to 53.9%. In addition, the driving volatility can be stabilized to a level with 0% extreme values. [ABSTRACT FROM AUTHOR]

The current non-connected autonomous vehicle scheme for speed changing along the road has limitations. Other alternatives require a central control or a complex communication system between the vehicles. We suggest a low cost and simple method for controlling the traffic of a line of autonomous vehicles using predetermined speed profiles imposed upon the vehicles along the road. We introduce a novel method to control autonomous vehicles traffic. Particularly, we investigate cases where specific velocities are required at some points along the road. This is done by comparing different velocity profiles for acceleration, deceleration or a combination of both. As traffic flow and speed limit may change due to upcoming road conditions, it is imperative to control vehicle line traffic, such that phantom jams will be prevented while preserving maximal traffic flow at minimum energy. We provide a comparison of these profiles for acceleration, deceleration, and for the combined case in terms of traffic flow, energy consumption, travel duration and the resulting jam characteristics. Following the comparison, we conclude that the best strategy would be to use linear speed profiles both to accelerate and to decelerate. Lastly, we suggest a tool to compare speed profiles for deceleration cases where the formation of an upstream propagated traffic congestion is inevitable. [ABSTRACT FROM AUTHOR]

The increasing urbanization in the last decades results in significant growth in urban traffic congestion around the world. This leads to enormous time people spent on roads and thus significant money waste and air pollution. Here, we present a novel methodology for identification, cost evaluation, and thus, prioritization of congestion origins, i.e., their bottlenecks. The presented work is based on network analysis of the entire road network from a global point of view. We identify and prioritize traffic bottlenecks based on big data of traffic speed retrieved in near-real-time. Our approach highlights the bottlenecks that have the most significant effect on the global urban traffic flow. We follow the evolution of every traffic congestion in the entire urban network and rank all the congestions, based on the cost they cause (in Vehicle Hours units). We show that the macro-stability that represents the seeming regularity of traffic load both in time and space, overshadows the existence of meso-dynamics, where the bottlenecks that create these congestions usually do not reappear on different days or hours. Thus, our method enables to identify in near-real-time both recurrent and nonrecurrent congestions and their sources. [ABSTRACT FROM AUTHOR]

Buses are the mainstay of public transport systems in many cities but are typically subject to significant delays due to traffic congestion. We examine the welfare effects of providing dedicated bus lanes in the city of Rome, Italy. We demonstrate that a dedicated bus lane reduces bus travel time by about 18 percent. Our welfare analysis focuses on the situation where mixed road lanes are turned into dedicated bus lanes. We find that bus lanes are undersupplied, despite the additional time costs due to reducing road capacity available to cars. [ABSTRACT FROM AUTHOR]

This paper presents a bottom-up methodology to estimate multi-pollutant hourly gridded on-road traffic emission using advanced traffic flow and speed data for Delhi. We have used the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. At first the traffic volume and congestion (travel time delay) relation is applied to model the 24-hour traffic speed and flow for all the major road links of Delhi. The modelled traffic flow and speed shows an anti-correlation behaviour having peak traffic and emissions in morning-evening rush hours. We estimated an annual emission of 1.82 Gg for PME (Exhaust particulate matter), 0.94 Gg for BC (Black Carbon), 0.75 Gg for OM (Organic matter), 221 Gg for CO (Carbon monoxide), 56 Gg for NOx (Oxide of Nitrogen), 64 Gg for VOC (Volatile Organic Carbon), 0.28 Gg for NH3 (Ammonia), 0.26 Gg for N2O (Nitrous Oxide) and 11.38 Gg for CH4 (Methane) for 2018. The hourly emission variation shows bimodal peaks corresponding to morning and evening rush hours and congestion. The minimum emission rates are estimated in the early morning hours whereas the maximum emissions occurred during the evening hours. Inner Delhi is found to have higher emission flux because of higher road density and relatively lower average speed. Petrol vehicles dominate emission share (> 50 %) across all pollutants except PME, BC and NOx, and within them the 2W (Two-wheeler motorcycles) are the major contributors. Diesel fuelled vehicles contribute most of the PME emission. Diesel and CNG vehicles have a substantial contribution in NOx emission. This study provides very detailed spatio-temporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic related pollution. Moreover, the developed methodology is a step forward in developing real-time emission with the growing availability of real-time traffic data. The complete dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.6553770 (Singh et al., 2022). [ABSTRACT FROM AUTHOR]

