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The research of traffic congestion control has been developed rapidly in recent years. Researching the bifurcation characteristics of traffic flow and designing control schemes for unstable bifurcation points can provide some new methods for relieving the traffic congestion. In this paper, we study the bifurcation characteristics and nonlinear control of traffic flow based on the continuum model considering the new energy vehicles effect. First, a macroscopic traffic flow model considering the number of new energy vehicles and their range is proposed. Then, we transform it into a traffic flow stability model suitable for branching analysis using the traveling wave transformation. Therefore, we can transform the traffic flow problem into the stability analysis problem of systems and reflect unstable traffic characteristics such as congestion. Based on the stability model, the existence conditions of the Hopf bifurcation are proved and some bifurcation points of the traffic system are found. Second, we study the congestion and stability abrupt change behaviors near the equilibrium point and branching point to seek for the formation mechanism of traffic congestion. Finally, for the unstable bifurcation point, the control scheme is designed by Chebyshev polynomial approximation, stochastic feedback control, and other methods to delay or disappear the unstable bifurcation point and alleviate traffic congestion. It has important theoretical significance and application value. [ABSTRACT FROM AUTHOR]

The current high rate of urbanization in developing countries and its consequences, like traffic congestion, slum development, scarcity of resources, and urban heat islands, raise a need for better Land Use Land Cover (LULC) classification mapping for improved planning. This study mainly deals with two objectives: 1) to explore the applicability of machine learning-based techniques, especially the Random forest (RF) algorithm and Support Vector Machine (SVM) algorithm as the potential classifiers for LULC mapping under different scenarios, and 2) to prepare a better LULC classification model for mountain terrain by using different indices with combination of spectral bands. Due to differences in topography, shadows, spectral confusion from overlapping spectral signatures of different land cover types, and a lack of access for ground verification, classification in mountainous terrain is difficult task compared to plain terrain classification. An enhanced LULC classification model has been designed using two popular machine learning (ML) classifier algorithms, SVM and RF, explicitly for mountainous terrains by taking into consideration of a study area of Gopeshwer town in the Chamoli district of Uttarakhand state, India. Online-based cloud platform Google Earth Engine (GEE) was used for overall processing. Four classification models were built using Sentinel 2B satellite imagery with 20m and 10m resolutions. Two of these models (Model 'i' based on RF algorithm and Model 'ii' based on SVM algorithm) were designed using spectral bands of visible and infrared wavelengths, and the other two (Model 'iii' based on RF algorithm and Model 'iv' based on SVM algorithm) with the addition of indices with spectral bands. The accuracy assessment was done using the confusion matrix based on the output results. Obtained result highlights that the overall accuracy for model 'i' and model 'ii' were 82% and 86% respectively, whereas these were 87.17% and 87.2% for model 'iii' and model 'iv' respectively. Finally, the study compared the performance of each model based on different accuracy metrics for better LULC mapping. It proposes an improved LULC classification model for mountainous terrains, which can contribute to better land management and planning in the study area. [ABSTRACT FROM AUTHOR]

Partial periodic pattern (3P) mining is a vital data mining technique that aims to discover all interesting patterns that have exhibited partial periodic behavior in temporal databases. Previous studies have primarily focused on identifying 3Ps only in row temporal databases. One can not ignore the existence of 3Ps in columnar temporal databases as many real-world applications, such as Facebook and Adobe, employ them to store their big data. This paper proposes an efficient single database scan algorithm, Partial Periodic Pattern-Equivalence Class Transformation (3P-ECLAT), to identify all 3Ps in a columnar temporal database. The proposed algorithm compresses the given database into a novel list-based data structure and mines it recursively to find all 3Ps. The 3P-ECLAT leverages the "downward closure property" and "depth-first search technique" to reduce the search space and the computational cost. Extensive experiments have been conducted on synthetic and real-world databases to demonstrate the efficiency of the 3P-ECLAT algorithm. The memory and runtime results show that 3P-ECLAT outperforms its competitor considerably. Furthermore, 3P-ECLAT is highly scalable and is superior to the previous approach in handling large databases. Finally, to demonstrate the practical utility of our algorithm, we provide two real-world case studies, one on analyzing traffic congestion during disasters and another on identifying the highly polluted areas in Japan. [ABSTRACT FROM AUTHOR]

To explore the mechanism of the end-to-end transmission delay of the communication network on the collaborative driving process for traffic flow in the vehicle-to-vehicle communication environment, based on the idea of the car-following model, this paper introduces characteristic parameters characterizing the end-to-end transmission delay of the network into Newell's following model and proposes a CD and OV model by considering the time delay characteristics of the collaborative driving process from information transmission to control decision and then to physical execution. To determine the cooperative driving system's stability criterion, the stability analysis of the new model is examined. By using the reductive perturbation approach, the spatiotemporal evolution mechanism of the traffic flow around the critical stability point under the influence of various transmission delays is analyzed. The resulting modified Korteweg-de Vries (mKdV) equations and density wave solutions are derived. The results show that the end-to-end transmission delay of the network has a significant shock effect on the stability of the vehicle-vehicle cooperative driving system, and the stability of the traffic flow and the ability to suppress traffic congestion becomes worse with the increase in the end-to-end transmission delay. [ABSTRACT FROM AUTHOR]

Safety at the gore areas near diverging ramps is very crucial during planning and implementation of highway safety improvement programs. Limited research has been conducted on safety at the gore areas on arterial roads. This study aims at investigating the impact of improving a sharp gore area in Lincoln, Nebraska by performing a micro simulation before-and-after study with respect to its underlying state of safety and congestion. Data on travel times and traffic volumes for peak hours are incorporated after successful calibration to find out how a geometric intervention can decrease mobility issues as well as the likelihood of crash involvement. This study has utilized VISSIM software package to run the simulation however, to perform safety analysis, Surrogate Safety Assessment Model (SSAM) is used which is developed by the Federal Highway Administration (FHWA). During the model calibration, custom driving behaviors are created to represent driving tendencies of familiar drivers. The simulation results indicated that by adding an auxiliary lane near the gore area, the mobility issues such as bottle necks, lane changing dilemmas and queue lengths are substantially decreased. However, geometric interventions such as provision of a separate lane, increasing ramp spacing, nose spacing, deceleration area and queue storage area considerably reduced the likelihood of rear-end and lane changing crashes. Surrogate safety assessment in diverging ramps, particularly for sharp gores, has not previously been studied, and this study can serve as a primary footmark for future research on ramp-gores safety. [ABSTRACT FROM AUTHOR]

To address the problems of high fuel consumption and severe traffic congestion caused by blindly cruising, we propose a Gurobi optimization algorithm combined with an A ngle and an A* algorithm (A2-Gurobi) to recommend the optimal taxi route based on big taxi trajectory data in the complex urban road network. Specifically, a r oad n etwork n ode e xtraction method (RNNE) based on the GPS direction of taxis is put forward to solve the difficulty of extracting road network nodes from the big Taxi GPS trajectory data. Next, an a ngle-based s harp p oint e limination approach (ASPE) is constructed to optimize the searching capability of the Gurobi algorithm to find the shortest path. Then, a Gurobi optimization algorithm (A-Gurobi) based on the A* algorithm is designed, which uses the heuristic function of the A* algorithm to enhance the ability of fast guidance from the origin to the destination, thereby improving the execution efficiency of the Gurobi algorithm. Finally, the A2-Gurobi algorithm is successfully applied to the optimal taxi route recommendation with real-world taxi trajectory big data. Compared with Gurobi, Dijkstra, Floyd, A*, Bi-A*-ACO, Bellman-Ford, BFS, Acyclic, and AC on the taxi trajectory data sets composed of 10, 20, 30, 40, and 50 nodes, the experimental results indicate that the distance of the A2-Gurobi algorithm for taxi route recommendation is 19.06%, 20.97%, 3.03%, 15.07%, 15.07%, and 3.85% shorter than that of Gurobi, A*, Bi-A*-ACO, BFS, Acyclic, and AC on average, respectively, and it also achieves the same distance as other algorithms. In terms of execution efficiency, our A2-Gurobi algorithm outperforms the baselines mentioned above by 38.56%, 96.63%, 66.66%, 66.66%, 90.87%, 97.60%, 97.27%, 97.21%, and 99.27%, respectively. [ABSTRACT FROM AUTHOR]

