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Traffic management is a critical activity, the population is increasing day by day and so the traffic on the road is also increasing. Traffic jams and long waiting queues of vehicles at the road crossing are now part of everyone's life. The traffic lights used at the crossing to regulate the traffic play a vital role in the smooth functioning of traffic movement. At a crossing of four roads, it has been observed that giving an equal amount of green light to all roads is meaningless since the arrival of traffic on different paths is different. Importantly, the arrival rate is responsible for all traffic jams, long queues, and increased waiting time. Therefore, this paper suggests a green light allocation scheme for all paths i depending on the arrival rate of the vehicles. Thus, the allocation of green light will be dynamic. Further, weight is also computed, where more arrival rate means more weight, thereby assigning more time to the green signal. This will help in reducing the long queue length, residual traffic, and long waiting times. On simulating the traffic with the traffic data, the proposed optimized green light allocation scheme to path i reduces the residue traffic to negligible, allowing smooth traffic flow even during peak hours. The work also provides a proficient optimization of the waiting time of vehicles accumulated during the red light. According to the simulation, the maximum time assigned for the green signal during the peak hour of 9:30 AM to 10:00 AM for paths i , where 1 ≤ i ≤ 4 is 39.96, 33.36, 26.64, and 20.04 seconds respectively. Similarly, during the second rush hour of 5:00 PM to 6:00 PM, the simulation assigns a green signal time of 41.4, 37.2, 24.84, and 16.56 seconds for corresponding paths 1–4. Thus, the proposed work suggests an effective traffic management scheme at the four-road crossing. [ABSTRACT FROM AUTHOR]

Constructing new roads and upgrading existing roads are two common approaches to address congestion caused by increasing traffic volumes. But they can also impact local communities if roads are built through neighborhoods or existing roads and intersections are widened and placed closer to nearby homes. In this paper, we estimate how local communities are impacted by these changes using data on housing prices near roads and roadway construction projects. We find that homes near major roads sell for less than homes further away, but that congestion-reducing road improvements only lead to small average increases in nearby home prices. Our results suggest that major roads in close proximity negatively impact housing prices by affecting neighborhood aesthetics, and this relationship is not contingent upon the level of traffic on the roads. Our results can help policymakers consider the external costs borne by local communities when conducting cost–benefit analyses of roadway expansions or improvements. • Homes near roadways face price penalty, regardless of traffic on the roadway. • Roadway construction increases home prices only slightly. • Co-located amenities likely mute effect of traffic on home prices. [ABSTRACT FROM AUTHOR]

Today, fossil fuels used in urban transportation vehicles have great harm to the environment. In order to minimize these damages, the demand for transportation vehicles that use renewable energy sources is increasing day by day. Their high energy efficiency, zero emission values, not causing traffic congestion, saving time, no parking problems and being affordable have made the use of electric scooters (e-scooter) more advantageous than other means of transportation. Studies carried out to increase the performance of e-scooters, which have become popular in modern city life, are of great importance. With this study, we aimed to eliminate the problems encountered in electric scooters, such as short charging life of the battery, long charging times and most importantly, short driving range. For this purpose, a scooter simulation model was proposed in which a fuel cell, the energy source of the future, is used instead of a battery in an electric scooter. In addition, it is powered from the battery to be able to compare the results obtained and a scooter model with regenerative recovery was also realized. The dynamic behaviors of both models presented are compared for the first time according to long-term driving cycle conditions, and it is aimed to provide motivation to the researchers for future studies on this subject. In order to make the driving conditions more realistic in the simulation were chosen from the most popular inner city driving cycles, the European Economic Commission-15 (ECE 15), the Worldwide Harmonized Motorcycle Emissions Certification Procedure (WMTC) and the Assessment and Reliability of Transport Emission Models and Inventory Systems (ARTEMIS) Urban driving cycles. During these driving cycles, the performance of the scooter models was compared and the results were evaluated. From the results obtained from three different driving cycles, it is seen that a scooter designed using a fuel cell can provide over 120% increase in range compared to a battery scooter. [Display omitted] • PEMFC use is recommended in e-scooters. • The system has been tested for different cycles in e-scooter model. • The obtained results are given graphically. • Results show the advantages of the model. [ABSTRACT FROM AUTHOR]

Transportation is an economic sector that contributes significantly to global warming due to its high consumption of fossil fuels, and sustainably produced hydrogen is a major contender for an alternative clean energy source. Public transit is vital for environmental sustainability via reducing individual vehicle usage and traffic congestion, and the prospect of powering buses using hydrogen fuel has been extensively studied lately. This paper seeks to comprehensively review the current status of research on hydrogen-powered buses considering triple bottom line sustainability perspectives. A brief technical overview of prospective environmentally benign hydrogen production processes has been presented. Technological, economic, and environmental findings and research trends seen in recent analyses on hydrogen-powered buses have been summarized, along with the status quo of global hydrogen refuelling stations. Identified focal points for future studies include performance enhancements, refuelling infrastructure propagation, and policy formulation. The conclusions derived from this review will benefit the accelerated deployment of hydrogen-fuelled public transit fleets. • Environmentally benign hydrogen production is reviewed techno-economically. • The prospects of using hydrogen for public bus transport are discussed thoroughly. • Blue and green hydrogen production are likely to coexist in the hydrogen economy. • Costs and energy consumption of hydrogen-powered buses need improvement. • Refuelling facility deployment and public awareness of safety should be enhanced. [ABSTRACT FROM AUTHOR]

Governments have implemented many regulations to solve traffic congestion and pollutant emissions. Traffic restriction (TR) policy is the most common and widely used traffic regulation. However, it is difficult to alleviate traffic pollution through TR policies. Only a few studies focus on TR policies in multiple cities. In this paper, we classify traffic restrictions by geographical factors and evaluate the impact of different policies based on PSM-DID first. Then, we classify the mixed restrictions and test their effects on air pollution. The results show that TR policy improves air quality regardless of the season. Moreover, the effect of TR policy is not affected by the superposition of the type of traffic restriction. For cities implementing the same TR policy, geographic factors can lead to different outcomes. This finding provides a reference for governments in reducing air pollution. • Traffic restrictions significantly affected air quality in different regions of China. • Cities implemented traffic restrictions have improved air quality in adjacent cities. • Effect of traffic restrictions does not depend on the number of mixed restrictions. [ABSTRACT FROM AUTHOR]

