Self-explaining Roads Research Articles

Self-Explaining Roads: Effects of road design on speed choice

One of the leading principles for road design is the notion of Self-Explaining Roads(SER). According to this notion, the design and layout of the road environment should automatically elicit behavior that is appropriate for that type of road. The current study investigated the effects of the road environment (i.e., the presence of particular road elements such as the presence/absence of edge lines, and a physical separation between driving lanes) on driving speed choice in the Netherlands for roads in- and outside the city limits (ICL and OCL). A total of 462 participants (all car drivers) were exposed to either 152 ICL or 152 OCL pictures of road environments for either a short (300 ms in Experiment 1, 200 ms in Experiment 2) or long duration (1500 ms) and indicated as fast as possible which speed they would drive given the road environment they viewed. All images were labeled with respect to the presence or absence of particular road elements. A linear mixed model was used to determine the effects of road elements on speed choice and response time. The results showed that the presence of certain road elements impacted the speed chosen. For example, inside the city limits, relative to no bicycle lane, a bicycle lane painted on the road reduced driving speed while a separate bike lane increased the speed chosen. Also, central line markings (relative to no line marking) and a road made of asphalt (relative to pavers) were associated with higher speeds. Outside city limits, having multiple lanes (versus one lane) was related to higher speeds and having two driving directions without separation (versus a one-way road) was related to lower speed choices. Importantly, exposure duration (200 /300 ms versus 1500 ms) only had a marginal effect, indicating that road users generally only need a brief glimpse of the road to be able to decide what speed to drive. Consistent with the principles of SER, we conclude that categorization and the associated speed decision is fast, operating within a single glance and is impacted by the road elements present in the environment. Finally, we believe our method constitutes a valuable tool in road design, as it allows one to efficiently and effectively gauge the impact of various road elements in large population samples.

Towards a safe system in low- and middle-income countries: vehicles that guide drivers on self-explaining roads

Road crashes cause a huge problem of public health in low- and middle-income countries (LMICs). The Safe System approach is generally considered as the leading concept on the way to road safety. Based on the fundamental premise that humans make mistakes, the overall traffic system should be ‘forgiving’. Sustainable safe road design is one of the key elements of the Safe System approach. Road design and speed control should help prevent crashes with a high level of kinetic energy. However, the road design principles behind the Safe System approach are certainly not leading in today’s infrastructure developments in most LMICs. Cities are getting larger with increasing motorization and expanding road networks. Existing through-roads in local communities are upgraded, resulting in heavy traffic loads and high speeds on places, that are absolutely not suited for this kind of traffic. Furthermore, a Safe System would require that functional design properties of vehicles and roads would be conceptually integrated, which is not the case at all. Although advanced driver assistance systems are on their way of development for quite a long period, their potential role in the Safe System concept is mostly unclear and at least strongly underexposed. The vision on future cars is dominated by the faraway concept of automation. This paper argues that the way to self-driving cars should take a route via the concept of guidance, i.e. vehicles that guide drivers, both on self-explaining roads and on more or less unsafe roads. Such an in-vehicle guidance system may help drivers to choose safe transport mode, a safe route and a safe speed, based on criteria related to safety and sustainability. It is suggested to develop driver assistance systems using relatively simple and cheap technologies, particularly for the purpose of use in LMICs. Such a guide may make roads self-explaining—not only by their physical design characteristics—but also by providing in-car guidance for drivers. In the future, the functional characteristics of both cars and roads may be conceptualized into one integrated Safe System, in which the user plays the central role. Such a guidance system may serve as the conceptual bridge between the roadway, the vehicle and the driver, and thus be considered as an indispensable component of the Safe System approach. It is argued that such a development is necessary to bring a breakthrough in road safety developments in LMICs and also give an acceleration towards zero fatalities in high-income countries.