With advancing vehicular technology, there are challenges related to the computing capabilities of the deployed infrastructure. In a dense vehicular network, the system performance quickly degrades due to the scarcity of computing capacity and the heavy workload on the coordinating nodes. In situations of a road accident or slow-moving traffic, many trigger messages are generated to enable situational awareness. Aforesaid events may lead to network congestion across the vehicular environment resulting in higher packet loss. Moreover, emergency messages incur increased service delays rather than getting preference for servicing. In this work, we propose a clustering-based re-routing framework for network traffic congestion avoidance on urban vehicular roads. We use queue optimization to categorize and execute tasks based on their priority level at each fog node. Moreover, threshold-based congestion detection is used to determine congested nodes, which, alongside clustering-based suitable node selection, is used for workload sharing. Furthermore, a re-routing mechanism is implemented to decrease packet loss, improving the delivery rate. The simulation results show that the proposed technique is reliable, effective, and robust in terms of packet loss, throughput, and delivery of emergency messages. [ABSTRACT FROM AUTHOR]

Traffic congestion not only has a great impact on people's travel, but also increases energy consumption and air pollution. The control analysis of the macroscopic traffic flow model considering the vehicle braking effect is particularly important, reflecting the impact on the actual traffic flow density wave, so as to better solve the actual traffic problems. In this paper, based on a speed difference optimization speed model, the micro–macro-variables are transformed into a high-order continuous traffic flow model. Then, a random function considering the physical correlation of random components is added to the high-order continuous traffic flow model to establish a random traffic flow model that can reflect the uncertain behavior of traffic flow acceleration or deceleration. Based on this stochastic traffic model, the existence of Hopf bifurcation and bifurcation control of the traffic flow system model considering stochastic characteristics are derived by using Hopf bifurcation theorem. By Chebyshev polynomial approximation method, the stochastic problem of the system is transformed into the bifurcation control problem of its equivalent deterministic system. A feedback controller is designed to delay the occurrence of Hopf bifurcation and control the amplitude of the limit cycle. Without changing the equilibrium point of the system, the complete elimination of Hopf bifurcation can be achieved by controlling the amplitude of the limit cycle. That is, the feedback controller is used to modify the bifurcation characteristics of the system, such as the bifurcation appearing at the equilibrium point in the control system moves forward, moves backward or disappears, so as to achieve the effect of preventing or alleviating traffic congestion. [ABSTRACT FROM AUTHOR]

In the process of collecting traffic information, traditional traffic flow control methods have some problems, such as high loss rate of sensing and detection information and long average queue length, which lead to the unsatisfactory effect of traffic control and evacuation in main roads. Therefore, based on multi-sensor information fusion technology, this study designs a new intelligent control method of traffic flow on main road. According to the multi-sensor information, the fusion algorithm and measurement equation are designed to obtain the main road traffic sensor position, vehicle linear velocity and other parameters, and update the fusion results in real time. Then, the short-term characteristics of traffic flow are used to control the appearance time of target section. Based on the ordinary differential equation, the main road traffic network is represented by intelligent directed graph, and the intelligent dredging model of urban main road traffic flow is constructed. The simulation results show that in five experimental roads, the average delay time of this method is less than 35 s, the maximum average number of stops is only 8.8, the average queue length is less than 30 m, the average travel speed is more than 25 km/h, the traffic flow per unit time is more than 250veh/10 min, and the traffic congestion index of main road is always more than 5.5. The above indexes of this method are better than those of traditional methods, which verifies that this method has better application performance. [ABSTRACT FROM AUTHOR]

Climate change is increasing the frequency and magnitude of floods in many parts of the world. Therefore, people are affected significantly during floods experienced in urban areas and road networks, as well as they, may be stranded at these points due to flooding, traffic jams, and flooding of vehicles. Thus, the quick search and rescue activities in these areas may reduce the loss of life and property. This study aims to create safe routes for emergency response teams and people in urban floods. For this purpose, 2D flood analysis is carried out, and flood risk classes are determined for vehicles and people according to the results of this analysis. According to these determined flood risk classes, the most appropriate point in the flood area is found for the emergency response teams to carry out their search and rescue activities effectively. The most suitable routes for emergency response teams are obtained in four different scenarios. In addition, 13 different safe points are identified for pedestrians in the flood area, and routes are created to reach these points. Consequently, the findings of the study demonstrate that during flooding, human evacuations and search and rescue activities may happen fast. [ABSTRACT FROM AUTHOR]

Traffic congestion has harmful implications on road users and leads to rise in both commuting time and the tendency of road rage, increasing the frequency of road crashes. At median openings (MO), due to complex movement of U-turns and approaching traffic, measurement and assessment of congestion is imperative. Identifying the level of congestion at median openings can help in strategizing location specific steps to ease the problem. In this study, the travel time reliability measures like planning time index (PTI) and travel time index (TTI) have been modified with relation to median opening by incorporating steady speed as compared to free flow speed used earlier. Video graphic analysis was conducted and speed data is extracted from 4500 vehicles travelling at different traffic volumes. PTI and TTI have been correlated with 95th, 85th, and 15th percentile speeds and have been used to develop the ranges of congestion severity index. It is observed that the congestion indices alone cannot justify the amount of congestion since their values saturated after a threshold traffic volume of 3000–4000 vph. 15th percentile speed along with the 85th and 95th percentile speeds gives a better picture of congestion since they encompass the slow moving vehicles too. The percentile speeds have been utilized to designate the congestion severity index by using k-mean clustering. Traffic situations where 95th, 85th, and 15th percentile speeds are below 39.6, 35.6, and 22.7 kmph, it can be attributed to severe traffic congestion whereas the same percentile speeds if are observed to be above 52, 46, and 31 kmph, the traffic flow can be represented as free flow/no sign of any congestion. The present study helps in categorizing traffic congestion at median openings in a rational way, thereby undertaking proper steps and actions to alleviate them. However, the results obtained are valid for median openings on 6-lane divided urban roads and the same methodology can be conducted for roads of different lane configuration for better understanding. [ABSTRACT FROM AUTHOR]

This paper revisits Coase's ideas on property rights in a scenario where the law allows the establishment of mandatory limits - caps- to specific harmful effects of economic activity such as air pollution or traffic congestion. We propose a general equilibrium model with a cap-and-trade mechanism where agents suffering the harm share a portfolio of Coasian rights designed according to the caps. Equilibrium allocations must comply with the cap, reducing polluting emissions or switching from private to public transport. In addition, the monetary transfers that result from the sale of rights to polluters yield compensation to the harmed population, increasing the non-polluters income. [ABSTRACT FROM AUTHOR]

