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Macroscopic fundamental diagram (MFD)-based perimeter control has a potential to improve traffic throughput and relieve the overall congestion. In practice, not only the MFD but also the traffic demand suffers from a variety of inherent uncertainties. Hence, this paper aims to contrive a robust control technique to address the perimeter traffic flow control issue in the oversaturated region. To this end, an integral sliding mode control is proposed to drive the traffic state to the desirable condition. The developed strategy deals with practical issues such as finite time stability and range of uncertainty in both MFD and traffic demand. Theoretical analysis verifies that the developed integral sliding mode control approach can guarantee the finite time convergence of the traffic state to the desired one. The performance of the developed scheme is attested by considering various traffic scenarios with uncertain MFDs and traffic demand, in which a comparative performance study with the proportional and integral control method is conducted. It is indicated that the developed scheme can alleviate the congestion in the urban network and improve the throughput of the urban network. Practical Applications: The research presented in this article proposes a novel perimeter control technique designed to enhance traffic flow management in densely populated urban road networks, particularly during peak congestion times. By leveraging the macroscopic fundamental diagram (MFD) and integral sliding mode control theory, the method significantly boosts traffic throughput and alleviates the impact of congestion. This advancement can be seamlessly integrated into current urban traffic management systems, leading to more efficient and convenient urban commutes. Additionally, it addresses the rise in vehicle emissions due to heavy traffic, aiding in the reduction of pollution and supporting sustainable development initiatives. The technique also curtails vehicle fuel consumption and cuts down on costs associated with congestion-related delays, thereby fostering resource optimization and economic growth within urban environments. Moreover, the method is designed to be resilient, taking into account the variability of traffic demand and ensuring the robustness of the traffic control strategy. Overall, the article demonstrates the practical application of advanced control technologies to address real-world traffic challenges, providing a scalable and robust solution adaptable to realistic situations with the uncertainties of urban traffic demand. [ABSTRACT FROM AUTHOR]

Traffic congestion is a serious problem in the US, and traffic signal control is one of the effective solutions to congestion. Previous research on model predictive control (MPC)-based traffic signal control showed substantial benefits over conventional methods. This study focused on implementing MPC over a large-scale network with complex intersections and the impact of cycle length, network size, and imperfect state estimation on performances. This study implemented a virtual phase link (VPL)-based model predictive control method which used the number of vehicles in each VPL as input state variables and was suitable for National Electrical Manufacturing Association (NEMA) ring-barrier control. To test the impact of network size, the performance of distributed MPC (36 intersections in the network are divided into five subnetworks) was compared with that of MPC over the full network for a set of cycle lengths. To test the impact of imperfect state estimation, we synthetically infused estimation error and developed two scenarios, MPC-error and MPC-error narrow, which had higher and lower estimation errors, respectively. The performance of these MPC methods was compared with that of the existing time-of-day (TOD) method and an offline method that used Webster's method for split and MULTIBAND for cycle length and offset optimization. Trajectory and linkwise signal performance measures were collected from the simulation to evaluate performance. The distributed MPC method with perfect state estimation had the lowest delay and highest energy efficiency of all the methods. The performance of MPC decreased as the prediction inaccuracy increased. MPC-error had 7% and 11% more delay than MPC-error narrow in the morning and evening peaks, respectively. Overall, simulation results suggest that even with imperfect state estimation, MPC methods will outperform offline methods significantly. [ABSTRACT FROM AUTHOR]

Despite the needs of assessing the time-varying queue length for incident traffic management, such imperative information has not yet been available in practice, due partly to the lack of reliable sensor data on many highways plagued by nonrecurrent congestion. Hence, this study first presents an incident queue impact assessment system that allows highway agencies to perform reliable estimate of the queue distance during the incident clearance period with available detector data, and then discusses two alternative models for supporting approximating such impacts on roadway networks without traffic sensors. The proposed system’s major model, grounded in the classical shockwave theory, has augmented the formulations for queue propagation dynamics with the impacts from incoming drivers’ perceptions and responses to the progressively constrained traffic conditions. To cope with the lack of real-time traffic flow information, the first alternative model integrates the increasing available speed data from probe vehicles or other sources with lane blockage patterns to approximate the discharge flow rate of the incident. The second alternative model centers on the methodology and calibration methods for constructing a set of speed– flow rate relationships for different times of day with archived traffic data to approximate the arriving flow rate. With such information, responsible agencies can then proceed the approximation of incident queue variation length and take necessary actions. Performance evaluation of the proposed system with both field incident data and simulated scenarios has confirmed its promising properties, especially for highway networks without reliable traffic surveillance systems. [ABSTRACT FROM AUTHOR]

In recent years, high-resolution traffic signal event data has provided valuable insights into understanding and managing congestion at signalized intersections. While existing applications primarily employ automated traffic signal performance monitoring (ATSPM) systems as postanalysis tools for identifying everyday congestion causes, traffic engineers are increasingly overwhelmed by the number of ATSPM-capable intersections. The workload increases extensively as the number of ATSPM-capable intersections rises mainly due to the necessity of manually checking and generating performance figures. Nonetheless, an advanced ATSPM system capable of automatically detecting time-of-day congestion bottlenecks among multiple intersections and suggesting "top intersections of interest" would significantly aid traffic managers in monitoring historical congestion and preventing future congestion occurrences. This paper introduces an efficient graphical automated congestion ranking method for capable intersections, leveraging high-resolution traffic signal event data as the basis for automated congestion ranking. To accomplish these objectives, we build upon ATSPM concepts by continuously generating ATSPM measures of effectiveness (MOEs). Utilizing continuously generated ATSPM performance measures in Frisco, Texas, over several months, we devise an efficient graphical method for ranking hourly congestion levels among the studied ATSPM-capable intersections. All intersections are assessed and ranked using a multiobjective optimization technique, the Pareto front method. The points on the Pareto front represent dominating intersections with at least one inferior performance measurement, warranting prioritized improvement. The dominating points identified from the test dataset were validated and further explained using Purdue coordination diagrams (PCD), along with another individual dataset--Wejo-connected vehicle data. The outcomes of this approach have proven the validity of the method. [ABSTRACT FROM AUTHOR]

Pavement markings serve to convey information to drivers, regulate driving behavior, and effectively mitigate traffic congestion and reduce accidents. Nonetheless, due to traffic exposure and temperature stress, pavement markings may develop defects to diverse degrees. Consequently, the inspection and maintenance of pavement markings has been paid high attention. Traditional manual detection methods prove time-consuming, subjective, and present security risks. Therefore, we employed four object detection models [You Only Look Once version 5 (YOLOv5), YOLOv7, faster region convolutional neural networks (Faster R-CNN) with visual geometry group laboratory (VGG), and Faster R-CNN with residual network (ResNet)] to achieve intelligent recognition of pavement marking defects through deep learning. Each model underwent 1,000 epochs of training and utilized 2,000 annotated road inspection images. Through data augmentation, module optimization, and anchor redesign, these models can locate pavement markings and classify their defects. The accuracy and efficiency of the model were evaluated by mean average precision (mAP) and frames per second. In addition, we introduced evaluation indicators that focused on defect types to assist in selecting models with high applicability in detecting markings. Among these models, the optimized Faster R-CNN with VGG as the backbone network has an mAP of 93.96% and can detect over 28 images per second, which meets the engineering requirements. Pavement markings play a crucial role in guiding driver behavior, and they significantly contribute to alleviating traffic congestion and reducing traffic accidents. As the demand for autonomous driving technology increases, the maintenance of pavement markings has become more important. To address this, it is essential to determine which pavement markings require maintenance through inspection. However, traditional manual inspection methods suffer from issues such as low efficiency, high cost, and safety hazards. In this work, we developed four models utilizing artificial intelligence to automatically detect pavement markings in road inspection vehicle images. This approach significantly reduces the need for manual operation and ensures efficient and safe detection of pavement markings. The automatic detection results accurately match the actual position of the pavement markings, thus providing valuable guidance for maintenance work. [ABSTRACT FROM AUTHOR]

