Search Results (63)

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

Urban population growth and expanding economic activity have intensified the demand on transportation networks, resulting in higher traffic volumes, increased spillbacks, and a declining level of service (LOS). Signalized intersections, as critical components, play a vital role in managing urban congestion. This study examines a heavily congested intersection in Miami, Florida, using Highway Capacity Software (HCS7) to assess operational performance and test improvement strategies. The baseline analysis revealed excessive delays, severe queue spillbacks, and LOS F during the PM peak period. Two data-driven scenarios were evaluated: (1) signal timing optimization, and (2) a combined approach involving both optimized timing and a proposed grade-separated pedestrian bridge. Scenario 2 achieved the most significant performance gains by reducing average delays by approximately 53% and improving the intersection’s LOS from F to E. Beyond operational benefits, the pedestrian bridge is supported by crash reduction evidence (CMF), complies with Americans with Disabilities Act (ADA) standards, and promotes long-term urban sustainability. The study’s methodology offers transferable insights for similar urban intersections facing high demand and multimodal conflict. Full article

Cities are increasingly integrating micromobility, which heightens the need for robust analytical methods to identify high-risk intersections. This study presents a three-stage methodological approach that combines six years of accident data, spatial hotspot analysis, and calibrated floating-car traffic data to estimate exposure and calculate intersection crash rates in Central Vilnius. Testing the proposed approach identified eight high-risk intersections, with intersection crash rates (ICR) ranging from 0.044 to 0.151, indicating substantial differences in exposure-adjusted risk across the network. The validation of floating-car data (FCD) produced a determination coefficient (R²) of 0.87, confirming reliable exposure estimates where traditional traffic counts are not available. One selected intersection was analyzed in greater depth using drone-based observations and conflict assessment, leading to two redesign alternatives. Both reduced conflicts, though the signalized option eliminated uncontrolled conflict points and offered the strongest expected safety improvement. The suggested methodological approach demonstrates how integrating accident data, exposure estimation, and behavioral analysis can support evidence-based scalable interventions to improve micromobility safety. Despite certain limitations, it enables the rapid identification of problematic intersections, provides site-specific safety diagnosis, and facilitates the development of data-driven design improvements to enhance the safety of micromobility users. As the world strives to shift towards greater sustainability, the concept of micromobility, defined as the use of lightweight, short-distance modes of transport, has gained growing attention among users and policymakers. Full article

Urban intersections are critical for pedestrian safety, as they usually account for high rates of traffic-related injury and fatalities. This study assesses smart crosswalks as an alternative approach to improve road safety that is inherently aligned with the development of intelligent transportation system technology. After a brief background on this technological advance, this study proceeds with the analysis of long-term crash records from Greek urban roads, concentrating on pedestrians’ behavior in incidents involving road crossing. Thereafter, challenges related to the adoption of an implementation framework are identified. The results confirmed the vulnerability of pedestrians, especially during cases with no specific crossing areas, based on a considerable number of available recorded crashes from a publicly available Greek database. Substantial reductions over the analysis period (i.e., years 2005–2022) in pedestrian-based incidents with injuries and fatalities at a rate of 44% and 52%, respectively, provide evidence-based insights that infrastructural interventions like improved crosswalk design can be translated into measurable benefits for pedestrian safety. Key factors toward a wider applicability framework for even safer interventions through smart crosswalks include maintenance strategies, user education, and systematic integration of funding into urban mobility plans. Full article

Intersections represent critical crash locations on road networks necessitating targeted safety interventions. This study employs a random parameters ordered logit (RPOL) model with heterogeneity in means to analyze injury severity contributing factors across 9108 Wyoming intersection crashes that occurred from 2007 to 2017. The analysis reveals that crashes on principal and minor arterial intersections are consistently associated with higher risks of severe/fatal injuries, while urban intersections exhibit less severe consequences, likely due to lower speeds and enhanced infrastructure. Adverse weather conditions, particularly snowy and icy road surfaces, increase the likelihood of property-damage-only (PDO) outcomes while reducing severe/fatal injuries. Temporal trends show a decline in crash severity over time, coinciding with advances in vehicle safety and policy improvements. Key behavioral factors, including left turn maneuvers and driver’s age heterogeneity, influence crash outcomes, whereas intersection sight distance (ISD) had no significant effect on crash severity underscoring data limitations requiring advanced analysis methods. This study’s findings prioritize the reconsideration of arterial intersection design, urban safety enhancements, and behavior-focused countermeasures for intersection safety. Full article