Short-term passenger flow prediction (STPFP) helps ease traffic congestion and optimize the allocation of rail transit resources. However, the nonlinear and nonstationary nature of passenger flow time series challenges STPFP. To address this issue, a hybrid model based on time series decomposition and reinforcement learning ensemble strategies is proposed. Firstly, the improved arithmetic optimization algorithm is constructed by adding sine chaotic mapping, a new dynamic boundary strategy, and adaptive T distribution mutations for optimizing variational mode decomposition (VMD) parameters. Then, the original passenger flow data containing nonlinear and nonstationary irregular changes of noise is decomposed into several intrinsic mode functions (IMFs) by using the optimized VMD technology, which reduces the time-varying complexity of passenger flow time series and improves predictability. Meanwhile, the IMFs are divided into different frequency series by fluctuation-based dispersion entropy, and diverse models are utilized to predict different frequency series. Finally, to avoid the cumulative error caused by the direct superposition of each IMF's prediction result, reinforcement learning is adopted to ensemble the multiple models to acquire the multistep passenger flow prediction result. Experiments on four subway station passenger flow datasets proved that the prediction performance of the proposed method was better than all benchmark models. The excellent prediction effect of the proposed model has important guiding significance for evaluating the operation status of urban rail transit systems and improving the level of passenger service. [ABSTRACT FROM AUTHOR]

A eukaryotic cell is a microscopic world within which efficient material transport is essential. Yet, how a cell manages to deliver cellular cargos efficiently in a crowded environment remains poorly understood. Here, we used interferometric scattering microscopy to track unlabeled cargos in directional motion in a massively parallel fashion. Our label-free, cargo-tracing method revealed not only the dynamics of cargo transportation but also the fine architecture of the actively used cytoskeletal highways and the long-term evolution of the associated traffic at sub-diffraction resolution. Cargos frequently run into a blocked road or experience a traffic jam. Still, they have effective strategies to circumvent those problems: opting for an alternative mode of transport and moving together in tandem or migrating collectively. All taken together, a cell is an incredibly complex and busy space where the principle and practice of transportation intriguingly parallel those of our macroscopic world. Leveraging a label-free interferometric scattering microscope, scientists tracked numerous cargos within a crowded cellular environment. Intriguingly, cells employ effective strategies echoing human transportation systems to manage such transportation hurdles. [ABSTRACT FROM AUTHOR]

Many highways are acquiring smart transportation systems to improve traffic efficiency, safety and management. Intelligent transportation systems can monitor traffic congestion by providing estimated time of arrival or suggesting a diversion route. However, due to the chaotic complexity of the highway traffic road network and the short-term mobility of the population, traffic flow prediction is affected by many complex factors, and the development of an effective traffic flow forecasting system is very challenging. This paper establishes a novel framework to enhance the prediction of highway traffic flow based on the integration of Complementary Ensemble Empirical Mode Decomposition and Gaussian Mixture Model (CEEMD-GMM). Firstly, the complementary ensemble empirical mode decomposition is used to decompose and reduce the noise of traffic flow data. The empirical modal components are combined by calculating the sample entropy (SE), which makes the time series stable and reduces the time cost of forecasting. Secondly, the histogram of oriented gradient (HoG) is computed to extract the distinct features of various traffic flows and enhance the traffic flow prediction. Furthermore, the highway traffic data is classified using Gaussian mixture model to realize the feature extraction of different types of highway traffics. The performance of the model is evaluated in terms of mean absolute error (MAE), root mean square error (RMSE), mean absolute percent error (MAPE), and average correlation. The model is extensively verified using traffic flow data under individual weather conditions and different traffic flow datasets. Compared with the traditional support vector machine (SVM), artificial neural network (ANN), random forest (RF) and Adaboost algorithms, the proposed model shows 2.90% MAE, 6.07% RMSE, 0.38% MAPE and average correlation coefficient of 0.74 for traffic flow detection. It is concluded that the model can predict the highways traffic flow with high accuracy as compared to other contemporary models. [ABSTRACT FROM AUTHOR]

Accurately estimated highway traffic flow info plays a decisive role in dynamic and real-time road management, planning, and preventing frequent/recurring traffic jams, traffic rule violations, and chain/fatal traffic accidents. Traffic flow information is extracted by processing raw camera images via vehicle detection and tracking algorithms. Object detectors including the Yolo, single-shot detector, and EfficientNet algorithms are used for vehicle detection; however, You only look once version 5 (Yolov5) has a clear advantage in terms of real-time performance. Due to this reason, the pre-trained Yolov5 models were utilized in the vehicle detection part, and in the vehicle tracking module, a novel tracker algorithm was developed using vehicle detection features. The performance of the proposed approach was measured by comparing it to the Kalman filter-based tracker. The evaluation results show that the proposed tracking approach outperformed the Kalman filter-based tracker with 5.82% (Buses), 2.24% (Cars), 36.50% (Trucks), and overall 2.58% better traffic counting accuracy for the 12 nighttime case study videos captured from the highways with different horizontal and vertical angle-of-views. [ABSTRACT FROM AUTHOR]

This study examined the concentration of BTEX in Tehran from 2018 to 2020 in five monitoring stations with different backgrounds, which has been accomplished using the combination of passive sampling and GC-FID method. The total concentration of BTEX was estimated to be 65.39 (µg/m3), with a higher average concentration in 2019–2020 (77.79 µg/m3) compared to 2018–2019 (53.48 µg/m3) due to the leaping concentration of Toluene in the pandemic era. Despite a Benzene concentration decline in recent years, the average annual concentration of Benzene (5.66 µg/m3) at five stations remained higher than the EU commission and India standards (5 µg/m3) as well as Japan and Iraq thresholds (3 µg/m3). Toluene dominated other species in terms of concentrations, mass distribution (~0.6%), followed by m,p–Xylene (~0.2%), Benzene (~0.05–0.1) and Ethylbenzene (< 0.05). The evidence regarding seasonal changes of BTEX in 2019 shows the maximum concentration of these compounds in autumn, which is probably due to heavier traffic compared to other seasons. In contrast, in the first half of 2020 (which encompasses the start of the pandemic period and urban lockdown), point sources seem to play a prominent role in concentration fluctuations, as confirmed by changes in interspecies relationships and lower traffic congestion. The highest mean concentrations were observed in high-traffic, residential and suburban sites, respectively. The study reveals that m,p-Xylene possess the highest Ozone formation potential (~109.46), followed by Toluene (~85.34), o-Xylene (~46.87), Ethylbenzene (~13.52) and Benzene (~2.61). Health risk assessment results indicated the high carcinogenic risk of Benzene (mean = 3.6 × 10–6) and the acceptable non-carcinogenic risk of BTEX (hazard index~0.03 < specified limit of 1). Finally, the estimated weighted exposures of BTEX emphasized that residents near the high-traffic districts are more exposed to BTEX. [ABSTRACT FROM AUTHOR]

In urban area vehicular adhoc network ascertaining appropriate channel for load balanced high data transferring, in dynamic network location is addressed by several industries and researchers using various routing algorithms. Particularly load balancing in traffic congestion and channel optimization in dynamic vehicular topology extends coverage and line-of-sight relay association issue and probability of hold-up link is high. For preventing problems instigated by traffic congestion and estimating optimistic channel for transferring high data rate without affecting network performances machine learning with orthogonal frequency division multiplexing and multi-input and multi-output (OFDM–MIMO) approach is incorporated for that takes care of load balancing and channel optimisation respectively in this paper for effective communication in vehicular network. Through miscellaneous simulations we implement Q-learning based load balancing routing (Q-LLBR) and OFDM–MIMO scheme is incorporated to achieve better performance in packet delivery ratio, latency, network utilization and signal to noise ratio are performed significantly to improve network performance in vehicular communications. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major concern in urban centers, as it can affect society, the environment, and the economy. There are many studies on the use of computational intelligence (CI) to improve mobility in urban centers. Some of these researches focus on developing strategies for traffic light programming, since traffic coordination is complex due to its many parameters, variables, and dynamic behavior, and also an inefficient traffic control plan can lead to increased delays and contribute to traffic congestion. Although there are many works in the literature on strategies for traffic control, there are still some contributions and gaps to be filled, especially because some studies do not consider the automatic optimization of traffic signals in real time, that is, according to the demand of vehicles on the roads, considering multiple objectives and the use of a network of intersections in their experiments. In addition, some of the proposed models are not independent of simulation to evaluate the solutions of CI algorithms, resulting in a more complex deployment in real situations. In this context, this paper presents a new method to optimize traffic light plan in a network of intersections and in real time, called Active Control of Traffic Signals (ACTS) associated with the Non-Dominated Sorting Genetic Algorithm, considering multiple objectives in the optimization process (minimizing the average delay time and the number of vehicles stops per cycle). To test the applicability of the model, a real dataset of vehicle demand collected by the Company of Transport and Traffic of Belo Horizonte (BHTrans) is loaded into the AIMSUN simulator, then the method is applied and compared with the current traffic control plan used by BHTrans. The results show that the ACTS method reduces the average vehicle delay by almost half compared to the results obtained with the current solution used by BHTrans. In real life, this means less time spent in traffic, which promotes faster traffic flow, reducing traffic congestions. [ABSTRACT FROM AUTHOR]