Predicting perceived risk of traffic scenes using computer vision

Perceived risk, or subjective risk, is an important concept in the field of traffic psychology and automated driving. In this paper, we investigate whether perceived risk in images of traffic scenes can be predicted from computer vision features that may also be used by automated vehicles (AVs). We conducted an international crowdsourcing study with 1378 participants, who rated the perceived risk of 100 randomly selected dashcam images on German roads. The population-level perceived risk was found to be statistically reliable, with a split-half reliability of 0.98. We used linear regression analysis to predict (r = 0.62) perceived risk from two features obtained with the YOLOv4 computer vision algorithm: the number of people in the scene and the mean size of the bounding boxes surrounding other road users. When the ego-vehicle’s speed was added as a predictor variable, the prediction strength increased to r = 0.75. Interestingly, the sign of the speed prediction was negative, indicating that a higher vehicle speed was associated with a lower perceived risk. This finding aligns with the principle of self-explaining roads. Our results suggest that computer-vision features and vehicle speed contribute to an accurate prediction of population subjective risk, outperforming the ratings provided by individual participants (mean r = 0.41). These findings may have implications for AV development and the modeling of psychological constructs in traffic psychology.

How does street space influence crash frequency? An analysis using segmented street view imagery

Road crashes in metropolitan areas are challenging to prevent because they stem from the interactions of drivers and other system users in intricate built environments. Recent theories indicate that features of the built environment may induce unsafe driving by shaping users’ expectations and behaviors. The availability of street view imagery and methods of scene parsing create new possibilities for understanding how features of the built environment influence crash incidence. Most previous crash research using street imagery has applied manual processing methods. In this paper, we develop and apply automated machine parsed imagery in conjunction with self-explaining roads theory to consider how the street space visible to drivers influences crash frequency, using data from Columbus, Ohio, USA. While controlling for road network and area characteristics, we model the association of individual street elements with crash frequency. We then conduct a cluster analysis to define four types of street spaces, which are used in a subsequent model. We find that an Open Road type of metropolitan street space, characterized by more visible sky, roadway, and signage are associated with the greatest increase in crashes, and that the majority of these spaces exist on arterial or collector class road segments. We theorize that the visual similarity of this type of street space to highways promotes faster, less careful driving, which combines with their mixed land uses to make them the least safe. This points to the importance of traffic calming for such roads in high-activity areas, and the need to differentiate environments of non-highways from highways to promote careful driving.

Road Infrastructure Deficiency and Road Safety Audit at Black Spot Area in Rural Road

Abstract One of the factors that caused traffic accidents is road infrastructure. Strategies to minimize the road deficiency infrastructure is implementation of the forgiving road, self-explaining road, and self-regulating road. The purpose of this study was analyzing the deficiency of road infrastructure and implementing the road safety audit at black spot areas in rural areas. Study location in Bojongsari Street, Purbalingga. Parameters that are analyzed in deficiency of road infrastructure include the road geometric condition, road pavement performance, and road equipment facilities. Parameters that analyzed in road geometric condition, namely stopping and passing sight distance, lane width of road, and road shoulder width. The results of this study show that the highest passing and stopping sight distance of all vehicles was motorcycles at 304.48 m and 64.67 m. The road pavement performance based on pavement condition index for the longitudinal/transverse cracking conditions is 94 and for the lane or shoulder drop-off condition is 77. Recommendations given based on road safety audit results for road geometric is to improve the road shoulder, for pavement condition improves shoulder drop-off and longitudinal/transverse cracking. For the road equipment facilities are to install the maximum speed limit sign of 40 km/h and install the warning sign.

How fast would you (or should you) drive here? Investigation of relationships between official speed limit, perceived speed limit, and preferred speed

Speed is a critical risk factor, which makes its management the central point of the Vision Zero approach. Driving speed is influenced by speed choice, and in turn by the perception of the road parameters, as well as by the characteristics of drivers. Credible speed limits and self-explaining roads have been suggested as promising countermeasures; however, these rather theoretical concepts have only rarely been operationalized and quantified.The aim of this study was to investigate the relationships between speed indicators (official speed limit, perceived speed limit, preferred speed), as well as their differences, and to determine which road and personality characteristics influence them. Compared to previous studies, we introduced several innovative features, including a richer personality dataset based on three different questionnaires, a focus on all three types of roads (urban, rural, transition), and representative data on observed speeds.Using statistical models of the speed indicators, we found that both speed limit belief and speed choice, as well as their differences, are often influenced by the same characteristics. These are mainly more generous road design (higher road class, higher speed limit, and wider road) and the presence of additional elements (vegetation, pavements, and pedestrian crossings). These characteristics may help improve the credibility of speed limits and self-explaining performance.