• Different motives explain acceptability of Connected Automated Vehicles. • Safety, instrumental, and environmental attributes most important for acceptability. • Perceived adoption norm directly and indirectly influences acceptability. Connected Automated Vehicles (CAVs) could be dominating the roads in the near future. CAVs are fully automated vehicles equipped to communicate and share data with other devices both inside and outside the vehicles, and can increase traffic safety and decrease greenhouse emissions from traffic, as they can ensure a more efficient traffic flow and reduce traffic jams. However, CAVs can only achieve this potential when they are accepted and widely adopted by the public. In this paper, we propose a model to explain the acceptability (i.e. evaluation before experience) of CAVs. We hypothesize that the acceptability of CAVs is higher when people evaluate its attributes more favourably, feel more able to use CAVs (i.e. higher perceived behavioural control), and think close others would consider adopting CAVs (i.e. the perceived adoption norm). We identified seven key attributes that could be important for the acceptability of CAVs, namely: safety, instrumental, hedonic, control, symbolic, environmental, and trustworthiness attributes. Results from a large-scale online questionnaire (N = 3783) showed that the proposed model explains acceptability well. Together, the evaluation of attributes of CAVs, perceived behavioural control, and perceived adoption norm explained 60 % of variance in acceptability. Positive evaluations of attributes were the strongest predictor of acceptability of CAVs, in particular safety, instrumental, and environmental attributes. Interestingly, we found that symbolic attributes predict acceptability better when the perceived adoption norm is low. The results suggest the acceptability of CAVs may be enhanced by improving the evaluations of its key attributes and by introducing it as a status product in the early adoption phase. [ABSTRACT FROM AUTHOR]

In recent years, vehicle traffic has become a major issue, with a significant increase in the number of vehicles leading to congestion on urban roads and motorways. This has resulted in an increase in accidents, causing more injuries and fatalities. In particular the cases with seriously injured road users who may not survive due to traffic congestion delaying emergency services. This paper tries to analyse the importance of the response time of the emergency services during road accidents, using real data from a hybrid environment (motorway and urban road). The presented model in this research aims to identifying the traffic problems, where the traffic lights are found to cause congestion issues and high travel times, even with low traffic volumes. The implementation of a traffic lights optimization system can be a crucial element in survival chances. The study also shows that an increase in traffic volume leads to drastic traffic congestion, highlighting further the importance of the traffic light optimization system. [ABSTRACT FROM AUTHOR]

There has been a significant growth in the number of private automobiles in the previous decade as a result of technological advancements and changes in people's lifestyles. This creates a major problem of congestion main reason being the transportation infrastructure's inadequately construction and maintenance, which is the case in most major cities. By broadcasting the vehicle's current location and surroundings in real time to other adjacent cars and roadside devices, VANETs may help alleviate traffic congestion by using intelligent algorithms to anticipate and avoid potential bottlenecks on the route. This can also be used to predict the shortest path from initial location to destination making it easy for the drivers in the choosing the right path. To implement all these functionalities, we need a robust and secure communication channel and also an intelligent and complex algorithm. Our aim is to predict a lane with least probability of accidents and find the shortest path with least travelling time and congestion. This keeps the user informed regarding the surrounding scenario's and makes more coordinated, safer, and optimal use of transport networks. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a critical issue in metropolitan areas, inflicting significant hardships on individuals on a daily basis. This adverse situation poses multifaceted challenges, including increased air pollution, heightened fuel consumption, and compromised road safety. Deep learning algorithms, encompassing both Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) have showcased remarkable performance in areas such as computer vision, natural language processing, healthcare, finance, and autonomous systems. This research paper proposes a novel methodology to develop a vehicle detection model - VehiNet, which can subsequently be refined to create an automated traffic signal system. The study explores the effectiveness of various CNN, RNN, and deep learning algorithms and techniques in accomplishing this objective. The dataset picked for the study in addition to being compiled from internet resources, was also painstakingly assembled manually using a variety of methods to collect over 2000 photographs from various angles, lighting conditions, and locations within Bengaluru city. The main motive is to apply CNN, RNN, and other Deep Learning classification techniques for object detection and classification of Indian vehicles and see how well these deep learning algorithms and techniques affect prediction efficiency. This research stands out from others and makes its novelty clear thanks to the carefully collected dataset and the detailed analysis of the methodologies. [ABSTRACT FROM AUTHOR]

Online ride-splitting has rapidly evolved worldwide, providing new means to enhance the efficiency of urban travel and reduce the number of vehicles and traffic congestion. While ride-splitting is currently believed to improve vehicle occupancy rates and decrease total travel distances, passengers may perceive it as resulting in additional travel due to ride sharing. To gain a more intuitive understanding of the current operations of ride-splitting, this study delves into carpooling behavior and analyzes its relative impact using GPS trajectory data from DiDi Chuxing in Chengdu, China. The investigation focuses on the behavior of ride-splitting and examines its relative impact based on disparities in total vehicle miles traveled (VMT) and travel duration between ride-splitting traveling (RST) and corresponding separated traveling (ST), aiming to estimate the effect of RST on urban mobility. Following are the three main findings of this study: (a) The existing operation of RST induces 7%–10% extra VMT and 20%–35% extra duration for RST users; (b) The RST achieves 30%–35% VMT and 10%–20% duration saving for drivers who are willing to provide RST service; (c) The RST saves up to 10% VMT and duration varying by time in a day. This paper offers some insights to quantify the current impact of RST and evaluate the orientation of mobility service quality improvement. • Introducing a novel approach to analyzing ride-splitting behaviors. • By leveraging rich GPS data, the research provides a comprehensive analysis of ride-splitting behaviors. • Identifying key limitations, including low ride-splitting rates, prompting future research directions to address this issue. • Providing valuable suggestions and emphasizing the integration of psychological perception benefits. [ABSTRACT FROM AUTHOR]

• The study investigated the psychological experiences of regular commuters in traffic congestion using a three-phase sequential exploratory mixed design. • Phase 1 established a thematic network of travel adversities, psychological experiences, and consequences with 57 fundamental themes categorized and assigned under the organizing themes of travel adversity, negative affect, fight, flight, and negative road occurrences. • In phase 2, a checklist was developed listing the fundamental themes from phase 1 with Likert-type responses ranging from 0 (never) to 5 (always). • Phase 3 statistically validated the model of the vicious cycle, based on which a mathematical model was formulated for further interdisciplinary inquiries on the prediction and control of traffic behavior in real-world settings. This study investigated the psychological experiences and consequences of travel adversities during traffic congestion using a three-phase sequential exploratory mixed-methods design. Phase 1 explored the travel adversities, psychological experiences, and consequences of a sample of ten (four women and six men) regular commuters of Bangalore's congested roads using semi-structured interviews. In phase 2, a checklist was developed listing the fundamental themes from phase 1 with Likert-type responses ranging from 0 (never) to 5 (always). Phase 3 gathered data in the checklist and tested the statistical validity of the thematic model in a sample of 190 (81 women and 103 men) regular commuters. Attride-Stirling model thematic network was established with 57 fundamental themes categorized and assigned under the organizing themes of travel adversities (n = 6), negative affect (n = 28), fight (n = 7), flight (n = 6), and negative road occurrences (n = 10), in the global theme, psychological experiences and consequences. Structural equation modeling indicated that (1) negative affect significantly predicted fight and flight, (2) fight is a significant predictor of negative road occurrences, and (3) psychological experiences and consequences create a self-perpetuating cycle, with travel adversity triggering negative emotions, which results in fight responses leading to negative road occurrences, further intensifying travel adversity. A mathematical model is established based on this statistical validation, which holds potential applications in real-time traffic algorithms. [ABSTRACT FROM AUTHOR]

