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This study presents a framework to employ naturalistic driving study (NDS) data to understand and predict crash risk at a disaggregate trip level accommodating for the influence of trip characteristics (such as trip distance, trip proportion by speed limit, trip proportion on urban/rural facilities) in addition to the traditional crash factors. Recognizing the rarity of crash occurrence in NDS data, the research employs a matched case-control approach for preparing the estimation sample. The study also conducts an extensive comparison of different case-to-control ratios including 1:4, 1:9, 1:14, 1:19, and 1:29. The model parameters estimated with these control ratios are reasonably similar (except for the constant). Employing the 1:9 sample, a multi-level random parameters binary logit model is estimated where multiple forms of unobserved variables are tested including (a) common unobserved effects for each case-control panel, (b) common unobserved factors affecting the error margin in the trip distance variable, and (c) random effects for all independent variables. The estimated model is calibrated by modifying the constant parameter to generate a population conforming crash risk model. The calibrated model is employed to predict crash risk of trips not considered in model estimation. This study is a proof of concept that NDS data can be used to predict trip-level crash risk and can be used by future researchers to develop crash risk models.

Run-off-road crashes are one of the most common crash types, especially in rural roadway environments contributing significantly to fatalities and severe injuries. These crashes are complex and multi-dimensional events, and factors like road geometry, driver behaviour, traffic characteristics and roadside features contribute to their occurrence, separately or interactively. Sudden changes in road geometry, in particular, can influence driver behaviour, and therefore, in developing a micro-level crash risk model for run-off-road crashes, one of the challenges is incorporating the effects of driver behaviour (disaggregated information) that may arise from the variations in road geometry (aggregated information). This study aims to examine the interaction between road geometry and driver behaviour through a set of measures for design consistency on two-lane rural roads. Multiple data sources, including crash data for 2014–18, traffic data, probe speed data and roadway geometric data, for twenty-three highways in Queensland, Australia, have been fused for this study. Seventeen types of design consistency measures with regard to alignment consistency, operating speed consistency and driving dynamics are tested. A run-off-road crash risk model is estimated by employing the Random Parameters Negative Binomial Lindley regression framework, which accounts for excess zeros in the crash counts and captures the effects of unobserved heterogeneity in the parameter estimates. Results indicate that the geometric design consistency capturing the interaction between driver behaviour and operational factors better predicts run-off-road crashes along rural highways. In addition, roadside attributes like clear zone width, infrastructures, terrain, and roadway remoteness also contribute to run-off-road crashes. The findings of the study provide a comprehensive understanding of the influence of variations in roadway geometry on driver behaviour and run-off-road crashes along rural highways.

An important tool to evaluate the influence of these public transit investments on transit ridership is the application of statistical models. Drawing on stop-level boarding and alighting data for the Greater Orlando region, the current study estimates spatial panel models that accommodate for the impact of spatial and temporal observed and unobserved factors on transit ridership. Specifically, two spatial models, Spatial Error Model and Spatial Lag Model, are estimated for boarding and alighting separately by employing several exogenous variables including stop-level attributes, transportation and transit infrastructure variables, built environment and land use attributes, and sociodemographic and socioeconomic variables in the vicinity of the stop along with spatial and spatiotemporal lagged variables. The model estimation results are further augmented by a validation exercise. These models are expected to provide feedback to agencies on the benefits of public transit investments while also providing lessons to improve the investment process.

The segmentation of highways is a fundamental step in estimating crash frequency models and conducting a before-after evaluation of engineering treatments, but the effects of segmentation approaches on the engineering treatment evaluations are not known very well. This study examined the effects of segmentation approaches on the before-after evaluation of engineering treatments. In particular, this study evaluated four segmentation approaches by applying the Empirical Bayes technique to a dataset for which the ground truth was known. Four segmentation approaches included Highway Safety Manual (HSM), Fixed (kilometre post), Fisher's, and K-means segmentation. This study utilized a 440 km stretch of rural two-lane two-way highway in Queensland, Australia, to prepare a dataset with known ground truth. The treatment under evaluation was a hypothetical treatment, which should yield a crash modification factor (CMF) of 1. For assigning hypothetical treatment, a total of fifteen datasets were prepared, including ten datasets based on the random assignment and five datasets based on the hotspot identification method. Following the before-after evaluation using the Empirical Bayes technique, the results showed that HSM and Fixed segmentation approaches predict the ground truth in both dataset types. From random assignment datasets, the estimated CMFs using HSM, Fixed, Fisher's, and K-means segmentation approaches deviated from the true CMF (i.e., 1) by 2.32 %, 5.30 %, 6.08 %, and 8.62 %, respectively. In the case of hotspots, the corresponding deviations of CMFs were 8.57 %, 9.37 %, 28.84 %, and 35.43 %, respectively. Overall, HSM segmentation best identified the actual treatment effect, followed by the Fixed segmentation. If the variables to define homogeneity for HSM segmentation are limited, then Fixed segmentation can yield reliable crash modification factors from the before-after treatment evaluations than the crash-based segmentation approaches.

