Accident Data Research Articles

Exploring associations between accident types and activities in construction using natural language processing

Existing accident analysis methods often fail to integrate the relationship of construction accidents with construction activities, restrict the potential for visualizing the accidents frequency in Building Information Modelling (BIM). This study proposes a decision support system that provides visual insights, and the frequency of the core construction accidents in relation to typical construction activities. K-mean clustering and keyword-matching techniques are used to group the leading causes of construction accidents and then compare them with the construction activities. The average hit rate computed for construction accidents was 91%, whereas the hit rate calculated for construction activities connected to these frequent accidents was 75%. The classification models are integrated into the Power BI platform to offer decision-makers deeper insights regarding the relationship of prevalent construction accident types and associated activities. The practical application of the system is demonstrated with a case study, exemplifying the fall accident data integrated in a BIM environment.

ANALYSIS OF ACCIDENT DATA AT PT X FOR THE 2018-2022 PERIOD USING THE HFACS-MINING INDUSTRY FRAMEWORK METHOD

The mining industry is a high-risk industrial activity. Human factors have been identified as the most common cause of major accidents in the mining industry. The Incident Management System of PT X, which is a company in exploration, mining, processing, and marketing sector of copper, gold and silver concentrate, part of the state-owned mining holding enterprise Mining Industry Indonesia, documented 322 cases of accidents within the 2018-2022 period. This research aims to analyze accident data at PT X using the Human Factor Analysis and Classification System-Mining Industry (HFACS-MI) framework. Methods. This research collected qualitative data for 322 accident cases at PT X in 2018-2022 from the incident management system database categorized as recordable injuries. Factors causing the accident were coded using the HFACS-MI framework. Accident data analysis used descriptive statistics. Results. The study findings revealed that 84% of all accidents involved contractor workers and 16% involved permanent workers at PT X. The results of the analysis using the HFACS-MI framework showed that each layer or level contributes to accidents, namely external factors by 44%, organizational influence by 68%, unsafe leadership by 90%, preconditions for unsafe acts by 99%, and unsafe acts by 99.7%. Conclusion. These findings emphasize the need to reduce the number of human errors during mining operations to reduce the current accident trend. The HFACS-MI framework has proven to be a valuable tool for robust accident analysis of human factors in mining.

Közúti közlekedési balesetben megsérült gyalogosok számának csökkentési lehetőségei

Összefoglalás. A közlekedési balesetekben sérült személyek, valamint a baleset miatti anyagi károk jelentős mértékű társadalmi-gazdasági veszteségértéket képviselnek. Kutatásommal célom a közlekedés biztonságosabbá tételéhez szükséges kulcsterületek feltárása és a lehetséges potenciális beavatkozási területek meghatározása volt. Munkám során a 2016–2020 közötti közlekedési balesetek adatait elemeztem leíró statisztikai módszerrel. Megállapítottam, hogy a leginkább kritikus pont a gyalogosok közlekedése. Azokban a balesetekben, ahol volt gyalogos sérült, a balesetek két okcsoportja dominál: gyalogosok hibája (34,3%), valamint a járművezetők gyalogosok részére történő elsőbbségadásának elmaradása (38,1%). Az eredmények lehetőséget adnak a további célirányos vizsgálatokra, valamint a hatékony beavatkozásokra. Summary. Transport is an essential part of everybody lives. The aim of transport users is to make their journeys without accidents in the shortest possible time, while optimising their individual resources and inputs. The number of people injured in road traffic accidents with personal injury and the material damage caused by a significant socio-economic loss. By preventing accidents, it is clear that the costs and resources spent on road accidents can be reduced. Therefore, I started my research by determining the share of road user groups in the number of accidents, the number of people injured and the amount of economic loss value associated with accidents. I analysed road accidents between 2016 and 2020. By the accident analysis, I found that among vulnerable road users, the proportion of pedestrians and cyclists injured was extremely high. Injuries in these two transport modes accounted for more than half of the injuries in cars. By calculating the loss value, I found that the damage to the national economy caused by pedestrian injuries was significant. In my research, by analysing the causes of accidents, I found that in accidents where there was a pedestrian injured, two groups of causes dominate: pedestrian error (34.3%) and drivers not giving priority to pedestrians (38.1%). Examining the accident data, it was evident that the number of pedestrian accidents due to pedestrian error is very high, so I continued my research in the area of other activities of pedestrians while walking. I measured pedestrians in 10 locations in Budapest. During the measurements, I recorded and analysed data of 11536 persons. The average rate of distraction was 54.3%, which means that one in two pedestrians is doing some other activity while walking. The most frequent activity was talking to another pedestrian (29.8%), but phone use (15.7%) and head/earphone use (13.5%) were also very significant. My research results so far show that if we want to improve pedestrian safety, we need to reduce accidents caused by pedestrians’ own fault and increase drivers’ willingness to give priority to pedestrians. My research plans also include investigating ways to reduce pedestrian errors, increasing pedestrian awareness and examining pedestrian-driver interactions in more depth. I already have results in this area, but my aim is to identify target areas (including human, infrastructure, vehicle field) for my research that can be implemented in practice, thus helping to improve pedestrian safety and road safety.