Connected and autonomous vehicles (CAVs), with their potential to enhance the interactive perception of vehicle behavior, are expected to benefit traffic congestion and travel efficiency. However, the research scenarios in most current literature are oversimplified and limited, such as a road section or an intersection. To address this issue, this paper proposes a congestion avoidance routing strategy for CAVs to reduce the occurrence and propagation of congestion at the network level. Unlike rerouting after detecting the congestion downstream, the floating-car data are utilized to extract the network congestion patterns, based on which the routes of CAVs are optimized and updated. A simulation framework was built to model the network consisting of CAVs and human-driven vehicles (HDVs). Cooperative adaptive cruise control (CACC) and intelligent driver model (IDM) car-following models were set to characterize the driving behavior of CAVs and HDVs. Simulation experiments were conducted to examine the performance of the proposed routing strategy. The results indicate that the proposed CAV routing strategy can significantly improve the overall congestion state of the network. Compared with the full HDV environment, the vehicles' average delay can be reduced by up to 46.7% and the travel time by up to 28.2% if all vehicles are switched to CAVs. The sensitivity analysis on CAV penetration rate and vehicle inflow rate shows that the vehicles' average delay and travel time decreases with the CAV penetration rate increase, and the travel efficiency of CAVs outperforms HDV users sufficiently. Moreover, the benefits of CAVs would be weakened with the increase in vehicle inflow rates. Finally, the findings also provide a reference for CAVs' centralized control strategy in urban intelligent transportation construction. [ABSTRACT FROM AUTHOR]

The public transport system is vital in any country and gives an idea of how well cities are organized. Recently, Saudi Arabia has turned its attention toward developing a new public transport system, as issues such as low mobility, traffic congestion, and environmental pollution have arisen. In Riyadh City (Saudi Arabia) in 2012, an effort was made to decrease the reliance on private cars by developing travel demand management strategies and a public transport system. This study aims to determine the new public transport system's impact on the economy, environment, and social practices. Qualitative semistructured interviews have been taken with Riyadh City public transport officers. Then, NVivo was used for data analysis. Interviews were transcribed verbatim and analyzed using a thematic approach. The findings of this study showed that the new public transport system would have significant environmental, social, and economic benefits, such as greenhouse gas (GHG) emissions, reduced traffic accidents, and economic opportunities. The Riyadh transportation system would promote flexibility, reduce driving time, and provide more job opportunities, significantly impacting the city's social and economic aspects. Barriers that are associated with the utilization of public transport include social norms and culture, higher reliance on private vehicles, and low fuel prices. This study's findings demonstrated that public transport would motivate people to use public transport. Strategies such as cleanliness, safety, strict gender segregation, and comfortable seats would encourage people to use public transport. Overall, introducing a new transport system would significantly change traffic congestion and positively influence the lives of citizens by reducing the time wasted in traffic and improving air quality. During the last few decades, Saudi Arabia, particularly Riyadh City, has witnessed a significant increase in motorization and urban development, which causes traffic congestion. This huge dependence on vehicles has undoubtedly brought about vehicle proprietorship, congestion, and pollution. Many researchers highlight the rapid increase in private vehicle possession, dangers to the human environment, and traffic safety. Therefore, the author conducted this study to determine the impact of the new public transport system on environmental, social, and economic aspects and the barriers and relevant measures to promote public transport use among Riyadh City residents. Qualitative semistructured interviews have been conducted with Riyadh City public transport officers. The main findings of this study are that the new public transport system would have significant environmental, social, and economic benefits, such as reduced greenhouse gas (GHG) emissions and traffic accidents, and economic opportunities. The major barrier to promoting public transport in Riyadh is changing people's attitude toward using public transport instead of private vehicles. This study could help the government and other authoritative departments to plan an effective public transport system for Riyadh and related interventions to improve urban quality of life and mobility. [ABSTRACT FROM AUTHOR]

Recently, traffic problems such as congestion and emission have been increasing. Transit travel is considered to be one of the effective ways to relieve these problems. The attractiveness of transit travel can be improved by integrating operational strategies with multitype services. Therefore, a study focusing on tapping the potential of integrated operational strategies, which combine local services, skip-stop services, and short-turning services, considering emissions under heterogeneous demand, was conducted. Two types of integrated service patterns were proposed. Pattern 1 comprised local services, skip-stop services, and short-turning services, all operating simultaneously and independently, while Pattern 2 comprised the combined local services and skip-stop services with a flexible route length. The estimation of heterogeneous travel demand for different services and high-resolution bus emissions based on short trips between stops and at stops was conducted. The optimized models to minimize system costs considering passengers, agencies, and environmental interests were developed to explore the effectiveness of integrated operational strategies for performing the transit service. The real-world cases of Bus Route 4 and Route 543 in Beijing, China, were studied. The performances of the proposed strategies under the two integrated service patterns were compared with the baseline. The results showed that the total costs of two integrated service patterns are reduced significantly, with a maximum reduction of above 30%. Moreover, under the route of limited vehicles and denes travel demand, Pattern 2 implementing skip-stop services in a flexible route length had more advantages over Pattern 1, where all three services existed simultaneously. The finding can be used to design the preferred transit development strategy and improve the service quality. [ABSTRACT FROM AUTHOR]

Overheight truck collision with a bridge superstructure can cause extensive structural damage and result in severe traffic congestion. Placing protection beams in front of the fascia girder is one of the common countermeasures to guard against such events. However, little research has been done on this topic in the past and there are no standard guidelines for designing these protection beams. High-fidelity numerical simulations are conducted to formulate a demand model for protection beams subjected to overheight truck impact scenarios involving three different trailer models (representing soft, semirigid, and rigid impactors), a range of truck weights and velocities, and a number of beam sections and lengths. A validated demand model is proposed to represent the loading pulses caused by the different types of impactors. Two performance levels for the protection beam are suggested. Simulation data are used to propose a reliable machine-learning (ML) model for the classification of beam performance as a function of the parameters of the impacting truck and protection beam. The ML model and demand function are complementary to each other and enable rapid and accurate assessments, respectively, of the performance of protection beams during impact scenarios. [ABSTRACT FROM AUTHOR]

This study developed a travel time congestion index (TTCI) keyed to the differences in actual versus desired travel times for various types of roadways, and found that the TTCI is influenced by traffic and geometric characteristics of the roadway other than volume and capacity. The TTCI provides a continuous scale for assessing the level of congestion in the network. In general, the measurement of the level of service (LOS) across the roadway sections is adopted to measure the level of congestion. However, measuring LOS alone does not provide better insight into the reasons for congestion other than the volume-to-capacity ratio. Therefore, we developed the TTCI by employing traffic and geometric parameters to measure the congestion along the roadways on the urban road network. In addition to the development of the TTCI, level of service is measured and compared with the TTCI values for the selected road sections. The results indicate that employing all the variables together in a single technique yields much better and more-reliable values for congestion indexes. The results indicate that providing grade-separated intersections reduces traffic congestion to a great extent. The results also were found to be in line with the field conditions. This study also proposes using TTCI values to design the cordon lines for delaminating the congestion zones and estimating the level of service of the roadway. The findings of this study can be used by policymakers and transportation planners to design effective countermeasures for decongesting urban roadways. [ABSTRACT FROM AUTHOR]

