World Academy of Science, Engineering and Technology

Authors: Gerhardus C. Doubell, Christian Kunneke

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Railway infrastructure in regions with high theft rates and harsh environmental conditions require monitoring solutions that balance advanced sensing capabilities with operational resilience. Current solutions’ typical reliance on extensive trackside hardware with substantial power requirements are still falling short of meeting, the required operational resilience requirements in these environments. This study presents a recently patented, novel approach to railway infrastructure monitoring using electrical pulse response analysis with the rails as a sensing medium. A low-power electrical pulse is sent onto the rail, and the subsequent pulse response is measured. Analysis of these measurements are subsequently done at the edge through a combination of time-domain reflectometry (TDR) and ‘track bed leakage current analysis’ (TLCA). This minimizes power consumption and the subsequent solution cost of a distributed IoT railway condition monitoring system. The low power pulse also does not interfere with existing signaling infrastructure, such as axle counters and track circuits. In-field testing results confirmed rail break and train detection up to an accuracy of 1m for up to 1km from the device. Other events that can be detected are the position, movement direction, and speed of trains, flooding, and loose or degrading electrical bonds (such as rail-to-rail or rail-to-mast bonds). The low-power nature of this monitoring approach enables solutions that are more robust to theft and vandalism, which addresses developing regions’ unique operational challenges. Accuracy of rail break detection, together with the possibility of a distributed IoT deployment strategy, provides real possibilities of risk reduction on derailments caused by rail breaks and can provide visibility to infrastructural conditions and train movements in remote, harsh, and unsafe regions. Future work will look to integrate machine learning models for predictive fault forecasting.

Keywords: edge analytics, IoT, low power sensing, non destructive sensing, railway monitoring

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Authors: Mattias Hejdesten

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The Volvo FH Aero marks a significant milestone in the evolution of energy-efficient transportation, showcasing Volvo's commitment to sustainability and innovation in the trucking industry. As the most energy-efficient truck in Volvo's lineup to date and the recipient of the prestigious Green Truck Award 2025, the FH Aero exemplifies advanced aerodynamics development that enhances fuel efficiency of up to 5% in long-haul applications and reduces environmental impact. A key contributor to the success of the FH Aero was the preceding advanced engineering project, culminating in the unveiling of Volvo Concept Truck in 2016. This paper analyzes the aerodynamic advancements achieved during the development of the Volvo FH Aero, emphasizing the successful transition from traditional physical development and verification methods to a robust virtual framework. In recent years, Volvo has embraced a paradigm shift in its approach to aerodynamics, leveraging advanced computational fluid dynamics (CFD) techniques to optimize vehicle performance. The development of the FH Aero involved over 6,000 simulations, from the initial concept phase of the Volvo Concept Truck to final product verification. Extensive correlation studies between wind tunnel tests, track tests and simulation methodologies have been conducted to ensure high prediction accuracy across methodologies. This focused virtual testing process has allowed our engineering teams to explore a wide range of design alternatives, assess their aerodynamic performance, and refine the truck's shape and detailing to minimize drag and maximize efficiency. The use of quantitative results and flow visualizations has significantly enhanced the quality of our analyses, allowing for more confident conclusions and visual recommendations to project stakeholders. The shift to virtual development and verification has not only streamlined the design process but has also fostered a culture of innovation within Volvo. By employing sophisticated simulation tools, we have been able to identify and address potential aerodynamic challenges early in the development cycle, reducing the need for costly physical prototypes and extensive wind tunnel testing. This approach has resulted in a more agile development process, enabling us to bring the FH Aero to market more quickly while ensuring it meets the highest standards of performance and sustainability. The implications for this shift extend beyond the FH Aero, as the methodologies and insights gained from this project will inform future developments across Volvo's product range. As we continue to prioritize sustainability and efficiency, the lessons learned from the FH Aero's aerodynamic development will play a crucial role in shaping the next generation of Volvo trucks. In conclusion, the Volvo FH Aero stands as a testament to our dedication to aerodynamic innovation and our commitment to reducing the transportation sector's environmental footprint. This paper will provide a comprehensive overview of the aerodynamic development process, the transition to virtual verification, and the significant achievements that position the Volvo FH Aero as a leader in energy efficiency and sustainability in the European trucking industry.

Keywords: Volvo, Volvo FH Aero, Energy efficiency, Fuel efficiency, sustainability, innovation, CO2 reduction, virtual development, Volvo Concept Truck, industrialization, performance, CFD methods, wind tunnel testing, track testing, correlation study, CFD method development

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Authors: Magnus Holmsteen Jørgensen, Helene Pehrsson, David Malmgren-Hansen, Leif Grønskov

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The Traffic Speed Deflectometer (TSD) is a non-destructive pavement evaluation tool that uses Doppler laser technology to measure pavement deflection responses at highway speeds. This enables high-resolution centimeter-level, continuous data collection across large networks without disrupting traffic. Building on this capability, two advanced analysis methods—Structural Curvature Index for TSD (SCI TSD) and visco-elastic back calculation (ViscBackCalc)—enhance the TSD’s utility in structural assessment and feature detection. SCI TSD quantifies pavement curvature derived from actual wheel load responses, providing direct insight into strain distribution in the upper layers. Its high sensitivity to structural anomalies makes it effective for identifying cracks, slab joints, and buried features such as drainpipes, even under asphalt overlays. Complementing this, ViscBackCalc interprets TSD data through a visco-elastic model to determine subgrade modulus and internal strains throughout the pavement structure. These modulus values serve as key indicators of pavement health and enable predictions of fatigue and rutting life. These tools transform raw TSD data into actionable intelligence for smart, network-level pavement maintenance planning and lifecycle optimization.