Railway Accident Causation Prediction with Improved Transformer Model Based on Lexical Information and Contextual Relationships

The railway system is a prime example of a safety-critical system. Predicting the causes of railway accidents holds immense significance in enhancing railway transportation safety. Previous approaches to railway causation analysis have encountered huge challenges regarding data processing and analytical capabilities. To address this concern, this paper proposes an innovative deep model framework based on the Transformer architecture that utilizes historical data on railway equipment accidents to predict the causes behind such incidents. Firstly, this paper proposes the utilization of Convolutional Block Attention in the domain of text processing, serving as a lexical encoder to augment word semantics acquisition in accident texts. Subsequently, in order to address the deficiency of traditional Transformers that lack positional representation information, we propose incorporating a BiGRU (Bidirectional Gated Recurrent Unit) as a contextual positional information encoder to capture contextual positional information in railway accident data effectively. Finally, considering that accident data reports are discrete tabular data, this study suggests employing cue word techniques for preprocessing accident data to alleviate the model's learning burden. We applied the proposed model to the FRA (Federal Railroad Administration) dataset. The results demonstrate that our model surpasses the current state-of-the-art language models, exhibiting superior performance compared to the optimal model with a notable improvement of 3.56%, 0.42%, and 0.76% in Precision, Recall, and F1-score, respectively. Furthermore, our model accurately predicts accident categories prone to misjudgment even when trained on limited data, outperforming existing language models. The study findings will contribute to the prevention and management of railway accidents.

Presentation of artificial neural network models based on optimum theories for predicting accident severity on rural roads in Iran

This study focuses on predicting the number and severity of rural accidents using nine accident-related variables by the multi-layer perceptron (MLP) approach as a type of artificial neural network modeling method. The models were developed using the input parameters of shoulder width, roadway width, roadside hazard, access density, passing zone ratio, speed limit, pavement condition index, shoulder rumble strips, and centerline rumble strips. MLP models were created based on accident data that occurred from 2019 to 2020 on the Tehran–Qom and Tehran-Saveh rural roads in Iran. In order to achieve the highest accuracy, twenty MLP models have been built with various structures. The MLP model, consisting of a multilayer feedforward network with hidden sigmoid and softmax output, has been used in this study. In this regard, an attempt was made to present an optimum theory to select the best model. The most accurate model was chosen based on the R-value and root mean square error (RMSE), mean absolute error (MAE), f, SD, and R2. Results indicated that the R-value obtained from the optimum model was 0.912, representing the accurate performance of the selected model. In addition, access density, roadside hazard, and roadway width were identified as the most significant variables.

한국제품 데이터기반의 소비자제품 정량적 리스크평가방법연구

The purpose of this study is to contribute to enhancing consumer safety by researching a quantitative product risk assessment method that allows for annual monitoring of product hazards. The study proposes a method for conducting rapid and straightforward evaluations annually using accident data from the Korea Consumer Agency, product usage survey data, and resident population data. Based on the number of accidents, usage rates, and population, probabilities of occurrences were derived, and severity was calculated based on injuries and affected body parts to perform quantitative evaluations. As a result of the research, 262 consumer products were evaluated using the proposed method, identifying 12 products with severe risk ratings, 6 products with increased risk, and 11 products with decreased risk annually. These evaluation results have the significant advantage of being able to observe changes in product hazards annually. They can be utilized not only for enhancing safety, easing regulations, and consumer education policy formulation but also as foundational data for various sectors including consumers, parents, children, and educators, thereby contributing to raising awareness and policy formulation regarding domestic consumer safety.