Quantifying the Effects of Visual Road Information on Drivers' Speed Choices to Promote Self-Explaining Roads.

Roads should deliver appropriate information to drivers and thus induce safer driving behavior. This concept is also known as “self-explaining roads” (SERs). Previous studies have demonstrated that understanding how road characteristics affect drivers’ speed choices is the key to SERs. Thus, in order to reduce traffic casualties via engineering methods, this study aimed to establish a speed decision model based on visual road information and to propose an innovative method of SER design. It was assumed that driving speed is determined by road geometry and modified by the environment. Lane fitting and image semantic segmentation techniques were used to extract road features. Field experiments were conducted in Tibet, China, and 1375 typical road scenarios were picked out. By controlling variables, the driving speed stimulated by each piece of information was evaluated. Prediction models for geometry-determined speed and environment-modified speed were built using the random forest algorithm and convolutional neural network. Results showed that the curvature of the right boundary in “near scene” and “middle scene”, and the density of roadside greenery and residences play an important role in regulating driving speed. The findings of this research could provide qualitative and quantitative suggestions for the optimization of road design that would guide drivers to choose more reasonable driving speeds.

Road Safety Audit and Comparison with Accident Case Studies

Objective: Road Safety Audit (RSA) gives scope for the reduction of accidents and helps us to provide safe, self-explaining and efficient roads. Analysis: The road safety audit was done for a stretch of 10 km from Kelambakkam Junction to VIT Chennai located at Kelambakkam to Vandalur Road- SH 121 in India. In this Road safety audit, the accident analysis was done by considering geometric features of the road, pedestrian safety, and accident-prone locations. The accident data for past 4 years 2015 – 2018 was considered and compared the RSA data with various identified accident case studies in that stretch. Findings: After conducting RSA, it was found that the effective width of carriageway was reduced by the trucks, which were parked on highway, and creating traffic hindrance to high speed moving vehicles. Numbers of street lights are not sufficient and most of the Street lights are not working properly throughout the stretch. Median openings were found at unauthorized places which should be immediately closed, Access and service lanes are also deficient which requires immediate improvement. Growth of uncontrolled vegetation on either side of the road should be removed. Road and median markings which are found missing should be corrected and speed signs should be indicated at various places. This study will be more useful if the RSA data would be updated, and the reason behind each accident, are recorded and corrective measures taken. Application: This research can be used by the government officials to monitor and alter the road conditions as per IRC codes. It aids in rectifying and reducing the future accidents. Keywords: Accident Analysis, Data Improvement

Effect of novel divergence markings on conflict prevention regarding motorcycle-involved right turn accidents of mixed traffic flow