Traffic congestion occurs when there is a mismatch between the demand for road use and the available capacity. The volume-delay function (VDF) can quantify the relationship between travel time and the volume of traffic on a particular link, and also provide insight into the state of a traffic system, such as whether it is congested or uncongested. In this paper, we present a VDF model that is based on the fundamental diagram and has two main components: (1) an improved VDF with fewer parameters that can handle both congested and uncongested traffic conditions, based on a fundamental diagram, and (2) a model-based VDF practical calibration framework for practical traffic applications that can determine key parameters for a link in a corridor. Our experiments using corridors in Los Angeles and Beijing demonstrate that our proposed analytical methods effectively calculate road impedance under congested conditions. The results indicate that the proposed model is superior to other existing models in terms of the root mean squared error (RMSE) and mean absolute error (MAE). In addition, our calibrated results indicate that the travel time index (TTI) in Los Angeles is 2.12, in Beijing is 1.74. The model proposed in this paper provides a useful calibration tool for enhancing model performance and improving the accuracy of travel time and speed estimates in traffic assignment. [ABSTRACT FROM AUTHOR]

As the main road pattern, multilane roads are prevalent on high-grade highways. In a connected vehicle environment, drivers can perceive the full view of traffic information on each lane, which provides more opportunities for flexibly changing lanes on multilane highways. In addition, drivers can predict the traffic status on short notice and regulate the vehicle's operating state in advance. This study determined the predictive effect of drivers in a multilane scenario using the lattice hydrodynamic model. The stability criteria of the proposed model were deduced via the reductive perturbation method; when the stability conditions do not hold, the modified Korteweg-de Vries (mKdV) equation can be deduced. By solving this above equation, we derived the kink-antikink soliton wave solution, which can be used to analyze and explain the formation and evolution process of traffic jams. The results show that the number of lanes and the prediction time of drivers considerably affect the stability of traffic flow. Simulation examples verified that when the predictive time is fixed and the number of lanes increases from 1 to 4, the fluctuation amplitude of traffic density decreases from 0.2 to 0.08 even with exogenous initial disturbance; when the number of lanes is fixed, the density fluctuation amplitude decreases as the predictive time increases; and when the predictive time increases to 0.7, the fluctuation amplitude of traffic density approximates to 0 and is a uniform flow. [ABSTRACT FROM AUTHOR]

Intelligent traffic control at nonsignalized intersections under a connected and automated vehicle (CAV) environment can reduce traffic congestion, vehicle emissions [e.g., carbon dioxide ðCO2Þ, carbon monoxide (CO), nitrogen oxide (NO)], but it is still not safe enough for an unpredictable emergency. For example, an accidental stop or slow down of a vehicle in the conflict area will cause secondary accidents of vehicles from other directions because they do not have enough distance to brake. Such risks have not been given sufficient attention in the literature. This paper proposed a safety guarantee mechanism of intersection control to avoid secondary accidents by adding a safe buffer area outside the conflict area of the intersection. To improve the traffic efficiency of intersections and reduce vehicle emissions under the mechanism, we proposed a heuristic vehicle trajectory planning control strategy based on vehicle kinematics. Three planning principles are applied to generate the microscopic trajectory for each vehicle: (1) the vehicle first decelerates and then accelerates before passing the intersection; (2) try not to change the running state of the vehicle; and (3) the vehicles do not collide with each other. Finally, to further improve efficiency, we proposed the concept of a dynamic safety area, which reduces the delay by self-adjusting the safety area to each vehicle’s trajectory. The paper selected three simulation scenarios of road intersections to validate our methodology. The simulation results show that the proposed control method with the safe buffer significantly reduces average vehicle delay and emissions compared to traditional signal control systems. Compared with the fixed buffer, in the dynamic safe buffer scenario, the average vehicle delay can be reduced by 66.94%, the throughput is increased by 18.75%, and CO2 emissions are reduced by 9.9%. The dynamic safe buffer can not only relax the safety concern of a nonsignalized intersection, which is not well addressed in the literature, but also has much better performance than the traditional intersection. [ABSTRACT FROM AUTHOR]

In general, socioeconomic factors play an essential role in traffic congestion. However, there is still doubt whether traffic congestion fluctuates indefinitely with the change in socioeconomic factors. Using a time analysis unit–based data envelopment analysis (DEA), this study aims to demonstrate urban development efficiency from the perspective of traffic congestion. We employ a linear model to illustrate the effect of internal correlation between independent variables on the model's accuracy. Through the Gray correlation model, we explore the correlation between socioeconomic factors and the traffic congestion index and select the key evaluation indices of development efficiency. Because the urban development efficiency evaluation belongs to a multi-input and single-output system, we introduce DEA to build a quantitative model for the coupling situation between socioeconomic factors and traffic congestion and to identify the factors with high redundancy. The results show that Beijing's urban development over the past two decades has been relatively efficient in terms of traffic congestion. This means that the change in relevant socioeconomic factors has played a rather good role in reducing traffic congestion. The discoveries presented here can make us understand the economic principle of socioeconomic factors to reduce traffic congestion and lay the foundation for adjusting socioeconomic factors' utilities in modeling traffic congestion. [ABSTRACT FROM AUTHOR]

With the ambitious goal of zero deaths but growing traffic and congestion at highway–rail grade crossings (HRGCs), grade separation of these conflicting points has become an increasing strategy that improves railway safety but also increases network capacity and enhances livability attributes. Meanwhile, the engineering costs of grade separation are expensive and can vary considerably, which results in difficulties in the rational selection of such projects. This paper implemented the data envelopment analysis (DEA) for the benchmarking of high-risk HRGCs for grade separation considerations in Oklahoma. The top 1% of HRGCs were identified based on the New Hampshire Hazard Index (NHI) rankings and used as the decision-making units (DMUs) for DEA. The outputs of the DEA process included safety benefits, travel time savings, vehicle operating cost savings, and environmental benefits, and the input was the estimated cost of a grade separation project. The DEA efficiency scores of the DMUs were calculated, and nine HRGCs were identified on the frontiers with efficiency scores of 1.0. To further discriminate the DEA-efficient HRGCs, the enhanced DEA (E-DEA), which combines self-evaluation and crossevaluation methods, was performed and the most efficient crossing was decreased to four with the E-DEA scores of 1.0. This study established a hierarchical framework combining the NHI, DEA, and E-DEA approaches, available data sets, and decision-making tools for the selection of the most efficient investment alternatives for highway–rail grade separation. [ABSTRACT FROM AUTHOR]

When traffic congestion occurs on freeway off-ramp bottlenecks, the traffic state becomes complicated and changeable, which leads to increased vehicle travel time and decreased traffic safety and traffic efficiency. Variable speed limit (VSL) control is an effective method to improve traffic conditions, increase bottleneck throughput, improve traffic efficiency, and reduce emissions. Currently, there is an emerging trend of using connected and autonomous vehicle (CAV) technology to develop VSL control. This paper proposes an optimal differential variable speed limit (DVSL) control strategy under mixed CAVs and human-driven vehicles (HVs) environment for freeway offramp bottlenecks. The proposed DVSL control considers the characteristics of on-ramp, off-ramp, and mixed traffic flow (i.e., CAVs coexist with HVs). The proposed optimal DVSL control can describe and forecast the dynamics of traffic flow, and can set different speed limits across each lane with a multiple-objective function of total travel time (TTT) and total travel distance (TTD). A model predictive control (MPC) approach was utilized to optimize the DVSL control algorithm. The designed DVSL control was tested on a real-word freeway section with a simulated off-ramp bottleneck. The simulation results show that the proposed control strategy outperforms other existing methods in terms of improving the mobility of a freeway off-ramp bottleneck and maximizing the environmental benefits. Sensitivity analysis shows that the proposed control strategy can improve performance with the increase of the penetration rate (PR) of CAVs. The proposed methods form the basis of VSL control at off-ramp sections under mixed traffic environment. [ABSTRACT FROM AUTHOR]