This article proposes innovative solutions in the field of traffic management – smart traffic lights with computer vision, aimed at improving traffic flow in the large cities of Kazakhstan. With a growing number of vehicles and increasing traffic volume, the issues of congestion and delays at intersections have become increasingly relevant. The article reviews the operational principle of such smart traffic lights, which is based on analyzing computer vision data using sensors and cameras. The benefits of applying this technology, including responsiveness, efficiency, and environmental friendliness, are considered. Furthermore, the potential of smart traffic lights in major megacities of Kazakhstan, such as Astana and Almaty, is analyzed in detail. The conclusion supports the assessment of traffic flow, travel time, and an overall evaluation of the traffic situation. Ultimately, this article highlights the improved performance of smart traffic lights with computer vision coverage in modern cities of Kazakhstan, aiming to ensure higher traffic safety and efficiency. [ABSTRACT FROM AUTHOR]

Recently, the Egyptian government has sought to create new urban spots. The New Administrative Capital was one of those new urban extensions. Architects and urban designers tended to vertical expansion in planning this city as a result of the high cost of the land. Accordingly, high-rise and super-high-rise buildings have been relied upon in the architectural design of the buildings. This study aims to evaluate the achievement of safe occupants' evacuation in case of fire for two high-rise and one super high-rise complex buildings based on a set of variables. Staircase design, the use of elevators, and the provision of refuge areas were evaluated, through simulating seven scenarios using Pathfinder software. The simulation results comprised an assessment of average stairs traffic jams, the occupant load density on emergency stairs, and the required safe evacuation time (RSET) for each scenario. It was concluded from the study that staircase design has a significant impact on (RSET) time. As well as, evacuation using elevators or relaying on refuge areas as auxiliary means with the emergency stairs may be safer for these types of buildings. Finally, the study provided technical requirements for using refuge areas and elevators in emergency evacuation in high-rise and super high-rise buildings. [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]

Illegal parking during peak hours and on heavily traveled streets can significantly impact the severity of traffic congestion and related problems. A double parking violation is among the most significant instances of illegal parking. Double parking violations reduce road capacity and increase traffic congestion. Intensified traffic congestion also increases travel time and reduces road user satisfaction. Thus, the management of double parking violations is critical to managing traffic congestion in urban streets. In this study, the factors influencing the drivers' tendency to perpetrate a double parking violation were identified using the ordinal logistic regression model in the central area of Mashhad. Then, by redesigning the ordinal logistic regression model and utilizing the identified effective factors, a model was developed to predict the level of drivers' propensity to perpetrate a double parking violation during urban trips. Mashhad is one of the largest cities in Iran, and its central district experiences a high volume of daily traffic. The results indicated that parking duration is the main factor affecting drivers' propensity to perpetrate a double parking violation. Driver education level and driver presence in the vehicle ranked second and third, respectively. The P-value in the goodness-of-fit test was obtained at less than 0.05, indicating the designed model's acceptable accuracy. • The greater the necessity of parking, the greater the likelihood that the driver will commit a double parking violation. • Reducing the educational level of drivers to B.Sc. or less increase the likelihood that they will perpetrate double parking. • If the parking duration is less than 25 min, the driver's visual search distance for parking will be significantly reduced. [ABSTRACT FROM AUTHOR]

As private car usage has negative effects, such as traffic congestion and air pollution, the effectiveness of Transportation Demand Management (TDM) policies could be considered as any reduction in car usage. However, the efficiency of TDM policies could be addressed by a combination of their effectiveness and public acceptability. Policy packaging is treated as a way to get more benefits from a set of policies in form of a package. Therefore, to improve the efficiency of a package containing a push TDM policy, which is usually effective but not acceptable, introducing a pull policy that is generally acceptable could be of interest. This research aimed to evaluate the efficiency of two packages of TDM policies containing a push policy (either cordon or parking pricing) and a pull policy (transit system development). Therefore, 535 working-purpose car commuters were interviewed to examine the trade-offs between the acceptability and effectiveness of each package. According to the results, although the transit development policy causes a 1.52% reduction in car usage, it significantly affects the acceptability of cordon pricing and transit development package. Finally, a discussion is provided regarding how to use the policy packages to achieve the highest potential effectiveness at a specific level of acceptability. • The efficiency of a TDM policy is related to its effectiveness and acceptability. • The effectiveness of a push policy is inversely related to its acceptability. • There is a positive relationship between the effectiveness and acceptability of a pull policy. • An auxiliary pull policy can increase both the acceptability and effectiveness of a policy package. [ABSTRACT FROM AUTHOR]

• A double flow controlled discrete-time two-lane traffic system with vehicle-to-infrastructure communication is proposed. • A stable condition of the traffic system is derived with lyapunov stability theorem and given by linear matrix inequality. • The double flow control scheme can suppress traffic congestion efficiently. • The relative flow is more effective than the delayed flow in stabilizing traffic flow. • The control scheme can lower energy consumption of the traffic system. Through vehicle-to-infrastructure communication, the road-side units could collect traffic information of a selected road section and send the refined traffic information to the vehicles on the road after information processing with edge computing servers. To study the impact of traffic information in different road sections on traffic flow, a double flow controlled two-lane traffic system considering vehicle-to-infrastructure communication is introduced in this paper. The new traffic system is analyzed with Lyapunov stability theorem and a sufficient stable condition is provided in linear matrix inequality. Through simulation, the traffic flow evolution properties and energy consumption rules under different control gains and lane changing parameters are exhibited. The results show that traffic congestion and energy consumption of the two-lane traffic system can be suppressed by taking the delayed flow control information, the relative flow control information, and lane changing effect into account. Also, the role of the control strategy on traffic flow is revealed microscopically through a two-lane car-following model by transforming the macroscopic traffic flow to vehicle's velocity. It further proved the stabilization effect of the control information on traffic flow. This research can provide some theoretical guidance for traffic control through vehicle-to-infrastructure information interaction. [ABSTRACT FROM AUTHOR]

[Display omitted] • This is the first time that secondary decomposition is applied to TFD prediction. • VMD improved by NGO is proposed. • KELM improved by ARO is proposed. • Error correction is introduced to further improve the prediction accuracy. • TFD prediction model based on second decomposition and error correction is proposed. With the aggravation of traffic congestion, traffic flow data (TFD) prediction is very important for traffic managers to control traffic congestion and for traffic participants to plan their trips. However, its effective prediction faces great difficulties and challenges. Aiming at handling complexity of TFD, a new TFD prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), neural network estimation time entropy (NNetEn), variational mode decomposition (VMD) improved by northern goshawk optimization (NGO) algorithm, kernel extreme learning machine (KELM) improved by artificial rabbits optimization (ARO) algorithm and error correction (EC) is proposed. Aiming at choosing the decomposition layers and penalty coefficient of VMD, VMD improved by NGO, named NVMD, is proposed. Aiming at handling the problem of selecting KELM parameters, KELM improved by ARO, ARO-KELM, is proposed. Firstly, CEEMDAN is used to decompose TFD into a limited number of IMF components. NNetEn is used to divide IMF components into high- and low-complexity components. The sum of high-complexity components is selected for secondary decomposition by NVMD. Then ARO-KELM is used to predict all decomposed components. Finally, error correction is introduced to further improve the prediction accuracy. TFD from England highway is used in the experiments. Taking TFD I as an example, the RMSE, MAE, MAPE and R2 are 4.5682, 3.3104, 0.0458 and 0. 9997 respectively. The results show that the proposed model is superior to the other six comparison models at 99% confidence level, which provides a theoretical and data basis for controlling traffic jams, accidents and pollution. [ABSTRACT FROM AUTHOR]