Introduction: In Taiwan, segregated traffic flow countermeasures have long been in place. Although these facilities have decreased the numbers of motorcycle left-turn collisions, right-angle collisions, and sideswipe collisions, they have also induced serious right-turn accidents. The purpose of this research was to evaluate an intervention intended to decrease conflicts and motorcycle-involved crashes. In this study, the reasons why the motorcycle accident rate is higher at intersections with slow lanes than at those without slow lanes are presented, and the theory of the self-explaining road was applied to create divergence markings for a mixed traffic flow environment. An intervention that guides motorcycles and cars into appropriate locations at intersections was applied to three intersection approaches. Method: The intervention effectiveness was evaluated by comparing the number of accidents at the intersections before and after the implementation of improvement measures. Moreover, video recordings were used to analyze the traffic distributions at the cross-sections of intersections. T-test was adopted to examine whether the traffic flows at the cross-sections of the intersections before and after the intervention were statistically different. In addition, this research applied the post-encroachment time (PET), the time between the first road user leaving the encroachment zone and the second road user arriving in it, to evaluate traffic conflicts. Finally, the PET and severity index between a straight-through motorcycle and a right-turn vehicle were analyzed. Results: PET increased by 3.2%–20.4%, and the rates of right-turn collisions, sideswipe collisions, and rear-end collisions decreased by 64.3%, 77.3%, and 61.5% respectively. Conclusions: Eliminating the slow traffic lane and setting divergence markings may not effectively cause vehicles in different driving directions to drive in the proper locations in the lanes. However, divergence markings both reduce the rate of right-turn collisions and decrease the incidence of sideswipe and rear-end collisions. Practical applications: The proposed design method may be a good design reference for countries having a high motorcycle density.

Children's perceptions of a 'self-explaining road' intervention to improve neighbourhood safety.

Safe neighbourhood environments can provide important spaces for child activity and well-being. Self-explaining roads (SERs), which reduce vehicle speeds by changing neighbourhood street design, are an intervention with known safety benefits. However, little is known about children's experiences of SERs. We engaged schoolchildren in developing a project to articulate children's perspectives of SERs. Students actively researched the experiences of other children at their school, and then participated in a researcher-led focus group. Children emphasised safer spaces for play as an important benefit of SERs. As well as using new designated play spaces adjoining footpaths, children also felt the roadway became safer, and more fun for cycling. In contrast, children reported that parents held mainly negative views of SERs, considering them annoying and inconvenient. From children's perspective, SERs can provide safe spaces for neighbourhood play, despite limited parental support. Potential benefits for children as well as established safety benefits should inform future implementation of SERs.

Improving the Self-Explaining Performance of Czech National Roads

Improving the road network according to the principles of self-explaining roads is a promising way to increase the level of safety; however, there are no universal guidelines on how to measure and improve the self-explaining performance of existing roads. To apply this approach on Czech national roads, the present study was conducted, consisting of five steps: ( a) automated segmentation into tangents and horizontal curves; ( b) collection of floating car data and calculation of speed; ( c) development of multivariate speed models for estimation of speed, including on segments not covered by floating car data; ( d) networkwide application of the models and evaluation of speed consistency, such as differences in speeds on tangents and following curves; and ( e) identification of substandard curves, and categorization and proposal of optimization for consistent placement of traffic control devices or reconstructions. The paper describes all the steps as well as several checks conducted along the way, such as comparison of profile speed and floating car speed, interpretation of regression models, and validation of predicted speed consistency against long-term average crash frequency. The methodology has been certified for use in practice and will be applied by the Czech national road agency.

The Identification of Infrastructure Characteristics Influencing Travel Speeds on Single-carriageway Roads to Promote Self-explaining Roads

Road design characteristics should deliver a clear message to drivers on the appropriate speeds. This approach is known in the literature as “self-explaining roads” (SER). In Israel, new guidelines for setting speeds on the road network were introduced aiming at a balance between the target and actual travel speeds on various road types and, thus, supporting the SER concept. However, engineering tools are needed to implement the new approach. The purpose of this study was to explore the relationship between travel speeds and road infrastructure characteristics, on single-carriageway roads, aiming to identify the features influencing the travel speeds’ selection by Israeli drivers. Statistical relations between road characteristics and speed indicators were explored using multivariate classification methods and regression models. The design speeds were reconstructed based on the infrastructure characteristics, and analyzed in relation to the travel speeds. Among the road characteristics most influencing the travel speeds were found: shoulder width and the recovery-zone width, where lower values of both characteristics were associated with lower speeds; junction density and road curvature, where higher presence of these characteristics had a moderating effect on speeds. It was concluded that changing shoulder width, recovery-zone width or junction density may be applied for promoting the SER concept and may affect travel speeds, yet a fine-tuning of existing design guidelines is required.

