Search Results (20)

In recent years, cycling mobility in urban environments across Spain has grown significantly, driven by sustainability policies and behavioral shifts following the COVID-19 pandemic. However, this growth has been accompanied by an increase in accidents in urban areas, where more than 72.6% of cyclist accidents are concentrated, with large cities being the most affected. This study aims to explore and analyze the factors influencing cycling accidents in Spanish municipalities with populations exceeding 50,000, during the period of 2020–2023. A total of 24 variables were analyzed, encompassing not only innovative cyclist infrastructure network features (line connectivity), but also urban morphology and street infrastructure, weather conditions and mobility (all transportation modes). The methodological approach combines Principal Component Analysis (PCA) with two negative binomial regression models: one addressing all cycling accidents, and another focusing specifically on collisions between cyclists and motor vehicles. PCA shows the complex relations between urban features when comparing cyclist accidents among cities. The main results from the Negative Binomial analysis show that increased bicycle lane length significantly reduces cycling accident risk, while higher intersections with traffic signal density are associated with a greater likelihood of car–bicycle crashes. These findings emphasize the importance of cycling infrastructure provision and intersection design and regulation as key policy levers for improving urban cyclist safety. Future research should seek to corroborate these results through micro-spatial analyses and accident geolocation, assessing their severity and accounting for more detailed data on cycling infrastructure. Finally, the results’ discussion underscores the importance of implementing holistic urban mobility strategies that prioritize cyclist safety. Full article

Pedestrian safety at signalized intersections remains a pressing concern in rapidly urbanizing cities. This study introduces a trajectory–signal behavior spectrum, grounded in Behavior Spectrum Theory (BST), to quantify crossing risk using readily observable data. Unmanned aerial vehicle (UAV) video is employed to record pedestrian movements, which are then detected with YOLOv8 and tracked with ByteTrack, producing frame-level trajectories without dependence on line-of-sight instrumentation. Five spatiotemporal features—speed, acceleration, crossing time, remaining pedestrian-signal green time, and red-phase duration—are compiled into the spectrum. Features are normalized using the interquartile range (IQR) method, and objective weights are determined with an improved CRITIC (Criteria Importance Through Intercriteria Correlation) scheme that incorporates a median-based coefficient of variation and absolute correlation for conflict measurement. The resulting risk eigenvalues are clustered with K-means into four levels: no risk, low, medium, and high. A case study of 1210 crossings at a two-way eight-lane intersection in Harbin, China (576 compliant, 634 non-compliant) demonstrates the approach. Results show greater variability among non-compliant speeds (mean 1.29 m/s) compared with compliant crossings (mean 1.40 m/s), with more extreme deviations. Clustering achieved silhouette coefficients of 0.60 for compliant and 0.69 for non-compliant groups, while expert validation on 20 samples yielded substantial agreement (Fleiss’ Kappa = 0.87). This study provides a systematic and interpretable method for risk classification, which supports both theoretical understanding and applied traffic safety management. Full article

Within urban tunnels, the lane boundary lines are typically solid, thereby prohibiting lane changes and overtaking. The establishment of a mobile operation zone in the slow lane can pose significant driving safety hazards not only to the slow lane within the tunnel but also to the middle and overtaking lanes at the tunnel exit. This article adopts the method of simulation of the establishment of an urban expressway three-lane VISSIM model, and selects the road traffic volume and speed of moving work zone as the independent variable parameters. Then, the influence range of a low-speed vehicle on the rear vehicles in the middle lane and slow lane and the traffic risk caused by a low-speed vehicle are analyzed. The results show that, irrespective of the variations in traffic volume and moving operation zone speed, the traffic flow within a 150 m range after the tunnel section was significantly influenced. This was because queuing and congested vehicles from the slow lane exited the tunnel, causing vehicles to change lanes and overtake in a concentrated manner. The moving operation zone has a substantial impact on the traffic flow in the slow lane. Under different moving operation zone speed conditions, the speed change trend of the following vehicles is consistent. When the moving operation zone speed was 5 km/h and the traffic volume exceeded 1200 pcu/h, the traffic flow behind the operation zone was directly affected, and within an observable longitudinal distance of 500 m, this impact did not dissipate. The research results can provide a scientific basis for the operation and management of urban tunnel low-speed vehicles. Full article

