Behram Wali

• Doctor of Philosophy
• PostDoc Position at Massachusetts Institute of Technology

With the advent of seemingly unstructured big data, and through seamless integration of computation and physical components, cyber-physical systems (CPS) provide an innovative way to enhance safety and resiliency of transport infrastructure. This study focuses on real world microscopic driving behavior and its relevance to school zone safety expand...

A novel evidence-based methodology is presented for determining place-based thresholds of objectively measured built environment features’ relationships with active travel. Using an innovative machine-learning based Generalized Additive Modeling framework, systematic heterogeneity fundamental to the development of well-justified and objective envir...

Compact walkable environments with greenspace support physical activity and reduce the risk for depression and several obesity-related chronic diseases, including diabetes and heart disease. Recent evidence confirms that these chronic diseases increase the severity of COVID-19 infection and mortality risk. Conversely, denser transit supportive envi...

An adequate understanding of consumer affinity towards automated vehicles (AVs) is necessary to more reliably forecast the potential impacts of AVs on transportation system. The road to fully automated driving systems pass through partially automated driving systems. This study contributes by simultaneously analyzing consumer affinity towards parti...

Most of the existing literature concerning the links between built environment and COVID-19 outcomes is based on aggregate spatial data averaged across entire cities or counties. We present neighborhood level results linking census tract-level built environment and active/sedentary travel measures with COVID-19 hospitalization and mortality rates i...

Motorcycling provides freedom and excitement, yet riders face a greater risk of crashes and injuries compared to other motorists. Understanding the factors contributing to motorcycle crash risk, especially rider age, experience, and training, is essential for developing effective safety measures. Using a unique and comprehensive matched case-contro...

The rise of shared mobility services, including carsharing and ride-hailing, has transformative impacts on transportation systems. We present a behavioral framework to jointly model individuals’ carsharing and ride-hailing use with a focus on deciphering the substitutive vs. complementary roles of the built environment, transit accessibility, and a...

Associations of built and natural environment and bike infrastructure features with neighborhood-level hypertension and obesity prevalence across the U.S. are not well explored. Identifying the environmental determinants of neighborhood-level disease prevalence can support community-based nonpharmacologic interventions. Additionally, little is know...

Walkable neighborhoods provide significant sustainability, health, and motorized user safety benefits. Far less consideration is given to the potential pedestrian/bicyclist safety-related implications of macro-level walkability. Making it desirable to walk and bike without providing the proper physical environment to make it safe is clearly problem...

Motorcycle riding offers travel options, freedom, and thrill to road users. However, motorcyclists are more vulnerable to a substantially higher risk of crashes and severe injuries than motorized users. Understanding the factors contributing to motorcycle crash risk, especially rider age, experience, and training, is essential for developing effect...

Driven by the emerging collaborative consumption trends, new shared ownership-based business models provide more flexible and accessible on-demand mobility options. This study simultaneously analyzes factors correlated with the consumers’ use of two interrelated disruptive on-demand mobility services, including ride-hailing (RH) and carsharing (CS)...

Future smart transportation systems are anticipated to integrate automation and sharing of vehicles. Responding to the expected changes in shared mobility services, this study used representative data from over 4100 households in California to examine consumers' affinity to use shared automated vehicles (SAV) and their willingness to renounce exist...

While existing research highlights the built and social environment impacts on COVID-19 mortality, no empirical evidence exists on how the built and social environments may interact to influence COVID-19 mortality. This study presents a rigorous empirical assessment of the interactive impacts of social vulnerability and walkability on neighborhood-...

Introduction: This study aims to increase the prediction accuracy of crash frequency on roadway segments that can forecast future safety on roadway facilities. A variety of statistical and machine learning (ML) methods are used to model crash frequency with ML methods generally having a higher prediction accuracy. Recently, heterogeneous ensemble...

