Design of Driving Behavior Pattern Measurements Using Smartphone Global Positioning System Data

The emergence of new technologies such as GPS,cellphone,Bluetooth device,etc.offers opportunities for collecting high-fidelity temporal-spatial travel data in a cost-effective manner.With the vehicle trajectory data achieved from a smartphone app Metropia,this study targets on exploring the trajectory data and designing the measurements of the driving pattern.Metropia is a recently available mobile traffic app that uses prediction and coordinating technology combined with user rewards to incentivize drivers to cooperate,balance traffic load on the network,and reduce traffic congestion.Speed and celeration(acceleration and deceleration)are obtained from the Metropia platform directly and parameterized as individual and system measurements related to traffic,spatial and temporal conditions.A case study is provided in this paper to demonstrate the feasibility of this approach utilizing the trajectory data from the actual app usage.The driving behaviors at both individual and system levels are quantified from the microscopic speed and celeration records.The results from this study reveal distinct driving behavior pattern and shed lights for further opportunities to identify behavior characteristics beyond safety and environmental considerations.

Studying Driving Risk Factors using Multi-Source Mobile Computing Data

Traffic congestion can largely be attributed to the issues related with driving behavior,which may cause vehicle crash,stop-and-go traffic due to frequent lane changing behaviors,etc.,and makes the driving behavior research also of significance in the realm of traffic management and demand management.The emergence and subsequent rapid advances with new information and communication technologies(ICT)now offers the capability of collecting high-fidelity and high-resolution trajectory data in a cost-effective manner.In this research,we use a smartphone app to collect data for the purpose of studying driving risk factors.What’s unique about the data in this research is its backend server also estimates traffic speed and volume for each link that the vehicle traverses.In order words,the data collected with build-in GPS modules in the smartphone include not only the vehicle spatial-temporal dimension location,which could be used to correlate the network geography attributes and/or real-time traffic condition,but also the detailed information about the vehicle dynamics including speed,acceleration,and deceleration,whereby a driver’s control and maneuver of a vehicle can be analyzed in detail.Such type of dataset combining both user trajectory and link speed/volume information is rarely seen in prior research,permitting a unique opportunity to link critical traffic congestion factors leading to driving behavior and crash potential.In this paper,the overall research framework used in this research is presented,which mainly includes data collection,data processing,calibration and analysis methodology.A preliminary case study-including data summary statistics and correlation analysis-is also presented.The results of our study will further existing knowledge about driving exposure factors that are closely linked to crash risk,and provide the foundation for advanced forms of Usage Based Insurance.

Tracking IBU Through the Brewing Process:The Quest for Consistency

International Bitterness Units(IBUs)were measured with a spectrophotometer throughout the brewing process for a wide variety of beer styles.Most of the data were collected at our 50BBL Scripps Ranch facility,although some data were also gathered from our 5 BBL R&D brewery and 150 BBL production facility.It was determined that mash hops add a small amount of IBUs to wort with an average utilization of 9.1%.As expected,60 min hop additions added substantially more IBUs;the average hop utilization was 44.0%.Whirlpool hops also contributed high IBUs w让h an average utilization of 29.9%.In addition,hot-side wet hopping had 8.0%utilization.A large amount of IBUs are lost during the first 2 days of fermentation.Losses were also observed during filtration.It was discovered that high amounts of dry hopping resulted in an increase of IBUs.However,the average overall IBU loss in the cellar was 33.7%.