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The team orienteering problem (TOP) is a well-studied optimization challenge in the field of Operations Research, where multiple vehicles aim to maximize the total collected rewards within a given time limit by visiting a subset of nodes in a network. With the goal of including dynamic and uncertain conditions inherent in real-world transportation scenarios, we introduce a novel dynamic variant of the TOP that considers real-time changes in environmental conditions affecting reward acquisition at each node. Specifically, we model the dynamic nature of environmental factors—such as traffic congestion, weather conditions, and battery level of each vehicle—to reflect their impact on the probability of obtaining the reward when visiting each type of node in a heterogeneous network. To address this problem, a learnheuristic optimization framework is proposed. It combines a metaheuristic algorithm with Thompson sampling to make informed decisions in dynamic environments. Furthermore, we conduct empirical experiments to assess the impact of varying reward probabilities on resource allocation and route planning within the context of this dynamic TOP, where nodes might offer a different reward behavior depending upon the environmental conditions. Our numerical results indicate that the proposed learnheuristic algorithm outperforms static approaches, achieving up to 25 % better performance in highly dynamic scenarios. Our findings highlight the effectiveness of our approach in adapting to dynamic conditions and optimizing decision-making processes in transportation systems. [ABSTRACT FROM AUTHOR]

The concept of a location's irreplaceability pertains to its unique characteristics that make it challenging for other locations to replace in terms of providing services and meeting needs. While scholars have shown great enthusiasm for studying location irreplaceability, accurately measuring it and understanding the effects of urban form have not been thoroughly explored through empirical research. This study selects Shenzhen, China, as the study area. First, the I‐index, which focuses on geographic flow, is utilized to measure the location's irreplaceability using mobile phone data. Subsequently, the factors related to urban form are analyzed from the perspectives of demography, building capacity, public services, and the location's conditions. Finally, a geographically and temporally weighted regression model (GTWR) is constructed to explore the spatiotemporal relationships between irreplaceability and urban form. The study demonstrates that the I‐index, considering flow volume and distance, can effectively measure the location's irreplaceability. The results of GTWR indicate that the location's irreplaceability and its influencing factors exhibit spatiotemporal heterogeneity. This study advances our understanding of the relationships between location irreplaceability and urban form both conceptually and methodologically. The findings have practical implications for urban design and planning, including urban space optimization, facility equity, and the alleviation of traffic congestion. [ABSTRACT FROM AUTHOR]

The popularity of working from home (WFH) in the US has surged over the past two decades, with the COVID‐19 pandemic further accelerating this trend. We hypothesize that WFH not only reduces the frequency of physical commutes but also lowers the time cost of commutes due to decreased urban congestion levels; both factors would flatten house‐price gradients. Analyzing big data from Google Maps on travel time in California, we first confirm that COVID‐19, as a WFH‐boosting shock, induced larger decreases in morning travel time in cities with a higher WFH potential. We then empirically validate the effect of WFH on house‐price gradients, channeled through its impact on commuting time; this effect explains 20% of the total WFH‐induced flattening of house‐price gradients during the pandemic in California. [ABSTRACT FROM AUTHOR]

Background: Given the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmost importance. Objective: Our aim is to propose and validate a HS segmentation model that is both training‐free and highly generalizable. This method should exhibit comparable accuracy and efficiency to state‐of‐the‐art techniques. The segmented HS can serve as a biomarker for studying the progression of AD. Methods: We utilized the functional magnetic resonance imaging of the Brain's Integrated Registration and Segmentation Tool (FIRST) to segment the entire hippocampus. By intersecting the segmentation results with the Brainnetome (BN) atlas, we obtained coarse segmentation of the four HS regions. This coarse segmentation was then employed as a shape prior term in the lattice Boltzmann (LB) model, as well as for initializing contours. Additionally, image gradients and local gray levels were integrated into the external force terms of the LB model to refine the coarse segmentation results. We assessed the segmentation accuracy of the model using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated the potential of the segmentation results as AD biomarkers on both the ADNI and Xuanwu datasets. Results: The median Dice similarity coefficients (DSC) for the left caudal, right caudal, left rostral, and right rostral hippocampus were 0.87, 0.88, 0.88, and 0.89, respectively. The proportion of segmentation results with a DSC exceeding 0.8 was 77%, 78%, 77%, and 94% for the respective regions. In terms of volume, the correlation coefficients between the segmentation results of the four HS regions and the gold standard were 0.95, 0.93, 0.96, and 0.96, respectively. Regarding asymmetry, the correlation coefficient between the segmentation result's right caudal minus left caudal and the corresponding gold standard was 0.91, while for right rostral minus left rostral, it was 0.93. Over time, we observed a decline in the volumes of the four HS regions and the total hippocampal volume of mild cognitive impairment (MCI) converters. Analysis of inter‐group differences revealed that, except for the right rostral region in the ADNI dataset, the p‐values for the four HS regions in the normal controls (NC), MCI, and AD groups from both datasets were all below 0.05. The right caudal hippocampal volume demonstrated correlation coefficients of 0.47 and 0.43 with the mini‐mental state examination (MMSE) and Montreal cognitive assessment (MoCA), respectively. Similarly, the left rostral hippocampal volume showed correlation coefficients of 0.50 and 0.58 with MMSE and MoCA, respectively. Conclusions: Our framework allows for direct application to different brain magnetic resonance (MR) datasets without the need for training. It eliminates the requirement for complex image preprocessing steps while achieving segmentation accuracy comparable to deep learning (DL) methods even with small sample sizes. Compared to traditional active contour models (ACM) and atlas‐based methods, our approach exhibits significant speed advantages. The segmented HS regions hold promise as potential biomarkers for studying the progression of AD. [ABSTRACT FROM AUTHOR]