A variety of statistical and machine learning methods are used to model crash frequency on specific roadways with machine learning methods generally having a higher prediction accuracy. Recently, heterogeneous ensemble methods (HEM), including stacking, have emerged as more accurate and robust intelligent techniques and are often used to solve patt...

Promoting sustainable transportation, ride-sourcing and dynamic ridesharing (DRS) services have transformative impacts on mobility, congestion, and emissions. As emerging mobility options, the demand for ride-sourcing and DRS services has rarely been simultaneously examined. This study contributes to filling this gap by jointly analyzing the demand...

In September 2015, a new light rail transit (LRT) line opened in metro Portland, Oregon, USA. We used this natural experiment to conduct an interrupted time series analysis of the effects of LRT introduction on health care costs. We hypothesized that such costs would decline over time based on demonstrated health benefits of increased transit-relat...

Evidence connecting health care expenditures with physical activity and built environment is rare. We examined how detailed urban form relates to mode specific moderate-to-vigorous physical activity (MVPA) and health care costs—controlling for transit access, residential choices/preferences, sociodemographic factors. We harness high resolution data...

Background and objective No research to date has causally linked built environment data with health care costs derived from clinically assessed health outcomes within the framework of longitudinal intervention design. This study examined the impact of light rail transit (LRT) line intervention on health care costs after controlling for mode-specifi...

Rapid advancements in vehicle automation and a shift towards collaborative consumption trends will disrupt future urban mobility systems. We present a joint model of consumers’ affinity towards shared automated vehicles (SAVs) with two distinct yet related configurations: automated vehicle (AV) carsharing and AV ride sourcing. Compact and walkable...

Introduction: Little evidence exists in the literature regarding the discrimination power of better anatomical injury measures in differentiating clinical outcomes in motorcycle crashes. Furthermore, multiple injuries to different body parts of the rider are seldom analyzed. This study focuses on comparing anatomical injury measures such as the in...

A variety of statistical and machine learning methods are used to model crash frequency on specific roadways – with machine learning methods generally having a higher prediction accuracy. Recently, heterogeneous ensemble methods (HEM), including “stacking,” have emerged as more accurate and robust intelligent techniques and are often used to solve...

Non-motorists involved in rail-trespassing crashes are usually vulnerable to receiving major or fatal injuries. Previous research has used traditional quantitative crash data for understanding factors contributing to injury outcomes of non-motorists in traininvolved collisions. However, usually overlooked crash narratives can provide useful context...

Active transportation (AT) is widely viewed as an important target for increasing participation in aerobic physical activity and improving health, while simultaneously addressing pollution and climate change through reductions in motor vehicular emissions. In recent years, progress in increasing AT has stalled in some countries and, furthermore, th...

Driving errors and violations are identified as contributing factors in most crash events. To examine the role of human factors and improve crash investigations, a systematic taxonomy of driver errors and violations (TDEV) is developed. The TDEV classifies driver errors and violations based on their occurrence during the theoretically based percept...

Accurate prediction of incident duration and response strategies are two imperative aspects of traffic incident management. Past research has applied various types of regression models for predicting incident durations and quantification of associated factors. However, an important methodological aspect is unobserved heterogeneity which may be pres...

Driving errors and violations are highly relevant to the safe systems approach as human errors tend to be a predominant cause of crash occurrence. In this study, we harness highly detailed pre-crash Naturalistic Driving Study (NDS) data 1) to understand errors and violations in crash, near-crash, and baseline (no event) driving situations, and 2) t...

Non-motorists involved in rail-trespassing crashes are usually more vulnerable to receiving major or fatal injuries. Previous research has used traditional quantitative crash data for understanding factors contributing to injury outcomes of non-motorists in train involved collisions. However, usually overlooked crash narratives can provide useful a...

Driving errors and violations are identified as contributing factors in most crash events. Different types of driving errors and violations may vary across diverse roadway environments. Due to unique nature of several types of driving errors and violations, crash risk associated with each type of these driving errors and violations can be different...

