Vehicle Speed Research Articles

Indirect measurement of bridge surface roughness using vibration responses of a two-axle moving vehicle based on physics-constrained generative adversarial network

This study addresses the challenge of indirectly measuring bridge surface roughness through the vibration responses of a moving vehicle, which is crucial for pavement maintenance and bridge safety assessment. A physics-constrained generative adversarial network (PC-GAN) was proposed for the probabilistic estimation of surface roughness. The method consists of two steps: initially, a GAN informed by physics-based knowledge extracts combined information of bridge vibration deflection and surface roughness from vehicle accelerations. Subsequently, a feed-forward network isolates the bridge surface roughness from the combined data. Numerical examples validate the PC-GAN method, demonstrating sustained high accuracy under challenging conditions, including ISO 8608 level C road roughness, vehicle speeds up to 8 m s-1, 10 % deviation in vehicle parameters, 10 % environmental noise, and 10 % vehicle damping ratio. Laboratory tests further confirmed the method's efficacy, with the successful detection of artificial barriers on the bridge surface and a mean relative error of 3.33 % in height estimation. The PC-GAN method is demonstrated to be a robust tool for estimating bridge surface roughness under various numerical and laboratory conditions. These findings provide valuable insights for the rapid inspection of bridge pavement conditions using vibration responses from moving test vehicles.

Research on the potential of integrated vehicle control with closed-loop phase portrait analysis

The phase portrait is an important tool for analyzing vehicle stable region widely utilized in vehicle dynamic control systems. At handling limits, traditional open-loop vehicle stable region constraints will restrict the vehicle control ability, while the closed-loop actuator interventions can help states outside the open-loop stable region converge back to stable origin, which is more suitable for constraining vehicle states under extreme working conditions. This article systematically analyzes the potential of integrated vehicle control with closed-loop phase portrait. Firstly, a vehicle dynamic model considering actuator dynamics and tire transient characteristics is established. Then, a numerical optimal control method for calculating closed-loop controllability region is introduced. The controllability region under different friction coefficients, vehicle speeds, convergence time, vehicle steering response characteristics, actuator time constants, and tire transient characteristics are discussed and analyzed. In terms of different control inputs combinations, this article displays the controllability regions under different front steering angles, rear steering angles, direct yaw moments, and their synergistic performances. Control input laws derived from numerical optimization are summarized in two specific directions. Finally, a comparative analysis of open-loop stable regions and closed-loop controllability regions confirms the superiority of the proposed method. Potential applications of the systematic analysis are also discussed.

Researching The Dynamic Response Law of High and Steep Subgrade Slopes Using ABAQUS

The study aimed to investigate the dynamic response of vehicles traveling on expressways across high and steep subgrade slopes under vehicle loads. This was done by creating a finite element model of high and steep subgrade slopes through ABAQUS software and its secondary program development. Specific engineering examples were used to analyze the dynamic response states and laws of high and steep subgrade slopes under varying vehicle speeds and load conditions. The study's results indicate that: (1) When the vehicle is travelling at 100 km/h, its vibration frequency is close to the natural frequency of the subgrade in the section, leading to strong dynamic responses. (2) As the number of axles increases, the pressure on the contact surface between the wheel and the roadbed rises, and dynamic responses become stronger. The four-axle vehicle shows the most significant dynamic response. (3) The maximum vertical stress fluctuates around an intermediate value over time. (4) The highest maximum vertical displacement value is observed in the penultimate analysis step, with a noticeable peak value. At this point, the vehicle load has the most prominent impact on the roadbed slope.

Optimal powertrain system design and V2V and V2I based hierarchical control strategy of FRIDEV under vehicle-following scenarios

In this paper, the front- and rear-independent-drive electric vehicle (FRIDEV) is selected as the target vehicle for its good dynamic and economy performance. Based on the analysis of the cost, efficiency, and loss characteristics of different motors, the interior permanent magnet synchronous motor (IPMSM) and induction motor (IM) are applied in the target powertrain system. Then, the proposed powertrain system is optimized based on the analysis of driving cycles. The optimal control strategy for the target powertrain system is developed, which consists of the driving mode switching algorithm and the driving torque distribution based on adaptive particle swarm optimization (APSO). Furthermore, in order to achieve the efficient autonomous driving of the target vehicle under the vehicle-following scenarios, a hierarchical control strategy is proposed with the combination of upper-level control of the speed planning strategy and the lower-level control of powertrain system control strategy. The upper-level control of speed planning strategy is designed, which can determine and track the target vehicle speed based on the information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Finally, simulation analysis in MATLAB/Simulink and virtual driving experiments are conducted to verify the superiority of the proposed powertrain system and hierarchical control strategy.

