conference paper
published journal article
published journal article
Computational efficiency and first in, first out (FIFO) are both important issues in applications such as dynamic traffic assignment. This paper first shows by example the FIFO properties of a computationally efficient commodity-based kinematic wave (CKW) model of network traffic flow. After the development of measurements of FIFO violation among commodities in location and time on the basis of the misplacement of vehicles in total and commodity traffic, it is argued theoretically that numerical CKW solutions converge to FIFO ones. Then, with numerical examples, it is shown that FIFO violation in CKW solutions is a function of the simulation time and the number of commodities but decreases with decreasing cell lengths, even for infinite number of commodities. Finally, the possible implications of this study are discussed.
conference paper
published journal article
conference paper
published journal article
Crash type is an informative indicator to infer driving behaviors and conditions that cause a crash. For example, rear-end and side-swipe crashes are typically caused by improper vehicle interaction such as sudden lane-changing or speed control while hit-object crashes are likely the result of a single driver’s mistake. This study investigated the impact of vehicles travelling as a group (platoon) and its configuration (i.e., types of vehicles consisting of the platoon) on crash type and severity since the vehicles could affect each other when travelling in close proximity. This study applied Generalized Structure Equation Modeling (GSEM) to capture the complex relationships among the various crash factors such as traffic condition, driver characteristics, environmental conditions, and vehicle interaction to the crash attributes including type and severity. This study collected over 3 million individual vehicle data from 39 traffic count sites in California to estimate the vehicle interactions and driving behaviors. The microscopic traffic data are matched to 1417 crash reports. Results showed that vehicles traveling in platoons are associated with more rear-end and side-swipe crashes. Speed difference in the platoon had a positive effect on hit-object crashes if the platoon comprises vehicles of homogeneous type – either trucks or non-trucks. In addition, human factors such as age and gender were identified as significant influential factors in all type of crashes, however truck involvement particularly played an important role amongst side-swipe crashes. Crash severity was negatively affected by total flow, and rear-end crashes were more likely to be severe compared with hit-object crashes. Based on findings, this study suggests practical operational strategies to reduce traffic instability associated with platooned vehicle patterns. Understanding the high-risk factors for different crash types and severities would provide valuable insights for decision-makers and transportation engineers to develop targeted intervention strategies in consideration of road users and traffic conditions such as fleet mix and speed.
published journal article
Since spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.
published journal article
conference paper
This study investigates the use of shared autonomous vehicles (SAVs) to enhance the resilience of fixed-route transit systems. This study focuses on the strategic deployment of SAVs during disruptive events to serve stranded transit passengers alongside regular SAV travelers. The study begins by detailing the Santa Clara Valley Transportation Authority (VTA) Light Rail Transit (LRT) system, highlighting its capacities, challenges, and the potential improvements offered by SAVs. We present three disruptive scenarios. In the first scenario, a temporary transit disruption caused by an earthquake prompts the deployment of SAVs to transport stranded passengers to their homes or designated drop-off locations. The second scenario assumes a short-term transit operation disruption due to an earthquake in which SAVs transport stranded passengers to their homes or designated SAV drop-off locations. Third scenario examines a complete, long-term transit shutdown resulting from a safety/security issue, such as a mass shooting-induced multi-month system closure. We propose and deploy an agent-based model to test various SAV deployment strategies in these three scenarios to understand the improvement of performance metrics such as traveler wait time and delay in the disrupted transit system. The results provide a concrete demonstration of the promising role of SAVs during transit system disruption. Through the disruption scenarios, we provide insights into the performance under various fleet sizes and their availability of use. This study provides a valuable analysis for urban planners, transportation authorities, and policymakers interested in leveraging shared mobility and automation technologies for improved urban mobility resilience.