Road pavement maintenance and upgrades (RPU) need to be scheduled in a way that minimises congestion across the road network and matches the schedule with the availability of equipment and skilled labourers to ensure that they are completed on time. RPU should not introduce unbearable congestion around blocked lanes or roads, and increased traffic due to ongoing road upgrades needs to be transferred to alternative routes. In situations where multiple upgrades are planned together, the scheduled road closures should be coordinated to not block alternative routes. While extensive studies have been conducted separately on scheduling and routing, limited research has linked these processes together, and only in restricted scenarios. In this paper, we investigate how to minimise the disruption of a set of road maintenance tasks based on their interaction with other tasks. We propose a new approach for scheduling a set of road maintenance tasks, some at the same time and others at different times, to minimise overall disruption. The proposed approach has two phases: (1) identify the local area affected by the RPU, and (2) determine which upgrades can be planned together to minimise congestion. The comparison of the results shows how the novel optimized approach identifies a better schedule to minimise congestion. [ABSTRACT FROM AUTHOR]

Smart cities can effectively improve urban life quality. But, with the rise in population size, there is an increase in the use of vehicles for transportation. In smart city development, traffic control and route planning are the primary tasks to address the problem of travel time estimation. It has become complex due to concerns of wrapped spatio-temporal data on dynamic real-time traffic conditions. The existing methods failed to estimate travel time efficiently due to not considering the spatio-temporal features and lack of computing resources. There is a need for a system like intelligent transportation system to monitor and accurately predict traffic flow to avoid traffic congestion and reduce the impact on the ecological system. This paper developed a novel hybrid deep learning model to estimate optimized travel time and possible trajectories. In this work, U-Net used to reduce the number of feature points for each temporal data and GNN is used to build the graph with connectivity between vehicular nodes. This hybrid model helps us predict traffic flow and aims to estimate accurate travel time from one location (node) to another. The model's performance is evaluated on the standard benchmark datasets Q-Traffic, TaxiBJ, and Chengdu. The experimental results show that the proposed framework significantly improves performance in extracting travel patterns and provides an optimal route with an estimated travel time than existing methods with RMSE 4%, MAE 20.49%, and MAPE 18%. The proposed model has accurately predicted the estimation of travel time of the vehicle for the given urban traffic data. [ABSTRACT FROM AUTHOR]

Traffic congestion can lead to negative driving emotions, significantly increasing the likelihood of traffic accidents. Reducing negative driving emotions as a means to mitigate speeding, reckless overtaking, and aggressive driving behaviors is a viable approach. Among the potential methods, affective speech has been considered one of the most promising. However, research on humor-based affective speech interventions in the context of driving negative emotions is scarce, and the utilization of electroencephalogram (EEG) signals for emotion detection in humorous audio studies remains largely unexplored. Therefore, our study first designed a highly realistic experiment scenario to induce negative emotions experienced by drivers in congested traffic conditions. Subsequently, we collected drivers' EEG signals and subjective questionnaire ratings during the driving process. By employing one-way analysis of variance (ANOVA) and t tests, we analyzed the data to validate the success of our experiment in inducing negative emotions in drivers during congested road conditions and to assess the effectiveness of humorous audio in regulating drivers' negative emotions. The results indicated that humorous audio effectively alleviated drivers' negative emotions in congested road conditions, with a 145.84% increase in arousal and a 93.55% increase in valence ratings compared to control conditions. However, it should be noted that humorous audio only restored drivers' emotions to the level experienced during normal driving. Our findings offer novel insights into regulating drivers' negative emotions during congested road conditions. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China has contributed to traffic events, such as traffic accidents and delays. It is difficult to detect and resolve highway traffic congestion in a timely manner using traditional methods because they are slow, require a large number of workers, and require the installation of a large amount of monitoring equipment. Therefore, it is imperative to introduce advanced technology to address these challenges. Recently, deep learning technology has made significant breakthroughs and has been widely applied to various fields with satisfactory results. Deep learning is among the most important technologies for the detection and evaluation of traffic congestion, enabling the accurate detection of the state of the expressway network's traffic congestion, the evaluation of traffic congestion, and the prediction of possible traffic congestion. This enables management to formulate traffic dredging strategies in advance to prevent the negative impact of traffic congestion on normal traffic flow. This paper proposes a framework based on deep learning for next-generation highway traffic management. This framework selects traffic congestion indicators to construct an index model, and then constructs a deep learning model based on self-coding. It predicts and classifies highway traffic environment data and excavates sample data based on the characteristics of traffic parameters. As soon as traffic data were classified, a prediction model based on SoftMax was established to detect and predict highway traffic congestion. We conducted a traffic congestion analysis of the Shanghai expressway network based on the speed performance data obtained from the China Traffic Management Bureau. As a result of their research, we developed an index to measure highway traffic congestion. For traffic control and management organizations to function effectively, it is crucial to have an accurate and clear picture of traffic network operations. We evaluated the proposed framework using data gathered from highway monitoring scenes, and the results indicated that 98.6% of the data could be correctly detected. Using the prediction model based on SoftMax for expressway vehicles during peak hours, the accuracy was 92%, and the misjudgment rate was 8%. This study demonstrates that detecting and evaluating the state of highway traffic environments using deep learning has high accuracy, can be applied to actual highway traffic systems, and is extremely useful for detecting highway congestion. This framework is a promising solution for next-generation highway traffic management and provides accurate and timely traffic congestion detection and evaluation. [ABSTRACT FROM AUTHOR]

Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance, which has become the key to avoiding traffic congestion and improving traffic management in intelligent transportation systems. To precisely characterize the spatial structure of road networks and discover temporal and spatial characteristics, we propose an attention-based spatial–temporal adaptive dual-graph convolutional network (ASTA-DGCN) for TFF in this paper. Specifically, we employ a spatial–temporal attention module to explore the hidden temporal correlation information of traffic data and the implicit influence of weights among road network nodes and to further capture the dynamic influence of different spatial–temporal positions on the current spatial–temporal position. Then, we utilize an adaptive graph modeling module to automatically extract the one-way relationship between variables and integrate external knowledge into the module. The FastDTW algorithm is exploited to measure the similarity of road network nodes, and the non-Euclidean pairwise association between regions is encoded into graphs to discover the hidden temporal pattern similarity effectively. Furthermore, temporal and spatial correlations are explicitly modeled using dual-graph convolution and sequential convolution based on the obtained graphs to mine the spatial–temporal patterns in dynamic traffic flow, and the final prediction result is produced based on the weighted fusion of the output values of the recent, daily, and weekly components. Finally, the ASTA-DGCN algorithm is successfully applied to TFF on two real-world traffic datasets. The experimental results indicate that our ASTA-DGCN algorithm outperforms ARIMA, VAR, FNN, GCN, GAT, GWNet, STGCN, ASTGCN, and STSGCN. [ABSTRACT FROM AUTHOR]

