Analysis of Phase Transition in Traffic Flow based on a New Model of Driving Decision

Different driving decisions will cause different processes of phase transition in traffic flow. To reveal the inner mechanism, this paper built a new cellular automaton （CA） model, based on the driving decision （DD）. In the DD model, a driver＇s decision is divided into three stages： decision-making, action, and result. The acceleration is taken as a decision variable and three core factors, i.e. distance between adjacent vehicles, their own velocity, and the preceding vehicle＇s velocity, are considered. Simulation results show that the DD model can simulate the synchronized flow effectively and describe the phase transition in traffic flow well. Further analyses illustrate that various density will cause the phase transition and the random probability will impact the process. Compared with the traditional NaSch model, the DD model considered the preceding vehicle＇s velocity, the deceleration limitation, and a safe distance, so it can depict closer to the driver preferences on pursuing safety, stability and fuel-saving and has strong theoretical innovation for future studies.

How much information is lost when sampling driving behavior data?Indicators to quantify the extent of information loss

Purpose–Individuals’driving behavior data are becoming available widely through Global Positioning System devices and on-board diagnostic systems.The incoming data can be sampled at rates ranging from one Hertz(or even lower)to hundreds of Hertz.Failing to capture substantial changes in vehicle movements over time by“undersampling”can cause loss of information and misinterpretations of the data,but“oversampling”can waste storage and processing resources.The purpose of this study is to empirically explore how micro-driving decisions to maintain speed,accelerate or decelerate,can be best captured,without substantial loss of information.Design/methodology/approach–This study creates a set of indicators to quantify the magnitude of information loss(MIL).Each indicator is calculated as a percentage to index the extent of information loss(EIL)in different situations.An overall information loss index named EIL is created to combine the MIL indicators.Data from a driving simulator study collected at 20 Hertz are analyzed(N=718,481 data points from 35,924 s of driving tests).The study quantifies the relationship between information loss indicators and sampling rates.Findings–The results show that marginally more information is lost as data are sampled down from 20 to 0.5 Hz,but the relationship is not linear.With four indicators of MILs,the overall EIL is 3.85 per cent for 1-Hz sampling rate driving behavior data.If sampling rates are higher than 2 Hz,all MILs are under 5 per cent for importation loss.Originality/value–This study contributes by developing a framework for quantifying the relationship between sampling rates,and information loss and depending on the objective of their study,researchers can choose the appropriate sampling rate necessary to get the right amount of accuracy.