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The acquisition of private property by right-of-way projects causes economic changes to the remainder of the property. An issue is the deviations in remainder parcel values between appraisers. Therefore, it is vital to understand whether appraisers in different locations consider and value the same or different factors. The objective of this paper is to identify spatial heterogeneity in the factors contributing to damages (as percentages) to remainders of affected parcels. Data on 507 appraisal reports for affected remainder parcels in Tennessee were collected and coded, creating a unique database with 23 variables. Applying a geographically weighted Gaussian regression model uncovered whether relationships were stationary over space. Results show that the local model outperforms the global model with an improved adjusted R2 of 0.81 compared with 0.77 in the global model. The most significant factors contributing to damage percentages that varied spatially are ratio of acquisition, adverse change in utility, major acquisition of landscape, highest and best use changed to assemblage, and major damage to access (landlocked). A larger area, corner parcels, and all categories of existing land use compared with residential use tend to lower the percentage damage to the remainder. Nashville is less severely affected by major damage to access, presumably for its high price of land. This study can assist appraisers in getting an early estimate of damage during partial takings. Property owners will have clarity about the impact of the eminent domain procedure on their land’s price.

A correct lane-changing plays a crucial role in traffic safety. Predicting the lane-changing behavior of a driver can improve the driving safety significantly. In this paper, a hybrid neural network prediction model based on recurrent neural network (RNN) and fully connected neural network (FC) is proposed to predict lane-changing behavior accurately and improve the prospective time of prediction. The dynamic time window is proposed to extract the lane-changing features which include driver physiological data, vehicle kinematics data, and driver kinematics data. The effectiveness of the proposed model is validated through the experiments in real traffic scenarios. Besides, the proposed model is compared with five prediction models, and the results show that the proposed prediction model can effectively predict the lane-changing behavior more accurate and earlier than the other models. The proposed model achieves the prediction accuracy of 93.5% and improves the prospective time of prediction by about 2.1 s on average.

Incident duration models are often developed to assist incident management and traveler information dissemination. With recent advances in data collection and management, enormous achieved incident data are now available for incident model development. However, a large volume of data may present challenges to practitioners, such as data processing and computation. Besides, data that span multiple years may have inconsistency issues because of the data collection environments and procedures. A practical question may arise in the incident modeling community—Is that much data really necessary (“all-in”) to build models? If not, then how many data are necessary? To answer these questions, this study aims to investigate the relationship between the data sample sizes and the reliability of incident duration analysis models. This study proposed and demonstrated a sample size determination framework through a case study using data of over 47,000 incidents. This study estimated handfuls of hazard-based duration models with varying sample sizes. The relationships between sample size and model performance, along with estimate outcomes (i.e., coefficients and significance levels), were examined and visualized. The results showed that the variation of estimated coefficients decreases as the sample size increases, and becomes stabilized when the sample size reaches a critical threshold value. This critical threshold value may be the recommended sample size. The case study suggested a sample size of 6,500 to be enough for a reliable incident duration model. The critical value may vary significantly with different data and model specifications. More implications are discussed in the paper.

Connected and automated vehicle technologies have the potential to significantly improve transportation system performance. In particular, advanced driver-assistance systems, such as adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC), may lead to substantial improvements in performance by decreasing driver inputs and taking over control of the vehicle. However, the impacts of these technologies on the vehicle- and system-level energy consumption, emissions, and safety have not been quantified in field tests. The goal of this paper is to study the impacts of automated and cooperative systems in mixed traffic containing conventional, ACC, and CACC vehicles. To reach this goal, experimental data based on real-world conditions are collected (in tests conducted by the Federal Highway Administration and the U.S. Department of Transportation) with presence of ACC, CACC, and conventional vehicles in a vehicle platoon scenario and a cooperative merging scenario. Specifically, a platoon of five vehicles with different vehicle type combinations is analyzed to generate new knowledge about potential safety, energy efficiency, and emission improvement from vehicle automation and cooperation. Results show that adopting the CACC system in a five-vehicle platoon substantially reduces the driving volatility and reduces the risk of rear-end collision which consequently improves safety. Furthermore, it decreases fuel consumption and emissions compared with the ACC system and manually-driven vehicles. Results of the merging scenario show that while the cooperative merging system slightly reduces the driving volatility, the fuel consumption and emissions can increase because of sharper accelerations of CACC vehicles compared with manually-driven vehicles.

Purpose Individuals’ driving behavior data are becoming available widely through Global Positioning System devices and on-board diagnostic systems. The incoming data can be sampled at rates ranging from one Hertz (or even lower) to hundreds of Hertz. Failing to capture substantial changes in vehicle movements over time by “undersampling” can cause loss of information and misinterpretations of the data, but “oversampling” can waste storage and processing resources. The purpose of this study is to empirically explore how micro-driving decisions to maintain speed, accelerate or decelerate, can be best captured, without substantial loss of information. Design/methodology/approach This study creates a set of indicators to quantify the magnitude of information loss (MIL). Each indicator is calculated as a percentage to index the extent of information loss (EIL) in different situations. An overall information loss index named EIL is created to combine the MIL indicators. Data from a driving simulator study collected at 20 Hertz are analyzed (N = 718,481 data points from 35,924 s of driving tests). The study quantifies the relationship between information loss indicators and sampling rates. Findings The results show that marginally more information is lost as data are sampled down from 20 to 0.5 Hz, but the relationship is not linear. With four indicators of MILs, the overall EIL is 3.85 per cent for 1-Hz sampling rate driving behavior data. If sampling rates are higher than 2 Hz, all MILs are under 5 per cent for importation loss. Originality/value This study contributes by developing a framework for quantifying the relationship between sampling rates, and information loss and depending on the objective of their study, researchers can choose the appropriate sampling rate necessary to get the right amount of accuracy.

A quick and accurate traffic incident duration prediction could greatly facilitate traffic incident management. However, at the very early stage of an incident, limited information is available for prediction. Information gathering for large-scale traffic incidents is a chronological process when a multi-agency response is required. At the early stage, information such as incident start time and roadway and weather conditions may be available, but information about response agencies and incident management solutions (e.g., lane closures) remains unknown. The objective of this study is to develop a sequential prediction method to handle the chronological process of incident information gathering. The method is based upon parametric survival modeling, which is often utilized to predict incident duration. This study took advantage of a unique incident database and identified over 600 large-scale incidents in the East Tennessee area from 2015 to 2016. A five-stage prediction method is proposed according to the chronological process by which information becomes available during incident operations. Using the data, this study compared three survival models: frailty model, multilevel mixed-effects model, and finite mixture model. Generally, with more information becoming available for modeling from the first to the last stage, the models’ performance improved according to the root mean square error and mean absolute percent error. The finite mixture model outperforms the other two models and its mean absolute percentage error is between 10% and 15%. Incident-associated factors at each stage are discussed and implications based on the study outcomes are also covered in the paper.