A macroscopic traffic flow model considering the effects of curves, ramps, and adverse weather is proposed, and nonlinear bifurcation theory is used to describe and predict nonlinear traffic phenomena on highways from the perspective of global stability of the traffic system. Firstly, the stability conditions of the model shock wave were investigated using the linear stability analysis method. Then, the long-wave mode at the coarse-grained scale is considered, and the model is analyzed using the reduced perturbation method to obtain the Korteweg-de Vries (KdV) equation of the model in the sub-stable region. In addition, the type of equilibrium points and their stability are discussed by using bifurcation analysis, and a theoretical derivation proves the existence of Hopf bifurcation and saddle-knot bifurcation in the model. Finally, the simulation density spatio-temporal and phase plane diagrams verify that the model can describe traffic phenomena such as traffic congestion and stop-and-go traffic in real traffic, providing a theoretical basis for the prevention of traffic congestion. [ABSTRACT FROM AUTHOR]

In recent decades, several Latin American cities have implemented policy instruments to reduce urban air pollution and traffic congestion such as license plate–based restrictions on car use. Our research analyzes the factors influencing the public acceptance of these urban policies in four cities: Bogotá, Mexico City, Santiago de Chile, and Quito. We use semi-structured interviews and digital diaries to examine participants' beliefs and emotions about vehicle restrictions as well as their daily mobility experiences. The study illustrates the importance of three main beliefs in shaping the public acceptance of the measures (perceived effectiveness, perceived fairness, and perceived personal impacts). The results also show a certain resignation on the part of the participants, accepting that the policies are necessary and, to some extent, effective in pacifying traffic and improving air quality in highly populated cities, even if the policies generate certain undesirable distributional and personal impacts. The study uncovers the importance of the local context in understanding why certain urban policies, successfully applied in other contexts, might have lower levels of public support in Latin American cities. A high population density, regulatory shortcomings, enforcement difficulties, and deficits in infrastructure and public transport all create a mobility context in which some policies to improve urban air quality and traffic congestion generate high levels of frustration and ambivalence among the residents. [ABSTRACT FROM AUTHOR]

Routing and parking problems are considered a real challenge in the populated cities. An intelligent routing and parking algorithm is needed to facilitate locating the most appropriate parking spots to vehicles in a way that efficiently utilizes parking resources, reduces the traffic congestion, and minimizes trip driving time. This work proposes a hierarchical optimal algorithm to solve the routing and parking problem (HOPRA) using multi-criteria decision concept and historical traffic data. The problem is divided into three parts: finding the preferred parking lot, identifying the parking entrance (in case there are multiple), and then selecting the preferred route to the destination point. The optimal parking lot is defined based on an objective function that depends on the availability rates of the parking lots. After finding the parking lot and the parking entrance, a multi-objective function is used to specify the preferred route to the selected parking lot. This function depends on the travel distance, the traffic rate of a specific route, and the driver's preferences. We use the Open Street Maps from the AnyLogic software in the implementation stage. We compare HOPRA against the Shortest Route (SR) algorithm that was implemented also in AnyLogic with historical traffic data obtained from HERE technologies for a sample city (Al-Madinah City in Saudi Arabia). The results show that the HORPA algorithm outperforms the SR algorithm in balancing the traffic congestion on the roads and reducing the driving time of the trip. [ABSTRACT FROM AUTHOR]

In recent years, traffic flow prediction has been extensively explored in Intelligent Transportation Systems, which is beneficial for reducing traffic jams and accidents as well as optimizing traffic network resources. Most of the previous methods divide cities into equal-sized grids and predict flows within one grid. However, we believe that each area is not independent, and there are interactions between areas. And the interaction between areas belonging to different attributes is more regular. Therefore, we propose a Multi-Attribute Graph Convolutional Network (MAGCN) for regional traffic flow prediction. Based on the attributes to which the areas belong, we divide cities into unequal-sized grids, and then a matrix is constructed using the flow of Functional area-based Origin-Destination pairs. GCN and dilated causal convolution allow the model to capture the spatial correlation and temporal dependence between functional regions while overcoming the under-fitting of local peaks. Extensive experimental results and evaluation metrics on two real-world datasets show that the MAGCN outperforms the baselines and has a higher accuracy for traffic flow prediction. [ABSTRACT FROM AUTHOR]

As a critical node and major bottleneck of the urban traffic networks, the control of traffic signals at road intersections has an essential impact on road traffic flow and congestion. Deep reinforcement learning algorithms have shown excellent control effects on traffic signal timing optimization. Still, the diversity of actual road control scenarios and real-time control requirements have put forward higher requirements on the adaptiveness of the algorithms. This paper proposes an Adaptive Weighted Averaged Double Deep Q Network (AWA-DDQN) based traffic signal optimal control method. Firstly, the formula is used to calculate the double estimator weight for updating the network model. Then, the mean value of the action evaluation is calculated by the network history parameters as the target value. Based on this, a certain number of adjacent action evaluation values are used to generate hyperparameters for weight calculation through the fully connected layer, and the number of action values for mean calculation is gradually reduced to enhance the stability of model training. Finally, simulation experiments were conducted using the traffic simulation software Vissim. The results show that the AWA-DDQN-based signal control method effectively reduces the average delay time, the average queue length and the average number of stops of vehicles compared with existing methods, and significantly improves traffic flow efficiency at intersections. [ABSTRACT FROM AUTHOR]

Accurate assessment of road conditions can effectively alleviate traffic congestion and guide people's travel plans, traffic control decisions of transportation departments, and formulation of traffic-related laws and regulations. This paper proposes a quantum particle swarm optimization (QPSO) and adaptive hybrid exponential smoothing with residual correction (AHES-RC) for the nonlinearity and randomness of traffic flow. In the proposed algorithm, the single–double-exponential smoothing method is mixed with adaptive weights to form AHES, a new method of mixing weights according to real-time traffic trend changes. After the residual correction of AHES is calculated by the extreme learning machine algorithm, the parameters of AHES-RC are optimized using QPSO to improve the prediction accuracy further. This paper comprehensively compares the QPSO-AHES-RC algorithm with other benchmark models through testing on 26 real-world data sets. The results show that the proposed algorithm can adaptive forecasting under various traffic conditions, yielding the best forecasting performance in terms of various forecast error metrics. Compared with advanced machine learning algorithms such as XGBoost and CatBoost, the mean RMSE and mean MAPE have been improved by over 20% on average. In addition, it is revealed that the real-time dynamic capture of traffic flow can effectively improve prediction accuracy. [ABSTRACT FROM AUTHOR]

Vehicles are getting equipped in technology, communication between vehicle and the user is getting better. The vehicular network is an emerging technology to provide mobile users with the flexibility to use various services such as entertainment and navigation on wheels. The users are served with data related to their travel in terms of road map, weather updates, traffic congestions, radio services, social network applications and place of interest. The data communication takes place with the help of Smart On-board Unit (SOBU) present on the vehicle. There is a requirement of compound data computations with firm latency. Vehicle Edge computing (VEC) is the emerging technology that has serves at the edge in the neighbourhood of vehicle that enables data offloading. There will be much of energy consumption and latency resulting from offloading and computations. In this paper smart offloading scheme is proposed that will efficiently harvest the energy and reduce the energy consumption problem. As a performance statistic, the execution cost is used, which accounts for both execution delay and task failure. And also, the offloading scheme is analyzed for single user and multiuser by simulations and the results are compared graphically for battery energy level, average execution cost and channel mode select parameters respectively. The factors affecting both Single-user and multi-user computation are identified from the results. Experimental results show that offloading schemes proposed for single user and multi user work better than the other state-of-the-art algorithms for vehicular networks. This single-user analysis will be a benchmark for developing auto pilot vehicle for people with special needs and multi-user analysis will help in developing to all kinds of Electric Vehicle communication. [ABSTRACT FROM AUTHOR]