Detailed ridership analytics requires refined data on transit ridership to understand factors affecting ridership (at the stop and/or route-level). However, detailed data for stop-based boarding and alighting information are not readily available for the entire bus system. Transit agencies usually resort to compiling ridership data on a sample of buses operating on the various routes. We propose an approach to infer stop-level ridership for transit systems that only compile route-level ridership information. A joint model structure of binary logit and fractional split model is proposed to estimate stop-level ridership data sourced from route-level ridership. The model is developed for the Greater Orlando region with ridership data for 8 quadrimesters (four-month time periods) from May 2014 through December 2016. In the presence of repeated data measures, panel version of the joint econometric models for boarding and alighting are estimated. The development of such an analytical framework will allow bus systems with only route-level ridership data to generate stop-level ridership data. The model results offer intuitive results and clearly supports our hypothesis that it is feasible to generate stop-level ridership with route-level ridership data. For transit agencies with ridership data at the stop-level, the proposed model can also be employed to understand how various stops along a route interact with one another toward affecting route-level ridership contributions.

Right-turn crashes (or left-turn crashes for the US or similar countries) represent over 40 % of signalized intersection crashes in Queensland, Australia. Protected right-turn phasings are a widely used countermeasure for right-turn crashes, but the research findings on their effects across different crash types and intersection types are not consistent. Methodologically, the Empirical Bayes and Full Bayes techniques are generally applied for before-after evaluations, but the inclusion of heterogeneous models within these techniques has not been considered much. Addressing these research gaps, the objective of this study is to evaluate the effectiveness of protected right-turn signal phasings at signalized intersections employing heterogeneous count data models with the Empirical Bayes and Full Bayes techniques. In particular, the Empirical Bayes approach based on random parameters Poisson-Gamma models (simulation-based Empirical Bayes), and the Full Bayes approach based on random parameters Poisson-Lognormal intervention models (simulation-based Full Bayes) are applied. A total of 69 Cross intersections (with ten treated sites) and 47 T intersections (with six treated sites) from Southeast Queensland in Australia were included in the analysis to estimate the effects of protected right-turn signal phasings on various crash types. Results show that the change of signal phasing from a permissive right-turn phasing to the protected right-turn phasing at cross and T intersections reduces about 87 % and 91 % of right-turn crashes, respectively. In addition, the effect of protected right-turn phasings on rear-end crashes was not significant. The heterogenous count data models significantly address extra Poisson variation, leading to efficient safety estimates in both simulation-based Empirical Bayes and simulation-based Full Bayes approaches. This study demonstrates the importance of accounting for unobserved heterogeneity for the before-after evaluation of engineering countermeasures.

In this study, we develop an advanced econometric model that considers the potential endogeneity of stop level headway in modeling bus ridership. We recognize that bus stops with higher potential demand are also like to have higher frequency of buses (or lower headway between buses). We consider headway endogeneity by proposing a simultaneous equation system that considers headway and ridership in a joint framework. The proposed model is developed employing stop level ridership data from the Orlando region for 11 quadrimesters (four-month time periods). The presence of multiple data points for each stop allows us to develop panel models for headway, boarding, and alighting. The headway variable is modeled using a panel ordered logit model while the ridership variables are modeled using a panel grouped ordered logit models. The model estimation results justify the consideration of headway endogeneity in bus ridership analysis. To illustrate the value of the proposed model, a validation exercise and a policy analysis exercise are conducted.

This study employs a copula-based multivariate temporal ordered probit model to simultaneously estimate the four common intersection crash consequence metrics - driver error, crash type, vehicle damage and injury severity - by accounting for potential correlations due to common observed and unobserved factors, while also accommodating the temporal instability of model estimates over time. To this end, a comprehensive literature review of relevant studies was conducted; four different copula model specifications including Frank, Clayton, Joe and Gumbel were estimated to identify the dominant factors contributing to each crash consequence indicator; the temporal effects on model estimates were investigated; the elasticity effects of the independent variables with regard to all four crash consequence indicators were measured to express the magnitude of the effects of an independent variable on the probability change for each level of four indicators; and specific countermeasures were recommended for each of the contributing factors to improve the intersection safety. The model goodness-of-fit illustrates that the Joe copula model with the parameterized copula parameters outperforms the other models, which verifies that the injury severity, crash type, vehicle damage and driver error are significantly correlated due to common observed and unobserved factors and, accounting for their correlations, can lead to more accurate model estimation results. The parameterization of the copula function indicates that their correlation varies among different crashes, including crashes that occurred at stop-controlled intersections, four-leg intersections and crashes which involved drivers younger than 25. The model coefficient estimates indicate that the driver's age, driving under the influence of drugs and alcohol, intersection geometry and control types, and adverse weather and light conditions are the most critical factors contributing to severe crash consequences. The coefficient estimates of four-leg intersections, yield and stop-controlled intersections and adverse weather conditions varied over time, which indicates that the model estimation of crash data may not be stable over time and should be accommodated in crash prediction analysis. In the end, relevant countermeasures corresponding to law enforcement and intersection infrastructure design are recommended to all of the contributing factors identified by the model. It is anticipated that this study can shed light on selecting valid statistical models for crash data analysis, identifying intersection safety issues, and helping develop effective countermeasures to improve intersection safety.