This study explored whether a policy with maximum velocity limitation prior to each jam point in a traffic flow system can mitigate jam situations in a highway. To quantify this question, a new cellular automata traffic model based on the Revised S-NFS model was established. We perused two specific scenarios: a jam naturally brought by a high traffic density, and one brought by a lane-closed section placed as an explicit bottleneck. The result of multi-agent simulation (MAS) reveals that such maximum velocity limitation policy can certainly mitigate jam situations when each jam is naturally bought up not by a bottleneck, as proved by several statistics, and cannot improve the traffic flux. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV) aka V2X is a growing area of research that aims at information exchange between vehicles and all other related objects to develop intelligent transportation systems. IoVs are characterized by high mobility, high-speed internet, varying node density, and dynamic topology and aim to minimize and communicate situations like traffic congestion, accidents, etc. Discovering a routing path in a highly unstable network environment to ensure the successful delivery of packets with minimal overheads. Finding reliable routing links as against shortest path routing is a necessity in IoV networks. In this paper, a routing protocol coined as the dynamic opportunistic routing protocol for IoV (DORP-IoV) is presented. DORP-IoV is an on-demand position-based protocol that seeks the advantage of wireless broadcast advantage to select a hop close to the virtual line of sight between source and destination for communicating the information to the destination. Vehicle movement direction and vehicle density around the ideal hop location are also considered while selecting the next hop for information forwarding. Communicating the information through an optimal number of intermediate nodes is the novelty of the work which ensures better packet delivery with minimized delay and routing overheads. The performance of DORP-IoV is evaluated and results are compared with the performance of Weighted- Greedy Perimeter Coordinator Routing (W-GPCR), Greedy Perimeter Coordinator Routing, and Greedy Perimeter Stateless Routing for varying node density and network connections for various metrics. DORP-IoV shows an improved performance in the range of 8–12% for packet delivery with similar performance for average end-to-end delay compared to W-GPCR. The optimal hop selection mechanism in DORP-IoV reduces the number of hops by 10–30% compared to W-GPCR. [ABSTRACT FROM AUTHOR]

Coastal cities in the Amazon lack basic infrastructure to treat and finally discharge urban domestic sewage. Implementing sub-river outfalls and waste-treatment pre-conditioning that significantly reduce pollution impact would be a sustainable alternative to cope with such an issue. The aim of the present research is to analyze the technical, economic and environmental feasibility of alternative projects applied to domestic waste's proper destination in the coastal zone of Amapá State (ZCEA), Brazil. Infrastructure basic sizing and the current treatment provided to domestic waste discharged in Amazonas River, in natura, were taken into consideration for study conduction. Research methodology was applied as follows: (a) pre-sizing the main infrastructure elements of sub-river outfalls (EM); (b) assessing the reduction of impacts on quality of water in the metropolitan seashore of Macapá and Santana cities; (c) economic assessment based on the alternative implementation of underwater outfalls in Macapá's Northern and Central zones, where one finds Amazonas River's high self-depuration ability and regular bathymetry. Technical feasibility, mainly in Fazendinha and Santana cities, was not satisfactory due to topobathymetric unevenness and traffic jam in Santana Channel's waterway. We have concluded that the self-depuration and sanitary features in Amazonas River's assessed stretches are a sustainable, effective and competitive technology extremely favorable for the implementation of sub-river outfalls, given its simplicity and, most of all, adjustment to the regional socioeconomic reality. [ABSTRACT FROM AUTHOR]