Vehicle merging is one of the main causes of reduced traffic efficiency, increased risk of collision, and fuel consumption. Connected and automated vehicles (CAVs) can improve traffic efficiency, increase safety, and reduce the negative environmental impacts through effective communication and control. Therefore, to improve the traffic efficiency and reduce the fuel consumption in on-ramp scenarios, this paper addresses the global and optimal coordination of the CAVs in a merging zone. Herein, a cooperative multi-player game-based optimization framework and an algorithm are presented to coordinate vehicles and achieve minimum values for the global pay-off conditions. Fuel consumption, passenger comfort, and travel time within the merging control zone were used as the pay-off conditions. After analyzing the characteristics of the merging control zone and selecting the appropriate control decision duration, multi-player games were decomposed into multiple two-player games. An optimal merging strategy was, thereby, derived from a pay-off matrix, and minimum payoffs were predicted for a number of different potential strategies. The optimal trajectory corresponding to the predicted minimum payoffs was then utilized as the control law to coordinate the vehicles merging. The proposed control scheme derives an optimal merging sequence and an optimal trajectory for each vehicle. The effectiveness of the proposed model is validated through simulation. The proposed controller is compared with two alternative methods to demonstrate its potential to reduce fuel consumption and travel time and to improve passenger comfort and traffic efficiency.

Abnormal driving behaviours, such as rapid acceleration, emergency braking, and rapid lane changing, bring great uncertainty to traffic, and can easily lead to traffic accidents. The accurate identification of abnormal driving behaviour helps to judge the driver's driving style, inform surrounding vehicles, and ensure the road traffic safety. Most of the existing studies use clustering and shallow learning, it is difficult to accurately identify the types of abnormal driving behaviours. Aimed at addressing the difficulty of identifying driving behaviour, this study proposed a recognition model based on a long short-term memory network and convolutional neural network (LSTM-CNN). The extreme acceleration and deceleration points are detected through the statistical analysis of real vehicle driving data, and the driving behaviour recognition data set is established. By using the data set to train the model, the LSTM-CNN can achieve a better result.

Hit-and-run crashes often delay emergency response and may result in increasing/secondary harms/damages to the victims in the crash. This study revisited hit-and-run crashes using a geo-spatial modeling approach, specifically, Geographically Weighted Regression (GWR), to explore geo-referenced crash data. The data cover motor vehicle crashes ( N = 138,529) in Southeast Michigan including 20,813 hit-and-run crashes in 2015. This study presented the results from both traditional regression and GWR models. GWR model results can be mapped in space, and the maps offer visual insights about the spatially varying correlates of hit-and-run crashes that are not available from previous studies. Results from traditional binary logit model are generally consistent with findings in previous studies. For example, hit-and-run is more likely to occur on weekends or during nighttime (especially without street lights on). Driving under impairment (DUI) seems to increase the likelihood of hit-and-run. GWR models also uncovered spatially varying correlates of hit-and-run. For example, DUI crashes in the northwest of the Detroit metropolitan area are associated with an even greater hit-and-run likelihood than those in other parts in this area. In addition, the local socio-economic factors are included in the analysis. Results show that hit-and-run is more likely to occur in census tracts with a higher unemployment rate, a lower household income, a smaller portion of college-educated population, and a greater population density. The study demonstrates a way of making sense of geo-referenced traffic safety data. The geo-spatial modeling method is useful for prioritizing specific geographic regions/corridors for safety improvement countermeasures, and outperforms traditional modeling techniques.

Walkability and walking activity are of interest to planners, engineers, and health practitioners for their potential to improve safety, promote environmental and public health, and increase social equity. Connected and automated vehicles (CAVs) will reshape the built environment, mobility, and safety in ways we cannot know with certainty—but which we may anticipate will change the meaning of “walkability.” The CAV era may provide economic, environmental, and social benefits, while potentially disrupting the status quo. This paper considers the concept of walkability in light of the approaching transition to CAVs, considering literature in engineering, information technology, built environment, land use, and public health, to support a discussion on research needs. To add depth, we subject a collection of research papers and technical reports to text analytics.

With the emergence of high-frequency connected and automated vehicle data, analysts can extract useful information from them. To this end, the concept of “driving volatility” is defined and explored as deviation from the norm. Several measures of dispersion and variation can be computed in different ways using vehicles’ instantaneous speed, acceleration, and jerk observed at intersections. This study explores different measures of volatility, representing newly available surrogate measures of safety, by combining data from the Michigan Safety Pilot Deployment of connected vehicles with crash and inventory data at several intersections. For each intersection, 37 different measures of volatility were calculated. These volatilities were then used to explain crash frequencies at intersection by estimating fixed and random parameter Poisson regression models. Given that volatility reflects the degree to which vehicles move, erratic movements are expected to increase crash risk. Results show that an increase in three measures of driving volatility are positively associated with higher intersection crash frequency, controlling for exposure variables and geometric features. More intersection crashes were associated with higher percentages of vehicle data points (speed & acceleration) lying beyond threshold-bands. These bands were created using mean plus two standard deviations. Furthermore, a higher magnitude of time-varying stochastic volatility of vehicle speeds when they pass through the intersection is associated with higher crash frequencies. These measures can be used to locate intersections with high driving volatilities. A deeper analysis of these intersections can be undertaken, and proactive safety countermeasures considered to enhance safety.

The existing model of sudden acceleration changes, referred to as the traffic jerk effect, is mostly based on theoretical hypotheses, and previous research has mainly focused on traditional traffic flow. To this end, this paper investigates the change in the traffic jerk effect between inactive and active vehicle-to-vehicle (V2V) communications based on field experimental data. Data mining results show that the correlation between the jerk effect and the driving behavior increases by 50.6% on average when V2V messages are received. In light of the data analysis results, a new car-following model is proposed to explore the jerk effect in a connected environment. The model parameters are calibrated, and the results show that the standard deviation between the new model simulation data and the observed data decreases by 38.2% compared to that of the full velocity difference (FVD) model. Linear and nonlinear analyses of the calibrated model are then carried out to evaluate the connected traffic flow stability. Finally, the theoretical analysis is verified by simulation experiments. Both the theoretical and simulation results show that the headway amplitude and velocity fluctuations are reduced when considering the jerk effect in a connected environment, and the traffic flow stability is improved.

