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To study the geographical distribution characteristics of maritime traffic risks, statistical representations of potential accident scenarios and macro collision risk models were established, and the waters with higher maritime traffic risks were generated. To better evaluate the risk of ship collision candidates during routing waterways, an improved adaptive bandwidth kernel density estimation (KDE) is proposed. This proposed algorithm is used for evaluating risk reduction of the ship routing system, which schedules and adjusts maritime traffic in congested harbor waterways. Larger bandwidth can make the hot spot region more obvious on a global scale. Moreover, the bandwidth is positively correlated with the dispersion of points. Concerning the data with sparse point distribution, a larger bandwidth should be used whereas, for data with dense points of interest, a smaller bandwidth should be considered. The results show that the KDE, with optimized bandwidth, can fit the ship encountering distribution and obtain the frequent spots for ship encountering. The comparison between KDE results before and after the ship routing system shows that the hot spots of ship collision candidates are reduced after the ship routing waterway is established. [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]

Due to the rapid increase of the population in the world, traffic signal controlling and monitoring has become an important issue to be solved with regard to the direct relation between the number of populations and the cars' usage. In this regard, an intelligent traffic signaling with a rapid urbanization is required to prevent the traffic congestions, cost reduction, minimization in travel time, and CO2 emissions to atmosphere. This paper provides a comprehensive review of computer vision techniques for autonomic traffic control and monitoring. Moreover, recent published articles in four related topics including density estimation investigation, traffic sign detection and recognition, accident detection, and emergency vehicle detection are investigated. The conducted survey shows that there is no fair comparison and performance evaluation due to the large number of involved parameters in the abovementioned four topics which can control the traffic signal controlling system such as (computation time, dataset availability, and an accuracy). [ABSTRACT FROM AUTHOR]

Data are generated at an unprecedented rate and scale these days across many disciplines. The field of streaming data analysis has emerged as a result of new data collection and storage technologies in various areas, such as air pollution monitoring, detection of traffic congestion, disease surveillance, and recommendation systems. In this paper, we consider the problem of model estimation for data streams in reproducing kernel Hilbert spaces. We propose an adaptive supervised learning method with a data sparsity constraint that uses limited storage spaces and can handle nonstationary models. We demonstrate the competitive performance of the proposed method using simulations and analysis of the bike sharing dataset. [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]

In the classical two-player decision-making scenario, individuals may have different tendencies to take a certain action, given that there exists a sufficient number of neighbors adopting a particular option. This is ubiquitous in many real-life contexts including traffic congestion, crowd evacuation, and minimal vertex cover problem. Under best-response dynamics, we investigate the decision-making behaviors of heterogeneous agents on complex networks. Results of the networked games are twofold: for networks of uniform degree distribution (e.g., the lattice) and fraction of the strategy is of a linear function of the threshold setting. Moreover, the equilibrium analysis is provided and the relationship between the equilibrium dynamics and the change of the threshold value is given quantitatively. Next, if the games are played on networks with non-uniform degree distribution (e.g., random regular and scale-free networks), influence of the threshold-switching will be weakened. Robust experiments indicate that it is not the value of the average degree, but the degree distribution that influences how the strategy evolves affected by the threshold settings. Our result shows that the decision-making behaviors can be effectively manipulated by tuning the parameters in the utility function (i.e., thresholds) of some agents for more regular network structures. [ABSTRACT FROM AUTHOR]

The current work presented a comparative analysis of traffic demand and safety skills before and after control measures during the COVID-19 epidemic, acquired time-series change data curves, and constructed a prediction model after determining the trend of traffic demand over time. From a data analysis perspective, the paper draws some interesting conclusions about long span, coarse sampling studies. In terms of the study population, the paper did focus on the specificity of the global epidemic. Kuwait was selected as a case study. Traffic demand analysis was conducted using a Structural Equation Model (SEM), Auto-Regressive Integrated Moving Average (ARIMA), and safety skills questionnaire along with flow charts and demographic variables. These methods were utilized to study the impact of COVID-19 on traffic congestion and safety skills as well as to forecast the future traffic volumes. Results showed that traffic congestion had a significant reduction during COVID-19 as a result of the preventive safety measures taken to control the spread of the virus. Such reduced traffic volume was associated with a decrease in traffic violations and an increase in the safety skills and PM skills of drivers. [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]

This article demonstrates how anti-trafficking media use the public memory of the Underground Railroad to racially legitimize US global policing regimes. From far-right paramilitary vigilante groups to liberal multicultural public history institutions, the anti-trafficking industry's reverence for 19th-century Black women abolitionist icons is mobilized, counter-intuitively, to build public support for carceral agendas. Through visual analysis of the media of two exemplar organizations—Operation Underground Railroad and the National Underground Railroad Freedom Center—I unpack the racial dynamics of anti-trafficking's carceral humanitarianism and the racial politics of anti-trafficking's memory of transatlantic abolition. I argue that incorporating icons of radical Black freedom struggle, such as Harriet Tubman, into anti-trafficking's neoliberal carceral agenda becomes a racial alibi for the perpetuation of ongoing racialized state violence in the name of abolition. US policing is thus racially legitimized as a set of freedom-granting institutions amid the ongoing Black women-led freedom struggles that name policing's role in perpetuating antiblack state violence. [ABSTRACT FROM AUTHOR]

