Suliman A. Gargoum

Exploring the association between speed and safety: A path analysis approach.

Road safety is influenced by many factors; these factors include characteristics of the road, climate, traffic and, most importantly, vehicle speeds. Previous research shows that increases in speed are typically associated with an increased collision risk. Moreover, previous studies have also found relationships between road and traffic characteristics and collisions. In addition, these features have also been found to affect speeds. This paper aims to model all the aforementioned relationships simultaneously using a Structural Equation Modelling approach. More specifically, the paper attempts to model the relationship between average speed and collision frequency, while taking into account the effects of factors that confound the relationship. Moreover, the analysis attempts to assess the mediated effects that some variables have on collisions through their effects on speed. The data used in this study originated from 353 two-lane urban roads in the city of Edmonton, Canada. The average speeds were obtained from 35 million speed survey observations collected over a five-year period. The speed data are linked to the crash frequency at each location during the same time frame, along with the other factors (road, traffic and climate). The results show that, among others, average speed, volume, segment length, medians and horizontal curves all have statistically significant effects on collisions. On the other hand, shoulders, speed limits and vehicle-lengths are some variables that significantly influence speeds. The results also show that the effects of some variables on safety are indeed mediated through speeds (both partial and full mediation is observed). These findings provide valuable insight that may assist decision makers in choosing and developing alternative speed management strategies, which, in turn, could help improve safety.

Automated Highway Sign Extraction Using Lidar Data

Traffic signs are integral elements of any transportation network; however, keeping records of those signs and their condition is a tedious, time-consuming, and labor-intensive process. As a result, many agencies worldwide have been working toward automating the process. One form of automation uses remote sensing techniques to extract traffic sign information. An algorithm is proposed that can automatically extract traffic signs from mobile light detection and ranging data. After the number of signs on a road segment has been determined, the coordinates of those signs are mapped onto the road segment. The sign extraction procedure involves applying multiple filters to the point cloud data and clustering the data into traffic signs. The proposed algorithm was tested on three highways located in different regions of the province of Alberta, Canada. The segments on which the algorithm was tested include a two-lane undivided rural road and four-lane divided highways. The highway geometry varied, as did vegetation and tree density. Success rates ranged from 93% to 100%, and the algorithm performed better on highways without overhead signs. Results indicate that the proposed method is simple but effective for creating an accurate inventory of traffic signs.

Towards setting credible speed limits: identifying factors that affect driver compliance on urban roads

Road geometry, vehicle characteristics, and weather conditions are all factors that impact a drivers perception of a safe or credible speed and, consequently, the drivers decision on whether or not to comply with the posted speed limit. In fact, the role a roads environment plays in a drivers perception of a credible speed limit is a topic that has attracted the interest of many researchers in recent years. Despite that, not many studies have considered using empirical data to investigate what features of the road environment influence a drivers compliance choice. This paper aims to address this matter by exploring the relationships between features of the road surroundings (geometric, temporal factors, and weather conditions) and driver compliance with speed limits. The paper uses data from almost 600 different urban roads in the city of Edmonton, at which over 35 million vehicle spot speeds were collected. Compliance was represented using a categorical ordered response variable, and mixed-effects-logistic-regression models were fitted. Two different models were built, one for arterials and another for collector roads. In general, the findings show that the more restricted drivers become, particularly on arterials, the more likely drivers are to comply with speed limits; potential restrictions include on-street parking and the absence of lateral shoulders. Furthermore, higher traffic activity during peak hours, and presumably on shoulder weekdays, both increase the likelihood of compliance on arterials. Similarly, posted speed limits and traffic volume are both positively correlated with compliance on both arterial and collector roads. The findings of this research provide evidence of the existence of an empirical relationship between road features and compliance, highlighting the importance of setting credible speed limits on roads and the possibility of achieving higher compliance rates through modifications to the road environment.

Automated Extraction of Horizontal Curve Attributes Using LiDAR Data

Horizontal curves are designed to provide a safe and smooth transition between straight segments on a highway network. Although curves are often designed to meet very stringent standards, imperfections during construction and high operating speeds mean that they are still prone to collisions. Therefore, it is essential that attributes of curves are surveyed to ensure they meet design requirements. Moreover, knowledge of the locations of horizontal curves and their attributes is also required to provide drivers with accurate information in advanced curve-warning systems, which are expected to enhance safety. Unfortunately, conventional techniques to obtain information about horizontal alignments are extremely tedious and, in some cases, impractical. This paper proposes a method by which horizontal curves can be automatically detected and their attributes automatically measured on scans of the highways obtained using light detection and ranging (LiDAR) technology. The proposed method is tested on two different highway segments at the Province of Alberta, Canada, where LiDAR data were collected. Moreover, testing was also conducted using virtual highways with curves with known attributes generated in AutoCAD Civil 3D. The results show that the code is successful in detecting all curves on a highway segment; moreover, the attributes of those curves were estimated with a high degree of accuracy (average difference <3%).