Traffic incidents, often known as nonrecurring events, impose enormous economic and social costs. Compared with short-duration incidents, large-scale incidents can substantially disrupt traffic flow by blocking lanes on highways for long periods. A careful examination of large-scale traffic incidents and associated factors can assist with actionable large-scale incident management strategies. For such an analysis, a unique and comprehensive 5-year incident database on East Tennessee roadways was assembled to conduct an in-depth investigation of large-scale incidents, especially focusing on operational responses, that is, response and on-scene times by various agencies. Incidents longer than 120 min and blocking at least one lane were considered large scale; the database contained 890 incidents, which was about 0.69% of all reported incidents. Rigorous fixed- and random-parameter, hazard-based duration models were estimated to account for the possibility of unobserved heterogeneity in large-scale incidents. The modeling results reveal significant heterogeneity in associations between operational responses and large-scale incident durations. A 30-min increase in response time for the first, second, and third (or more) highway response units translated to a 2.8%, 1.6%, and 4.2% increase in large-scale incident durations, respectively. In addition, longer response times for towing and highway patrol were significantly associated with longer incident durations. Given large-scale incidents, associated factors included vehicle fire, unscheduled roadwork, weekdays, afternoon peaks, and traffic volume. Notably, the associations were heterogeneous; that is, the direction could be positive in some cases and negative in others. Practical implications of the results for large-scale incident management are discussed.

Highway infrastructure requires periodic maintenance, reconstruction, and rehabilitation. As a result, highway users have to deal with work zone activities such as lane closures and lane shifts or crossovers, and they must be aware of changes in road conditions to drive safely through work zones. A large-scale statewide crash database from the Virginia Department of Transportation was used in this study to examine correlations between precrash actions and the severity of the injuries of drivers involved in crashes. An innovative aspect of this study is that it accounts for the injury severity of each vehicle driver involved in a crash by estimating hierarchical models. The correlates of the severity of a driver’s injury are nested in crashes. Modeling revealed that for work zone crashes the chances of driver injury are 9.9% to 10.3% higher than for the base behavior (i.e., no improper actions by the driver) if the driver intentionally commits an improper action or a violation. For non–work zone crashes, the chances of injury are higher by only 1.7% to 5.7% compared with the base behavior. Such actions and violations are mainly the following: ( a) speeding, ( b) following too closely, and ( c) disregarding officers, flaggers, signals, and signs. The correlations between precrash actions and injury severity provide insights into safety improvements (e.g., effective speed enforcement and traffic regulations) that could reduce the risk of injury in work zones. Hierarchies embedded in highway crash data were explored in this study to make methodological and empirical contributions to the understanding of work zone safety and to improve understanding of behaviors that lead to injuries and fatalities in work zone crashes.

Traffic incidents occur frequently on urban roadways and cause incident-induced congestion. Predicting incident duration is a key step in managing these events. Ordinary least squares (OLS) regression models can be estimated to relate the mean of incident duration data with its correlates. Because of the presence of larger incidents, duration distributions are often right-skewed; that is, the OLS model underpredicts the durations of larger incidents. Therefore, this study applies a modeling technique known as quantile regression to predict more accurately the skewed distribution of incident durations. Quantile regression estimates the relationships between correlates and a chosen percentile—for example, the 75th or 95th percentile—while the OLS regression is based on the mean of incident duration. With the use of incident data related to more than 85,000 (2013 to 2015) incidents for highways in the Hampton Roads area of Virginia, quantile regression results indicate that the magnitudes of parameters and predictions can be quite different compared with OLS regression. In addition to predicting durations of larger incidents more accurately, quantile regressions can estimate the probability of an incident lasting for a specific duration; for example, incidents involving congestion and delay have an approximately 25% chance of lasting more than 100.8 min, while incidents excluding congestion and delay are estimated to have a 25% chance of lasting more than 43.3 min. Such information is helpful in accurately predicting durations and developing potential applications for using quantile regressions for better traffic incident management.

To improve transportation safety, this study applies Highway Safety Manual (HSM) procedures to roadways while accounting for unobserved heterogeneity and exploring alternative functional forms for Safety Performance Functions (SPFs). Specifically, several functional forms are considered in Poisson and Poisson-gamma modeling frameworks. Using five years (2011-2015) of crash, traffic, and road inventory data for two-way, two-lane roads in Tennessee, fixed- and random-parameter count data models are calibrated. The models account for important methodological concerns of unobserved heterogeneity and omitted variable bias. With a validation dataset, the calibrated and uncalibrated HSM SPFs and eight new Tennessee-specific SPFs are compared for prediction accuracy. The results show that the statewide calibration factor is 2.48, suggesting rural two-lane, two-way road segment crashes are at least 1.48 times greater than what HSM SPF predicts. Significant variation in four different regions in Tennessee is observed with calibration factors ranging between 2.02 and 2.77. Among all the SPFs considered, fully specified Tennessee-specific random parameter Poisson SPF outperformed all competing SPFs in predicting out-of-sample crashes on these road segments. The best-fit random parameter SPF specification for crash frequency includes the following variables: annual average daily traffic, segment length, shoulder width, lane width, speed limit, and the presence of passing lanes. Significant heterogeneity is observed in the effects of traffic exposure-related variables on crash frequency. The study shows how heterogeneity-based models can be specified and used by practitioners for obtaining accurate crash predictions., Comment: Accepted for Publication in Transportation Research Record: Journal of the Transportation Research Board

Transportation engineers and researchers heavily use traffic data, which are generally aggregated by predetermined time intervals (e.g., 5 to 15 min). The aggregation process often discards essential information of traffic state transition (e.g., breakdowns). However, the transition of traffic conditions within an aggregation interval is not well understood. This study explored traffic state transition from uncongested to congested regimes that occurred within a predetermined time interval. From two urban freeway locations in Norfolk, Virginia, traffic data archived at 15-min intervals were obtained. A heuristic method based on a Gaussian mixture model was developed to detect the aggregate traffic data that exhibit the transition of traffic states as well as to partition the data statistically into uncongested and congested traffic states. Results show a substantial difference in travel speed (approximately 20 mph) between the two states. In addition, these results illustrate that aggregating these different traffic conditions can cause substantial traffic data aggregation bias by lowering travel speed and flow rates, especially in high traffic flow situations. Finally, new insights into valid traffic data aggregation and speed–flow–concentration relationship development are discussed.