With the rise in traffic congestion in urban centers, predicting accidents has become paramount for city planning and public safety. This work comprehensively studied the efficacy of modern deep learning (DL) methods in forecasting traffic accidents and enhancing Level-4 and Level-5 (L-4 and L-5) driving assistants with actionable visual and language cues. Using a rich dataset detailing accident occurrences, we juxtaposed the Transformer model against traditional time series models like ARIMA and the more recent Prophet model. Additionally, through detailed analysis, we delved deep into feature importance using principal component analysis (PCA) loadings, uncovering key factors contributing to accidents. We introduce the idea of using real-time interventions with large language models (LLMs) in autonomous driving with the use of lightweight compact LLMs like LLaMA-2 and Zephyr-7b- α. Our exploration extends to the realm of multimodality, through the use of Large Language-and-Vision Assistant (LLaVA)—a bridge between visual and linguistic cues by means of a Visual Language Model (VLM)—in conjunction with deep probabilistic reasoning, enhancing the real-time responsiveness of autonomous driving systems. In this study, we elucidate the advantages of employing large multimodal models within DL and deep probabilistic programming for enhancing the performance and usability of time series forecasting and feature weight importance, particularly in a self-driving scenario. This work paves the way for safer, smarter cities, underpinned by data-driven decision making. [ABSTRACT FROM AUTHOR]

The congestion caused by ramp overflow has an essential impact on the energy consumption of autonomous vehicles. It is urgent to solve the energy waste of ramp overflow on connected and autonomous vehicles and improve the energy utilization rate. Aiming at the traffic congestion caused by ramp overflow, this paper obtains the data of autonomous vehicles and traffic flow based on vehicle road cooperative perception, analyzes the impact mechanism of vehicle energy consumption, and summarizes the energy consumption modes of autonomous vehicles into three categories. Second, an energy consumption evaluation framework is proposed based on the CSANS strategy. This strategy can make up for the deficiency of constructing neighborhoods within European distance, find important influencing variables on connected and autonomous vehicles' energy consumption, and accurately capture the manifold characteristics of energy consumption data. Finally, flow control is carried out from the macro perspective of traffic engineering to optimize ramp overflow's impact on autonomous vehicles' energy consumption. Through multiple groups of experiments, it has been found that it can effectively reduce the energy consumption of autonomous vehicles. [ABSTRACT FROM AUTHOR]

To study how traffic congestion affects logistics and transportation in terms of cost and carbon emission and countermeasures, we designed congestion-avoidance strategies to reduce vehicle waiting time and vehicle speed. The delivery time in a day is divided into three periods to calculate the roadway travel time under the congestion avoidance strategy. The mathematical models of cold chain logistics route planning with the objectives of minimizing carbon emissions and total costs are constructed, and the congestion-avoidance strategy is applied to plan the cold chain logistics route. An improved ant colony algorithm is proposed to solve the objective model. The reduced speed strategy generates fewer carbon emissions than the waiting strategy. The cost of distribution under different strategies is influenced by the form of distribution and the size of the customer. The results of the study show that the use of congestion-avoidance strategies can reduce the total cost of distribution and carbon emissions. The waiting strategy is more effective in general logistics, and the speed-reduction strategy is more effective in cold chain logistics. By comparing the carbon emissions generated for various average speeds under the reduced-speed strategy, the optimal driving speed is about 45 km/h. [ABSTRACT FROM AUTHOR]

In winter, ice on the roads causes traffic accidents and congestion. In recent years, various techniques have been developed to provide more effective, efficient, and environmentally friendly solutions to the problem of road icing. One of these is electrically conductive asphalt concrete (CAC). This study aims to develop an intelligent anti-icing system by increasing the conductivity and mechanical properties of asphalt concrete. Two different conductive asphalt series were produced. Series 1 uses a binder modified with 3% styrene butadiene styrene (SBS) block copolymer and Series 2 uses a binder modified with both 3% SBS and 14% carbon black (CB). In both series, 0.3% carbon fiber (CF) by weight was added to the dry mixture. Thermal dissipation status, ice melting performance, freezing, and electrical conductivity heating cycle tests were conducted. The long-term mechanical and conductivity performances of CAC specimens were assessed with pre-and post-cycle experiments. CAC, developed for its conductivity and mechanical properties, was integrated with the intelligent anti-icing system. A comparative cost analysis was also performed to assess conventional asphalt concrete and the CAC. The binder phase was found to contribute to the mixture conductivity. Such systems can be integrated with Intelligent Transportation Systems (ITS). [ABSTRACT FROM AUTHOR]