Optimization of household medical waste recycling logistics routes: Considering contamination risks.

The escalating generation of household medical waste, a byproduct of industrialization and global population growth, has rendered its transportation and logistics management a critical societal concern. This study delves into the optimization of routes for vehicles within the household medical waste logistics network, a response to the imperative of managing this waste effectively. The potential for environmental and public health hazards due to improper waste disposal is acknowledged, prompting the incorporation of contamination risk, influenced by transport duration, waste volume, and wind velocity, into the analysis. To enhance the realism of the simulation, traffic congestion is integrated into the vehicle speed function, reflecting the urban roads' variability. Subsequently, a Bi-objective mixed-integer programming model is formulated to concurrently minimize total operational costs and environmental pollution risks. The complexity inherent in the optimization problem has motivated the development of the Adaptive Hybrid Artificial Fish Swarming Algorithm with Non-Dominated Sorting (AH-NSAFSA). This algorithm employs a sophisticated approach, amalgamating congestion distance and individual ranking to discern optimal solutions from the population. It incorporates a decay function to facilitate an adaptive iterative process, enhancing the algorithm's convergence properties. Furthermore, it leverages the concept of crossover-induced elimination to preserve the genetic diversity and overall robustness of the solution set. The empirical evaluation of AH-NSAFSA is conducted using a test set derived from the Solomon dataset, demonstrating the algorithm's capability to generate feasible non-dominated solutions for household medical waste recycling path planning. Comparative analysis with the Non-dominated Sorted Artificial Fish Swarm Algorithm (NSAFSA) and Non-dominated Sorted Genetic Algorithm II (NSGA-II) across metrics such as MID, SM, NOS, and CT reveals that AH-NSAFSA excels in MID, SM, and NOS, and surpasses NSAFSA in CT, albeit slightly underperforming relative to NSGA-II. The study's holistic approach to waste recycling route planning, which integrates cost-effectiveness with pollution risk and traffic congestion considerations, offers substantial support for enterprises in formulating sustainable green development strategies. AH-NSAFSA offers an eco-efficient, holistic approach to medical waste recycling, advancing sustainable management practices.

Multi-Modal Contextualization of Trajectory Data for Advanced Analysis

SummaryRising amounts of generated geospatial data, either trajectory-like tracking data, raster-like imagery, or vector-like mappings as in OpenStreetMap (OSM), grow the need for multi-modal algorithmic analysis. Existing machine-learning-based algorithms contradictly mainly focus on image and textual input representations and cannot deal with other modes of geospatial data. Therefore, we propose a novel method to contextualize vector-like trajectory data with surrounding data to create easy-to-be-analyzed image-like representations. Our approach includes the proposition of a chase-cam-like scanline over space according to the trajectory’s speed and possibly smoothed orientation. Thereby, surrounding pixels in the vicinity of the trajectory points are accumulated along the scanline and are combined into a visual representation of the trajectory. To show the potential effects of our work, we predict traffic regulations for trajectory sections in the vehicle speed dataset based on our proposed trajectory-based sampling of orthophotos in the same region. This proposes a new way of using multi-modal data sources (trajectories and airborne imagery) to extract road metadata.

Wide angle wide band polarization insensitive metamaterial absorber for C, X and Ku band application with hexagonal packaging

Abstract In this paper wide angle wide band polarization insensitive Metamaterial absorber (MA) has been proposed. The proposed unit cell structure consists of hexagonal concentric ring along with hexagonal packing. The structure is fabricated on FR4 substrate. The advantage of hexagonal packing is that it covers less area as compared to square packing. The unit cell structure is scaled w.r.t hexagonal ring and resistance of the substrate. The absorptivity achieved range from 6.54 to 14.85 GHz having Bandwidth (BW) of 8.31 GHz. The metamaterial behaviour of the structure is proven by plotting real and imaginary pat of permittivity and permeability. Absorption mechanism is shown by plotting surface current distribution at the top and bottom of the surface. The proposed MA structure is analysed for normal and oblique incidence. From the normal incidence it was found that structure has wide angle polarization insensitive. The absorptivity of the fabricated structure is measured inside the anechoic chamber. The simulated and measured plot is compared and found that both the results are very close to each other. At last, the proposed and already reported MAs are compared and from the observation it was found that proposed unit cell have larger BW compact in size, polarization insensitive and have hexagonal packing. The proposed MA covers partial C band, full X band and partial Ku band. The Proposed MA found practical applications for satellite communication, Wi-Fi devices, cordless telephone, military, air traffic control, defence tracking, vehicle speed detection.

