conference paper
conference paper
published journal article
Unplanned disaster events can greatly disrupt access to essential resources, with calamitous outcomes for already vulnerable households. This is particularly challenging when concurrent extreme events affect both the ability of households to travel and the functioning of traditional transportation networks that supply resources. This paper examines the use of volunteer-based crowdsourced food delivery as a community resilience tactic to improve food accessibility during overlapping disruptions with lasting effects, such as the COVID-19 pandemic and climate disasters. The study uses large-scale spatio-temporal data (n = 28,512) on crowdsourced food deliveries in Houston, TX, spanning from 2020 through 2022, merged with data on community demographics and significant disruptive events occurring in the two-year timespan. Three research lenses are applied to understand the effectiveness of crowdsourced food delivery programs for food access recovery: 1) geographic analysis illustrates hot spots of demand and impacts of disasters on requests for food assistance within the study area; 2) linear spatio-temporal modeling identifies a distinction between shelter-in-place emergencies and evacuation emergencies regarding demand for food assistance; 3) structural equation modeling identifies socially vulnerable identity clusters that impact requests for food assistance. The findings from the study suggest that volunteer-based crowdsourced food delivery adds to the resilience of food insecure communities, supporting its effectiveness in serving its intended populations. The paper contributes to the literature by illustrating how resilience is a function of time and space, and that similarly, there is value in a dynamic representation of community vulnerability. The results point to a new approach to resource recovery following disaster events by shifting the burden of transportation from resource-seekers and traditional transportation systems to home delivery by a crowdsourced volunteer network.
published journal article
book/book chapter
conference paper
Vehicle classification is the process of separating vehicles according to various predefined classes. Vehicle-classification information can be used in many transportation applications, including road maintenance, emissions/pollution estimation, traffic modeling and simulation, traffic safety, and toll setting. An example of a classification scheme using the following seven vehicle classes is presented: cars, sport-utility vehicles/pickups, vans, limousines, buses, two-axle trucks, and trucks with more than two axles. This system uses vehicle inductive signatures collected from existing loop-detector infrastructure. It also uses a heuristic-discriminant algorithm for classification and a multi-objective optimization for training the heuristic algorithm. Feature vectors obtained by processing inductive signatures are used as inputs into the classification algorithm. Three different heuristic algorithms were developed, yielding encouraging results of 81 to 91 percent overall classification rates. The results demonstrate the potential of collecting network-wide vehicle-classification data from inductive loops. The availability of vehicle-classification data helps to improve traffic surveillance and better defines dynamic traffic networks.
research report
Data from the second year (1979-80) of the Section 15 statistics are used, first to test the validity of a small set of performance indicators for fixed route bus operations, and second to define relatively homogeneous groups of operators (peer groups) that can be compared. Agencies operating 304 bus systems are included. Rail operations were excluded, as were exclusive, demand-responsive operations. The second year data is both more complete and accurate than that reported for the inaugural year. However, data from the magnetic tape had to be reorganized before it could be used with any of the major statistical software packages . A large set of performance variables are analysed with factor analysis to establish seven dimensions of transit performance. Seven marker indicators were chosen rather than the nine proposed in previous research. Cluster analysis is used to create a typology for transit based upon characteristics of operations that are available in the Section 15 statistics. Agency size (measured by total vehicle miles and number of peak vehicles operated), peak to base demand and average bus speed are used to create 12 peer groups. Results from this research confirm the validity of using a small set of indicators to ^represent dimensions of transit performance. They will also allow meanlQ||fuX comparisons between similar systems.
conference paper
Drayage trucks play a crucial role in the logistics and supply chain, facilitating the movement of goods over short distances, typically between ports, rail yards, distribution centers, and other cargo-handling facilities. However, they are mostly powered by internal combustion engines and impact nearby communities with elevated noise and air pollution. This study proposes a drayage-focused truck data acquisition method that leverages existing infrastructures to provide a better understanding of drayage activities with the potential to support efficient policy-making. A single loop-based truck body type classification was designed with a specific focus on the identification of drayage trucks. The proposed model is capable of accurately distinguishing forty truck body type trucks with an average correct classification rate (CCR) of 81 percent. The model performance on drayage trucks (trucks hauling 20ft, 40ft, and 53ft intermodal containers) was improved over the weighted cross-entropy loss function without significantly compromising the model performance on other classes. The CCR value of drayage trucks is 91 percent which significantly outperforms the state-of-the-art models and has the potential to yield accurate real-world drayage truck activity metrics.
research report |
Aug 2023
working paper
research report