The advanced travel information system is a prominent intelligent transportation system for which variable message signs (VMSs) are the major components. In Iran, more attention has been paid to sign placement than to other components which are complementary to placement and combine to create relevant and effective messages for the driver. The current study used a stated preference survey to investigate the attitudes and reactions of drivers to VMS information that then were used to build models to reveal the reasons behind driver reactions to a VMS request to change route. The survey included variables affecting driver decisions, such as driver characteristics, attitude and perceptions of current VMS information and traffic in general. The influence of personality traits as subjective factors for driver behavior regarding route choice also was investigated. The results of the estimation of sequential hybrid binary logistic regression model showed that visual health had a significant positive effect on the driver decisions to divert to an alternate route when the VMS messages related to an accident or traffic congestion. Provision of expected delay information about the current route was found to be the most significant factor causing drivers to change route when a VMS indicated traffic congestion ahead. Personality traits such as Agreeableness, neuroticism, extraversion and openness significantly affected driver decisions about changing route when the VMS indicated traffic congestion or adverse weather conditions. [ABSTRACT FROM AUTHOR]

Recent advances in GPS and telecommunications technologies have made ridesharing a widespread practice around the world. By matching drivers and passengers with near-distance destinations and similar time schedules, ridesharing saves individuals travel costs by enabling them to share vehicles. It is also an effective way to reduce traffic congestion and greenhouse gas emissions. A ridesharing problem can be modeled as a dial-a-ride problem with time windows. This study looks at a solution to the dynamic ridesharing problem in which passengers share travel costs at the same ratio. We formulate the problem as a mixed-integer programming model and suggest a column generation approach. As the system's status is updated in real time, riders and drivers are matched, and new paths are created via column generation. Computational experiments show that our approach is superior to an existing algorithm when it is tested on instances of various sizes. [ABSTRACT FROM AUTHOR]

Road safety is a matter of paramount concern on a global scale, given the substantial loss of life and injury resulting from road accidents every year. This comprehensive study is dedicated to a meticulous examination of road safety awareness and responsible conduct among drivers and motorists in Zamboanga Peninsula, situated in the Philippines. The central aim of this research is to conduct a detailed evaluation of the current status of road safety awareness and the responsible behaviors of drivers and motorists within Zamboanga Peninsula. Our intention is to identify the pivotal factors that influence road safety awareness and to recognize the barriers obstructing the translation of this awareness into responsible road behavior. To accomplish this aim, the researchers have employed quantitative techniques. A meticulously structured questionnaire survey was administered to a diverse cohort of drivers and motorists, which facilitated the collection of multifaceted data encompassing demographics, the level of awareness regarding road safety regulations, and the determinants impacting their on-road conduct. The quantitative dataset underwent rigorous analysis employing an array of statistical methods, with the mean scores and ranking system employed to dissect the collected data. The outcomes of this comprehensive research endeavor unveil a myriad of critical insights. Foremost, a conspicuous gender disparity is evident among road users in Zamboanga Peninsula, with males constituting the majority. Intriguingly, a substantial proportion of drivers falls within the age bracket of 26-35, signifying the demographic significance of this cohort. On the aspect of road safety awareness, the study affirms the presence of a robust understanding of fundamental road safety principles, yet it highlights the imperative need for focused attention to areas such as overtaking rules and the usage of mobile phones during driving. The research points to inconsistent law enforcement and high levels of traffic congestion as the most influential factors contributing to deficiencies in road safety awareness. This comprehensive study serves to illuminate the complex road safety landscape in Zamboanga Peninsula. It underscores the compelling necessity for region-specific interventions that cater to the prevalent gender and age disparities among road users. Moreover, the research findings emphasize the pivotal roles of law enforcement, traffic management, education, and targeted awareness campaigns in the quest for an improved road safety scenario. [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]

The increasing pressure of traffic congestion on socio-economic development has made the construction of cross-water transportation ever more crucial. The immersed tunnel method is among the most extensively employed. However, a critical challenge of the immersed tunnel technique is to ensure the compactness and stability of concrete during the casting process. Conventional laboratory methods face challenges in achieving large-volume concrete casting, resulting in the notable waste of human and material resources. Hence, this study employs a simulation approach to investigate the casting parameters and the fresh properties of concrete, exploring their impacts on concrete stability and compactness. The results indicate that when the surface tension of concrete exceeds 0.03 N/m, and the yield stress and plastic viscosity are 50 Pa and 50 Pa·s, respectively, the concrete exhibits excellent casting compactness. A design incorporating three large and six small outlets, paired with a casting speed of 3 cm/s, achieves superior compactness. Additionally, when the yield stress of concrete exceeds 3 Pa, there is no segregation of aggregates. In cases where segregation occurs, the thixotropic property of the cement paste contributes to a significant reduction in the velocity of aggregate segregation. [ABSTRACT FROM AUTHOR]