On application of sound power radiation of rolling tyre measured using CPX-based methodology

The present paper provides a comprehensive account of sound power radiation measurement from a rolling tyre, utilising a methodology that employs an advanced PolyU Mark III Close-Proximity (CPX) trailer enclosure which is aimed to provide CPX measurement per relevant ISO stsandard with suppressed background noise within the interior. The interior walls and ceiling of the enclosure are equipped with diffuser panels, creating a reverberant environment suitable for sound power measurement using the comparison method. The optimal design of the enclosure, which minimizes acoustic resonance and ensures a uniform distribution of acoustic energy, is determined through extensive numerical simulations. During tyre/road sound power measurements, the spatially averaged sound pressure level (SPL) distributions within the enclosure are obtained with a reference sound source (RSS) of known sound power. The relationships between the RSS SWLs and the corresponding SPLs are established at all vehicle speeds. These relationships can then be utilised to deduce the total SWL, as well as its 1/3-octave spectrum, from the captured averaged SPL radiated by a tyre rolling at any vehicle speed. The effectiveness of the CPX-based sound power measurement are illustrated. Additionally, the potential of utilising the measured SWLs for predicting pass-by noise radiation to roadside is discussed.

Development of a feed-forward audio-visual notification system for reducing motion sickness in autonomous vehicles

To mitigate motion sickness occurring in seat positions where driving conditions cannot be predicted due to the changed interior structure of autonomous vehicles, a feed-forward audio-visual notification system was developed. The design, implemented in the driving environment, provides information through ambient light and notifying sound by predicting signal processing information, including steering angle and turn signals, to address the audio-visual impact of changes in the vehicle's direction, speed, and acceleration with feed-forward alerts. The correlation model between psychological and physiological responses was developed through measurements such as MSQ, MISC, EEG, fNIRS, and HRV during driving, quantifying the degree of motion sickness and improvement situations. The effect of visual variation by ambient light and the customized effect of notifying sound were combined to compare and analyze the degree of motion sickness reduction following the installation of the notification system.

On the influence of AVAS directivity on electric vehicle speed perception

Electric vehicles (EVs) typically produce minimal noise at low driving speeds, increasing the risk of accidents for pedestrians and other vulnerable road users. Therefore, regulations require EVs to use acoustic vehicle alerting systems (AVAS) that emit artificial warning sounds. Investigating the human response to these AVAS sounds requires laboratory listening experiments, often based on auralizations. One of the challenges when auralizing electric vehicles is to include AVAS radiation directivity. However, it is currently unknown how this directivity affects the perception of a vehicle passing by and whether an accurate reproduction is necessary for auralizations. We present a study on the influence of AVAS directivity on perceived vehicle speed, comparing different radiation patterns in combination with narrowband and tonal AVAS signals in a paired comparison listening experiment with 31 participants. The results show that AVAS radiation directivity can significantly influence the perception of vehicle pass-by speed, with a tendency for omnidirectional patterns to be perceived slower than more directional patterns. Additionally, most participants consistently rated either the tonal or the narrowband noise AVAS signal as faster throughout most comparisons. This indicates that AVAS signal type can affect vehicle speed perception with a subjective preference between tonal and narrowband noise AVAS.

The problem of obtaining vehicles speed input for road traffic noise microscopic models: a comparison between field measurements and a stochastic approach

Mitigating environmental noise harmful effects is a relevant goal in the EU "Zero Pollution Action Plan", which targets a 30% reduction in chronic annoyance from transportation noise by 2030. Since road traffic is a major noise source in urban and non-urban settings, much effort has been invested in analyzing and modeling it. To assess noise levels, numerous Road Traffic Noise Models (RTNMs) exist, each requiring multiple inputs like vehicle flows, percentage of heavy vehicles, and average speed. Specifically, when dealing with microscopic models, the single vehicle speeds are needed. To obtain them, on-site measurements offer accurate data but are time consuming and expensive. This paper explores two approaches: an alternative measurement approach, based on video processing and object detection tools, and a stochastic approach, which randomly assigns a speed to each vehicle from a distribution curve, depending on traffic conditions and vehicle category. These methodologies are used to provide input data to a road traffic noise microscopic model, whose results are compared with field measured data. This comparison will allow to get useful insights on the performances of the proposed techniques in providing reliable inputs and their potential applications in different scenarios where single vehicle measured speeds are not available.