Vehicle operating speed plays a significant role in many fields of transportation engineering including safety, operation, design and management. The current research effort contributes to literature on examining vehicle speed on arterial roads methodologically and empirically. Specifically, we propose and estimate a panel mixed generalized ordered probit fractional split (PMGOPFS) model to examine critical factors contributing to vehicle operating speed on roadways. The proposed modeling framework allows for the exogenous variable impacts to vary across the alternatives. Further, the model is formulated to allow for the impact of common unobserved factors across multiple levels (roadway, segment, direction, day and time period). To the best of the authors’ knowledge, this is the first time such an econometric model is proposed and estimated in any literature (not just in transportation). The proposed model is estimated employing a maximum simulated quasi-likelihood based objective function. Vehicular speed data obtained from 8 arterial roads in Orlando for the year 2016 is used for estimating the model. The data is obtained for weekday morning and evening peak and off-peak hours for one randomly chosen week for each roadway throughout the year. The exogenous variables that are considered in the current empirical study include geometry, roadway, traffic, land use and environmental attributes. The model estimation results are further augmented by conducting elasticity analysis to highlight the important factors affecting the vehicular speed profile.

The study develops a comprehensive and statistically valid framework to study the impact of new public transportation infrastructure (SunRail commuter train) on existing public transit infrastructure (Lynx bus) in the Orlando metropolitan region. The data for the study is drawn from bus ridership information for six 4-month time periods - 3 prior and 3 after SunRail began operations - allowing us to study time varying effects of SunRail on bus ridership. The current research formulates and estimates an innovative grouped ordered response structure for the ridership analysis. To measure the impact of commuter rail on stop level bus ridership (defined as boarding and alighting), the model system controls for a host of independent variables including stop level attributes, transportation infrastructure, transit infrastructure, land use, built environment, demographic and socioeconomic variables. The proposed model also accommodates for common unobserved factors affecting ridership as well as repeated ridership measures for each stop.

Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users—pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with different VRU groups—for pedestrian, bicyclist, motorcyclist and all VRU groups together. The critical factor of crash severity outcomes for these VRU groups is estimated in identifying the similarities and differences across different important features associated with different VRU groups. The crash data for the study is sourced from the state of Queensland in Australia for the years 2013 through 2019. The supervised machine learning algorithms considered for the empirical analysis includes the K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest (RF). In these models, 17 distinct road crash parameters are considered as input features to train models, which originate from road user characteristics, weather and environment, vehicle and driver condition, period, road characteristics and regions, traffic, and speed jurisdiction. These classification models are separately trained and tested for individual and unified VRU to assess crash severity levels. Afterwards, model performances are compared with each other to justify the best classifier where Random Forest classification models for all VRU modes are found to be comparatively robust in test accuracy: (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%). Based on the Random Forest model, the road crash features are ranked and compared according to their impact on crash severity classification. Furthermore, a model-based partial dependency of each road crash parameters on the severity levels is plotted and compared for each individual and unified VRU. This clarifies the tendency of road crash parameters to vary with different VRU crash severity. Based on the outcome of the comparative analysis, motorcyclists are found to be more likely exposed to higher crash severity, followed by pedestrians and bicyclists.

Traditionally, in developing non-motorized crash prediction models, safety researchers have employed land use and urban form variables as surrogate for exposure information (such as pedestrian, bicyclist volumes and vehicular traffic). The quality of these crash prediction models is affected by the lack of "true" non-motorized exposure data. High-resolution modeling frameworks such as activity-based or trip-based approach could be pursued for evaluating planning level non-motorist demand. However, running a travel demand model system to generate demand inputs for non-motorized safety is cumbersome and resource intensive. The current study is focused on addressing this drawback by developing an integrated non-motorized demand and crash prediction framework for mobility and safety analysis. Towards this end, we propose a three-step framework to evaluate non-motorists safety: (1) develop aggregate level models for non-motorist generation and attraction at a zonal level, (2) develop non-motorists trip exposure matrices for safety evaluation and (3) develop aggregate level non-motorists crash frequency and severity proportion models. The framework is developed for the Central Florida region using non-motorist demand data from National Household Travel Survey (2009) Florida Add-on and non-motorist crash frequency and severity data from Florida. The applicability of the framework is illustrated through extensive policy scenario analysis.

The current study contributes to safety literature both methodologically and empirically by developing a macro-level multivariate copula-based crash frequency model for crash counts. The multivariate model accommodates for the impact of observed and unobserved effects on zonal level crash counts of different road user groups including car, light truck, van, other motorized vehicle (including truck, bus and other vehicles), and non-motorists (including pedestrians and cyclists). The proposed model is estimated using Statewide Traffic Analysis Zone (STAZ) level road traffic crash data for the state of Florida. A host of variable groups including land-use characteristics, roadway attributes, traffic characteristics, socio-economic characteristics and demographic characteristics are considered. The model estimation results illustrate the applicability of the proposed framework for multivariate crash counts. Model estimation results are further augmented by evaluation of predictive performance and policy analysis.

