conference paper
working paper
In this paper a simultaneous modeling system for dichotomous endogenous variables is developed and applied empirically to longitudinal travel demand data of modal choice. The reported research is motivated by three factors. First, the analysis of discrete data has become standard practice among geographers, sociologists, and economists. In the seventies a number of new tools were developed to handle multivariate discrete data (Bishop, et al., 1975; Fienberg, 1980; Goodman, 1972). However, while these methods are invaluable in studying empirical relationships among sets of discrete variables, they have a limited ability to reveal the underlying causal structure that generated the data. Second, in travel demand analysis and housing market modeling, attention has been focused largely on single-equation models. It can be argued that this scope is too limited. Human decisions are usually not taken in isolation but in conjunction with other decisions and events. There may be complex feedback relations, recursive, sequential, and simultaneous decision structures that cannot be adequately described in a single equation. This has been a major motivation in the seventies in sociology for the development of a new modeling approach: linear structural equations with latent variables. Such models combine the classical simultaneous equation system model with a linear measurement model. Original developments, particularly the LISREL model (Jtireskog, 1973, 1977), did not allow for discrete dependent variables. More recently, Muthen (1983, 1984, 1987) and others (e.g., Bentler, 1983, 1985) developed models that incorporate various types of non-normal endogenous variables, including censored/truncated polytomous and dummy variables. This paper explores the possibilities of this method for simultaneous equation models in dynamic analysis of mobility. A third motivation for the present research is the rapid growth of longitudinal data sets. In recent years many longitudinal surveys have become available for geographical, economic, and transportation analyses. In labor and housing market analysis the Panel Study of Income Dynamics (PSID, 1984) has played an important role (Heckman and Singer, 1985; Davies and Crouchley, 1984, 1985). In consumer behavior, the Cardiff Consumer Panel has been a major motivation for the development and testing of dynamic discrete choice models (Wrigley, et al., 1985; Wrigley and Dunn, 1984a, 1984b, 1984c, 1985; Dunn and Wrigley, 1985; Uncles, 1987). In the Netherlands a large general mobility panel has been conducted annually since 1984 (J. Golob, et al., 1985; van Wissen and Meurs, 1989). Here analyses have focused on discrete data on modal choice (T. Golob, et al., 1986), as well as on dynamic structural modeling (Golob and Meurs, 1987, 1988; Kitamura, 1987; Golob and van Wissen, 1988; Golob, 1988). The present paper is an extension of this line of research to incorporate dynamic structural models of modal choice, using data from the Dutch Mobility Panel.
working paper |
Jun 1991
working paper |
Mar 1989
working paper |
Oct 1991
working paper |
Nov 1991
working paper |
Jun 1989
policy brief
The advent of e-commerce has changed consumer behavior and brought about a growing last-mile delivery system. These deliveries provide consumers with access to goods and services that would otherwise require personal trips to brick-and-mortar locations or not be available. To improve the efficiency of last-mile delivery and mitigate potential effects on traffic, communities, and the environment, e-retailers are trying out a diverse set of distribution strategies. These include: (1) using light-duty vehicles such as electric vans and cargo bikes in conjunction with micro-hubs, consolidation centers, and staging areas to reduce heavy traffic and operational costs; (2) establishing collection points (e.g., parcel lockers) that allow customers to pick up their orders at convenient locations, without the need for additional delivery vehicle travel; (3) engaging independent drivers who can provide flexible and cost-effective delivery; (4) deploying autonomous delivery robots and unmanned aerial vehicles; and (5) replacing conventional fuel vehicle fleets with zero- or near-zero emissions vehicles. A team at the University of California, Davis explored the economic viability, environmental efficiency, and social equity impacts of these strategies with state of the art modeling techniques.
published journal article |
Sep 2025
published journal article |
Apr 2024
research report |
Jan 2025
research report
The goal of this research project is to improve the operational efficiency of shared-ride mobility-on-demand services (SRMoDS). SRMoDS ranging from UberPool to micro-transit have the potential to provide travelers mobility benefits that are comparable to existing ride-hailing services without shared rides such as UberX, but at a lower cost and with fewer harmful externalities. To meet the project’s goal, this study proposes a bi-criteria network pathfinding approach that considers proximity to potential future traveler requests in addition to travel time. This pathfinding approach was built on top of a state-of-the-art dynamic vehicle routing and matching modules. The study tests the proposed pathfinding approach using the network of the City of Anaheim. The results indicate that the proposed bi-criteria pathfinding can potentially reduce both traveler waiting and in-vehicle travel time; however, the effectiveness depends on several factors. Important factors include the relative supply-demand imbalance as well as several hyperparameters in the optimization-based control policy. Moreover, the results indicate that the bi-criteria policy is only advisable when the SRMoDS vehicle has one or fewer in-vehicle passengers. Although the operational benefits found in this study are relatively small, future research efforts related to tuning hyperparameters should allow bi-criteria pathfinding to significantly improve SRMoDS.
conference paper
Robust perception is crucial for autonomous vehicle security. In this work, we design a practical adversarial patch attack against camera-based obstacle detection. We identify that the back of a box truck is an effective attack vector. We also improve attack robustness by considering a variety of input frames associated with the attack scenario. This demo includes videos that show our attack can cause endto-end consequences on a representative autonomous driving system in a simulator.
