Apostolos Ziakopoulos defended his Phd thesis on spatial analysis of harsh event frequencies, on July 20th 2020

Apostolos Ziakopoulos has successfully defended his PhD dissertation titled: “High resolution spatio-temporal analysis of road safety and traffic behaviour”, on Monday July 20th 2020 via a teleconference (https://centralntua.webex.com/centralntua/j.php?MTID=m57b7271d3b11cc79727d3e39d536ae1e).

This PhD thesis was carried out at the Department of Transportation Planning and Engineering at the School of Civil Engineering of the National Technical University of Athens under the supervision of Professor G. Yannis.

The main objective of the present doctoral dissertation is the spatial analysis of harsh event frequencies in road segments using multi-parametric data, including (i) high resolution naturalistic driving and driver behavior data from smartphone sensors, (ii) microscopic road segment geometry and road network characteristic data from digital maps and (iii) high resolution traffic data. Naturalistic driving data were collected and processed with purpose-made spatial processing algorithms, performing critical functions such as derivation of additional geometrical characteristics, data merging and map-matching. The resulting spatial data-frames were then analyzed and modelled on a road segment basis. Moran’s I coefficients, as well as merged and directional variograms were calculated. Spatial analyses were performed on two parallel pillars: (i) Prediction models were developed in an urban road network training area, with the intent to transfer them to a second urban road network testing area and assess their predictive performance and (ii) Causal models including road user behavior and traffic input data were calibrated in an urban arterial study area per traffic state, in order to investigate additional underlying correlations in an effort to further understand the phenomena of harsh braking and harsh acceleration frequencies. Geographically Weighted Poisson Regression (GWPR) models, Bayesian Conditional Autoregressive Prior (CAR) models and Extreme Gradient Boosting algorithms with random cross-validation (RCV XGBoost) and spatial cross-validation (SPCV XGBoost) were implemented.