The current study develops a trivariate framework that accommodates for inherent dependencies across the various components of incident duration. A unique ordered response model structure (sometimes referred to as grouped ordered response model) is introduced for modeling three durations—reporting time, response time, and clearance time—in this study. Further, as opposed to employing a simulation oriented multivariate model approach, the study proposes and estimates a copula-based methodology that allows for a closed-form probability computation. The approach is the first application of this model for incident duration analysis. The proposed copula framework is estimated to identify factors affecting incident duration components from a host of characteristics including incident characteristics, traffic conditions, roadway, and environmental characteristics. The data for the analysis are obtained from the 2015 Florida Department of Transportation incident database. The model estimates were also augmented by conducting policy analysis by generating a three-dimensional representation of incident frequencies as a function of reporting, response, and clearance time. Traffic congestion can generally be attributed to either recurring or nonrecurring events. Congestion arising from recurring events is generally a result of mismatched transportation demand and supply (or capacity). Nonrecurring congestion, on the other hand, is a result of unexpected (or irregular) events such as abandoned vehicles, adverse weather, spilled loads, highway debris, and traffic crashes. The potential solutions for congestion arising from these two sources are vastly different. This research focuses on nonrecurrent congestion. Nonrecurring congestion is responsible for approximately one-quarter of all traffic delay on U.S. roadways (1). According to Roper (2), every minute that a freeway travel lane is blocked results in 4–5 minutes of traffic delay after the incident is cleared. The U.S. Department of Transportation Strategic Plan for Fiscal-Year 2010–2015 reports that 2.8 billion gallons of gasoline are consumed every year in the U.S. as a result of incident-related congestion events (3). Moreover, longer incident durations can increase the risk of secondary incidents (4). Consequently, transportation agencies are developing traffic incident management strategies to reduce the overall duration of incidents to minimize their impacts on travelers and the environment. The overall incident duration, as identified by the Highway Capacity Manual (5), is composed of the following four phases: notification time, response time, clearance time and traffic recovery time (6). Incident clearance (third phase) is usually the longest component of the incident duration time (4). The traffic recovery time (fourth phase) is a function of total duration of the first three phases and the traffic demand on the facility. Given the importance of different phases of incident duration, the objective of the proposed research effort is to study the factors influencing incident reporting, response, and clearance times—with a goal of reducing the congestion impacts of nonrecurring events while providing improved traffic incident management plans. An important factor affecting incident clearance is the response personnel involved. In the state of Florida, in addition to the traditional agencies, a road ranger service patrol assists in the incident clearance process. In particular, Florida Department of Transportation (FDOT) offers a unique service via road ranger service patrol to offer free assistance to road users on highways to reduce delay while enhancing safety for the public. Since its inception, the Road Rangers (RR) service has offered nearly 4.3 million assists (7). The objectives of the program include reducing traffic crashes, assisting the Florida Highway Patrol to reduce incident duration, providing assistance to disabled or stranded vehicles, removing road debris, and increasing safety at incident sites. Toward meeting these goals, the RR trucks monitor congested areas and high-incident locations of the urban expressway for different nonrecurrent incidents. This research examines incident clearance duration with a specific emphasis on the impact of RR service patrol program. The analysis would allow recommendations to be made on the performance of the RR patrol while offering recommendations for similar programs in other states.

The study proposes and estimates a new econometric framework for analysing crash count events labeled as the Mixed Grouped Response Ordered Logit Count model. The proposed framework relates the crash count propensity to the observed counts directly while also accommodating for heteroscedasticity and unobserved heterogeneity. The proposed model is demonstrated by using Traffic Analysis Zone level bicycle crash count data for the Island of Montreal. The model framework employs a comprehensive set of exogenous variables − accessibility measures, exposure measures, built environment, road network characteristics, sociodemographic and socioeconomic characteristics. Further, we also compare the performance of the proposed model to the most commonly used negative binomial model and the generalized ordered logit count model by generating a comprehensive set of measures to evaluate model performance and data fit. The alternative modeling approaches considered for the comparison exercise include: (1) negative binomial model without parameterized overdispersion, (2) negative binomial model with parameterized overdispersion, and (3) mixed negative binomial model with parameterized overdispersion, (4) generalized ordered logit count model and (5) mixed generalized ordered logit count model, (6) grouped response ordered logit count model without parameterized variance, (7) grouped response ordered logit count model with parameterized variance and (8) mixed grouped response ordered logit count model with parameterized variance. The comparison exercise clearly highlights that the proposed mixed grouped response ordered logit count model with parameterized variance relative to the mixed negative binomial model with parameterized overdispersion offers either equivalent or superior data fit across various measures in the current study context. The fit measures for comparing the predictive performance also indicate that the proposed grouped response model offers better predictions both at the aggregate and disaggregate levels. Overall, the results from this comparison exercise points out that the grouped response ordered logit count model is a promising alternate econometric framework for examining crash count events.