Contending with various recurrent congestion patterns on major freeway corridors has long been a prioritized task of the transportation community. Most of these patterns take place on a freeway interchange's on-ramp and off-ramp segments where both exiting and merging flows often trigger a series of lane changes, resulting in significant freeway speed reduction and propagation of traffic queues to neighboring surface streets. To concurrently mitigate freeway traffic congestion and minimize any negative impacts on the nearby local arterials, this study has developed an Integrated Real-time Interchange Control (IRIC) system, which maximizes the benefits of both freeway and arterial users by fusing an integrated off-ramp signal control (IOSC) module, an off-ramp queue impact (OQI) model, a lane group-based (LGB) traffic model, and a ramp metering control strategy as well as arterial signal optimization modules. The proposed system with both ramp metering functions and local arterial signal design capabilities can determine whether to implement system-wide optimization or merely the ramp metering based on real-time detected traffic conditions. The evaluation results from experiments have confirmed the effectiveness of the developed real-time interchange control system, especially with respect to its ability of preventing ramp queue overflows to the neighboring arterials and minimizing the off-ramp spillback onto the freeway mainline. [ABSTRACT FROM AUTHOR]

Automated platooning is gaining increasing attention because it is regarded as a highly efficient transportation mode to solve traffic congestion problem. Although different intraplatoon spacing strategies have been proposed, little research has focused on quantitative optimization of spacing policy from a safety perspective considering differences between automated and human-driven platooning. In addition, there still is a lack of comprehensive research into string stability and delay robustness of cooperative adaptive cruise control (CACC) for heterogeneous platoons. This paper proposes a quantitative spacing policy in quadratic form based on safety index optimization under hard braking scenarios. The expected value of collision severity is taken as the main index for spacing optimization. The CACC strategy then tracks the proposed spacing considering fluctuation of multitime delays and uncertainty of vehicle dynamics. Based on sliding model control theory and frequency stability criteria, the sufficient conditions satisfying CACC stability and string stability were given. The robustness of the CACC system considering communication latency fluctuation was shown using simulation results. Compared with the existing control strategy, the proposed control strategy is more robust to communication latency, and string stability can be guaranteed. The proposed spacing strategy effectively can reduce collision risk, and the proposed CACC strategy improves latency robustness on the premise of ensuring string stability. [ABSTRACT FROM AUTHOR]

With the increase in the number of ride-hailing, traffic congestion and environmental pollution have been exacerbated. A new ride-hailing policy was formed as a means of administrative control to reduce the negative externalities. However, the improvement in air quality caused by the new ride-hailing policy has not been effectively verified. This study employed regression discontinuity design (RDD) to explore the causal relationship between the air quality in Beijing and the new ride-hailing policy to reduce the endogeneity problems. The main results show that the implementation of the new policy reduces air quality index (AQI), particulate matter 10 (PM10), and ozone (O3) by 14%, 12%, and 16%, respectively, in the ordinary least squares (OLS) regression analysis. After applying the RDD, this study finds that the implementation of the new policy reduces AQI, PM10, and O3 by 43%, 30%, and 42%, respectively. The regression value of RDD is higher than that of OLS regression. Additionally, the robustness test reveals that RDD is reasonable. The findings suggest that the implementation of the new ride-hailing policy exerts an improvement effect on air quality. Policy implications to further promote the new ride-hailing policy are hence discussed. [ABSTRACT FROM AUTHOR]

Rapid urbanization has brought along with it many environmental and social problems such as ecosystem damage and traffic congestion. Therefore, forecasting the trend of urban expansion and providing a reasonable urban planning basis for government departments have become the focus of researchers. An artificial neural network (ANN) can be used to consider spatial nonstationarity when obtaining the changing characteristics of urban land types. Therefore, in this study, we use cellular automata (CA) based on ANN (ANN-CA) to simulate and forecast urban expansion and discuss the parameter sensitivity of the model in detail. In addition, we propose a new Urban Expansion Early Warning Indicator system to warn about the deterioration of future land distribution patterns. Chengdu is selected as the study area, and the study period is from 2000 to 2020. The results showed the following: (1) The best accuracy was achieved when the neighborhood size is 7 × 7 and the number of model iterations is 250, and overall accuracy (OA), Kappa coefficient, and figure of merit (FOM) are 91.47%, 0.855, and 0.354, respectively; (2) ANN-CA is more suitable for predicting the urban expansion of Chengdu than CA based on logistic regression (LR-CA) and CA based on decision tree (DT-CA). Compared with the worst performance model, the score of OA increased by 6.23%, that of kappa increased by 0.062, and that of FOM increased by 0.056. (3) According to the current development trend, artificial built-up areas will increase substantially. The comprehensive evaluation results of the morphology effect, ecological effect, and intensity effect of urban expansion predict severe early warning for Jinniu District, Qingyang District, and Wuhou District by 2030. [ABSTRACT FROM AUTHOR]

Significant private motorization to meet increased mobility requirements has resulted in severe congestion and parking problems in urban areas. In such a scenario, Park-and-Ride (P&R) has been a potential and established travel demand management strategy that efficiently improves urban mobility by significantly reducing traffic congestion and parking burden in city centers. Thus, it is essential that urban planners and policy makers establish P&R facilities at appropriate locations. The current manuscript presents a state-of-the-art review of past and present practices in selecting appropriate locations for establishing P&R facilities. The present study provides useful insights into the critical factors influencing the identification of potential P&R locations, establishment of P&R facilities at optimal locations, and development of commuters' P&R location choice behavior models. The study findings could help researchers, planners, and policy makers collate basic ideas of P&R facility planning and develop context-specific guidelines to suitably locate P&R facilities as potential urban travel demand management solutions in the context of developing countries. [ABSTRACT FROM AUTHOR]

Road crashes cause significant traffic delay, which can bring unnecessary financial losses. The objective of this study is to predict the level of delay caused by crashes (LDC) and discuss significant risk factors. To ensure the efficiency and accuracy of prediction, an improved stacking model was developed using Texas crash data of 2020. The first layer integrates seven base classifiers and the second layer tests three classifiers with different advantages. To improve and simplify the stacking model, three state-of-the-art methods-Bayesian hyperparameter optimization (BO), multiobjective feature selection (FS), and ensemble selection (ES)-were used. First, the hyperparameters and the least and most effective features were selected for each base classifier by BO and FS, respectively. Then ES, considering diversity and performance, selects the least base classifiers to reduce the input of the second layer. Finally, permutation feature importance was used to interpret the best stacking model. The results indicate that the stacking model achieves superior performance on four indicators: recall, G mean, F1 score, and area under the receiver operating characteristic (ROC) curve (AUC-ROC). FS significantly improves the efficiency of the stacking model and ES obtains a simplified stacking model without significantly reducing performance. In addition, the combination of the two methods (FS and ES) tends to achieve the best performance, and six risk factors have the greatest contributions in prediction using permutation feature importance. The prediction of LDC and the analysis of the main contributing factors help road managers respond to the rescue strategies to mitigate traffic congestion caused by crashes in a timely manner, thus minimizing economic losses. [ABSTRACT FROM AUTHOR]