Understanding vehicle acceleration behavior during intersection departures is critical for advancing traffic safety, sustainable mobility, and intelligent transport systems. This study presents a high-resolution kinematic analysis of 714 vehicle departures from signalized intersections, encompassing straight crossings, left turns, and right turns, and involving a diverse sample of internal combustion engine (ICE), hybrid electric (HEV), and battery electric vehicles (BEV). Using synchronized Micro Electro-Mechanical Systems (MEMS) accelerometers and Real-Time Kinematic (RTK)-GPS systems, the study captures longitudinal acceleration and velocity profiles over fixed distances. Results indicate that BEVs exhibit significantly higher acceleration and final speeds than ICE and HEV vehicles, particularly during straight crossings and longer left-turn maneuvers. Several mathematical models—including polynomial, arctangent, and Akçelik functions—were calibrated to describe acceleration and velocity dynamics. Findings contribute by modeling jerk and delay propagation, supporting better calibration of AV acceleration profiles and the optimization of intersection control strategies. Moreover, the study provides validated acceleration benchmarks that enhance the accuracy of forensic engineering and road accident reconstruction, particularly in scenarios involving intersection dynamics, and demonstrates that BEVs accelerate more rapidly than ICE and HEV vehicles, especially in straight crossings, with direct implications for traffic simulation, ADAS calibration, and urban crash analysis. Full article

High Friction Surface Treatments (HFSTs) are frequently used to increase skid resistance and reduce collisions, particularly in crash-prone zones, including horizontal curves and intersections. Epoxy-based binders traditionally have been the sole option for HFSTs, but their drawbacks, such as high costs and compatibility challenges, have led to the search for substitute binders, including asphalt-based options. This study investigates the comparative performance of highly modified asphalt-based binders, including polymer-modified, mastic, and highly modified emulsions, in HFST applications using two aggregate types, Calcined Bauxite (CB) and Rhyolite with different gradations, with an emphasis on their frictional properties, durability, and resistance to polishing. Laboratory evaluations, including the Pendulum Tester (BPT), Dynamic Friction Testing Equipment (DFT), Surface Texture Measurement Apparatus (CTM), and Binder Bond Strength Test (BBS), were carried out to examine the Coefficient of Friction (COF), Mean Profile Depth (MPD), and aggregate bonding and retention. In terms of durability and friction, this study indicated that highly modified asphalt-based binders performed better than PG binders and conventional emulsions. The highest BPT values, both prior to and following polishing, were consistently observed for CB, with the emulsion containing the highest reactive polymer modifier showing the smallest decrease in BPT value (12.86% for CB and 10.34% for Rhyolite). Epoxy showed a greater COF retention over lengthy polishing cycles; however, highly polymer-modified (PM) binders like PG82-22 (PM) performed better than Epoxy under specific conditions. The macrotexture analysis revealed that Epoxy-based samples retained surface texture for further polishing cycles, while Mastic2 and PG82-22 (PM) also showed strong MPD retention. These findings highlight the importance of optimizing aggregate–binder combinations to ensure durable and effective HFST applications. Full article

Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian crash data are indispensable for safety research, as the most detailed descriptions of crash scenes and pedestrian actions are typically found in crash narratives and diagrams. However, extracting and analyzing this information from police crash reports poses significant challenges. This study tackles these issues by introducing innovative image-processing techniques to analyze crash diagrams. By employing cutting-edge technological methods, the research aims to uncover and extract hidden features from pedestrian crash data in Michigan, thereby enhancing the understanding and prevention of such incidents. This study evaluates the effectiveness of three Convolutional Neural Network (CNN) architectures—VGG-19, AlexNet, and ResNet-50—in classifying multiple hidden features in pedestrian crash diagrams. These features include intersection type (three-leg or four-leg), road type (divided or undivided), the presence of marked crosswalk (yes or no), intersection angle (skewed or unskewed), the presence of Michigan left turn (yes or no), and the presence of nearby residentials (yes or no). The research utilizes the 2020–2023 Michigan UD-10 pedestrian crash reports, comprising 5437 pedestrian crash diagrams for large urbanized areas and 609 for rural areas. The CNNs underwent comprehensive evaluation using various metrics, including accuracy and F1-score, to assess their capacity for reliably classifying multiple pedestrian crash features. The results reveal that AlexNet consistently surpasses other models, attaining the highest accuracy and F1-score. This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. These outcomes are critical for minimizing errors in image classification, especially in transportation safety studies. In addition to evaluating model performance, computational efficiency was also considered. In this regard, AlexNet emerged as the most efficient model. This understanding is precious in situations where there are limitations on computing resources. This study contributes novel insights to pedestrian safety research by leveraging image processing technology, and highlights CNNs’ potential use in detecting concealed pedestrian crash patterns. The results lay the groundwork for future research, and offer promise in supporting safety initiatives and facilitating countermeasures’ development for researchers, planners, engineers, and agencies. Full article

