Individual Characteristics and Stated Preferences for Alternative Energy Sources and Propulsion Technologies in Vehicles: A Discrete Choice Analysis

This paper empirically examines the determinants of the demand for alternative energy sources and propulsion technologies in vehicles. The data stem from a stated preference discrete choice experiment with 598 potential car buyers. In order to simulate a realistic automobile purchase situation, seven alternatives were incorporated in each of the six choice sets, i.e. hybrid, gas, biofuel, hydrogen, and electric as well as the common fuels gasoline and diesel. The vehicle types were additionally characterized by a set of attributes, such as purchase price or motor power. Besides these vehicle attributes, our study particularly considers a multitude of individual characteristics, such as socio-demographic and vehicle purchase variables. The econometric analysis with multinomial probit models identifies some population groups with a higher propensity for alternative energy sources or propulsion technologies in vehicles, which can be focused by policy and automobile firms. For example, younger people and people who usually purchase environment-friendly products have a higher stated preference to purchase biofuel, hydrogen, and electric automobiles than other population groups. Methodologically, our study highlights the importance of the inclusion of taste persistence across the choice sets. Furthermore, it suggests a high number of random draws in the Geweke-Hajivassiliou-Keane simulator, which is incorporated in the simulated maximum likelihood estimation and the simulated testing of statistical hypotheses.Alternative energy sources and propulsion technologies in vehicles, stated preferences, discrete choice, multinomial probit models, unobserved heterogeneity, simulated maximum likelihood estimation

Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles

The advancements in connected and autonomous vehicles in these times demand the availability of tools providing the agents with the capability to be aware and predict their own states and context dynamics. This article presents a novel approach to develop an initial level of collective awareness (CA) in a network of intelligent agents. A specific collective self-awareness functionality is considered, namely, agent-centered detection of abnormal situations present in the environment around any agent in the network. Moreover, the agent should be capable of analyzing how such abnormalities can influence the future actions of each agent . Data-driven dynamic Bayesian network (DBN) models learned from time series of sensory data recorded during the realization of tasks (agent network experiences) are here used for abnormality detection and prediction. A set of DBNs, each related to an agent , is used to allow the agents in the network to reach synchronously aware possible abnormalities occurring when available models are used on a new instance of the task for which DBNs have been learned. A growing neural gas (GNG) algorithm is used to learn the node variables and conditional probabilities linking nodes in the DBN models; a Markov jump particle filter (MJPF) is employed for state estimation and abnormality detection in each agent using learned DBNs as filter parameters. Performance metrics are discussed to asses the algorithm’s reliability and accuracy. The impact is also evaluated by the communication channel used by the network to share the data sensed in a distributed way by each agent of the network. The IEEE 802.11p protocol standard has been considered for communication among agents. Performances of the DBN-based abnormality detection models under different channel and source conditions are discussed. The effects of distances among agents and of the delays and packet losses are analyzed in different scenario categories (urban, suburban, and rural). Real data se..

Electric Vehicle Adoption Behavior and Vehicle Transaction Decision: Estimating an Integrated Choice Model with Latent Variables on a Retrospective Vehicle Survey

Electric vehicles (EVs) promise a sustainable solution to mitigating negative emission externalities of transportation systems caused by fossil-fueled conventional vehicles (CVs). While recent developments in battery technology and charging infrastructure can help evolve the niche market of EVs into the mass market, EVs are yet to be widely adopted by the public. This calls for an in-depth understanding of public adoption behavior of EVs as one dimension of vehicle decision making, which itself may be intertwined with other vehicle decision-making dimensions, especially vehicle transaction. This study presents an integrated choice model with latent variables (ICLV) to investigate households’—as a decision-making unit—decisions on vehicle transaction type (i.e., no transaction, sell, add, and trade) and vehicle fuel type (i.e., CVs and all EV types, including hybrid EV, plug-in hybrid EV, and battery EV) choice. To analyze the ICLV model empirically, one of the first revealed preferences national vehicle survey involving CVs and all EV types was conducted, which retrospectively inquired about 1,691 American households’ dynamics of vehicle decision making and demographic attributes over a 10-year period as well as their attitudes/preferences. The model estimation results highlight that EV adoption and vehicle transaction choice is influenced mainly by (1) the dynamics of household demographic attributes and (2) four latent constructs explaining attentiveness to vehicle attributes, social influence, environmental consciousness, and technology savviness. Notably, EV adoption promotion policies are found to be likely most effective on socially influenced individuals, who tend to consider advertisement and social trend more when making vehicle decisions