The accuracy of short-term traffic flow prediction is one of the important issues in the construction of smart cities, and it is an effective way to solve the problem of traffic congestion. Most previous studies could not effectively mine the potential relationship between the temporal and spatial dimensions of traffic data flow. Due to the large variability in the traffic flow data of road conditions, we analyzed it with "dynamic", using a dynamic-aware graph neural network model for the hidden relationships between space-time in the deep learning segment. In this paper, we propose a dynamic perceptual graph neural network model for the temporal and spatial hidden relationships of deep learning segments. This model mixes temporal features and spatial features with graphs and expresses them. The temporal features and spatial features are connected to each other to learn potential relationships, so as to more accurately predict the traffic speed in the future time period, we performed experiments on real data sets and compared with some baseline models. The experiments show that the method proposed in this paper has certain advantages. [ABSTRACT FROM AUTHOR]

The economic development in China has brought about the urban traffic problems such as traffic congestion, long traffic waiting time, and inappropriate vehicle transfer. Therefore, under the technical background of China's Internet of Things platform, the fusion technology based on big data is studied and an algorithm for Chinese urban intelligent traffic safety scheduling is designed. In this paper, first of all, the urban traffic safety big data is clustered. Secondly, the gray distribution model of the big data is established by extracting the association rule features. Thirdly, the elements in Chinese urban traffic safety big data are fused, including the text, location, picture, audio, and video. On the condition of meeting highly time-sensitive needs of urban traffic intelligence, the video information after data fusion is applied to detect traffic flow parameters, so that an evaluation strategy for urban traffic safety state under the urban traffic speed dispersion is established. According to the fuzzy value of urban traffic drivers' satisfaction with the waiting time, the effect of traffic dispatching is measured, the convergence formula of urban traffic in the morning and evening peaks is constructed, and the optimal solution of the objective function of dispatching strategy is calculated by the particle swarm optimization algorithm. In this way, more efficient urban traffic safety scheduling in China is realized. As can be learned from the experimental results, the proposed algorithm can reasonably judge the urban traffic safety situation and reduce the time of waiting for urban vehicles with reasonable data fusion results, so it is proved to improve the urban traffic safety. [ABSTRACT FROM AUTHOR]

This paper evaluated the congestion reduction benefits over a 6-year period from the implementation of an adaptive traffic control system (ATCS) in a congested area of Manhattan, New York, bounded by 2nd and 6th Avenues and 42nd and 57th Streets—known as the Midtown-in-Motion (MIM) area. A methodology using taxi Global Positioning System (GPS) sensors was used to measure traffic speed. Traffic speeds were calculated for May weekdays before the ATCS was installed (May 2011) and for each subsequent year (May 2012 to May 2016). Within 1 to 2 years after the implementation of the ATCS, the area’s traffic speed increased on avenues and cross streets. This gain, however, could not be sustained in subsequent years because of intervening changes in key factors impacting traffic congestion. These factors included reduction in roadway space/capacity for motor vehicles, lack of effective traffic enforcement to maintain/protect available roadway capacity, increased vehicle miles traveled (VMT) from transportation network company vehicles, and increasing volume of bicycle trips sharing street space with vehicles/pedestrians. This paper’s two key findings are, first, traffic speed gains initially seen in the MIM area after 1 year of ATCS implementation could not be sustained because intervening external factors reduced capacity and increased VMT. However, during the same analysis period, the rest of the Midtown Core area (without ATCS deployment) experienced a greater speed loss than the MIM area, indicating the effectiveness of ATCS deployment in minimizing losses in traffic speed. Second, to protect street network capacity and to minimize VMT growth, midtown Manhattan requires adopting proactive, collaborative, and coordinated strategies by the three key agencies involved with traffic management in New York City (NYC): the NYC Department of Transportation (traffic control system technology), the NYC Police Department (traffic enforcement), and the Mayor’s office (traffic demand mitigation policies). [ABSTRACT FROM AUTHOR]