Keywords: bearing capacity, bending theory, centimeter level data, network level management, pavement evaluation, pavement properties

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Authors: K. Thriveni, P. Vinay, Ch. V. Rama Rao

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The advent of self-driving vehicles has revolutionized the automotive industry, promising safer and more efficient transport solutions. Central to the development of these autonomous vehicles is an advanced control mechanism that dictates their behavior on the road. Autonomous vehicle speed in dynamic environments presents significant challenges, particularly in accurately predicting steering angles and estimation of vehicle speed in real-time applications. To address these issues, a vision-based end-to-end system is proposed for real-time steering angle prediction and velocity estimation under varying driving conditions. The system employs ResNet-18 with a Spatial Pyramid Pooling (SPP) layer for feature extraction from road images and dense layers for steering angle prediction. Here, random forest regressor (RFR) is used to estimate vehicle speed with the predicted steering angle, torque, and wheel acceleration. The developed system is simulated on a desktop and also implemented on the resource-constrained Jetson Nano board. The evaluation of the proposed system is carried out in diverse driving scenarios. The system’s performance is evaluated using mean squared error (MSE), mean absolute error (MAE), and (R²) metrics. The experimental results show that the developed system takes less execution time in predicting the steering angle and estimating the speed of the vehicle. This reveals that the system can be implemented in real-time applications.

Keywords: autonomous vehicles, steering angle prediction, velocity estimation, deep learning

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Authors: Oluwapelumi John Oluwalana, Katarzyna Grzesik

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This comprehensive review examines the evolution, current state, and future potential of solar-powered electric vehicles (SEVs) and vehicle-integrated photovoltaics (VIPV). The study analyzes 77 relevant scientific papers published up to March 2025, identifying significant advancements in photovoltaic efficiency, lightweight materials, and integration techniques. While SEVs and VIPV show promising potential for sustainable mobility, challenges remain in areas such as energy yield optimization, climate adaptability, and economic viability. The review highlights research gaps and proposes future directions, emphasizing the need for standardized testing protocols, improved energy management systems, and innovative material solutions. Key findings include the development of SEVs from early prototypes to limited commercial applications, the importance of design and integration of solar photovoltaic systems, advancements in energy management and optimization, the use of lightweight materials, and the impact of climate and shading factors on performance. The review concludes with recommendations for future research and commercialization efforts to realize the full potential of solar-powered transportation as a sustainable solution.

Keywords: solar-powered electric vehicles, vehicle-integrated photovoltaics, sustainable mobility, electric vehicles

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Authors: Mizanur Rashid, Yunquan Dong, Md. Musa Haque

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Achieving real-time and precise detection of all objects around the vehicle is essential for the safe operation of vehicles at high speeds. Consequently, achieving equilibrium between the effectiveness and efficiency of the object detection system is imperative. To tackle this issue, we introduced a holistic approach that integrates Cascade R-CNN with state-of-the-art methods. The fusion of Cascade R-CNN and Feature Pyramid Network enhances the model's capability to effectively manage multi-scale features inherent in diverse environments. Furthermore, we employ Deformable Net in conjunction with ResNext101, adapting to geometric variations of objects. This ensures precise detection of even the smallest and intricate features. Subsequently, we replaced RoI Pooling with the advanced Deformable Pooling method to enhance the precision of multi-object localization information. Our model undergoes rigorous evaluation on the BDD1K and OpenImage datasets. The experimental findings indicate the exceptional performance of our proposed method in detecting complex and occluded objects. Achieving higher 𝑨𝑷𝟓𝟎 than the baseline on the dataset underscores its effectiveness.

Keywords: object detection, autonomous vehicles, deformable networks, cascade R-CNN

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Authors: Marcell Misznéder, Ulrich Nieken

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Efficient thermal management is essential in Battery Electric Vehicles (BEVs) to ensure optimal performance, safety, and battery longevity. This study proposes a Model Predictive Control (MPC) framework for managing the Thermal Management System (TMS) of BEVs, targeting improved thermal and electrical efficiency over traditional control methods such as PID control. A physics-based, modular TMS model was developed,including key components like pumps, fans, and water-circuit mixing valves. These are coordinated by a Thermal Control Unit (TCU) executing various MPC strategies. Unlike conventional feedback controllers, MPC predicts future system behaviour and solves an optimization problem at every control step, allowing it to account for system constraints, nonlinearities, and dynamic conditions more effectively. By integrating digital component and system models early in development process, the approach utilizes modular, physics-based simulations to replicate real-world conditions and enable comprehensive testing of theTMS without the need for physical prototypes. Classical feedback controllers adjust control variables based on deviations between reference and measured values, relying on past and current states. This reactive approach restricts their effectiveness in dynamic, multi-variable systems with constraints, delays, or nonlinearities. While these controllers are easy to implement and computationally efficient, they face tuning challenges in complex systems like vehicle TMS. In contrast, MPC continuously solves an optimization problem at each control step, predicting future system behavior and proactively adjusting control inputs to maintain optimal conditions. To ensure precise control inputs and prevent overheating, undercooling, and actuator overload, an effective MPC requires an accurate model of the real system. MPC's weighting factors prioritize objectives, such as energy efficiency and optimal thermal performance, enabling adaptability to changing conditions. However, MPC's complexity introduces challenges such as high computational demands and sensitivity to model accuracy. The study explores different MPC variants, including adaptive and learning-based approaches, to enhance control robustness and adaptability. To ensure MPC efficiency, the physics-based models were validated using a specially designed and constructed test bench. This research examines the energy efficiency of actuators and their adherence to temperature limits in the developed TMS, comparing the performance of MPC with conventional control methods. Both simulations and experiments were conducted using the same dynamic driving profiles under varying ambient conditions to analyze component temperatures and actuator power consumption consistently. A sensitivity analysisof MPC parameters is performed to assess their impact on controller performance.

Keywords: thermal management, model predictive control, battery electric vehicles, energy efficiency

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Authors: Ferney Quiñones Siniestra, Estefanía Bermúdez Castañeda, Mariana Torres, Carlos Calero Valenzuela

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The construction industry is experiencing rapid growth, leading to an increase in the number of buildings, demolitions, and infrastructure construction activities, generating large amounts of construction and demolition waste (CDW). This issue has economic, social, and environmental repercussions, especially when its final disposal is not controlled, complicating the reuse process. Consequently, it is crucial to mitigate the damage these materials cause by assessing their behavior for use as aggregates in granular layers and in hot mix asphalt for pavements.This study presents case studies for the cities of Cali (Colombia) and Brasilia (Brazil), where a comprehensive characterization of CDW was carried out, identifying its composition, hardness, shape, cleanliness, and durability, as well as monitoring the increase in resistance over time due to the presence of cementitious material. For asphalt mixtures, an experimental program was designed to evaluate the effect on resistance due to the total replacement of natural aggregate with CDW, using conventional asphalt binder and rubber-modified asphalt. The mechanical properties investigated included: indirect tensile strength, abrasion resistance (Cantabro test), resilient modulus, resistance to permanent deformation, fatigue resistance, and moisture damage resistance (modified Lottman test). Regarding the use of CDW as a granular layer, good performance was observed, further supported by the fact that unconfined compressive strength increased by approximately three times the initial resistance. Regarding asphalt mixtures made with CDW, adequate values were obtained, along with significantly better rutting resistance.