Traditionally, evaluation of intersection safety has been largely reactive and based on historical crash frequency data. However, the emerging data from connected and autonomous vehicles can complement historical data and help in proactively identifying intersections with high levels of variability in instantaneous driving behaviors before the occurrence of crashes. On the basis of data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, this study developed a unique database that integrated intersection crash and inventory data with more than 65 million real-world basic safety messages logged by 3,000 connected vehicles; this database provided a more complete picture of operations and safety performance at intersections. As a proactive safety measure and a leading indicator of safety, location-based volatility was introduced; this quantified variability in instantaneous driving decisions at intersections. Location-based volatility represented the driving performance of connected-vehicle drivers traveling through a specific intersection. As such, with the use of the coefficient of variation as a standardized measure of relative dispersion, location-based volatility was calculated for 116 intersections in Ann Arbor. Rigorous fixed- and random-parameter Poisson regression models were estimated to quantify relationships between intersection-specific volatilities and crash frequencies. Although exposure-related factors were controlled for, the results provided evidence of a statistically significant (at the 5% level) positive association between intersection-specific volatility and crash frequencies for signalized intersections. The implications of these findings for proactive intersection safety management are discussed.

The transportation sector is the second largest contributor to human-generated carbon dioxide (CO2) emissions. A key goal of the U.S. Department of Transportation is to implement environmentally sustainable policies that can reduce carbon emissions from transportation sources. Smart growth—characterized by compact, mixed use; greater network connectivity; and environments friendly to alternative modes—may encourage reductions in vehicle travel and emissions. A better understanding of travel behavior in conventional and smart growth communities is needed to inform policies. A behavioral data set is analyzed to determine whether smart growth developments are associated with lower CO2 emissions. Sample selection models are estimated from a 2009 travel behavior survey of 15,213 households to capture the conditionality of emissions on the decision to drive (or not) by household members on a given day. Results indicated that 12% of responding households used alternative modes or did not travel from home; the rest of the sample traveled in an automobile and therefore contributed to CO2 emissions. CO2 emissions were calculated from vehicle miles traveled and the fuel efficiency of the vehicle used for specific trips taken by household members. The developed framework models whether CO2 emissions are associated with land use, sociodemographics, and preferences for adopting information technology. Tailpipe CO2 emissions are lower for households that reside in mixed land use neighborhoods with good network connections (on the order of 9%). As a long-term strategy, CO2 emissions reductions from smart growth developments can be substantial.

National and regional household travel surveys have conventionally sampled landline telephone households through list-assisted random digit dialing. However, recent increases in “mobile phone-only” households result in either noncoverage or undercoverage of a growing segment of the population. This result could cause a substantial bias in the representativeness of travel behavior toward the target population. To cover mobile phone-only households, an address-based sampling method is of interest. This study explores whether the characteristics and travel behavior of mobile phone-only households differ from those of households with landline telephones. In addition, this study quantifies the extent of noncoverage errors in the surveys in respondents’ travel behavior. Along with census data, the mobile phone-only sample (N = 2,988) was compared with the landline telephone sample (N = 7,774) drawn from the 2008 National Capital Region Household Travel Survey. Results show that the mobile phone-only sample consisted of relatively more single-person households; younger individuals; and Blacks, Asians, and Hispanics, who were generally identified as hard-to-reach groups. Statistical models were estimated to examine differences in travel behavior and suggested that the mobile phone-only households made more transit (41%) and walking (29%) trips. This study shows that the inclusion of the mobile phone-only households can reduce the noncoverage errors, especially for alternative modes. The implications for travel survey methods are discussed.

Identifying and minimizing potential errors in household travel surveys can facilitate collecting more representative and accurate data. Through a comparison of two recent travel surveys with census data, this paper presents how sampling, noncoverage, nonresponse, and measurement errors work their way into surveys. The 2009 National Household Travel Survey (NHTS) Add-On in Virginia was implemented with a comprehensive survey instrument and random-digit-dial (RDD) sampling. The 2008 National Capital Region Household Travel Survey collected behavioral data with a concise instrument, while adopting address-based sampling (ADD). Focusing on a common area of Northern Virginia, this study examined differences in sociodemographics and travel behavior of the extracted samples (N = 597 and N = 3,581, respectively). Results show that the ADD survey collected data on more single-person households, younger individuals, and Hispanics and Mexicans, which are generally identified as hard-to-reach groups. A comparison of the two samples with the census data shows that the ADD sample was more representative of the population and area, partly because of the inclusion of mobile phone-only households (28%), which were not fully covered in RDD. To quantify a measurement error, this study estimated rigorous statistical models in regard to reported trip frequency. Results show that the NHTS captured 10% more trips, partly as a result of diary instructions and the presence of walking and biking questions in the instrument. Details of other errors and implications for reducing key survey errors are discussed.

Traffic incidents on urban freeways are a major source of congestion and travel time uncertainty. In particular, large-scale incidents have longer durations and need more incident response resources. These incidents cause severe problems such as longer traffic queues, substantial delays, and secondary incidents. Large-scale incidents deserve more attention by practitioners and researchers. The objective of this study was to analyze large-scale incidents and explore their correlates and implications for traffic operations. An innovative analysis method based on a detailed incident data set from Hampton Roads, Virginia, was developed. This study defined large-scale incidents as having a minimum duration of 2 h, according to guidance from the Manual of Uniform Traffic Control Devices. The study discovered how the spatial and temporal patterns of large-scale incidents varied. Forty percent of the large-scale incidents blocked all lanes of traffic during some point in their duration. Rigorous statistical models were estimated to quantify associated key factors that included incident characteristics, roadway geometry, traffic flow, and operational responses. Results indicated that given large-scale incidents, their longer durations were associated with extreme events, for example, occurring in a work zone, the presence of curvature on the segment where the incident occurred, morning peak hours, and occurrence of secondary incidents. The new findings provide insights concerning the understanding of large-scale incidents and have certain implications for effective incident management.