Toward environmental noise estimation according to the road surface characteristics and traffic volume

U radu su dani prvi rezultati istraživanja koje ima za cilj analizirati mogućnost relevantne ocjene stupnja smetnji uzrokovanih bukom od prometa na osnovu parametara koje uprave za ceste redovito prikupljaju. To su za opis površinskih svojstva kolnika ravnost (IRI100), tekstura i vizualni pregled stanja dan PCI-indeksom te prometno opterećenje koje uključuje i strukturu prometnog toka. Pri tome bi uz dosadašnje parametre pri donošenju odluke o rekonstrukciji bilo potrebno uključiti i ocjenu stanja bučnosti. Na taj način bi i stanje bučnosti u okolišu, ne mijenjajući uvriježenu praksu, moglo postati još jedan važan čimbenik prilikom donošenja odluke o rekonstrukciji postojećih prometnica.The paper gives the first results of the research aimed at analysing the possibility of the relevant estimation of the level of disturbance caused by traffic noise on the basis of parameters collected regularly by road administrations. For the description of road surface characteristics these are road evenness (IRI100), road texture and visual assessment of pavement condition given by the PCI-index, as well as the traffic load including the structure of the traffic flow. When making a decision about the reconstruction, together with already included parameters, the environmental noise estimation should also be taken into consideration. In doing so, environmental noise conditions would, without altering standard procedure, become another important factor in deciding about the reconstruction of the existing roads

Crash Risk Reduction at Signalized Intersections Using Longitudinal Data

This study extends the previous work of Burkey and Obeng (2004) that examined the impact of red light cameras on the type and severity of crashes at signalized intersections in Greensboro, NC. The extension takes the following form. First, we extend the data to cover 57 months, and to include demographics, technology variables, the condition of a driver at the time of the crash, vehicle characteristics, land use and visual obstruction. Second, instead of examining the impact of red light cameras, we focus on identifying the determinants of crash severity, two-vehicle crashes, and property damage costs. The major findings are that the safety impacts of seatbelt use outweigh the impacts of airbags deploying because the latter tends to increase evident injuries and property damage costs, while the former reduces these injuries. We also find that head-on collisions and under rides increase evident injuries. For two-vehicle crashes, we find that the risk of severe injuries increases in pickup-pickup crashes and SUV-pickup crashes, while the risk of possible injuries increases in car-truck crashes. For property damage costs, we found the condition of the driver at the time of the crash (i.e., illness, impaired, medical condition, driver falling asleep, driver apparently normal) to be important determinants in increasing these costs. The types of accidents that we found to increase property damage costs are running into a fixed object and under rides. Finally, we found that property damage costs of crashes are low where the land uses are commercial and institutional suggesting that the accidents that occur in these areas are minor.longitudinal data; accidents; intersections

Dynamic Bayesian Collective Awareness Models for a Network of Ego-Things

A novel approach is proposed for multimodal collective awareness (CA) of multiple networked intelligent agents. Each agent is here considered as an Internet-of-Things (IoT) node equipped with machine learning capabilities; CA aims to provide the network with updated causal knowledge of the state of execution of actions of each node performing a joint task, with particular attention to anomalies that can arise. Data-driven dynamic Bayesian models learned from multisensory data recorded during the normal realization of a joint task (agent network experience) are used for distributed state estimation of agents and detection of abnormalities. A set of switching dynamic Bayesian network (DBN) models collectively learned in a training phase, each related to particular sensorial modality, is used to allow each agent in the network to perform synchronous estimation of possible abnormalities occurring when a new task of the same type is jointly performed. Collective DBN (CDBN) learning is performed by unsupervised clustering of generalized errors (GEs) obtained from a starting generalized model. A growing neural gas (GNG) algorithm is used as a basis to learn the discrete switching variables at the semantic level. Conditional probabilities linking nodes in the CDBN models are estimated using obtained clusters. CDBN models are associated with a Bayesian inference method, namely, distributed Markov jump particle filter (D-MJPF), employed for joint state estimation and abnormality detection. The effects of networking protocols and of communications in the estimation of state and abnormalities are analyzed. Performance is evaluated by using a small network of two autonomous vehicles performing joint navigation tasks in a controlled environment. In the proposed method, first the sharing of observations is considered in ideal condition, and then the effects of a wireless communication channel have been analyzed for the collective abnormality estimation of the agents. Rician wireless channel and the usage of two protocols (i.e., IEEE 802.11p and IEEE 802.15.4) along with different channel conditions are considered as well