In recent years, there is growing recognition that common unobserved factors that influence crash frequency by one attribute level are also likely to influence crash frequency by other attribute levels. The most common approach employed to address the potential unobserved heterogeneity in safety literature is the development of multivariate crash frequency models. The current study proposes an alternative joint econometric framework to accommodate for the presence of unobserved heterogeneity – referred to as joint negative binomial-multinomial logit fractional split (NB-MNLFS) model. Furthermore, the study undertakes a first of its kind comparison exercise between the most commonly used multivariate model (multivariate random parameter negative binomial model) and the proposed joint approach by generating an equivalent log-likelihood measure. The empirical analysis is based on the zonal level crash count data for different collision types from the state of Florida for the year 2015. The model results highlight the presence of common unobserved effects affecting the two components of the joint model as well as the presence of parameter heterogeneity. The equivalent log-likelihood and goodness of fit measures clearly highlight the comparable performance offered by the proposed joint model relative to the commonly used multivariate approach. Overall, the model interpretations and fit measures clearly highlight the potential complementary role of the proposed approach for crash frequency analyses.

The current research effort bridges the gap between traditional crash risk and real-time crash risk models by developing a joint model that accommodates for both dimensions in developing crash risk analysis models. Specifically, we develop a joint reactive and proactive crash modeling framework by coupling the monthly crash risk and real-time crash risk in a unified econometric framework for a microscopic analysis unit. In the joint modeling approach, we propose and estimate an alternative to the case-control binary logit based real-time crash risk analysis by proposing a multinomial logit based approach where time periods serve as alternatives and the chosen alternative is the time period in which crash occurs. The joint model also allows us to accommodate for the common unobserved factors that increase the likelihood of a crash in microscopic unit to affect the real-time crash risk propensity. We demonstrate the application of the proposed approach by using data on roadway segments from three expressways in Central Florida (State Roads 408, 417, and 528) for 29 months. The monthly crash risk component is examined by using binary logit model employing different static roadway attributes (roadway geometry and operational attributes). The real-time crash risk component is examined by using a multinomial logit model employing different real-time traffic attributes (volume, speed, lane occupancy and environmental conditions). The outcome of the proposed approach allows us to predict both the monthly and real-time crash risk components simultaneously in a single econometric framework.

In traffic safety literature, crash frequency variables are analyzed using univariate count models or multivariate count models. In this study, we propose an alternative approach to modeling multiple crash frequency dependent variables. Instead of modeling the frequency of crashes we propose to analyze the proportion of crashes by vehicle type. A flexible mixed multinomial logit fractional split model is employed for analyzing the proportions of crashes by vehicle type at the macro-level. In this model, the proportion allocated to an alternative is probabilistically determined based on the alternative propensity as well as the propensity of all other alternatives. Thus, exogenous variables directly affect all alternatives. The approach is well suited to accommodate for large number of alternatives without a sizable increase in computational burden. The model was estimated using crash data at Traffic Analysis Zone (TAZ) level from Florida. The modeling results clearly illustrate the applicability of the proposed framework for crash proportion analysis. Further, the Excess Predicted Proportion (EPP)-a screening performance measure analogous to Highway Safety Manual (HSM), Excess Predicted Average Crash Frequency is proposed for hot zone identification. Using EPP, a statewide screening exercise by the various vehicle types considered in our analysis was undertaken. The screening results revealed that the spatial pattern of hot zones is substantially different across the various vehicle types considered.

The latent class clustering and latent segmentation-based models are employed to account for heterogeneity across different groups. Further, the random parameter variants of these modeling frameworks are employed to consider heterogeneity within the group. Both of these approaches have recently gained significant attention in road safety literature. However, the similarities and differences between these two methods are seldom explained and investigated. To that end, this study proposes to compare the performance of latent class clustering and latent segmentation-based random parameter models in examining crash injury severity outcomes. These models have been developed based on an ordered logit modeling framework to accommodate the ordinal nature of injury severity levels. For examining crash injury severity outcomes, this is the first study to consider the random parameter variant of ordered modeling structure within a latent segmentation modeling scheme. The current study also tests for and incorporates temporal instability of exogenous variables across multiple years of crash data in examining injury severity outcomes. The models have been estimated by using motorcycle crash data of Queensland, Australia, from the year 2012 through 2016. The comparison exercise is also augmented by estimating aggregate level elasticity effects of exogenous variables. The comparison exercise highlights the superiority of the latent segmentation approach in examining injury severity compared to the latent class clustering-based modeling approach. Moreover, the random parameter variants of both frameworks performed better than their fixed-parameter counterparts, which highlights the need to account for both across- and within-group heterogeneity. The temporal stability tests indicate that the effects of exogenous variables on the rider injury severity are different across year-wise models.