Road Traffic Noise Levels Estimations by means of Fully Dynamic and Microscopic Approach Compared to CNOSSOS-EU Model

In the last decades, research has been dedicated to refining models for assessing traffic noise levels, supporting national authorities in addressing associated problems. Progress has shifted from basic statistical models to dynamic ones, typically composed of a Noise Emission Model (NEM) for estimating source sound power levels and a sound propagation model for assessing equivalent noise levels at specific receiver points. These models commonly integrate the average speed of traffic flow as an input variable, placing them in the macroscopic or mesoscopic realm. The CNOSSOS-EU model (reference in Europe) exemplifies this macroscopic approach. However, due to the modular structure of road traffic noise models, a NEM can be coupled with different sound propagation models, facilitating the transition from macroscopic to microscopic assessments. This paper demonstrates the coupling of a NEM with two distinct simplified sound propagation models: CNOSSOS-EU and the sum of Sound Exposure Levels (SEL) models. The latter approach showcases the potential of using individual on-road vehicle speeds as inputs, enabling microscopic noise assessments. Vehicle speed data is extracted from video records, while the goodness of the proposed approaches is tested by comparing the models' estimations with ground truth values recorded on a National Road in Portugal.

A review of vehicle speed measurement methods for the statistical pass-by noise testing

The Statistical Pass-By method (SPB, ISO11819-1) is used in environmental impact studies to evaluate, classify, and compare different road surfaces according to their influence on traffic noise. It relates the noise level to the type of vehicles and their speed. There are different methods to measure vehicle speed and the cost plays a significant role in their practicality and widespread adoption. Radar, Lidar, and Photocells-based method systems typically involve higher initial costs due to the sophisticated technology employed, but they offer real-time and accurate data, justifying their investment. On the other hand, Video camera systems may have lower initial costs, but their accuracy can be influenced by environmental conditions and calibration requirements. This paper will assess the cost-effectiveness of these technologies, considering not only the initial setup expenses but also maintenance and operational costs over time. Cost-benefit analyses are crucial in determining the most economical yet reliable solution for specific budget constraints. As the research in this field progresses, a key focus lies in optimizing the balance between cost and performance to make these systems more accessible and practical also for a wide range of applications, ensuring that advancements benefit both traffic management authorities and users alike.

Impact of road noise components (rolling/propulsion) by vehicle categories (light-duty, heavy-duty and two-wheeled vehicles) on population's noise exposure and associated adverse health effects

Exposure to road traffic noise can lead to various adverse health effects, ranging from annoyance and night-time sleep disturbances to ischemic heart diseases. The aim of this paper is to identify the contribution of the road noise components (rolling/propulsion) for each vehicle category (light-duty, heavy-duty and two-wheeled vehicles) on population's noise exposure and the associated adverse health effects. This analysis focuses on the case study of Lyon, France. The input data align with the requirements of the 4th deadline of Directive 2002/49/CE and adhere to the CNOSSOS calculation standard. The results reveal that the rolling noise component of light-duty vehicles is predominant due to vehicle speed and traffic volume. It is the sole noise component (i) significantly exposing a portion of the population to Lden > 68 dBA, noise limit set by the French decree of April 4, 2006, and (ii) where over half of the population is exposed to Lden > 53 dBA, recommended threshold by the World Health Organization (2018). By using transfer functions established between noise components for each vehicle category, and population's noise exposure, noise source-abatement solutions can be quantified in terms of their impact on population's noise exposure, thus determining the reduction in health impacts.