The inconvenience of travel, air pollution and consequent economic losses caused by traffic congestion have seriously restricted the healthy and sustainable development of cities in China. In this context, as the main component of current and future urban traffic management measures, intelligent transportation system is an important means to improve the traffic efficiency of road network and alleviate urban traffic congestion. Traffic flow prediction plays an important role in this connection. In this paper, an ensemble short-term traffic flow prediction method based on optimized variational mode decomposition (OVMD) and combined long short-term memory network (LSTM) is proposed. The method consists of three main components: 1. Use the improved bat algorithm to optimize the parameters of VMD to achieve better decomposition effect; 2. Use the optimized variational mode decomposition algorithm (OVMD) to decompose the unstable original traffic flow time series data into relatively stable multiple Intrinsic Mode Functions (IMFs); 3. The optimized L-BILSTM model is established by combining the basic long short-term memory network with the bidirectional long short-term memory network. It can better extract information from traffic flow data and improve the accuracy of prediction results. In the empirical study part, the traffic flow data of Changsha City is used to verify the prediction model proposed in this paper. The influence of the application of the variational mode decomposition algorithm to the training set data and the overall data on the final prediction results is also compared and analysed. [ABSTRACT FROM AUTHOR]

• The study analysed attendees' departure time choices before special events and explain how pre-event activities change attendees' departure time. • A distribution of attendees' pre-event utility sensitivity that results in no-queue and capacity waste at the bottleneck is given. • Relationship amongst attendees' departure times choices, the attractiveness of the event venue and venue profit was examined. • The optimal choice of the overall price level and facility or service level is determined to maximize venue profit and attendees' trip utilities. This study aims to investigate attendees' trips to venues before special events considering pre-event activities. We focus on how pre-event activities change attendees' departure-time choices and affect traffic congestion near a venue. By describing attendees' pre-event utility and the attractiveness of the venue, a bottleneck model is proposed to model attendees' departure times with heterogeneous pre-event utility. Attendees' heterogeneity in pre-event utility is depicted by a continuous distribution of pre-event utility sensitivity, which changes with the attractiveness (e.g., the overall price level, facility, or service levels) of the venue. Different distributions are adopted to depict the attendees' pre-event utility sensitivity and are further used to analyse the equilibrium at the bottleneck. The conditions to eliminate the queue at the bottleneck are determined, which are related to the distribution of the pre-event utility sensitivity of the attendees. We further analyse the impact of the venue's attractiveness to attendees on the distribution of attendees' pre-event utility sensitivity. The optimal choice of the overall price level and facility or service level is determined to maximise the profit of the venue and the total trip utilities of all attendees. Finally, numerical examples are conducted to illustrate the equilibrium at the bottleneck and examine the no-queue condition and the conditions to maximise total trip utility and venues' profit. [ABSTRACT FROM AUTHOR]

• Investigate the issues of income distribution, implementation sequence, and equity in the auto ownership rationing schemes. • Implementing auto ownership rationing schemes can cause inequity issues due to different changes in individual costs of different-income residents. • Pareto-improvement strategy with self-financing constraint is presented to balance the equity and efficiency. • The optimal lottery + auction (L+A) hybrid scheme outperforms the optimal auction + lottery (A+L) hybrid scheme in terms of expected social surplus. • The optimal A+L Pareto-improvement strategy is better than the optimal L+A Pareto-improvement strategy in terms of expected social surplus. • Residents' income distribution has no significant effects on the properties of the models. Auto ownership rationing schemes, including lottery, auction and hybrid schemes, have been recognized as effective measures to mitigate urban traffic congestion. The implementation sequence of auction and lottery in the hybrid schemes has two possible scenarios: auction is implemented before lottery (i.e., A+L), and lottery is implemented before auction (i.e., L+A). This paper addresses the issues of income distribution, implementation sequence, and equity in the auto ownership rationing schemes. Expected social surplus maximization models are proposed for determining the optimal auto quota for each scheme and the optimal proportion allocated to the lottery and auction for the hybrid schemes. In the proposed models, a trade-off between positive externality (social contribution) and negative externality (traffic congestion) caused by auto industrial development is considered, together with effects of residents' income distribution. Inequity issue incurred by the rationing schemes (who gains and who loses) is investigated, and Pareto-improvement strategy with self-financing constraint is presented to balance the equity and efficiency. The solution properties of the proposed models are analytically explored, and the solutions of different schemes are compared. The results show that (i) residents' income distribution has no significant effects on the properties of the models; (ii) the optimal L+A hybrid scheme outperforms the optimal A+L hybrid scheme in terms of the expected social surplus; (iii) however, it is just reverse for the Pareto-improvement strategy that solves the inequity issue, i.e., the optimal A+L Pareto-improvement strategy is better than the optimal L+A Pareto-improvement strategy in terms of the expected social surplus. [ABSTRACT FROM AUTHOR]

• Studies network Macroscopic Fundamental Diagram of urban network with hierarchical streets. • Presence of hierarchy leads to non-unimodal patterns in MFD when hierarchical streets present and users route according to UE principles. • Findings obtained with simple analytical model and confirmed with simulations and empirical data. Unimodal, concave relationships between average network productivity and accumulation or density aggregated across spatially compact regions of urban networks—so called network Macroscopic Fundamental Diagrams (MFDs)—have recently been shown to exist on homogeneous street networks. When present, MFD relationships facilitate the modeling of traffic congestion at a regional level and have led to the development of various regional traffic control strategies. However, real street networks are not homogeneous—they generally have a hierarchical structure where some streets (e.g., arterials) promote higher mobility than others (e.g., local roads). This paper examines how the presence of hierarchical roadway structures may potentially cause non-unimodal patterns in a network's MFD. These are observed using three types of tools: analytical models of simple network structures, simulations of various idealized roadway networks, and empirical data. The impacts of street hierarchy depend on how vehicles use different roadway types to move within the network; i.e., their routing strategy. The findings suggest that the presence of roadway hierarchies may lead to MFDs that have non-unimodal or non-concave patterns on the free-flow branch when vehicles route themselves according to user equilibrium principles, which is closest to what would be observed in realistic situations. Such patterns are contrary to what is traditionally assumed in most MFD-based modeling frameworks. However, the unimodal and concave MFD should be expected under system optimal routing conditions that maximize network productivity for a given traffic state. [ABSTRACT FROM AUTHOR]