Rapid urbanisation has led to a remarkable increase in travel demand as well as the number of vehicles globally. Inadequate road networks, especially in developing countries fail to serve this increasing vehicle population resulting in frequent traffic congestion. Proportional vehicular emissions are found to be responsible for serious consequences on human health and the environment. The nature and impact of these emissions depend on the traffic, environmental and meteorological factors. The present study focuses on vehicular emissions in a typical identified urban road stretch. The dispersion of vehicular pollutants in the selected urban stretch was studied using Fluidyn PANACHE version 5.2.6 software. The analysis of dispersion revealed that the highest concentration of pollutants is observed along the road sections, with a gradual decrease in concentration as you move away from the streets in both directions. From the vertical profile plotted, the maximum concentration is found to be on the ground and decreases as the height increases, which reveals the influence of meteorological parameters on urban air quality. These results indicate the significant influence of vehicular emissions and meteorological characteristics on ambient air quality. [ABSTRACT FROM AUTHOR]

As the adoption of digital twins increases steadily, it is necessary to determine how to operate them most effectively and efficiently. In this article, the digital twin synchronization problem is introduced and defined formally. Frequent synchronizations would increase cost and data traffic congestion, whereas infrequent synchronizations would increase the bias of the predictions and yield wrong decisions. This work defines the synchronization problem variants in different contexts. To discuss the problem and its solution, the problem of determining when to synchronize an unreliable production system with its digital twin to minimize the average synchronization and bias costs is formulated and analyzed analytically. The state-independent, state-dependent, and full-information solutions have been determined by using a stochastic model of the system. Solving the synchronization problem using simulation is discussed, and an approximate policy is proposed. Our results show that the performance of the state-dependent policy is close to the optimal solution that can be obtained with full information and significantly better than the performance of the state-independent policy. Furthermore, the approximate periodic state-dependent policy yields near-optimal results. To operate digital twins more effectively, the digital twin synchronization problem must be considered and solved to determine the optimal synchronization policy. [ABSTRACT FROM AUTHOR]

Vehicular Ad-Hoc Network is one of the most growing research fields and has become a promising topic for researchers. Day by day, vehicle density increases, causing very serious issues like road accidents and traffic jams. These issues can be minimized by a periodic exchange of awareness messages among the vehicles. Traffic density increases transmission of awareness messages, which causes channel congestion and degrades safety services, so congestion control schemes must ensure that the channel load should be below a particular threshold, boost the quality of services and minimize information loss and delay in the network. This paper proposes a machine learning-based risk-aware congestion control (MLB-RACC) scheme which is an efficient congestion control scheme based on the modified k -means machine learning algorithms. MLB-RACC technique processes the input data and helps to improve the quality of services in the automotive industry. It works in three phases: detecting congestion, clustering of vehicles and controlling the broadcasting of awareness messages for decreasing the channel load. MLB-RACC scheme works by grouping the nearest vehicles and the number of groups or clusters is decided on the basis of transmission range/transmission power. The group member selects adaptive transmission rate by looking at the channel busy ratio (CBR). In this technique, message generation rate is controlled at two levels: first is at the clustering level and second is during the checking of CBR. The scheme is implemented through MATLAB, NS2 and the SUMO platform and provides evidence for the minimization of information loss by analyzing the throughput, packet loss and end-to-end delay in comparison to ordinary decentralized congestion control (DCC) technique. [ABSTRACT FROM AUTHOR]

Current traffic simulation approaches analyze vehicle loads and load effects from a statistical perspective; however, they fail to reproduce the spatiotemporal distribution of bridge loads and resultant load effects at every moment, hindering real‐time bridge health management. This paper proposes a two‐step hybrid virtual–real traffic simulation (HvrTS) approach to reproduce the spatiotemporal distribution of bridge loads. Vehicle load sequences are first identified using computer vision based on traffic video from two surveillance cameras installed along the bridge. Next, traffic microsimulation is optimized with the identified vehicle load sequences from the two cameras serving as the known input and validated output. Using a cable‐stayed bridge under free‐flowing traffic, the HvrTS approach achieved a weighted mean square matching error of ≤0.3 m for the vehicle longitudinal location and a matching error of ≤7% for the vehicle lane position, whereas with congested traffic, the matching errors were much higher due to the inherent complexities and challenges associated with reproducing vehicle behavior in heavily congested situations. The traffic load effects calculated via HvrTS presented excellent spatiotemporal matching with those measured by a structural health monitoring system, especially in free‐flow traffic conditions. Applications on a continuous rigid frame girder bridge further validate these findings. Hence, the proposed HvrTS approach can overcome the challenge of spatiotemporal matching between vehicle loads and load effects in field monitoring. [ABSTRACT FROM AUTHOR]

