Do more trucks lead to more motor vehicle fatalities in European roads? Evaluating the impact of specific safety strategies.

Truck operations have recently become an important focus of academic research not only because road freight transport is a key part of logistics, but because trucks are usually associated with negative externalities including pollution, congestion and traffic accidents. While the negative environmental impacts of truck activities have been extensively analyzed, comparatively little attention has been paid to the role of trucks in road accidents. A review of the literature identifies various truck-traffic safety related issues: frequency of accidents and their determinants; risk factors associated with truck driver behavior (including cell phone use, fatigue, alcohol and drugs consumption); truck characteristics and facilities (roadway types, specific lanes and electronic stability programs) to improve performance of vehiclemaneuvering; and the safety characteristics of heavy and large trucks. However, to date, there seems to have been developed few studies evaluating the complex coexistence of trucks and cars on roads and that may support the implementation of differential road safety strategies applied to them. This paper focuses on the impact on the traffic fatalities rate of the interaction between trucks and cars on roads. We also assess the efficiency of two stricter road safety regulations for trucks, as yet not harmonized in the European Union; namely, speed limits and maximum blood alcohol concentration rates. For this, econometric models have been developed from a panel data set for European Union during the years 1999–2010. Our findings show that rising motorization rates for trucks lead to higher traffic fatalities, while rising motorization rates for cars do not. These effects remain constant across Europe, even in the most highly developed countries boasting the best highway networks. Furthermore, we also find that lower maximum speed limits for trucks are effective and maximum blood alcohol concentration rates for professional drivers are only effective when they are strictly set to zero. Therefore, our results point to that the differential treatment of trucks is not only adequate for mitigating an important source of congestion and pollution, but that the implementation of stricter road safety measures in European countries for the case of trucks also contributes significantly to reducing fatalities. In summary, and as a counterpoint to the negative impact of trucks on road traffic accidents, we conclude the effectiveness of efforts made in road safety policy (based on specific traffic regulations by vehicle type imposed by member States) to counteract the safety externalities of freight transportation in the European Union. In certain sense, our study might provide indirect support to public policies implemented at the macro European level to promote multimodal transport corridors. In this respect, there is an increasing focus at the European level on how freight transport can be moved from trucks on roads to more environmentally-sustainable modes, such as rail and ship.Dirección General de Tráfico SPIP2014127

Car accidents: How much is due to external factors and conditions? A data science approach for the Portuguese road network

The main objective of this research is to find out which and what weight external factors have in accidents and victims resulting from them. Within the variable accidents, all accidents that happened in Mainland Portugal in 2018 are counted, according to INE, as the victims include all victims, in Mainland Portugal in 2018 resulting from an accident. The data used was taken from several sources, namely, PORDATA, IPMA, INE, DGTerritório and Here. After collecting the data, the data was thoroughly analyzed and new variables were created with the help of QGIS and SPSS Statistics software, all of them organized by municipalities belonging to the country under study. After all the analysis and selection of variables with the Geoda software and the literature, different models were performed in order to draw conclusions about the selected variables. For this study, two different models were made, for accidents and victims (per 1000 meters and per 1000 inhabitants respectively), because these two variables (targets) didn’t have a strong linear correlation, presenting a value of 0.036 (Pearson correlation) since there was no relationship between the variables. In order to generalize to Portuguese road structures and to other countries with similar characteristics to Portugal, the bootstrap method was used as a simulation strategy, thus generating 300,000 new data. After evaluation the data, it was found that the external factors used in these models have an explanatory capacity of less than 50%, but spatial dependence is a key and very important factor in geospatial problems.O principal objetivo desta pesquisa é encontrar quais e qual o peso dos fatores externos nos acidentes e das vítimas que resultam do mesmo. A variável dos acidentes contém todos os acidentes que aconteceram em Portugal Continental em 2018, de acordo com o INE e a variável das vítimas contém todas as vítimas desde ligeiras, graves e mortais em Portugal Continental em 2018 resultantes de acidentes. Os dados foram retirados de fontes como a IPMA, PORDATA, INE e Here (rede viária de Portugal). Após a recolha dos dados, a análise dos mesmos e a criação de novas variáveis, com a ajuda dos softwares QGIS e SPSS Statistics, foram todas organizas por município pertencentes ao país em estudo. Após toda a seleção das variáveis, de acordo com a literatura, foram criados diferentes modelos de forma a retirar conclusões sobre as varáveis (fatores externos). Para este estudo foram criados dois modelos diferentes, para acidentes e vítimas pois estas duas variáveis (targets) não tinham uma forte correlação linear apresentando um valor de Sig de 0,554. De modo a generalizar para as estruturas rodoviárias portuguesas e para outros países com características semelhantes a Portugal, foi utilizado o método de bootstrap como uma estratégia de simulação, deste modo gerou-se 300000 novos dados. Após a avaliação dos dados verificou-se que os fatores externos, utilizados nestes modelos têm uma capacidade explicativa inferior a 50%, mas a dependência espacial é um fator chave e muito importante em problemas geo-espaciais