The process of rapid urbanization in contemporary cities has resulted in a rise in traffic congestion, accidents, and pollution, leading to negative impacts on transportation efficiency and urban development. In response, Intelligent Transportation Systems (ITS) have emerged as a crucial field of research and development, with a goal to leverage technological advancements to enhance transportation safety, efficiency, and sustainability. However, traditional expert systems and traffic control methods have displayed limitations in handling the complexities of urban transportation. To address these challenges in Advanced Traffic Management Systems (ATMSs), this paper pro- poses a new approach utilizing Graph Neural Network-Reinforcement Learning (GNN-RL). By using GNNs for pre-training and information aggregation from the entire grid, our method enables downstream RL tasks, allowing for fine-tuning of effective global information aggregation and dynamic traffic signal control policy adjustment. The GNN-RL approach offers enhanced adapt- ability and performance in managing long-term dependencies, rendering it a promising alternative for traffic control in contemporary urban transportation systems. [ABSTRACT FROM AUTHOR]

The increase in population and vehicle ownership causes high levels of traffic density. The very high mobility of the population ultimately has an impact on routine congestion which is felt to be getting worse over time. There are several factors that cause traffic jams to get worse. One of them is due to the increasing number of motorized vehicles. The number of vehicles that continues to increase is not in accordance with the road capacity that can accommodate these cars. For this reason, a system is needed that can categorize cars that can run on certain days according to the number plate (eg odd or even) to reduce congestion. This built system can extract the license plates of vehicles passing on the highway using image processing and character recognition methods. This research paper proposed an Automatic Number Plate Recognition ANPR system is an image processing and character recognition system that is used to recognize a car's license plate using Optical Character Recognition (OCR). The inputted license plate is automatically localized, segmented, and recognized using the OCR algorithm provided in the Tesseract library. The experiment shows 83.3% accuracy due to the difference in license plate format, background, and fonts. [ABSTRACT FROM AUTHOR]

Traffic management is essential for preventing traffic congestion and regulating traffic flow. Jakarta has implemented several methods to reduce traffic congestion in response to the growing number of vehicles on the road. This study proposes integrating computer vision and fuzzy logic into traffic lights for use in traffic management. The study focuses on comparing and implementing three computer vision libraries: ImageAI, Cloud Vision, and OpenCV Libraries. This comparison is based on Jakarta traffic data captured by closed-circuit television (CCTV). After evaluating the libraries, the result indicates the ImageAI achieves better performance than the other libraries across testing measurements and it was used to develop a module simulator. This simulator was used to compare the amber time produced by each lane at a simple intersection under fixed time versus proposed-actuated traffic lights. The simulation of the proposed actuated traffic light scenario demonstrates that the use of computer vision and fuzzy logic in the decision-making process can marginally improve traffic simulation compared to the fixed-time traffic light scenario. This study illustrates the application of computer vision and fuzzy logic to traffic management. The application of these technologies to traffic lights has the potential to increase traffic flow and decrease congestion. [ABSTRACT FROM AUTHOR]

This work analyzes why the classic cold start-up model cannot match experimental data well in high initial membrane water content due to the unboundedness of membrane water content conservation equation, and a mathematical framework to ensure the boundedness of membrane water content is developed to solve these issues. Based on vehicle mechanism and Grotthuss mechanism, a "traffic jam" model is applied to describe the transport of membrane water reaching its maximum content. To keep the membrane water content within the upper bound, a new governing equation is proposed and the corresponding boundary conditions and discretization schemes are set. This model has been proved in eleven cases by comparing with experimental data including different initial membrane water content and different current density. Some of these cases are considered impossible to exist in classical cold start-up theory. The results illustrate that the boundedness model can show a clearer physical process and obtain more accurate and reasonable calculation results. • "Traffic jam" model is proposed to describe transport of membrane water. • Actual experimental conditions that do not exist in classic theory are analyzed. • Boundedness model avoids unreasonable physical results. • Mathematical boundedness of membrane water content has been analyzed and ensured. [ABSTRACT FROM AUTHOR]

Healthcare offers a rich palette of potential applications of RFID technology. Healthcare provides a rich palette of possible applications of RFID technology. Besides traditional uses such as tracking medical equipment and devices or access control, healthcare can benefit even more significantly from RFID technology. However, using the RFID technology in healthcare raises various problems of scalability, timely identification of tags, security, privacy, and efficient implementation in practice. That is because such systems contain many tags, operate with private personal data, and must respond promptly in concrete, practical situations to avoid malfunctions (errors in the decision process, traffic congestion, and so on). This paper discusses the fundamental requirements of RFID systems raised by healthcare and the limitations of existing schemes. Then, we propose a new RFID scheme that achieves mutual authentication, strong privacy, and constant-time identification in the HPVP model. The scheme employs a secure symmetric-key encryption scheme, making it very efficient in implementation and physically unclonable functions (PUFs) to protect the secret key against adversaries with corruption capabilities. [ABSTRACT FROM AUTHOR]

One of JST and JICA's technical cooperation projects, "Smart Transportation Strategy to Realize Thailand 4.0," aims to reduce traffic congestion to simultaneously achieve low carbon emissions and improve citizens' quality of Life (QoL). To achieve this goal, it is necessary to develop an AI-based system that allows users to select a combination of transportation modes that meets the various needs of people of all ages and genders, and to realize this goal, evaluation of QoL from images is required. The project suggests how to improve QoL when people moves from some point to another point and some application software to recommend the time scheduling about how to optimize the QoL in a day. It recommends the highest QoL route when we have several path candidate of roads and further some application is necessary to show how QoL dynamically changes in the route. That is scenario of the QoL project. In the previous research, a method for QoL estimation from images was proposed based on semantic segmentation and MLP (Multi Layer Perceptron) by constructing a unique questionnaire form and collecting data in a car view scene. However, the previous method estimates QoL without considering differences in the attributes of the questionnaire data, so the constructed model does not satisfy the attributes of all people. In the proposed method, multiple subjects are asked to subjectively evaluate QoL in 5 levels for the same driving image data set, and the subjects are grouped by clustering based on the evaluation results. The effectiveness of the proposed method was demonstrated by comparing the MAE (mean absolute error) of the model constructed by the proposed method with that constructed by other subjects in a 5-step cross-validation. [ABSTRACT FROM AUTHOR]

