Congestion Reduction Research Articles

Providing real-time en-route suggestions to CAVs for congestion mitigation: A two-way deep reinforcement learning approach

This research investigates the effectiveness of information provision for congestion reduction in Connected Autonomous Vehicle (CAV) systems. The inherent advantages of CAVs, such as vehicle-to-everything communication, advanced vehicle autonomy, and reduced human involvement, make them conducive to achieving Correlated Equilibrium (CE). Leveraging these advantages, this research proposes a reinforcement learning framework involving CAVs and an information provider, where CAVs conduct real-time learning to minimize their individual travel time, while the information provider offers real-time route suggestions aiming to minimize the system’s total travel time. The en-route routing problem of the CAVs is formulated as a Markov game and the information provision problem is formulated as a single-agent Markov decision process. Then, this research develops a customized two-way deep reinforcement learning approach to solve the interrelated problems, accounting for their unique characteristics. Moreover, CE has been formulated within the proposed framework. Theoretical analysis rigorously proves the realization of CE and that the proposed framework can effectively mitigate congestion without compromising individual user optimality. Numerical results demonstrate the effectiveness of this approach. Our research contributes to the advancement of congestion reduction strategies in CAV systems with the mitigation of the conflict between system-level and individual-level goals using CE as a theoretical foundation. The results highlight the potential of information provision in fostering coordination and correlation among CAVs, thereby enhancing traffic efficiency and achieving system-level goals in smart transportation.

Enhancing routing efficiency in social internet of things: R-OPTICS and vEBT based congestion free model

The emergence of the social internet of things (SIoT) network has brought forth distinctive challenges, including node mobility and varying densities, leading to congestion and hampered network efficiency. To overcome these issues, a congestion-free routing model for SIoT is proposed. This model combines the relationship-ordering points to identify the clustering structure (R-OPTICS) algorithm for intelligent node clustering based on relationships and ordering,along with a van emde boas tree (vEBT) for efficient path selection. R-OPTICS enables effective network management by clustering nodes appropriately. The model’s performance is evaluated using metrics such as Rand-Index (1.5765),Davies-Bouldin (-0.4305), and Silhouette Coefficient (1.71685) to assess average goodness values. vEBT identifies optimal paths between clusters, facilitating smart routing decisions. The primary objective of the model is to enhance network efficiency and alleviate congestion by intelligently routing data between clusters. Through extensive simulations, the proposed model outperforms existing routing methods, resulting in improved efficiency and congestion reduction. This congestion-free routing model presents a promising solution to address the unique challenges of SIoT networks, ensuring optimal performance and effective resource management.

Traffic simulation-emission modelling to evaluate impact of signal cycle on automobile emissions

Vehicular traffic comprises different types of vehicles with varying static and dynamic characteristics, under heterogeneous traffic conditions in India. The continuous growth of vehicles results in high congestion and becoming the main source of air pollution. The reduction in emissions is directly linked to the reduction in traffic congestion. At urban corridors, higher concentration of pollutants is observed near the traffic signals because of the influence of traffic signal operations on vehicular speed. The present research is carried out with the principal objective as the study of the effect of intersection control on emissions in the urban intersection of a metropolitan city. Microscopic traffic simulation models have been used to simulate real traffic conditions, and effectively represent the extensive performance of vehicles associated with speed and acceleration states. The simulation model is revised with optimum signal cycle time considering the signal phase system shows a substantial reduction in automobile emissions.