Characterization of Black Spot Zones for Vulnerable Road Users in São Paulo (Brazil) and Rome (Italy)

Non-motorized transportation modes, especially cycling and walking, offer numerous benefits, including improvements in the livability of cities, healthy physical activity, efficient urban transportation systems, less traffic congestion, less noise pollution, clean air, less impact on climate change and decreases in the incidence of diseases related to vehicular emissions. Considering the substantial number of short-distance trips, the time consumed in traffic jams, the higher costs for parking vehicles and restrictions in central business districts, many commuters have found that non-motorized modes of transportation serve as viable and economical transport alternatives. Thus, local governments should encourage and stimulate non-motorized modes of transportation. In return, governments must provide safe conditions for these forms of transportation, and motorized vehicle users must respect and coexist with pedestrians and cyclists, which are the most vulnerable users of the transportation system. Although current trends in sustainable transport aim to encourage and stimulate non-motorized modes of transportation that are socially more efficient than motorized transportation, few to no safety policies have been implemented regarding vulnerable road users (VRU), mainly in large urban centers. Due to the spatial nature of the data used in transport-related studies, geospatial technologies provide a powerful analytical method for studying VRU safety frameworks through the use of spatial analysis. In this article, spatial analysis is used to determine the locations of regions that are characterized by a concentration of traffic accidents (black zones) involving VRU (injuries and casualties) in Sao Paulo, Brazil (developing country), and Rome, Italy (developed country). The black zones are investigated to obtain spatial patterns that can cause multiple accidents. A method based on kernel density estimation (KDE) is used to compare the two cities and show economic, social, cultural, demographic and geographic differences and/or similarities and how these factors are linked to the locations of VRU traffic accidents. Multivariate regression analyses (ordinary least squares (OLS) models and spatial regression models) are performed to investigate spatial correlations, to understand the dynamics of VRU road accidents in Sao Paulo and Rome and to detect factors (variables) that contribute to the occurrences of these events, such as the presence of trip generator hubs (TGH), the number of generated urban trips and demographic data. The adopted methodology presents satisfactory results for identifying and delimiting black spots and establishing a link between VRU traffic accident rates and TGH (hospitals, universities and retail shopping centers) and demographic and transport-related data. Document type: Articl

Influence of Street Trees on Roadway User Safety

This thesis aims to understand trends of trees in transportation planning and to determine if street trees have a negative or positive influence on crash frequency and severity. As roadways become more walkable and livable, they become safer. Street trees are a vital component of this trend. Planners must understand the impacts of trees on roadway user safety as they work to reduce crash risk. Although spatial analysis suggests there may be a negative relationship between trees and crash frequency, correlation models find a significant correlation between trees and crash severity, but no significant correlation between trees and crash frequency. Regression models of crash reports, tree inventory data, and other related variables in the city of Des Moines, Iowa, show that the presence of trees has a positive relationship on crash severity but no relationship on crash frequency. For every one unit increase in trees there is a 1.428 increase in predicted severe crashes, but an increase in trees does not result in any statistically significant influence on crash frequency. These findings are useful in gaining an understanding of tree influences on crash frequency and severity at the block group level, but further analysis of other variables is necessary for any further conclusions to occur. Advisor: Daniel Piatkowsk

SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data

The problem of urban event ranking aims at predicting the top-k most risky locations of future events such as traffic accidents and crimes. This problem is of fundamental importance to public safety and urban administration especially when limited resources are available. The problem is, however, challenging due to complex and dynamic spatio-temporal correlations between locations, uneven distribution of urban events in space, and the difficulty to correctly rank nearby locations with similar features. Prior works on event forecasting mostly aim at accurately predicting the actual risk score or counts of events for all the locations. Rankings obtained as such usually have low quality due to prediction errors. Learning-to-rank methods directly optimize measures such as Normalized Discounted Cumulative Gain (NDCG), but cannot handle the spatiotemporal autocorrelation existing among locations. In this paper, we bridge the gap by proposing a novel spatial event ranking approach named SpatialRank. SpatialRank features adaptive graph convolution layers that dynamically learn the spatiotemporal dependencies across locations from data. In addition, the model optimizes through surrogates a hybrid NDCG loss with a spatial component to better rank neighboring spatial locations. We design an importance-sampling with a spatial filtering algorithm to effectively evaluate the loss during training. Comprehensive experiments on three real-world datasets demonstrate that SpatialRank can effectively identify the top riskiest locations of crimes and traffic accidents and outperform state-of-art methods in terms of NDCG by up to 12.7%.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023