• Urban commuters are exposed to several daily stress-related factors in transport. • This study presents the development and validation of the MCSS. • The scale has shown fair psychometric properties, reliability and validity insights. • MCSS dimensions show significant differences by gender, age, and modality. • This tool is useful for measuring stress-related factors in different commuting means. Although most of the literature on stress-related factors uniformly points out several negative health, performance, and safety issues as potential outcomes attributable to a prolonged exposition to stressful situations, the applications of these assumptions to the field of urban mobility (and more specifically to commuting) remain considerably scarce. Indeed, while commuters worldwide usually face daily situations such as traffic jams, dearth of transport choices, crowded public transport services, long trips, and road conflicts, there are only a few studies that have methodologically addressed the issue of commuting stress (CS). This paper aims to present in detail the development and validation of the Multimodal Commuting Stress Scale (MCSS), a self-report tool for assessing commuting stress-related factors among urban commuters of different modalities. This cross-sectional study retrieved data from a nationwide sample of 1,429 commuters (aged between 18 and 78; average 34.42 years) from all 17 Spanish regions. Data were analyzed through Structural Equation Model-based competitive Confirmatory Factor Analyses, thus testing the psychometric performance of the scale under different structural assumptions, as well as its reliability, consistency, and validity insights. The outcomes of this study suggest that the developed MCSS has a clear four-dimensional structure, an optimal set of factor loadings, fair goodness-of-fit indexes, and satisfactory internal consistency and reliability. Additionally, validity analyses show that commuting stress factors measured through the MCSS have coherent associations with both physical/mental health indexes and trip-related factors, while it also discriminates commuting stress scores by sex, age, and commuting modality. All in sum, the findings of this study endorse the suitability of the Multimodal Commuting Stress Scale for measuring stress-related factors affecting urban commuters, as well as to make comparisons among different groups of them. [ABSTRACT FROM AUTHOR]

Traffic at urban intersections constitute a series of complex conflicting vehicular movements and contribute greatly to the problems of traffic congestion and air pollution. The emerging of connected and automated vehicle (CAV) technologies inspires innovative ideas in traffic management at intersections; it not only enables new control paradigms, but also allows for possibilities to revolutionize the geometric layouts of intersections. This study proposes a novel intersection design called knotted intersection (KI), to resolve the complexity of conflicting relations at intersections in a full CAV environment. This new design is associated with a set of neat control rules for vehicles to realize the smooth traffic operation at these intersections. The design problem of intersection geometry and control strategy is optimized jointly and formulated as a mixed-integer linear program (MILP), and some basic theoretical properties of KI design are derived, including nearly throughput-maximizing property and bounded within-intersection delay of vehicles. Furthermore, the KI design is then extended to a road network composed of multiple intersections to improve the overall efficiency. The optimal design of multiple knotted intersections (MKI) is developed to coordinate the traffic organization methods among intersections and a two-level solution method is proposed to solve the optimization efficiently. Numerical and simulation experiments are conducted at both the intersection level and network level under various cases, and the results validate the superior performance of the proposed methods compared to classical traffic signal control (TSC) strategies. • Knotted intersection design for resolving the complexity of intersection conflicts • Joint design of intersection geometric layout and traffic control strategy • Theoretical properties for nearly-maximum throughput and bounded delay • Optimal design of multiple knotted intersections to enhance network efficiency [ABSTRACT FROM AUTHOR]

In this study, the problem of quantized H ∞ filtering is addressed for networked switched piecewise-affine systems, in which the switching mechanism obeys the persistent dwell-time constraint. To process the measurement output and schedule the transmission sequence for alleviating the burden of the limited communication channel, a logarithmic quantizer is employed, which can further improve the bandwidth utilization in networked switched systems. On the basis of state-space division, the objective is to design a mode-dependent and region-dependent filter that ensuresthe filtering error system is globally uniformly exponentially stable and meets an H ∞ performance. By using the mode-dependent Lyapunov function approach and employing appropriate decoupling methods, sufficient conditions for obtaining desired filter gains are deduced. Eventually, two illustrated examples are given to demonstrate the effectiveness of our proposed method. • A logarithmic quantizer is used to mitigate traffic jams and improve communication quality. • Compared with DTS and ADTS, PDTS is applied to the filter design for investigated systems. • Sufficient criteria are provided to ensure the error system meets an H ∞ performance. [ABSTRACT FROM AUTHOR]

• We investigated technology readiness and intentions to use CAVs in Finland. • People with optimistic views towards technology have stronger intentions to use a CAV. • A sense of security regarding technology influences intentions to use CAVs. • Previous experience with ADAS was a strong indicator of intentions to use. Connected and autonomous vehicles (CAVs) are an emerging technology predicted to become available to the public over the next several decades. CAVs offer a range of potential benefits to the driver and broader society including reducing road trauma, decreasing traffic congestion, and offering new mobility choices. However, there is uncertainty regarding uptake of this new technology. If users do not see the value in the technology, it is unlikely to be fully adopted and the potential benefits of CAVs will not be realised. One commonly used instrument to investigate user acceptance of new technology is the Technology Readiness Index (TRI). The TRI has been used to investigate intentions to use CAVs in various countries, however no such study has been undertaken in Finland. As such the aim of this research was to investigate technology readiness and intentions to use conditionally automated vehicles amongst Finnish residents. A total of 384 participants (M = 46.9yrs; SD = 14.9yrs, 24.5 % female) completed a survey consisting of the TRI 2.0, demographics, car ownership, driving information and intentions to use conditionally automated vehicles. A multilinear regression was conducted to examine the factors that influence self-reported intentions to use a conditionally automated vehicle. When controlling for the other variables, Optimism and Insecurity were significantly associated with intentions to use a conditionally automated vehicle. Optimism had a positive correlation, with stronger intentions to use a conditionally automated vehicle among those who were more optimistic towards technology. Participants with lower levels of insecurity, that is, those who felt more secure with technology, also had stronger intentions to use a conditionally automated vehicle. The other significant variable in the model was previous experience with a CAV which had a significant positive relationship. The study adds to the existing literature regarding CAV adoption and provides added insight within a Finnish context of how exposure to technology and the factors of the TRI can influence intentions to use conditionally automated vehicles. [ABSTRACT FROM AUTHOR]

Traffic prediction is an important component in Intelligent Transportation Systems(ITSs) for enabling advanced transportation management and services to address worsening traffic congestion problems. The methodology for traffic prediction has evolved significantly over the past decades from simple statistical models to recent complex integration of different deep learning models. In this paper, we focus on evaluating recent hybrid deep learning models in the task of traffic prediction. To this end, we first conducted a review and taxonomize the reviewed models based on their feature extraction methods. We analyze their constituent modules and architectural designs. We select ten models representative of different architectural choices from our taxonomy and conducted a performance comparison study. For this, we reconstruct the selected models and performed a series of comparative experiments under identical conditions with three well-known real-world datasets collected from large-scale road networks. We discuss the findings and insights based on our results, highlighting the differences in the achieved prediction accuracy by models with different design decisions. • A specific review and taxonomy on the recent models for traffic prediction. • Architectural analysis of hybrid deep learning models based on our taxonomy. • A comparison study of representative models. • Experiments conducted on real-world datasets under identical conditions. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major concern for many metropolises. Although it is difficult to regulate traffic flow because of numerous complexities and uncertainties, the traffic congestion problem must be mitigated in order to reduce the environmental problems related to traffic and the time lost on the roads in big cities. Intelligent traffic control methods, the use of which is increasing with the development of new methods, as opposed to conventional methods, and provide more efficient solutions, especially in traffic intersections with high traffic density. In this paper, we propose a new agent-based Fuzzy Logic assisted traffic light signal timing for traffic intersections. Deep Q-Learning algorithms and Fuzzy Logic Control (FLC) are used together in the proposed method. In this study, the proposed method and many traffic light control methods in the literature were simulated. In order to demonstrate the effectiveness of the proposed method, some of the important metrics of evaluation such as traffic congestion, air pollution, and waiting time were used in the assessment of the simulation results. In addition, with the proposed method, it has been shown that the stability and robustness of the system are increased. [ABSTRACT FROM AUTHOR]