Smart Autonomous Vehicles (AVSs) are networks of Cyber-Physical Systems (CPSs) in which they wirelessly communicate with other CPSs sub-systems (e.g., smart -vehicles and smart-devices) to efficiently and securely plan safe travel. Due to unreliable wireless communication among them, such vehicles are an easy target of malware attacks that may compromise vehicles' autonomy, increase inter-vehicle communication latency, and drain vehicles' power. Such compromises may result in traffic congestion, threaten the safety of passengers, and can result in financial loss. Therefore, real-time detection of such attacks is key to the safe smart transportation and Intelligent Transport Systems (ITSs). Current approaches either employ static analysis or dynamic analysis techniques to detect such attacks. However, these approaches may not detect malware in real-time because of zero-day attacks and huge computational resources. Therefore, we introduce a hybrid approach that combines the strength of both analyses to efficiently detect malware for the privacy of smart-cities. [ABSTRACT FROM AUTHOR]

With the increasing density of the freeway network, frequent traffic incidents on road segments have a significant impact on the operational efficiency of the road network. Therefore, it has become urgent and important to study traffic route guidance strategy on the road network level. The previous traffic route guidance method primarily focused on the congestion on the road segments where incidents occurred, with insufficient attention given to the impact of congestion on the road network level. In this study, a route guidance model with limited overlap is proposed to improve freeway network reliability under traffic incidents. Specifically, in order to explore alternative paths, we conducted a study on the problem of finding k-short paths with limited overlap. The objective is to identify a set of k -paths that are both sufficiently dissimilar and as short as possible. Then, we promptly update the route guidance information using a stochastic dynamic traffic assignment model that aligns with travelers' path choice psychology. Moreover, we use the reliability of the road network to evaluate the network performance. To illustrate the model, the Jinan freeway network is selected as an experimental study. The effectiveness of this method was validated through SUMO simulations, comparing it with alternative route guidance methods, including Yen's algorithm, A ∗ algorithm, and ant colony algorithm. These results show that the proposed method has proven effective in mitigating traffic congestion arising from incidents and performs well in regard to the reliability of the road network under the impact of incidents. [ABSTRACT FROM AUTHOR]

This paper presents a Laplace transform model for an urban tunnel ventilation system. This model allows one to witness higher performance for supervisory control and data acquisition (SCADA) in terms of monitoring and control of an urban area tunnel based on measurement systems. This proposed model illustrates the ventilation control system framework as well as the emergency response system for urban area tunnels such that smoother controllability and higher security in the operation of tunnels can be envisioned. The salient contributions of this work can be stated as a novel method for modeling tunnel ventilation systems and the implementation of an emergency response plan for a futuristic intelligent transportation system. The simulation results exhibit that the proposed model outperforms the ventilation system in the high-density traffic jams and further the efficient operation of the tunnel. Likewise, comparison results and experimental results are addressed to emphasize the validation of this method and to be helpful in proving the reliability of the results obtained in this study. These results show that the ventilation control system reaches the desired CO value either in high-traffic volume conditions or in normal traffic conditions. [ABSTRACT FROM AUTHOR]

Rainfall has a significant impact on urban population mobility, posing great challenges to traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we devised a multiscale comparative research framework to explore the spatiotemporal effects of rainfall on taxi travel patterns, aiming to provide a new perspective on the investigation of rainfall's impact on urban human mobility. More specifically, at the macroscopic scale, we computed taxi travel indicators across the entire study area and used kernel density estimates to observe the spatiotemporal distribution patterns influenced by rainfall. Subsequently, complex traffic networks were constructed by considering urban road intersections as nodes and combined with visualization methods to understand changes in taxi travel patterns visually at the microscopic level. We selected Wuhan City, a typical urban area in southern China with frequent rainfall, as the study area and used meteorological data along with a large volume of taxi spatiotemporal trajectory data for investigation. Results indicated a 4.16% decrease in weekly travel volume due to rainfall, with a 3.96% decrease on workdays and a 4.64% decrease on weekends. However, nighttime rainfall between 19:00 and 22:00 on weekdays increased the demand for taxi travel. Furthermore, the impact of rainfall on weekends exceeded that on workdays, restricting people's mobility and leisure activities, resulting in reduced travel to recreational tourist spots and commercial pedestrian streets. Rainfall altered residents' travel preferences to some extent, with more residents choosing taxis during rainy weather, which led to decreased transportation efficiency and increased traffic congestion. These findings contribute to a deeper understanding of the complex relationship between population mobility patterns and the urban ecological environment, providing valuable insights for planning resident travel and taxi dispatching under adverse weather conditions. [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]