#1748 The burden of cardiorenal syndrome: epidemiological data and treatment: a one-center experience

Abstract Background and Aims Cardiorenal syndrome (CRS) is an entity combining cardiac and renal dysfunction in a bi-directional way. Six subtypes of CRS have been described, all of which are associated with high morbidity and mortality rates. CRS treatment—no matter the subtype—is until nowadays a field of investigation, aiming to the reduction of fluid overload and venous congestion. The primary endpoint of this study was to estimate the frequency of cardiorenal induced AKI compared to other AKI etiologies in hospitalized patients. We furthermore aimed to determine the burden of the therapeutic regimen in hospitalization duration and renal deterioration. Method This is a retrospective, quantitative analysis of hospitalized adult patients in General Hospital of Athens Hippokration suffering from AKI of various etiologies. No age or sex limitations were used for the purposes of this study. Results A total amount of 1163 patients were enrolled over the course of 18 months. 430 (36.9%) of these patients were hospitalized in the cardiologic department, suffering from either acute or chronic heart failure (66.5% of which were men) with a mean age of 74.4 ± 11.4 years. Mean creatinine level at hospital admission was 2.7 ± 2 mg/dl. Out of these patients, 33.9% were treated using diuretics alone, while 25.5% also needed hemodialysis. Mean number of days of hospitalization was 9.4 ± 5.8 days and 11 ± 8.8 days, while creatinine exit was 2.9 ± 1.5 mg/dl and 4.2 ± 3 mg/dl respectively. Conclusion Cardiorenal syndrome is a quite common cause of AKI in patients being hospitalized. In our study, patients that needed treatment with diuretics compared to those requiring a combination of diuretics and hemodialysis had less days of hospitalization and a better kidney function outcome.

Streamlining Toll Management: A Survey of Business Requirement Solutions

Effective toll control performs a pivotal role in improving traffic flow, decreasing congestion, and enhancing overall transportation performance. However, toll plaza operators frequently face demanding situations in efficaciously handling statistics and monitoring transactional activities. This studies paper proposes a secure, fast, and dependable statistics control answer tailor-made to the precise wishes of toll plaza operators. By implementing this solution, toll plaza proprietors can successfully track income and loss states, examine regular transaction statistics, and optimize traffic control measures. Through the improvement and implementation of this solution, massive enhancements in traffic flow efficiency and congestion reduction may be achieved, ultimately leading to more advantageous transportation systems. This paper offers a practical method to addressing the complexities of toll control, imparting insights into the advantages and impacts of adopting such answers withinside the transportation sector. Keywords-Toll management, Traffic flow, Congestion reduction, Transportation efficiency, Data management solution, Profit tracking, Transaction analysis, Traffic control, Toll plaza operators, Transportation systems

CHANGES IN LEFT ATRIAL VOLUME DURING ECHOSTRESS IN PATIENTS UNDERGOING MITRAL VALVE PLASTIC CARDIAC SURGERY

Abstract Mitral Regurgitation (MR) is characterized by a high morbidity and mortality that often finds a solution only in the reparative surgical treatment, in particular in presence of myxomatous degeneration that cause the prolapse of one or both leaflets. This results in an increase in diastolic pressure that leads to a progressive dilation of the left atrium. The increase in Left Atrium Volume Index (LAVI) during stress test represents a negative prognostic factor in the clinical evolution of many pathologies, such as ischemic heart disease, hypertrophic cardiomyopathy, heart failure. An increase in peak/rest LAVI >6.8 ml/m2 correlates with a higher incidence of heart failure decompensation, reduced left ventricular contractile reserve (LVCR), pulmonary congestion. We evaluate the effect of MR reparative surgery on the changes of LAVI during stress–echo. We enrolled 19 patients, 11 females and 8 males, affected by MR candidate to cardiac surgery. We performed a physical stress–echo test by using the ABCDE protocol suggested by the Italian Society of Echocardiography and Cardiovascular Imaging (SIECVI), before and after 12 weeks from surgery. During each test, the changes in LAVI (ΔLAVI) expressed as peak/rest difference in ml/m2 was measured. In addition, the prognostic score deriving from the sum of the ABCDE criteria was calculated, such as the LVCR (C parameter of the score) that represents a parameter of the force deriving from the hemodynamic changes during the test. In all patients we observed a significant reduction in ΔLAVI (8.4+0.5 mL/m2 vs 9.7+0.7 mL/m2; p<0.0001). In addition, there was a significant reduction in the ABCDE prognostic score (from 4.19+0.43 to 3.48+0.37; p=0.002), secondary to the increase in the LVCR observed approximately 3 months after surgery (from 1.77 mmHg/ml to 2.44 mmHg/ml, p<0.0001), as well as a significant reduction in pulmonary congestion during stress (B parameter). There were no significant changes in ejection fraction, wall motion score index–WMSI (A parameter), coronary reserve (D parameter) and heart rate reserve (E parameter). These data demonstrate for the first time that mitral valve plastic surgery is able to reduce the ΔLAVI during stress echocardiography in patients with severe MR. The surgical procedure also resulted in both an improvement in the LVCR and a reduction in the degree of pulmonary congestion, that ultimately led to an improvement in the ABCDE prognostic score.