Simulation studies on Shared Autonomous Vehicles (SAVs) could offer several insights to transport policymakers about future mobility. Case studies using the agent-based approach have recently emerged worldwide to test the impacts of SAVs on local networks. However, as the most densely populated area in Belgium, a relevant SAV study for Brussels is currently lacking. In this paper, we have simulated the impacts of substituting Brussels private car trips for people who either only travel in Brussels or do not travel to external places by car. The simulation was carried out with various SAV fleet sizes implementing vehicle relocation strategy. This paper describes how we transferred certain car trips to SAVs, and the respective SAV parameters used for simulation. The results suggest that under a 10% current travel demand, if all current studied drivers groups are willing to carpool as long as the additional travel times do not exceed a certain threshold, 1000 SAVs will be sufficient to replace these car trips in Brussels with the average daily request waiting time of around 5 minutes. By deploying SAVs with the proposed operational scheme, agents' overall travel time and traffic congestion in Brussels can also be significantly reduced. [ABSTRACT FROM AUTHOR]

Smart traffic congestion reduction is actually a real challenge for big cities. Machine learning algorithms can play a significant role in traffic analysis, congestion prediction, and rerouting. In this paper, we propose a new prediction approach to reduce the traffic congestion problem by studying a scheme for predicting traffic flow information using four machine learning techniques: Feed Forward Neural Networks (FFNN), Radial Basis Function Neural Networks (RBFNN), simple linear regression model, and polynomial linear regression model. This prediction scheme is based on the following parameters: the average waiting time at entry and exit street pairs, the days of the week, hours of movement, holidays, and the rain rate. The results indicate that the FFNN technique overcomes the other techniques producing 97.6% prediction accuracy. [ABSTRACT FROM AUTHOR]

Communication and collaboration in transport systems are overdeveloped through Vehicular Ad hoc Networks (VANET). In VANETs, various messages are sent to activate warnings about road accidents, sharp turns, traffic jams, etc. Data security in VANETs is then essential to prevent the intrusion of hackers. Although security and safety in VANETs have been the subject of several research works, some intelligent vehicles contain extremely sensitive data that require specific high-level and real-time security solutions. This is the case for connected medical ambulances in which surgery can be performed with the help of an outside doctor. In such cases, video is the most complicated type of data to handle due to its size, content, and structure. In this paper, an elliptical approach to video representation is introduced, and a video crypto-compression scheme is proposed. The scheme described is built on Elliptic Curve Cryptography, which demonstrates good performance in computer security in recent years. [ABSTRACT FROM AUTHOR]

To solve the city's traffic congestion problem, micro-traffic simulation is widely used to analyze the current situation and implement a plan to solve the problem. With the nature of the simulation, it is possible to design multiple scenarios and predict future traffic conditions. The state of the network is usually represented visually through 3D graphics and numbers such as speed, delay time, etc. However, analysis results are generally not intuitive, making it difficult for non-experts to judge traffic indicators. This study presents the possibility of utilizing an open-sourced real-time web-based platform linking simulation results to dashboards. Here, the micro traffic simulation, VISSIM is used. Traffic flow at intersections and location information of individual vehicles are stored in the cloud from VISSIM at analysis intervals. In real-time, the saved results are loaded into Elasticsearch, an open-source visualization tool. Data are visualized according to temporal and spatial characteristics and constitute the dashboard layout. Users intuitively analyze and predict traffic conditions through this visualization platform. In addition, traffic conditions can be easily communicated to general users who use web-based information. [ABSTRACT FROM AUTHOR]

Traffic congestion forms a large problem in many major metropolitan regions around the world, leading to delays and societal costs. As people resume travel upon relaxation of COVID-19 restrictions and personal mobility returns to levels prior to the pandemic, policy makers need tools to understand new patterns in the daily transportation system. In this paper we use a Spatial Temporal Graph Neural Network (STGNN) to train data collected by 34 traffic sensors around Amsterdam, in order to forecast traffic flow rates on an hourly aggregation level for a quarter. Our results show that STGNN did not outperform a baseline seasonal naive model overall, however for sensors that are located closer to each other in the road network, the STGNN model did indeed perform better. [ABSTRACT FROM AUTHOR]

Road accidents and traffic congestion severely impact road safety, resulting in significant loss of life and property. Therefore, there is a need for road safety and traffic management. The wireless networks of connected vehicles called Vehicular Adhoc Networks (VANETs) are designed to improve the traffic management on highways with comfort and safety for drivers and passengers. However, there is a need to develop an authentication scheme that can protect the privacy of vehicles and secure them against attacks such as insider attacks and repudiation attacks. In this article, we present a scheme that performs vehicle registration and message forwarding in the VANETs and appends the transactions to the blockchain. The proposed privacy-preserving authentication scheme using blockchain technology enables vehicles and Trusted Authorities (TA) to authenticate vehicles while preserving privacy. The proposed scheme facilitates privacy-preserving authentication by deploying the scheme on a decentralized network. The proposed scheme ensures privacy, confidentiality, integrity, and transparency during vehicle authentication for VANETs. In the proposed scheme, an authentication process is automated with the use of a smart contract. We comparatively evaluate the security and performance of the proposed privacy-preserving authentication scheme with the state-of-the-art literature. The proposed privacy-preserving authentication scheme is significantly reliable and transparent in authenticating vehicles while preserving privacy. [ABSTRACT FROM AUTHOR]