Connected and automated vehicles can reduce the traffic congestion level of the entire network through platoon-driving technologies compared to human-driven vehicles. One promising approach to enhancing platoon-driving technology's efficiency is deploying dedicated lanes or roads for connected and automated vehicles. Since asymmetric interactions between different vehicle types increase road congestion, it is necessary to distinguish routes for efficient traffic management. However, the traditional traffic assignment problem, which uses only user equilibrium as a constraint with no difference in travel time between users, could not be proposed as a globally optimal solution because it generates an infinite number of locally optimal solutions. Recent studies have attempted to overcome the limitations by considering the sum of system-wide travel times as an additional constraint. Their research sought to help propose optimal deployment strategies through the lowest total travel time solution (best-case) or design robust transport planning strategies through the highest total travel time solution (worst-case). However, past studies have not focused on the possibility of the best/worst case appearing in reality. This study focused on the relationship between the two solutions pointed out in past studies and traffic patterns likely to appear in reality. This study interprets the Karush–Kun–Tucker condition of the static traffic assignment problem, considering the asymmetric interaction, and proposes a solution algorithm using discrete dynamics. The proposed algorithm extends the most widely used method in transportation planning research, which can overcome the limitations of asymmetric interaction problems through simple variations. The proposed algorithm can reliably derive two solutions, and entropy theory shows that both solutions are unlikely to appear in reality without additional policies such as dedicated lanes or roads. [ABSTRACT FROM AUTHOR]

In order to solve the network congestion problem caused by burst service flows in heterogeneous cognitive radio networks (Het‐CRN), a two‐stage dual‐threshold random early detection (TD‐RED) control mechanism is proposed here. Meanwhile, a three‐state Markov modulated Poisson process (MMPP‐3) is used to model the burst service to analyze its arrival in depth. Moreover, a performance evaluation system (PES) is proposed to quantify the interference of burst services to different classes of services and their quality of service, which can comprehensively evaluate the performance enhancement of the TD‐RED control mechanism for each service. The system examines performance metrics such as packet loss rate, average delay, average captain etc. PES simulation results show that TD‐RED significantly reduces the packet rejection rate and transmission delay compared to the no congestion control (NCC) and random early detection (RED) methods, thus providing superior quality of service to all services. thereby providing a superior quality of service to cognitive users. Its lower average captain fully proves that TD‐RED can effectively alleviate congestion. [ABSTRACT FROM AUTHOR]

Accurate air traffic flow prediction assists controllers formulate control strategies in advance and alleviate air traffic congestion, which is important to flight safety. While existing works have made significant efforts in exploring the high dynamics and heterogeneous interactions of historical air traffic flow, two key challenges still remain. (1) The transfer patterns of air traffic are intricate, subject to numerous constraints and limitations such as controllers, flight regulations, and other regulatory factors. Relying solely on mining historical traffic evolution patterns makes it difficult to accurately predict the constrained air traffic flow. (2) Weather conditions exert a substantial influence on air traffic, making it exceptionally difficult to simulate the impact of external factors (such as thunderstorms) on the evolution of air traffic flow patterns. To address these two challenges, we propose a Spatiotemporal Knowledge Distillation Network (ST-KDN) for air traffic flow prediction. Firstly, recognizing the inherent future insights embedded within flight plans, we develop a "teacher-student" distillation model. This model leverages the prior knowledge of upstream-downstream migration patterns and future air traffic trends inherent in flight plans. Subsequently, to model the influence of external factors and predict air traffic flow disturbed by thunderstorm weather, we propose a student network based on the "parallel-fusion" structure. Finally, employing a feature-based knowledge distillation approach to integrate prior knowledge from flight plans and extract meteorological features, our method can accurately capture complex and constrained spatiotemporal dependencies in air traffic and explicitly model the impact of weather on air traffic flow. Experimental results on real-world flight data demonstrate that our method can achieve better prediction performance than other state-of-the-art comparison methods, and the advantages of the proposed method are particularly prominent in modeling the complicated transfer pattern of air traffic and inferring nonrecurrent flow patterns. [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]