Keywords: construction and demolition waste (CDW), aggregates, recycled, hot mix asphalt

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Authors: Raphael Damasceno Marotta, Luiz Antonio Silveira Lopes, Giuseppe Miceli Junior

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This paper investigates the effects of harmonic excitation in railway vehicles exposed to cyclic dynamic forces, employing multibody modeling tools. Drawing on a application case with a rail operator, the research illustrates how such vibrations influence train performance and operational safety under real-world conditions. It identifies key excitation frequencies capable of initiating resonance, heightening the risk of derailment, particularly in tangent track zones where cross-level variations are prevalent. Track geometry deviations were measured using a Track Evaluation Vehicle, and harmful wavelength intervals were detected through advanced signal analysis based on the Short-Time Fourier Transform (STFT). The vehicles’ natural vibration modes were obtained through eigenvalue extraction and transient response simulations using dynamic rail vehicle software. Further numerical studies determined the amplitude of dynamic responses for parameters related to derailment risk, focusing on L/V ratios and wheel load reduction, in line with international safety norms. The generated response charts provide a fast means to identify hazardous pairings of vehicle dynamics, track state, and speed regimes. The methodology aids in evaluating speed enhancement proposals, prioritizing maintenance activities, and supporting accident analysis—offering a robust basis for realistic simulations of track-vehicle interaction.

Keywords: harmonic excitation, railway safety, multibody dynamics, track geometry, vehicle-track interaction, derailment risk

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Authors: Raphael Damasceno Marotta, Luiz Antonio Silveira Lopes, Giuseppe Micelli Junior

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AbstractThis paper proposes an analytical framework for the objective definition of optimal tamping intervals in heavy haul railway systems. In light of increasing freight transport demands, the efficient planning of maintenance and renewal activities is vital for ensuring long-term infrastructure performance and cost-effectiveness. The proposed approach is grounded in the analysis of track quality indicators, benchmarked against reference data from comparable heavy haul networks. The railway track was divided into segments to allow for localized assessment. For each segment, the degradation trajectory of track geometry quality indices was evaluated as a function of cumulative axle loads and historical maintenance interventions. In addition to estimating degradation rates, post-tamping recovery curves were modeled to quantitatively assess the effectiveness of tamping operations. These curves provide predictive insight into the expected condition of the track following each intervention. The methodology supports maintenance decision-making by recommending the most appropriate intervention type and timing based on the degradation profile, recovery behavior, and typical tamping cycles of each segment. By integrating these insights with available maintenance windows, machine capacity, and associated costs, a medium-term planning model was developed. This model applies linear programming techniques to optimize resource allocation while maintaining track quality within prescribed safety and operational standards.

Keywords: tamping intervals, track geometry, heavy haul railway, maintenance optimization, degradation modeling, linear programming

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Authors: Raphael Damasceno Marotta, Luiz Antonio Silveira Lopes, Giuseppe Miceli Junior

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This study presents a methodology for determining energy losses in railway vehicle suspensions and track ballast due to geometric irregularities in the railway track. These irregularities induce oscillations in the vehicle's mass system, resulting in energy dissipation primarily as heat within suspension friction wedges and through friction between ballast particles. The vehicle-track interaction was modeled using a multibody system with 62 degrees of freedom, enabling an accurate representation of the dynamic behavior of both the vehicle and the track structures. To accurately characterize track stiffness properties, field instrumentation was conducted. Track irregularities were measured, and the corresponding track quality indices were calculated, effectively describing the geometric condition of the track. The resulting energy losses negatively impact the train's overall energy efficiency, causing additional fuel consumption directly proportional to the degree of track degradation. Beyond a certain threshold, track deterioration may influence operational costs related to train energy consumption. The developed methodology provides a reliable means of estimating diesel fuel losses based on track geometry data and train operational speeds, facilitating a quantifiable relationship between railway pavement condition and energy loss.

Keywords: railway energy efficiency, track irregularities, multibody simulation, track quality index, diesel fuel consumption

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Authors: Erik Santiago Vidal Lara, Yesi Natalia López Sánchez, Carlos Aníbal Calero Valenzuela, Aldemar José González Fernández, Víctor Uribe Forez

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Tertiary roads in Colombia shows precarious infrastructure conditions and low institutional presence, leading to a high risk of traffic crash. Among the identified risk factors, inadequate speed stands out as one of the primary causes of incidents on these roads, given the combination of irregular surfaces, poor geometry, and lack of proper signage. Despite its relevance, this factor is often overlooked in road improvement interventions, such as the commonly implemented plate-track system, which primarily focuses on structural enhancements without considering its impact on driving behavior. This study analyzes the influence of speed on road safety in tertiary roads using a methodology based on the identification and categorization of risk factors with community participation and expert validation. Field observations and interviews with local stakeholders were conducted to assess the impact of speed on the occurrence of road incidents and their relationship with other critical factors, such as road conditions and environmental factors. The results highlight that users' risk perception, and the lack of effective control measures contribute to the presence of inappropriate speeds. Finally, mitigation strategies are discussed, and an assessment tool is proposed to facilitate the identification and management of this risk in the context of tertiary roads, emphasizing the need to integrate speed control considerations into future infrastructure improvement projects.