working paper
Increasing emphasis is found on the objective evaluation of public transit performance. In the past, transit management has often attracted little attention; growing public interest in transportation issues and increasing costs in public transit have brought transit management increased visibility. With such visibility, clear evaluation procedures become necessary. Performance indicators may be used to evaluate the performance of individual transit routes in much the same manner in which they evaluate the performance of the entire transit system. The selection of appropriate performance indicators requires the clear definition of goals and objectives for each transit system. Once selected, there exist several different ways in which performance indicators may be implemented and their desired standards defined. This report suggests techniques for the development of route evaluation procedures and the range of goals which transit might be expected to facilitate. It then reviews the route evaluation procedures used by three transit properties in California and two properties in other states.
working paper
Phd Dissertation
During the last two decades, a large body of empirical research has focused on the relationship between land use and travel behavior, and also on the impacts of transportation accessibility on land value. However, significant gaps remain in our understanding of these relationships. In this dissertation, I present three essays on accessibility, carless households, and long-distance travel that will enhance our understandings of relationships among land use, land value, and transportation.In my first essay, I provide empirical evidence about the magnitude of the value of transportation accessibility as reflected by residential rents in Rajshahi City, Bangladesh. Results of my SARAR (spatial autoregressive model with spatial-autoregressive disturbances) model show a small but statistically significant capitalization of accessibility. Results of this study should be useful for planning transportation infrastructure funding measures in least developed country cities like Rajshahi City.In my second essay, I assess the joint effects of various socio-economic, life-cycle stage, and land use variables on the likelihood that a household is carless, voluntarily or not, by analyzing data from the 2012 California Household Travel Survey (CHTS). Results of my binary logit models show the importance of land use diversity and of good transit service to help households voluntarily forgo their vehicles, and downplay the impact of population density and pedestrian-friendly facilities. Results of this study should help planners and policy makers formulate policies to curb automobile dependency and help promote sustainable urban transportation.My third essay analyzes long-distance data from the 2012 CHTS to understand the influence of different socio-economic, land use, and land value variables on the likelihood that a household commutes long-distance in California. Results of my Generalized Structural Equation Model (GSEM) show that long-distance commuting is negatively associated with mixed density and residential home values (around commuters’ residences), but positively related with households’ car-ownership. My results also confirm the presence of residential self-selection. The empirical evidence of this study should help formulate land use planning strategies to curb long-distance commuting and thus help reducing vehicle-miles traveled, which is one way of reducing the emission of greenhouse gases from transportation.
Phd Dissertation
Society has an interest in reducing pollutants emitted from the vehicles used for transporting people and goods. The main goal of heavy-duty natural gas vehicle (NGV) incentive projects is to offer upfront monetary incentives to reduce greenhouse gas emissions and the production of regulated pollutants in the state. However, these incentives are often based on vehicle weight and do not account for environmental impacts. In addition, although heavy-duty NGVs are being used in a variety of vocation types, conventional emission models only support a limited number of these vocation types. Because of this, it is challenging to assess the precise impacts of the heavy-duty NGV (HD NGV) adoption and predict the specific environmental benefits per given operational conditions and vocation type. If government agencies realize the environmental benefits of alternative fuel vehicles (AFVs), like NGVs, with respect to vocation type and operating characteristics, it would be beneficial to design cost-effective incentive structures and implementation plans. This study primarily focused on the operational characteristics and environmental impacts of the HD NGVs incentivized in California. This study conducted pattern clustering and classification analyses to obtain drive mode compositions (DMC) over duty cycles and showed the heterogeneity of operational and emission characteristics of the vocational HD NGVs. The vocational impact analysis computed the adoption impact of 40 NGVs operating in California across ten different vocation types. The proposed evaluation framework included life-cycle nitrogen oxides (NOx) and carbon dioxide (CO2) emissions of natural gas, renewable natural gas and diesel fuel pathways and compared the lifetime NOx emission reduction potential of the considered vocation type vehicles. The resulting emission benefits of the fuel pathways were used to determine the most incentive-effective vocation types among the considered NGV applications. The multi-criteria decision-making analysis prioritized the fuel pathways based on multiple criteria which are related to an incentive effectiveness index as well as life cycle emissions. Refuse truck and transit bus pathways are likely to achieve the highest return for the total incentive granted when the vehicles are renewable natural gas (RNG)-powered. For compressed natural gas (CNG) fuel pathways, school and transit buses take the highest ranks over the various analysis scenarios. Each vocation type showed different incentive effects and emission reduction potential, which means that some vocational vehicles can play a critical role in the state’s funding and emission reduction plans. The suggested decision-making tool and assessment framework can provide useful reference data to improve the performance of future alternative fuel vehicle incentive programs.
published journal article |
Jul 2023
Phd Dissertation |
Jan 2020
other |
Aug 2023
research report |
May 2022
working paper
If truck drivers could use internet-enabled wireless devices to access traveler information, what type of information would they most want to have? The answer almost surely varies according to the type of trucking operation, location, and many other factors. We analyzed preferences for traveler information from managers of 700 trucking companies to determine how they valued information about such things as locations of freeway incidents and lane closures, port and rail terminal schedules and clearances, delays at terminal train arrivals at grade crossings, weather, and travel times at alternative routes. Using a factor-analytic model with regressor variables, we found clear differences in preferences across types of trucking operations.