• The deviations between measured information and true information are quantified by information uncertainty level. • A generic model is developed to describe the scenario where multiple information uncertainties exist. • The impacts of multiple information uncertainties on stability of connected automated vehicles flow are explored. • A novel car-following controller is developed to address the adverse effects of multiple information uncertainties. Connected automated vehicles have the capability to operate autonomously by monitoring real-time traffic information through on-board sensors, such as velocity and distance. However, no measurement can be perfect, and sensors are no exception, especially in challenging road and weather conditions, leading to the deviations between multiple information measured by vehicles and true information. Since the sizes of sensor detection errors are uncertain, we call this issue as multiple information uncertainties. This issue affects not only the normal operation of host vehicle directly but also the connected automated vehicular flow indirectly through wireless communication, resulting in the instability of car-following behavior and further deteriorating traffic congestion. So far, it is hard for us to obtain the repeatable, transferable, and even comparable results due to the lack of generic model. Therefore, this study develops a generalized model by using the uncertainty levels of multiple information to describe the dynamics of connected automated vehicles under the influence of sensor detection errors based on car-following theory. The theoretical and simulation-based investigations present a complete method to analyze the stability of traffic flow under multiple information uncertainties. Analytical results show that traffic stability will be reduced when the velocity measured by sensors is smaller than true velocity (i.e., negative uncertainty level of velocity information) or the headway monitored by sensors is bigger than real headway (i.e., positive uncertainty level of headway information), whereas the velocity and headway of equilibrium state will be enlarged. Otherwise, the opposite. These findings indicate that the impacts of multiple information uncertainties are double-edged swords, depending on the uncertainty levels of different information. To improve the adverse impacts of multiple information uncertainties on traffic stability, this study proposes a novel car-following controller and verifies its effectiveness. Overall, the present study provides a set of theoretical frameworks to investigate and improve traffic stability under multiple information uncertainties. All results contribute to enhancing the stability of traffic flow and further easing traffic congestion. [ABSTRACT FROM AUTHOR]

• We studied the advanced air mobility aircraft scheduling problem at vertiports. • We considered separation rules at touchdown and lift-off pads and blocking constraints. • Two mixed integer linear programming formulations are presented for optimally solving small instances. • We propose two heuristic algorithms for solving real-life sized instances. • The computational results provide insights into vertiport operations. Recent progress in electric vertical take-off and landing (eVTOL) vehicles suggests that soon these vehicles could safely and efficiently transport people and cargo in urban areas. Therefore, advanced air mobility vehicles could become an alternative means of transport to overcome traffic congestion in cities in the upcoming years. There has been enormous interest from companies and governments in recent years in developing such technologies and enabling markets for new air transportation services. Despite the interest in the topic, little research has been done to address the aircraft scheduling problem in advanced air mobility take-off and landing areas (vertiports). The vertiports serve as the airports of eVTOL vehicles and could experience congestion problems similar to those of airports. This work proposes two optimization models for scheduling departing and landing aircraft at the vertiports' common ground taxi routes (taxiways), gates, and touchdown and lift-off (TLOF) pads. The mathematical models include advanced air mobility features such as separation rules and blocking constraints. As scheduling objectives, the first model maximizes the vertiport throughput, and the second model minimizes the deviation from the expected take-off/landing time. In addition, as a solution methodology, we developed two heuristic algorithms that use scheduling rules to assign and sequence the aircraft to the vertiport components. Computational results show that the optimization models find optimal schedules for small-sized instances of up to 10 aircraft, while the heuristic algorithms provide good results in terms of solution quality and computational time for large instances. [ABSTRACT FROM AUTHOR]

Reasonable transportation planning policies can effectively reduce traffic congestion, improve traffic operation efficiency, and improve the quality of residents' travel. The paper studied the correlation analysis between rail transit policies and traffic congestion under different urban scales. Firstly, construct a system of factors affecting rail transit congestion and implement clustering analysis of rail transit data based on an improved k-means clustering algorithm. Then, normalize the factors that affect rail transit congestion, and use the normalized traffic congestion coefficient factors as a reference sequence. Finally, the grey entropy correlation analysis method is used to calculate the entropy correlation between urban scale factors, rail transit policy factors, and traffic congestion. The research results of correlation analysis indicate that urban scale factors and rail transit policy factors are highly correlated with traffic congestion; The biggest factor affecting traffic congestion is the comprehensive density distribution policy of population and jobs, while the smallest factor is the population density of employees. [ABSTRACT FROM AUTHOR]

One of the main impediments of good urban transportation system is poor parking policy and its management. Poor parking management is the reason for high traffic congestion and climate pollution [1], so the importance of a paid parking policy has received much attention. The research thoroughly examines the impact of the cost of parking in both on-street and off-street locations on the demand for parking spaces in the central business district (CBD) of Silchar. It also estimates the relationship between them by controlling income, frequency of visit, occupation, trip purpose, family size, car ownership, mode of travel, average visitor per hour, on-street and off-street parking price. Four locations in Silchar were selected for this study. Linear regression model has been used for estimating the parking demand using SPSS. Two separate model have been used for estimating the on-street and off-street parking demand. The result shows the R2 values of 0.57 and 0.58 for on-the street and the off-street parking model, respectively. All variables show significant influence on parking demand except occupation. [ABSTRACT FROM AUTHOR]

