Developing a New Spatial Unit for Macroscopic Safety Evaluation Based on Traffic Density Homogeneity

Abstract

Macrolevel crash modeling has been extensively applied to investigate the safety effects of demographic, socioeconomic, and land use factors, in order to add safety knowledge into traffic planning and policy-making. In recent years, with the increasing attention to regional traffic management and control, the safety effects of macrolevel traffic flow parameters may also be of interest, in order to provide useful safety knowledge for regional traffic operation. In this paper, a new spatial unit was developed using a recursive half-cut partitioning procedure based on a normalized cut (NC) minimization method and traffic density homogeneity. Two Bayesian lognormal models with different conditional autoregressive (CAR) priors were applied to examine the safety effects of traffic flow characteristics at the NC level. It was found that safety effects of traffic flow exist at such macrolevel, indicating the necessity of considering safety for regional traffic control and management. Furthermore, traffic flow effects were also examined for another two spatial units: Traffic Analysis Zone (TAZ) and Census Tract (CT). It was found that ecological fallacy and atomic fallacy could exist without considering traffic flow parameters at those planning-based levels. In general, safety needs to be considered for regional traffic operation and the effects of traffic flow need to be considered for spatial crash modeling at various spatial levels.

On the Impact of the Rules on Autonomous Drive Learning

“…Many driving simulators have been designed, from the low-level ones that allow the drivers to control the hand brake of their car [6], to higher-level ones, in which the drivers can control their car acceleration and lane-change [7]. Some simulators model the traffic in an urban road network [8], some others model car's intersection access [9][10][11][12], or roundabout insertion [13].…”

Assessment of Crash Occurrence Using Historical Crash Data and a Random Effect Negative Binomial Model: A Case Study for a Rural State

“…Thus, here is where it relies on the importance of creating a method to assess crash occurrence using specialized methods to forecast crash frequencies. A handful of studies focused on identifying risk factors of crash occurrence using historical data at the TAZ level ( 17 – 20 ). For instance, Wang et al showed that at the macro-level—for example, TAZ and census tract—traffic flow influenced crash occurrence, by using Bayesian models to account for the spatial dependency ( 20 ).…”

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