Network Level Clearance Assessment using LiDAR to Improve the Reliability and Efficiency of Issuing Over-Height Permits on Highways

Commercial vehicles on highway networks are only permitted to use routes that are designed to accommodate their sizes and weights. When issuing an over-height permit, agencies consider vertical clearances on all route options on a highway network before directing a vehicle to the optimal route. Inefficient routing of commercial vehicles could cause excessive delays, which would result in undesirable economic impacts. Moreover, inaccurate assignment of vehicles to routes where the road infrastructure cannot handle the vehicle’s size could cause significant damage to the roadway, resulting in potential safety risks. Although routing programs and permit-issuing agencies try to avoid inaccurate assignments, this is not always possible since they rely on a database of information prone to human error and one which is not always up to date. To create a more reliable database of information, Departments of Transport need more efficient methods to collect information on highways. This paper aims to increase the efficiency of collecting data required to issue over-height permits by utilizing LiDAR data to automatically assess vertical clearance on highways. The method used involves detecting all overhead objects on a highway corridor and estimating the clearance at each object before mapping the data on a GIS map. The method was tested on three different highway corridors in Alberta, Canada ranging in length from 130 to 400 km. Testing revealed that the proposed method is effective in performing network-level assessment of vertical clearance, which has significant impacts on the efficiency of routing over-height vehicles on a network.

Automated extraction of road features using LiDAR data: A review of LiDAR applications in transportation

Mobile Light Detection and Ranging (LiDAR) integrates laser scanning equipment, Global Positioning Systems, and inertial navigation technologies into one system that can acquire positional data and intensity information about surrounding objects. In Mobile Laser Scanning, data collection equipment is mounted on a truck which travels through a highway creating a 3D point cloud image of the entire road segment. The high point density of such datasets enables automated extraction of multiple features on highways, which are typically collected manually during long site visits. In addition, the LiDAR data sets could also be used to perform geometric assessments of highway attributes such as available stopping sight distance. If used to their full potential, LiDAR datasets could create a paradigm shift in how geometric assessment and safety audits on highways are conducted. Despite the huge potential, only limited research has attempted extraction of geometric design data from the LiDAR images. This could be a matter of researchers not realizing the full potential of such data or believing that, due to their size, processing such datasets might be impractical. To highlight the full potential of LiDAR data in transportation and to address doubts about the feasibility of extracting information from LiDAR images, this paper provides a thorough review of the potential applications of LiDAR in the field of transportation. The paper includes a thorough review of the previous attempts of transportation data extraction from LiDAR while also providing an overview of other applications which researchers are yet to explore. The paper also discusses the challenges associated with the extraction process and future research in this area.

A Voxel-Based Method for Automated Detection and Mapping of Light Poles on Rural Highways using LiDAR Data

The number of light poles and their position (in terms of density and offset off the roadside) have significant impacts on the safe operation of highways. In current practice, inventory of such information is performed in periodic site visits, which are tedious and time consuming. This makes inventory and health monitoring of poles at a network level extremely challenging. To relieve the burden associated with manual inventory of poles, this paper proposes a novel algorithm which can automatically obtain such information from remotely sensing data. The proposed algorithm works by first tiling point cloud data collected using light detection and ranging (LiDAR) technology into manageable data tiles of fixed dimensions. The data are voxelized and attributes for each data voxel are calculated to classify them into ground and nonground points. Connected components labeling is then used to perform 3D clustering of the data voxels. Further clustering is performed using a density-based clustering to combine connected components of the same object. The final step involves classifying different objects into poles and non-poles based on a set of decision rules related to the geometric properties of the clusters. The proposed algorithm was tested on a 4 km rural highway segment in Alberta, Canada, which had substantial variation in its vertical alignment. The algorithm was accurate in detecting nonground objects, including poles. Moreover, the results also highlight the importance of considering the length of the highway and its terrain when detecting nonground objects from LiDAR.