Keywords: risk factors, road safety, speed management, tertiary roads

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Authors: Raphael Damasceno Marotta, Pedro Henrique Oliveira, Luiz Antonio Silveira Lopes, Giuseppe Miceli Junior

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Railway networks play a vital role in worldwide transportation, yet preserving their infrastructure poses significant challenges. Routine evaluations are necessary to guarantee operational safety, as the degradation of track geometry can result in major incidents like derailments. Conventional track geometry inspection vehicles are costly and often restrict the frequency of evaluations, highlighting the need for innovative and more practical solutions. This research introduces a method for assessing track conditions by integrating dynamic multi-body simulations with machine learning techniques. Measurements obtained from rail vehicles outfitted with accelerometers and gyroscopes serve as input for training a classification model that identifies different condition categories. An interactive, geo-referenced visualization platform supports the methodology and enables indirect estimation of wheel-rail contact forces. The developed model reached an F1-score of 82.5% in identifying the most critical cases and 97.5% for standard track states. Beyond strong predictive performance, the approach delivers meaningful insights into the dynamic behavior of the system, presenting a cost-efficient and adaptable strategy to enhance inspection routines and infrastructure maintenance planning in rail operations.

Keywords: condition monitoring, railway safety, machine learning, multi-body simulation, predictive maintenance

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Authors: Pongphatana Puttima, Zhihua Chen, Tongtong Zhou

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This research presents a domain-informed machine learning framework for the real-time prediction of traffic congestion on the Chalong Rat Expressway in Bangkok, Thailand. Departing from conventional data-driven approaches that frequently neglect system dynamics, the approach methodology systematically integrates three foundational traffic engineering models into feature engineering: The Cell Transmission Model (CTM) for macroscopic flow simulation, Kerner's Three-Phased Traffic Theory for understanding the phase transition of a congested state, and Helming's Microscopic Traffic Dynamics modelling vehicular interactions through pressure gradients and driver response parameters. These models track simple, interpretable theory-driven features, such as vehicle densities, flow rates, headway, jam propagation, and phase-transition probabilities. Two empirical time-series sensor datasets and indexes on historical traffic data from January 2023 to June 2024 were synchronised and processed to construct a multidimensional feature space. The traffic state prediction models were then evaluated using three different methods: i.e. Hybrid Random Forest with XGBoost (RF—XGBoost), Support Vector Machine (SVM), and Gaussian Process Regression (GPR). The RF component executes dimensionality reduction through feature importance ranking, subsequently feeding optimised inputs into the XGBoost ensemble regressor. Model performance was evaluated across multiple temporal windows and traffic states using conventional regression metrics (MAE, RMSE, R²) with particular emphasis on transitional flow regimes. Experimental results demonstrate the hybrid RF-XGBoost architecture achieves superior predictive accuracy under unstable flow conditions and near phase-transition boundaries. The proposed approach maintains accuracy and transparency combined with interpretability based on domain structure. The visualisation analysis containing residual plots, timelines, and feature importance ranks further substantiate both model robustness and explanatory capacity. The proposed framework includes a scalable real-time solution for intelligent transportation systems (ITS) integration from the Expressway Authority of Thailand (EXAT), providing a practical means to forecast congestion, facilitate dynamic routing, and generally control traffic on the urban expressways.

Keywords: domain-informed machine learning framework, microscopic driver interactions, phase transition probabilities, macroscopic flow behaviour, hybrid ensemble learning, congestion phase segmentation, hybrid random forest with XGBoost

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Authors: Allan Pimenta, Liton Kamruzzaman, Fuad Huda, Graham Currie

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The rise of fully autonomous vehicles (FAVs) is expected to reshape urban parking dynamics by enabling vehicles to relocate autonomously. While this may reduce parking demand in city centres, it could also increase congestion due to empty-cruising vehicles. This study examines how privately-owned fully autonomous vehicle (PAV) users trade off parking fees, empty-cruising costs, and pick-up waiting times when choosing parking locations. A stated choice experiment was conducted among 526 car-based commuters to Central Melbourne, Australia. Nine pricing scenarios were tested, varying in parking fees, empty-cruising costs, and pick-up waiting times. Using mixed logit models, we find that users perceive parking and empty-cruising costs as nearly interchangeable—each additional AUD in daily parking fees or empty-cruising costs reduces the odds of selecting a parking option by 27.5%, while each additional minute of pick-up waiting time reduces it by 13.1%. Scenario analysis indicates that if Central Melbourne’s daily parking fee meets or exceeds the survey participants’ average (AUD 16.80), about 98% of parking demand would shift to free suburban zones. However, when the daily parking fee is AUD 8.40, raising empty-cruising costs from AUD 15/hour to AUD 45/hour increases the share of PAV commuters parking within 5 km of work from 30% to 70%. This increase in empty-cruising costs reduces commuters’ total vehicle kilometers traveled by approximately 44%, from 1.8 million km to 1 million km, demonstrating its effectiveness in mitigating congestion. Key policy interventions include: (1) encouraging PAV parking at transit hubs with return-home incentives to reduce PAV travel demand to urban centres; (2) implementing dynamic joint road pricing systems that adjust parking and route-based empty-cruising costs in real-time to optimize demand distribution; (3) designating dedicated cruising lanes to connect urban centres with peripheral parking zones in low-density areas (e.g., industrial and warehousing zones) to minimize congestion and prevent urban decay in residential zones.

Keywords: parking management, empty-cruising, congestion, land use, urban planning

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Authors: Ing. Hana Pechova

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Global warming is a critical global issue, which is why research is being conducted to address it. The goal is to find ways to reduce CO₂ emissions. This experiment involved driving tests to assess the fuel consumption and CO₂ emissions of a small road vehicle. The tests were conducted on different types of roads, including motorways and Class I, II, and III roads. The vehicle was loaded with varying amounts of cargo in the cargo area. During the tests, data was collected on fuel consumption, vehicle speed, driving time, and distance traveled. It is essential for the vehicle to operate efficiently and in an environmentally friendly manner in order to reduce CO₂ emissions.