This report focuses on safety aspects of connected and automated vehicles (CAVs). The fundamental question to be answered is how can CAVs improve road users' safety? Using advanced data mining and thematic text analytics tools, the goal is to systematically synthesize studies related to Big Data for safety monitoring and improvement. Within this do...

Abstract: This report focuses on safety aspects of connected and automated vehicles (CAVs). The fundamental question to be answered is how can CAVs improve road users' safety? Using advanced data mining and thematic text analytics tools, the goal is to systematically synthesize studies related to Big Data for safety monitoring and improvement. With...

As a key indicator of unsafe driving, driving volatility characterizes the variations in microscopic driving decisions. This study characterizes volatility in longitudinal and lateral driving decisions and examines the links between driving volatility in time to collision and crash-injury severity. By using a unique real-world naturalistic driving...

As a key indicator of unsafe driving, driving volatility characterizes the variations in microscopic driving decisions. This study characterizes volatility in longitudinal and lateral driving decisions and examines the links between driving volatility in time to collision and crash injury severity. By using a unique real-world naturalistic driving...

To enhance safety, the Tennessee Department of Transportation (TDOT) is in the process of adopting the Highway Safety Manual (HSM) as a resource to facilitate decision making based on the safety performance of its roadways. The predictive models which are known as Safety Performance Functions (SPFs) are used to forecast the expected crash frequency...

With the advent of seemingly unstructured big data, and through seamless integration of computation and physical components, cyber-physical systems (CPS) provide an innovative way to enhance safety and resiliency of transport infrastructure. This study focuses on real-world microscopic driving behavior and its relevance to school zone safety – expa...

The transportation system of the future is anticipated to integrate automation, connectivity, electrification, and sharing of rides and vehicles—a concept known as ACES. Driven by the growing computational power, ubiquity of sensors, big data, and Artificial Intelligence, the public and private sectors have new opportunities to improve the quality...

Adoption of Alternative Fuel Vehicles, especially by the commercial sector can reduce emissions and fossil fuel dependence. Despite the incentives to adopt AFVs, the current situation shows that about 86% of new vehicle sales for the commercial fleet are diesel and 13% are gasoline vehicles with a very small share of AFVs. A better understanding of...

Automated vehicles (AVs) represent an opportunity to reduce crash frequency by eliminating driver error, as safety studies reveal human error contributes to the majority of crashes. To provide insights into the contributing factors of AV crashes, this study created a unique database from the California Department of Motor Vehicles 124 manufacturer-...

Large-scale truck-involved crashes attract great attention due to their increasingly severe injuries. The majority of those crashes are passenger vehicle–truck collisions. This study intends to investigate the critical relationship between truck/passenger vehicle driver’s intentional or unintentional actions and the associated injury severity in pa...

The sequence of instantaneous driving decisions and its variations, known as driving volatility, prior to involvement in safety critical events can be a leading indicator of safety. This study focuses on the component of "driving volatility matrix" related to specific normal and safety-critical events, named "event-based volatility." The research i...

Motorcyclists are vulnerable road users at a particularly high risk of serious injury or death when involved in a crash. In order to evaluate key risk factors in motorcycle crashes, this study quantifies how different “policy-sensitive” factors correlate with injury severity, while controlling for rider and crash specific factors as well as other o...

The enormous loss of life, as well as economic loss, incurred by road traffic crashes means that rigorous research efforts, especially in developing countries, are needed to investigate risk factors that significantly influence crash severity. The objective of this study is to explore empirically the impact of driver and vehicle characteristics, en...

This paper develops key elements of the safe systems framework; analyzing human errors and violations and their contributions to crashes; bringing together and analyzing behavioral, infrastructure/built environment, and vehicle, and data analytic features in order to find ways to reduce crashes and prevent injuries. Driver errors and violations are...

Driving behavior and school zone safety is a public health concern. The sequence of instantaneous driving decisions and its variations prior to involvement in safety critical events, defined as driving volatility, can be a leading indicator of safety. By harnessing unique naturalistic data on more than 41,000 normal, crash, and near-crash events fe...