INCORPORATING SPEED INTO CRASH MODELING FOR RURAL TWO-LANE HIGHWAYS

Rural two-lane highways account for 76% in mileages of the total paved roads in the US. In Kentucky, these roads represent 85 % of the state-maintained mileages. Crashes on these roads account for 40% of all crashes, 47% of injury crashes, and 66% of fatal crashes on state-maintained roads. These statistics draw attention to the need to investigate the crashes on these roads. Several factors such as road geometries, traffic volume, human behavior, etc. contribute to crashes on a road. Recently, studies have identified speed as one of the key factors of crashes as well as the severity associated with them and indicated the need to incorporate speed into predicting crashes and severity. Such studies are limited for rural two-lane highways due to the lack of measured speed data in the past. This study fills this gap by utilizing widely available measured speed data on these roads and investigates the relationship between speed and crashes on rural two-lane highways. This study collected crash, speed, traffic, and road geometric data for rural two-lane highways in Kentucky. Particularly for the speed, this study utilized GPS-based probe data. The speed data was integrated with the crash data and road attributes for the rural two-lane highways. This study utilized the speed measures directly calculated from the measured speed data and evaluated the effect of speed on the crashes of these roads. At first, this study investigated the effect of speed by incorporating average speed along with traffic volume and length in the crash prediction model for total number of crashes. A zero-inflated negative binomial model was utilized to account for the overdispersion from excess zero crashes in the dataset. From the model, a negative relationship was identified between average speed and number of crashes. One possible explanation is that rural two-lane roads with higher speeds tend to be those main corridors with better geometric conditions. Furthermore, the significance of speed in the model varies with the operating speed on these roads. This suggested considering speed as a categorizer to develop separate models for different speed ranges. Separating models based on speed provided improved prediction performance compared to an overall model. Operating speed often reflects geometric conditions. Therefore, this study also evaluated how the change in the 85th percentile speed from one section to another road section affects the crashes of a road. The analysis showed that more crashes tend to occur when the 85th percentile speed differential between consecutive segments increases. However, further investigation showed that speed differential may not be a suitable indicator of identifying the locations with a high risk of crashes, rather it can be applied for design improvement of the roads. Later, this study investigated spatial heterogeneity of the effect of speed in addition to other factors utilizing a geographically weighted regression model. The model accounted for the geographical location of the data and helped to investigate the spatially varying effect of speed. The results from this model showed that the significance of speed can vary at different locations, which is not observed in the global model. In some regions, speed actually reflects the local geometric conditions of the roads. On the road with poor geometric conditions, crashes tend to be higher. The safety improvement strategies for these roads can focus on improving the geometric conditions such as providing shoulders, realigning the sharp curves, etc. Furthermore, speed seemed to increase crashes in some locations with good geometric conditions and low traffic volume. Speed was indeed a critical factor for these locations and safety countermeasures should be recommended considering the operating condition. Utilizing measured speed data, this study also explored the effect of speed separately on KABC and PDO crashes for these roads. Separate models were developed for KABC and PDO crashes using a zero-inflated Poisson model form. Results from the models showed that speed had a positive relationship with KABC crashes, but a negative relationship with PDO crashes. For the KABC crashes, more KABC crashes tend to occur on high-speed roads. In contrast, PDO crashes tend to be higher on low-speed roads with poor geometric conditions. Furthermore, this study separated the models for each severity level using speed as a categorizer. The models developed at individual speed ranges revealed a varying effect of speed over the different speed ranges of these roads. For example, speed had a positive effect on KABC crashes of low and medium-speed roads, whereas it had a negative influence on crashes of high-speed roads. Further investigation of the study data showed that most of the low and medium-speed roads had poor geometric conditions (narrow shoulder and lane widths with the presence of sharp curves), whereas, high-speed roads had standard geometric conditions. Especially on low-speed roads, it is understandable that a crash can be severe when speed goes up under such restrictive geometric conditions of the roads. In contrast, on high-speed roads, the number of severe crashes tends to be low under standard geometric conditions. Additionally, separating models considering speed ranges provided 19% and 6.5% improvement respectively for KABC and PDO crashes compared to the overall models. Such models can help the agencies to adopt strategies for minimizing crashes at different severity levels based on the speed condition of the road. This study further looked at the effect of speed using Random Forest model since it can deal with multicollinearity between explanatory variables and requires no assumptions on the functional form. After including all the traffic and geometric variables in the model, speed showed 11.5% importance. Compared to the traditional count model, the model provided a better fit with an improved performance of 13%. For better predictability, planning level safety analysis can utilize such machine learning model