Accidental Survey of Different Intersection of Junagadh City

Abstract: Intersections are known to be high-risk areas for traffic accidents due to the convergence of different streams of traffic. By conducting an accidental survey, identify patterns and common causes of accidents. This study attempts investigation and analysis to prevent accidents at intersection which have high number of accidents. To get an insight into the trend of road accidents we collect last 3 years of accident data of different intersection of Junagadh city from District Traffic Office of Junagadh by taking permission from SP office Junagadh. And selected intersection is “Majevadi gate” Because in last 3 years, maximum number of accidents have occurred at this intersection. To reduce accidents at this intersection, we design a rotary intersection at the Intersection.

Pattern recognition of urban traffic accidents: Application of cluster analysis and support vector machine in accident pattern recognition

Urban traffic accidents impose a significant threat to public safety because of its frequent occurrence and potential for severe injuries and fatalities. Hence, an effective analysis of accident patterns is crucial for designing accident prevention strategies. Recent advancement in data analytics have provided opportunities to improve the pattern of urban traffic accidents. However, the existing works face several challenges in adapting the complex dynamics, and heterogeneity of the accident data. To overcome these challenges, we proposed an innovative solution by combining the K-means clustering and Support Vector Machine to precisely predict the traffic accident patterns. By leveraging the efficiencies of clustering technique and machine learning, this work intends to identify the intricate patterns within the traffic database. Initially, a traffic accident database was collected and fed into the system. The collected database was pre-processed to improve and standardize the raw dataset. Further, cluster analysis is employed to identify distinct patterns within the dataset and group similar accidents into clusters. This clustering enables the system to recognize common accident scenarios and identify recent accident trends. Subsequently, a Support Vector Machine is deployed to classify accidents into distinct categories through intensive training with identified clusters. The combination enables the system to understand the complex relationships among diverse accident variables, making it an effective framework for real-time pattern recognition. The proposed strategy is implemented in Python and validated using the publicly available traffic accident database. The experimental results manifest that the proposed method achieved 99.65% accuracy, 99.53% precision, 99.62% recall, and 99.57% f-measure. Finally, the comparison with the existing techniques shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones.

Explainable artificial intelligence in transport Logistics: Risk analysis for road accidents

Automobile traffic accidents represent a significant threat to global public safety, resulting in numerous injuries and fatalities annually. This paper introduces a comprehensive, explainable artificial intelligence (XAI) artifact design, integrating accident data for utilization by diverse stakeholders and decision-makers. It proposes responsible, explanatory, and interpretable models with a systems-level taxonomy categorizing aspects of driver-related behaviors associated with varying injury severity levels, thereby contributing theoretically to explainable analytics. In the initial phase, we employed various advanced techniques such as data missing at random (MAR) with Bayesian dynamic conditional imputation for addressing missing records, synthetic minority oversampling technique for data imbalance issues, and categorical boosting (CatBoost) combined with SHapley Additive exPlanations (SHAP) for determining and analyzing the importance and dependence of risk factors on injury severity. Additionally, exploratory feature analysis was conducted to uncover hidden spatiotemporal elements influencing traffic accidents and injury severity levels. We developed several predictive models in the second phase, including eXtreme Gradient Boosting (XGBoost), random forest (RF), deep neural networks (DNN), and fine-tuned parameters. Using the SHAP approach, we employed model-agnostic interpretation techniques to separate explanations from models. In the final phase, we provided an analysis and summary of the system-level taxonomy across feature categories. This involved classifying crash data into high-level causal factors using aggregate SHAP scores, illustrating how each risk factor contributes to different injury severity levels.

Hazardous traffic scenarios for motorcyclists in Indonesia: a comprehensive insight from police accident data and self-reports.

Motorcycle safety remains a concern in low- and middle-income countries. This study addresses this issue by identifying hazardous scenarios for motorcyclists in Indonesia. We conducted a two-step cluster analysis and injury analysis to examine motorcycle accidents based on the police accident dataset (2020-2021) of Brebes Regency, Indonesia. We integrated the findings with accident self-reports from 104 young motorcyclists using a joint display to obtain a more comprehensive insight. As a result, we identified four hazardous traffic scenarios: motorcycle-to-vehicle collisions on median roads, motorcycle-to-vehicle collisions on non-median roads, motorcycle-to-pedestrian collisions, and single-motorcycle collisions. We suggest countermeasures for each scenario and propose a public transport policy as a safer mobility solution. Applying a two-step cluster analysis on accident data and integrating the findings of accident data and self-report analysis proved beneficial in this study. Therefore, we encourage the use of this novel approach in future studies.