PB-Trajectron: Physics bounded neural network for generalized trajectory prediction

Vehicle trajectory prediction is a critical technology in autonomous driving systems, as the quality of prediction directly affects downstream path planning and vehicle control. Although recent studies have shown that models combining kinematic rules with data-driven methods exhibit better interpretability in trajectory prediction, these models often adopt fixed constraint strengths, limiting their generalization ability in diverse traffic scenarios. This fixed constraint strength design restricts the model’s adaptability to different traffic environments.To address this issue, we propose PB-Trajectron, a dynamic integration mechanism that enhances the model’s prediction performance and generalization capability. PB-Trajectron is a dynamic integration mechanism that combines vehicle kinematic rules with deep learning prediction models for generalized vehicle trajectory prediction. The model integrates physics-based motion models and Deep Neural Networks (DNNs) to provide reasonable physical explanations for predicting vehicle trajectories at different speeds. First, we establish two vehicle kinematic constraints applicable to different prediction scenarios, enabling the agent to switch between these constraints based on the causal relationships between vehicle motion states to avoid generating non-physical trajectory results. Second, we propose a kinematic constraint decision framework based on velocity thresholds, which demonstrates adaptive adjustment, allowing the agent to switch tasks according to real-time state conditions and adaptively adjust the contribution of different physical constraints based on actual vehicle speeds. Finally, we investigate the advantages of PB-Trajectron in Out-of-Domain(OOD) generalization.Cross-scenario experiments on the nuScenes dataset show that, compared to previous methods, PB-Trajectron reduces the final displacement error by 7.69% when the prediction step length is 4. Furthermore, out-of-domain generalization tests on the INTERACTION dataset demonstrate that PB-Trajectron achieves a 12.23% reduction in average prediction error on datasets from different countries and scenarios compared to previous methods. The proposed mechanism can better adapt to complex and diverse traffic scenarios, laying the foundation for explainable and robust autonomous driving systems.

Ego-Vehicle Speed Correction for Automotive Radar Systems Using Convolutional Neural Networks.

The development of autonomous driving vehicles has increased the global demand for robust and efficient automotive radar systems. This study proposes an automotive radar-based ego-vehicle speed detection network (AVSD Net) model using convolutional neural networks for estimating the speed of the ego vehicle. The preprocessing and postprocessing methods used for vehicle speed correction are presented in detail. The AVSD Net model exhibits characteristics that are independent of the angular performance of the radar system and its mounting angle on the vehicle, thereby reducing the loss of the maximum detection range without requiring a downward or wide beam for the elevation angle. The ego-vehicle speed is effectively estimated when the range-velocity spectrum data are input into the model. Moreover, preprocessing and postprocessing facilitate an accurate correction of the ego-vehicle speed while reducing the complexity of the model, enabling its application to embedded systems. The proposed ego-vehicle speed correction method can improve safety in various applications, such as autonomous emergency braking systems, forward collision avoidance assist, adaptive cruise control, rear cross-traffic alert, and blind spot detection systems.

Quantifying perceived risk in driving: A Monte Carlo approach for obstacle avoidance

Objective This study aims to develop a model for quantifying perceived risk in obstacle avoidance, emphasizing how drivers’ perceived risk characteristics influence their driving decisions. The research addresses the lack of attention given to modeling risk from the perspective of drivers’ risk perceptions. Methods Monte Carlo methods are employed to account for the uncertainties and complexities of driving behavior, restoring the probabilistic nature of risk. The proposed method quantifies perceived risk by incorporating drivers’ fuzzy perceptions, enabling a quantitative evaluation during obstacle avoidance. A logit model is used to link perceived risk with driving decisions, identifying key factors influencing driver behavior in obstacle avoidance scenarios. Results Experimental data revealed significant variations in vehicle trajectories and speed distributions due to differences in drivers’ experience and proficiency. The perceived risk indicator (PRI) values for leftward bypasses were higher compared to rightward bypasses, and the receiver operating characteristic (ROC) curve confirmed the PRI’s strong predictive ability with an area under the curve (AUC) of 0.820. The logit model showed that both PRI and speed significantly influenced the probability of choosing a rightward bypass, achieving 90% accuracy. Building on the model, the study predicted and visualized the probability of vehicles turning right to avoid obstacles at different positions and speeds within 200 m of the obstacle. Conclusions The research offers a framework for traffic professionals to understand driver-perceived risk and decision-making mechanisms. This understanding is beneficial for improving traffic safety and highlights the importance of considering drivers’ risk perceptions in modeling driving behavior.

