Rishabh Chauhan

Princeton University | PU · Department of Civil and Environmental Engineering

This study compares the performance of a causal and a predictive model in modeling travel mode choice in three neighborhoods in Chicago. A causal discovery algorithm and a causal inference technique were used to extract the causal relationships in the mode choice decision making process and to estimate the quantitative causal effects between the va...

The COVID-19 pandemic has brought about transformative changes in human activity-travel patterns. These lifestyle changes were naturally accompanied by and associated with changes in transportation mode use and work modalities. In the United States, most transit agencies are still grappling with lower ridership levels, thus signifying the onset of...

This study focuses on an important transport-related long-term effect of the COVID-19 pandemic in the United States: an increase in telecommuting. Analyzing a nationally representative panel survey of adults, we find that 40-50% of workers expect to telecommute at least a few times per month post-pandemic, up from 24% pre-COVID. If given the option...

This study focuses on an important transport-related long-term effect of the COVID-19 pandemic in the United States: an increase in telecommuting. Analyzing a nationally representative panel survey of adults, we find that 40-50% of workers expect to telecommute at least a few times per month post-pandemic, up from 24% pre-COVID. If given the option...

Causal discovery identifies causal relationships between variables in a dataset. This study investigates the potential of causal discovery in extracting causal connections from transportation behavioral data. To do so, four causal discovery algorithms are tested: Peter-Clark (PC), Fast Causal Inference (FCI), Fast Greedy Equivalence Search (FGES),...

The COVID-19 pandemic is an unprecedented global crisis that has impacted virtually everyone. We conducted a nationwide online longitudinal survey in the United States to collect information about the shifts in travel-related behavior and attitudes before, during, and after the pandemic. The survey asked questions about commuting, long distance tra...

Crash is one of the leading causes of death in the United States. Real time detection of crashes plays a pivotal role in increasing safety of highways. In this study, a deep ensemble modelling approach is proposed in which we first employed three powerful deep learning techniques, Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Dee...

The COVID-19 pandemic has impacted billions of people around the world. To capture some of these impacts in the United States, we are conducting a nationwide longitudinal survey collecting information about activity and travel-related behaviors and attitudes before, during, and after the COVID-19 pandemic. The survey questions cover a wide range of...

The explosive nature of Covid-19 transmission drastically altered the rhythm of daily life by forcing billions of people to stay at their homes. A critical challenge facing transportation planners is to identify the type and the extent of changes in people's activity-travel behavior in the post-pandemic world. In this study, we investigated the tra...

Human behavior is notoriously difficult to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring about long-term behavioral changes. During the pandemic, people have been forced to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. A critical question going forward i...

The utility of attitudes in travel demand forecasting requires predictability. Any attempt to simulate future attitudes, as is done in such models, would be impractical if they were subject to substantial unpredictable variation over time. We investigate the stability of attitudes using waves of the COVID Future survey answered 3.5–11 months apart....

This study identifies differences in COVID-19 related attitudes and risk perceptions among urban, rural, and suburban populations in the US using data from an online, nationwide survey collected during April-October 2020. In general, rural respondents were found to be less concerned by the pandemic and a lower proportion of rural respondents suppor...

This article uses data from the first wave of the COVID Future Panel study to evaluate attitudes towards COVID-19 and their influence on traveler behaviors. An exploratory factor analysis identified two underlying constructs based on the measured attitudes, namely “Concern about Pandemic Response” and “COVID Health Concern.” A cluster analysis base...

Human behavior is notoriously difficult to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring about long-term behavioral changes. During the pandemic, people have been forced to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. A critical question going forward i...

The COVID-19 pandemic has impacted billions of people around the world. To capture some of these impacts in the United States, we are conducting a nationwide longitudinal survey collecting information about travel-related behaviors and attitudes before, during, and after the COVID-19 pandemic. The survey questions cover a wide range of topics inclu...

This study focuses on the short-term prediction of traffic delays, for passenger cars, at the three Niagara Frontier Border Crossings, namely the Peace Bridge, the Lewiston–Queenston Bridge, and the Rainbow Bridge. Predictions are made for up to 60 min into the future, using a delay dataset, collected by Bluetooth readers recently installed at thes...

Accident detection is a vital part of traffic safety. Many road users suffer from traffic accidents, as well as their consequences such as delay, congestion, air pollution, and so on. In this study, we utilize two advanced deep learning techniques, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), to detect traffic accidents in Chicag...