To alleviate traffic congestion, an improved car-following model for connected vehicles is proposed in this paper by considering backward-looking effect and motion information of multiple vehicles. The backward-looking effect is common in traffic and motion information of multiple vehicles and is beneficial for alleviating traffic congestion. The linear and nonlinear stability of the proposed model is analyzed, and the stability condition and modified Korteweg-de Vries equations of the proposed model are derived. The theoretical analysis results prove the effectiveness of the proposed model in alleviating traffic congestion and improving the stability of the traffic system. Further, numerical simulation is designed to verify the promoting effect of the introduced parameters on the traffic stability and to test the effect of the improved model on large-scale car-following queues. Finally, the next-generation simulation (NGSIM) data sets are used to calibrate the parameters of the improved model. The results show that the improved model can effectively avoid traffic congestion and enhance the stability of traffic flow. The improved model can be used as active safety technology to prevent collision accidents or as a car-following strategy in driverless algorithms. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning has strong perception and decision-making capabilities that can effectively solve the problem of continuous high-dimensional state-action space and has become the mainstream method in the field of traffic light timing. However, due to model structural defects or different strategic mechanisms of models, most deep reinforcement learning models have problems such as convergence and divergence or poor exploration capabilities. Therefore, this paper proposes a multi-agent Soft Actor–Critic (SAC) for traffic light timing. Multi-agent SAC adds an entropy item to measure the randomness of the strategy in the objective function of traditional reinforcement learning and maximizes the sum of expected reward and entropy item to improve the model’s exploration ability. The system model can learn multiple optimal timing schemes, avoid repeated selection of the same optimal timing scheme and fall into a local optimum or fail to converge. Meanwhile, it abandons low reward value strategies to reduce data storage and sampling complexity, accelerate training, and improve the stability of the system. Comparative experiments show that the method based on multi-agent SAC traffic light timing can solve the existing problems of deep reinforcement learning and improve the efficiency of vehicles passing through in different traffic scenarios. Practical Applications: This paper is devoted to research on the timing method of traffic lights at multiple intersections. The experimental results show that the method proposed in this paper can effectively improve the throughput of each intersection, the waiting time of vehicles, and the number of queued vehicles. In comparison with related algorithms, it is fully proven that the method proposed in this paper can effectively solve the problems pervasive in existing algorithms. In the actual application process, the traffic status information is obtained via interactions with real traffic environment, and the timing scheme of the traffic lights is dynamically adjusted according to the information, so as to achieve the effect of alleviating traffic congestion at the intersection. [ABSTRACT FROM AUTHOR]

The demand for high-performance asphalt pavements has been increasing in recent years due to increased traffic volume, automobile size and weight, and rising temperatures due to climate change. It is necessary to modify the bitumen to achieve long-life high-performance pavements in many cases. Consequently, polymers obtained from plastic wastes emerged as a modifier to enhance the bitumen properties. Polymers offer a number of advantages. Polymers such as polyethylene (PE) or polypropylene (PP) offer a number of advantages when used as binder modifier. Using PE or PP in infrastructure projects can be an innovative method to reduce the environmental damage of these nonbiodegradable wastes and the high cost of recycling these wastes. Therefore, this paper extensively investigates the experimental studies carried out by many researchers to evaluate polymer-modified binders' properties as an alternative for pure bitumen in asphalt mixtures under different percentages and production processes. The main goal is to establish a better understanding of the engineering properties of polyethylene-modified asphalt mixtures and ultimately argue that waste polyethylene has a viable use as a modifier for asphalt binders. [ABSTRACT FROM AUTHOR]

In winter, the ice and snow on the asphalt pavement reduce the friction coefficient of the pavement, which may lead to serious traffic accidents and large-scale congestion. Taking preventive measures to ensure traffic safety by accurately predicting road surface temperature is an economical and environmentally friendly solution. However, road surface temperature (RST) prediction is a challenging task due to the complicated uncertainty and periodicity. To improve the accuracy of RST prediction, this paper aims to propose an advanced ensemble deep learning model using a gated recurrent unit (GRU) network and long short-term memory (LSTM) network. The ensemble model predicts RST by extracting the periodicity of RST and incorporating the lag and accumulation effects of meteorological factors. To verify the applicability of the ensemble model, RST data and climatic data were collected from a road weather station in Jiangsu, China. Extensive experiments are conducted including predictions for 1, 3, and 6 h ahead. The results demonstrated that the performance of the proposed ensemble deep learning model is validated for 1-, 3-, and 6-h nowcasts of RST, with mean absolute error (MAE) of 0.345, 0.833, and 1.743, respectively, and the prediction accuracy was higher than that of the baseline models [convolutional neural networks (CNN)-LSTM networks, support vector regression (SVR), and backpropagation neural network (BP) networks]. [ABSTRACT FROM AUTHOR]

Lane management is expected to alleviate traffic congestion and improve mobility on roadways. Previous studies have mainly analyzed the impacts of lane management on the road segment rather than the road network. Because lane management strategies can affect traffic flows in the neighboring traffic regions and the entire road network, it is suitable to assess traffic impacts in the entire road network. This study proposed an analytical framework to evaluate lane management’s impacts and economic effects using location-based data, including road segments, traffic zones near road segments, and the road network. Traffic assignments with estimated origin-destination matrices from location-based data allow spatial and temporal impact analysis of lane management. This study analyzed the contraflow lane with movable median barriers installed at the Alex Fraser Bridge (AFB) in Vancouver, British Columbia, Canada, as a case study for lane management. In terms of traffic characteristics, the results showed that the contraflow lane with movable median barriers contributed significantly to improving the states of traffic flow on the AFB (traffic flow increased about 7.4%, travel speed increased about 48.3%, travel time decreased about 31.8%, and volume/capacity ratio decreased about 19.3% on average). This study showed that the contraflow lane on the AFB improved traffic flow and generated an economic benefit of $1.1 M per year (AFB, $12.7 M; zones near the AFB, −$1.4 M; Vancouver area, −$10.1 M) by estimating the changes in the value of travel time before and after lane management. This study contributes to a better understanding of using location-based data for assessing traffic impact and the economic effect of lane management operations at the network level. [ABSTRACT FROM AUTHOR]

Maintaining roadway pavements and bridge decks is key to providing high levels of service for road users. However, improvement actions incur downtime. These actions are typically scheduled by asset class, yet implemented on any asset type, they have network-wide impacts on traffic performance. This paper presents a bilevel program wherein the upper level involves a Markov decision process (MDP) through which potential roadway improvement actions across asset classes are prioritized and scheduled. The MDP approach considers uncertainty in component deterioration effects, while incorporating the benefits of implemented improvement actions. The upper level takes as input traffic flow estimates obtained from a lower-level user equilibrium traffic formulation that recognizes changes in capacities determined by decisions taken in the upper level. Because an exact solution of this bilevel, stochastic, dynamic program is formidable, a deep reinforcement learning (DRL) method is developed. The model and solution methodology were tested on a hypothetical problem from the literature. The importance of obtaining optimal activity plans that account for downtime effects, traffic congestion impacts, uncertainty in deterioration processes, and multiasset classes is demonstrated. [ABSTRACT FROM AUTHOR]

Using a bicycle for commuting is still uncommon in US cities, although it brings many benefits to both the cyclists and to society as a whole. Cycling has the potential to reduce traffic congestion and emissions, increase mobility, and improve public health. To convince people to commute by bike, the infrastructure plays an important role because safety is one of the primary concerns of potential cyclists. This paper presents a method to find the best way to improve the safety of a bicycle network for a given budget and maximize the number of riders that could now choose bicycles for their commuting needs. This optimization problem is formalized as the bicycle network improvement problem (BNIP): it selects which roads to improve for a set of traveler origin–destination pairs, taking both safety and travel distance into account. The BNIP is modeled as a mixed-integer linear program (MIP) that minimizes a piecewise linear penalty function of route deviations of travelers. The MIP is solved using Benders decomposition to scale to large instances. The paper also presents an in-depth case study for the Midtown area in Atlanta, GA, using actual transportation data. The results show that Benders decomposition algorithm allows for solving realistic problem instances and that the network improvements may significantly increase the share of bicycles as the commuting mode. Multiple practical aspects are considered as well, including sequential road improvements, uneven improvement costs, and how to include additional data. [ABSTRACT FROM AUTHOR]

