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This paper develops a multivariate spatial model for the joint analysis of daytime and nighttime crash frequencies by injury severity in traffic analysis zones (TAZs). The model specification allows for spatial correlation across TAZs, heterogeneous effects specific to crash time and severity, and correlations across response types under a Bayesian multivariate conditional autoregressive framework. One hundred and thirty one TAZs in Hong Kong, China, with traffic crash, traffic flow, roadway network, and land use data for a one-year period are selected to calibrate the advocated model. Considerable spatial and heterogeneous effects are found for each type crash frequency. Significant correlations exist in the heterogeneous effects for various severity levels and those for daytime and nighttime. The Bayesian estimates of the regression coefficients reveal that there are significant inconsistencies in the set of factors contributing to zonal daytime and nighttime crashes at various severity levels. [ABSTRACT FROM AUTHOR]

Travel times are generally stochastic and spatially correlated in congested road networks. However, very few existing route guidance systems (RGS) can provide reliable guidance services to aid travellers planning their trips with taking account explicitly travel time reliability constraint. This study aims to develop such a RGS with particular consideration of travellers' concern on travel time reliability in congested road networks with uncertainty. In this study, the spatially dependent reliable shortest path problem (SD-RSPP) is formulated as a multi-criteria shortest path-finding problem in road networks with correlated link travel times. Three effective dominance conditions are established for links with different levels of travel time correlations. An efficient algorithm is proposed to solve SD-RSPP by adaptively using three established dominance conditions. The complexities of road networks in reality are also explicitly considered. To demonstrate the applicability of proposed algorithm, a comprehensive case study is carried out in Hong Kong. The results of case study show that the proposed solution algorithm is robust to take account of travellers' multiple routing criteria. Computational results demonstrate that the proposed solution algorithm can determine the reliable shortest path on real-time basis for large-scale road networks. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• One major challenge of bicycle safety is lack of complete exposure data. • A simultaneous-equation model was proposed to tackle incomplete exposure data. • Our model can reveal link of built environment, cycling levels, and bicycle crashes. • Our model is promising in imputation of missing exposure values. • Ignoring uncertainty in exposure results in biased inferences. One major challenge faced by neighborhood-level bicycle safety analysis is the lack of complete and reliable exposure data for the entire area under investigation. Although the conventional travel-diary surveys, together with the emerging smartphone fitness applications and bike-sharing systems, provide straightforward and valuable opportunities to estimate territory-wide bicycle activities, the obtained ridership suffers inherently from underreporting. We introduced the Bayesian simultaneous-equation model as a sound methodological alternative here to address the uncertainty arising from incomplete exposure data when modeling bicycle crashes. The proposed method was successfully fitted to a crowdsourced dataset of 792 bicycle–motor vehicle (BMV) crashes aggregated from 209 neighborhoods over a 3-year period in Hong Kong. Our analysis empirically demonstrated the bias due to omission of activity-based exposure measures or to the direct use of cycling distance extracted from the travel-diary survey without correcting for incompleteness. By modeling bicycle activities and the frequency of BMV crashes simultaneously, we also provided new evidence that an expansion of bicycle infrastructure was likely associated with a significant increase in cycling levels and a substantial reduction in the risk of BMV crashes, despite a slight increase in the absolute number of BMV crashes. Our approach is promising in adjusting for the uncertainty in raw exposure data, extrapolating the missing exposure values, and untangling the linkage among built environment, bicycle activities, and the frequency of BMV crashes within a unified framework. To promote safer cycling, designated facilities should be provided to consecutively separate cyclists from motor vehicles. [ABSTRACT FROM AUTHOR]

• Using population as a surrogate of pedestrian exposure leads to biased estimates. • Activity-based exposure measures should be used when modeling pedestrian crashes. • Travel-diary survey offers a straightforward means to estimate pedestrian activity. • Spatial heterogeneity is worthy of note in zonal pedestrian crash-frequency models. Although numerous efforts have been devoted to exploring the effects of area-wide factors on the frequency of pedestrian crashes in neighborhoods over the past two decades, existing studies have largely failed to provide a full picture of the factors that contribute to the incidence of zonal pedestrian crashes, due to the unavailability of reliable exposure data and use of less sound analytical methods. Based on a crowdsourced dataset in Hong Kong, we first proposed a procedure to extract pedestrian trajectories from travel-diary survey data. We then aggregated these data to 209 neighborhoods and developed a Bayesian spatially varying coefficients model to investigate the spatially non-stationary relationships between the number of pedestrian–motor vehicle (PMV) crashes and related risk factors. To dissect the role of pedestrian exposure, the estimated coefficients of models with population, walking trips, walking time, and walking distance as the measure of pedestrian exposure were presented and compared. Our results indicated substantial inconsistencies in the effects of several risk factors between the models of population and activity-based exposure measures. The model using walking trips as the measure of pedestrian exposure had the best goodness-of-fit. We also provided new insights that in addition to the unstructured variability, heterogeneity in the effects of explanatory variables on the frequency of PMV crashes could also arise from the spatially correlated effects. After adjusting for vehicle volume and pedestrian activity, road density, intersection density, bus stop density, and the number of parking lots were found to be positively associated with PMV crash frequency, whereas the percentage of motorways and median monthly income had negative associations with the risk of PMV crashes. The use of population or population density as a surrogate for pedestrian exposure when modeling the frequency of zonal pedestrian crashes is expected to produce biased estimations and invalid inferences. Spatial heterogeneity should also not be negligible when modeling pedestrian crashes involving contiguous spatial units. [ABSTRACT FROM AUTHOR]