The use of traffic simulation to analyze traffic safety and performance has become common in transportation engineering. Microsimulation methods are increasingly used to analyze driving performance for different road geometries and environmental elements. Drivers’ perception has an important impact on driving performance factors contributing to traffic safety on transportation facilities (highways, arterials, intersections, etc.). Impaired vision leads to failure in drivers’ perception and making right decisions. Various studies investigated the impact of environmental elements (fog, rain, snow, etc.) on driving performance. However, there is limited research examining the potentially detrimental effects on driving capabilities due to differing exposure to natural light brightness, in particular sun exposure. Autonomous vehicles (AVs) showed a significant impact enhancing traffic capacity and improving safety margins in car-following models. AVs may also enhance and/or complement human driving under deteriorated driving conditions such as sun glare. This study uses a calibrated traffic simulation and surrogate safety assessment model to improve traffic operations and safety performance under impaired visibility using different types of autonomous vehicles. A combination of visibility reduction, traffic flow characteristics, and autonomy levels of AVs was simulated and assessed in terms of the number of conflicts, severity level, and traffic operations. The simulation analysis results used to reveal the contribution of conflicts to the risk of crashes varied based on the influence of autonomy level on safe driving during sun glare exposure. The outcome of this study indicates the benefits of using different levels of AVs as a solution to driving under vision impairment situations that researchers, traffic engineers, and policy makers can use to enhance traffic operation and road safety in urban areas. Full article

Rollover crashes are among the most prevalent types of accidents in developing countries. Various factors may contribute to the occurrence of rollover crashes. However, limited studies have simultaneously investigated both traffic stream and road-related variables. For instance, the effects of T-intersection density, U-turns, roadside parking lots, the entry and exit ramps of side roads, as well as traffic stream characteristics (e.g., standard deviation of vehicle speeds, speed violations, presence or absence of speed cameras, and road surface deterioration) have not been thoroughly explored in previous research. Additionally, the simultaneous modeling of crash frequency and intensity remains underexplored. This study examines single-vehicle rollover crashes in Yazd Province, located in central Iran, as a case study and simultaneously evaluates all the variables. A dataset comprising three years of crash data (2015–2017) was collected and analyzed. A crash index was developed based on the weight of crash intensity, road type, road length (as dependent variables), and road infrastructure and traffic stream properties (as independent variables). Initially, the dataset was refined to determine the significance of explanatory variables on the crash index. Correlation analysis was conducted to assess the linear independence between variable pairs using the variance inflation factor (VIF). Subsequently, various models were compared based on goodness of fit (GOF) indicators and odds ratio (OR) calculations. The results indicated that among ten crash modeling techniques, namely, Poisson, negative binomial (NB), zero-truncated Poisson (ZTP), zero-truncated negative binomial (ZTNB), zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), fixed-effect Poisson (FEP), fixed-effect negative binomial (FENB), random-effect Poisson (REP), and random-effect negative binomial (RENB), the FENB model outperformed the others. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) values for the FENB model were 1305.7 and 1393.6, respectively, demonstrating its superior performance. The findings revealed a declining trend in the frequency and severity of rollover crashes. Full article