Traffic incidents are a primary cause of traffic delays, which can cause severe economic losses. Effective traffic incident management requires integrating intelligent traffic systems, information dissemination, and the accurate prediction of incident duration. This study develops a clustering-based machine learning model to predict the incident duration. Unlike similar studies that train separate machine learning models for a fixed number of clusters, this study proposes an ensemble learning method based on multiple clustered individual models that can provide good and diverse prediction performance. The K-means clustering method is used in this study as a bootstrapping technique in the ensemble learning approach, with the individual models based on the artificial neural network model and random forest regression model. The models are tested using the incident data from Singapore, and the results show that the ensemble model outperforms both the traditional model with fixed clusters and the classical model without clustering. Additionally, this study attempted to determine the significance of different variables on traffic incident durations using the random forest feature importance function. The prediction of incident duration and the analysis of influence factors can contribute to several aspects of traffic management, such as improving traffic dissemination to mitigate traffic congestion caused by incidents. [ABSTRACT FROM AUTHOR]

It is common knowledge that rainfall and rain-induced waterlogging have negative impacts on urban road traffic in that they could cause decreased traffic flow capacity and induce traffic congestion from a macroscopic view. What is the ultimate cause behind the phenomenon from a micro perspective, however, is still unclear. This paper proposes a variable cell transmission model (VCTM) considering the influence of rainfall and waterlogging on traffic parameters based on the key findings from a previous experiment-simulation study. A typical local traffic network in a city with seven road sections was chosen as a study area, and a case study was conducted in a scenario of rainfall and waterlogging based on the proposed model. The results comprehensively profile how rainfall and waterlogging induce flow fluctuation and density fluctuation. It is found that road capacity is a dominant factor influencing the flow fluctuation, and the flow fluctuation tends to be much more violent near the ponding areas. It is also found that density increases with increased rainfall intensity and water depth, and the density increase caused by waterlogging seems to be more significant. Another important discovery is made that density of some roads could increase violently due to insufficient capacities of downstream roads. These valuable findings from this study help better understand the influence of rainfall and waterlogging on urban traffic flow. This work can also help guide the emergency response by the corresponding government department when encountering rainfall and waterlogging. [ABSTRACT FROM AUTHOR]

In this paper, the authors present their efforts in exploring a new type of traffic data, referred to as internet-connected vehicle (ICV) data, for traffic congestion management and operational planning. Most currently manufactured vehicles contain onboard GPS and cellular modules, and they constantly connect to automobile manufacturers' clouds via cellular networks and upload their status. Some automobile manufacturers have recently redistributed the nonpersonal part of such data, such as geolocation, to third-party organizations for innovative applications. Compared with the traditional vehicle GPS data, the ICV data contain high-resolution GPS waypoints accompanied with the vehicles' abnormal moving events (e.g., hard braking). The ICV data also have huge potential in congestion management and operational planning. They explore to identify and analyze traffic congestion on both freeways and arterials using the ICV data. The ICV data adopted for this research are redistributed by Wejo Data Service, representing 10%–15% of all moving vehicles in the Dallas–Fort Worth (DFW) area in Texas. Through one case study for a freeway segment and one for an arterial segment, new traffic performance metrics based on the characteristics of ICV data have been presented. The highlights of these efforts are as follows: (I) queue length and propagation at freeway bottlenecks can be directly measured based on where and when most internet-connected vehicles slow down and join the queue; (II) an internet-connected vehicle's actual delay time on arterials can be directly measured according to its slow movement percentage, without assuming the nondelay travel speed; and (III) the ICV data set are also combined with the high-resolution traffic signal events to generate a ground-truth time-space diagram (TSD) on arterials—a common visualization of arterial signal performance for transportation planning and operations. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a significant problem that hinders the proper functioning of the transportation system. Traffic congestion forewarning methods can provide traffic management with accurate congestion prediction information, thus taking timely measures to avoid or alleviate traffic congestion. In this paper, we propose a data-plus-model framework-based expressway congestion forewarning method that can be used in the absence of high-quantity and high-quality data. First, we applied historical traffic flow data to determine the approximate extent of the traffic congestion forewarning and fitted a proper traffic fundamental diagram to characterize the traffic flow. Then, the critical congestion forewarning parameters were obtained according to the slope change of the fundamental diagram. Finally, the proposed method was applied to an expressway section in Beijing to forewarn of traffic congestion with multiday historical traffic data. The experimental results show that the proposed method can effectively analyze the trend of traffic flow accurately and quickly give early forewarning of congestion, which is helpful in reducing the occurrence and persistence of congestion. [ABSTRACT FROM AUTHOR]

The emergence of online taxi-hailing fills the shortages of the taxi supply, but the reservable feature of online taxi-hailing has led to the increment of road parking, which has aggravated traffic congestion. Improving the prediction accuracy of online taxi-hailing demand is crucial to reducing congestion. Moreover, the traffic demand of different modes that influence each other is affected simultaneously by the environment, land-use characteristics, and geographic location. Therefore, we introduced a forecasting framework to improve online taxihailing demand forecasting accuracy based on multimode traffic in metro station areas and explored the best predictive range of metro station areas with different land-use characteristics. The paper extracted the origin and destination (OD) information from taxi, online taxi-hailing, and metro data. Next, we extracted the essential factors from the environmental data through Pearson's coefficient analysis. Finally, we selected the best predictive model from several models that contain different information and explored the best predictive range. The result indicates that multisource traffic data and considering multimode traffic could improve traffic demand prediction accuracy. Furthermore, we found that the best traffic demand predictive ranges in metro station areas with different land-use characteristics are different. [ABSTRACT FROM AUTHOR]

Following major hazards and incidents, it is crucial to mitigate the congestion at intersections of urban traffic systems with disruptions; maximize the evacuation, rescue, and recovery efficiency; and prevent a hazard from turning into a disaster. An optimized traffic signal design strategy can effectively contribute to maintaining an efficient traffic system operation despite various disruptions. Most of the existing studies focused on static and generic congestion scenarios during the recovery stage, rather than realistic time-progressive scenarios covering the whole process following a disruption. An adaptive traffic signal control strategy in response to traffic disruptions at a single intersection is proposed by covering both the incident and recovery stages. Dynamic phase selection (DPS) technology is applied to adjust the traffic signal control plan adaptively during the incident stage, while the queue length dissipation (QLD) algorithm is adopted to carry out optimal green time calculation during the recovery stage. The proposed methodology is demonstrated by considering disruptions caused by several typical vehicle crashes at intersections. The proposed DPS+QLD traffic signal strategy is found to improve the resiliency of a typical intersection against disruptions by clearing queues faster, reducing overall traffic loss time, and maintaining stable mobility with superior performance over conventional fixed and actuated traffic signal plans. DOI: 10.1061/JTEPBS.0000671. [ABSTRACT FROM AUTHOR]

Congestion detection is one of the key steps in reducing delays and associated costs in traffic management. With the increasing use of global positioning system (GPS)-based navigation, promising speed data are now available. This study used extensive historical probe data (year 2016) in Des Moines, Iowa. We used Bayesian change point detection to segment the speed signal and detect temporal congestion. The detected congestion events were then classified as recurrent congestion (RC) or nonrecurrent congestion (NRC). This paper thus presents a robust statistical, big-data-driven expert system and a big-data-mining methodology for identifying both recurrent and nonrecurrent congestion. [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]