Based on big data analysis technology, the sustainable travel prediction of intelligent transportation system (ITS) monitors road network congestion in real time and predicts future transportation demand, thus adjusting traffic signals in a targeted manner, optimizing route planning, reducing traffic congestion, and improving road capacity. The highly dynamic traffic conditions are influenced by various factors, such as weather, holidays, and emergencies, which leads to limitations of existing methods, possibly causing unsatisfactory prediction results in practical applications. Therefore, this research studied the sustainable travel prediction of ITS based on big data analysis technology. First, the sustainable travel demand prediction of ITS in the context of big data was defined. Then the spatial correlation of urban areas in the sustainable travel prediction was modeled based on Graph Attention Network (GAT), thus improving the demand prediction accuracy of the model. In order to further improve the accuracy and practicality of prediction, various spatial dependence of sustainable travel demand was modeled using multi-graph convolutional network (GCN). Finally, the experimental results demonstrated the constructed model was effective. [ABSTRACT FROM AUTHOR]

Radius of road curvature and the information of preceding vehicles have a significant influence on traffic flow in the vehicle-infrastructure cooperation environment. However, the existing car-following model is subjective in considering the type of preceding vehicles' information, and does not comprehensively consider the synergistic effect of road curvature radius and preceding vehicles' information. Therefore, in this study, an intelligent driver model for curved roads considering multi-preceding vehicles' information is established in the vehicle-infrastructure cooperation environment. Then, using the Lyapunov method to analyze the stability of the extended model, the linear stability condition is obtained, and the influence law of radius of road curvature and weight coefficient of the preceding vehicle on traffic flow is mastered. Finally, the theoretical analysis of the model is verified by numerical simulation experiments. The simulation results are consistent with the theoretical analysis, and both show that the stability of traffic flow increases with the increase of radius of road curvature and weight coefficient of the preceding vehicle. This shows that the extended model can not only accurately describe the car-following behavior of vehicles on a curved road in the vehicleinfrastructure cooperation environment but also stabilize the traffic flow and suppress traffic jams. [ABSTRACT FROM AUTHOR]

In order to alleviate traffic congestion, transfer the regional orientation of macroscopic traffic flow to microscopic routes, and form a unified and coordinated framework of macro path decision and micro path decision, a vehicle route hierarchical control strategy is proposed from the perspective of network macro aggregated information. Firstly, according to the homogeneity of vehicle density distribution, a network is divided into several sub-regions, and then the macro-aggregate dynamic mapping relationship of each sub-area is analyzed. Secondly, a traffic equilibrium distribution model is established under heterogeneous macro path conditions to minimize the travel time of traffic flow. To bring macro traffic flow solved by the above model to the projected into the actual network level, the logit route model is used to guide macro traffic flow to the micro-scale road network with the help of a simplified network. Finally, the SUMO simulation platform is employed to achieve the solution and verification of the hierarchical vehicle path control method. The experimental results prove that the strategy can effectively reduce the congestion of the urban road network during peak hours and improve the traffic capacity of the road network. It is also demonstrated that macroscopic aggregate information is one of the reliable information sources for vehicle path control and guidance in a road network. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel approach for alleviating traffic congestion in freeways with multiple access locations through the use of dynamic toll pricing. The pricing strategy is determined using model-based feedback control, with the underlying model derived using a combination of both traffic flow modeling and driver behavior. The traffic segment we focus on is a suburban freeway with multiple access locations. A model derived from the cell transmission method was utilized to develop the traffic flow model, with past traffic information from on-road sensors utilized for determining the model parameters. The behavior of the driver with respect to the choice of whether or not to enter the freeway segment is modeled using utility theory and the Value of Time (VOT) relative to the toll value. The proposed toll-pricing scheme is tested with traffic data from Portuguese freeway A5 and with different hypothesis on the driver's VOT distribution, showing a significant improvement of the overall traffic conditions. The algorithm developed here provides an opportunity to improve on existing toll policy by guaranteeing more stable traffic conditions for the freeway users and optimizing the overall traffic throughput. [ABSTRACT FROM AUTHOR]

The combination of various Intelligent Transportation System (ITS) strategies generally has manifold effects, much greater than when strategies are implemented individually. This research introduces a novel dynamic control strategy, which includes the dynamic use of a Model Predictive Control (MPC) based Variable Speed Limit (VSL) integrated with the dynamic use of Hard Shoulder Running (HSR). Both VSL and HSR are proactively triggered to allow for necessary control measures to be taken to delay and possibly avoid the formation of a bottleneck. For traffic prediction, a modified METANET model is developed which takes into consideration the complex nature of driver's behavior along with driver's compliance, capacity drop and posted speed limits. The modified METANET model is shown to be more efficient than conventional macroscopic prediction models in detecting traffic congestions. This MPC based strategy was tested on a section of Deerfoot Trail, Calgary, Alberta using an exclusively developed integrated VISSIM-COM-MATLAB interface. The results from this study suggested that the integrated VSL and HSR control strategy results in a 21.09% increase in average speed and 33.44%. in vehicle-throughput. Furthermore, there was a noticeable reduction in the average travel time by 39.98% and in the total number of stops, by 32.43%. Importantly, the safety analysis performed using Surrogate Safety Assessment Model (SSAM) revealed a notable reduction in collisions, by 29.73%. [ABSTRACT FROM AUTHOR]