Thrombosis inhibited by Corydalis decumbens through regulating PI3K-Akt pathway

Ethnopharmacological relevanceCorydalis decumbens (Thunb.) Pers. was used as stasis-eliminating medicine traditionally to treat cardiovascular disease potentially attributed to its antithrombotic effect, but lack of pharmacological research on it. Aim of the studyTo investigate the antithrombotic effect of C. decumbens and its preliminary mechanism. Materials and methodsA carrageenan-induced mouse thrombus model and adenosine diphosphate stimulated platelet aggregation of rabbits were used to confirm the inhibitory effect of C. decumbens extract and compounds on thrombosis in vivo. Then, H2O2-induced human umbilical vein endothelial cells (HUVECs) injury model was further adopted to verify the effects of bioactive compounds in vitro. Moreover, in silico network pharmacology analyses and molecular docking were performed to predict the underlying mechanisms, targets, and pathways, and which were further confirmed through western blotting assay. ResultsThe administration of total extract (TE), total alkaloids (TA) and tetrahydropalmatine (TET) resulted in a significant reduction in black tail thrombus and congestion, along with a decreasing in platelet aggregation of rabbits. A superior antithrombotic effect indicated the bioactive fraction, and then the isolated bioactive compounds, TET and protopine (PRO) increased cell survival, and decreased reactive oxygen species (ROS) and lactate dehydrogenase (LDH) release in H2O2-induced HUVECs injury model. Moreover, the two alkaloids targeted 33 major proteins and influenced 153 pathways in network pharmacology prediction. Among these, HSP90AA1, COX-2, NF-κB/p65, MMP1 and HIF-1α were the key proteins and PI3K-Akt emerged as the major signaling pathway. Further western blotting results supported that five key proteins were downregulated by the two bioactive compounds in H2O2-stimulated HUVECs model. ConclusionC. decumbens exerted protective effect on thrombosis through inhibiting PI3K-Akt pathway and related key proteins, which supported the traditional use and presented potential antithrombotic alkaloids for further investigation.

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

Assessing the Role of Autonomous Vehicles in Urban Areas: A Systematic Review of Literature

Autonomous vehicles (AVs) aim to improve safety and comfort of road users while contributing to the reduction of traffic congestion, air pollution, fuel consumption, and enabling mobility and accessibility of disabled and older people. As AV technology is rapidly advancing, there is an urgent need to explore how those new mobility services will impact urban transport systems, including the users, the infrastructure, and the design of future urban areas. This paper applies a systematic review to assess the role of AVs in urban areas. It reviews 41 articles published between 2003 and 2023, and uses inductive and deductive coding approaches to identify seven themes and thirty sub-themes within the literature. The seven include: benefits, attitudes, and behaviours and user perception, climate adaptation, climate mitigation, legislation and regulations, sustainability, and infrastructure. Studies related to benefits accounted for 25% of the sample, followed by behaviours and user perception (24%) and sustainability (22%). The least amount of research has been undertaken on the role of AVs to support climate adaptation. Geographically, almost half (#22) of the papers originate within Europe, followed by America (#10) and Asia (#7). There is only limited research originating from the Global South. This systematic review sets the scene for considering how AVs in public transport can be implemented in urban areas by establishing the current state of knowledge on user attitudes, perceptions, and behaviour, the benefits of AVs, the infrastructure and legislation and regulations required for AVs, and the role AVs have in climate mitigation, adaptation, and sustainability.