Urban road stormwater reuse is one of the most effective ways to mitigate water resource shortage. However, due to a diversity of human activities such as traffic, various toxic pollutants can be deposited on road surfaces during dry periods and washed off during wet periods, threatening stormwater reuse safety. Among these pollutants, polycyclic aromatic hydrocarbons (PAHs) have been widely found in road stormwater. This study selected twelve road sites in Shenzhen, China, and investigated PAHs deposited on urban roads and their influential factors (traffic characteristics, land use and road surface condition). The research outcomes showed that high-molecular-weight PAH species (5–6 benzene rings) had higher concentrations and variability on spatial distributions than light-molecular-weight ones (2–4 benzene rings). Additionally, more PAHs were attached to dusts with small particle sizes (< 150 µm), and among influential factors, commercial land use showed a stronger correlation with PAHs distributions, regardless of particle sizes. Furthermore, it is noteworthy that traffic volume did not have an important influence on PAH generations on roads, while the source tracking results did indicate that traffic activities were the main contributor of PAHs. This implies that other traffic characteristics such as frequent go-and-stop activities might also contribute PAHs on roads. This means that areas with frequent traffic congestions could be the "hot spot" areas of PAHs, although the traffic volume might be not high. These research outcomes can provide useful insight into effective stormwater management and ensuring their reuse safety. [ABSTRACT FROM AUTHOR]

Drivers always try to plan their trips over the road network by avoiding highly congested areas. Several research studies have considered image processing techniques and vehicular network technologies to predict the level of traffic congestion over pre-determined road scenarios. In this paper, we aim to use big data and machine learning regression techniques to provide the driver ahead of time with the expected level of traffic congestion on the investigated road scenarios. Three basic regression techniques have been experimentally tested over some main roads in Amman, Jordan. Fast and correct traffic characteristics have been predicted on the investigated road scenarios, such as positions, speed, and locations of each vehicle. After that, the best regression technique has been used to predict the level of traffic congestion on the traversed road network during future periods. These predictions are also reported to the moving traffic to be considered in the route options of each moving vehicle. [ABSTRACT FROM AUTHOR]

Urban traffic congestion is a critical problem for port-city areas. The truck restriction time window (TRTW) aims to curb congestion by restricting truck use on city roads during peak hours. But TRTW can cause congestion at ports after the restriction ends, leading to economic losses and higher emissions. This paper proposed a buffer stack model to reduce truck delays and emissions from TRTW at port-city areas. The model was implemented for Port of Izmir with using Discrete Event SimulationF (DES). A cost model was used for evaluating DES results. The results showed that buffer stack reduced truck delays by 28.1–43.9% and gate queue lengths by 31.2–43.2% in high and low truck traffic scenarios. The congestion costs (NOX, CO2 PM2.5, emission, and truck delay costs) were also reduced by 35.8–43.9% and the total cost including the buffer stack operation was reduced by 25.6–36.3% for high and low truck traffic. The comparison between buffer stack and chassis exchange terminal (CET) system showed that both systems had comparable emission reduction values, however, CET had a higher reduction in truck delays (83.8% compared to 28.0%). Results showed buffer stacks are effective and affordable solution for ports to mitigate the impact of peak truck traffic resulted from TRTW, lower truck emissions, and reduce the logistics cost. [ABSTRACT FROM AUTHOR]

The idea of applying AI in embedded systems is growing in all sectors, from airplanes to drones, cars, cell phones and robots. The challenge for this type of embedded systems is to ensure real-time functioning, with accurate results under a robust system. In this paper, we propose an embedded and real-time road traffic classification system based on AI techniques. The software part includes the different blocks dedicated to vehicle counting and classification, which is performed by using background subtraction, Histograms of Oriented Gradients and Support Vector Machine. The proposed method also includes a set of linear and non-linear filtering techniques for noise removal. The occlusion handling is applied during classification process by training the model on classes containing occlusion images. The hardware part of the system consists of Raspberry Pi board with an operating system installed for flexibility purposes. The implemented algorithm has been improved to guarantee a real time operation and a minimal storage in the memory. The proposed system achieved a counting accuracy of 96.34%, and a processing speed around 4.55 FPS, which is adequate with all the performance and real time criteria. [ABSTRACT FROM AUTHOR]

Intelligent traffic light control (ITLC) algorithms are very efficient for relieving traffic congestion. Recently, many decentralized multi-agent traffic light control algorithms are proposed. These researches mainly focus on improving reinforcement learning method and coordination method. But, as all the agents need to communicate while coordinating with each other, the communication details should be improved as well. To guarantee communication effectiveness, two aspect should be considered. Firstly, a traffic condition description method need to be designed. By using this method, traffic condition can be described simply and clearly. Secondly, synchronization should be considered. As different intersections have different cycle lengths and message sending event happens at the end of each traffic signal cycle, every agent will receive messages of other agents at different time. So it is hard for an agent to decide which message is the latest one and the most valuable. Apart from communication details, reinforcement learning algorithm used for traffic signal timing should also be improved. In the traditional reinforcement learning based ITLC algorithms, either queue length of congested cars or waiting time of these cars is considered while calculating reward value. But, both of them are very important. So a new reward calculation method is needed. To solve all these problems, in this paper, a new ITLC algorithm is proposed. To improve communication efficiency, this algorithm adopts a new message sending and processing method. Besides, to measure traffic congestion in a more reasonable way, a new reward calculation method is proposed and used. This method takes both waiting time and queue length into consideration. [ABSTRACT FROM AUTHOR]

Due to the continuous development of economy and society, the pace of life is also accelerated, so people pay more and more attention to the time cost, and the transportation time cost is also a very important part of it. The traffic system is an important carrier to realize the traffic operation. The increase of automobile ownership requires the traffic system to be higher and higher. Moreover, vehicles in congested traffic flow inevitably start and stop operations with high frequency, which undoubtedly increases vehicle exhaust emissions, environmental pollution, noise pollution and other problems. For today's traffic system, vehicle dynamics information is also an important factor which affects the traffic system. Therefore, adding throttle, vehicle dynamics information, to the macro-traffic flow modeling research in this paper is a supplement and improvement to the current traffic flow theory research. By analyzing the equilibrium point, this paper proves the conditions for the existence of Hopf branch and saddle junction branch. Finally, numerical simulation is carried out, and the space–time diagram of density and phase plane are obtained through simulation, which can be used to describe the actual traffic phenomenon. Through numerical simulation, it is found that this model can better describe the congestion phenomenon of the actual traffic system, and provide scientific theoretical support for macroscopic traffic flow state analysis. [ABSTRACT FROM AUTHOR]

Traditionally, traffic congestion was alleviated through significantly upgrading the infrastructure of transportation networks. However, building new roads or adding more lanes to a main road needs huge expenses. A better cost-effective approach is to redesign and fine-tune transportation networks by closing and reversing existing lanes. This paper aims at developing an optimal scheme for lane-level closure and reversal to improve the performance of existing transportation networks with a fairly tight budget. We call this new problem Lane-level Closure and Reversal Problem (LCRP). By considering the capacities of all lanes of a road, two different bi-objective bi-level programs (called the arc-based and lane-based models) are developed to formulate the LCRP. Furthermore, our proposed formulations consider the elastic traffic demand and the elimination of conflict points resulting from reversing lanes. A hybrid machine learning and bi-objective optimization (MLBO) algorithm is developed to overcome the curse of dimensionality of the bi-level programs, especially for the land-based model that is more general but with higher computational complexity. The proposed methodology is illustrated by a small-size numerical example and verified by a real-world case study from Winnipeg (i.e., a benchmark transportation network). Computational results show that the integrated lane-level closure and reversal model can achieve a 0.52% reduction in the total travel time, which is significantly better than the 0.05% reduction individually obtained by arc-level closure or the 0.10% reduction obtained by arc-level reversal. The proposed methodology is beneficial for the traffic management bureau to make a more precise decision in redesigning transportation networks in practice. [ABSTRACT FROM AUTHOR]