Increasing visitor numbers in wildlife tourism destinations creates a number of issues in relation to visitor management. Developing traffic and visitor policies, while taking into consideration the varying desires and needs of different visitor and organisational stakeholder groups, is a daunting task. Using the situation in the Kruger National Park (KNP) in South Africa, this paper demonstrates how through the combined use of focus groups and the Delphi technique, negotiated stakeholder solutions can be reached between seemingly opposed wildlife tourism and conservation user groups. Traffic congestion has been a major problem in the southern section of the Kruger National Park and has contributed towards reduced visitor satisfaction with the wildlife watching experience in the Park. Focus group discussions with nine different stakeholder groups identified major traffic related problems that participants agreed needed to be addressed and an extensive range of suggestions for improving perceived traffic congestion problems. Applying a Delphi process to these suggestions resulted in a high level of consensus across all stakeholder groups regarding actions management should take to address traffic problems in the Park. [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]

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

In the realm of transportation system optimization, enhancing overall performance through the proactive coordination of traffic guidance and signal control in a divergent network can tackle the challenges posed by traffic congestion and inefficiency. Thus, we propose an innovative approach to first allow the information on variable message signs (VMS) that deviates from estimated travel times. This proactive approach guides drivers towards optimal routes from a system-wide perspective, such as minimizing vehicle hours traveled. The deviation is constrained both by the lower bound of drivers' long-term compliance rate and the upper bound of the favored traffic signal operation. The proposed approach coordinates the traffic guidance system with the signal control system. The traffic signal control system sets the upper limit for information deviation in the traffic guidance system, while the traffic guidance system provides demand predictions for the traffic signal control system. Overall, the objective function of the approach is the network-level performance of all users. We gauge traveler satisfaction as a measure of system credibility, using both a route choice module and a satisfaction degree module established through stated preference surveys. Numerical results demonstrate that proactive-coordinated (PC) strategies outperform reactive-coordinated (RC), proactive-independent (PI), and reactive-independent (RI) strategies by improving the system performance, meanwhile keeping the system trustworthy. Under the normal traffic scenario, the PC strategy reduces total travel time by approximately 10%. Driver satisfaction with the PC strategy increases from a baseline of 76% to 95%. Moreover, in scenarios with sudden changes in either traffic demand or supply, e.g., accidents or large events, the proactive guidance strategy is more flexible and can potentially improve more from the system perspective. [ABSTRACT FROM AUTHOR]

The timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into account external factors such as weather and accidents is still a challenge. In this research, we propose a hybrid deep learning framework, double attention graph neural network BiLSTM (DAGNBL), that utilizes a graph neural network to represent spatial characteristics and bidirectional LSTM units to capture temporal dependencies between features. Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. It can be utilized as a useful tool for predicting traffic flow on certain stretches of road. [ABSTRACT FROM AUTHOR]

The high level of population and activities in the city of Mataram also increases the use of vehicles as a secondary need for the community to help with daily activities in land transportation. As the use of vehicles increases, the demand for fuel oil (BBM) also increases. Fuel is a material that can be converted into energy by burning fuel where the fuel contains heat energy that can be released and manipulated. Vehicle emissions are a major source of pollution in big cities in Indonesia. CO2 is a compound consisting of the elements carbon and oxygen with the writing CO2. Carbon dioxide is a gaseous element that exists at atmospheric temperature and pressure. The hope for this research is to determine CO2 levels in the city of Mataram and prevent CO2 emissions from increasing. The results of calculating the highest vehicle volume are located in segment III Jalan Majapahit of 3506 pcu/hour which occurs at 17:00 - 17:15 WITA. The results of the regression analysis between vehicle volume as the X variable and CO2 gas emissions as the Y variable on the five road segments conducted by the study were y = 134.57 ln(x) - 970.14 with an R2 value of 0.8661 included in the 0.80 interval – 1,000 which means that the strength of the relationship between vehicle volume and CO2 emissions has a very strong relationship and vehicle volume contributes 86.6165% to CO2 emissions. Based on the results of the discussion that was carried out, the CO2 gas emission levels in all road segments carried out had an average CO2 gas emission level value of 103.31 – 108.56 kg/hour. [ABSTRACT FROM AUTHOR]

