Chowdhury Siddiqui

Macroscopic spatial analysis of pedestrian and bicycle crashes

This study investigates the effect of spatial correlation using a Bayesian spatial framework to model pedestrian and bicycle crashes in Traffic Analysis Zones (TAZs). Aggregate models for pedestrian and bicycle crashes were estimated as a function of variables related to roadway characteristics, and various demographic and socio-economic factors. It was found that significant differences were present between the predictor sets for pedestrian and bicycle crashes. The Bayesian Poisson-lognormal model accounting for spatial correlation for pedestrian crashes in the TAZs of the study counties retained nine variables significantly different from zero at 95% Bayesian credible interval. These variables were - total roadway length with 35 mph posted speed limit, total number of intersections per TAZ, median household income, total number of dwelling units, log of population per square mile of a TAZ, percentage of households with non-retired workers but zero auto, percentage of households with non-retired workers and one auto, long term parking cost, and log of total number of employment in a TAZ. A separate distinct set of predictors were found for the bicycle crash model. In all cases the Bayesian models with spatial correlation performed better than the models that did not account for spatial correlation among TAZs. This finding implies that spatial correlation should be considered while modeling pedestrian and bicycle crashes at the aggregate or macro-level.

Integrating Trip and Roadway Characteristics to Manage Safety in Traffic Analysis Zones

A transportation network is a conglomeration of road-traffic-environment modules and features multicategories of interdependent factors. This mix makes the management of safety in traffic analysis zones (TAZs) explicitly challenging. This study investigated the association between crash frequencies and various types of trip productions and attractions in combination with the road characteristics of 1,349 TAZs of four counties in the state of Florida. Crash safety management of these TAZs is emphasized through prioritizing them by examining the effects of trip and roadway factors on the aggregated crash frequencies. Models were developed separately for total crashes, severe crashes (fatal and severe injury crashes), total crashes during peak hours, and pedestrian- and bicycle-related crashes on the basis of various groups of estimators. It was found that the total crash model and the peak-hour crash model were best estimated by total trip productions and total trip attractions. The severe crash model was best fit by trip-related variables only, and the pedestrian- and bicycle-related crash model was best fit by road-related variables only. The results from this study pave the way for better safety management and the incorporation of safety measures in travel and network planning.

Indexing crash worthiness and crash aggressivity by vehicle type

Crash aggressivity (CA), along with conventional crash worthiness (CW), has been recently studied to deal with the crash incompatibility between vehicles on roads. Clearly, injury severity depends on the attacking ability of striking vehicle as well as the protective ability of struck vehicle. This study proposes a systematic crash-based approach to index CA and CW of various vehicles. The approach deviates from existing methods in three aspects: (a) an explicit definition and specification in the model for CW and CA; (b) Bayesian hierarchical analysis to account for the crash-vehicle two-level data structure; (c) a five-level ordinal model to explicitly consider all levels of crash severity. The case study on major vehicle types illustrated the method and confirmed the consistency of results with previous studies. Both crash worthiness and crash aggressivity significantly vary by vehicle types, in which we identified the dominating effect of vehicle mass, and also highlighted the extraordinary aggressivity of Light Trucks and Vans (LTVs). While it was not surprising to identify least CA and CW of motorcycles, buses were unconventionally found to be less aggressive than other motor vehicles. The method proposed in this research is applicable to detailed crash-based vehicle inspection and evaluation.

Aggregate nonparametric safety analysis of traffic zones

Exploring the significant variables related to specific types of crashes is vitally important in the planning stage of a transportation network. This paper aims to identify and examine important variables associated with total crashes and severe crashes per traffic analysis zone (TAZ) in four counties of the state of Florida by applying nonparametric statistical techniques such as data mining and random forest. The intention of investigating these factors in such aggregate level analysis is to incorporate proactive safety measures in transportation planning. Total and severe crashes per TAZ were modeled to provide predictive decision trees. The variables which carried higher weight of importance for total crashes per TAZ were - total number of intersections per TAZ, airport trip productions, light truck productions, and total roadway segment length with 35 mph posted speed limit. The other significant variables identified for total crashes were total roadway length with 15 mph posted speed limit, total roadway length with 65 mph posted speed limit, and non-home based work productions. For severe crashes, total number of intersections per TAZ, light truck productions, total roadway length with 35 mph posted speed limit, and total roadway length with 65 mph posted speed limit were among the significant variables. These variables were further verified and supported by the random forest results.

