Abstract
Traffic flow parameters have been found to significantly affect crash risk at micro-levels. If such effects do exist at macro-levels, at least two benefits could be expected: (1) the performance and estimates of planning-based crash models could be improved and (2) useful safety knowledge could be provided for regional traffic management. In this article, a flow-based spatial unit was developed by a graph-cut minimization method, based on which regional management strategies are often applied. The graph-cut method partitioned the central area of Kunshan, China, into multiple sub-regions (i.e. graph-cut unit), considering traffic density homogeneity. Bayesian Poisson lognormal models with conditional autoregressive priors were utilized to examine the safety effects of traffic flow parameters, based on the traditional planning-based units and the flow-based graph-cut units. According to the results, no significant traffic flow effect was found for the traffic analysis zone–based model. Traffic flow parameters resulted in a decreased model performance and potential endogeneity issues for the census tract–based model. However, traffic flow effects were found significant for the graph-cut-based model, with an improved model performance. In general, the safety effects of macro-level traffic flow need to be considered for flow-based units developed for regional management.
Highlights
Macro-level crash models have been extensively developed to identify the safety effects of demographic, socioeconomic, and lane use factors, providing safety knowledge for decision-makers in the planning stage
Since the main purpose of this study is to examine the effects of traffic flow parameters rather than comparing multiple spatial models, Bayesian Poisson lognormal models with conditional autoregressive (CAR) priors are applied to analyze crash data, which has been widely applied in many different research fields such as epidemiology.[26]
Traffic flow parameters were found to be correlated with crash risk at micro-levels according to the previous literature.[33]
Summary
Macro-level crash models have been extensively developed to identify the safety effects of demographic, socioeconomic, and lane use factors, providing safety knowledge for decision-makers in the planning stage. Macro-level crash models with planning purposes may be improved by considering such traffic flow effects (if significant), in terms of model performance and estimates. As for macro-level crash models, the modifiable areal unit problem (MAUP) is a critical issue that model performance and estimates (i.e. effects) could significantly vary by different spatial units.[11,12,13,14,15,16,17,18,19]
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