Generally, urban traffic management evaluates micro-scale projects based on traffic indices, such as travel time, that ignore the sustainability criteria and impacts of changes on adjacent areas. Therefore, the aim is to assess the sustainability of micro-scale traffic congestion removal projects on highways with a wider vision. Five proposed scenarios were evaluated using ten sustainable transportation indicators (STIs) that represent various aspects of sustainability. Scenarios were simulated in Aimsun, and STIs were derived directly and indirectly from the outputs. The results showed that even small changes could improve the operation of the network. Finally, a decision-making method was implemented to prioritize the scenarios. The combination of STIs based on the criteria for the Tehran 2025 horizon as a composite sustainability index (CSI) helped to determine impacts of each scenario. Although all scenarios improved traffic indices of the network, some failed sustainability criteria. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to store the local area roads' average speed of the vehicles. Second, the discrete snapshot set is proposed to characterize traffic flow's spatial and temporal features over a continuous period. Third, the evolution of traffic congested flow in various time dimensions (weekly days, weekend days, and one week) is examined by transforming the global urban transportation network into traffic zones. Finally, the data-driven model is constructed to predict urban road traffic congestion by using the extracted spatio-temporal characteristics of traffic zones' traffic flow, the snapshot set of which serves as inputs for this model. The model adopts the convolutional LSTM network to learn the temporal and local spatial features of traffic flow, while utilizing a convolutional neural network to effectively capture the global spatial features inherent in traffic flow. The numerical experiments are conducted on two cities' transportation networks, and the results demonstrate that the performance of the proposed model outperforms traditional traffic flow prediction models. [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]

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]

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]

The global concern over traffic safety conditions has escalated due to the growing number of vehicles and pedestrians. Governments are putting significant efforts into implementing systems to alleviate and reduce traffic congestion. This study aimed to identify the factors contributing to traffic congestion in the 2nd district of Albay through quantitative research. It examined traffic congestion status in terms of Data Traffic Volume, Rush Hour, Classification of Vehicles, and Road Users. The research pinpointed key contributors to congestion, including insufficient road infrastructure, high vehicle density, rapid population growth, poor public transport, and road quality. A survey questionnaire was administered to personnel from Camalig MPS, Daraga MPS, and Legazpi City Police Station, as well as Public Safety Officers from the mentioned areas. The community, representing riders and commuters in the Poblacion area, also participated. The findings revealed that traffic congestion is prevalent in the 2nd district of Albay, primarily driven by traffic volume, rush hour, vehicles, and road users. As a result, the study emphasizes the need for a standardized traffic management framework to be implemented. This framework aims to coordinate stakeholders' perceptions regarding the factors contributing to traffic congestion, serving as a guide for effective traffic management in the region. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is an effective strategy to assess traffic conditions and alleviate traffic congestion. Influenced by external non-stationary factors and road network structure, traffic flow sequences have macro spatiotemporal characteristics and micro chaotic characteristics. The key to improving the model prediction accuracy is to fully extract the macro and micro characteristics of traffic flow time sequences. However, traditional prediction model by only considers time features of traffic data, ignoring spatial characteristics and nonlinear characteristics of the data itself, resulting in poor model prediction performance. In view of this, this research proposes an intelligent combination prediction model taking into account the macro and micro features of chaotic traffic data. Firstly, to address the problem of time-consuming and inefficient multivariate phase space reconstruction by iterating nodes one by one, an improved multivariate phase space reconstruction method is proposed by filtering global representative nodes to effectively realize the high-dimensional mapping of chaotic traffic flow. Secondly, to address the problem that the traditional combinatorial model is difficult to adequately learn the macro and micro characteristics of chaotic traffic data, a combination of convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM) is utilized for capturing nonlinear features of traffic flow more comprehensively. Finally, to overcome the challenge that the combined model performance degrades due to subjective empirical determined network parameters, an improved lightweight particle swarm is proposed for improving prediction accuracy by optimizing model hyperparameters. In this paper, two highway datasets collected by the Caltrans Performance Measurement System (PeMS) are taken as the research objects, and the experimental results from multiple perspectives show that the comprehensive performance of the method proposed in this research is superior to those of the prevalent methods. [ABSTRACT FROM AUTHOR]

The demolition of old bridges and the construction of new bridges are obviously not as good as retaining old bridges through the transformation in a short period of time. To continue using the old bridge and implement the new bridge and the old bridge at the same elevation, the old bridge overall jacking of 2.57 m is the only feasible plan (Figure 2). The old bridge in the middle is retained and still used, and the new bridge on both sides is adopted to broaden the bridge. [Extracted from the article]

The increase in the number of vehicles brings parking needs in city centers. Many studies are being carried out to solve the problem and new parking lots are constructed. With the developing technology, smart systems are effectively used to increase the capacity of existing car parks. Unfortunately, both putting new car parks into service and equipping existing car parks with technological facilities cannot solve the problem. This highlights that public transportation is used effectively instead of vehicles. On the other hand, the idea of enabling people to reduce parking time is a partial solution. Thus, new smart parking mobile application called “Let's Share a Single Vehicle Park-TAPP was developed conceptually. In the system, utilization period must be specified by the user. Users who specify the duration of utilization are rewarded as a fee when they comply. If they do not, they are faced with overpayment and parking utilization restriction. The parking capacity will be planned instantly and an effective operating performance will be achieved. The factors affecting the proposed system were determined by SWOT analysis and the weighting of each factor using the AHP method was obtained by pairwise comparisons. With the A'WOT analysis, it has been determined that the increase in the need for parking spaces with the increase in the number of vehicles. The need for parking, which is the most important factor due to the increasing number of vehicles, shows the need for such innovative systems when all general factor weights of factors are evaluated together. [ABSTRACT FROM AUTHOR]

