published journal article
conference paper
ALINEA, a local feedback romp-metering strategy, has been shown to be a remarkably simple, highly efficient and easy application. This paper presents a microscopic simulation-based method to optimize the operational parameters of the algorithm, as an alternative to the difficult task of fine-tuning them In real-world testing. Four parameters, including the update cycle of the metering rate, a constant regulator, the location and the desired occupancy of the downstream detector station, are considered. A Genetic Algorithm that searches the optimal combination of parameter values Is employed. Simulation results show that the genetic algorithm is able to find a set of parameter values that can optimize the performance of the ALINEA algorithm.
published journal article
published journal article
conference paper
In recent years, there has been increasing interest in inter-vehicle communications (IVC) based on wireless networks to collect and distribute traffic information in various Intelligent Transportation Systems applications. In this paper, we study the performance of IVC under various traffic and communication conditions by means of simulation analysis. We consider impacts of shock waves, transportation network, traffic densities, transmission ranges, and multiple information sources. We used a state-of-the-art communication network simulator ns-2 to measure success rate and message delivery ratio (MDR) for flooding-based IVC communication. For reasonable realism in the deployment scenario, we assume that only a partial set of vehicles on the road are equipped with communication devices, according to the market penetration rate. A Monte-Carlo simulation method is used with repeated random sampling of IVC-equipped vehicles. The results indicate how these parameters can impact the performance of IVC communications. By comparing the flooding-based approach (theoretical and simulation) and simulation results using AODV (Ad Hoc On-Demand Distance Vector), we conclude the importance of traffic environment and network protocol for IVC communication.
published journal article
published journal article
Volunteered sharing of resources is often observed in response to disaster events. During evacuations the sharing of resources and vehicles is a crucial mechanism for expanding critical capacity and enabling inclusive disaster response. This paper examines the complexity of rideshare decision-making in the wake of simultaneous emergencies. Specifically, the need for physical distancing measures during the coronavirus (COVID-19) pandemic complicates face-to-face resource sharing between strangers. The ability of on-demand ridesharing to provide emergency transportation to individuals without access to alternatives calls for an understanding of how evacuees weigh risks of contagion against benefits of spontaneous resource sharing. In this research, we examine both sociodemographic and situational factors that contribute to a willingness to share flood evacuation rides with strangers during the COVID-19 pandemic. We hypothesize that the willingness to share is significantly correlated with traditional emergency resource sharing motivations and current COVID-19 risk factors. To test these hypotheses, we distributed an online survey during the pandemic surge in July 2020 to 600 individuals in three midwestern and three southern states in the United States with high risk of flooding. We estimate a random parameter multinomial logit model to determine the willingness to share a ride as a driver or passenger. Our findings show that willingness to share evacuation rides is associated with individual sociodemographics (such as being female, under 36 years old, Black, or republican-identifying) and the social environment (such as households with children, social network proximity, and neighborly sharing attitudes). Moreover, our findings suggest higher levels of income, COVID-19 threat perception, evacuation fear, and household preparedness all correspond with a lower willingness to share rides. We discuss the broader implications of emergency on-demand mobility during concurrent disasters to formulate strategies for transportation agencies and on-demand ridehailing providers.
conference paper
This paper describes an ongoing project to develop a demand forecasting model for clean-fuel vehicles in California. Large-scale surveys of both households and commercial fleet operators have been carried out These data are being used to calibrate a new micro-simulation based vehicle demand forecasting system. Based on pre-specified attributes of future vehicles (including specified clean-fueled vehicle incentives), the system will produce annual forecasts of new and used vehicle demand by type of vehicle and geographic region. The system will also forecast annual vehicle miles traveled for all vehicles and recharging demand by time of day for electric vehicles. These results are potentially useful to utility companies in their demand-side management planning, to public agencies in their evaluation incentive schemes, and to manufacturers faced with designing and marketing clean-fuel vehicles.
conference paper
published journal article