In safety literature, there are two ways to incorporate the potential correlation between multiple crash frequency variables: (1) simulation-based approach and (2) analytical closed-form approach. The current research effort undertakes a comparison between simulation-based multivariate model and copula based closed-form approach to analyze zonal level crash counts for different crash types. Further, the research builds on earlier copula based models by incorporating random parameters thus proposing a hybrid (combination of analytical and simulation based system) approach to incorporating unobserved heterogeneity. Within the proposed hybrid copula model, the empirical analysis involves estimation of count models using four different copula structures which cover a wide range of dependency structures, including radial symmetry and asymmetry, and asymptotic tail independence and dependence. Further, to the best of authors’ knowledge, this study is the first of its kind to incorporate attribute variability (random parameters) effect within the copula framework. The empirical analysis is based on traffic analysis zone (TAZ) level crash count data for both motorized and non-motorized crashes from Central Florida for the year 2016. A comprehensive set of exogenous variables including roadway, built environment, land-use, traffic, socio-demographic and spatial spillover characteristics are considered for the analysis. The resulting data fit and prediction performance offered by the proposed approach clearly highlights the hybrid model – Random Parameter Copula based approach’s superiority over the purely simulation-based multivariate model in our study context. The comparison exercise is further augmented by undertaking an in-depth comparison for different count events across different crash types and a correct classification analysis. The estimated results further reinforce the improved performance of the Random Parameter Copula-based multivariate approach. The applicability of the model for hot spot identification is illustrated by generating plots identifying high-crash and low- crash zones by crash type in the Central Florida region.

Estimating the future state of air quality associated with transport policies and infrastructure investments is key to the development of meaningful transportation and planning decisions. This paper describes the design of an integrated transportation and air quality modelling framework capable of simulating traffic emissions and air pollution at a refined spatio-temporal scale. For this purpose, emissions of Nitrogen Oxides (NOx) were estimated in the Greater Montreal Region at the level of individual trips and vehicles. In turn, hourly Nitrogen Dioxide (NO2) concentrations were simulated across different seasons and validated against observations. Our validation results reveal a reasonable performance of the modelling chain. The modelling system was used to evaluate the impact of an extensive regional transit improvement strategy revealing reductions in NO2 concentrations across the territory by about 3.6% compared to the base case in addition to a decrease in the frequency and severity of NO2 hot spots. This is associated with a reduction in total NOx emissions of 1.9% compared to the base case; some roads experienced reductions by more than half. Finally, a methodology for assessing individuals daily exposure is developed (by tracking activity locations and trajectories) and we observed a reduction of 20.8% in daily exposures compared to the base case. The large difference between reductions in the mean NO2 concentration across the study domain and the mean NO2 exposure across the sample population results from the fact that NO2 concentrations dropped largely in the areas which attract the most individuals. This exercise illustrates that evaluating the air quality impacts of transportation scenarios by solely quantifying reductions in air pollution concentrations across the study domain would lead to an underestimation of the potential health gains. An integrated transportation, emissions, air quality modelling chain is developed.Our modelling framework is able to account for the spatio-temporal distribution of NO2.The modelling framework was used to test the effect of transit investments.Increased transit service was associated with reductions in population exposure to NO2.

This paper describes the design and application of an integrated model for the prediction of exposure to traffic related air pollution in an urban area as a result of transport policy scenarios. For this purpose, a travel demand model linked with models for traffic assignment, emissions, and air quality was used to simulate population exposure to ambient Nitrogen Dioxide (NO2) in a base year (2008) and in a horizon year (2031) while incorporating population and demographic projections. The integrated model was used to evaluate the impacts of the planned regional transit and vehicle technology improvements on exposure to NO2. In the 2031 business as usual scenario, an average decrease of 19% in exposure to NO2 is observed across the sample population, compared to the 2008 base case. This decrease is primarily attributed to projected improvements in vehicle technology. In the 2031 transit scenario, we observed an average 10% decrease in exposure compared to the 2031 business as usual. In terms of the spatial variability in air pollution, the transit scenario was observed to achieve large reductions in NO2 concentrations within the downtown area and moderate reductions throughout the suburbs.

Location-based social networks (LBSN) are social media sites where users check-in at venues and share content linked to their geo-locations. LBSN, considered to be a novel data source, contain valuable information for urban planners and researchers. While earlier research efforts focused either on disaggregate patterns or aggregate analysis of social and temporal attributes, no attempt has been made to relate the data to transportation planning outcomes. To that extent, the current study employs LBSN service-based data for an aggregate-level transportation planning exercise by developing land-use planning models. Specifically, we employ check-in data aggregated at the census tract level to develop a quantitative model for activity intensity as a function of land use and built-environment attributes for the New York City (NYC) region. A statistical exercise based on clustering of census tracts and negative binomial regression analyses are adopted to analyze the aggregated data. We demonstrate the implications of the estimated models by presenting the spatial aggregation profiling based on the model estimates. The findings provide insights on relative differences of activity engagements across the urban region. The proposed approach thus provides a complementary analysis tool to traditional transportation planning exercises.

