Studies of spatial effects on road traffic safety are highly skewed in relation to aggregation units. As a result, the spatial effect of road traffic crashes at segment-level aggregation is rarely investigated. In light of that, this study investigates spatial spillover effects to determine the effects of independent variable changes initiated in one segment on the dependent variable of its neighboring segments. Additionally, the performance of the implemented models is evaluated under five different spatial weighting approaches. Spatial spillover effect of fatal and injury crashes aggregated at the segment level is estimated with the spatial lag of explanatory variables model under the general framework of Poisson and negative binomial models. The results indicate (i) spatial spillover effects are better modeled with spatial relation conceptualized by inverse distance spatial weighting schemes; (ii) spatial lag of explanatory variables under negative binomial and inverse distance spatial weighting provides the best model fitting; (iii) exogenous interactions among influence factors of fatal and injury crashes have a noteworthy effect in determining the crash frequency of neighboring segments. Convincingly, spatial spillover effect seems to significantly affect the results of conventional count modeling at the segment levels. To that effect, this study expects to provide empirical evidence and complement the existing literature on the impact of changes in explanatory variables initiated in a given segment on the safety of segments in proximity.

Travel demand is commonly predefined as a constant during the planning period in transit service design, but it varies daily with many factors, for example, weather, vacation, and social activity. Under the uncertain demand, the transit system operates in two states, that is, unsaturation and saturation, distinguished by whether or not the capacity of transit vehicle satisfies the possible demand. Thus, we propose a continuum approximation (CA) model for transit service design, including headway and station location, to account for the effects of the stochastic demand via a penalty cost, a service-reliability constraint, and equilibrium. The penalty cost is utilized to describe the saturation state. The service-reliability constraint is applied to ensure the robustness of the transit system. The equilibrium is introduced to allocate the household location where trip demand is generated in a corridor. Furthermore, we build a bilevel framework to find the solutions to the proposed model. In the numerical experiment, the proposed model is applied in the impact analyses of the service-reliability constraint, as well as the sensitivity analyses of the household numbers and value of time. The impact analyses indicate that the transit service design integrated with the effect of housing location choice is necessary under the stochastic demand. The sensitivity analyses show that the number of households and the value of time play a significant role in the performance of transit systems accounting for service reliability. The proposed model and findings serve to improve the design of the transit system under stochastic demand.