Impact of Speed Limits and Road Characteristics on Free-Flow Speed in Urban Areas

The paper documents studies of posted speed limit (PSL) changes on the free-flow speed on urban roads. Before and after field measurements were conducted, changing the existing PSL from 50 to 40 or 60 km/h. The analysis was conducted on the mean free-flow speed difference and speed variability. The data collected were also used for multiple regression analysis, including PSL changes and selected self-explaining road characteristics. The results showed that a decreased PSL caused a small (1.6 km/h) but significant reduction in the mean free-flow speed and speed variance, which might lead to a 10% reduction of severe injury accidents. Furthermore, the PSL reduction had a larger impact on faster drivers and higher road network classes. Conversely, an increased PSL resulted in a 2.6 km/h increase in the mean free-flow speed but no change on speed variability. The regression results indicated that the free-flow speed was heavily influenced by road characteristics, such as carriageway width, road environments, and the presence of on-street parking and sidewalks. Arterial roads presented the largest impact. The PSL had a relatively small impact.

Distinction of Road Categories by Road Users

In order to create self-explaining roads, a remarkable difference should exist between road categories, whereas within a given road category the layout should be homogenous. The paper analyses, how many and which road categories are identified and distinguished by road users. A picture sorting task was completed to find out how road users group 45 different road scenes, and how these groups correspond to road categories according current standards.

Distinction of road categories by road users compared to road classification in design guidelines

On self-explaining roads drivers automatically drive according to the expected behavior and speed. In order to create this type of roads, within a given road category the layout should be homogenous, whereas a remarkable difference should exist between road categories. Using a sample of 500 persons, the paper analyses, which road categories are identified and distinguished by road users.A picture sorting task was completed to find out how road users group 45 different road scenes, and how these groups correspond to the road categories according to current standards.In addition, an analysis of a questionnaire survey was used to determine how individual road scene images are grouped on the basis of chosen speeds, and how this grouping corresponds to road categories. For processing of speed data, cluster analysis was used.The result of both surveys show that road users can clearly distinguish only 4–5 road categories. The paper compares these results with the design guidelines of some countries.

Road safety audit on a major freeway: implementing safety improvements

The paper presents (i) the method followed in a road safety audit of Attica Freeway (“Attiki Odos”), (ii) the findings and recommendations which arose from the RSA, (iii) the improvements made by the freeway operator to improve safety, and (iv) the pilot safety treatments which were proposed with a view to broader implementation in order to mitigate similar problems at other locations. The Road Safety Audit team conducted the RSA with the goal of identifying potentially dangerous roadway or traffic features of the freeway operating environment, as well as potentially misleading or missing information by the applying the safety principles of positive guidance and self-explaining roads. The RSA findings were recorded in the RSA report together with recommendations for implementation. On the basis of the RSA findings, the freeway operator decided on the prompt implementation of several countermeasures, and in cooperation with the RSA team submitted to the Ministry of Infrastructure a proposal for pilot implementation of safety improvements included in the RSA report. An important factor determining the effectiveness of the RSA was that the RSA recommendations were implemented shortly after the RSA was completed. Preliminary results from a before-and-after analysis of data regarding skid resistance characteristics, speeds and crashes at locations where RSA recommendations had been implemented showed improvements in skid resistance characteristics and a reduction in the number of crashes and incidents. The RSA of Attica Freeway promoted the implementation of innovative road safety measures such as shot blasting and motorcyclist-friendly restraint systems.

Strategic analysis of stakeholder preferences regarding the design of ITS‐based road safety measures

In this study, a multi-stakeholder/multi-actor multi-criteria analysis (MAMCA) is applied to obtain a selection and preliminary ranking for a number of alternative ways to design innovative ITS-based tools that have the potential to improve road safety by creating a more forgiving road (FOR) and a more self-explaining road (SER) environment. The aim of this MCA is to allow various stakeholders with an interest in improving road safety (in particular public policymakers, but also users and manufacturers), to assess alternative designs for future FOR and SER environments. For each of these stakeholders a separate MCA is carried out taking into account their specific objectives. By analysing the differences in preferences among stakeholders and using the highest expected ‘value-added’ from the community of stakeholders in its entirety on various FOR and SER environments, it has been possible to uncover information on the chances of successful implementation.