An Examination of Nepal's Aviation Sectors: An Analysis of the Aviation Industry as an Expanding Economic Sector

This research paper is prepare using data from studies on the aviation sector in Nepal and how it affects the country's economy. It is acknowledged that Nepali aviation currently influences the country's economic growth in three ways: directly, indirectly, and inducedly. Even though fixed-wing aircraft are being employed less frequently in Nepal, their use in other nations, combined with the employment of helicopters, has helped to strengthen the country's aviation sector. This has also made a significant contribution to the nation's improving economic standing. This industry's growth and development seem to be happening at a snail's pace, despite the enormous potential of this business. Thus, the basis of this research paper is the explanation for the industry's delayed advancement, with a primary focus on its impact on the economy of the nation along with the data of accident occurred in recent ten years. The results of this study will be helpful for the global aviation industry in achieving rapid economic growth and decrease accidents.

Review of Road Traffic Accident Burdens in Transportation Sector of Ethiopia

The transportation sector has a very immense role in the economic development of a given country. Because human beings' day-to-day activities re highly depends on the transport service. But this sector has its own drawbacks, like road traffic crashes and environmental pollution. In Ethiopia, the road transport sector is poor in terms of quality and service delivery systems. Due to this, the frequent occurrence of road traffic crashes has been recorded in the country continuously. To that end, this paper was mainly intended to review road traffic accident burdens in the transportation sector of Ethiopia. In detail, reviewing the costs of road traffic accidents, the causing factors of road traffic accidents, the types of vehicles highly involved in road traffic accidents, and the victim groups of communities affected by road traffic accidents at the national level were the specific objectives of this paper. Meanwhile, all road traffic accident data found in Ethiopia from the federal police commission report papers, journal articles, and dissertation papers were used for analysis purposes. Besides, the collected data is presented in tables and figures. Furthermore, the data was analyzed and interpreted based on a descriptive research design. Finally, the result of this review shows that trends of road traffic accidents in Ethiopia have been increasing over time by killing, injuring people and damaging resources. Drivers’ bad behaviors, low know-how of road users, poor road infrastructure, old vehicle conditions, and environmental factors are aggravating the occurrence of road traffic accidents in the country. Thus, to tackle this problem, educating people, enforcing traffic laws, enhancing engineering measures, and giving emergency responses were the recommendations given in this paper.

Settlement of Traffic Offences: A Shift in the Presumption of Innocence and the Impact on Legal Justice

Traffic offences are a serious issue in Indonesia, creating complex challenges for the justice system and social costs. Although Law No. 22/2009 has defined the rules, the high number of accidents and offences indicates the ineffectiveness of law enforcement. This study aims to investigate the impact of traffic offences on legal justice, the effectiveness of sanctions, and the shifting presumption of innocence in Indonesia. The research methodology included both quantitative and qualitative approaches, involving analyses of accident data and interviews with authorities. The results showed a high number of offences, a lack of effectiveness of Law No. 22 of 2009, and the phenomenon of "peace in place." The implementation of ETLE cameras became an alternative, despite internet access constraints. Perma No. 12/2016 changed the judicial process, moving from a presumption of innocence to a presumption of guilt. This shift provides uniform sanctions without considering individual circumstances. The limitation of violators' rights in ticket hearings violates the principle of justice. The implementation of e-ticket technology shows advantages, but the dependence on the internet and the potential for misuse are obstacles. The shift to the presumption of innocence in the Perma raises doubts about the fairness of the justice system. Improving fairness requires a re-evaluation of the application of sanctions, technological improvements, and a deeper understanding of the rights of offenders. These recommendations are expected to improve the justice system related to traffic offences in Indonesia.