Speeding is one of the main contributing factors to road crashes and their severity; therefore, this study aims to investigate the complex dynamics of speeding and uses a multivariable analysis framework to explore the diverse factors contributing to exceeding vehicle speeds on rural roads. The analysis encompasses diverse measured variables from Croatia’s secondary road network, including time of day and supplementary data such as average summer daily traffic, roadside characteristics, and settlement location. Measuring locations had varying speed limits ranging from 50 km/h to 90 km/h, with traffic volumes from very low to very high. In this study, modeling of influencing factors on speeding was carried out using conventional and more advanced methods with speeding as a binary dependent variable. Although all models showed accuracy above 74%, their sensitivity (predicting positive cases) was greater than specificity (predicting negative cases). The most significant factors across the models included the speed limit, distance to the nearest intersection, roadway width, and traffic load. The findings highlight the relationship between the variables and speeding cases, providing valuable insights for policymakers and law enforcement in developing measures to improve road safety by determining locations where speeding is expected and planning further measures to reduce the frequency of speeding vehicles. Full article

The increasing presence of autonomous vehicles (AVs) in transportation, driven by advances in AI and robotics, requires a strong focus on safety in mixed-traffic environments to promote sustainable transportation systems. This study analyzes AV crashes in California using advanced machine learning to identify patterns among various crash factors. The main objective is to explore AV crash mechanisms by extracting association rules and developing a decision tree model to understand interactions between pre-crash conditions, driving states, crash types, severity, locations, and other variables. A multi-faceted approach, including statistical analysis, data mining, and machine learning, was used to model crash types. The SMOTE method addressed data imbalance, with models like CART, Apriori, RF, XGB, SHAP, and Pearson’s test applied for analysis. Findings reveal that rear-end crashes are the most common, making up over 50% of incidents. Side crashes at night are also frequent, while angular and head-on crashes tend to be more severe. The study identifies high-risk locations, such as complex unsignalized intersections, and highlights the need for improved AV sensor technology, AV–infrastructure coordination, and driver training. Technological advancements like V2V and V2I communication are suggested to significantly reduce the number and severity of specific types of crashes, thereby enhancing the overall safety and sustainability of transportation systems. Full article

In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer’s own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers’ speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals. Full article

With the acceleration of population aging, the elderly driving safety issue is increasingly prominent. Method: With the crash data of Pennsylvania from 2010 to 2019, this study exclusively discusses features of single-vehicle hit-fixed-object crashes (SVHFOCs), one of the most common and deadliest crash types for elderly drivers. Results: Firstly, we demonstrate that elderly drivers are much more likely to be injured and killed than young drivers in SVHFOCs by checking crash consequences. The descriptive analysis indicates that elderly drivers have very different crash features from young drivers. They are found to drive with more caution in many aspects, such as more low-speed local travels, fewer illegal behaviors, fewer nighttime travels, etc. Then, a logistic regression model is built to find the factors significantly influencing the severity of SVHFOCs from driver, vehicle, roadway, and environment. The estimation results indicate that female sex, not wearing a seatbelt, DUI, rural area, and SUV involvement tend to be associated with more severe SVHFOCs. Additionally, illumination, weather, and road type could also significantly affect crash severity. Especially, SVHFOCs in adverse weather, in dark conditions, and at intersections are found to be less severe, which implies that elderly drivers might drive more carefully in complex environments. Practical Applications: These findings are expected to provide new insights for agencies in formulating customized measures to prevent elderly drivers from being involved in SVHFOCs. Full article

Intersection safety has been traditionally evaluated using three to five years of crash data. Recent literature suggests that connected vehicle (CV)-derived hard braking (HB) events can provide a surrogate for crashes with only a few weeks or months of data collection. This study used CV trajectories to derive HB events. Then, the HB events were normalized as the ratio of HB events to sampled CV trajectories. The normalized HB ratios were evaluated and compared at 435 signalized intersections, roundabouts, and all-way stops in Indiana. The analysis showed that signalized intersections and roundabouts had the highest counts of HB events, and all-way stops had the highest HB ratios. Through movements at signalized intersections showed the lowest HB ratios, whereas left turns at all-way stops had the highest ratios. A density analysis of the geospatial occurrence of HB events concluded that they tend to occur closest to the intersection center at all-way stops, but are more evenly distributed at signalized intersections. Additionally, a speed analysis indicated that HB events at signalized intersection through movements tend to occur at higher speeds, roughly between 26 and 36 MPH, perhaps due to the driver reaction during the onset of yellow. The findings presented in this study provide transportation agencies with insights on the occurrence of normalized HB ratios at three different intersection types. The data provided in this paper provide a framework for agencies to use HB ratios to screen different types of intersections for further evaluation. Full article