Traffic congestion is a pervasive issue that affects urban areas worldwide. This study investigates congestion on urban roads in an Indian smart city. Video graphic technique was employed to collect data across various traffic volumes. Travel time for different vehicle categories was estimated, and the travel time reliability congestion index was assessed. Multiple linear regression modelling was utilized to assess the attributes influencing congestion. Traffic signal synchronization and enforcement of traffic regulations, decentralization of important offices, and regulating roadside activities received strong recommendations from the majority of inhabitants. For improving the functionality and accessibility, related to the urban road network, these attributes should be enforced. The research intended to assess the functional effectiveness of urban roads using travel time reliability measures. [ABSTRACT FROM AUTHOR]

Traffic congestion incurs long delay in travel time, which seriously affects our daily travel experiences. Exploring why traffic congestion occurs is significantly important to effectively address the problem of traffic congestion and improve user experience. Traditional approaches to mine the congestion causes depend on human efforts, which is time consuming and cost-intensive. Hence, we aim at discovering the known and unknown causes of traffic congestion in a systematic way. However, to achieve it, there are three challenges: (1) traffic congestion is affected by several factors with complex spatio-temporal relations; (2) there are a few samples of congestion data with known causes due to the limitation of human label; (3) more unknown congestion causes are unexplored since several factors contribute to traffic congestion. To address above challenges, we design a congestion cause discovery system consisting of two modules: (1) congestion feature extraction module, which extracts the important features distinguishing between different causes of congestion; and (2) congestion cause discovery module, which designs a deep semi-supervised learning based framework to discover the causes of traffic congestion with limited labeled data. Specifically, in pre-training stage, it first leverages a few labeled data as prior knowledge to pre-train the model. Then, in clustering stage, we propose two different clustering methods to discover the congestion causes. For the first clustering method, we extend the classic deep embedded clustering model to produce clusters via soft assignment. For the second one, we iteratively use k-means to group the latent features extracted from the pre-trained model, and use the cluster results as pseudo-labels to fine-tune the network. Extensive experiments show that the performance of our methods is superior to the state-of-the-art baselines, which demonstrates the effectiveness of the proposed cause discovery system. Additionally, our system is deployed and used in the practical production environment at Amap. [ABSTRACT FROM AUTHOR]

Using an existing coordination (traffic) experiment, we investigate information's effect on traffic congestion when subjects already have a history of past play. In contrast to previous studies, our interventions neither alter aggregate nor individual payoffs. A second study isolates individual‐subject response to information using a fixed distribution of past subjects. We find information alters subject play: subjects switch roads more often and receive higher payoffs conditional on switching roads. Because switching reduces payoffs unconditionally, information does not generally improve payoffs overall. Only subjects that receive information upon starting the game appear to increase their payoffs due to the information treatment. [ABSTRACT FROM AUTHOR]

Along with reducing traffic congestion, electric car-sharing (ECS) can also solve travel demand, utilize idle resources, and enhance forms of transportation. Accordingly, university students are identified in Guangzhou, China as a promising customer group for ECS companies. Although university students in Guangzhou, China are unique in the context of ECS, few researchers have studied the factors that influence their willingness to use such services. Hence, this paper investigates the key determinants that affect university students' behavioral intention (BI) to use ECS in Guangzhou, China. Based on an extension of the unified theory of acceptance and use of technology (UTAUT), this paper examines the effects of the constructs on BI to use ECS and the moderating role of trust in ECS. Based on results from a questionnaire of 486 university students in Guangzhou, China, conditional value (CV) was the most critical driver of BI, followed by personal attitude (PA) and sustainability (SUST). The moderating analysis showed that taking gender, education, and driving experience of university students as control variables, trust was found to have a significant positive moderating impact on the relationship between PA and BI to use ECS, and a significant negative moderating effect of trust on the relationship between CV and BI to use ECS. The implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

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

With the rapid growth of trajectory big data, there is a need for more efficient methods to extract, analyze, and visualize these data. However, existing research on trajectory big data visualization mainly focuses on displaying trajectories for a specific period or showing spatial distribution characteristics of trajectory points in a single time slice using clustering, filtering, and other techniques. Therefore, this paper proposes a vector field visualization model for trajectory big data, aiming to effectively represent the inherent movement trends in the data and provide a more intuitive visualization of urban traffic congestion trends. The model utilizes the motion information of vehicles to create a travel vector grid and employs WebGL technology for vector field visualization rendering. The vector field effects are effectively displayed by generating many particles and simulating their movements. Furthermore, this research also designs and implements congestion trend point identification and hotspot congestion analysis, thus validating the practicality and effectiveness of trajectory big data vector field visualization. The results indicate that compared to traditional visualization methods, the vector field visualization method can demonstrate the direction and density changes in traffic flow and predict future traffic congestion. This work provides valuable data references and decision support for urban traffic management and planning. [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]