This research addresses the challenge of predicting operating vehicles’ speeds (V85) on single-carriageway road tangents. While most previous models rely on preceding segment speeds or focus on curves, this research develops an independent prediction model specifically for road tangents, based on empirical data collected in Croatia. A total of 46 locations across 23 road cross-sections were analysed, with operating speeds measured using field radar surveys and fixed traffic counters. Following a comprehensive correlation and multicollinearity analysis of 24 geometric, environmental, and traffic-related variables, a multiple linear regression model was developed using a training dataset (36 locations) and validated on a separate test set (10 locations). The model includes nine statistically significant predictors: shoulder type (gravel), edge line quality (excellent and satisfactory), pavement quality (excellent), average summer daily traffic (ASDT), crash ratio, edge lane presence, overtaking allowed, and heavy goods vehicle share. The model demonstrated strong predictive performance (R² = 0.89, RMSE = 5.24), with validation results showing an average absolute deviation of 2.43%. These results confirm the model’s reliability and practical applicability in road design and traffic safety assessments. Full article

Bicycle use has become more important today, but more information and planning models are needed to implement bike lanes that encourage cycling. This study aimed to develop a methodology to predict the speed a cyclist can reach in an urban environment and to provide information for planning cycling infrastructure. The methodology consisted of obtaining GPS data on longitude, latitude, elevation, and time from a smartphone of two groups of cyclists to calculate the speeds and slopes through a model based on a recurrent short-term memory (LSTM) type neural network. The model was trained on 70% of the dataset, with the remaining 30% used for validation and varying training epochs (100, 200, 300, and 600). The effectiveness of recurrent neural networks in predicting the speed of a cyclist in an urban environment is shown with determination coefficients from 0.77 to 0.96. Average cyclist speeds ranged from 6.1 to 20.62 km/h. This provides a new methodology that offers valuable information for various applications in urban transportation and bicycle line planning. A limitation can be the variability in GPS device accuracy, which could affect speed measurements and the generalizability of the findings. Full article

To study the internal formation mechanisms of risky riding behaviors of takeaway e-bikers at urban intersections, we designed a takeaway riding risky behavior questionnaire and obtained 605 valid samples. An exploratory factor analysis was then conducted to extract five scales: individual characteristics, safety attitude, riding confidence, risk perception, and risky riding behavior. On this basis, a structural equation model was constructed to explore the intrinsic causal relationships among the variables that affect the risky riding behaviors of takeaway e-bikers. The results show that the influence of incentive compensation driven by the takeaway platform was the greatest one. Takeaway riders tend to fight against time to improve punctuality and income by red-light running and speeding. They usually need to pay attention to order information and the delivery routes and communicate with customers to pick up meals in real-time, which inevitably lead to the use of cell phone while riding. Road factors such as “no turnaround at the intersection” and “no non-isolation facilities between on-motorized and motorized lane” lead riders to riding against the traffic, riding on the motor lane, and parking outside the stop line. In addition, lax traffic regulations lead to frequent loopholes for takeaway riders. It means that improving the takeaway platform system, strengthening traffic safety education, and adopting mandatory restraint measures are extremely important. The empirical results provide theoretical support for the benign and healthy development of the takeaway industry, which is significant for preventing and reducing risky behaviors of takeaway riders and improving safety at urban intersections. Full article

Accurate, ubiquitous and reliable navigation can make transportation systems (road, rail, air and marine) more efficient, safer and more sustainable by enabling path planning, route optimization and fuel economy optimization. However, accurate navigation in urban contexts has always been a challenging task due to significant chances of signal blockage and multipath and non-line-of-sight (NLOS) signal reception. This paper presents a detailed study on environmental context detection using GNSS signals and its utilization in mitigating multipath effects by devising a context-aware navigation (CAN) algorithm that detects and characterizes the working environment of a GNSS receiver and applies the desired mitigation strategy accordingly. The CAN algorithm utilizes GNSS measurement variables to categorize the environment into standard, degraded and highly degraded classes and then updates the receiver’s tracking-loop parameters based on the inferred environment. This allows the receiver to adaptively mitigate the effects of multipath/NLOS, which inherently depend upon the type of environment. To validate the functionality and potential of the proposed CAN algorithm, a detailed study on the performance of a multi-GNSS receiver in the quad-constellation mode, i.e., GPS, BeiDou, Galileo and GLONASS, is conducted in this research by traversing an instrumented vehicle around an urban city and acquiring respective GNSS signals in different environments. The performance of a CAN-enabled GNSS receiver is compared with a standard receiver using fundamental quality indicators of GNSS. The experimental results show that the proposed CAN algorithm is a good contributor for improving GNSS performance by anticipating the potential degradation and initiating an adaptive mitigation strategy. The CAN-enabled GNSS receiver achieved a lane-level accuracy of less than 2 m for 53% of the total experimental time-slot in a highly degraded environment, which was previously only 32% when not using the proposed CAN. Full article

