SNUS-2.5, a Multimoment Analysis of Road Demand, Accidents and their Severity in Germany, 1968 – 1989

The present article presents an improved and refined version of the SNUS-1 model (GAUDRY and BLUM 1993) documented only in French. The greatest difficulty faced in the development of the model did not have to do with structure – the multilevel structure is straightforward – but with the specification of the employment activity variable, due to the specifics of the German economy,and with the proper formulation of the role of vehicle stocks in the road demand models. Moreover,we consider the following aspects to be special in the context of an analysis of Germany: • there exist no general speed limits on motorways, i.e. about 70% allow unlimited speed today,and in the Sixties, when our analysis starts, this share was even higher; • the country is large compared with other regions were the DRAG-methodology is employed, and it possesses high car ownership levels and an important car industry that sees the German infrastructure as an appropriate testing ground; • Germany is poly-central, its infrastructure resembles a grid, whereas France’s is almost a huband-spoke system, as compared for instance to Norway’s line; • unification is not yet included because of lagging data availability and, thus, problems to compensate for the structural break in data series.Classification-JEL:

Analysing speeding behaviour: A multilevel modelling approach

This paper examines the variability in speeding for 147 motorists over a five-week period using data collected from Global Positioning System (GPS) technology. A multilevel modelling approach is employed to decompose speeding behaviour into four major levels of variation, namely: inter-individual variation, temporal variation, trip-level variation, and segment level variation. Initially, we estimate a null model (i.e., excludes the explanatory variables) to assess the variations at each level. Results suggest that the driver is more of a factor in speeding as the speed limit increases but that the majority of variation in speeding goes unexplained. This is followed by progressively including explanatory variables (e.g., age, gender, vehicle type, trips purpose etc) at each of the four levels to assess how much more of the variation in speeding can be explained. Results suggest that the reduction in unexplained variance in speeding varies markedly by speed zone, indicating the disproportionately different impacts of explanatory factors

Cycling area can be a confounder and effect modifier of the association between helmet use and cyclists’ risk of death after a crash

The effect of helmet use on reducing the risk of death in cyclists appears to be distorted by some variables (potential confounders, effect modifiers, or both). Our aim was to provide evidence for or against the hypothesis that cycling area may act as a confounder and effect modifier of the association between helmet use and risk of death of cyclists involved in road crashes. Data were analysed for 24,605 cyclists involved in road crashes in Spain. A multiple imputation procedure was used to mitigate the effect of missing values. We used multilevel Poisson regression with province as the group level to estimate the crude association between helmet use and risk of death, and also three adjusted analyses: (1) for cycling area only, (2) for the remaining variables which may act as confounders, and (3) for all variables. Incidence–density ratios (IDR) and their 95% confidence intervals were calculated. Crude IDR was 1.10, but stratifying by cycling area disclosed a protective, differential effect of helmet use: IDR = 0.67 in urban areas, IDR = 0.34 on open roads. Adjusting for all variables except cycling area yielded similar results in both strata, albeit with a smaller difference between them. Adjusting for cycling area only yielded a strong association (IDR = 0.42), which was slightly lower in the adjusted analysis for all variables (IDR = 0.45). Cycling area can act as a confounder and also appears to act as an effect modifier (albeit to a lesser extent) of the risk of cyclists’ death after a crash

A flexible multivariate conditional autoregression with application to road safety performance indicators.

There is a dearth of models for multivariate spatially correlated data recorded on a lattice. Existing models incorporate some combination of three correlation terms: (i) the correlation between the multiple variables within each site, (ii) the spatial autocorrelation for each variable across the lattice, and (iii) the correlation between each variable at one site and a different variable at a neighbouring site. These may be thought of as correlation, spatial autocorrelation and spatial cross-correlation parameters respectively. This thesis develops a exible multivariate conditional autoregression model where the spatial cross-correlation is asymmetric. A comparison of the performance of the FMCAR with existing MCARs is performed through a simulation exercise. The FMCAR compares well with the other models, in terms of model fit and shrinkage, when applied to a range of simulated data. However, the FMCAR out performs all of the existing MCAR models when applied to data with asymmetric spatial crosscorrelations. To demonstrate the model, the FMCAR model is applied to road safety performance indicators. Namely, casualty counts by mode and severity for vulnerable road users in London, taken from the STATS19 dataset for 2006. However, by exploiting correlation between multiple performance indicators within local authorities and spatial auto and cross-correlation for the variables across local authorities, the FMCAR results in considerable shrinkage of the estimates of local authority performance. Whilst this does not enable local authorities to be differentiated based upon their road safety performance it produces a considerable reduction in the uncertainty surrounding their rankings. This is consistent with previous attempts to improve performance rankings. Further, although the findings of this thesis indicate that there is only mild evidence of asymmetry in the spatial cross-correlations for road casualty counts, the thesis provides a demonstration of the applicability of this model to real world social and economic problems

Rollover prevention and path following of a scaled autonomous vehicle using nonlinear model predictive control