Keywords: road transport, small cars, logistics, fuel consumption, CO₂ emissions, global warming

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Authors: Salvatore Strano, Mario Terzo, Ciro Tordela

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The safety, comfort, and operational efficiency of rail vehicles depend significantly on the performance of their suspension systems. The secondary suspension system, which is designed to isolate the vehicle body from track irregularities and dynamic disturbances, plays a crucial role in maintaining stability and ride quality. However, faults in this system, such as air spring leaks, damping degradation, or sensor failures, can compromise vehicle performance and passenger comfort. This paper presents a model-based approach for estimating faults in the secondary suspension system of a rail vehicle, leveraging advanced fault detection and isolation (FDI) techniques to enhance reliability and maintenance efficiency. The proposed methodology employs a mathematical model of the secondary suspension system, incorporating key dynamic parameters such as stiffness, damping, and mass distribution. A state-space representation is used to describe the vehicle's dynamic behaviour, allowing for the simulation of various fault scenarios. By integrating observer-based estimation techniques, particularly Kalman filters and unknown input observers (UIOs), the system can identify discrepancies between measured and predicted states, facilitating real-time fault diagnosis. The approach also utilizes residual analysis, where the difference between actual sensor readings and model predictions serves as an indicator of potential faults. To validate the proposed method, a set of simulation tests were conducted under different fault conditions. Simulated faults include changes in damping characteristics due to oil leakage in dampers, loss of air pressure in air springs, and sensor drift errors. The performance of the fault estimation framework was evaluated based on its sensitivity, accuracy, and robustness to external disturbances. Results demonstrate that the model-based approach successfully detects and estimates faults with a high degree of precision, outperforming conventional threshold-based fault detection methods. The integration of adaptive filtering techniques further enhances robustness by compensating for modelling uncertainties and measurement noise. One of the key advantages of this approach is its ability to provide early fault detection, enabling predictive maintenance strategies that can reduce downtime and maintenance costs. By implementing real-time fault estimation, railway operators can make informed decisions regarding component replacements and maintenance scheduling, improving overall system reliability. Additionally, the proposed model can be integrated into onboard diagnostic systems, allowing for continuous monitoring of suspension performance during operation. The findings of this study highlight the effectiveness of model-based estimation techniques in diagnosing faults in rail vehicle secondary suspension systems. The combination of dynamic modelling, observer-based estimation, and residual analysis offers a reliable framework for fault detection and isolation. Future work will focus on extending the methodology to account for more complex multi-body interactions, incorporating machine learning techniques for enhanced fault classification, and testing the approach in real-world rail environments. The implementation of such advanced diagnostic systems can contribute to the development of next-generation intelligent rail vehicles with improved safety, comfort, and efficiency.

Keywords: estimation, rail vehicle dynamics, fault detection, Kalman flters

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Authors: Chandramahanti Tejaswi Ram, Sriram Sundar

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A path-constrained spatial mechanism involving vehicles is useful in accelerated pavement testing facilities to replicate an actual road condition. The system’s dynamic analyses are essential in precise control of the motion and estimation of the joint forces (and moments). Hence, this study focuses on quantifying the influence of the system’s kinematic parameters on the vehicle's motion, along with the associated joint reaction forces (and moments). The mechanism under investigation consists of several kinematically connected bodies, including the pavement (ground), central shaft, intermediate arm, vehicle, and tires. Transient and steady-state analyses were performed on the system as the vehicle moved/traversed along a constrained circular path. These analyses capture the vehicle’s motion, including bounce, pitch, roll, and yaw, while also examining the force interactions in both transient and steady states. Multiple simplified variants of the multibody dynamics model were developed, and a comparative study was performed. The joint forces (and moments) derived from these models provide valuable insights for designing and optimizing constrained mechanisms. Additionally, this work provides a foundation for the control and stability of the vehicle on a constrained circular path. The tire-pavement interaction forces obtained from the dynamic analysis further serve as critical inputs for pavement models, ensuring optimal road design and enhancing overall performance.

Keywords: circular path, kinematic parameters, multibody dynamic, path constrained

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Authors: Priyanshu Aman, Geetam Tiwari, Kalaga Ramachandra Rao

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In many low- and middle-income countries, pedestrians in rural areas often reside along high-speed roads, putting them at elevated risk of fatal crashes. The expansion of built-up areas along these road networks further exacerbates this risk. This study investigates the factors influencing pedestrian fatalities on high-speed rural roads at a macroscopic level. Police First Information Reports (FIRs) documenting pedestrian fatalities from 2017 to 2022 were collected and geocoded in ArcGIS to generate a statewide distribution of pedestrian fatality locations. The study considers a road network comprising National Highways (NH), State Highways (SH), and Major District Roads (MDR), excluding sections within urban boundaries, to identify high-speed rural roads. A total of 83 road segments were selected for analysis. Key variables, including road length, number of lanes, minor access density, village density, population within a 500m buffer, and land use, were estimated for each road segment. Land use and population data were derived from satellite imagery and high-resolution population density maps. To account for over-dispersion in the dataset, count data models—Generalized Poisson (GP) and Negative Binomial (NB)—were applied to identify significant predictors of pedestrian fatalities. The GP model demonstrated superior performance, as indicated by lower Akaike Information Criterion (AIC) values and higher log-likelihood and ρ² values. Findings indicate that road length, population within 500m, multi-lane roads (4, 6, and 8 lanes), minor access density, NH classification, and village density positively correlate with pedestrian fatalities. Notably, pedestrian fatality risk on multi-lane roads is 1.5 times higher than on two-lane roads. These results highlight the critical factors contributing to pedestrian fatalities on high-speed rural roads and underscore the need for targeted road design interventions. Implementing pedestrian safety measures on high-speed roads traversing settlements is essential to mitigate fatality risks and enhance pedestrian safety.

Keywords: low and middle-income country, pedestrian safety, rural roads, risk factors

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Authors: Arjun Radhakrishnan, Martin Thomas Schlecht