We study a multi-agent system for the modeling maritime crime. The model involves three interacting populations of ships: commercial ships, pirate ships, and coast guard ships. Commercial ships follow commercial routes, are subject to traffic congestion, and are repelled by pirate ships. Pirate ships travel stochastically, are attracted by commercial ships and repelled by coast guard ships. Coast guard ships are controlled. We prove well-posedness of the model and existence of optimal controls that minimize dangerous contacts. Then we study, in a two-step procedure, the mean-field limit as the number of commercial ships and pirate ships is large, deriving a mean-field PDE/PDE/ODE model. Via Γ-convergence, we study the limit of the corresponding optimal control problems. [ABSTRACT FROM AUTHOR]

In order to explore the long-term effect of driving-restriction policies on the traffic congestion level and traffic accident rate, we combined system dynamics, the back propagation neural network, and gray system theory (SD-BP-GM integrated algorithm) to construct a dynamic nonlinear function of the private car growth rate. On this basis, an urban traffic congestion management model was constructed, which includes the driving-restriction policies, degree of parking shortage, traffic congestion level, and traffic accident rate. Through simulation, the following results were found: (1) The driving-restriction policies had a dual impact on the traffic congestion level and the traffic accident rate. The short-term effect was significant, but in the long run, the policies induced a new demand for car purchases, further aggravating the degree of parking shortage, traffic congestion level, and traffic accident rate. (2) The single strategy of strengthening publicity guidance, strengthening policy guidance, and strengthening pavement management had different preference effects, while the combination strategy had multiple benefits. (3) Compared with single strategies and driving restriction policies, the cumulative effect of the combined strategies reduced the traffic congestion level by 2.1% and 2.5%, respectively, and also reduced environmental pollution by 1.4% and 2.9%, respectively. [ABSTRACT FROM AUTHOR]

Intelligent transportation system (ITS) is widely employed in dynamic traffic management to alleviate roadside congestion and increase traffic efficiency for solving the increasingly critical traffic congestion problems. With the advancement in ITS, real-time data collection of vehicles on the road has become a reality. A vast amount of traffic data ensures that the state of the road network may be analysed and predicted in real time for vehicle detection. Decision support system plays a significant role in the early decision making based on some defined criteria again available options. If the decision is made in the right and precise way, then eventually, it lead to success. This research investigates and designs a vehicle recognition algorithm and the road environment discrimination algorithm, which greatly increase the accuracy of highway vehicle detection, using a deep learning framework. In this work, we collect the highway video surveillance's images in various environments, create an original database, build a deep learning model of environment discrimination, and train the classification model to achieve real-time highway environment as the basic condition of vehicle recognition and traffic event discrimination. The proposed work uses a decision support system which is feed with basic information for vehicle detection and selection. Labeling the vehicle target and sample preparation of various environments are carried out to improve the accuracy of detecting the vehicles on a highway. The vehicle recognition algorithm is investigated in this context, and a vehicle detection technique based on weather environment recognition and a rapid RCNN model is presented. The performance of the vehicle recognition algorithm developed in this study is then verified by comparing detection accuracy with the existing state-of-the-art approaches. The comparison with these approaches in terms of accuracy, sensitivity, and F-score shows that our algorithm outperforms these approaches for detecting and classifying vehicles on the highway. [ABSTRACT FROM AUTHOR]

Urban online ride-hailing demand forecasting is an important component of smart city transportation systems. An accurate online ride-hailing demand prediction model can help cities allocate online ride-hailing resources reasonably, reduce energy waste, and reduce traffic congestion. With the massive popularity of online ride-hailing, we can collect a large amount of order data, and how to use deep learning models for improving order prediction accuracy has become a hot research topic. Most of the urban online taxi demand forecasting methods do not sufficiently consider the influencing factors and cannot model the complex nonlinear spatio-temporal relationships. Therefore, we propose a multi-view deep spatio-temporal network framework (MVDSTN) architecture to obtain the spatio-temporal relationships for online ride-hailing demand prediction. Our proposed model includes five views,up-passenger order view, down-passenger order view, POI view, spatial GCN view, POI view and weather view, applies LSTM with attention mechanism to achieve demand prediction for urban online taxi bodies. Experiments Haikou Didi Taxi datasets and Wuhan Taxi datasets prove that our model has good robustness and the prediction method outperforms current methods. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITS) have advanced significantly over the past years as an incredible technology for averting congested traffic and enhancing traffic safety. Recent researchers show that incorporating Wireless Sensor Networks (WSN) into ITS can decrease the necessary investment and permits the creation of intelligent collaborative applications that enhance traffic efficiency and driver safety. In this paper, we propose a Trust Aware Clustering Technique based on Rat Swarm Optimizer for WSN-based Intelligent Transportation System (TACTIRSO), which is a secure method for selecting cluster heads (CHs) based on nodes' trust value. We employed the Rat Swarm Optimizer (RSO), one of the most recent swarm-based optimization methods, to efficiently choose CHs. For the selection of CH, the proposed fitness function takes into account the node remaining energy and trust value. Moreover, the exponential moving average model is employed to dynamically change the predefined threshold values according to the network state. In order to enhance the performance of RSO, we applied different local search strategies in addition to an energy and trust-aware method of initializing the rat population. The simulation results reveal that TACTIRSO outperforms existing studies in terms of energy efficiency, selection of most trustworthy nodes, and average network lifetime. Numerical results indicate that TACTIRSO improves average network stability by at least 1.13 times, average trust value by at least 3.31 times, and reliability by at least 4.45 times over other schemes in a heterogeneous network, while these improvements are by 1.02 times, 0.33 times, and 3.52 times, respectively, in a homogeneous network. [ABSTRACT FROM AUTHOR]

Road transportation is among the global grand challenges affecting human lives, health, society, and economy, caused due to road accidents, traffic congestion, and other transportation deficiencies. Autonomous vehicles (AVs) are set to address major transportation challenges including safety, efficiency, reliability, sustainability, and personalization. The foremost challenge for AVs is to perceive their environments in real-time with the highest possible certainty. Relatedly, connected vehicles (CVs) have been another major driver of innovation in transportation. In this paper, we bring autonomous and connected vehicles together and propose TAAWUN, a novel approach based on the fusion of data from multiple vehicles. The aim herein is to share the information between multiple vehicles about their environments, enhance the information available to the vehicles, and make better decisions regarding the perception of their environments. TAWUN shares, among the vehicles, visual data acquired from cameras installed on individual vehicles, as well as the perceived information about the driving environments. The environment is perceived using deep learning, random forest (RF), and C5.0 classifiers. A key aspect of the TAAWUN approach is that it uses problem specific feature sets to enhance the prediction accuracy in challenging environments such as problematic shadows, extreme sunlight, and mirage. TAAWUN has been evaluated using multiple metrics, accuracy, sensitivity, specificity, and area-under-the-curve (AUC). It performs consistently better than the base schemes. Directions for future work to extend the tool are provided. This is the first work where visual information and decision fusion are used in CAVs to enhance environment perception for autonomous driving. [ABSTRACT FROM AUTHOR]

