Urban Roadside Safety

In order to evaluate roadside crash severity and help making decision on roadside safety improvement alternatives, this article proposes a roadside crash severity evaluation method based on vehicle kinematics metric during the crash: Acceleration Severity Index. Based on the field investigation on 1917 km of representative roads, roadside crash test standards and parameters were determined. A total of 59 crash scenarios, involving 5 typical roadside obstacles, 2 types of guardrails, 15 embankment slopes, and 3 types of vehicles (car, bus, and truck), were designed for simulated crash testing with VPG3.2 and LS-DYNA971 software. The x-, y-, and z-direction acceleration (or deceleration) curves of a test vehicle’s center of mass during each crash test were collected for the calculation of the Acceleration Severity Index values. The Fisher optimal partition algorithm was used to cluster the Acceleration Severity Index values to identify an appropriate number of roadside crash severity levels and the corresponding threshold values that demarcate these levels. The results showed that the roadside crash severity classification produced by Acceleration Severity Index–based method is consistent with handbook Guideline for Implementation of Highway Safety Enhancement Project. Therefore, when crash data are missing, crash test could be a feasible surrogate method for roadside crash severity evaluation.

The article aims to examine how the armed conflict affects the environmental safety and resilience of the urban environment in Ukraine. The author analyzes the impact of infrastructural, social, economic, and environmental resistance on the city’s ability to maintain a socioeconomic and ecological balance and minimize the mass destruction of its infrastructure. The article employs general scientific and special methods, including theoretical generalization, comparison, systematization, abstract-logical, prognostic, logical, and analytical methods. The article relies on an integrated approach when assessing environmental safety and its preservation; it primarily involves implementing preventive measures necessary to ensure the viability and sustainability of urban areas in the conditions of war and after its end. The findings confirm that a close interaction between public authorities, scientific institutions, public organizations, and the population can help to achieve sustainable development and urban environment safety in Ukraine. It is suggested to focus on climate-neutral post-war recovery and strengthen the potential of territorial communities in further scientific research.

Driving is a critical everyday function that helps older adults remain independent. The Advanced Cognitive Training in Vital Elderly (ACTIVE) study has shown cognitive training improves driving safety outcomes for older adults. Few studies have examined the longitudinal effects of cognitive training on crash risk beyond 5 years. To address this empirical gap, we aimed to determine the effects of cognitive training on crash risk over 20 years, and how urbanicity moderates these relationships. Using participants from the no-contact control and intervention (speed of processing, memory, and reasoning training) groups in the ACTIVE study (n=2170) and state-recorded crash reports, cox proportional hazards models were used to examine the effect of three cognitive training interventions as predictors of time to first at-fault crash over 20 years. Participants (mean age 73, 73% White, 23% Male) reported driving 90.6 miles/week on average and 12.6% experienced an at-fault crash. Overall, speed of processing training was the only intervention related to lower crash risk, but this varied by urbanicity. Namely, speed of processing training was related to lower crash risk for individuals living in rural environments (HR=.16, 95% CI [.04-.73], p=.018) but not for individuals in urban environments (HR=.89, 95% CI [.54-1.50], p=.679). Findings highlight the safety benefits of speed of processing training for older drivers living in rural environments where more fatal crashes occur. However, interventions are still needed to improve safety in urban environments where more crashes occur generally. Consideration of location in future interventions may help improve safe driving for older adults.

The Arizona Department of Transportation (ADOT) recognizes that a new paradigm in highway safety evaluation was brought about by the advent of advanced technologies such as photo log, geographic information systems (GIS), and global-positioning satellite systems. Whereas these technologies are known to serve distinct singular purposes in a highway agency, ADOT has endeavored to explore the possibilities of integrating these technologies for the purpose of providing an all-encompassing perspective of crash history and roadside characteristics in a multimedia display of GIS maps and related photo imagery. The research provides the account of an analytic tool-development process aimed at improving the recognition of highway safety hazards. These hazards might otherwise be apparent if not for the relative complexity of existing relational databases and spatial GIS infrastructure at ADOT. Previous methods of mining data from the ADOT crash databases were limited in functionality as well as in reliability. By promoting the “visualization” of highway safety conditions, the advanced technologies open a wealth of new opportunities in identifying problematic roadside conditions and crash histories. This is expected to lead to an improved economy of implementing safety improvements that are designed properly to mitigate the “real” conditions that can be identified. The research is a companion to the larger, FHWA-sponsored research into establishing a corridor safety-improvement program for Arizona (FHWA Report FHWA-AZ 98-458).