Congestion patterns in severe tricuspid regurgitation and transcatheter treatment: Insights from a multicentre registry.

While invasively determined congestion holds mechanistic and prognostic significance in acute heart failure (HF), its role in patients with tricuspid regurgitation (TR)-related right- heart failure (HF) undergoing transcatheter tricuspid valve intervention (TTVI) is less well established. A comprehensive understanding of congestion patterns might aid in procedural planning, risk stratification, and the identification of patients who may benefit from adjunctive therapies before undergoing TTVI. The aim of this study was to investigate the role of congestion patterns in patients with severe TR and its implications for TTVI. Within a multicentre, international TTVI registry, 813 patients underwent right heart catheterization (RHC) prior to TTVI and were followed up to 24 months. The median age was 80 (interquartile range 76-83) years and 54% were women. Both mean right atrial pressure (RAP) and pulmonary capillary wedge pressure (PCWP) were associated with 2-year mortality on Cox regression analyses with Youden index-derived cut-offs of 17 mmHg and 19 mmHg, respectively (p < 0.01 for all). However, RAP emerged as an independent predictor of outcomes following multivariable adjustments. Pre-interventionally, 42% of patients were classified as euvolaemic (RAP <17 mmHg, PCWP <19 mmHg), 23% as having left-sided congestion (RAP <17 mmHg, PCWP ≥19 mmHg), 8% as right-sided congestion (RAP ≥17 mmHg, PCWP <19 mmHg), and 27% as bilateral congestion (RAP ≥17 mmHg, PCWP ≥19 mmHg). Patients with right-sided or bilateral congestion had the lowest procedural success rates and shortest survival times. Congestion patterns allowed for discerning specific patient's physiology and specifying prognostic implications of right ventricular to pulmonary artery coupling surrogates. In this large cohort of invasively characterized patients undergoing TTVI, congestion patterns involving right-sided congestion were associated with low procedural success and higher mortality rates after TTVI. Whether pre-interventional reduction of right-sided congestion can improve outcomes after TTVI should be established in dedicated studies.

Transformative Applications of IoT in Diverse Industries: A Mini Review

By integrating physical objects and facilitating data-driven decision-making, the Internet of Things (IoT) is transforming several sectors. Through the provision of individualised treatment plans, real-time health data analysis, and remote patient monitoring, it is vital to the modernization of healthcare systems. IoT technologies are essential to the development of smart cities, resource allocation optimisation, public safety improvement, and traffic congestion reduction. IoT-driven smart farming automates machinery, optimises irrigation, and monitors crop conditions. As IoT makes it possible to create smart grids, save energy waste, and increase grid dependability, the energy landscape is changing. IoT makes it easier to apply Industry 4.0 ideas in the manufacturing sector, converting conventional factories into networked, intelligent systems. Reducing operating costs and increasing productivity are the outcomes of implementing IoT-enabled sensors, robots, and data analytics to improve supply chain management, predictive maintenance, and production efficiency. Innovation, sustainability, and efficiency are becoming more and more possible as a result of the Internet of Things' integration across many industries. This review also showcases the relevant prospects of IoT applications in the fields mentioned.