With the increasing traffic congestion in cities, the priority of public transit has become a consensus for the development and management of urban transportation. The traffic pre-signal mechanism, which gives priority in time and space to buses by optimizing road right-of-way allocation, has gained wide attention and application. In order to broaden the action exploration range of the agent and avoid the pre-signal decision from falling into suboptimal strategy or local optimal strategy. For the exploration strategy of the DDQN algorithm, this paper reduces the probability of directly selecting the local optimal action and increases the probability of selecting non-greedy actions based on the principle that "the action with a larger value function is more likely to be selected." This paper addresses the problem that the existing urban traffic pre-signal mechanism cannot adaptively adjust the advance time, and proposes a traffic pre-signal adaptive timing mechanism based on a Hybrid Exploration Strategy Double Deep Q Network (HES-DDQN) by combining the ϵ -greedy strategy and Boltzmann strategy. We have used the traffic simulation software VISSIM to conduct simulation experiments on an intersection. The experimental results show that, compared with the method of setting no pre-signal and the formula method of setting pre-signal, the HES-DDQN pre-signal mechanism can significantly reduce the average delay of buses, the waiting queue length, and the number of stops of social vehicles. [ABSTRACT FROM AUTHOR]

With the enormous growth in the public and private vehicles fleet, traffic congestion is increasing at a very high rate. To deal with this, an intelligent mechanism is required.Therefore, this work proposes a novel Neuro-fuzzy based intelligent traffic light control system, which accounts for vehicle heterogeneity by dynamically generating traffic light phase duration considering the real-time heterogeneous traffic load. For this purpose, the proposed model establishes peer-to-peer connections among neighboring traffic light junctions to fetch the respective real-time traffic conditions and congestion. A fuzzy membership function is utilized to generate an intelligent traffic light phase duration. Further, to obtain an effective fuzzy membership function input value considering real-time heterogeneous traffic scenarios, an adaptive neural network is utilized. The proposed system adopts three execution modes: Congestion Mode (CM), Priority Mode (PM), and Fair Mode (FM). It automatically activates and switches to the best mode based on the live traffic conditions. The performance of the proposed model is evaluated via a realistic simulation on the Gwalior city map of India using an open-source simulator known as Simulation of Urban Mobility (SUMO). The results evident the effectiveness of the proposed model over the existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Cooperation between intersections in large-scale road networks is critical in traffic congestion. Currently, most traffic signals cooperate via pre-defined timing phases, which is extremely inefficient in real-time traffic scenarios. Most existing studies on multi-agent reinforcement learning (MARL) traffic signal control have focused on designing efficient communication methods, but have ignored the importance of how agents interact in cooperative communication. To achieve more efficient cooperation among traffic signals and alleviate urban traffic congestion, this study constructs a Graph Cooperation Q-learning Network Traffic Signal Control (GCQN-TSC) model, which is a graph cooperation network with an embedded self-attention mechanism that enables agents to adjust their attention in real time according to the dynamic traffic flow information, perceive the traffic environment quickly and effectively in a larger range, and help agents achieve more effective collaboration. Moreover, the Deep Graph Q-learning (DGQ) algorithm is proposed in this model to optimize the traffic signal control strategy according to the spatio-temporal characteristics of different traffic scenes and provide the optimal signal phase for each intersection. This study also integrates the ecological traffic concept into MARL traffic signal control, which aims to reduce traffic exhaust emissions. Finally, the proposed GCQN-TSC is experimentally validated both in a synthetic traffic grid and a real-world traffic network using the SUMO simulator. The experimental results show that GCQN-TSC outperforms other traffic signal control methods in almost all performance metrics, including average queue length and waiting time, as it can aggregate information acquired from collaborative agents and make network-level signal optimization decisions. [ABSTRACT FROM AUTHOR]

Car congestion is a pressing issue for everyone on the planet. Car congestion can be caused by accidents, traffic lights, rapid accelerations, deceleration, and hesitation of drivers, as well as a small low-carrying capacity road without bridges. Increasing road width and constructing roundabouts and bridges are solutions to car congestion, but the cost is significant. TLR (traffic light recognition) reduces accidents and traffic congestion caused by traffic lights (TLs). Image processing with convolutional neural network (CNN) lakes dealing with harsh weather. A semi-automatic annotation for traffic light detection employs a global navigation satellite system, raising the cost of automobiles. Data was not collected in harsh conditions, and tracking was not supported. Integrated channel feature tracking (ICFT) combines detection and tracking, but it does not support sharing information with neighbors. This study used vehicular ad-hoc networks (VANETs) for VANET traffic light recognition (VTLR). Information exchange as well as monitoring of the TL status, time remaining before a change, and recommended speeds are supported. Based on testing, it has been determined that VTLR performs better than semi-automatic annotation, image processing with CNN, and ICFT in terms of delay, success ratio, and the number of detections per second. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the original path forwarding traffic caused by congestion that cannot be recovered in time in Smart Grid Communication, a congestion control scheme RFCC (Router Forwarding Congestion Control) based on path recovery is proposed, which not only supports the receiving end combined with router nodes to relieve congestion, it can also solve the problem of low transmission performance caused by not being able to restore the original path in time after the traffic is transferred. Active queue management technology is used to detect congestion and notify downflow nodes (users and routers) in time, which trigger downflow users to adjust the request sending window and intermediate routers to transfer packets. In addition, the original path traffic is restored in time through the path recovery policy. Firstly, the path traffic is recovered according to the congestion mark carried by the subsequent content packets. Secondly, the transmission frequency of the probe packets is calculated according to the information such as the packet queue length of routers and the available bandwidth of the congested path, and the probe packets are periodically sent to the congested path, and the traffic is sent to the congested path in time. The alternate path is transferred back to the original path. This paper implements the scheme in NS-3, and extensive experiments show that the proposed scheme outperforms the comparison schemes, which not only higher and more stable throughput can be obtained, but also shorter and more stable transmission delay can be obtained. In addition, the optimal path transmission volume and link utilization are improved. [ABSTRACT FROM AUTHOR]

The selection of a proper international freight transport route is one of the crucial tasks for decision-makers since it can affect costs, efficiency, and transportation performance. Besides, the selection of suitable and appropriate freight routes can also reduce external costs of transportation such as emissions, noise, traffic congestions, accidents, and so on. Route selection in international transportation is a complicated decision-making problem as many conflicting factors and criteria affect the assessment process. It has been observed that there is no mathematical model and methodological frame used for solving these selection problems, and decision-makers make decisions on this issue based on their own experiences and verbal judgments in the research process. Therefore, a methodological frame is required to make rational, realistic, and optimal decisions on route selection. From this perspective, the current paper proposes using the IVAIF CODAS, an extended version of the traditional CODAS techniques, and using the Atanassov interval-valued intuitionistic fuzzy sets (IVAIFS) for processing better the existing uncertainties. The proposed model is applied to solve the route selection, a real-life decision-making problem encountered in international transportation between EU countries and Turkey. According to the results of the analysis, option A6 (i.e., Route-6 (Bursa–Istanbul–Pendik–Trieste (Ro-Ro)–Austria–Frankfurt/Germany) has been determined as the best alternative. These obtained results have been approved by a comprehensive sensitivity analysis performed by using different MCDM techniques based on interval-valued intuitionistic fuzzy sets. Hence, it can be accepted that the proposed model is an applicable, robust, and powerful mathematical tool; also, it can provide very reliable, accurate, and reasonable results. As a result, the proposed model can provide a more flexible and effective decision-making environment as well as it can provide valuable advantages to the logistics and transport companies for carrying out practical, productive, and lower cost logistics operations. [ABSTRACT FROM AUTHOR]