working paper
Air pollution is frequently the stated reason for special measures aimed at controlling motor vehicles. In the United States, motor vehicle emission standards are set explicitly in clean air legislation, while policies at several levels of government are designed to reduce the use of cars for particular purposes like commuting. In Europe, high fuel taxes and subsidies to urban mass transit and intercity rail travel in large part aim to reduce car use.
working paper |
Sep 1994
working paper |
Oct 1993
working paper |
Jul 1997
book/book chapter |
Oct 1998
research report
working paper |
Sep 1978
published journal article |
Dec 1978
published journal article
Drawing insights from a random survey of Californians conducted in May 2021, we explore how the COVID-19 pandemic impacted Californians’ preferences for various transportation modes and analyze obstacles to increasing transit use. As COVID-19 disrupted habits and created health concerns for shared modes, many Californians expressed a desire to use transit and transportation network companies less after the pandemic, while intentions for driving were mixed. However, intentions to drive less did not materialize, possibly because the pandemic reinforced Californians’ car use habits, and many firms have been urging their employees to return to the office. Without new incentives and measures that increase the generalized cost of driving, the future of transit appears grim, as the intentions of the 28.9% of Californians 18 and over who intended to use transit less post-COVID (versus only 7.3% who wanted to use it more) appear to be realized. Hispanics, choice riders, and those who intend to telecommute more post-pandemic stated that they would use transit less. A silver lining is the substantial uptick in intentions to walk and bike more (25.9%), with just 8% of Californians stating the opposite. The main reasons Californians would not take transit post-pandemic do not include health concerns and are the same in 2021 as in 2017: a personal vehicle offers more flexibility and convenience. Younger adults, people with more education, and affluent households (the so-called “choice riders”) also deplore transit’s insufficient reach and frequency. Our work underscores the intricate interplay between the pandemic, transportation intentions, and actual behavior.
policy brief |
Apr 2022
research report |
Jan 2022
conference paper
published journal article
This paper considers the dynamic response of a taut string, of infinite extent and resting on an elastic foundation, to a moving concentrated load, whose position X(t) can be regarded as a sample function of a stationary random process. An expression for the asymptotic value of the mean deflection is obtained without specification of the particular random process. The results obtained are applied to an example of practical interest.
conference paper
Phd Dissertation
Modern automotive Cyber-Physical Systems (CPS) are increasingly adopting a variety of wireless communications (Radio Frequency and Visible Light) as a promising solution for challenges such as the wire harnessing problem, collision detection and avoidance, traffic control, and environmental hazards. Regrettably, this new trend results in security challenges that can put the safety and privacy of the automotive CPS and passengers at great risk. Further, automotive wireless communication security is constrained by strict energy and performance limitations of electronic controller units and sensors. As a result, the key generation and management for secure automotive wireless communication is an open research challenge. This thesis aims to help solve these security challenges with a novel key management scheme built upon a physical layer key generation technique that exploits the reciprocity and high spatial and temporal variation properties of the automotive wireless communication channel. A key length optimization algorithm is also developed to help improve performance (in terms of time and energy) for safety-related applications. Channel models, simulations and real-world experiments with vehicles and remote-controlled cars were performed to validate the practicality and effectiveness of the scheme. Lastly, it is shown that generated keys may have high security strength (67% min-entropy for the Radio Frequency domain and high randomness according to NIST tests for the Visible Light domain) and that code size overhead is 20 times less than state-of-the-art security techniques.
published journal article
We analyze the complex travel behavior of workers who utilize public transit as part of their work tours (“transit commuters”). Here, complex travel behavior is defined in terms of tours, where a tour is defined as a sequence of trips and activities that begins and ends at the same location and a work tour contains at least one non-home, work activity. The objective of this study is to investigate how transit commuters link non-work activities as part of work tours under transit operational constraints. In particular, we identify dominant patterns of work tours made by transit commuters and analyze these tours using a set of activity-travel analytics and data from the 2017 National Household Travel Survey (NHTS). The primary insights are: (1) about 80 percent of work tours consist of 7 dominant patterns whereas the remaining 20 percent of tours demonstrate a total of 106 diverse and more complicated patterns; (2) half of the transit work tours are complex; (3) most simple tours are transit-only tours whereas most complex tours are multi-modal tours; and (4) transit use is more complex than the traditional home to work commute with a diverse set of choices at various stages of activity scheduling. While policies associated with public transit typically focus only on the journey to work, this study considers the complete set of trips starting and ending at home including intermediate non-work activity, which can provide insights for land use and transit-related policies to better accommodate the complex travel behavior of commuters who utilize transit.
published journal article
conference paper
Gross Vehicle Weight Rating (GVWR)-based vehicle activity data is widely used in freight planning, fuel efficiency evaluation, and on-road emission estimation. However, a vehicle’s GVWR remains challenging to obtain using existing highway sensor infrastructure. This paper describes a novel approach to acquire GVWR-based classification data through the fusion of two complementary infrastructure-based sensing technologies: inductive loop sensors and side-fire video cameras. While inductive loops are widely deployed in the U.S., they only provide single-dimensional data with limited information. Side-fire cameras can offer richer details to enhance vehicle classification. Accordingly, an open-source intelligence (OSINT) method was used to establish a GVWR-based vehicle dictionary, linking vehicle specifications from online data sources to GVWR classes. A dataset comprising 9,154 vehicle inductive loop signatures paired with images was then collected and annotated according to the pre-defined dictionary. Next, signature-based and image-based classification models were developed for GVWR classification. Each model was designed to function independently. A signature-based GVWR classification model was trained with a multi-layer perceptron (MLP) neural net architecture and optimized through the implementation of a weighted cross-entropy loss function. A two-stage image-based GVWR classification framework was designed to extract vehicle objects and classify them based on the GVWR scheme. Finally, a linear fusion model was implemented to combine the output of the signature- and image-based models to achieve an improvement over each standalone classification model. The sensor fusion framework significantly outperformed each individual sensing technology, achieving an average correct classification rate of 0.97 and an score of 0.96, which surpasses state-of-the-art methods.