The key objective of this study is to investigate the interrelationship between fuel economy gaps and to quantify the differential effects of several factors on fuel economy gaps of vehicles operated by the same garage. By using a unique fuel economy database (fueleconomy.gov), users self-reported fuel economy estimates and government fuel economy...

To analyze key risk factors in motorcycle crashes, this study quantifies how different “policy-sensitive” factors correlate with injury severity, while controlling for rider and crash specific factors, and other observed/unobserved factors. Data on 321 motorcycle injury crashes from a comprehensive US DOT FHWA’s Motorcycle Crash Causation Study (MC...

The main objective of this study is to quantify how different “policy-sensitive” factors are associated with motorcycle crash risk, while controlling for rider-specific, psycho-physiological, and other observed/unobserved factors. The analysis utilized data from a matched case-control design collected through FHWA’s Motorcycle Crash Causation Study...

The sequence of instantaneous driving decisions and its variations, known as driving volatility, prior to involvement in safety critical events can be a leading indicator of safety. The research issue is to characterize volatility in instantaneous driving decisions, and how it varies across drivers involved in normal driving, crash, and/or near-cra...

To address pedestrian and bicycle safety goals, new data collection techniques can be used to provide deeper understanding and insights on combating such crashes. Using the SHRP2 Naturalistic Driving Study data, we explore how driving volatility well before a crash or a near-crash happens can be used as a leading indicator of pedestrian and bicycle...

Driving behavior and school zone safety is a public health concern. The sequence of instantaneous driving decisions and its variations prior to involvement in safety critical events, defined as driving volatility, can be a leading indicator of safety. By harnessing unique naturalistic data on more than 41,000 normal, crash, and near-crash events fe...

Road infrastructure is used by various types of vehicles among which heavy vehicles imposes the most critical loading, causing damage in pavement structure, which ultimately leads to an increased maintenance and rehabilitation costs. During the design of road pavements, each type of vehicle is converted into equivalent standard axle load (ESAL) to...

The key objective of this study is to investigate the interrelationship between fuel economy gaps and to quantify the differential effects of several factors on fuel economy gaps of vehicles operated by the same garage. By using a unique fuel economy database (fueleconomy.gov), users’ self-reported fuel economy estimates and government’s fuel econo...

The main objective of this study is to quantify how different policy-sensitive factors are associated with risk of motorcycle injury crashes, while controlling for rider-specific, psycho-physiological, and other observed/unobserved factors. The analysis utilizes data from a matched case-control design collected through the FHWA Motorcycle Crash Cau...

Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous dri...

A key purpose of the U.S. government fuel economy ratings is to provide precise and unbiased fuel economy estimates to assist consumers in their vehicle purchase decisions. For the official fuel economy ratings to be useful, the numbers must be relatively reliable. This study focuses on quantifying the variations of on-road fuel economy relative to...

A key purpose of the U.S. government fuel economy ratings is to provide precise and unbiased fuel economy estimates to assist consumers in their vehicle purchase decisions. For the official fuel economy ratings to be useful, the numbers must be relatively reliable. This study focuses on quantifying the variations of on-road fuel economy relative to...

The main objective of this study is to quantify how different policy-sensitive factors are associated with risk of motorcycle injury crashes, while controlling for rider-specific, psycho-physiological, and other observed/unobserved factors. The analysis utilizes data from a matched case-control design collected through the FHWA Motorcycle Crash Cau...

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...

Driving behavior in general is considered a leading cause of intersection related traffic crashes. However, due to unavailability of real-world driving data, intersection safety performance evaluations are largely reactive where state-of-the-art methods are applied to analyze historical crash data. In this regard, the emerging connected vehicles te...