The superblock has become a typical land use in China and many growing Asian cities. Superblock access points generate traffic congestion and many conflicts among all road users. Driveway design is a critical process and has a major impact on traffic conditions around superblocks. There are various guidelines for the two key factors for driveway design, driveway width and curb radius, but they provide reference values corresponding to traffic volume and speed; these are not sufficient for managing the complex traffic environment around superblocks. To improve driveway design, we develop detailed access point design models that account for conflicts between turning and through motorized vehicles, conflicts between motorized and nonmotorized traffic, speed differential larger than 10 km/h, and lane encroachments of entering and exiting vehicles. The crash risk models evaluate and optimize the combination of driveway width and curb radius with respect to three traffic safety indexes: traffic conflict, lane encroachment, and speed differential. A case study evaluation shows that the updated driveway design models produce a lower crash risk; seven of the ten driveways improved by 16.14% or more. The updated driveway design for superblocks would be beneficial for analyzing, permitting, and managing traffic operations at superblocks and oversized development with many and complex driveways. [ABSTRACT FROM AUTHOR]

Transportation agencies are implementing traffic management strategies to improve mobility and safety on freeways. Ramp metering is a traffic management strategy deployed to mitigate congestion on freeways by using traffic signals installed at on-ramps to control and regulate vehicle entry onto the freeway mainline. Estimating the mobility benefits of ramp metering is critical not only to determine the strategy's effectiveness but also to inform the decision-making process regarding its deployment. The before-and-after approach and ramp metering shutdown experiments are conventional methods for estimating the benefits of ramp metering. These methods could overestimate or underestimate the benefits. This study aimed to estimate the expected mobility benefits of ramp metering by leveraging ramp metering downtime due to system breakdowns. Buffer index (BI), a travel time reliability measure, was selected as the performance measure. The study was based on data collected from 2016 to 2018 on a corridor with ramp metering signals (RMSs) along I-95 in Miami- Dade County, Florida. Penalized regression methods were used to identify factors that could predict the buffer indices of the freeway segment with RMSs. Factors evaluated include ramp metering operations (on/off), freeway traffic congestion levels, freeway mainline traffic speed, ramp traffic volume, and density of on-ramps and off-ramps. Results showed a 23% reduction in BIs during moderate congestion and a 28% reduction in BIs during severe congestion. Transportation agencies could use the results when evaluating RMSs operational performance and comparing their mobility impact with other alternatives. [ABSTRACT FROM AUTHOR]

We estimate economic impacts from decaying water infrastructure in the United States. Using water main breaks in Washington, DC, and a yearlong panel of hourly traffic speeds, we estimate causal effects of water main failures on traffic congestion. We use k‐means clustering to create clusters of streets that are similar to each other: treated observations are compared to other units in the cluster. We identify heterogeneous treatment effects algorithmically while retaining straightforward standard error calculations. We find strong evidence of heterogeneous treatment effects across clusters but small welfare impacts of water main breaks on traffic patterns overall. [ABSTRACT FROM AUTHOR]

Road traffic is an important factor restricting economic development. With the rapid growth of the number of motor vehicles, traffic accidents, traffic congestion and traffic pollution have become the persistent diseases of contemporary society, and become more and more serious. The existing traffic accident analysis is difficult to comprehensively and objectively reflect the process of traffic accidents. In order to improve the effectiveness of traffic accident prevention and measures, it is particularly important to carry out road traffic accident analysis. The paper proposes a new method for modeling the relationship between vehicle speed dispersion and traffic accidents in mixed traffic flow. Simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Traffic congestion in the adjacent region between the highway and urban expressway is becoming more and more serious. This paper proposes a traffic speed forecast method based on the Macroscopic Fundamental Diagram (MFD) and Gated Recurrent Unit (GRU) model to provide the necessary traffic guidance information for travelers in this region. Firstly, considering that the road traffic speed is affected by the macroscopic traffic state, the adjacent region between the highway and expressway is divided into subareas based on the MFD. Secondly, the spatial-temporal correlation coefficient is proposed to measure the correlation between subareas. Then, the matrix of regional traffic speed data is constructed. Thirdly, the matrix is input into the GRU prediction model to get the predicted traffic speed. The proposed algorithm's prediction performance is verified based on the GPS data collected from the adjacent region between Beijing Highways and Expressway. [ABSTRACT FROM AUTHOR]