Nature of Modeling Boundary Pedestrian Crashes at Zones

Traffic analysis zones are often delineated by the existing street network. This practice may result in a considerable number of crashes on or near zonal boundaries. Although the traditional macrolevel approach to crash modeling assigns zonal attributes to all crashes that occur within the zonal boundary, this paper acknowledges the inaccuracy resulting from relating crashes on or near the boundary of the zone to merely the attributes of that zone. This paper proposes a novel approach to account for the spatial influence of neighboring zones on crashes that occur specifically on or near the zonal boundaries. Predictive models for pedestrian crashes per zone were developed with a hierarchical Bayesian framework and with separate predictor sets for boundary and interior (nonboundary) crashes. The hierarchical Bayesian model that accounted for spatial autocorrelation was found to have better goodness-of-fit measures than did models that had no specific consideration for crashes located on or near the boundaries. In addition, the models were able to capture some unique predictors associated explicitly with interior and boundary-related crashes. For example, two variables, total roadway length with a posted speed of 35 mph and long-term parking cost, were not statistically significant from zero in the interior crash model but were significantly different from zero at the 95% level in the boundary crash model.

Implications of Pedestrian Safety Planning Factors in Areas with Minority and Low-Income Populations

ABSTRACT Minority status and low-income are two important criteria on which environmental-justice areas are being identified. These unique areal characteristics have been of particular interest to traffic safety analysts in investigating pedestrian safety planning factors in deprived areas. In this study, a pedestrian crash model was developed for these areas explicitly accounting for the spatial autocorrelations of different zonal factors commonly used in the traditional transportation planning models. It was found that the Bayesian Poisson-lognormal model with a spatial effect term performed the best compared to the models without accounting for spatial autocorrelations.

Evaluating Long-Range Regional Safety with Scenario Planning Analysis

This study attempted to evaluate the long-range regional safety of alternative growth scenarios. In doing so, a scenario planning process was undertaken for the midregion of New Mexico. This paper discusses the manifold aspects of data preparation that went into a scenario planning process. The study used land use and travel demand models to parameterize future scenarios and applied these parameters as covariates to model total and severe crashes separately for the future planning horizon with the use of three statistical modeling techniques: a negative binomial model, a Bayesian Poisson–lognormal model without accounting for spatial heterogeneity, and a spatial Bayesian Poisson–lognormal model. These models were investigated and compared for their forecasts for three alternative scenarios that were developed during the scenario planning process. The method adopted for generating and assembling future covariates from the land use and travel demand model accounted for their intricate nature and complexity to be geographically appropriate so that they might reliably synthesize future scenarios. The widest band of increase in crashes was observed from nonspatial Bayesian models. Despite their better predictive fit compared with the negative binomial models, they provided similar or worse safety forecasts for the alternative scenarios. The spatial models provided the least variability in their forecasts for all the alternative scenarios and had the best goodness of fit values. It was concluded that the approach of a safety forecast (short term versus long term) can be dictated by the accuracy of the predictability needed and expected from a regional safety model.

Managing Roadway Safety at the Traffic Analysis Zones Level

This paper aims at managing safety at traffic analysis zones (TAZs) by investigating best sets of covariates for total, severe, peak hour, and pedestrian and bicycle-related crashes considering various types of trip productions and attractions in combination with roadway characteristics of the TAZs as explanatory variables. It is found that the severe crash model is the best fit by trip related predictors only; total crash and peak hour crash model are best fit for total trip attractions and total trip productions whereas pedestrian and bicycle related crash model is best described as road related predictors only.

Geographical Boundary Dependency Versus Roadway Hierarchy in Macroscopic Safety Modeling: Analysis with Motor Vehicle Crash Data

This study investigated two methodologies for allocating crashes within a zone and compared them in light of crash prediction models. The assumption that crashes within a zone were influenced only by the characteristics of that zone was examined with motor vehicle crash data. Models were specified by a hierarchical structure with submodels that provided separate estimates of covariate sets for crashes that occurred at or near boundaries and that occurred within a zone away from zonal boundaries. The proposed model structure was compared with spatial and nonspatial statistical models fitted for a wide array of independent variables. Two layers of zonal influences were investigated: (a) the influence of immediate neighbors and (b) the additional influence of neighbors of immediate neighbors. The roadway network hierarchy was considered in model sublevel specifications. Comparison of the candidate models showed that the complex nested model structure specifying interior and boundary crashes did not necessarily provide the best fit. Spatial models with submodels based on roadway network hierarchy were found to have the best goodness-of-fit measures for both total and severe crashes. Significant variables were common between total and severe crashes. However, only on-system crashes were significantly associated with the exposure variable, indicating that socioeconomic characteristics and land use types play an important role in off-system crash propensity.

Integrating Climate Change into Scenario Planning: Can Mitigation Measures Also Make a Region More Resilient?

The Central New Mexico Climate Change Scenario Planning Project integrated climate change analysis into a land use and transportation scenario planning process in the Albuquerque Metropolitan Planning Area. In addition to traditional transportation and accessibility indicators, the Mid-Region Council of Governments and federal and local project partners used spatial analysis to test the benefits of a preferred scenario against climate change–related performance measures. The project found that in central New Mexico, emphasizing growth in priority development areas, such as activity centers and transit nodes, not only reduced vehicle miles traveled and greenhouse gas emissions but also proved to be more sustainable by attracting development to desired locations and reducing the amount of growth in areas subject to the risk of climate change impacts such as wildfires and flooding.