Urban plans that will shape the city's future should include sustainable transportation systems so that it becomes a livable city. Today, delimiting individual transportation and increasing the share of mass transit play an essential role in planning a sustainable urban transportation system. Several practices can be utilized for attracting private car users into public transportation. Among these practices, park-and-ride application precedes because it provides significant benefits in sustainable transport. Therefore, city planning should be considered together with parking lot areas. In this evaluation, it would be more appropriate to use multiple-criteria decision methods (MCDMs) such as analytical hierarchy process (AHP) and analytical network process (ANP) instead of cost–benefit analysis (CBA) that focuses only on costs and revenues. Therefore, it is not accurate to evaluate the park-and-ride application in cities only according to the monetized criteria by CBA, because there are severe problems in monetizing negativities such as traffic congestion, loss of time, and harmful environmental effects arising from the city's structure. For this reason, it was thought that it would be more accurate to make a multiple-criteria evaluation with methods such as AHP and ANP for the park-and-ride application. In addition, using multiple criteria decision-making methods will positively increase the effects of urban planning on the city. In this direction, a new approach to the subject is presented in our study. In this study, the park-and-ride system and other applicable scenarios have been evaluated in the city of Eskisehir with both AHP and ANP methods by taking expert views. In this respect, the study shows differences from the previous studies on this subject. The alternatives evaluated in the study comprise the Protection of the Current Situation, the Introduction of Only Restrictive Factors, and the Park-and-Ride Implementation. As a result, in applying both methods, the overall relative priority values of alternatives were calculated and found to be the highest in the case of Park-and-Ride Implementation. Therefore, the park-and-ride application came forward in both methods. In this regard, the results obtained are consistent. From this study, it was determined that the park-and-ride application ensured a clear superiority compared with other alternatives. This study provides a general framework for the park-and-ride application in medium-sized cities whose centers are congested and attract many passengers in developing countries. Although it seems that an application has been made for the city of Eskisehir, the study also contains general characteristics. [ABSTRACT FROM AUTHOR]

This paper introduced a new intersection design, the double contraflow intersection (DCI), that could help at big, busy suburban sites, and explored how well it would work. The DCI uses contraflow left turn lanes on each approach to minimize the conflicting traffic flows. The DCI has the potential to provide slightly better capacity than the continuous flow intersection (CFI) and median U-turn (MUT), and much better capacity than a comparable conventional intersection, while mitigating some of the known limitations of the CFI and MUT. Our results showed that the DCI was generally superior to the CFI, MUT, and conventional designs in capacity and travel time. The DCI likely looks about as safe as the conventional and CFI designs, and is as good in through progression as the CFI. The DCI looks to be generally behind the MUT in pedestrian service, and behind the CFI in bicyclist service, but not by much in either case. The DCI looks to be generally behind the conventional design, but better than the CFI, in driveway service and right-of-way needed. In sum, the DCI looks like it could have a future in helping relieve traffic congestion at some suburban intersections and deserves attention from researchers and project teams. [ABSTRACT FROM AUTHOR]

The advantage of self-relocation of connected and automated vehicles (CAVs) can eliminate heavy searching-for-parking traffic in areas with limited parking availability. However, the floating trips will exacerbate local traffic congestion and parking competition if relocated CAVs are not well distributed in the network. To address these issues, this paper proposes a centralized dispatching-for-parking system to dynamically dispatch CAVs between different regions to optimize parking resource utilization and traffic distribution. A macroscopic modeling approach is presented with the consideration of mixed traffic flows of human-driven vehicles (HDVs) and CAVs. The system dynamics are modeled with the representation of the macroscopic fundamental diagram (MFD) in a multiregion road network. The objective of the system is to minimize the total network delay, which is formulated by the framework of model predictive control (MPC). Results of the numerical experiments in a two-region network show that the approach improves the performance of system operation and alleviates traffic congestion and imbalance between parking supply and demand in downtown areas. The sensitivity analysis on the level of CAV penetration reveals that the total network delay gradually decreases with the penetration increase, and HDVs benefit more from the MPC controller. The study demonstrates the applicability and implication of the dispatching-for-parking system in an era of CAVs. [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]

The COVID-19 pandemic has caused worldwide lockdowns and similar containment measures aiming to curb the spread of the virus. Lockdown measures have been implemented in cities amid the COVID-19 outbreak. After the pandemic is under control, cities will be gradually reopened. This study aims to investigate the variations in urban travel behavior during the lockdown and reopening phases. On the basis of long-term traffic congestion index data and subway ridership data in eight typical cities of China, this study carried out comparisons on urban travel behaviors with and without the pandemic. Changes in the multimodal travel behaviors in different times of day and days of week are analyzed during the lockdown and reopening phases. Multivariate and one-way analyses of variance are conducted to show the statistical significance of the changes. This study further investigates the relationship between the returned-to-work (RTW) rate and travel behaviors in the reopening phase. A stepwise multiple regression is conducted to quantify the impacts of influencing factors (i.e., population migration index, RTW rate, socioeconomic indices, and pandemic statistical indicators) on vehicular traffic after reopening. Results show that the lockdown measure has a significant impact on reducing the traffic congestion during the peak hours on workdays, and the subway ridership dropped to below 10% of the prepandemic level during the lockdown phase. Travel demands tended to switch from subways to private vehicular travel modes during the reopening phase, leading to a rapid recovery of vehicular traffic and a slow recovery of subway ridership. The recovery of vehicular traffic is proved to be related to the RTWrate, certain city characteristics, and new COVID-19 cases after city reopening. [ABSTRACT FROM AUTHOR]

The comprehensive coverage of weighing detection systems at the entry point of every highway in China in 2020 caused congestion and unsafe traffic. In this study, weigh-in-motion data on overloaded vehicles were obtained from the Department of Highway Transportation Management of Jiangsu Province, and a spatiotemporal analysis was performed by utilizing the spatial analysis function of a geographic information system. In addition, the weight of the overloaded vehicles was calculated using the overload rate. Furthermore, the clustering and concentration of the overloaded vehicles were obtained using the global spatial autocorrelation model. The kernel density estimation method was then used to identify areas with severe overloading and calculate the local probability of overloading in that area. Our results revealed that the spatial distribution of the overloading severity was mainly influenced by the per capita income, density of highways, industry type, and freight policy in a given region, and that it was mainly concentrated in transportation hubs and areas with high traffic and complex logistics (e.g., municipal and provincial boundaries). Finally, the temporal aggregation of overloading was primarily affected by the level of law enforcement and freight policy because vehicles with high overload rates were mainly concentrated between 12:00 and 4:00 a.m. [ABSTRACT FROM AUTHOR]

The study aims to propose an integrated travel demand and accessibility model to examine the impact of new infrastructures on accessibility for households and employment. The cumulative opportunity measure and the spatial and temporal accessibility measure are used to describe region accessibility layout. And a zonal accessibility measure is proposed to measure attractiveness of the central business district (CBD). Further, accessibility measures are obtained by various transportation modes and times of day considering travel behavior and network traffic congestion. The complete methodology is demonstrated using a case study from the state of Maryland, US. Maryland Statewide Transportation Model is used to build an integrated travel demand and accessibility model. The investment in different projects for constrained long range plan (CLRP) (known as the vision for future growth) is compared to a case without transportation improvement but still has the same growth. Analysis results show that (1) due to lack of transit facilities, car accessibility is significantly higher than transit during peak hours; (2) because of traffic congestion, car accessibility is significantly lower during peak hours than off-peak hours, while transit is the opposite due to high frequency service during peak hours; (3) transit facility improvement can not only increase accessibility but also narrow the gap in accessibility between peak and off-peak hours; and (4) the affected region of accessibility is primarily concentrated in the metropolitan area. The study results demonstrate that it is necessary to fully assess the effect of planned transportation improvements on regional accessibility using the multimeasure accessibility analysis. [ABSTRACT FROM AUTHOR]