To improve regional travel demand models, transportation engineers and planners desire appropriate representation of subpopulations. University students are a relatively neglected group of the population, often missed in regional behavioral surveys and not well represented in travel demand models. Many students attending a university reside, take classes, work, and perform other activities in the university environment, which is often mixed use, alternative mode friendly, higher density, and livable. The purpose of this paper is to understand the travel behavior of university students and to model associations with their attributes that include personal characteristics, residential location (residing on campus or off campus), and academic status. The data used in this study are from a unique Internet-based survey (N = 1,468) of students at Old Dominion University in Virginia. This effort was conducted in 2010 and was part of the Virginia University Student Travel Survey (USTS) supplement. With USTS data combined with spatial data, rigorous statistical models of automobile and walk–bicycle trip rates are estimated to explore associated factors. Results showed that students living on campus or near campus were significantly more likely to walk and bicycle and less likely to drive automobiles and indicated the value of living in a campus environment with greater accessibility to activities and a walk-and bicycle-friendly network. The behavioral models provide helpful information that can be used to represent better the behavior of university students in regional travel demand models and to improve strategic transportation planning.

Very high traffic volumes may lead to extensive congestion during hurricane evacuations. Evacuation planners reduce this congestion by careful planning for multiple hurricane scenarios and assignment of evacuation routes and timing. This planning may be for naught if obstructions block key roadways. An advanced traveler information system (ATIS) may be used to guide evacuees to alternate routes, but how effective will that guidance be? Should the use of alternate routes be encouraged? How are drivers likely to respond to delays and information? Will information shorten or improve the reliability of travel times in emergency conditions? Integration of a dynamic evacuation simulation and a decision-making model (representative of the decisions made by potential hurricane evacuees when provided with information on downstream traffic congestion and alternate routes) can help emergency planners prepare for the unexpected. Advance modeling of likely accident locations and the severity can forecast the effects of alternate route use, help determine the best locations and timing of alternate route information, and support decision making. This study integrated an evacuee route choice decision model and a mesoscopic evacuation transportation simulation for southeastern Virginia. Study results show how the effects of ATIS can be tested in advance, thus allowing more comprehensive planning by emergency management and transportation professionals. Simulations of ATIS’ effectiveness in evacuation scenarios have been largely unexplored. Methods presented can be applied in a variety of evacuation scenarios and may be of particular value to emergency planners.

The application of a gravity model in freight modeling work on both short-haul and long-haul trips is discussed. A commodity-based gravity model was developed to assess the distribution of freight by long-haul trucks in southeastern Virginia. Although gravity models have been used extensively in transportation studies, little work has been done to address the special characteristics of freight transportation, such as the definition of friction factors and the differences between long-haul and short-haul trips. Results of a recent study of these and similar problems provide valuable insight into freight distribution modeling. A new calibration method that used a genetic algorithm was applied, various commodities were modeled, and the impact of the commodities on the accuracy of the gravity model was studied. Both travel time and travel distance were tested to generate the impedance for friction factors; results showed that for commodity-based long-haul models, travel times were more appropriate for friction factor calculations. In addition, results showed that the gamma function was more suitable than the exponential function for friction factor calculations. Extensive analyses of the causes of variation between observed values and the gravity model outputs are provided. The analyses and conclusions may help modelers better understand characteristics specific to freight transportation and can promote model constructions with greater accuracy and efficiency.

Advanced traveler information systems provide pretrip and en route information, which can improve the travel experience of individuals and increase the efficiency of the transportation system. While research on travelers’ acquisition and use of relevant information on their intended routes and modes has been conducted, behavioral responses of sub-populations that might be particularly sensitive to information are not well understood. A key segment of the population is university students, who are often technologically savvy, are early adopters of new information technology, have widespread access to computers and the Internet, and often use a multitude of travel modes. This study explores student responses to travel information. As part of a larger study, behavioral surveys were conducted to collect and analyze data on university students’ travel behavior. This study focuses on a subset of the collected data that deals with acquisition and use of travel information. It explores how students at four universities in Virginia acquire and respond to travel information, and it identifies important factors associated with these decisions. Statistical models are estimated to test hypotheses. Results show that travel information acquisition is higher when students report longer travel times and on urban campuses. The Internet and variable message signs have the strongest associations with travel decision changes regardless of campus location. Students alter their routes and their modes of travel relatively frequently, especially on suburban campuses, pointing to the importance of delivering multimodal information. Implications of the findings are discussed.

Incidents on urban freeways are a leading cause of traffic congestion. Secondary incidents can occur in the vicinity of primary incidents, complicating traffic operations. While studies have examined factors associated with incident duration and secondary incident occurrence, a significant number of spatiotemporal variables in incident management are often overlooked. For example, how soon does a secondary incident happen after a primary incident? How far is the secondary from the primary incident? What factors are associated with near versus far secondary incidents? To answer these questions, a deeper analysis of primary and secondary incidents was conducted on the basis of a unique 2008 incident and roadway inventory database for Hampton Roads, Virginia. Time gaps and distances for secondary incidents in the same direction were analyzed with appropriate statistical methods. This research contributes to incident management by rigorously analyzing time-gap distances between primary and secondary incidents and exploring their implications. The results can support more informed planning and operational decisions needed to respond in complex incident situations.

The access and the use of advanced traveler information systems (ATISs) by drivers during normal commuting have been extensively assessed and analyzed. Emergency managers and transportation officials have extended the results of studies of ATIS use under routine conditions to emergency scenarios under the assumption that drivers' responses to information under emergency conditions mimic those seen under normal driving conditions. A recent survey of potential hurricane evacuees suggests the need to revisit this assumption. Results indicate that although commercial radio reports and variable message signs continue to be the sources of traffic information cited the most frequently, other information sources (mobile phones, in-car systems such as Global Positioning System devices, and the Internet) have significantly increased in importance. Rapid growth in user rates and the relatively low cost of implementation suggest that a revision of plans for emergency transportation information communications may be warranted. Better, more effective use of ATISs during emergency situations, especially when traffic incidents occur, may lead to improved and more reliable travel times and improved safety and emergency response. With the use of factor analysis, four driver personalities are identified, with each one characterized by the proclivity for and response to traffic information. This information will be of interest to developers and users of ATISs and to those responsible for emergency management and transportation planning.

To improve regional travel demand models, transportation engineers and planners want to represent subpopulations appropriately. A key segment of the population is university students, and their behavior is neither well understood nor well represented in travel demand models. Furthermore, universities provide a unique context for behavioral research because they are livable, are friendly to alternative travel modes, have a higher density than other contexts, and offer mixed travel modes. This study collected and analyzed data on the travel behavior of university students. With the use of an Internet-based survey instrument, the study collected data on travel behavior, sociodemographics, and context variables at four major universities in Virginia. This paper provides information about the design and implementation of the survey, the instrument structure, and a descriptive analysis of students’ personal and travel characteristics. The results indicated that the sociodemographics and travel behavior of university students were different from those of the general population. Moreover, differences in travel behavior were found between students living on campus and students living off campus and between students attending urban campuses and those attending suburban campuses. The insights gained from this study serve as a basis for further such surveys and help provide an understanding of travel behavior in and around university campuses.