Vehicle platoon safety considering the vehicle cut-in: A coupled reinforcement learning and model predictive control approach

A longitudinal platoon control method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) and Model Predictive Control (MPC) is proposed to solve the problems of low following efficiency and system instability in longitudinal platoon control. Firstly, Dynamic Bayesian Network (DBN) and Long Short-Term Memory (LSTM) network are introduced to identify the driving behavior of bystanders and derive the MPC objective constraint function containing three indicators of following, comfort and fuel consumption according to the platoon dynamics equation. Secondly, the system prediction model and cost function are introduced into the action-critic network of TD3 to solve the problem of no model training in the traditional TD3 algorithm and to speed up the training speed and accuracy of the network. On this basis, a Bellman equation is proposed to calculate the time-domain difference error and the expected loss function to solve the TD3 network overestimation problem. Finally, the joint simulation platform is built to simulate the platoon driving conditions and compare with the DDPG optimization algorithm and the traditional MPC algorithm respectively. The results show that the improved TD3-MPC algorithm satisfies the constraints and the spacing error is controlled within 0.3 m, and the vehicle speed changes more smoothly in the scenario of speed fluctuation in the front vehicle, and the ride comfort of the platoon is improved, and it has better robustness in the scenario of vehicle cut-in. The experimental results show that when the vehicle speeds are 20, 40, and 60 km/h, compared to MPC, the average spacing errors are reduced by 27.56%, 25.64%, and 28.04%, respectively, and compared to DDPG-MPC, the average spacing errors are reduced by 6.59%, 8.77%, and 7.86%, respectively.

Correlation relationships of processes in the combustion engine in the RDE test

The article presents considerations on the processes taking place in the combustion engine in the in real driving operating conditions of a vehicle performing the RDE (Real Driving Emissions) test. The tests were carried out using a passenger car with a spark-ignition engine. The processes considered in the article were related to the engine operating states, exhaust emissions and fuel consumption, and the vehicle speed, which determines the engine operating conditions. The RDE test were carried out using PEMS (Portable Emissions Measurement System) equipment, and the following variables were recorded: vehicle speed, control, rotational speed, relative torque and relative engine power, emission pollutant intensity of: carbon monoxide, hydrocarbons, nitrogen oxides and carbon dioxide, the intensity of particle number and the fuel consumption intensity. The recorded signals were digitally processed, and the statistical properties of the variables and the mutual relation between the engine operating states were examined. The properties of the measured variables were investigated in the entire RDE test and in its constituent phases: the first, corresponding to vehicle movement in cities, the second – outside cities, and the third – on highways and expressways. The pollutant specific distance emission and the particle number specific distance as well as the specific distance fuel consumption were determined in relation to the average vehicle speed, and based on these results, the exhaust emissions and fuel consumption characteristics were created. Correlational studies of the considered variables were also performed. Pearson's linear correlation coefficients for the measured variables combinations were determined.

Impact of urban road characteristics on vehicle speed: Insights from Brescia, Italy

In managing road infrastructures, a key benchmark is the 85th percentile of vehicle speeds (V85). While V85 can be derived from spot speed samples, these are often lacking on each urban road. Thus, prediction models become valuable tools for examining the relationship between V85 and road characteristics. Although various models exist for rural roads, the impact of roadside characteristics and markings on V85 in urban road networks has been partially investigated, and the effect of traffic calming measures remains fragmented.This study aims to address these gaps by applying a methodology that sheds light on the effects of some variables that influence V85 on urban roads. Specifically, the methodology selects and segments roads along the urban road network of the municipality of Brescia (Italy) and collects data on both road characteristics and 48,000+ spot speed information. Following data cleansing, it processes these data according to three different multiple regression models to analyse the influence of various predictors on V85. Once the best model is estimated, its performance is evaluated, and the final list of significant predictors is obtained.The results revealed that V85 increases with longer homogeneous segments, greater distance to successive intersections, bituminous conglomerate roads with more lanes, and the presence of trees, visible road markings, and posted speed limits. Conversely, V85 decreases in the presence of on-street parking and other obstacles (e.g., walls and road posts), when the density of road intersections and pedestrian crossings increases, when the left crossbar width increases and when the land use crossed is commercial or office, residential or industrial. Nevertheless, no significant effect was found for several traffic calming measures included in the model.These findings can assist road authorities in verifying road operating conditions and planning infrastructure interventions to reduce speeds, thereby creating a safer urban environment for all users.