The hydrodynamic model can be used to describe traffic problems in transport. When the speed of the first car is less than the speed behind it, it leads to traffic jams. When the first car's speed is faster than the cars behind it, it leads to traffic evacuation. If we consider the first car to be a piston, then the speed of the piston will cause traffic jams and traffic evacuation. In this paper, we study the piston problem for the hydrodynamic model. The formation and propagation of shock wave, rarefaction wave, delta-shock wave, and vacuum can describe the phenomena of traffic jams, traffic evacuation, severe traffic jams, and traffic evacuation with traffic volume of zero, respectively. Therefore, for different traffic phenomena, we prove the existence of shock solution, rarefaction solution, delta shock solution, and vacuum solution. In addition, we perform some representative numerical simulations. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel sim-learnheuristic method designed to address the team orienteering problem (TOP) with a particular focus on its application in the context of unmanned aerial vehicles (UAVs). Unlike most prior research, which primarily focuses on the deterministic and stochastic versions of the TOP, our approach considers a hybrid scenario, which combines deterministic, stochastic, and dynamic characteristics. The TOP involves visiting a set of customers using a team of vehicles to maximize the total collected reward. However, this hybrid version becomes notably complex due to the presence of uncertain travel times with dynamically changing factors. Some travel times are stochastic, while others are subject to dynamic factors such as weather conditions and traffic congestion. Our novel approach combines a savings-based heuristic algorithm, Monte Carlo simulations, and a multiple regression model. This integration incorporates the stochastic and dynamic nature of travel times, considering various dynamic conditions, and generates high-quality solutions in short computational times for the presented problem. [ABSTRACT FROM AUTHOR]

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

The study concentrates on two different genetic programming approaches for determining passenger car equivalent (PCE) values and observing the impact on capacity estimation at urban unsignalized intersections. Considering heterogeneous traffic conditions, a new PCE value is introduced to encompass sustainable modes of public transit vehicles, specifically slow-moving three-wheelers (SM3W), commonly known as E-Rickshaws. Since PCE value is considered an important parameter for capacity calculations, the present study considered 14 unsignalized intersections located in Ranchi city of India. An automatic plate recognition system is employed to have the count of vehicular traffic. The methodologies include age-layered population structure genetic programming (ALPSGP), and the offspring selection genetic programming (OSGP) approach that incorporates static and dynamic variables. Based on the significance test and ranking of the genetic programming (GP) models, the OSGP model is recommended as the most appropriate model for heterogeneous traffic. Sensitivity analysis reported that lagging headway () is the most contributing factor in PCE estimation. The PCE value of SM3W is found to be 0.81 and that could be incorporated as a new classification of vehicles in Indo-HCM. It is observed that evaluated capacity based on PCE values of OSGP performed admirably in both normal and congested traffic situations. [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]

Due to the increasing demand for transport of electrical energy, measures for power flow control, congestion management, and higher utilisation of the existing grid play a decisive role in the transformation of the power system. Hence, enormous efforts must be undertaken using measures of congestion management. Modelling and integration of corresponding measures in optimisation tools to support grid and system operation and therewith reduce the resulting efforts become more important. This is especially true because of the high intermittency and decentralisation of renewable generation leading to increased complexity of the power system, higher loading of assets, and a growing need for control over flexible alternating current transmission systems (FACTS) and high-voltage direct current (HVDC) converters. This work therefore describes the implementation of optimised congestion management in an A Mathematical Programming Language (AMPL)-based nonlinear optimisation problem. AMPL is an effective tool to deal with highly complex problems of optimisation and scheduling. Therefore, the modelling of assets and flexibilities for power flow control in AC/DC systems in combination with an innovative grid operation strategy using predefined curative measures for the optimised use of the existing grid is introduced. The nonlinear mathematical optimisation aims at the optimal cost selection of flexibility measures. The application of the optimisation technique in a combined AC/DC system shows the optimal preventive and curative use of measures in operational congestion management. Simulation results prove that, by using predefined curative measures, the volume of cost-intensive preventive measures can significantly be reduced, especially in association with power flow control. [ABSTRACT FROM AUTHOR]