This study addresses the critical issue of road safety in urban environments, with a specific focus on the Greater London Area. Utilizing a novel, theory-driven approach, the study investigates the multifaceted impact of urban fabric factors on road safety, operationalized through a severity-weighted index of road accident frequency per capita. Through factorial analysis, six key factors (Urban Integration, Socioeconomic Challenges, Urban Amenities, Commuter Patterns, Housing and Mobility Barriers, and Major Urban Infrastructure) are identified. These factors are examined in relation to road safety using a structural equation model to uncover theoretical relationships, which inform predictive modeling with an XGBoost machine learning framework, enhanced by SHAP value analysis. Our findings reveal significant insights into the interplay between urban physical and social environments and road safety, revealing that integrated urban development strategies—encompassing improved urban integration, enhanced sustainable density, robust infrastructure development, alleviation of socioeconomic disparities, fostering of local employment, and integration of residents with transportation—are imperative for increasing road safety.

More With the rise of AI-driven technologies, urban cycling is becoming more accessible and appealing to many due to benefits such as health improvement and cost efficiency. Governments worldwide are promoting cycling as a sustainable transportation option to address environmental challenges. Ensuring seamless bicycle mobility in cities is essential to incentivize cycling. AI-powered Traffic Engineering can significantly enhance the flow of bicycle traffic in urban areas by optimizing infrastructure and safety. This article explores the benefits of cycling and the rationale for investing in AI-integrated cycling infrastructure. It provides examples of smart solutions such as AI-based vehicle-cycle segregation (including London's Cycle Superhighways), protected intersections enhanced by machine learning algorithms, and Intelligent Transport Systems (ITS) that incorporate AI for dynamic traffic management. Their implementation and impact on cyclists and overall traffic flow are analyzed, demonstrating that these advanced systems reduce accidents, boost road efficiency, and make cycling more enjoyable. Quantitative data on these improvements is also presented. In conclusion, AI-enabled Traffic Engineering solutions play a vital role in enhancing bicycle mobility and safety in urban environments.

This paper presents an advanced approach for helmet detection at traffic signals using deep learning techniques, addressing the critical issue of motorcycle safety in urban environments. With the rising number of road accidents involving two-wheeled vehicles, effective monitoring of helmet usage is essential for enhancing compliance with safety regulations. We propose a convolutional neural network (CNN)-based model that analyses video feeds from traffic signals to detect and classify helmeted riders. Our methodology involves the collection and annotation of a diverse dataset that includes various riding scenarios, helmets, and lighting conditions to ensure robust model training. Leveraging transfer learning, we fine-tuned pre- trained models such as YOLOv5 and EfficientDet, achieving high accuracy in real-time detection. We also implemented data augmentation techniques to counteract potential overfitting and improve the model's generalization capabilities. The experimental results demonstrated that our helmet detection system achieves an impressive mean average precision (mAP) score, outperforming existing solutions in both speed and accuracy. Furthermore, we integrated this detection system into a prototype traffic monitoring framework, allowing for immediate feedback regarding helmet compliance at traffic signals. Our system not only enhances road safety by encouraging helmet use among riders but also contributes valuable data for traffic management authorities. By automating helmet detection, we facilitate proactive intervention strategies and support public safety initiatives. This work underscores the significance of applying deep learning in transportation safety measures and sets a foundation for further research in the realm of intelligent traffic systems. The implementation implications extend beyond just helmet monitoring, offering a scalable solution for various vehicular safety assessments that can potentially inform policy- making and enhance public health outcomes