Incident-induced traffic congestion is a major source of travel uncertainty. Sometimes multiple incidents occur sequentially because of queue backups, which substantially increase uncertainty. Such cascading incidents can be grouped into one event because of their spatial and temporal proximity. Events consisting of a primary and its secondary incidents are expected to have longer durations than single incidents and therefore to result in larger impacts on traffic. Though relatively rare, such cascading events are a major concern for transportation operations managers, and they are the focus of this paper. A unique event database, based on incident and road inventory data from Hampton Roads, Virginia, is created. Single-pair events (one primary and one secondary incident) and large-scale events (one primary and multiple secondary incidents) are identified and analyzed. “Event duration” is defined as the time elapsed from the notification of a primary incident to the departure of the last responder from the event scene after removal of the primary and associated secondary incidents. Events are further categorized as either contained or extended. If the primary incident is the last one being cleared during such an event, then it is a contained event; otherwise, it is an extended event. Correlates of contained and extended event durations are identified through a set of rigorous statistical models. The findings of this study provide knowledge that can aid in mitigating the impacts of cascading incidents.

Successful evacuations of metropolitan areas require overcoming unexpected congestion that reduces traffic flows. Congestion may result from accidents, incidents, or other events that reduce road capacity. Traffic professionals and emergency managers may promote deviations from planned routes to bypass an area of congestion and speed mass exit. However, some route changes may actually reduce traffic flow rates, and in these cases decision makers may want to discourage use of alternate routes. By using results of a behavioral survey of potential hurricane evacuees, this study identifies variables associated with the decision to alter routes and also identifies frequently used information sources. A dynamic traffic simulation with a decision-making model using this information is proposed as a means for evacuation decision makers to assess impacts of driver decisions. Results from more than 800 responses showed the potentially strong influence of effective advanced traveler information systems to support decisions made by hurricane evacuees on whether to use an alternate route when faced with congestion. Results of this study are a timely contribution to those seeking a better understanding of driver behavior during evacuations and improvement of emergency management efficiency and efficacy.

Advanced traveler information systems are particularly helpful in supporting route diversion decisions. The effect of information on diversion behavior on nontolled roads is well documented in the literature. Revealed and stated preference studies traditionally have been conducted to analyze route diversion behavior. However, the effect of realtime traffic information on the behavior of toll road users is unexplored. This study examines the behavior of toll road users in Orlando, Florida. Orlando is serviced by a toll road network where dynamic message signs (DMSs) provide real-time travel time information to travelers. The response of toll road users to information was expected to differ from that of non-toll users. To capture the effects of information, specifically the DMS, a survey was conducted in two phases: predeployment (with only one DMS installed) and postdeployment (after 29 DMSs were installed). A detailed behavioral data set with rigorous modeling was used to investigate the relationship between information and travel decisions in the context of toll road trips. The surveys revealed that higher travel time savings due to diversion, 511 use, and toll payment by cash were associated with a greater propensity to divert in postdeployment. Travelers who experienced abnormal travel times or who reported that DMS helped them during congestion were more likely to divert. The study further showed that toll road users might have more inertia and avoid leaving the toll road than non-toll road users. Implications of the results are discussed.

Travelers can often benefit from acquiring relevant information on their intended modes and routes. By providing pretrip and en route information, advanced traveler information systems present real opportunities for improving the travel experience of individuals and increasing efficiency of the transportation system. In this regard, it is important to understand how consumers acquire and respond to travel information. This study develops a conceptual framework identifying important factors influencing travelers’ information acquisition behavior and their response to dynamic information. The model is empirically tested with the use of a recent and comprehensive regional travel survey. A sample selection model is estimated to be consistent with the two-stage processing of travel information (i.e., acquisition and response). Results show that information acquisition and changes in travel plans are sensitive to different sets of factors. In the data set analyzed, normal travel time to work is found to be a critical factor in information acquisition, but has an insignificant association with the change of travel plans. Furthermore, travelers respond differently to various information technologies. In an examination of change behavior (including route change, mode change, and trip cancellation), Internet access had the strongest association with change. With examination of specific route change behavior, transportation information obtained through radio had the strongest association with change. The study generates useful implications on how to improve existing and future traveler information systems.

Incidents impose substantial social and personal costs on drivers. Some of the larger incidents that cause delays are also associated with secondary incidents. However, the nature of interdependence between primary and secondary incidents is not fully known. The objective of this study is to understand how primary incident duration and secondary incident occurrence are related. Specifically, secondary incidents are more likely to occur if the primary incident lasts a long time; at the same time, the durations of primary incidents are expected to be longer if secondary incidents occur. After traffic incident and road inventory data in the Hampton Roads, Virginia, area were obtained, secondary incidents were then identified. Secondary incidents were defined as incidents occurring on the same roadway segment (which average 1 mi in length) as the primary incident and within the actual duration of the primary incident. If the primary incident blocked lanes, then the actual duration plus 15 min was used as the threshold. Models for primary incident durations and whether a secondary incident occurs are estimated. The interdependence is modeled by considering incident duration as endogenous in the secondary incident occurrence models. Results show statistical evidence for interdependence, but when it is taken into account, no substantial differences in the magnitudes and statistical significance for the estimated independent variables are found (compared with when the interdependence is not accounted for). Statistically significant correlations found between secondary incident occurrence and other variables allow the recommendation of specific operational strategies.

A practical method is presented for systematically evaluating the network impacts of advanced traveler information systems (ATISs) to support well-informed project decisions and well-founded funding priorities. Detailed comparative assessments of available evaluation tools are given on the basis of a common set of desirable criteria. A mesoscopic network modeling and dynamic traffic assignment (DTA) tool, namely, DYNASMART-P, was identified as a promising candidate model and was applied to the evaluation of case study scenarios in a subnetwork of the Triangle Regional Model in North Carolina. The case study involved planned work zone activities occurring during nonpeak time periods. The case study demonstrated that the DYNASMART-P DTA tool is capable of providing reasonable evaluation results, including realistic estimates of the effectiveness of ATISs in mitigating the congestion caused by work zone activities. The case study analyses further illustrated the high value of traveler information through an example benefit–cost analysis based on the scenario modeling. The simulation results were validated for reasonableness through comparison with field speed data. Finally, several potential DTA functionality enhancements are identified that will further support ATIS evaluation.

