Are multiple speed cameras more effective than a single one? Causal analysis of the safety impacts of multiple speed cameras

Assessing the effect of automated speed enforcement and comprehensive measures on road safety in Rwanda

OBJECTIVES

Daily, approximately 3,400 traffic-related deaths occur globally, with over 90% concentrated in low and middle-income countries (LMICs). Notably, Rwanda has one of the highest road traffic death rates in the world (29.7 per 100,000 people) and is the first low-income country to implement a national Automated Speed Enforcement (ASE) policy. The primary goal of this study is to evaluate the effectiveness of ASE cameras in reducing the primary outcome of road traffic deaths and secondary outcomes of serious injury crashes and fatal crashes.

METHODS

The study used data on road traffic deaths, and serious injury and fatal crashes collected by the Rwanda National Police between 2010 and 2022. Interrupted time series (ITS) models were fit to quantify the association between ASE and change in road traffic crash outcomes, adjusted for COVID-19-related variables (such as the start of the pandemic, the closure of schools and bars), along with exposure variables (such as GDP and population), and other concurrent road safety measures (such as road safety campaigns).

RESULTS

The ITS models show that the implementation of ASE cameras significantly reduced road traffic deaths, serious injury crashes, and fatal crashes at the provincial level. For instance, the implementation of ASE cameras in the whole of Rwanda in April 2021 was significantly associated with a 0.14 (95% CI [0.072, 0.212]) reduction in monthly death incidence, equating to a 38.16% monthly decrease compared to the period before their installation (January 2010-March 2021).

CONCLUSION

This study emphasizes the significant association of ASE in Rwanda with improved road traffic crash outcomes, a result that may inform road safety policy in other LMICs. Rwanda has become the first low-income country to implement nationwide scaling of ASE in Africa, paving the way for the generation of valuable evidence on speed-related interventions. In addition to new knowledge generation, African road safety research efforts like this one are opportunities to grow academic and law enforcement cooperations while improving data systems and sources for future research benefits.

Spot speed cameras in a series - Effects on speed and traffic safety

Reduced speeds and increased speed compliance are crucial for achieving increased road traffic safety, cutting across most Safe System interventions. Speed cameras have been shown to be effective in increasing speed compliance and reducing the number of fatalities and seriously injured. The speed cameras system in Sweden is different compared to many other countries, spot speed cameras are almost always placed in series along a road stretch. The aim of this study is to investigate the effects of this system on mean speeds, speed compliance, and on the number of fatalities and seriously injured. Including 20 years of data, the study applies before-after analysis to 361 speed measurement spots, and Empirical Bayes before-after analysis with control to crash outcomes on 202 road sections. The results show a mean speed decrease of 3.5 km/h for all vehicles and road sections, 7.9 km/h at cameras and 3.0 km/h between cameras. Furthermore, follow-up measurements showed that the effects were maintained long-term. Speed compliance increased 16 %-units, 42 %-units at cameras and 13 %-units between cameras. Though larger effects can be seen at cameras, there are still substantial effects on the enforced road sections between cameras. The cameras had an average effect of 38.6 % on decreasing fatalities and may also suggest a decrease for seriously injured, though not statistically significant. This study also shows that for roads that received both a decreased speed limit from 90 to 80 km/h and speed cameras, the mean speeds were reduced by additionally 3.6 km/h compared to roads with unchanged limits of 90 km/h. The combined effect on fatalities and seriously injured was a reduction by 61.6 % and 33.4 %. In conclusion, the Swedish strategy with spot speed cameras in a series led to an increased speed compliance and a comprehensive reduction in mean speeds and of the number of fatalities.

How yielding cameras affect consecutive pedestrian-vehicle conflicts at non-signalized crosswalks? A mixed bivariate generalized ordered approach

Yielding cameras are considered to be an effective means of preventing drivers' non-yielding behavior. Notably, as pedestrians' perceived risk and behavior change dynamically during the crossing, the safety effectiveness of such facility could also vary across the consecutive conflicts. This study contributes to the literature by assessing the safety effectiveness of yielding camera from a novel perspective, focusing on the consecutive pedestrian-vehicle conflicts (primary conflict and secondary conflict), using Unmanned Aerial Vehicle (UAV) and roadside camera data. Another key contribution lies in the consideration of primary conflict related factors in the secondary conflict analysis, providing new insights into conflict analysis. The mixed bivariate generalized ordered probit model is proposed to analyze the consecutive conflicts simultaneously. The model results indicate that the yielding camera could decrease both slight and severe conflict probability in primary conflict. However, in secondary conflict, the yielding camera would lower severe conflict probability but increase slight conflict probability. Moreover, several primary conflict related factors reveal significant effects on the secondary conflict severity. Specifically, higher pedestrian speed and driver's yielding behavior in primary conflict could lead to higher crossing risks in the secondary conflict. Conversely, more unsuccessful attempts before primary conflict could decrease the severity level of secondary conflict. Based on the results, several practical implications are provided to improve the effectiveness of yielding camera and enhance pedestrian safety.