Accelerated bridge construction (ABC) has been used extensively to achieve rapid construction, to reduce traffic congestion and environmental impacts, and to ensure reliable quality control. Precast columns are typically precast with a solid section and then inserted into a socket foundation that is deeper than the maximum dimensions of the column section. To reduce the weight of the precast column and foundation for easy transportation and assembly, an alternative solution was proposed that uses a hollow section column with high-strength concrete and a foundation with a shallow socket. After the column was erected, postpour concrete (PPC) was then poured into the hollow column to a certain depth to strengthen the shear capacity in the plastic hinge region. Two precast columns with different depths of the PPC were investigated experimentally by cyclic loading test and were compared with a cast-in-place (CIP) reference column. The cyclic loading test results showed that the precast, hollow, high-strength concrete column with socket connection exhibited emulative seismic performance of the CIP reference. A deeper PPC section shows to improve the energy-dissipating capacity of the precast column. The effect of the PPC depth on the precast column and footing was investigated both experimentally and numerically. [ABSTRACT FROM AUTHOR]

The exit lane for left-turn (EFL) intersection is a newly proposed unconventional intersection that can alleviate left-turn traffic congestion at intersections. The EFL design characteristic lies in that a part of exit lanes can be provisionally used as left-turn lanes to provide additional capacity for left-turn traffic. The inappropriate lane selection of human drivers decreases the effectiveness of such design. This paper establishes an optimal trajectory planning model for left-turn vehicles at EFL intersections under the automated driving environment. The longitudinal acceleration and lane changing variables are optimized simultaneously. The optimization model is formulated as a mixedinteger linear model, which ensures the computational efficiency. The effectiveness of the proposed model is explored under various traffic, geometric, and signal control conditions. The results show that the model can provide a suitable control strategy for left-turn vehicles. On average, traffic delay can be reduced by 47.3%. The proposed model has a promising control effect under high volume conditions. Moreover, the proposed model performs stably under the various length of the mixed-usage area. [ABSTRACT FROM AUTHOR]

This paper presents an on-board advance warning system based on a probabilistic prediction model that advises vehicles approaching a freeway diverge area on when to change lanes to reach an off-ramp, with the goal of reducing delay caused by congestion at the diverge area. The prediction model estimates the probability of reaching a goal state on the road using one or multiple lane changes. This estimate is based on several traffic- and driver-related parameters such as the distribution of intervehicle headway distances and lane-changing maneuver duration. For an upcoming off-ramp, the advance warning system utilizes the prediction model to continuously calculate the probability of reaching that off-ramp and advises vehicles to change lanes when the probability drops below a certain threshold. To evaluate the impact of the proposed system on reducing traffic delay at congested freeway diverge areas, it was used in a simulation case study of a two-lane segment of the I-66 interstate highway to advise vehicles taking an off-ramp on when to change lanes. The results indicate that incorporating the proposed system can reduce the average delay by up to 83%--effectively wiping out the congestion in some cases--depending on traffic flow and the ratio of vehicles taking the off-ramp. [ABSTRACT FROM AUTHOR]

Newly emerging (inexperienced) public-private partnership (PPP) markets require private sector participation to change the current situation in which governments are the only entity responsible for funding and supporting infrastructure projects. The decision to implement a PPP approach in an inexperienced market requires identification of potential PPP solutions to mitigate the problems associated with the public delivery of road projects. However, this issue of identification has not been addressed in previous studies. Therefore, the present study aims to develop a framework that identifies potential PPP solutions to public delivery problems. To address this issue, documents that provide first-hand PPP experiences were collected from five toll roads and bridge projects in Senegal, Ghana, Nigeria, Mozambique, and Cote d'Ivoire. Furthermore, the authors conducted interviews with the key stakeholders of toll road projects in Ethiopia—a newly emerging, inexperienced PPP market. The resulting data were analyzed to explore the potential drivers of PPP adoption through a concept-mapping technique, along with case study aggregation. Based on the findings, a framework was developed that consisted of nine PPP-associated factors acting as a solution to 12 public delivery-associated problems. The results demonstrate that fostering economic development with the help of the private sector to raise project financing and constructing roads that can ease traffic congestion are the main potential drivers of PPP implementation in inexperienced PPP markets. This reveals that PPP implementation motives in the inexperienced PPP markets of developing countries are different from the motives in the experienced PPP markets of developing countries. The study findings would help inexperienced PPP markets to find PPP solutions to the problems associated with public delivery. [ABSTRACT FROM AUTHOR]

Connected and automated vehicle (CAV) exclusive lanes are an important application of intelligent transportation technologies on highways. However, current research on CAVexclusive lanes mainly focuses on basic road segments rather than on on-ramp junctions. This paper analyzed the impact of CAV exclusive lanes on the traffic capacity of on-ramp junctions through theoretical analysis and numerical simulation. This paper established a capacity model for heterogeneous traffic consisting of CAVs and manual vehicles (MVs) considering the CAV degradation mechanism. Three key parameters, namely the CAV penetration rate, the proportion of CAVs on regular lanes, and a CAV platoon coefficient, were introduced into the heterogeneous traffic capacity model. Based on this input, the relationship between traffic capacity and the three parameters was analyzed on regular lanes. The critical CAV penetration rate at which a CAV exclusive lane reaches saturation was expressed mathematically. An on-ramp junction scenario with a CAV exclusive lane was simulated numerically with microscopic heterogeneous traffic flow models. Experimental results showed that congestion at the junction area no longer spread upstream after the CAV penetration rate exceeded 70% without CAVexclusive lanes. However, for the case in which a CAVexclusive lane was used, CAVs on the exclusive lane traveled quickly through the junction and effectively relieved traffic congestion when the CAV penetration rate reached 40%. The results also showed that a long acceleration lane slightly reduced the CAV platoon coefficient. In addition, a CAVexclusive lane improved traffic capacity when the CAV penetration rate was higher than 30%, regardless of the length of the acceleration lane. [ABSTRACT FROM AUTHOR]

Aging infrastructure has become a safety issue for local communities. For example, aging and deteriorating utility poles in strong winds have a high risk of falling onto roads, adjacent houses, or vehicles, which could cause traffic jams, power outages, property damage, or casualties. To prevent such accidents, the infrastructure condition needs to be monitored on a regular basis, thereby facilitating proactive maintenance and repair. However, available monitoring resources are limited for inspecting numerous existing infrastructures in a timely manner, which hinders obtaining up-to-date records representing the current condition status. As an alternative monitoring method, participatory sensing has the potential for infrastructure inspection, leveraging the prevalence of citizens' smartphones as a ubiquitous sensing device. Nonetheless, the reliability of crowdsourced data for infrastructure monitoring and how to improve it have not been investigated fully, although participatory sensing increasingly has been adopted in many studies. Because citizens generally do not have expertise in infrastructure condition assessment, a lack of understanding of the crowdsourced data reliability prevents participatory sensing from being implemented in practice. To advance the understanding of the crowdsourced data reliability for infrastructure assessment, this study investigated the discrepancy in infrastructure assessment results by citizens and by an expert. Wood utility poles were selected as a target infrastructure. This study investigated a way to reduce the deviation between the expert's and the citizens' responses through a fuzzy inference system along with particle swarm optimization and pattern search algorithms. The experimental results showed that the proposed fuzzy inference system reduced the evaluation error by 21.18%. The findings of this study have the potential to fill the knowledge gap for enhancing participatory sensing in infrastructure monitoring, thereby promoting future study to enhance its applicability for citizen-driven urban resilience. [ABSTRACT FROM AUTHOR]