Energy efficient optimal deployment of industrial wireless mesh networks using transient trigonometric Harris Hawks optimizer

Wireless mesh networks (WMNs) play a vital role in modern communication systems, and optimizing the placement of wireless mesh routers is crucial for achieving efficient network performance in terms of coverage and connectivity. However, network congestion caused by overlapping routers poses challenges in WMN optimization. To address these issues, researchers have explored metaheuristic algorithms to strike a balance between coverage and connectivity in WMNs. This study introduces a novel hybrid optimization algorithm, namely Transient Trigonometric Harris Hawks Optimizer (TTHHO), specifically designed to tackle the optimization problems in WMNs. The primary objective of TTHHO is to find an optimal placement of routers that maximizes network coverage and ensures full connectivity among mesh routers. Notably, TTHHO's unique advantage lies in its efficient utilization of residual energy, strategically placing the sink node in areas with higher energy levels. The effectiveness of TTHHO is demonstrated through a comprehensive comparison with seven well-known algorithms, including Harris Hawks optimization (HHO), Sine Cosine Algorithm (SCA), Gray Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO), Equilibrium Optimizer (EO), and Transient Search Optimizer (TSO). The proposed algorithm is rigorously validated using 33 benchmark functions, and statistical analyses and simulation results confirm its superiority over other algorithms in terms of network connectivity, coverage, congestion reduction, and convergence. The simulation outcomes demonstrate the effectiveness and efficacy of the proposed TTHHO algorithm in optimizing WMNs, making it a promising approach for enhancing the performance of wireless communication systems.

Understanding high-frequent bus riders: Congestion, education, and riders’ preferences

In this article, we quantify the impact of individual and contingent factors on citizens’ decisions to regularly use the public bus transportation in Barcelona, Spain. Using data from an original survey, we approximate the effect of traffic congestion as a deterrent to becoming a high-frequent bus rider and show that level of education and employment status, as indicators of environmental concerns, fuel the regular use of bus services. Simulations conducted to identify the group of citizens most prone to changing their transportation habits additionally show that regular commuters in the urban area and with a higher level of education are more responsive to reductions in congestion that increase their probability of becoming frequent bus users.

Poor in-hospital congestion improvement in acute heart failure patients classified according to left ventricular ejection fraction: prognostic implications.

Residual congestion in acute heart failure (AHF) is associated with poor prognosis. However, there is a lack of data on the prognostic value of changes in a combined assessment of in-hospital congestion. The present study sought to assess the association between in-hospital congestion changes and subsequent prognosis according to left ventricular ejection fraction (LVEF) classification. Patients (N = 244, 80.3 ± 7.6 years, 50.8% male) admitted for acute HF in two European tertiary care centres underwent clinical assessment (congestion score included dyspnoea at rest, rales, third heart sound, jugular venous distention, peripheral oedema, and hepatomegaly; simplified congestion score included rales and peripheral oedema), echocardiography, lung ultrasound, and natriuretic peptides (NP) measurement at admission and discharge. The primary outcome was a composite of all-cause mortality and/or HF re-hospitalization. In the 244 considered patients (95 HF with reduced EF, 57 HF with mildly reduced EF, and 92 HF with preserved EF), patients with limited improvement in clinical congestion score (hazard ratio 2.33, 95% CI 1.51-3.61, P = 0.0001), NP levels (2.29, 95% CI 1.55-3.38, P < 0.0001), and the number of B-lines (6.44, 95% CI 4.19-9.89, P < 0.001) had a significantly higher risk of outcome compared with patients experiencing more sizeable decongestion. The same pattern of association was observed when adjusting for confounding factors. A limited improvement in clinical congestion score and in the number of B-lines was related to poor prognosis for all LVEF categories. In AHF, the degree of congestion reduction assessed over the in-hospital stay period can stratify the subsequent event risk. Limited reduction in both clinical congestion and B-lines number are related to poor prognosis, irrespective of HF subtype.

A Novel Hypobaric Perfusion Method to Remove Microthrombi in Kidney Grafts with Prolonged Circulatory Arrest: A Pilot Study on a Porcine Model.