• Design a Correlated equilibrium Routing Mechanism (CeRM) for traffic congestion mitigation. • Develop a distributed algorithm to solve the CeRM efficiently involving a large scale of travelers. • Prove the solution algorithm's robustness against random communication failures. • Design an acceleration scheme-aided solution algorithm and prove the resilience of the convergence. • Conduct experiments to validate the performance of the CeRM and its solution algorithms. With the rapid development of wireless communication, mobile computing, and GPS technologies, drivers' route decisions nowadays rely more on navigation services, such as Google or Waze. However, these navigation services don't always come with improved traffic conditions. Individual drivers often make independent and selfish route decisions that are not systematically favorable and thus often result in severe congestions. This study aims to alleviate such problems by exploiting the information gaps between individuals and the central planner (CP). Specifically, we develop a correlated equilibrium routing mechanism (CeRM) for the CP, which drives a group of vehicles' route choices to an equilibrium with a systematically optimal traffic condition while still satisfying individuals' selfish nature. Participating drivers would only be better off by following the suggested routing guidance than navigating on their best responses to real-time traffic information. The CeRM is modeled as a nonconvex and nonlinear program involving a large-scale of users. A distributed Augmented Lagrangian algorithm (D-AL) is developed to efficiently solve the CeRM to provide online real-time navigation service, taking advantage of the on-board computation resources of individual vehicles. Considering the D-AL relies on the wireless communications between vehicles and the CP, we proved the convergence robustness of the D-AL against random communication failures and derived the convergence rate upper bound as a function of the communication failure probability. It is noticed that the convergence rate of the D-AL degrades dramatically as the communication failure probability increases, which hampers the applicability of implementing the CeRM in practice. To improve the solution algorithm's resilience in the computation performance, we further designed and proved an acceleration scheme aided D-AL (aD-AL) to expedite the convergence rate under the high likelihood of communication failures. Numerical experiments conducted on the Sioux Falls city network confirmed the D-AL's convergence properties, robustness against random communication failures, and the resilience of the aD-AL to solve the CeRM. The experiments also show that the CeRM results in better system performance (have less system cost) compared with the existing Independent Routing (IR) mechanism and user-oriented Equilibrium Routing (uoER) mechanism. [ABSTRACT FROM AUTHOR]

Traffic flow and travel time-based measures are used to quantify and characterize congestion levels on urban roadway networks. However, under low-density conditions, contradictory results are observed when these two measures are used to identify the prevailing congestion levels. This accentuates the need to develop a new measure of congestion that effectively integrates traffic flow and travel time-based measures. The present study develops a new measure termed modified congestion index (MCI) by integrating traffic flow and travel time-based congestion measures for identifying prevailing congestion levels. As proof of the concept, the application of the methodology was demonstrated using empirical data collected at the corridor and network level and simulated data developed for a real-world urban network. The traffic flow and travel time-based congestion measures were integrated using a generalized least square framework (GLS) to derive MCI. Investigations revealed that traffic states bifurcated based on MCI follow the general philosophy of traffic state bifurcation using the macroscopic fundamental diagram (MFD) and fundamental diagram (FD). MCI, when correlated with existing performance measures, revealed that MCI could characterize both reliability and congestion simultaneously. As an important outcome from a policy and practical application perspective, MCI-based level of service (LOS) thresholds were developed. The application of the LOS thresholds was demonstrated by (a) analysing the effect of traffic signal control using a before-after perspective and (b) ranking and prioritizing segments in an urban network for optimal allocation of funds for developing congestion mitigation strategies. The developed LOS thresholds are expected to facilitate planners and engineers to comprehend the operational level of urban networks in terms of congestion and develop mitigation measures accordingly. • Modified congestion index (MCI) is proposed for characterizing and quantifying congestion. • Volume-to-capacity ratio and congestion index are integrated using a generalized least square framework for deriving MCI. • Congestion-based level of service (LOS) thresholds are developed to quantify service quality. • The application of LOS is demonstrated by evaluating the effect of traffic signal control on congestion. • Segments are ranked and prioritized based on MCI for optimal allocation of funds. [ABSTRACT FROM AUTHOR]

The establishment of future intelligent transport systems is dependable on the reliable and seamless function of Connected and Autonomous Vehicles (CAV). Reinforcement learning (RL), which allows autonomous vehicles (AVs) to learn an ideal driving strategy through constant contact with the environment, plays a significant part in the decision-making process of autonomous driving (AD). The networking of CAV is advantageous since it allows for the transmission of traffic-related data to vehicles via Vehicle-to-External (V2X) communication. Recognition and anticipation of driving behaviour are critical for avoiding collisions because they can provide useful information to other drivers and vehicles. The fundamental challenge in developing CAV is the construction of an autonomous controller that can effectively perform close real-time control selections, such as a fast acceleration while merging onto a highway and rapid speed adjustments in stop-and-go traffic congestion. CAV driving behaviours can be considerably improved by utilizing shared information, resulting in more accountable, intelligent, and efficient driving. In the present work, a deep reinforcement learning approach is proposed that integrates the information gathered through connectivity capabilities and sensing from neighbour automobiles in the vicinity of CAV. The fused information is used for providing safe and cooperative lane-changing behaviour. The deployment of an algorithm in CAV is expected to improve the transportation safety of CAV driving behaviours. [ABSTRACT FROM AUTHOR]

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

Microtubule disruption is a common downstream mechanism leading to axonal degeneration in a number of neurological diseases. To date, most studies on this topic have focused on the loss of microtubule mass from the axon, as well as changes in the stability properties of the microtubules and/or their tubulin composition. Here we posit corruption of the normal pattern of microtubule polarity orientation as an underappreciated and yet treatable contributor to axonal degeneration. We include computational modeling to fortify the rigor of our considerations. Our simulations demonstrate that even a small deviation from the usual polarity pattern of axonal microtubules is detrimental to motor-based trafficking of organelles and other intracellular cargo. Additional modeling predicts that axons with such deviations will exhibit significantly reduced speed and reliability of organelle transport, and that localized clusters of wrongly oriented microtubules will result in traffic jams of accumulated organelles. [ABSTRACT FROM AUTHOR]

• Systematically consider time-varying traffic conditions and system-wide impact in urban city logistic problem. • Incorporate parsimonious road link travel time functions with First-In-First-Out property. • Develop three unified optimization models for characterizing problem-specific features in real-life rich arc routing applications. • Exact solution algorithms using Lagrangian relaxation and branch-and-price methods. City logistics, as an essential component of the city operation system, aims at managing the complex flow of goods and services from providers to customers efficiently. Delays associated with peak-period traffic congestion exists in both large and small metropolitan areas. As many of the service tasks in city logistics are needed to be performed during peak hours, operators of urban management movement should consider reducing the total trip time and delay when designing service plans. Equally important, the congestion impact of service vehicles to other road users should also be considered. In this paper, we focus on formulating and solving rich arc routing problems (RARPs) in city logistics with a congested urban environment. We highlight the needs of embedding a structurally parsimonious time-dependent travel time model in RARP for producing high-quality and practically useful solutions. A fluid queue model based analytical approach is presented for link travel time calibration in the form of polynomial arrival rate functions. Accordingly, system-wide (societal) impact of vehicles routing is analytically derived and incorporated into the RARP models which enables traffic managers to systematically consider operation costs and societal impacts when designing routing policies in real-life city logistics applications. Additionally, we develop two new representation schemes for time-dependent travel time modeling in RARPs, including a discretized time-expanded representation scheme and a nonlinear polynomial representation scheme. Three modeling approaches for RARPs are proposed, with different perspectives of capturing time-dependent travel time and formulating problem-specific constraints. With a real-life sprinkler truck routing problem as the representative example of RARP, we develop two efficient exact solution algorithms, including a Lagrangian relaxation-based method and a branch-and-price based method. The latter one is embedded with an enhanced parallel branch-and-bound algorithm. Extensive numerical experiments are conducted based on real-world networks and traffic flow data to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]