Traffic speed prediction is an emerging paradigm for achieving a better transportation system in smart cities and improving the heavy traffic management in the intelligent transportation system (ITS). The accurate traffic speed prediction is affected by many contextual factors such as abnormal traffic conditions, traffic incidents, lane closures due to construction or events, and traffic congestion. To overcome these problems, we propose a new method named fuzzy optimized long short‐term memory (FOLSTM) neural network for long‐term traffic speed prediction. FOLSTM technique is a hybrid method composed of computational intelligence (CI), machine learning (ML), and metaheuristic techniques, capable of predicting the speed for macroscopic traffic key parameters. First, the proposed hybrid unsupervised learning method, agglomerated hierarchical K‐means (AHK) clustering, divides the input samples into a group of clusters. Second, based on parameters the Gaussian bell‐shaped fuzzy membership function calculates the degree of membership (high, low, and medium) for each cluster using Takagi‐Sugeno fuzzy rules. Finally, the whale optimization algorithm (WOA) is used in LSTM to optimize the parameters obtained by fuzzy rules and calculate the optimal weight value. FOLSTM evaluates the accurate traffic speed from the abnormal traffic data to overcome the nonlinear characteristics. Experimental results demonstrated that our proposed method outperforms the state‐of‐the‐art approaches in terms of metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). [ABSTRACT FROM AUTHOR]

The HOV carpooling lane offers a feasible approach to alleviate traffic congestion. The connected vehicle environment is able to provide accurate traffic data, which could optimize the design of HOV carpooling schemes. In this paper, significant tidal traffic flow phenomenon with severe traffic congestion was identified on North Beijing road (bidirectional four-lane) and South Huaihai road (bidirectional six-lane) in Huai'an, Jiangsu Province. The historical traffic data of the road segments were collected through the connected vehicle environment facilities. The purpose of this study is to investigate the effect of adopting two HOV schemes (regular HOV scheme and reversible HOV carpooling scheme) on the urban arterial road under connected vehicle environment. VISSIM was used to simulate the proposed two HOV carpooling schemes at the mentioned road segment. The simulation results showed that the reversible HOV carpooling scheme could not only mitigate the traffic congestion caused by traffic tidal phenomenon but also improve the average speed and traffic volume of the urban arterial road segment, while the regular HOV scheme may exert a negative impact on the average speed and traffic volume on the urban arterial road segment. [ABSTRACT FROM AUTHOR]

• Maneuvers of mainline facilitating vehicles and on-ramp merging vehicles are coordinated. • Facilitating speeds are bounded from below to restrain impact on following traffic for safety. • The Pontryagin Maximum Principle is applied to rigorously derive analytical solutions. • The proposed strategy evaluated for mixed traffic through MATLAB-Aimsun interaction. • Sensitivity test conducted to check effects of different allowable minimum facilitating speeds. This paper presents a trajectory planning strategy for connected automated vehicles (CAVs) to cooperatively carry out mainline facilitating (i.e. gap development) and on-ramp merging maneuvers. The trajectory planning tasks of the mainline facilitating vehicle and the merging vehicle are formulated as two related optimal control problems. The motivation behind the proposed strategy is to restrain a facilitating maneuver's impact on following traffic. To this end, the proposed strategy bounds the speed of the facilitating maneuver from below and meanwhile ensures that the task of gap development can still be fulfilled. Because of the existence of the speed bound, the optimal control problem of the facilitating vehicle becomes constrained in state, in addition to the control constraints. A Pontryagin Maximum Principle (PMP) with state constraints is applied to rigorously derive the analytical solution. The main difficulty of the analytical procedure exists in the fact that the first-order necessary condition on the extremality of the Hamiltonian cannot yield useful information on the property of the optimal control history with regard to making the optimal speed trajectory to satisfy the speed constraint. As a result, additional conditions have to be explored, notably the so-called "jump conditions", among others. Taking advantage of the analytical solution, the proposed strategy is then implemented under a model predictive control framework. Simulation assessments of the proposed strategy are conducted at two levels – individual vehicle level and traffic flow level. At the individual vehicle level, the proposed strategy shows potential to reduce the risk of rear-end collision between the facilitating vehicle and the following vehicle, the most vulnerable pair of vehicles under the influence of gap development. At the traffic flow level, coupled with Aimsun, the proposed strategy is assessed under mixed traffic flow conditions, with various penetration rates of CAVs. The results show that it has potential to generate lower speed variations of traffic flow, a critical factor in traffic flow safety. Meanwhile it does not show negative impact on traffic efficiency in the simulation, and is likely to improve traffic efficiency in the real world. A sensitivity analysis of the effect of the facilitating maneuver's lower speed bound is also conducted. Although there exist several limitations with this study, it sheds some light on future research. [ABSTRACT FROM AUTHOR]