Highway Work Zones (HWZs) present a major hazard for road users, construction workers and equipment, and significantly contribute to occurrence of road crashes worldwide. The present study focuses on analysing the current state of safety measures at HWZs in Pakistan. A more direct approach is adopted by comparing safety measures at randomly selecte...

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 y...

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 y...

For practical considerations, Annual Average Daily Traffic (AADT) and segment 6 length are often used as the main correlates for predicting crash frequencies on segments. Typically, 7 a linear or simple non-linear dependence of crash frequencies on traffic exposure-related factors 8 is assumed which may not realistically represent the underlying co...

The main objective of this study is to simultaneously investigate the degree of injury severity sustained by drivers involved in head-on collisions with respect to fault status designation. This is complicated to answer due to many issues, one of which is the potential presence of correlation between injury outcomes of drivers involved in the same...

Safety Performance Functions (SPFs) provide a basis for identifying locations where countermeasures can be effective. While SPFs in the Highway Safety Manual (HSM) were calibrated based on data from select states, calibration factors can be developed to localize SPFs to other states. Calibration factors typically provide a coarse adjustment—time an...

Traditionally, evaluation of intersection safety has been largely reactive, based on historical crash frequency data. However, the emerging data from Connected and Automated Vehicles (CAVs) can complement historical data and help in proactively identify intersections which have high levels of variability in instantaneous driving behaviors prior to...

Traditionally, evaluation of intersection safety has been largely reactive, based on historical crash frequency data. However, the emerging data from Connected and Automated Vehicles (CAVs) can complement historical data and help in proactively identify intersections which have high levels of variability in instantaneous driving behaviors prior to...

Understanding the fuel economy of vehicles in actual use has important implications for fuel economy, greenhouse gas emission and consumer information policies. This study explores how fuel economy varies with intensity of daily vehicle use, cumulative mileage, and ambient temperature. Using a unique longitudinal database, we quantify variations in...

The effectiveness of speed limit enforcement (SLE) is a critical factor in reducing the global burden of fatalities and injuries due to road crashes. A random-parameter ordered-probit model was developed to explore the relationship between the effectiveness of SLE and different explanatory variables using data from a 2013 World Health Organization...

The contemporary traffic safety research comprises little information on quantifying the simultaneous association between drink driving and speeding among fatally injured drivers. Potential correlation between driver’s drink driving and speeding behavior poses a substantial methodological concern which needs investigation. This study therefore focu...

Traffic incidents often known as non-recurring events impose enormous economic and social costs. Compared to short duration incidents, large-scale incidents can substantially disrupt traffic flows by blocking lanes on highways for long periods of time. A careful examination of large-scale incidents and associated factors can assist with actionable...

Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous dri...

– For practical considerations, Annual Average Daily Traffic (AADT) and segment length are often used as the main correlates for predicting crash frequencies on segments. Typically, a linear or simple non-linear dependence of crash frequencies on traffic exposure related factors is assumed which may not realistically represent the underlying comple...

Creating a sustainable energy system will require transition to renewable and low-carbon energy sources. A key part of such a system is refueling infrastructure, which may in large part determine the transition to alternative fuel vehicles and reduce dependence on fossil fuels. This paper focuses on providing information about the availability of p...

Safety Performance Functions (SPFs) provide a basis for identifying locations where countermeasures can be effective. While SPFs in the Highway Safety Manual (HSM) were calibrated based on data from select states, calibration factors can be developed to localize SPFs to other states. Calibration factors typically provide a coarse adjustment—time an...

– Collisions between heavy trucks and passenger vehicles are a major societal concern primarily due to the severity of injuries involved. This research focuses on investigating the associations between injury severity and unsafe pre-crash driving behaviors (both intentional and unintentional) of passenger vehicle and truck drivers. Due to complex i...

Accurate prediction of incident duration and response strategies are two imperative aspects of traffic incident management. Past research has applied various types of regression models for predicting incident durations and quantification of associated factors. However, an important methodological aspect is unobserved heterogeneity which may be pres...

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 distr...