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Globally, over 1.19 million fatalities and 20-50 million injuries occur annually due to road traffic accidents, with vulnerable road users—pedestrians, cyclists, and motorcyclists—representing over half of these deaths. Despite advancements in traffic management systems, the human factors and psychological aspects influencing road safety remain underexplored. There is a significant need to understand how safety interventions affect vulnerable users’ perceptions of risk and safety in their daily mobility. This research aims to investigate the impact of AI-enabled accident prevention interventions on vulnerable road users' perception of road safety through a mixed-methods approach. By analyzing 200 pre-post-intervention survey responses and 10 in-depth interviews conducted in Sarajevo, the capital of Bosnia and Herzegovina, this study explores the psychological and behavioral shifts in response to interventions such as improved pedestrian crossings, enhanced signage, and better road infrastructure. The study focuses on how AI-enabled targeted road safety measures shape users' awareness of risks, influence their attitudes toward road safety, and prompt behavioral changes. By combining quantitative data on risk perception and qualitative insights into user experiences, this research provides a holistic understanding of the social and psychological dimensions of data-driven road safety interventions. The SAFELY tool will play a pivotal role in deploying AI-enabled accident prevention interventions in Sarajevo, specifically targeting vulnerable road users (VRUs) such as pedestrians, cyclists, and motorcyclists. By analyzing diverse data sources—including local accident records, traffic flow patterns, weather conditions, and user feedback—SAFELY identifies high-risk road segments. AI algorithms, including support vector regression and decision trees, will be used to detect hazardous locations and assign a risk score, helping authorities pinpoint areas that require urgent safety improvements. These insights are crucial for understanding the causes of accidents in Sarajevo, considering factors such as road geometry, traffic behavior, and environmental conditions. Once high-risk areas are identified, SAFELY will provide urban planners with actionable insights through visualizations like heat maps and time-series charts, enabling the evaluation of accident trends over time. These visual tools will facilitate the implementation of targeted interventions, such as enhancing pedestrian crossings, improving signage, and optimizing road markings in Sarajevo’s most dangerous zones. Additionally, SAFELY’s predictive capabilities will allow authorities to simulate the effects of proposed safety measures, proactively addressing future risks. By continuously monitoring accident patterns and evaluating the impact of implemented interventions, SAFELY will support both immediate improvements and long-term, data-driven strategies to reduce accidents and fatalities among vulnerable road users in Sarajevo. The research further aims to examine how community involvement and localized interventions contribute to changes in road user behavior and perceived safety. Findings are expected to provide actionable insights for municipal traffic authorities and urban planners in Sarajevo to design socially informed and effective safety measures. In the long term, this study offers a scalable framework for integrating psychological and social perspectives into urban road safety strategies, contributing to Vision Zero’s goal of eliminating traffic fatalities

Keywords: road safety, artificial intelligence, vulnerable road user, pre–post analysis

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Authors: Mahesh Patil, S. Hemachandra

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The rise of electric vehicles (EVs) is revamping the landscape of the automotive sector and will bring in huge demand on the power grid. This will bring huge opportunities for sustainability and also challenges to overcome. One of the most pressing issues is the efficient management of power distribution across EV charging stations. As EV adoption accelerates, the impact on grid stability, charging infrastructure, and power quality becomes more pronounced. Dynamic load balancing plays a vital role in mitigating these challenges by converging power distribution and real-time demand. The outcome of the dynamic load balancing techniques to reduce grid instability, power quality issues, and user satisfaction are reviewed. Leveraging technologies like smart grids, AI-based management systems, and pricing strategies are discussed. The paper concludes by examining future trends in advancing load-balancing solutions.

Keywords: dynamic load balancing, time of use, distributed energy resources, artificial intelligence

Procedia PDF Downloads 21

Authors: Jwan Kamla

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In order to reduce accident risk, highway authorities prioritise maintenance budgets partly based on previous accident history. However, as accident rates have continued to fall in most contexts, this approach has become problematic as accident ‘black spots’ have been treated, and the number of accidents at any individual site has fallen. Another way of identifying sites of higher accident risk might be to identify near-miss accidents (where an accident nearly happened but was avoided), which are likely to be much more prolific than actual accidents; therefore, they are useful in identifying high-risk sites. The principal aim of this research is to analyse potentially unsafe truck driving conditions that involve harsh braking incidents (HBIs) that may indicate accident risk. Most modern truck fleets now record position as part of fleet management. This research used position data collected by a truck fleet management company for 8000 trucks in the United Kingdom (UK) over a 2-year period (2011-2012) to identify incidents of harsh braking. This data was compared with STATS19 accident data events (specifically truck accidents) occurring in 70 selected roundabouts (284 approaches) over an 11-year period (2002-2012) to test the hypothesis that the HBIs could represent accident near-misses and, therefore, increased accident risk. The data used for model prediction comprised all vehicle accidents, truck accidents, HBIs, geometric properties, and traffic characteristics for whole roundabouts, within the circulatory lanes, and at approaches to the selected roundabouts. Random-parameters negative binomial (NB) count data models were used to estimate model parameters, and the models were compared with fixed-parameters NB count data models. It was found that random-parameters count data models provide better goodness of fit and more variables were found to be significant, giving a better prediction of events. It is concluded that HBIs are influenced by traffic and geometric variables in a similar way to total and truck accidents; therefore, they may be useful in considering accident risk at roundabouts. They are a source of higher volumes of data than accidents, which is important in considering changes or trends in accident risk over a much shorter time. The most important variables were Average Annual Daily Traffic (AADT) and percentage of truck traffic, which were found to have a positive influence on accidents and HBIs. Regarding the geometric variables, signalisation, circulatory roadway width, number of arms and two-lane indicator were the most important factors influencing accidents and HBIs. In addition to these models, the number of HBIs was used as an independent variable in the models of total and truck accidents, along with traffic and geometric variables. From the results it can be concluded that at all approaches, HBIs are related to total accidents along with traffic and geometric variables, which can be used to study safety measures. These results for truck HBIs could help highway authorities identify sites of increased accident risk more rapidly and without waiting for an accident history to develop.

Keywords: road accidents, near-miss accidents, position data, truck accidents, harsh braking, roundabouts, random parameter negative binomial distribution, telematics data

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Authors: Haytham M. El-Zomor

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The aim of this work is to implement a quarter-car-model test rig to examine a semi-active dampers to characterize the parameters of the suspension system. In a further step, analytical design methods are developed to implement a mechanical and instrumentational setup ready for testing the suspension system components, using a data acquisition system (DAQ) connected to a commercial LabVIEW software directly collecting the sensors data. In the second step, the current semi-active damper with has been tested under different operating conditions to examine the wheel, and body oscillations. The results showed that the tested damper vertical stiffness extensively affected by the operating pressure in the air spring, moreover the damper has been tested under static and dynamic tests to evaluate its response during different modes of operations. This study showed that the semi-active damper has a significant improvements in ride comfort in vertical dynamics tests and better vehicle stability can be achieved compared to a passive suspension.