Lateral driving behavior analysis is the foundation of freeway cross-section design and the focus of road safety research. However, the factors that influence vehicle lateral driving behavior have not been clearly explained. The dataset of the natural driving trajectory of freeways is used in this study to analyze vehicle lateral driving behavior and trajectory characteristics. As vehicle trajectory characteristic indicators, parameters such as preferred trajectory deviation and standard deviation are extracted. The effects of lane position, speed, road safety facilities, and vehicle types on freeway trajectory behavior are investigated. The results show that lane width and lane position significantly impact vehicle trajectory distribution. As driving speed increases, the lateral distance between vehicles in the inner lane and the guardrail tends to increase. In contrast, vehicles in the outside lane will stay away from the road edge line, and vehicles in the middle lane will stay away from the right lane dividing line when the speed increases. Statistical analysis shows that the preferred trajectory distribution of the same vehicle type in different lane positions is significantly different among groups (Cohen’s d > 0.7). In the same lane, the lateral position characteristics of the center of mass of different vehicle types are basically the same (Cohen’s d < 0.35). This work aims to explain what variables cause trajectory deviation behaviors and how to design traffic safety facilities (guardrail and shoulder) and lane width to accommodate various vehicle types and design speeds. Full article

This paper aims to investigate the internal mechanisms of bottlenecks in bike-sharing travel. We perform kernel density analysis to obtain analysis points and areas designated by buffer areas. Additionally, we improve the spatial lag model through Tobit regression, so as to avoid the interference of autocorrelation and to set reasonable constraints for dependent variables. The proposed model distinguishes between bike-sharing demand determined by land use and other built environmental factors, which helps to define and identify bottlenecks in bike-sharing travel. Based on a Bayesian network fault tree, we define the diagnosis mode of evidence nodes to calculate the posterior probabilities and to determine the most sensitive factors for bottlenecks. We use Beijing city as the case study. The results show that the most sensitive factors that induce bottlenecks in bike-sharing travel are few subway stations, few bus stops, few bus lines, a low density of bike lanes, and more serious home–work separation. The findings presented here can enhance the generation of bike-sharing trips in response to bike-sharing development and contribute to adjusting the urban structure and reconstructing the green infrastructure layout. Full article

Due to inertia, heavy trucks are often involved in serious losses in accidents. To prevent such accidents, since 2020, the transportation department has promoted the free installation of intelligent video surveillance systems on key vehicles of “two passengers, one danger, and one cargo”. The system can provide real-time warnings to drivers for various risky driving behaviors. The data collected by the system are often managed by third-party platforms, and such platforms do not have authority beyond the information that the authority system can collect. Therefore, it is necessary to use the trajectory data and warning behavior records that the system can collect for behavior analysis and accident prevention. To analyze the correlation between different warning factors, 88,841 warning records and 1033 trip records of heavy trucks for construction in the second half of 2021 were collected from a third-party supervision platform. The research associated the warning records with the vehicle operation records according to the warning time and the license plate and established a multiple linear regression equation associated with operational attributes and warning factors. The factor selection results showed that only two warning factors, “too close distance” and “lane change across solid line”, can be used as dependent variables to construct a regression model. The results showed that many distracted behaviors had a significant impact on aggressive driving behavior. Companies need to focus on behaviors that are prone to other warning behaviors. This paper provides a theoretical basis for the optimization of the warning function of the electrified supervision system and the continuing education of drivers by exploring the internal correlation between different warning factors. Full article

Road cycling, both individually and in groups, is common in Spain, where most two-lane rural roads have no cycle lanes. Due to this, and the difference in speed between drivers and cyclists, the overtaking manoeuvre is one of the most dangerous interactions. This study analyses how road geometry influences the overtaking manoeuvre performance. Field data of 1355 overtaking manoeuvres were collected using instrumented bicycles, riding along different rural road segments, and considering individual, medium and large groups of cyclists. The safety variables that characterise the overtaking manoeuvre are overtaking vehicle speed and lateral clearance. These variables have been correlated to geometric characteristics of the road, such as the type of centre line, the horizontal alignment, the speed limit, and the road cross section. Regression models have been fitted considering each cyclist group size and configuration. For individuals and medium groups, wider roads generate higher lateral clearances and lower overtaking speeds, while for large groups only the solid centre line was significant, generating lower clearances and higher speeds. Results suggest that other factors need to be considered, especially for large groups. Results offer a deeper understanding of the phenomenon by providing key points for improving road geometry design, such as widening the shoulders. Full article