Vehicle safety remains an important topic in the automotive industry due to the large number of vehicle accidents each year. One of the causes of vehicle accidents is due to vehicle instability phenomena. Vehicle instability can occur due to unexpected road profile changes, during full braking, obstacle avoidance or severe manoeuvring. Three main instability phenomena can be distinguished: the yaw-rate instability, the rollover and the jack-knife phenomenon. The main goal of this study is to develop a yaw-rate and rollover stability controller of an Autonomous Scaled Ground Vehicle (ASGV) using Nonlinear Model Predictive Control (NMPC). Open Source Software (OSS) known as Automatic Control and Dynamic Optimisation (ACADO) is used to design and simulate the NMPC controller based on an eight Degree of Freedom (8 DOF) nonlinear vehicle model with Pacejka tire model. Vehicle stability limit were determined using load transfer ratio (LTR). Double lane change (DLC) steering manoeuvres were used to calculate the LTR. The simulation results show that the designed NMPC controller is able to track a given trajectory while preventing the vehicle from rolling over and spinning out by respecting given constraints. A maximum trajectory tracking error of 0.1 meters (on average) is reported. To test robustness of the designed NMPC controller to model mismatch, four simulation scenarios are done. Simulation results show that the controller is robust to model mismatch. To test disturbance rejection capability of the controller, two simulations are performed, with pulse disturbances of 0.02 radians and 0.05 radians. Simulations results show that the controller is able to reject the 0.02 radians disturbance. The controller is not able to reject the 0.05 radians disturbance

ANALYSIS OF LARGE-SCALE TRAFFIC INCIDENTS AND EN ROUTE DIVERSIONS DUE TO CONGESTION ON FREEWAYS

En route traffic diversions have been identified as one of the effective traffic operations strategies in traffic incident management. The employment of such traffic operations will help relieve the congestion, save travel time, as well as reduce energy use and tailpipe emissions. However, little attention has been paid to quantifying the benefits by deploying such traffic operations under large-scale traffic incident-induced congestion on freeways, specifically under the connected vehicle environment. New Connected and Automated Vehicle technology, known as “CAV”, has the potential to further increase the benefits by deploying en route traffic diversions. This dissertation research is intended to study the benefits of en route traffic diversion by analyzing large-scale incident-related characteristics, as well as optimizing the signal plans under the diversion framework. The dissertation contributes to the art of traffic incident management by 1) understanding the characteristics of large-scale traffic incidents, and 2) developing a framework under the CAV to study the benefits of en route diversions.Towards the end, 4 studies are linked together for the dissertation. The first study will be focusing on the analysis of the large-scale traffic incidents by using the traffic incident data collected on East Tennessee major roadways. Specifically, incident classification, incident duration prediction, as well as sequential real-time prediction are studied in detail. The second study mainly focuses on truck-involved crashes. By incorporating injury severity information into the incident duration analysis, the second study developed a bivariate analysis framework using a unique dataset created by matching an incident database and a crash database. Then, the third study estimates and evaluates the benefit of deploying the en route traffic diversion strategy under the large-scale traffic incident-induced congestion on freeways by using simulation models and incorporating the analysis outcomes from the other two studies. The last study optimizes the signal timing plans for two intersections, which generates some implications along the arterial corridor under connected vehicles environment to gain more benefits in terms of travel timing savings for the studies network in Knoxville, Tennessee. The implications of the findings (e.g. faster response of agencies to the large-scale incidents reduces the incident duration, penetration of CAVs in the traffic diversion operations further reduces traffic network system delay), as well as the potential applications, will be discussed in this dissertation study

SafetyNet final activity report

SafetyNet final activity repor

Weather Conditions and Telematics Panel Data in Monthly Motor Insurance Claim Frequency Models

Risk analysis in motor insurance aims to identify factors that increase the frequency of accidents. Telematics data is used to measure behavioural information of drivers. Contextual variables include temperature, rain, wind and traffic conditions that are external to the driver, but may also influence the probability of having an accident, as well as vehicle and personal characteristics. This paper uses a monthly panel data structure and the Poisson model to predict the expected frequency of claims over time. Some meteorological information is included. Two types of claims are considered separately: only those related to at-fault third-party liability accidents, and all types of claims including assistance on the road. A sample of drivers in Spain in 2018-2019 is analysed with information on claiming frequency per month. Drivers were observed for seven months. Our analysis is novel because monthly summaries of telematics information are combined with weather data in a panel structure, revealing that external factors affect the expected claims frequencies. Reckless speeding behaviours and intense urban circulation increase the risk of an accident, which also increases with windy conditions

Intention-Aware Risk Estimation for General Traffic Situations, and Application to Intersection Safety

This work tackles the risk estimation problem from a new perspective: a framework is proposed for reasoning about traffic situations and collision risk at a semantic level, while classic approaches typically reason at a trajectory level. Risk is assessed by estimating the intentions of drivers and detecting conflicts between them, rather than by predicting the future trajectories of the vehicles and detecting collisions between them. More specifically, dangerous situations are identified by comparing what drivers intend to do with what they are expected to do according to the traffic rules. The reasoning about intentions and expectations is performed in a probabilistic manner, in order to take into account sensor uncertainties and interpretation ambiguities. This framework can in theory be applied to any type of traffic situation; here we present its application to the specific case of road intersections. The proposed motion model takes into account the mutual influences between the maneuvers performed by vehicles at an intersection. It also incorporates information about the influence of the geometry and topology of the intersection on the behavior of a vehicle, and therefore can be applied to arbitrary intersection layouts. The approach was validated with field trials using passenger vehicles equipped with Vehicle-to-Vehicle wireless communication modems, and in simulation. The results demonstrate that the algorithm is able to detect dangerous situations early and complies with real-time constraints