• A novel vehicle speed management strategy is proposed for preventing crashes. • Vehicle interactions are evaluated in terms of comprehensive crash risks. • VISSIM simulation experiments are conducted to investigate the feasibility of the proposed methodology. • The outcome will be valuable for supporting the management of individual vehicle speeds in automated driving environments. Recent technological advancements have facilitated the implementation of speed harmonization based on connected and automated vehicles (CAV) to prevent crashes on the road. In addition, trajectory-level vehicle controls are receiving substantial attention as sensors, wireless communications, and control systems are rapidly advancing. This study proposes a novel vehicle speed control strategy to minimize inter-vehicle crash risks in automated driving environments. The proposed methodology consists of the following three components: a risk estimation module, a risk map construction module, and a vehicle speed control module. The essence of the proposed strategy is to adjust the subject vehicle speed based on an analysis of the interactions among a subject vehicle and the surrounding vehicles. Crash risks are quantified by a fault tree analysis (FTA) method to integrate the crash occurrence potential and crash severity at every time step. A crash risk map is then constructed by projecting the integrated risk of the subject vehicle into a two-dimensional space composed of relative speed and relative spacing data. Next, the vehicle speed is continuously controlled to reach the target speed using risk map analysis to prevent a crash. The performance of the proposed methodology is evaluated by a VISSIM simulator with various traffic congestion levels and market penetration rates (MPR) of controlled vehicles. For example, an approximate 50% reduction rate of the crash potential was achievable without a loss of the operational performance of the traffic stream when all vehicles were controlled by the proposed methodology under the level of service (LOS) C conditions. This study is meaningful in that vehicle speed control is performed for the purpose of speed harmonization in a traffic stream based on a comprehensive analysis of inter-vehicle risks. It is expected that the outcome of this study will be valuable for supporting the development of vehicle control systems for preventing crashes in automated driving environments. [ABSTRACT FROM AUTHOR]

Historical cities around the world have serious traffic congestions due to old infrastructure and urbanization. To mitigate traffic problems in such cities, infrastructure investments are channeled to tunnels, bridges, and highways. The solution becomes more complicated as the city centers are expected to become fully pedestrian-friendly as a European Union target for 2050. Any modification to the city transport network will lead to changes in traffic density patterns. Investment decisions become more pronounced as the speed of urbanization increases. However, this decision-making processes in urban transport networks pose a serious risk to city managers as wrong decisions are expensive and will not solve the problem. This paper proposes a practical, cost-effective model to assist decision makers by providing them with estimated changes in the traffic patterns due to the addition of new roads to existing infrastructure. The study adopts the Extreme Gradient Boosting (XGboost) which is a tree-based algorithm that provides 85% accuracy for estimating the traffic patterns in Istanbul, the city with the highest traffic volume in the world. The proposed model is a static model that allows city managers to perform efficient analyses between projects that involves changes to the city's transport network. • The study includes a traffic control model developed for a 6104 km complex network. • The study provides a productivity analysis for long-term road infrastructure investments. • The study provides an important tool for decision makers in pedestrian friendliness studies. • The study provided an important feature selection for a static traffic forecast. • The study created an application for a congested city with the most severe traffic problem. [ABSTRACT FROM AUTHOR]

Widespread use of single-occupancy cars often leads to traffic congestion. Using anonymized traffic speed data from Android phones collected through Google Maps, we investigated whether high-occupancy vehicle (HOV) policies can combat congestion. We studied Jakarta’s “three-in-one” policy, which required all private cars on two major roads to carry at least three passengers during peak hours. After the policy was abruptly abandoned in April 2016, delays rose from 2.1 to 3.1 minutes per kilometer (min/km) in the morning peak and from 2.8 to 5.3 min/km in the evening peak. The lifting of the policy led to worse traffic throughout the city, even on roads that had never been restricted or at times when restrictions had never been in place. In short, we find that HOV policies can greatly improve traffic conditions. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency and safety of urban expressway, the macroscopic dynamic traffic flow model is extended using speed guidance control. Speed guidance as control variable was introduced into the urban expressway control system, and the macroscopic dynamic traffic flow model was established. Model predictive control is presented to calculate the optimal speed guidance under the objective function of total travel time and changes of speed guidance. Speed guidance control of urban expressway was designed and optimized. Simulation analysis is carried out in simulation platform based on the case of mutations traffic in the downstream. The results show that the speed guidance under safety constraints has a good control effect to smooth traffic flow, alleviate traffic congestion and improve traffic safety which can be applied in practice to provide a theoretical basis for active traffic management [ABSTRACT FROM AUTHOR]