Evaluation of cardiovascular risk factors in hypertensive subjects: comparison between patients' self-assessment and objective measurement by the physician

In contrast to motorways, rural roads are characterised by a large variation in design, appearance and function which is reflected in a comparatively large number of rural road categories. Depending on these categories, a certain (normative) behaviour is usually expected from the driver. These normative behavioural expectations are conveyed to the driver either by formal cues (e.g. speed limit signs) or are expected to be inferred from the road appearance or the affordance (Gibson, J.J., 1986. The Ecological Approach to Visual Perception. Lawrence Erlbaum, Hillsdale (New Jersey)) of the respective road situation. Unsafe situations are likely to occur if the perceived message conveyed by cues or affordances does not match the normative behavioural expectations of the official road category. In order to avoid such mismatch it is important to know how drivers categorise (rural) roads and which elements are used for this subjective and behaviourally relevant road categorisation. We therefore summarized the processes behind this categorisation in a model and conducted a study in a laboratory setting during which subjects were asked to rate a variety of rural road pictures. The study revealed that drivers distinguish between three different rural road categories which can be distinguished with comparatively few objective criteria. Applying these criteria helps to categorise and design rural roads along self-explaining road principles.

A Review of the Evolution of Shared (Street) Space Concepts in Urban Environments

This paper aims to clearly establish the origin and evolution of the shared space concept from a New Zealand perspective by reviewing the literature in the disciplines of both urban design and transportation engineering. The review process involves investigating the use and function of a public road space in the context of the changing of public expectations and how this can relate to a number of interconnected street design approaches (e.g. traffic calming and self-explaining roads (SERs)). These approaches have been used to minimise the influence of motor vehicles since the beginning of the automobile era. The shared space concept, when applied in public road environments in activity centres, has increasingly been embraced by urban planners, transportation engineers and regulatory agencies. A shared space diverges from a conventional road where all road users are encouraged to legitimately occupy the same road space with little physical separation. To achieve this in a safe and efficient manner, the design aims to reduce the dominance of the motor vehicle by promoting pedestrian and cycling activity and utilising the road space as a ‘place’ in addition to its ‘transport’ mobility and access purposes. Given the fundamental conceptual differentiation between a traffic-calmed street and a shared space, the paper argues that there are certain design elements, constituting a shared space, and without them, it would be difficult for a public street to function as a genuine shared space for all road users.

Road user behaviour changes following a self-explaining roads intervention

The self-explaining roads (SER) approach uses road designs that evoke correct expectations and driving behaviours from road users to create a safe and user-friendly road network. Following the implementation of an SER process and retrofitting of local and collector roads in a suburb within Auckland City, lower speeds on local roads and less variation in speed on both local and collector roads were achieved, along with a closer match between actual and perceived safe speeds. Preliminary analyses of crash data shows that the project has resulted in a 30% reduction crash numbers and an 86% reduction in crash costs per annum, since the road changes were completed. In order to further understand the outcomes from this project, a study was carried out to measure the effects of the SER intervention on the activity and behaviour of all road users. Video was collected over nine separate days, at nine different locations, both before and after SER construction. Road user behaviour categories were developed for all potential road users at different location types and then used to code the video data. Following SER construction, on local roads there was a relatively higher proportion of pedestrians, less uniformity in vehicle lane keeping and less indicating by motorists along with less through traffic, reflecting a more informal/low speed local road environment. Pedestrians were less constrained on local roads following SER construction, possibly reflecting a perceptually safer and more user-friendly environment. These behaviours were not generally evident on collector roads, a trend also shown by the previous study of speed changes. Given that one of the objectives of SER is to match road user behaviour with functionally different road categories, the road user behaviour differences demonstrated on different road types within the SER trial area provides further reinforcement of a successful SER trial.