Collaborative Optimization of Dynamic Lane Assignment and Signal Timing for Incident-Affected Intersections

Traffic accidents can lead to rapid changes in the traffic supply and demand at urban intersections, thus causing a severe traffic supply/demand imbalance during specific periods. Responding to accidents requires dynamic and accurate adjustments to optimize intersection resources collaboratively and enhance the real-time reliability and stability of traffic flows during such accidents. Currently, research on traffic incidents rarely considers real-time data-based collaborative optimization theories. Therefore, this study, supported by real-time incident detection technology and accident data, first considers the location and intensity of traffic incidents to update dynamically the changes in intersection traffic demand and supply. Subsequently, a dynamically collaborative optimization method is proposed based on lane assignment and signal timings to minimize the sum of variances of the degree of saturation of various approach lanes. Finally, various traffic demand scenarios are set, and the effectiveness of the proposed model is validated based on numerical and sensitivity analyses. The results demonstrate that compared with signal-only optimization and the highway capacity methods (HCM), the collaborative optimization method presented in this study reduces the average vehicular delay percentages by 8.54% and 16.47%, respectively. Sensitivity analysis indicates that, under various detour rates and detour modes, collaborative optimization methods have effectively mitigated the average vehicular delay at upstream intersections to varying degrees. In the context of real-time accident response, collaborative optimization methods demonstrate a capacity to promptly address the urgency of incidents occurring and maintain a sustained reduction in overall delay levels following detours.

Analysis of Fishing Vessel Accidents and Suggestions for Safety Policy in South Korea from 2018 to 2022

Commercial fishing, one of the most dangerous sectors globally and, particularly, in South Korea, faces fatal accident rates significantly exceeding other industries, and an urgent improvement in safety measures is necessary to reduce these numbers effectively. The main objective of this study is to analyse fishing vessel accidents in South Korea and present the direction of safety policy for fishing vessels based on the lessons learnt from previous accidents, and, ultimately, to contribute to reduce fishing vessel accidents. For this purpose, we have analysed the marine accident data from the last five years provided by the Korea Maritime Safety Tribunal (KMST), the statistical yearbook of oceans and fisheries provided by the Ministry of Oceans and Fisheries (MOF), and the statistical survey of marine fisheries provided by the Korean Statistical Information Service (KOSIS), using both quantitative and qualitative approaches. After thorough review and analysis of the accident data, we have proposed seven suggestions for the direction of future safety policy for fishing vessels in South Korea: adopt international safety benchmarks, focus on occupational and collision accidents, tailor safety measures to vessel size and age, enhance lookout practices to prevent collisions, implement comprehensive strategies to mitigate human errors, and address communication issues among Korean and international crews.

A Review on the 3D Cartographic and Spatiotemporal GIS Models for Safety of Accidents in Deep Underground Coal Mines

AbstractThe aim of this study was the safety risk assessment of accidents that occur because of production dynamics in deep coal mining, one of the highly dangerous business class, with spatiotemporal GIS. In this study, accidents that occur at certain times during production in underground mining workplaces are discussed based on evaluations of spatial and temporal dimensions. Accordingly, various analyses have been handled using criminology science tactics and strategies such as 80–20, kernel density, space–time cube, and hot spot, which have been adapted to GIS tools. Implementations have been realized on 3D underground cartographic models covering accident data from deep coal mine workplaces in the Zonguldak-Kozlu basin in Turkey between 2019 and 2021, at an average elevation of – 400 m. Space–time cube, kernel density, and time series analyses suggest that the Annual Bonus Incentives, which are repeated every year and given before the new year, increase the psychosocial risks for employees. It has been determined that this situation can lead to problems such as poor concentration among miners and loss of focus in some shifts. Additionally, significant relationships were found between the number of accidents and monthly production progress rates as a temporal parameter. The results demonstrate that applying a reactive approach with spatiotemporal GIS to accidents in deep coal mines is becoming increasingly important.

Interpretable machine learning models for failure cause prediction in imbalanced oil pipeline data

