Methods of analysis for vehicle soak time data

Vehicle soak time, the duration of time a vehicle’s engine is at rest prior to being started, and its distribution function are important transportation activity data inputs for mobile emissions inventory estimation due to their impacts on vehicle start and evaporative emissions. This paper provides vehicle emission researchers with an overview of statistical analysis methods relevant to analyzing vehicle soak time data. Many of these methods are already in use in emissions research and have appeared in the literature. These methods are reviewed and further details regarding the implementation and interpretation of these methods are provided. Statistical methods relevant to the analysis of soak time data that have yet to appear in the emissions literature, including kernel density estimation and generalized linear models, are also introduced. Advantages and disadvantages of the methods are compared and theoretical justification is provided. Issues of correlated observations and censored data are discussed. General guidelines for the analysis of soak time data, such as stratification by start type and geographical region, are established. Finally, a subset of the statistical methods discussed is used to analyze the US Environmental Protection Agency’s 3-city data.     

Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors

A practical system is described for the real-time estimation of travel time across an arterial segment with multiple intersections. The system relies on matching vehicle signatures from wireless sensors. The sensors provide a noisy magnetic signature of a vehicle and the precise time when it crosses the sensors. A match (re-identification) of signatures at two locations gives the corresponding travel time of the vehicle. The travel times for all matched vehicles yield the travel time distribution. Matching results can be processed to provide other important arterial performance measures including capacity, volume/capacity ratio, queue lengths, and number of vehicles in the link. The matching algorithm is based on a statistical model of the signatures. The statistical model itself is estimated from the data, and does not require measurement of ‘ground truth’. The procedure does not require measurements of signal settings; in fact, signal settings can be inferred from the matched vehicle results. The procedure is tested on a 1.5 km (0.9 mile)-long segment of San Pablo Avenue in Albany, CA, under different traffic conditions. The segment is divided into three links: one link spans four intersections, and two links each span one intersection.