Transportation safety is a highly contentious issue in the design of cities and communities. While urban designers, architects, and planners often encourage the use of aesthetic streetscape treatments to enhance the liability of urban streets, conventional transportation safety practice regards roadside features such as street trees as fixed-object hazards and strongly discourages their use. In this study, I examine the subject of livable streetscape treatments and find compelling evidence that suggests they may actually enhance the safety of urban roadways. Concerns about their safety effects do not appear to be founded on empirical observations of crash performance, but instead on a design philosophy that discounts the important relationship between driver behavior and safety. This study traces the origin and evolution of this philosophy, and proposes an alternative that may better account for the dynamic relationships between road design, driver behavior, and transportation safety. It has taken many decades for roadway designers to begin to recognize that how road and road environments are designed affects safety, and to identify what particular features enhance or detract from safety in a given environment. This process is still evolving. Historically, there has been a tendency for those funding research to focus on rural or high-speed environments, and on pavements and structures. One outcome of the resulting underemphasis on safety and urban design concerns, as author Eric Dumbaugh identified in this article, is the problem with the published research about urban roadside design in the U.S.: it is limited in both scope and quantity. With these limitations, it is understandable that city street designers extrapolate from principles learned in a rural highway environment. They may not be standing on the firmest ground when they do this, but they judge that it is the best ground they have.

This paper presents traffic speed models developed on the basis of numerous measurements. In studies of the speed–flow relationships the following research issues were analyzed: impacts of the speed limit, traffic flow, traffic flow composition, and time of day on speed–flow and impacts of qualitative road and roadside features (cross sections, road function, accessibility, intensity of development, roadside pedestrian traffic) on free-flow speed. The research included road sections with numerous constraints of traffic freedom. Such sections include sections of roads through built-up areas (small communities and towns). Regression analysis using generalized multiple regression was the basic tool in investigations. In addition, in analyzing data from speed measurements, the classic form of the logit function for a multiple-valued regression was applied. The analyses showed that even at general and posted speed limits the characteristic traffic flow–speed relations can be identified. The shape of these relations is characteristic of empirical results in which the spread of recorded values is typical for randomness and impacts of local factors. Average speed in traffic flow depends not only on geometric alignment of a road cross section but also on local factors and speed limits. Speed in free-flow traffic is significantly influenced by qualitative road and roadside characteristics. Most important of these factors are type of cross section, intensity of roadside development, and accessibility of the road.

This paper examines the operational effects of converting a 14-ft-wide curb lane to an 11-ft-wide travel lane with a 3-ft-wide undesignated lane at various locations in Broward County, Florida. Six midblock sites with various configurations were selected for study. Two of the midblock sites had previously been striped with the 3-ft undesignated lane, and these served as comparison sites. Videotapes were taken of bicyclists riding through the midblock and intersection locations before and after placement of the 3-ft undesignated lane striping. At the locations where the 3-ft stripe was already in place, the videotaping was done to examine whether changes were occurring over time. Software was used to extract images at all midblock locations so that before and after lateral spacing measurements could be obtained. After the new striping, (a) bicycles were ridden, on average, 7 to 9 in. further away from the gutter pan seam; (b) motor vehicles were driven, on average, 6 to 12 in. farther away from the gutter pan seam; (c) passing motor vehicles were driven, on average, 3 to 5 in. closer to bicycles at curb and gutter sites; conversely, passing motor vehicles were driven, on average, 4 to 5 in. farther away from bicycles at the sites where the stripe was already in place; and (d) the addition of the stripe at new locations had the effect of reducing the amount of motor vehicle encroachment into the adjacent lane on these multilane roadways.