Expressway traffic information is important for guiding driving routes and alleviating traffic congestion. However, the current research on expressway guidance information focuses on existing expressways. In this study, strategies for providing expressway guidance information under reconstruction and expansion scenarios are investigated. Multiple factors of expressway reconstruction and expansion, such as the length of construction areas and the number of lanes occupied by construction areas, are extracted. A panel latent class logit model considering individual heterogeneity is established to fit the survey data collected by 825 respondents. The results show that the proposed panel latent class logit model fits the data best, and the studied drivers could be categorized into three classes, i.e., the information provision time-sensitive class, the information promotion detour class, and the information suppression detour class. The research results can support expressway operators in designing appropriate traffic information provision strategies, providing personalized guidance to drivers, and ensuring the safe operation of expressways in construction areas. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model. [ABSTRACT FROM AUTHOR]

Forecasting citywide traffic congestion on large road networks has long been a nontrivial research problem due to the challenge of modeling complex evolution patterns of congestion in highly stochastic traffic environments. Arguing that purely data‐driven methods may not perform well for congestion forecasting, we propose a deep marked graph process model for predicting the congestion indices and the occurrence time of traffic congestion events for complex signalized road networks. Traffic congestion is considered as a nonrigorous spatiotemporal extreme event. We extend the traditional point process model by integrating a specially designed spatiotemporal graph convolutional network. This hybrid strategy takes advantage of the simple form of the point process model as well as the ability of graph neural networks to emulate the evolution of congestion. Experiments on real‐world congestion data sets show that the proposed method outperforms state‐of‐the‐art baseline methods, yielding satisfactory prediction results on a large signalized road network with superior computational efficiency. [ABSTRACT FROM AUTHOR]

The connected and automated car-following model can provide a model reference for the queue control algorithm of connected and automated driving and has become a hot research topic in the field of connected vehicles and intelligent transportation. A queue of fast-moving vehicles on urban roads can cause traffic congestion when forced to slow down and, in serious cases, can cause rear-impact accidents. Therefore, this paper introduces information on the time delay of information reception and processing, a collision risk quantification factor reflecting the speed characteristics of the front vehicle, and the speed limit and proposes an improved intelligent driver collision quantification model that considers drastic changes in the speed of the front vehicle. Additionally, the model parameters are calibrated using real vehicle data from urban roads combined with an improved salp swarm algorithm. Finally, the evolution rule of disturbance in the traffic flow under different states is analyzed using a time-space diagram, and the DIDM-CSCL model is compared with the classical IDM. The results show that the improved IDM can better describe the following behavior at the microscopic level, which provides a basis for research related to connected and automated driving. [ABSTRACT FROM AUTHOR]

One dominant aspect of cities is transport and massive passenger mobilization which remains a challenge with the increasing demand on the public as cities grow. In addition, public transport infrastructure suffers from traffic congestion and deterioration, reducing its efficiency. In this paper, we study the capacity of transport in 33 worldwide metro systems under the accumulation of damage. We explore the gradual reduction of functionality in these systems associated with damage that occurs stochastically. The global transport of each network is modeled as the diffusive movement of Markovian random walkers on networks considering the capacity of transport of each link, where these links are susceptible to damage. Monte Carlo simulations of this process in metro networks show the evolution of the functionality of the system under damage considering all the complexity in the transportation structure. This information allows us to compare and classify the effect of damage in metro systems. Our findings provide a general framework for the characterization of the capacity to maintain the transport under failure in different systems described by networks. [ABSTRACT FROM AUTHOR]

For the dynamic takeout delivery vehicle routing problem, which faces fluctuating order demand and time-varying speeds, this study presents a novel approach. We analyze the time distribution of takeout orders and apply a Receding Horizon Control (RHC) strategy to convert the dynamic challenge into a static one. The driving speed of delivery vehicles on different roads at different times is determined based on the subdivision criteria of the urban road network and a traffic congestion measurement method. We propose a dynamic takeout delivery vehicle routing optimization model and a time-varying subdivision road network is established to minimize the total delivery cost. We validated the model through simulation examples. The optimization results show that the total distribution cost is reduced after considering the time-varying subdivision road network, with the penalty cost decreasing by 39%. It is evident that considering the subdivision of the road network can enhance order delivery efficiency and optimize the overall dining experience. The sensitivity analysis of various parameters reveals that the delivery platform must appropriately determine the time domain and allocate the number of delivery personnel based on order scale to avoid escalating delivery costs. These findings provide theoretical guidance for vehicle routing planning in the context of delivery platforms. [ABSTRACT FROM AUTHOR]