Intragraft microthrombi prevent complete organ perfusion, thereby compromising the viability maintained by preservation solutions or machine perfusion. Herein, we developed and evaluated a hypobaric perfusion method for flushing microthrombi from kidney grafts with prolonged circulatory arrest in a porcine model. Porcine renal grafts with 1-h warm ischemia were flushed with heparin-containing perfusate in a normobaric environment (control group) or a hypobaric environment of -20 to -30 mm Hg (hypobaric perfusion group) for 10 min using a gravity drip from a 1-m height. Perfusion parameters, histological findings in ex vivo blood perfusion experiments (2 control and 4 hypobaric perfusion kidneys), and safety in allogeneic porcine transplantation experiments (1 donor to 2 recipients) were evaluated. The -20 mm Hg hypobaric perfusion group exhibited greater maximal flow than the control group (20.4 versus 6.9 mL/min; P = 0.028). Histological evaluation following 3 h of static cold storage and 10 min ex vivo porcine whole-blood perfusion revealed statistically significant reductions in congestion and edema (1.5 versus 3, and 0.5 versus 4 on a 5-point scale, from 0 to 4; P = 0.014 and 0.006, respectively) in the medulla along with improved ischemia-reperfusion injury scores (4.0 versus 4.7 on a 6-point scale, from 0 to 5; P = 0.004) in the -20 mm Hg hypobaric perfusion group. Kidney grafts perfused under -30 mm Hg hypobaric environment followed by 3 h of static cold storage could be used for porcine allogeneic transplantation without any macroscopic damage to the graft, effect on intraoperative handling, or perioperative adverse events. Thus, the hypobaric perfusion method was considered safe. Perfusion in a hypobaric environment may prevent graft congestion, edema, and further reperfusion injury by flushing out erythrocytes occluding the medullary capillaries, improving marginal renal graft quality, and reducing the number of discarded grafts.

Impact of Static Urban Traffic Flow-Based Traffic Weighted Multi-Maps Routing Strategies on Pollutant Emissions

Addressing urban traffic congestion is a pressing issue requiring efficient solutions that need to be analyzed regarding travel time and pollutant emissions. The traffic weighted multi-maps (TWM) method has been proposed as an efficient mechanism for congestion mitigation that enables differential traffic routing and path diversity by strategically distributing different network views (maps) to the drivers. Previous works have focused on TWM generation by creating optimal edge weights, but the complexity exponentially increases with the network size and traffic group diversity. This work describes how congestion and emissions can be addressed using TWM maps based on the k-shortest paths for the traffic flows (instead of individuals) that are optimally assigned and distributed to the components of the traffic flow. The map allocation strategies optimal TWM (OTV), optimal TWM per path flow with linear constraints (LCTV), and its variant unconstrained optimal TWM per path flow (UCTV) are described. They use maps generated from the k-shortest paths of the traffic flows (kSP-TWM). The heuristic solution obtained is compared with the theoretical static traffic assignment estimation baseline with different configurations, regarding congestion reduction, total travel time enhancement, and pollutant emissions. Experiments are developed using a synthetic traffic grid network scenario with a mesoscopic simulation. They show that the solution provided is adequate for its proximity to the theoretical equilibrium solutions and can generate minimum emissions patterns. The presented solution opens new possibilities for further congestion and pollutant management studies and seamless integration with existing traffic management frameworks.

An analysis of travel behavior and willingness to pay on Express Lanes before and during the COVID-19 pandemic

In March 2020, governments worldwide implemented various social restriction measures, including lockdowns, to mitigate the spread of the COVID-19 virus. This led to a profound shift in travel behavior, accompanied by a notable reduction in congestion. This paper investigates travel demand on Express Lanes during two time periods: before the pandemic (from January 2020 to mid-March 2020) and during the pandemic (from mid-March 2020 to the end of May 2020). A large data set of trips observed on Express Lanes derived from transponders is combined with vehicle probe measures of travel times and used to estimate the willingness to pay to travel on Express Lanes. The analysis reveals that not all Express Lane assets are equal, with the longer Express Lane demonstrating better performance than the shorter one. Peak hours experienced a more significant impact than off-peak hours, reflecting the heightened flexibility in travelers' schedules during the pandemic. The proposed econometric model quantifies the increase in willingness to pay for Express Lanes, which went up to $36 and $34 per hour on the two assets from the values of $26 and $28 per hour estimated before the pandemic, which can be justified by the observed lower market shares during the pandemic.