A shockwave-based speed harmonization algorithm for the longitudinal movement of automated vehicles is presented in this paper. In the advent of Connected/Automated Vehicle (C/AV) environment, the proposed algorithm can be applied to capture instantaneous shockwaves constructed from vehicular speed profiles shared by individual equipped vehicles. With a continuous wavelet transform (CWT) method, the algorithm detects abnormal speed drops in real-time and optimizes speed to prevent the shockwave propagating to the upstream traffic. A traffic simulation model is calibrated to evaluate the applicability and efficiency of the proposed algorithm. Based on 100% C/AV market penetration, the simulation results show that the CWT-based algorithm accurately detects abnormal speed drops. With the improved accuracy of abnormal speed drop detection, the simulation results also demonstrate that the congestion can be mitigated by reducing travel time and delay up to approximately 9% and 18%, respectively. It is also found that the shockwave caused by nonrecurrent congestion is quickly dissipated even with low market penetration. [ABSTRACT FROM AUTHOR]

In the future, there will inevitably be a mixed driving situation of intelligent connected vehicles and traditional manual driving vehicles. How intelligent connected vehicles affect traditional traffic flow has attracted increasing attention. Based on the different characteristics of these two types of vehicles a two-lane heterogeneous traffic flow cellular automata model of expressway is proposed, and the effects of whether intelligent connected vehicles form platoons and the size of platoons on traffic flow congestion and energy consumption are simulated. The results show that: as the proportion of intelligent connected vehicles and the size of platoons increase, the maximum traffic capacity can be effectively improved. Besides, with the increase of the mixed ratio, the larger the platoon size, the higher the average speed of traffic flow. The platoon can alleviate even eliminate the congestion faster and more thoroughly. However, in the high-density fully intelligent connected vehicles environment, the larger platoon size leads a negative impact on the traffic flow in the form of "moving bottleneck". When the platoon size is 4, it can maximize the positive effect of the platoon on the traffic flow. Besides, increasing the proportion and shortening the reaction time of intelligent connected vehicles can effectively reduce the average energy consumption. And platoon mode is more conducive to reduce the average energy consumption than the discrete mode, but the platoon size should be controlled within a reasonable range, otherwise too larger platoon size will aggravate the energy consumption in traffic flow. • The effects of intelligent connected vehicles and platoons on traffic flow and energy consumption are simulated. • When the vehicle platoon size is 4, it can maximize the positive effect of the platoon on the traffic flow. • Platooning is conducive to reduce energy consumption, but too larger platoon size will aggravate the energy consumption. [ABSTRACT FROM AUTHOR]

This paper firstly show that 2 Dimensional Intelligent Driver Model (Jiang et al., 2014) is not able to replicate the synchronized traffic flow. Then we propose an improved model by considering the difference between the driving behaviors at high speeds and that at low speeds, which is in the framework of three-phase traffic theory. Simulations show that the improved model can reproduce the phase transition from synchronized flow to wide moving jams, the spatiotemporal patterns of traffic flow induced by traffic bottleneck, and the concave growth pattern of traffic oscillations (i.e. the standard deviation of the velocities of vehicles increases in a concave/linear way along the platoon). Validating results show that the empirical time series of traffic speed obtained from Floating Car Data can be well simulated as well. [ABSTRACT FROM AUTHOR]

A new multi-phase lattice hydrodynamic traffic flow model is proposed by considering the effect of multi-forward looking sites on a unidirectional highway. We examined the qualitative properties of proposed model through linear as well as nonlinear stability analysis. It is shown that the multi-anticipation effect can significantly enlarge the stability region on the phase diagram and exhibit three-phase traffic flow. It is also observed that the multi-forward looking sites have prominent influence on traffic flow when driver senses the relative flux of leading vehicles. Theoretical findings are verified using numerical simulation which confirms that the traffic jam is suppressed efficiently by considering the information of leading vehicles in unidirectional multi-phase traffic flow. [ABSTRACT FROM AUTHOR]