Crash cushions vary in geometry and cost. In this study, crash cushions were categorized in three different categories: redirecting with repair costs greater than $1,000 (RGM), redirecting with repair costs less than $1,000 (RLM), and nonredirecting sacrificial (NRS). Typically, RGM systems are less expensive initially, but life-cycle costs are high. RLM systems typically reciprocate this trend. NRS crash cushions (e.g., sand barrels) are generally less expensive but require total replacement after a crash has occurred, which may be impractical at high-traffic volume locations. Due to limited funding, there is often a need to identify the most cost-effective crash cushion category for highway scenarios with different roadway, traffic, and roadside characteristics. This study was commissioned to determine benefit-cost ratios for each crash cushion category in a wide range of roadway and roadside characteristics using the probability-based encroachment tool, Roadside Safety Analysis Program. Only RGM and RLM systems were cost-effective for freeways and divided rural arterials, but all three categories competed against the unprotected condition on undivided rural arterials and local roads.

Speeding is an important factor in traffic safety. Speed not only affects crash severity, but is also related to the possibility of crash occurrence. This study presents results from an analysis of 27,968 speed violation cases collected from 36 red light and speed enforcement cameras at signalized intersections in the city of Cheongju. Data included details of their violation history such as speeding tickets within a recent 3-year span and their demographic characteristics. The goal of this analysis is to understand the correlation between speed violations and various factors in terms of humans, vehicles and road environments. This study used descriptive statistics and Binary Logistics Regression(BLR) analysis with SPSS 20.0 software. The major results of this study are as follows. First, speed violations occurred at rural and suburban area. Second, about 25.6% of the violators committed to more than 20km/h over a speed limit. Third, the difference between speed violators and normal drivers clearly appeared in location of intersection(urban/rural/suburban area), gender and age. Finally, a statistically significant model(Hosmer and Lemeshow test: 11.586, p-value: 0.171) was developed through the BLR.Keywords: binary logistic regression(BLR), red light and speed enforcement camera, signalized Intersection, traffic safety

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.

Rapid urbanization in developing countries has increased pressure on urban land resources, often resulting in incompatible land-use patterns and heightened environmental safety risks. One manifestation of this challenge is the proliferation of fuel stations within residential neighbourhoods. Although fuel stations are essential for urban transportation and economic activity, their close proximity to homes, schools, and markets raises concerns about environmental safety, planning compliance, and public health. This study evaluates the spatial compatibility and urban safety implications of fuel station locations within residential areas of Port Harcourt Municipality using Geographic Information Systems (GIS). Spatial data used in the analysis include GPS-surveyed fuel station coordinates, building footprints, road networks, and land-use data obtained from Nigerian Midstream and Downstream Regulatory Authority (NMDRA), OpenStreetMap and field verification. Spatial mapping, nearest neighbour analysis, buffer analysis, and proximity modelling were applied to examine the distribution of fuel stations and their relationship with nearby residential buildings and other sensitive land uses. The results reveal a clustered spatial pattern, with fuel stations concentrated along major transportation corridors and mixed residential-commercial zones. Buffer analysis shows that 357 residential buildings are located within 50 m of fuel stations, 1,043 within 100 m, and 3,068 within 200 m, exposing a significant number of residents to hazards such as fire outbreaks, explosions, air pollution, and traffic accidents. The findings highlight weak enforcement of development control regulations and emphasize the need for GIS-based monitoring systems and risk-sensitive urban planning.

In order to solve the problem of vehicle speed planning that meets the constraints of safety and efficiency in complex road environments (large curvature and low adhesion), a differential equation planning method based on vehicle dynamics analysis is proposed. First, the structural parameter expression of the limit speed that meets the lateral tire force constraint during steady-state steering is derived. Secondly, the tire force execution space of the front and rear wheels is combined into an FF diagram, and an implicit differential equation that considers load transfer and driving mode factors is obtained. The differential equation is solved to obtain the limit speed along the path, and a limit speed calculation method for discrete path information is given. Finally, a lateral and longitudinal collaborative model predictive controller is designed, and a CarSim and Simulink joint simulation platform is built. Trajectory tracking simulation experiments are carried out on both continuous and discrete information paths with the planned limit speed. The results show that the limit speed planning method proposed in this paper can complete the trajectory tracking task in complex road environments as quickly as possible while controlling the tire force within the range of the stable friction circle.