Comparative Analysis of Spatial Decision Tree Algorithms for Burned Area of Peatland in Rokan Hilir Riau

 Over one-year period (March 2013 – March 2014), 58 percent of all detected hotspots in Indonesia are found in Riau Province. According to the data, Rokan Hilir shared the greatest number of hotspots, about 75% hotspots alert occur in peatland areas. This study applied spatial decision tree algorithms to classify classes before burned, burned, and after burned from remote sensed data of peatland area in Kubu and Pasir Limau Kapas subdistrict, Rokan Hilir, Riau. The decision tree algorithm based on spatial autocorrelation is applied by involving Neigborhood Split Autocorrelation Ratio (NSAR) to the information gain of CART algorithm. This spatial decision tree classification method is compared to the conventional decision tree algorithms, namely, Classification and Regression Trees (CART),  C5.0, and C4.5 algorithm. The experimental results showed that the C5.0 algorithm generate the most accurate classifier with the accuracy of  99.79%. The implementation of spatial decision tree algorithm succesfuly improve the accuracy of CART algorithm

Modeling travel demand and crashes at macroscopic and microscopic levels

Accurate travel demand / Annual Average Daily Traffic (AADT) and crash predictions helps planners to plan, propose and prioritize infrastructure projects for future improvements. Existing methods are based on demographic characteristics, socio-economic characteristics, and on-network (includes traffic volume) characteristics. A few methods have considered land use characteristics but along with other predictor variables. A strong correlation exists between land use characteristics and these other predictor variables. None of the past research has attempted to directly evaluate the effect and influence of land use characteristics on travel demand/AADT and crashes at both area and link level. These land use characteristics may be easy to capture and may have better predictive capabilities than other variables. The primary focus of this research is to develop macroscopic and microscopic models to estimate travel demand and crashes with an emphasis on land use characteristics. The proposed methodology involves development of macroscopic (area level) and microscopic (link level) models by incorporating scientific principles, statistical and artificial intelligent techniques. The microscopic models help evaluate the link level performance, whereas the macroscopic models help evaluate the overall performance of an area. The method for developing macroscopic models differs from microscopic models. The areas of land use characteristics were considered in developing macroscopic models, whereas the principle of demographic gravitation is incorporated in developing microscopic models. Statistical and back-propagation neural network (BPNN) techniques are used in developing the models. The results obtained indicate that statistical and neural network models ensured significantly lower errors. Overall, the BPNN models yielded better results in estimating travel demand and crashes than any other approach considered in this research. The neural network approach can be particularly suitable for their better predictive capability, whereas the statistical models could be used for mathematical formulation or understanding the role of explanatory variables in estimating AADT. Results obtained also indicate that land use characteristics have better predictive capabilities than other variables considered in this research. The outcomes can be used in safety conscious planning, land use decisions, long range transportation plans, prioritization of projects (short term and long term), and, to proactively apply safety treatments

C4.5 Decision Tree Algorithm for Spatial Data, Alternatives and Performances

Using data mining techniques on spatial data is more complex than on classical data. To be able to extract useful patterns, the spatial data mining algorithms must deal with the representation of data as stack of thematic layers and consider, in addition to the object of interest itself, its neighbors linked through implicit spatial relations. The application of the classification by decision trees combined with the visualization tools represents a convenient decision support tool for spatial data analysis. The purpose of this paper is to provide and evaluate an alternative spatial classification algorithm that supports the thematic-layered data organization, by the adaptation of the C4.5 decision tree algorithm to spatial data, named S-C4.5, inspired by the SCART and spatial ID3 algorithms and the adoption of the Spatial Join Index. Our work concerns both data organization and the algorithm adaptation. Decision tree construction was experimented on traffic accident dataset and benchmarked on both computation time and memory consumption according to different experimentations: study of phenomenon by a single and then by multiple other phenomena, including one or more spatial relations. Different approaches used show compromised and balanced results between memory usage and computation time