With the advancement in traffic management systems and improving accessibility to traffic information through various sources such as mobile apps, radio, variable message sign; road users tend to choose their route based on a complex interaction of various attributes including travel time, delay, travel cost and information provision mechanisms. While earlier research has examined route choice preferences in relation to travel time and travel cost (or toll), there is little guidance on the influence of information provision mechanisms. By accommodating for information provision attributes, the proposed research contributes to our understanding of the design of an active traffic management (ATM) system by quantitatively estimating the inherent trade-offs across the various attributes affecting route choice. Specifically, the research designs and elicits data using a web-based stated preference (SP) survey to understand road users’ preferencesin the Greater Orlando Region, USA. In the empirical analysis, the data compiled is utilized to develop random utility maximization and random regret minimization based panel mixed multinomial logit models. Route choice behavior is modeled using a comprehensive set of exogenous variables including trip characteristics, roadway characteristics and traffic information characteristics. The model results are utilized to conduct a comprehensive trade-off analysis across various attributes for the two model frameworks. In this research effort, we also customize the trade-off computation for regret minimization model for accommodating variable interactions. The trade-offs results provide useful insights on travel information provision (when and how).

The paper presents a model system that recognizes the distinct traffic incident duration profiles based on incident types. Specifically, a copula-based joint framework has been estimated with a scaled multinomial logit model system for incident type and a grouped generalized ordered logit model system for incident duration to accommodate for the impact of observed and unobserved effects on incident type and incident duration. The model system is estimated using traffic incident data from 2012 through 2017 for the Greater Orlando region, employing a comprehensive set of exogenous variables, including incident characteristics, roadway characteristics, traffic condition, weather condition, built environment and socio-demographic characteristics. In the presence of multiple years of data, the copula-based methodology is also customized to accommodate for observed and unobserved temporal effects (including heteroscedasticity) on incident duration. Based on a rigorous comparison across different copula models, parameterized Frank-Clayton-Frank specification is found to offer the best data fit for crash, debris, and other types of incident. The value of the proposed model system is illustrated by comparing predictive performance of the proposed model relative to the traditional single duration model on a holdout sample.

This paper focuses on developing an analysis framework to study the impact of cell phone treatment (cell phone type and call status) on driver behavior in the presence of a dilemma zone. Specifically, we examine how the treatment influences the driver maneuver decision at the intersection (stop or cross) and the eventual success of the maneuver. For a stop maneuver, success is defined as stopping before the stop line. Similarly, for a cross maneuver, success is defined as clearing the intersection safely before the light turns red. The eventual success or failure of the driver's decision process is dependent on the factors that affected the maneuver decision. Hence it is important to recognize the interconnectedness of the stop or cross decision with its eventual success (or failure). Toward this end, we formulate and estimate a joint framework to analyze the stop/cross decision with its eventual success (or failure) simultaneously. The study is conducted based on driving simulator data provided online for the 2014 Transportation Research Board Data Contest at http://depts.washington.edu/hfsm/upload.php. The model is estimated to analyze drivers' behavior at the onset of yellow by employing exogenous variables from three broad categories: driver characteristics, cell phone attributes and driving attributes. We also generate probability surfaces to identify dilemma zone distribution associated with different cell phone treatment types. The plots clearly illustrate the impact of various cellphone treatments on driver dilemma zone behavior.

In crash frequency models, frequency by severity level is examined with multivariate count models. In these multivariate approaches the impact of exogenous variables is quantified through the propensity component of count models. The main interaction between variables across severity levels is sought through unobserved effects; that is, there is no interaction of observed effects across the multiple count models. Although this is not necessarily a limitation, it could be beneficial to evaluate the impact of exogenous variables in a framework that directly relates a single exogenous variable to all severity count variables simultaneously. An alternative approach to examining crash frequency by severity is proposed. Specifically, instead of modeling the number of crashes, a fractional split modeling approach is used to study the fraction of crashes by each severity level on a road segment. Given the ordered nature of injury severity, an ordered probit fractional split model is used to study crash proportion by severity levels. The model is estimated for roadway segment data for single-vehicle and multivehicle crashes in Florida for 2009 through 2011. The model estimation results highlight the effect of traffic volume, lane width, shoulder width, proportion of divided segments, and speed limit on crash proportion by severity. The model results are used to predict hot spots for various crash types. The results highlight how the ordered probit fractional split models can be used for highway safety screening.

This study contributes to the safety literature on active mode transportation safety by using a copula-based model for crash frequency analysis at a macro level. Most studies in the transportation safety area identify a single count variable (such as vehicular, pedestrian, or bicycle crash counts) for a spatial unit at a specific period and study the impact of exogenous variables. Although the traditional count models perform adequately in the presence of a single count variable, these approaches must be modified to examine multiple dependent variables for each study unit. The presented research developed a multivariate model by adopting a copula-based bivariate negative binomial model for pedestrian and bicycle crash frequency analysis. The proposed approach accommodates potential heterogeneity (across zones) in the dependency structure. The formulated models were estimated with pedestrian and bicycle crash count data at the statewide traffic analysis zone level for the state of Florida for 2010 through 2012. The statewide traffic analysis zone level variables considered in the analysis included exposure measures, socioeconomic characteristics, road network characteristics, and land use attributes. A policy analysis was conducted—along with a representation of hot spot identification—to illustrate the applicability of the proposed model for planning purposes. The development of such spatial profiles allows planners to identify high-risk zones for screening and subsequent treatment identification.