Consumer purchase response to e-bike incentives: Results from a nationwide stated preference study

Electric bike (e-bike) incentives are gaining popularity to increase ownership of e-bikes, with goals of increased mobility while reducing emissions. This study is among the first to experimentally analyze prospective purchase behavior of e-bike consumers to assess price sensitivity, behavioral response to incentives, and consumer value of e-bike attributes. We present results of a nationwide (20 cities) stated-preference survey and find e-bike incentives are powerful levers to shift behavior. Different incentive mechanisms have different behavioral utility. Point-of-purchase discounts are the most influential at shifting behavior, followed by tax credits, then mail-in rebates. Point-of-purchase discounts are 30% more effective than mail-in rebates. Across point-of-purchase scenarios, the average inframarginal cost of an added e-bike purchase is $4252 (range $3719 to $4799). Income-based schemes that are often aimed at increasing equity result in cost-effectiveness trade-offs. Policy makers should evaluate the investment in e-bike purchase incentives across benefits from added e-bike ownership that include not only greenhouse gas reductions, but also health benefits, increased mobility, travel affordability, safety, and congestion reductions.

The Positive Impacts of Artificial Intelligence in Highway Transport

This Master's thesis titled "The Positive Impacts of Artificial Intelligence in Highway Transport," explores the transformative effects of artificial intelligence in highway transport from the perspective of users and stakeholders. Focusing on key areas such as traffic control/management, smart traffic lights, predictive maintenance, and autonomous vehicles [1,2], the study employs an in-depth survey analysis using questionnaires. Results indicate notable improvements, with a 24.9% increase in traffic safety, 23.6% reduction in congestion, 15% boost in mobility, 14.7% enhancement in sustainability, and a 21.7% increase in efficiency. These findings offer insights into user/stakeholder perceptions of current artificial intelligence applications in highway transport and their potential benefits. The thesis also evaluates limitations to artificial intelligence implementation in highway transport, providing valuable perspectives for policymakers, industry stakeholders, and researchers.

Determining optimal location and size of PEV fast-charging stations in coupled transportation and power distribution networks considering power loss and traffic congestion

The placement and sizing of plug-in electric vehicle fast-charging stations (PEVF-CS) can significantly influence traffic flow in urban transportation networks. Consequently, suboptimal choices regarding the location and size of PEVF-CSs may lead to increasing travel time and traffic congestion in transportation networks and deterioration of the power quality indexes in power distribution networks. Also, each PEVF-CS is equipped with power electronic converters that can be used as power line conditioners and active filters to compensate for the reactive power and current harmonic components due to nonlinear loads and reduce the power loss. Thus, PEVF-CSs can improve the voltage profile, decrease voltage THD, and reduce the power loss in power distribution networks. Accordingly, this paper develops a mixed-integer linear programming model (MILP) to determine the optimal locations and sizes of PEVF-CSs. In addition, the proposed model considers traffic congestion and power quality in the coupled transportation and power distribution networks. The model considers various load profiles and origin-destination demands to address the different operational aspects of transportation and power distribution networks. The main features of the proposed model are 1- road congestion reduction, 2- traveling time reduction, 3- power loss reduction, and 4- power quality improvement. The proposed model is implemented using GAMS software and applied to different-scale test systems in different scenarios. The results show that the proposed method decreases traffic congestion and improves power quality.