Advanced traveler information systems (ATISs) help individuals make informed travel decisions. Current ATIS applications encompass a variety of delivery mechanisms, including the Internet, telephone, television, radio, variable message signs, and in-vehicle navigation devices to support decisions about destinations, travel mode, departure time, routes, parking, and trip cancellation. It is important for researchers and practitioners to review the status of ATIS technologies and to understand travelers’ access and response to current ATIS deployment. Focusing on largely public-sector delivery mechanisms, this study answers two fundamental questions: whether accessing more information sources is associated with a higher likelihood of travel decision adjustments and which technologies are more likely to elicit substantive adjustments to routine travel. These questions are answered by using a comprehensive and recent behavioral data set, collected in the Research Triangle area of North Carolina. The study generates useful knowledge about how to operate existing traveler information systems more efficiently and how to improve them in the future.

Physical planning can benefit from deeper insight into the space-use options that individuals have. This paper examines how individuals’ uses of space are related to urban form factors at their residences, after controlling for traffic congestion, weather, and individual or household characteristics. The behavioral data analyzed came from the 2006 Greater Triangle Region Travel Study in North Carolina. Individuals’ uses of space were measured by daily activity space–the minimum convex polygon that contains all the daily activity locations–and daily travel distance, and were estimated by the use of spatial regression models. The results showed that the residents of densely developed neighborhoods with more retail stores and better-connected streets generally have a smaller area of daily activity space and a shorter daily travel distance. In addition, urban form factors were compared in terms of their importance in explaining individuals’ space-use behavior. It was found that retail mix and street connectivity are key factors relating to individuals’ uses of space, whereas building density was less important. The findings shed light on possible land use solutions toward the better coordination of services in space.

Incidents and the resulting congestion on Interstate highways add uncertainty to travel times, imposing significant costs on business operations and regional economic development. This paper describes the results of an effort to quantify the economic impact of traffic incidents on North Carolina's Interstate facilities. The businesses to be interviewed were carefully chosen on the basis of their substantial shipping needs. Analyses of 29 selected businesses, including carriers, were conducted and showed an average hourly cost of unexpected delay of $145 to the businesses surveyed (2005 dollars). A more focused analysis by sector and region showed that various types of businesses and regions differ in their sensitivities to unexpected congestion. Of the industrial sectors sampled, transportation and warehousing had the highest hourly costs, followed by the retail trade and manufacturing sectors. Case studies further showed that although a majority used information technology to track shipments, few businesses sought preshipment traffic information or were aware of the traffic information services available. The businesses surveyed generally expressed a desire for better communication and information services from the state department of transportation. It is clear from this study that businesses incur costs because of unexpected delays on the Interstate system, adding to the cost of production. The implications of the results are discussed.

Incidents often account for nearly half of traffic congestion in urban areas and add uncertainty to transportation networks. The costs of incident-induced congestion, often in the form of delays, are borne by motorists and commercial carriers or associated businesses. In fact, a higher burden is borne by commercial carriers, given their higher costs and value of time. Dynamic traveler information about incidents disseminated through electronic media can benefit users. The extent of benefits associated with dynamic traveler information and whether network delays increase or decline were explored when (a) travelers can observe incidents, (b) commercial truck percentages increase in traffic, (c) truck drivers divert to alternate routes in the same way motorists do, as opposed to having lower diversion rates, and (d) commercial trucks have a higher value of time compared with passenger vehicles. With a behavioral route diversion model, the movement of commercial trucks and passenger vehicles in a simple transportation network was simulated. The results show how dynamic traveler information may or may not benefit commercial and noncommercial users under different scenarios.

Transportation analysts have long recognized a role for the environment in travel behavior; techniques for incorporating the built environment into travel research remain in active development. This study uses multiple environmental representations to model automobile ownership and travel decisions with a single data set and model structure to test relationships already reported in the literature and to lay the foundation for extending this framework to additional travel modeling. Simple environmental measures, indices generated by factor analysis, and a neighborhood typology derived from cluster analysis of the factors, along with common household measures, are used to find the factors to provide information about travel that the clusters and direct measures do not. Automobile ownership and trips showed the expected relationships, with the former sensitive to sociodemographic factors and the latter sensitive also to the environment. Modes related differently to environmental factors; specifically, walk trips were strongly associated with accessibility and walkability, whereas drive trips were insensitive to these factors but were associated with other factors.

Planners and engineers may consider both conventional capacity improvements and intelligent transportation systems (ITS) to address transportation problems. Examples of ITS include freeway service patrols, advanced vehicle location systems, and certain elements of employee transit pass programs, each with various advantages. Details of a system to help planners and engineers make informed decisions about ITS deployment are provided. ITS Decision is designed to provide comprehensive information about state-of-the-art ITS technologies in a relevant form—the user can access desired information without sifting through irrelevant material. In addition, ITS Decision offers innovative tools to help users identify ITS appropriate for particular transportation problems. An expert system queries the user about specific conditions, diagnoses the problem, and suggests ITS remedies. The case-based reasoning tool lets users match historical cases that are most similar and see the effects. Given the still limited penetr...

An extensive body of literature has developed on the relationship between the physical environment and travel behavior. Although many studies have found that neotraditional neighborhood development supports nonautomobile travel by providing good street connectivity, pedestrian and cycling facilities, and internal destinations, questions remain about the travel behavior of individuals within such neighborhoods. This study uses travel diaries to examine utilitarian trip-making behavior within a neotraditional neighborhood and compares total trips with mode-specific (i.e., walk and drive) trips. Negative binomial regression is used to examine the effect of a set of independent variables, including personal and household characteristics, select attitudinal factors, and distance from residences to the commercial center. It is found that within the neotraditional neighborhood, walk trips drop off quickly with increasing distance to destinations, whereas drive trips increase. The analysis demonstrates the importance of short distances for within-neighborhood travel and the merit in considering trips separately for walk and drive modes to avoid obscuring important factors associated with trip making.

Planners and engineers may consider both conventional capacity improvements and intelligent transportation systems (ITS) to address transportation problems. Examples of ITS include freeway service patrols, advanced vehicle location systems, and certain elements of employee transit pass programs, each with various advantages. Details of a system to help planners and engineers make informed decisions about ITS deployment are provided. ITS Decision is designed to provide comprehensive information about state-of-the-art ITS technologies in a relevant form—the user can access desired information without sifting through irrelevant material. In addition, ITS Decision offers innovative tools to help users identify ITS appropriate for particular transportation problems. An expert system queries the user about specific conditions, diagnoses the problem, and suggests ITS remedies. The case-based reasoning tool lets users match historical cases that are most similar and see the effects. Given the still limited penetration of ITS into appropriate settings, the expert system and case-based reasoning tools are meant to stimulate greater deployment of promising technologies in localities that have not adopted such systems.