Intervention analysis of the safety effects of a legislation targeting excessive speeding in Canada

ABSTRACT Excessive speeding is a major traffic safety concern consequently, numerous countermeasures have been considered to mitigate this problem. Excessive speeding, street racing and stunt driving activities subject all road users to extreme risk. To address this problem, three Canadian provinces introduced severe sanctions against drivers who exceed speed limits by high margins. Under the laws offenders were subject to immediate license suspension and vehicle impoundment. In this paper, intervention analysis of the collision data from the three provinces was conducted to identify the safety effects of the legislation. The analysis aims to identify changes in the time series behaviour of collision data after the adoption of the law. The changes were assessed for statistical significance, and the magnitude of the change was quantified. In general, the paper showed that the legislative changes were associated with drops in province-wide fatal collisions substantiating the safety benefits of introducing such legislation against excessive speeders.

Available sight distance on existing highways: Meeting stopping sight distance requirements of an aging population

An important element of highway design is ensuring that the available sight distance (ASD) on a highway meets driver needs. For instance, if the ASD at any point on a highway is less than the distance required to come to a complete stop after seeing a hazard (i.e. Stopping Sight Distance (SSD)), the driver will not be able to stop in time to avoid a collision. SSD is function of a number of variables which vary depending on the driver, the vehicle driven and surface conditions; examples of such variables include a drivers perception reaction time or PRT (i.e. the time required by the driver to perceive and react to a hazard) and the deceleration rate of the vehicle. Most design guides recommend deterministic values for PRT and deceleration rates. Although these values may serve the needs of the average driver, they may not satisfy the needs of drivers with limited abilities. In other words, even if the ASD exceeds required SSD defined in the design guide, it might not always satisfy the needs of all drivers. While it is impossible to design roads that satisfy the needs of all drivers, the fact that most developed countries suffer from an aging population, means that the number of old drivers on our roads is expected to increase. Since a large proportion of old drivers often have limited abilities, it is expected that the general population of drivers with limited abilities on our roads will increase with time. Accordingly, more efforts are required to ensure that existing road infrastructure is prepared to handle such a change. This paper aims to explore the extent to which ASD on highways satisfies the needs of drivers with limited abilities. The paper first develops MATLAB and Python codes to automatically estimate the ASD on highway point cloud data collected using Light Detection and Ranging (LiDAR) remote sensing technology. The developed algorithms are then used to estimate ASD on seven different crash prone segments in the Province of Alberta, Canada and the ASD is compared to the required SSD on each highway. Three different levels of SSD are defined (SSD for drivers with limited ability, AASHTOs SSD requirements and SSD for drivers with high skill). The results show that, when compared to SSD requirements which integrate limitations in cognitive abilities, a substantial portion of the analyzed segments do not meet the requirements (up to 20%). Similarly, when compared to AASHTOs SSD requirements, up to 6% of the analyzed segments do not meet the requirements. In an attempt to explore the effects of such design limitations on safety, the paper also explores crash rates in noncompliant regions (i.e. regions that do not provide sufficient SSD) and compares them to crash rates in compliant regions. On average, it was found that noncompliant regions experience crash rates that are 2.15 and 1.25 times higher than compliant regions for AASHTOs SSD requirements and those integrating driver limitations, respectively. Furthermore, the study found that a significantly higher proportion of drivers involved in collisions in the noncompliant regions were old drivers.

Factors Affecting Classification of Road Segments into High- and Low-Speed Collision Regimes

The safety of locations operating under high-speed conditions could significantly differ from that of locations operating under low-speed conditions. Therefore, different approaches must be adopted when speed and safety are analyzed and managed at locations operating under different regimes. However, it is necessary first to understand the factors affecting the speed–collision classification of a site. Locations operating under high speeds are typically expected to have more collisions compared with locations in which speeds are low. Some locations, however, might experience a high collision rate even when speeds are low, or vice versa. This study aimed to identify the factors that affected the site classification into any of those categories by using data collected on roads in Edmonton, Alberta, Canada. Locations were divided into four speed–collision bins (high collision, high speed; high collision, low speed; low collision, high speed; low collision, low speed), and geographic information system maps of locations were produced to explore the spatial distribution of those locations. Moreover, logistic regression was used to understand the role of different factors in identifying the speed–collision bin to which a certain location belonged. The results reveal that locations with high collision rates but low speeds have a relatively high population of heavy vehicles and trucks as well as high speed variability. As for locations with low collision rates and high speeds, these sites were found to have a high level of protection through the presence of medians and shoulders with relatively low access density.