Pipelines are critical arteries in the oil and gas industry and require massive capital investment to safely construct networks that transport hydrocarbons across diverse environments. However, these pipeline systems are prone to integrity failure, which results in significant economic losses and environmental damage. Accurate prediction of pipeline failure events using historical oil pipeline accident data enables asset managers to plan sufficient maintenance, rehabilitation, and repair activities to prevent catastrophic failures. However, learning the complex interdependencies between pipeline attributes and rare failure events presents several analytical challenges. This study proposes a novel machine learning (ML) framework to accurately predict pipeline failure causes on highly class-imbalanced data compiled by the United States Pipeline and Hazardous Materials Safety Administration. Natural language processing techniques were leveraged to extract informative features from unstructured text data. Furthermore, class imbalance in the dataset was addressed via oversampling and intrinsic cost-sensitive learning (CSL) strategies adapted for the multi-class case. Nine machine and deep learning architectures were benchmarked, with LightGBM demonstrating superior performance. The integration of CSL yielded an 86% F1 score and a 0.82 Cohen kappa score, significantly advancing prior research. This study leveraged a comprehensive Shapley Additive explanation analysis to interpret the predictions from the LightGBM algorithm, revealing the key factors driving failure probabilities. Leveraging sentiment analysis allowed the models to capture a richer, more multifaceted representation of the textual data. This study developed a novel CSL approach that integrates domain knowledge regarding the varying cost impacts of misclassifying different failure types into ML models. This research demonstrated an effective fusion of text insights from inspection reports with structured pipeline data that enhances model interpretability. The resulting AI modeling framework generated data-driven predictions of the causes of failure that could enable transportation agencies with actionable insights. These insights enable tailored preventative maintenance decisions to proactively mitigate emerging pipeline failures.

Geometric Evaluation of Road Construction on The Ir Sutami PJR Climb Bandar Lampung Using Topographic Measurements

In general, steep inclines are made winding for the reason that they reduce the energy required to reach the same height. The slope of the incline will be gentler with curves so it is easier to climb, because the car does not have enough power to climb steep slopes so the road is made winding. Based on accident data from the KNKT, the highest number of accidents that occurred on the PJR Bandar Lampung ramp was 21 incidents in a year. Geometric road on an incline. The winding and narrow condition of PJR Bandar Lampung makes it difficult for vehicles to pass this section of road. The aim of this research is to carry out geometric analysis and evaluation of roads on the PJR Bandar Lampung incline based on Road Geometric Design Guidelines Number 13/P/BM/2021 using topographic measurements. The results of the geometric evaluation of the road were suitable for corner 4 with a superelevation below 6% and with a minimum radius from a speed of 20 km/h to 50 km/h, while the results were not suitable for the average ideal lane width of 3 m. maximum superelevation is 10.01%, maximum slope is 9.81% and critical slope length is 9.33%. So the road geometry does not comply with Road Geometric Design Guidelines Number 13/P/BM/2021.

Road Safety Audit Tangerang - Merak Toll Road

Tangerang-Merak Toll Road is a toll road connecting Tangerang and Merak Port, which includes Cikupa Toll Road, East Balaraja, West Balaraja, Ciakande, Ciujung, East Serang, West Serang, East Cilegon, West Cilegon, and Merak. With the more congested, will make the risk of car accidents also increased. In an effort to improve of road safety, especially toll roads, a Road Safety Audit (AKJ) is needed. Aspects examined and observed include geometric, traffic management, pavements, street furnitures and complementary buildings. As a result, it is concluded that in geometric aspect, in general has been designed in accordance with the provisions, but nevertheless there are parts that still require improvement. This research will discussed the existing condition of Tangerang-Merak Toll Road based on direct observation. The aspects reviewed are geometric, pavement, street furnitures and road complementary buildings such as Signs, Markings and Median roads. In the end, accident-prone areas will be obtained on tangerang- merak toll road with a comparison analysis of direct observation data, and traffic accident data.

Deerly departed: Using motor-vehicle accidents to determine factors influencing white-tailed deer rut timing in Ontario, Canada

In seasonal environments conception is timed such that offspring are born at the peak of resource abundance. Although largely determined by photoperiod, reproductive timing can also be modulated by fluctuating environmental conditions. Some studies show that reproduction is occurring earlier, coinciding with an earlier spring as the climate warms. A failure to synchronize parturition with the advancing spring flush of resources can lower reproductive fitness; on the other hand, a longer growing season could be beneficial to some species. We asked whether breeding phenology of white-tailed deer (Odocoileus virginianus) has changed with changing environmental conditions. Using 29 years of wildlife motor-vehicle accident data (1988–2016) as an index of deer rut behaviour in southern Ontario, Canada, we assessed four deer management areas (DMAs) to determine: 1) whether timing of the white-tailed deer rut has changed; 2) whether the growing season has changed; and 3) whether changes in growing season and rut are correlated. We found that in the southwestern-most DMA the peak of the rut is 9 days earlier than in the northeastern-most DMA. The peak of the rut did not change over three decades. We detected an increase in the number of growing degree days, signifying both an earlier start to spring green-up and a later end to fall. The trend toward a longer growing season could have positive impacts on deer populations throughout the province.