The assessment of exposure to traffic-related air pollution has seen advances along various dimensions. Air pollution dispersion models with fine spatial resolution and ability to reflect near-road...

In safety literature, simulation-based multivariate framework is the most commonly employed approach for analyzing multiple crash frequency dependent variables. The current research effort contributes to literature on crash frequency analysis by suggesting an alternative and mathematically simpler approach for analyzing multiple crash frequency variables for the same study unit. The proposed recasts a multivariate distributional problem as a repeated measure univariate problem. Specifically, we employed a simpler panel random parameter based univariate model framework to analyze zonal level crash counts for different crash types. The empirical analysis is based on the traffic analysis zone (TAZ) level crash count data for both motorized and non-motorized crashes from Central Florida for the year 2016. The performance of the proposed framework is compared with the performance of the random parameter multivariate negative binomial model (RPMNB) using a host of metrics for estimation sample and hold-out sample. The resulting goodness of fit and predictive measures clearly highlight the comparable performance offered by the proposed framework relative to the commonly used RPMNB model with substantially fewer parameters. The comparison exercise is augmented by computing aggregate level elasticity effects for both PMNB and RPMNB models. The results clearly highlight the comparable performance offered by the proposed PMNB model relative to the traditional RPMNB model. In summary, the proposed framework allows for a parsimonious specification without compromising the model explanatory power and provides similar performance as the most traditional multivariate NB model for analyzing different crash dimensions.

In the United States, safety researchers have focused on examining fatal crashes (involving at least one fatally injured vehicle occupant) by using Fatality Analysis Reporting System (FARS) dataset. FARS database compiles crashes if at least one person involved in the crash dies within 30 consecutive days from the time of crash along with the exact timeline of the fatal occurrence. Previous studies using FARS dataset offer many useful insights on what factors affect crash related fatality, particularly in the context of fatal vs. non-fatal injury categorization. However, there is one aspect of fatal crashes that has received scarce attention in the traditional safety analysis. The studies that dichotomize crashes into fatal vs. non-fatal groups assume that all fatal crashes in the FARS dataset are similar. Keeping all else same, a fatal crash that results in an immediate fatality is clearly much more severe than another crash that leads to fatality after several days. Our study contributes to continuing research on fatal crashes. Specifically, rather than homogenizing all fatal crashes as the same, our study analyzes the fatal injury from a new perspective by examining fatality as a continuous spectrum based on survival time ranging from dying within 30 days of crash to dying instantly (as reported in the FARS data). The fatality continuum is represented as a discrete ordered dependent variable and analyzed using the mixed generalized ordered logit (MGOL) model. By doing so, we expect to provide a more accurate estimation of critical crash attributes that contribute to death. In modeling the discretized fatality timeline, the Emergency Medical Service (EMS) response time variable is an important determinant. However, it is possible that the EMS response time and fatality timeline are influenced by the same set of observed and unobserved factors, generating endogeneity in the outcome variable of interest. Hence, we propose to estimate a two equation model that comprises of a regression equation for EMS response time and MGOL for fatality continuum with residuals from the EMS model to correct for endogeneity bias on the effect of exogenous factors on the timeline of death. Such research attempts are useful in determining what factors affect the time between crash occurrence and time of death so that safety measures can be implemented to prolong survival. The model estimates are augmented by conducting elasticity analysis to highlight the important factors affecting time-to-death process.

In the transportation safety field, in an effort to improve safety, statistical models are developed to identify factors that contribute to crashes as well as those that affect injury severity. This study contributes to the literature on severity analysis. Injury severity and vehicle damage are two important indicators of severity in crashes and are typically modeled independently. However, there are common observed and unobserved factors affecting the two crash indicators that lead to potential interrelationships. Failure to account for the interrelationships between the indicators may lead to biased coefficient estimates in crash severity prediction models. The focus of this study was to explore interrelationships between injury severity and vehicle damage and to also identify the nature of these correlations across different types of crashes. A copula-based methodology that could simultaneously model injury severity and vehicle damage while also accounting for the interrelationships between the two indicators was employed. Furthermore, parameterization of the copula structure was used to represent the interrelationships between the crash indicators as a function of crash characteristics. In this study, six specifications of the copula model—Gaussian, Farlie–Gumbel–Morgenstern, Frank, Clayton, Joe, and Gumbel—were developed. On the basis of goodness-of-fit statistics, the Gaussian copula model was found to outperform the other copula-based model specifications. Results indicated that interrelationships between injury severity and vehicle damage varied with different crash characteristics including manner of collision and collision type.