Amir Sobhani

Simulation of safety: A review of the state of the art in road safety simulation modelling

Recent decades have seen considerable growth in computer capabilities, data collection technology and communication mediums. This growth has had considerable impact on our ability to replicate driver behaviour and understand the processes involved in failures in the traffic system. From time to time it is necessary to assess the level of development as a basis of determining how far we have come. This paper sets out to assess the state of the art in the use of computer models to simulate and assess the level of safety in existing and future traffic systems. It reviews developments in the area of road safety simulation models. In particular, it reviews computer models of driver and vehicle behaviour within a road context. It focuses on stochastic numerical models of traffic behaviour and how reliable these are in estimating levels of safety on the traffic network. Models of this type are commonly used in the assessment of traffic systems for capacity, delay and general performance. Adding safety to this assessment regime may allow more comprehensive assessment of future traffic systems. To date the models have focused primarily on vehicular traffic that is, cars and heavy vehicles. It has been shown that these models have potential in measuring the level of conflict on parts of the network and the measure of conflict correlated well with crash statistics. Interest in the prediction of crashes and crash severity is growing and new models are focusing on the continuum of general traffic conditions, conflict, severe conflict, crash and severe crashes. The paper also explores the general data types used to develop, calibrate and validate these models. Recent technological development in in-vehicle data collection, driver simulators and machine learning offers considerable potential for improving the behavioural base, rigour and application of road safety simulation models. The paper closes with some indication of areas of future development.

A kinetic energy model of two-vehicle crash injury severity

An important part of any model of vehicle crashes is the development of a procedure to estimate crash injury severity. After reviewing existing models of crash severity, this paper outlines the development of a modelling approach aimed at measuring the injury severity of people in two-vehicle road crashes. This model can be incorporated into a discrete event traffic simulation model, using simulation model outputs as its input. The model can then serve as an integral part of a simulation model estimating the crash potential of components of the traffic system. The model is developed using Newtonian Mechanics and Generalised Linear Regression. The factors contributing to the speed change (ΔV(s)) of a subject vehicle are identified using the law of conservation of momentum. A Log-Gamma regression model is fitted to measure speed change (ΔV(s)) of the subject vehicle based on the identified crash characteristics. The kinetic energy applied to the subject vehicle is calculated by the model, which in turn uses a Log-Gamma Regression Model to estimate the Injury Severity Score of the crash from the calculated kinetic energy, crash impact type, presence of airbag and/or seat belt and occupant age.

Modeling Pedestrian Crowd Exit Choice Through Combining Sources of Stated Preference Data

One crucial aspect of pedestrian behavior when a facility is being evacuated is exit selection. This phenomenon, however, is difficult to capture. Recording revealed choices as the exact situations or moments in which individuals make or change their subconscious exit decisions when evacuating a place is highly ambiguous. The approach in which stated choice data are collected offers an appealing solution to tackle the problem. For the underlying factors that influence peoples exit decisions to be examined, two types of stated preference (SP) data were collected and pooled: traditional stated preference data and stated preference–off–revealed preference (RP) data. The latter is from the state-of-the-art class of stated choice methods that design experiments with reference to an alternative in an individuals actual choice set. The nested logit trick model and a customized version of the generalized mixed multinomial logit model were applied to estimate the difference in variance scale of the two sectors of data and to quantify the relative contribution of the factors of distance, density, visibility, and herding behavior to exit decisions. Results showed that the SP-off-RP method, compared with the classical SP method, led to lower variance for random noise by a small margin. Compared with the nested logit trick method, the generalized mixed multinomial logit approach allowed researchers to consider more behavioral dimensions of the problem as well as accommodate the difference in scale of variance, including heterogeneity in utility weights and utility scale of individuals, correlation between alternatives, correlation of unobserved utility factors over time, and correlation between utility coefficients.

Impacts of different angles and speeds on behavior of pedestrian crowd merging

Many forms of complex pedestrian crowd behaviors, including merging, can be identified in built environments such as public transport stations and public buildings. Understanding and capturing this phenomenon in a robust model is a challenging task; it is also a significant opportunity for research, given the international demand for models of this type. Despite the frequent occurrence of merging of crowd streams, this complex behavior has not received enough attention so far. The literature that is related to crowd merging is limited to T-shaped intersections and studies conducted on staircases. In this study using experimental data, the crowd merging phenomenon was investigated. The impacts of different merging angles and different pedestrian speeds were investigated. The results showed that flow rates and headway distributions are affected by variety in pedestrian speeds and merging angles.