Traffic prediction plays a crucial role in an intelligent transportation system (ITS) for enabling advanced transportation management and services. In this paper, we address the problem of multi-step traffic speed prediction, including both short- and long-term predictions. We assert that it is important to consider not just the fixed spatial dependency of the road network (i.e., the connections between road segments) but also the dynamic spatial dependency of traffic within the static topology that intertwines with the temporal evolution of traffic condition across the entire network. We propose a novel deep learning model, named Self-Attention Graph Convolutional Network with Spatial, Sub-spatial and Temporal blocks (SAGCN-SST) model, that specifically capture such complex dynamic spatial–temporal processes. In SAGCN-SST, we integrate self-attention mechanism into graph convolutional networks in a novel framework design while using a sequence-to-sequence model in an encoder–decoder architecture for extracting long-temporal dependency of traffic speed. Two real-world datasets with frequent traffic congestion and accidents from large-scale road networks (i.e., Seattle and Los Angeles) are used to train and test our model. Our experiment results indicate that the proposed deep learning model consistently achieves the most accurate predictions (higher than 98% accuracy on both datasets for the short- and long-term predictions) when compared against well-known existing models in recent literature. The results also indicate that SAGCN-SST is robust against emergent traffic situations. • Novel traffic deep learning prediction model on large-scale road networks. • Graph convolution network and attention mechanism for spatial feature analysis. • Sequence-to-sequence architecture for temporal feature analysis. • Result validated using real-world traffic datasets. [ABSTRACT FROM AUTHOR]

In this paper, an extended cellular automaton model is proposed to simulate the complex characteristics of traffic flow and the probability of the occurrence of traffic accidents by considering the modified conditions for determining whether traffic accidents happen and the effect of multi-slowdown sections on a highway. The simulation results show that the multi-slowdown sections can lead to multiphase coexistences (i.e. free flow phase, congestion phase and saturation phase) in traffic system. The fundamental diagram shows that the number of slowdown section does not influence the mean velocity and the mean flow under the periodic boundary condition, but the existence of slowdown sections can effectively reduce the occurrence of traffic accident. In particular, it is found that the probability of car accidents to occur is the largest at the joint of the normal-speed section and slowdown section, and the underlying mechanism is analyzed. In addition, to design the appropriate limited speed and reduce the differences between the normal speed and limited speed will alleviate traffic congestion and reduce the occurrence of traffic accidents obviously. [ABSTRACT FROM AUTHOR]

Abstract: While many microscopic models of traffic flow describe transitions between different traffic phases, such transitions are difficult to quantify in measured traffic data. Here we study long-term traffic recordings consisting of days of flow, density, and velocity time series with minute resolution from a Spanish motorway. We calculate fluctuations, cross-correlations, and long-term persistence properties of these quantities in the flow–density diagram. This leads to a data-driven definition of (local) traffic states based on the dynamical properties of the data, which differ from those given in standard guidelines. We find that detrending techniques must be used for persistence analysis because of non-stationary daily and weekly traffic flow patterns. We compare our results for the measured data with analysis results for a microscopic traffic model, finding good agreement in most quantities. However, the simulations cannot easily reproduce the congested traffic states observed in the data. We show how fluctuations and cross-correlations in traffic data may be used for prediction, i.e., as indications of increasing or decreasing velocities. [Copyright &y& Elsevier]

Highlights: [•] We use a modeling approach for reducing greenhouse gas emissions in a presence of traffic congestion. [•] We show that idle waiting can be used as effective strategy to reduce vehicle emissions. [•] We present analytical properties based on a single-arc version of the problem. [•] We provide insights on the trade-off between emission costs and driver wage. [•] We describe a procedure to optimize departure times and travel speeds on a fixed route. [ABSTRACT FROM AUTHOR]

Highlights: [•] Network fundamental diagrams, based on a reduced amount of measurements, are derived. [•] Reduced NFDs exhibit a critical range of traffic states equivalent to the complete NFD. [•] Efficient feedback-based gating is possible by applying few real-time measurements. [•] Delay and mean speed improved by 30% in cases with complete and reduced measurements. [ABSTRACT FROM AUTHOR]