Effect of urban street trees on pedestrian safety: A micro-level pedestrian casualty model using multivariate Bayesian spatial approach

In the past decades, trees were considered roadside hazard. Street trees were removed to provide clear zone and improve roadside safety. Nowadays, street trees are considered to play an important role in urban design. Also, street tree is considered a traffic calming measure. Studies have examined the effects of urban street trees on driver perception, driving behaviour, and general road safety. However, it is rare that the relationship between urban street trees and pedestrian safety is investigated. In this study, a micro-level frequency model is established to evaluate the effects of tree density and tree canopy cover on pedestrian injuries, accounting for pedestrian crash exposure based on comprehensive pedestrian count data from a state in Australia, Melbourne. In addition, effects of road geometry, traffic characteristics, and temporal distribution are also considered. Furthermore, effects of spatial dependency and correlation between pedestrian casualty counts of different injury severity levels are accounted using a multivariate Bayesian spatial approach. Results indicate that road width, bus stop, tram station, on-street parking, and 85th percentile speed are positively associated with pedestrian casualty. In contrast, pedestrian casualty decreases when there is a pedestrian crosswalk and increases in tree density and canopy. Also, time variation in pedestrian injury risk is significant. To sum up, urban street trees should have favorable effect on pedestrian safety. Findings are indicative to optimal policy strategies that can enhance the walking environment and overall pedestrian safety. Therefore, sustainable urban and transport development can be promoted.

Unveiling the relevance of traffic enforcement cameras on the severity of vehicle-pedestrian collisions in an urban environment with machine learning models

PURPOSE

One of the leading causes of violent fatalities around the world is road traffic collisions, and pedestrians are among the most vulnerable road users with respect to such incidents. Since walking is highly promoted in urban areas to alleviate motor-vehicle externalities, it is paramount to understand the causes associated with vehicle-pedestrian collisions and their severity to provide safe environments. Although traffic enforcement cameras can address vehicle-vehicle collisions, little is known about their effectiveness with respect to vehicle-pedestrian incidents.

METHODOLOGY

In this study, we trained a set of machine learning models to forecast if a vehicle-pedestrian collision will turn into an injury or fatality, and the most suitable model was used to investigate the contributing features associated with such events with emphasis on the impact of traffic enforcement cameras. In addition to traffic enforcement camera proximity, features associated with the collision, weather, vehicle, victim, and infrastructure are included in the model to reduce unobserved heterogeneity.

RESULTS

Results show that a Linear Discriminant Analysis model surpasses other machine learning models considering the evaluation metrics. Results reveal that the age and gender of the victim, the involvement of larger vehicles in the collision, and the quality of the illumination are the causes associated with pedestrian fatalities. On the other hand, involvement of motorcycles and collisions that occurred in densely populated locations are the causes associated with pedestrian injuries.

CONCLUSIONS

This investigation demonstrates how to articulate machine learning into a vehicle-pedestrian crash analysis to understand the direction and magnitude of covariates in the corresponding severity outcome. Furthermore, it highlights the remarkable effect that traffic enforcement cameras and other features have on vehicle-pedestrian crash severity. These results provide actionable guidance for educational campaigns, enhanced traffic engineering, and infrastructure improvements that could be implemented in the analyzed region to provide safer transportation.

Evaluations of Speed Camera Interventions Can Deliver a Wide Range of Outcomes: Causes and Policy Implications

Speeding (travelling at speeds above the speed limit) is proven to be a major contributor to serious crashes, and speed management interventions including speed cameras are shown to reduce speeds, crashes, and trauma. However, the present review identifies that the range of outcomes reported in evaluations of speed cameras is large, complicating the understanding of effects, and inviting scepticism about the value of speed cameras despite the large numbers of reported successes, as well as systematic reviews and meta-analyses that demonstrate their life- and injury-saving value. Therefore, this review is focused on the factors that contribute to the large range of findings, including reasons for genuine differences in the outcomes delivered by different camera programs and variations in evaluation methodology that influence the extent to which real benefits are detected. Finally, recommendations are offered to maximise the safety benefits of speed-camera programs (including ensuring the full chain of requirements for general deterrence is met; strong communications about new programs and expansions at least several weeks in advance of implementation; and unpredictability of enforcement versus signposted cameras) and to improve evaluation methods (especially around determining the road lengths/locations assumed to be treated by the cameras and use of control locations).

Estimating heterogeneous treatment effects in road safety analysis using generalized random forests

Numerous evaluation studies have been conducted on a variety of road safety measures. However, the issue of treatment heterogeneity, defined as the variation in treatment effects, has rarely been investigated before. This paper contributes to the literature by introducing generalized random forests (GRF) for estimation of heterogeneous treatment effects (HTEs) in road safety analysis. GRF has high functional flexibility and is able to search for complex treatment heterogeneity. We first perform a series of simulation experiments to compare GRF with three causal methods that have been used in road safety studies, i.e., outcome regression method, propensity score method, and doubly robust estimation method. The simulation results suggest that GRF is superior to these three methods in terms of model specification, especially with the existence of nonlinearity and nonadditivity. On the other hand, a large dataset is required for accurate GRF estimation. Then we conduct a case study on the UK's speed camera program. Our results indicate significant reductions in the number of road accidents at speed camera sites. And the heterogeneity in treatment effects is found to be statistically significant. We further consider the associations between the baseline accident records, traffic volume, local socio-economic characteristics, and the safety effects of speed cameras. In general, the effect of speed cameras is larger at the sites with more baseline accident records, higher traffic volume, and in more densely-populated and deprived areas. Several policy suggestions are provided based on these findings. The evaluation of HTEs likely offers more comprehensive information to local authorities and policy makers, and improves the performance of speed camera programs. Moreover, GRF can be a promising approach for revealing treatment effect heterogeneity in road safety analysis.