Evaluating the Effectiveness of a School-Based Intervention on Driving-Related Carbon Emissions Using Real-Time Transportation Data

The development of tools that can measure the efficiency of individual driving behaviors offers unique opportunities to encourage drivers towards more efficient driving behaviors. As states make progress towards reducing carbon emissions through the adoption of renewable energy for electricity generation, transportation remains the largest sources of carbon emissions. Although numerous local or regional campaigns have encouraged consumers to conserve energy at home and at work, less interest has been shown in encouraging drivers to adopt more energy efficient driving behaviors. In this study, a smartphone application was used to gather driving data (e.g., hard accelerations, hard braking and time over speed limit) within a university course on climate change to investigate whether environmental appeals could encourage more efficient driving behavior in students. The results show that through this intervention, average student driving scores improved by between 2 and 5% in the classes studied, with larger changes found in students who did not initially identify as having pro-environmental attitudes. These results suggest that educational programs and campaigns using real-time data on driving behavior may provide opportunities to reduce carbon emissions

Effect of State Coalitions to Reduce Underage Drinking: A National Evaluation

Summarizes an evaluation of how the RWJF-funded Reducing Underage Drinking Through Coalitions Project to reduce youth drinking changed media coverage of alcohol-related issues, state policy, youth drinking behaviors, and alcohol-related driving behaviors

Modeling Pipeline Driving Behaviors: A Hidden Markov Model Approach

Driving behaviors at intersection are complex because drivers have to perceive more traffic events than normal road driving and thus are exposed to more errors with safety consequences. Drivers make real-time responsesin a stochastic manner. This paper presents our study using Hidden Markov Models (HMM) to model driving behaviors at intersections. Observed vehicle movement data are used to build up the model. A single HMM is used to cluster the vehicle movements when they are close to intersection. The re-estimated clustered HMMs provide better prediction of the vehicle movements compared to traditional car-following models. Only through vehicles on major roads are considered in this paper.

A Survey of Taxi Drivers’ Aberrant Driving Behavior in Beijing

This is the author's accepted manuscript. The original publication is available from http://www.tandfonline.com/doi/abs/10.1080/19439962.2013.799624#.VD_cxxYXNWs.Taxis are an important component in Beijing's urban integrated transport system. They provide passengers with convenient, comfortable, and efficient service. However, aberrant driving behaviors occur frequently among Beijing taxi drivers, leading to frequent violations and passenger complaints. This study explores Beijing taxi drivers’ aberrant driving behaviors and the factors influencing them. Questionnaires were designed to obtain different views of taxi drivers’ aberrant driving behaviors from taxi drivers, traffic police, and passengers, and to sample problems in the Beijing taxi industry. Responses from 52 professional taxi drivers, 40 traffic police officers, and more than 500 taxi passengers were obtained. The results show that taxi drivers generally under-report their own aberrant driving behaviors, whereas passengers and police appear to have a very negative impression of taxi drivers’ driving behaviors. Environmental influences such as economic pressure, ownership of taxi management, and the nature of the complaint system were found to contribute to taxi drivers’ aberrant driving behaviors. Some suggestions to increase the efficiency and safety of the taxi system in Beijing were proposed, such as improving drivers’ working and economic conditions, developing a better passenger loading system, establishing more effective license and termination policies for drivers, and improving the complaint system

Promoting Eco-driving through Persuasive Visualization

The goal of sustainability is to preserve resources for future generations. Climate change is a major environmental problem that violates the goal of sustainability. A key strategy to combat climate change is to reduce carbon emission that is largely generated by road transport. Traditional interventions on promoting eco-driving behaviors often fail to convince people to alter their driving behaviors. The growing use of persuasive visualization allows individuals to become aware of the relationship between their driving behaviors and the associated environmental impact. Drawing on the expectancy theory of motivation, this study plans to explore ways to design effective visualization to promote eco-driving behaviors. Additionally, this study proposes a unique lab experiment that enables the manipulation of visualizations and presents an opportunity to observe individuals’ driving behavioral changes