Spatio-temporal modeling of traffic risk mapping on urban road networks

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesOver the past few years, traffic collisions have been one of the serious issues all over the world. Global status report on road safety, reveals an increasing number of fatalities due to traffic accidents, especially on urban roads. The present research work is conducted on five years of accident data in an urban environment to explore and analyze spatial and temporal variation in the incidence of road traffic accidents and casualties. The current study proposes a spatio-temporal model that can make predictions regarding the number of road casualties likely on any given road segments and can generate a risk map of the entire road network. Bayesian methodology using Integrated Nested Laplace Approximation (INLA) with Stochastic Partial Differential Equations (SPDE) has been applied in the modeling process. The novelty of the proposed model is to introduce "SPDE network triangulation" precisely on linear networks to estimate the spatial autocorrelation of discrete events. The result risk maps can provide geospatial baseline to identify safe routes between source and destination points. The maps can also have implications for accident prevention and multi-disciplinary road safety measures through an enhanced understanding of the accident patterns and factors. Reproducibility self-assessment : 3, 1, 1, 3, 2 (input data, preprocessing, methods, computational environment, results)

A Dynamic Spatiotemporal Analysis Model for Traffic Incident Influence Prediction on Urban Road Networks

Traffic incidents have a broad negative impact on both traffic systems and the quality of social activities; thus, analyzing and predicting the influence of traffic incidents dynamically is necessary. However, the traditional geographic information system for transportation (GIS-T) mostly presents fundamental data and static analysis, and transportation models focus predominantly on some typical road structures. Therefore, it is important to integrate transportation models with the spatiotemporal analysis techniques of GIS to address the dynamic process of traffic incidents. This paper presents a dynamic spatiotemporal analysis model to predict the influence of traffic incidents with the assistance of a GIS database and road network data. The model leverages a physical traffic shockwave model, and different superposition situations of shockwaves are proposed for both straight roads and road networks. Two typical cases were selected to verify the proposed model and were tested with the car-following model and real-world monitoring data. The results showed that the proposed model could successfully predict traffic effects with over 60% accuracy in both cases, and required less computational resources than the car-following model. Compared to other methods, the proposed model required fewer dynamic parameters and could be implemented on a wider set of road hierarchies