Keywords: airmatic suspension, electric vehicle suspension, quarter-car test bench, ride comfort, semi-active dampers, vertical wheel dynamics

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Authors: Xinyue Gu

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Air pollution is a significant issue in urban areas globally. While human mobility plays a crucial role in determining air quality, its effects vary by travel purposes due to distinct spatiotemporal patterns. The study utilizes a trip purpose inference algorithm to classify mobility based on three-month taxi trajectory data of Beijing and examines its heterogeneous relationship with air pollution using interpretable XGBoost-SHAP models. The results indicate that human mobility has a smaller impact on air pollution than natural environments yet a larger impact than built environments. In the long term, the influence of mobility becomes more pronounced, with clearer correlations. Notably, work- and home-purpose mobility exhibit a negative correlation with pollution, challenging the assumption that more mobility always increases pollution. These findings provide actionable insights for urban planning, including promoting mixed-use development and work-residence integration, creating urban wind corridors and open green spaces, and adopting low-emission transportation while avoiding blanket traffic restrictions. This study contributes to more effective data-driven urban management and the advancement of sustainable environments.

Keywords: human mobility, air pollution, taxi trajectory data, trip purpose inference, interpretable machine learning, SHAP

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Authors: Carlos A. Calero, Nelson Rivas, Jaime R. Obando, Ferney Quiñones, Victor M. Uribe, Wilson D. Rodriguez, Andres F. Gomez, Harlenson S. Artunduaga

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The rapid growth of the vehicle fleet has led to problems in urban areas, such as congestion and a shortage of parking spaces. This issue is also evident on the Tulcán campus of the University of Cauca, where the increase in vehicles and motorcycles has exceeded the available parking capacity, causing delays and congestion during peak hours. This study aims to optimize parking spaces through an analysis of supply and demand, as well as the use of technological tools to enhance management. The expansion of the university community has increased parking demand, negatively impacting mobility and service efficiency, which, in turn, affects academic and work activities. Based on a literature review and data collection using a vehicle counting system on two selected dates, it was determined that the campus has 295 parking spaces for motorcycles (250 of which are marked) and 192 for vehicles, revealing a deficit of 155 spaces, as regulations require 347. Additionally, it was identified that the university fails to meet the required provision of spaces for individuals with reduced mobility, as only 2 of the 7 mandated spaces are available. The analysis revealed that 74% of users are students and that motorcycles account for 70% of campus vehicles. Peak hours were recorded between 7:00-7:15 a.m. and 8:45-9:00 a.m., during which occupancy reached 100%, leading to congestion. The areas with the highest demand were Zones 5, 1, and 2, mainly due to their proximity and associated daily living costs. To gain deeper insights into user behavior, two additional processes were conducted: monitoring vehicular flow using cameras and interviewing university community members. Based on this data, a simulation was developed using VISSIM software to analyze the current system. Cameras were installed in selected parking areas to record vehicular movements (entries, exits, and internal maneuvers), identifying occupancy patterns and average dwell times. Using this information, a baseline model was built in VISSIM, defining the study area's geometry, configuring vehicular routes and parking zones, and incorporating observed parameters. The model was calibrated by adjusting speeds, reaction times, and route preferences, comparing simulated results with real data. Based on this validated model, improvement alternatives were explored using literature reviews and expert recommendations. The most viable option was the allocation of parking spaces according to university roles (faculty, administrative staff, and students), optimizing space usage and reducing search times. In the proposed system, faculty members were assigned parking spaces near their respective faculties to facilitate quick access to classes and meetings. Administrative staff, whose work requires continuous office presence, were allocated spaces near administrative areas. Meanwhile, students were assigned parking areas further away, ensuring minimal impact on academic mobility. Simulations in VISSIM demonstrated that this reorganization reduced search times by 25%, improved vehicular flow, and minimized user conflicts. Additionally, a positive impact on driver satisfaction was observed, as users benefited from a clearer and more organized allocation system.

Keywords: parking lots, university, simulation, parking management

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Authors: Carlos Aníbal Calero Valenzuela, Aldemar José Gonzalez Fernández, Ferney Quiñones Sinisterra, Jaime Rafael Obando Ante, Erik Santigo Vidal Lara, Yesi Natalia López Sánchez

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This exploratory research addresses the issue of road safety on tertiary roads in the Cauca Department, Colombia. Due to the lack of reliable data, this topic is critical and understudied. The study aims to identify the factors that influence road safety on these roads and develop an accessible tool for implementation by local communities. The methodology involved an exhaustive review of global and national literature on road safety on rural roads, the analysis of existing instruments to assess road safety, and the development and pilot testing of a tool adapted to the local context. The community was involved in the application and refinement of this tool. The results reveal the most significant factors that affect safety on tertiary roads in Cauca, which are consolidated and evaluated in an optimized tool for assessment. This research contributes to the knowledge of road safety on tertiary roads and provides a practical tool to empower local communities to identify and manage road risks. In addition, it can be useful in the applied context of the Department of Cauca and other geographic contexts of the country and worldwide. The study concludes by highlighting the importance of involving communities in evaluating road safety. It suggests directions for future research and public policies in this important and often ignored field, in addition to addressing the variables that are critical and specific to each section to remedy the road safety deficiencies associated with these factors.

Keywords: safety, rural roads, local rural roads, community involvement

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Authors: Hermen Cléusia Dabo

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Traffic congestion remains a persistent challenge in urban centers worldwide, leading to economic inefficiencies, increased pollution, and diminished quality of life. While congestion pricing has proven effective in high-income countries, its implementation and outcomes in low- and middle-income cities (LMICs) are not as well understood. These cities often face unique challenges, including inadequate public transportation systems, informal transport networks, and socioeconomic inequalities that complicate the adoption of congestion pricing policies. This study evaluates the effectiveness of congestion pricing in LMICs, with a particular focus on Maputo, Mozambique, and its impacts on traffic patterns, environmental sustainability, and equity. The research employs a mixed-methods approach, combining quantitative analyses of traffic flow and emissions with qualitative insights from stakeholder interviews and policy reviews. Maputo serves as the primary case study, offering a unique perspective on the intersection of urban growth, informal transport dependency, and the socioeconomic dynamics prevalent in LMICs. Supporting data from other cities, such as Lagos and Bogotá, provides a comparative framework to contextualize findings. Key variables analyzed include reductions in vehicle kilometers traveled (VKT), changes in air quality indices, revenue generation, and the redistribution of funds to improve public transit infrastructure. The study also examines behavioral responses to congestion pricing, including shifts to alternative modes of transport and changes in travel patterns. Findings indicate that congestion pricing can significantly reduce traffic congestion and improve air quality in Maputo when designed with attention to local conditions. However, challenges such as public resistance, limited administrative capacity, and the need for robust enforcement mechanisms are critical barriers to successful implementation. The research underscores the importance of equitable policy design, particularly in a city like Maputo, where significant income disparities and reliance on informal transport systems complicate mobility solutions. Programs that include exemptions, tiered pricing, or revenue reinvestment in affordable public transit are more likely to gain public acceptance and achieve long-term benefits. Moreover, the study highlights the necessity of integrating congestion pricing within a broader urban mobility framework. Complementary policies, such as investments in non-motorized transport infrastructure, modernization of public transit systems, and public education campaigns, enhance the overall efficacy of congestion pricing initiatives. This research contributes to the growing body of knowledge on sustainable urban mobility in LMICs by providing actionable insights for policymakers and urban planners in Maputo. It emphasizes that while congestion pricing is a powerful tool for managing urban traffic, its success in Maputo depends on context-sensitive implementation, inclusive policymaking, and sustained public engagement.