Rapid urban expansion has brought new challenges to firefighting, with the speed of firefighting rescue being crucial for the safety of property and life. Thus, fire prevention and rescuing people in distress have become more challenging for city managers and emergency responders. Unfortunately, existing research does not consider the negative effects of the current spatial distribution of fire-risk areas, land cover, location, and traffic congestion. To address these shortcomings, we use multiple methods (including geographic information system, multi-criterion decision-making, and location–allocation (L-A)) and multi-source geospatial data (including land cover, point-of-interest, drive time, and statistical yearbooks) to identify suitable areas for fire brigades. We propose a method for identifying potential fire-risk areas and to select suitable fire brigade zones. In this method, we first remove exclusion criteria to identify spatially undeveloped zones and use kernel density methods to evaluate the various fire-risk zones. Next, we use analytic hierarchy processes (AHPs) to comprehensively evaluate the undeveloped areas according to the location, orography, and potential fire-risk zones. In addition, based on the multi-time traffic situation, the average traffic speed during rush hour of each road is calculated, a traffic network model is established, and the travel time is calculated. Finally, the L-A model and network analysis are used to map the spatial coverage of the fire brigades, which is optimized by combining various objectives, such as the coverage rate of high-fire-risk zones, the coverage rate of building construction, and the maintenance of a sub-five-minute drive time between the proposed fire brigade and the demand point. The result shows that the top 50% of fire-risk zones in the central part of Wuhan are mainly concentrated to the west of the Yangtze River. Good overall rescue coverage is obtained with existing fire brigades, but the fire brigades in the north, south, southwest, and eastern areas of the study area lack rescue capabilities. The optimized results show that, to cover the high-fire-risk zones and building constructions, nine fire brigades should be added to increase the service coverage rate from 93.28% to 99.01%. The proposed method combines the viewpoint of big data, which provides new ideas and technical methods for the fire brigade site-selection model. [ABSTRACT FROM AUTHOR]

In this paper, we explore spatio-temporal clusters using massive floating car data from a complex network perspective. We analyzed over 85 million taxicab GPS points (floating car data) collected in Wuhan, Hubei, China. Low-speed and stop points were selected to generate spatio-temporal clusters, which indicated the typical stop-and-go movement pattern in real-world traffic congestion. We found that the sizes of spatio-temporal clusters exhibited a power law distribution. This implies the presence of a scaling property; i.e., they can be naturally divided into a strong hierarchical structure: long time-duration ones (a low percentage) whose values lie above the mean value and short ones (a high percentage) whose values lie below. The spatio-temporal clusters at different levels represented the degree of traffic congestions, for example the higher the level, the worse the traffic congestions. Moreover, the distribution of traffic congestions varied spatio-temporally and demonstrated a multinuclear structure in urban road networks, which suggested there is a correlation to the corresponding internal mobile regularities of an urban system. [ABSTRACT FROM AUTHOR]

Variable speed limit (VSL) can be used on freeways to manage traffic flow with the goal of improving capacity. To achieve this objective, it is necessary that both speed and density dynamics be represented accurately. In this study, to deeply understand the effectiveness of VSL control, an analytical model was developed to represent drivers' response to updated speed limits and macroscopic speed dynamical change with respect to changeable speed limits. Specifically, to model the freeway links having VSL control, the fundamental diagram (FD) was replaced with the VSL control variable in the relaxation term of the METANET. This modification led to the speed control variable appearing linearly, which is preferable for online computation. The density dynamics are based on the cell transmission model (CTM), which is introduced to estimate the transition flow among successive links with some practical constraints. It also offers flexibility in designing active bottleneck in which there is a capacity drop once feeding flow exceeds its capacity. To exploit this benefit, a modification was introduced in the FD of the density dynamics. A VSL control strategy was proposed that explicitly considers traffic characteristics at active bottleneck and its upstream-downstream segments. It can control traffic flow into any type of active bottleneck. Then, the proposed traffic dynamics with the control strategy are implemented in a freeway corridor using the model predictive control (MPC) approach. The analysis was carried out in the calibrated microsimulation model, VISSIM, within a scenario in which shock waves were present. The microsimulation model functions as a proxy for the real-world traffic system. This study reveals that, in terms of mobility, VSL is mostly effective during congestion periods. [ABSTRACT FROM AUTHOR]