The insufficient development of intelligent dynamic monitoring systems, which operate with big data, obstructs the control of traffic-related air pollution in regulated urban road networks. The study introduces mathematical models and presents a practical comparative assessment of pollutant emissions at urban intersections, with two typical modes of vehicle traffic combined, i.e., freely passing an intersection when the green signal appears and uniformly accelerated passing after a full stop at the stop line. Input data on vehicle traffic at regulated intersections were collected using real-time processing of video streams by Faster R-CNN neural network. Calculation models for different traffic flow patterns at a regulated intersection for dynamic monitoring of pollutant emissions were obtained. Statistical analysis showed a good grouping of intersections into single-type clusters and factor reduction of initial variables. Analysis will further allow us to control and minimize traffic-related emissions in urban road networks. A comparative analysis of pollutant emissions in relation to the basic speed of passing at the intersection of 30 km/h was performed according to the calculations of the mathematical models. When reducing the speed to 10 km/h (similar to a traffic jam), the amount of emissions increases 3.6 times over, and when increasing the speed to 50 km/h, the amount of emissions decreases by 2.3 times. Fuzzy logic methods allow us to make a comparative prediction of the amount of emissions when changing both the speed of traffic and the capacity of the intersection lanes. The study reveals the benefits of using a real-life measurement approach and provides the foundation for continuous monitoring and emission forecasting to control urban air quality and reduce congestion in the road network. Full article

In order to alleviate the influence of low-speed vehicles on tunnel safety, this paper discusses the setting method of variable lane boundaries in urban tunnels. VISSIM simulation software is used to analyze the influence of low-speed vehicles on tunnel traffic flow when lane changes are allowed and when lane changes are prohibited. The results show that the influence of low-speed vehicles on the average speed of traffic flow in urban tunnels is the greatest, and the influence of low-speed vehicles on the average speed of traffic flow can be significantly alleviated when lane changes are allowed in the lane dividing line. When the speed of low-speed vehicles is 40 km/h and the variable lane is set, the average delay time is reduced by 30–50%. The existence of low-speed vehicles significantly increased the average delay time of the local lane, and the lower the vehicle speed and the greater the road traffic volume, the longer the average delay time. When the speed of low-speed vehicles is 40 km/h and the traffic volume is 1200 pcu/h, the traffic density of the right-hand lane decreases by 43.5% after the variable lane is set. While lane changing is prohibited, the presence of low-speed vehicles causes a backlog of vehicles in the rear of the lane, which leads to a significant increase in traffic density. Setting lane-changing permits can alleviate the impact of low-speed vehicles on traffic flow. The research results can provide a scientific basis for the operation and management of urban tunnels. Full article

In recent years, the number of fatal traffic accidents has been on the rise in Taiwan, with a total of 2865 fatalities in 2019, representing a 3% increase from the previous year, and indicating an urgent need for the improvement of Taiwan’s traffic conditions. This research focuses on the three-way intersections on provincial roads. In Taiwan, such three-way intersections account for more than 70% of all intersections on provincial roads. From 2018 to 2019, there were 41,137 accidents at three-way intersections, accounting for about 50% of the traffic accidents in Taiwan. Relevant research in Taiwan has been mostly focused on driver characteristics and other factors were less addressed. In this study, we looked into the environmental factors, conducted analysis and the results were proposed for future roadway safety improvements. This study uses a regression model for analysis, in which the dependent variable is the number of traffic accidents at each intersection. However, for 68% of the three-way intersections surveyed, the number of traffic accidents recorded during the observation period was zero. Therefore, this study uses zero-inflated models to construct a model to identify important variables that affect the severity of traffic accidents at three-way intersections. The research results show that two types of factors are related to the risk of traffic accidents at three-way intersections. The first type includes the width of the provincial road, the width of the shoulder, the width of the dividing line and the number of lanes, while the second type relates to the presence of convenience stores, gas stations, supermarkets, and other attractions, such as public retail markets at the intersection. Full article

The presence of cyclists on Spanish rural roads is ever increasing and currently frequent, and thus becoming a serious safety concern. In rural environments, the risk of a crash is higher than in rural areas. The main cause is the higher speed of motor vehicles during overtaking manoeuvres. This manoeuvre is especially challenging when cyclists ride in groups as they may change size, length, shape, and speed along their route. These variables and those related to road cross-section can influence driver behaviour when overtaking a group of cyclists. To study this, instrumented bicycles were used to ride along five road segments with different geometric and traffic characteristics. Cyclists rode individually and in groups. Overtaking was evaluated by analysing the lateral distance, the speed, and other characteristics of the manoeuvre. Wider roads presented higher lateral clearances and overtaking speeds. Narrower roads had a high opposing lane invasion but a high level of compliance with the minimum lateral clearance. A higher clearance and lower speed of overtaking vehicles was registered when cyclists rode in line. Compliance with the 1.5 m clearance depended on the group configuration, being higher when cyclists rode in line. However, overtaking cyclists riding two abreast presented more accelerative manoeuvres, especially on narrow roads. Full article