Keywords: congestion pricing, urban mobility, transport equity, Low and middle income countries

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Authors: Philippe Barbosa Silva, Gisele da Silva Almeida Vilalba, Luanna Oliveira Lima

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Sustainable urban mobility is crucial for balancing development with environmental and social needs, particularly in rapidly growing cities such as Rio Verde, Brazil. This study employed the Sustainable Urban Mobility Index (IMUS), focusing on the Integrated Planning Domain, to assess the sustainability of Rio Verde’s urban mobility. With an IMUS score of 0.601, the city demonstrated performance comparable to similar municipalities while identifying critical areas for improvement. The methodology involved a literature review, data collection, IMUS score calculation (a consolidated Brazilian methodology), and the development of a public perception survey. Data were gathered from municipal departments, satellite imagery, and public databases, addressing indicators related to manager training, transparency, land use, and infrastructure planning. Specific aspects evaluated included urban areas, public transportation, parks and green spaces, and the number of schools. The public survey further enriched these findings by capturing community perspectives on urban mobility issues, enabling comparisons between calculated scores and user perceptions. The findings revealed notable strengths in transparency and adherence to urban legislation, alongside high scores for mixed land use and urban vacancy management. However, deficiencies were identified in intermunicipal consortia, population density, and urban growth. Limitations in the professional training of urban planning personnel were also observed, indicating a need for enhanced capacity-building initiatives to strengthen the city's planning framework. Rio Verde’s potential for improving sustainable urban mobility is significant, particularly through targeted investments in infrastructure and governance reforms. While the city has shown progress in areas such as mixed land use and legislative compliance, weaknesses in transportation infrastructure and urban planning highlight opportunities for development. This research contributes to the broader understanding of sustainable urban mobility by demonstrating the application of IMUS in medium-sized municipalities. It also provides actionable recommendations to address Rio Verde’s specific challenges. Improving data availability, expanding public transportation networks, and fostering collaboration between municipal agencies are key strategies to align urban growth with sustainability goals. Additionally, addressing critical areas such as intermunicipal collaboration and population density management will strengthen Rio Verde’s urban mobility framework.

Keywords: sustainable urban mobility, integrated planning, sustainable urban mobility index (IMUS), urban sustainability indicators

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Authors: Sifat Md. Iftekhar Bhuiyan

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This study investigates the dynamic correlation between fuel prices and traffic congestion, a topic of significant importance in the fields of urban planning and transportation economics. The primary objective is to examine the correlation between variations in fuel prices and their impact on traffic volume and transportation mode preferences, ultimately affecting congestion in urban regions. Drawing upon an extensive dataset from the Texas A&M Transportation Institute’s Urban Mobility Division, this study encompasses data from various Metropolitan Statistical Areas (MSAs) across the United States over a 37-year period (1982-2019). The study used data till 2019 to avoid COVID-19’s impact on the result. The study used a two-way fixed effects econometric model to examine the relationship between gasoline and diesel prices and Total Vehicle Miles Traveled (Total_VMT), which serves as an indicator of traffic congestion. The study offers a nuanced understanding of the elastic response of vehicular mobility to changes in fuel costs. The analysis distinguishes between the effects of gasoline and diesel pricing, recognizing their distinct use in private and commercial transportation. The results demonstrate a noteworthy inverse relationship between gasoline costs and Total_VMT, indicating that higher gasoline prices result in a decrease in traffic volume. In contrast, the costs of diesel exhibit a diverse effect, which mirrors the distinct market dynamics of commercial transportation. Additionally, the number of commuters is found to be a strong predictor of traffic congestion, emphasizing the role of urban population dynamics in shaping traffic patterns. These observations have significant ramifications for urban policy and transportation planning. The study highlights the capacity of fuel pricing as a mechanism for controlling traffic congestion, addressing environmental goals, and promoting sustainable urban mobility. It advocates for tailored strategies that consider the distinct roles of various fuel types and their broader economic and environmental impacts. The study contributes to a deeper understanding of the interplay between economic factors and urban transportation dynamics, providing vital assistance for policymakers and urban planners in their endeavor to establish cities that are more efficient and sustainable.

Keywords: traffic congestion, urban population dynamics, economic implications, urban planning

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Authors: Siliang Luan

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In this paper, an integrated-decentralized bi-level optimization framework is developed to coordinate intersection signal operations and vehicle driving behavior at an isolated signalized intersection in a mixed traffic environment. The framework takes advantage of both signal control and conflict elimination by incorporating an integrated level and a decentralized level. Two distinct signal control methods are introduced: the classical green phase control strategy and the white phase control strategy. The latter allows certain vehicles to pass through the intersection during a red phase, thereby reducing idle time. Besides, various vehicle trajectory optimization strategies are tailored to different vehicle-following types, leveraging the capabilities of CAV technology. Enhanced microscopic behavior control strategies, such as car-following and lane-changing controls, are also developed for CAVs to improve their performance in mixed traffic. These strategies are integrated into the proposed framework. The effectiveness of the framework is validated through numerical experiments and sensitivity analysis, demonstrating its advantages in terms of traffic effectiveness, stability, and energy economy.

Keywords: traffic signal optimization, connected and automated vehicles, vehicle microscopic control, traffic control and information technology

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