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The continued transition towards electric mobility will decrease energy tax revenues worldwide, which has substantial implications for government funds. At the same time, demand for transportation is ever increasing, which in turn increases…
A statistical analysis implemented in the Python programming language was performed on the available MassDOT car accident data to identify whether a certain set of traffic circumstances would increase the likelihood of injuries. In the…
Berkson errors are commonplace in empirical microeconomics. In consumer demand this form of measurement error occurs when the price an individual pays is measured by the (weighted) average price paid by individuals in a specified group…
National Highway Traffic Safety Administration reported 7,345 pedestrian fatalities in the United States in 2022, making pedestrian safety a pressing issue in urban mobility. This study presents a novel probabilistic simulation framework…
Graduated driver licensing systems effectively reduce adolescent traffic fatalities but create unintended health consequences. Using state-level variation in licensing policies from 1999-2020 and difference-in-differences analysis, we…
Option pricing is mainly based on ideal market conditions which are well represented by the Geometric Brownian Motion (GBM) as market model. We study the effect of non-ideal market conditions on the price of the option. We focus our…
Modelling crash rates in an urban area requires a swathe of data regarding historical and prevailing traffic volumes and crash events and characteristics. Provided that the traffic volume of urban networks is largely defined by typical work…
We believe that "all men are created equal". With the rise of the police shootings reported by media, more people in the U.S. think that police use excessive force during law enforcement, especially to a specific group of people. We want to…
This study investigates the predictive capacity of environmental, temporal, and spatial factors on traffic accident severity in the United States. Using a dataset of 500,000 U.S. traffic accidents spanning 2016-2023, we trained an XGBoost…
Pedestrian fatalities continue to rise in the United States, driven by factors such as human distraction, increased vehicle size, and complex traffic environments. Advanced Driver Assistance Systems (ADAS) offer a promising avenue for…
In this paper we adopted state-of-the-art machine learning algorithms, namely: random forest (RF) and least squares boosting, to model crash data and identify the optimum model to study the impact of narrow lanes on the safety of arterial…
Approximately 30% of all traffic fatalities in the United States are attributed to alcohol-impaired driving. This means that, despite stringent laws against this offense in every state, the frequency of drunk driving accidents is alarming,…
Understanding holistic impact of planned transportation solutions and interventions on urban systems is challenged by their complexity but critical for decision making. The cornerstone for such impact assessments is estimating the…
Whether the Millennials are less auto-centric than the previous generations has been widely discussed in the literature. Most existing studies use regression models and assume that all factors are linear-additive in contributing to the…
We consider the problem of traffic accident analysis on a road network based on road network connections and traffic volume. Previous works have designed various deep-learning methods using historical records to predict traffic accident…
Traumatic Brain Injuries (TBIs) resulting from Road Traffic Crashes (RTCs) can have fatal and disabling effects on patients. In this study, we evaluated the TBIs outcomes of patients involved in RTCs and identify key contributing factors…
Traffic prediction is a spatiotemporal predictive task that plays an essential role in intelligent transportation systems. Today, graph convolutional neural networks (GCNNs) have become the prevailing models in the traffic prediction…
Motivated by empirical studies investigating treatment effects in survival analysis, we propose a bivariate transformation model to quantify the impact of a binary treatment on a time-to-event outcome. The model equations are connected…
This paper studies natural disasters and the psychological costs of climate change. It presents what we believe to be the first evidence that higher temperature variability and not a higher level of temperature is what predicts natural…
Approach-level models were developed to accommodate the diversity of approaches within the same intersection. A random effect term, which indicates the intersection-specific effect, was incorporated into each crash type model to deal with…