The concept of Urban Air Mobility (UAM), an on-demand aviation service using air vehicles for urban short-distance trips, such as electric vertical takeoff and landing aircraft (eVTOL), has recently drawn public attention as one of possible solutions to traffic congestion in metropolitan areas. We present an optimal planning framework for fleet size and vertiport numbers using generalized cost models of UAM trip chains with two different ground access modes for first and last mile trips, including taxis and robotaxis (i.e. autonomous taxis or shared autonomous vehicles). As it is a relatively new and untested mode of transportation, our research aims to provide a decision-making tool for initial UAM system planning and analysis of its economic feasibility, appropriately considering its unique characteristics, such as high free-flow speed, low circuity, needs for ground access, and specific cost factors, such as vertiport and electric vertical takeoff and landing aircraft costs. Through a parametric study, we quantitatively examine the necessary conditions for UAM, with or without ridesharing, to become more economically viable than other ground-only modes. The study confirms that UAM can have potential economic benefits especially in large cities with longer average trip lengths and severe traffic congestion, where the average ground vehicle speed is low. [ABSTRACT FROM AUTHOR]

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

Rapid global urbanization has led to a growing urban population, posing challenges in transportation management. Persistent issues such as traffic congestion, environmental pollution, and safety risks persist despite attempts to mitigate them, hindering urban progress. This paper focuses on the critical need for accurate traffic flow forecasting, considered one of the main effective solutions for containing traffic congestion in urban scenarios. The challenge of predicting traffic flow is addressed by proposing a two-level machine learning approach. The first level uses an unsupervised clustering model to extract patterns from sensor-generated data, while the second level employs supervised machine learning models. Although the proposed approach requires the availability of data from traffic sensors to realize the training of the machine learning models, it allows traffic flow prediction in urban areas without sensors. In order to verify the prediction capability of the proposed approach, a real urban scenario is considered. [ABSTRACT FROM AUTHOR]

Due to increasing urbanization, nowadays, cities are facing challenges spanning multiple domains such as mobility, energy, environment, etc. For example, to reduce traffic congestion, energy consumption, and excessive pollution, big data gathered from legacy systems (e.g., sensors not conformant with modern standards), geographic information systems, gateways of public administrations, and Internet of Things technologies can be exploited to provide insights to assess the current status of a city. Moreover, the possibility to perform what-if analyses is fundamental to analyzing the impact of possible changes in the urban environment. The few available solutions for scenario definitions and analyses are limited to addressing a single domain and providing proprietary formats and tools, with scarce flexibility. Therefore, in this paper, we present a novel scenario model and editor integrated into the open-source Snap4City.org platform to enable several processing and what-if analyses in multiple domains. Different from state-of-the-art software, the proposed solution responds to a series of identified requirements, implements NGSIv2-compliant data models with formal descriptions of the urban context, and a scenario versioning method. Moreover, it allows us to carry out analyses on different domains, as shown with some examples. As a case study, a traffic congestion analysis is provided, confirming the validity and usefulness of the proposed solution. This work was developed in the context of CN MOST, the National Center on Sustainable Mobility in Italy, and for the Tourismo EC project. [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]

Although the EVRP (Electric Vehicle routing problem) has promising results on the environmental scale, its implementation has proved challenging. The difficulty of the EVRP resides in the limited driving range of the electric vehicles, combined with the significant charging time. While the charging cost is less than the cost of fuel, this charge time adds to the overall travel time and may overlap with customers' time windows. All these factors increased the computational time exponentially and resulted in the need to overlook some constraints such as traffic congestion, road conditions, weather impact on energy consumption, and driving style, to name a few, in order to speed up execution time. While this method proved effective in accelerating the process of the EVRP, it did, however, render the approach unrealistic, as it steered far from real-life settings and made the approach unpredictable when facing dynamic and changing parameters. In this paper, we try to remedy this issue by proposing an approach in which we try to replicate real-life parameters such as heterogenous fleets, energy consumption, and infrastructure data. The objective of our approach was to minimize the total travel time, travel distance, energy consumed, and the number of vehicles deployed. To solve this problem, we propose a three-stages approach, in which the first stage consists of a newly developed dispatching approach where customers are assigned to vehicles. The second stage uses the genetic algorithm to find a set of optimal paths, and, finally, in the third stage, charging stations are inserted in the selected paths. Upon testing our approach on Solomon's instances, our approach proved effective in finding optimal solutions in a reasonable time for five- to fifteen-customer datasets. However, when trying to solve larger datasets, the approach was slowed down by the extreme number of constraints it had to satisfy. [ABSTRACT FROM AUTHOR]