Traffic counts collect information that is valuable, for example, in bridge and road design or maintenance processes. The average daily traffic volume is often the most collected measure of vehicular traffic, which is used in the design or assessment of major highways. Permanent control stations, situated in key locations of the highway network, gather data the entire year. However, one of the disadvantages of traffic count data is that most counters used, do not measure total vehicle weight and axle load data. Traffic counts display only the classification of vehicles, traffic volume, average daily traffic, and annual average daily traffic. Axle loads on the other hand are required, for example, as input in the design of pavement and new bridges, and the reliability assessment of existing ones. Weighin-motion (WIM) systems are usually used to collect vehicle load data. The State of Mexico (in central Mexico) has 115 permanent vehicle counting stations with 745 traffic counting points in its federally administered road network. However, due to the lack of WIM stations, it is not possible to obtain axle load data. In this paper, a Bayesian Network (BN) quantified with data from WIM stations in the Netherlands is used to describe the weight and length distribution of heavy vehicles registered in the permanent vehicle counting stations of the State of Mexico federal highways. The Dutch and Mexican vehicle types are matched according to similar characteristics. Later, synthetic WIM observations from the BN model are analysed through extreme value theory and vehicle loads with selected return periods are computed for all study counting points. The outcome is a mapping methodology with a linked database. The traffic volumes and extreme loads can then be easily found and compared with other highways in the network. This work shows that hazard maps can be implemented to provide importantly and summarized information to understand the risks of extreme traffic loads and to help in the reliability assessment and maintenance strategies of pavements and bridges.
This study demonstrates the feasibility of fusing large-scale travel and health surveys and uses the new comprehensive data set generated to model the relationship between health and multimodal (walking, biking, transit, and vehicle usage) long-term (weekly, monthly, and yearly) travel choices. Two measures of health, the body-mass index (BMI) and a self-assessed physical health score (SAPHS), were fused from a health survey onto a travel survey at the disaggregate (individual) level. The probabilistic record linkage software, Link Plus, was used for the data fusion purposes. The methodology was validated by using the eating and health module (EH) of the American Time Use Surveys (ATUS). Subsequently, the algorithm was used to match the health information from the ATUS to the National Household Travel Surveys (NHTS) of 2008 to 2009, and the resulting master data set was used to develop models for multimodal travel choices and health. The statistical analysis indicates that although increasing walking and transit use were associated with better health (relative to nonusers of the mode), those with the highest levels of walking and transit use were also found to be in poor health relative to moderate users of the mode. Similarly, those at the two ends of the vehicle miles traveled spectrum (first and fourth quartiles) had higher BMI compared with those in the middle of the spectrum. There were no statistically significant effects of weekly bike trips on health measures. Overall, this study is envisioned as a proof-of-concept of how data fusion techniques may be used to integrate multiple data sets to facilitate a comprehensive study of multimodal travel choices and health.
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