Although the commonly accepted link between automobile ownership and automobile use has inspired some municipalities to experiment with neighborhood design in an attempt to influence both automobile ownership and travel behavior, the underlying relationship between neighborhood design and automobile ownership is still unclear. Evidence suggests that automobile ownership is tightly linked to income and household size and is less responsive to urban design. This research uses data from a matched pair of neighborhoods–-one conventional and one neotraditional–-to consider the relationship between neighborhood design and automobile ownership and the relationship between these factors and automobile use. Statistically significant differences were found for automobile ownership in the two neighborhoods. In addition, there were clear differences in automobile use–-residents of neotraditional developments made fewer automobile trips, traveled fewer miles in their vehicles, and spent less time driving. This has implications for planning strategies that may help reduce automobile trips and miles separately from changes in automobile ownership.

Society pays a high cost for work zone crashes in terms of operational disruptions, property damage, injuries, and loss of life. Given narrow lanes in work zones, large trucks are of particular concern. Truck-involved collisions in work zones, as opposed to non-truck-involved collisions in North Carolina, are empirically examined. This examination helps in understanding which work zone attributes are empirically associated with the most seriously injured occupant and total harm in a crash. Specifically, with a unique data set, effects of the following variables were explored: type of work zone, presence of warning signs and cones, type of activity in the work zone, location of the crash in the work zone, and construction impact of the work zone on the roadway. The results show that work zone crashes in North Carolina, especially those involving large trucks, were more injurious than were non—work zone crashes. Rigorous modeling results suggest that truck-involved multivehicle crashes were most injurious and harmful when ( a) they occurred on two-way undivided or two-way divided but unprotected (without a median barrier) roadways; ( b) the roadway was closed and a detour was required on the opposite side; ( c) they occurred adjacent to the work area; and ( d) the posted speed limits were higher. The results provide valuable information on high-risk factors in work zones.

Freeway service patrols (FSPs), classified as part of intelligent transportation systems, are programs that use roving vehicles to patrol congested and high-incident sections of freeways; they help to smooth traffic flow by aiding stranded motorists and assisting in incident clearance. Many major urban areas currently have patrols, and most medium-sized urban areas are following suit. The success of FSPs has resulted in frequent requests for service expansion. However, the decision on where to put the next patrol is becoming more difficult because an assessment of greatest need typically indicates that high-priority areas already have the service, whereas the possible effects of the service are often indistinguishable on lower-priority facilities. A new approach was developed to help determine the most beneficial locations for patrol deployment by using expanded placement criteria. North Carolina was used as a case study. Analysis of three incident and crash indexes was combined with spatial analysis, incident type distributions, average hourly freeway traffic volumes, and incident delay estimations to identify, evaluate, and compare candidate facilities for FSP expansion. Results of the research were incorporated into a decision support tool that allows easy planning and operational assessment of candidate sites by comparing performance values between sites, modeling the effect of FSPs, and estimating their key potential benefits. By using the tool, decision makers can quickly assess the needs of different facilities and make an informed, cost-effective decision on where to implement the next service patrol.

Crashes involving large trucks and passenger cars are important topics for research and countermeasure development since they represent more than 60% of all fatal truck crashes and because the passenger car occupant is much more likely to be killed. This study ( a) examined “fault” in total car–truck crashes using North Carolina Highway Safety Information System (HSIS) data for comparison with fault analyzed in previous studies of fatal crashes, ( b) used general estimates system (GES) crash data to verify unsafe driving acts (UDAs) identified by expert panels in past studies, and ( c) used North Carolina HSIS data to identify critical combinations of roadway facility type, roadway location, and crash type based on “total harm”—a measure combining both the frequency and severity of the crash. Fault in total North Carolina car–truck crashes was found to differ significantly from past fatal crash studies, with the truck driver being at fault more often than the car driver both overall and in certain crash types. Car drivers continue to be at fault much more often in head-on and angle crashes. While it was not possible to analyze all UDAs identified in prior studies, when possible, the current analyses revealed differences between the GES crash data results and the expert-based results, pointing to the need for better UDA methods if they are to be used to target treatments. Finally, using the total-harm analysis with North Carolina car–truck crashes indicated that undivided rural arterials and collectors should be primary targets for further investigation and for treatment.

The role of human capital and occupational factors in influencing driver safety has gained increased attention from trucking firms and policy makers. The influence of these factors, along with demographic factors, on the crash frequency of truck drivers is examined. A unique driver-level data set from a large truckload company collected over 26 months was used for estimating regression models of crash counts. On the basis of estimates from a zero-inflation Poisson regression model, results suggest that human capital and occupational factors, such as pay, job tenure, and percentage of miles driven during winter months, have a significantly better explanatory power of crash frequency than demographic factors. Relative to the zero-inflation and count models, results suggest that higher pay rates and pay increases are related to lower expected crash counts and to a higher probability of no crashes, all else held equal. Although the data come from one company, the evidence provided is a first step in examining the structural causes of unsafe driving behavior, such as driver compensation. These results may motivate other companies to modify operations and driver hiring practices. Also, the need for a comprehensive study of the relationship between driver compensation and driver safety is demonstrated.

With increasing speed limits and more light trucks penetrating the market, concern over their rollover risk is growing. In particular, the general public and automobile manufacturers would like to know if the increasingly popular sport utility vehicles (SUVs) are indeed safer than other vehicle platforms. The influences of various vehicle platforms on rollovers and driver injuries were investigated. Specifically, ( a) the rollover intensities of SUVs vis-à-vis those of other vehicle types in single-vehicle crashes and ( b) the severity of the resulting driver injury were explored. Data from a good-quality federally maintained database were used for crash analysis. The database contains a relatively clean stratified sample of police-reported tow-away crashes nationwide, and it contains detailed information about vehicle rollovers. Rollover intensity, captured by the number of quarter turns, was investigated by using weighted negative binomial models; injury severity, measured on the abbreviated injury scale, was examined by using weighted ordered logit models. New insights emerged about the factors that increase rollover intensity and injury severity. As expected, SUVs are more likely to roll over and therefore injure their occupant drivers more severely. However, SUVs also protect their drivers during collisions because of their greater crashworthiness. In fact, the SUV crashworthiness effect exceeds the rollover effect, on average. The implications of these findings are discussed.