24,227 research outputs found
New Physics in after the Measurement of
The recent measurement of is yet another hint of new physics (NP),
and supports the idea that it is present in decays. We
perform a combined model-independent and model-dependent analysis in order to
deduce properties of this NP. Like others, we find that the NP must obey one of
two scenarios: (I) or (II) . A third scenario, (III) , is rejected largely because it
predicts , in disagreement with experiment. The simplest NP models
involve the tree-level exchange of a leptoquark (LQ) or a boson. We show
that scenario (II) can arise in LQ or models, but scenario (I) is only
possible with a . Fits to models must take into account the additional
constraints from - mixing and neutrino trident production.
Although the LQs must be heavy, O(TeV), we find that the can be light,
e.g., GeV or 200 MeV.Comment: 18 pages, 1 figure; final version accepted for publication in
Physical Review
Temporal Variations on Allocation of Time
This study investigates the allocation of time and trip-making across time-of-day, day-of-week, and month-of-year, as well as over the past forty years. Some interesting findings result. People are working much more, shopping somewhat more on weekends, and stay at home less today than forty years ago. Time spent in travel on each weekend day (Saturday or Sunday) exceeds that on any weekday, as it did forty years ago. Time spent shopping on a typical day in the busiest month (December) is more than double that in the least busy month (September). Monthly variations in daily time in travel exceed 10 percent. The time of day patterns of shop and other trips for workers and nonworkers are both rational: nonworkers peak in mid-day away from rush hour while workers peak just after work, indicating trip chaining. .
Chained Trips in Montgomery County, Maryland
This paper analyzes the 1987-88 Metropolitan Washington Council of Governments home interview survey to understand how work trips are combined into trip chains and to relate trip chaining with demographic and travel characteristics. The focus is on the work trips during the morning and afternoon peak period and the stops made on the way for performing nonwork activities. The work trips during the afternoon period are much more likely to involve trip chaining as compared to the morning period. Women are more likely to link work trips with other activities as compared to men. Stops are closer to home than work. .
A Multi-modal Trip Distribution Model
This paper presents a multimodal trip distribution function estimated and validated for the metropolitan Washington region. In addition, a methodology for measuring accessibility, which is used as a measure of effectiveness for networks, using the impedance curves in the distribution model is described. This methodology is applied at the strategic planning level to alternative HOV alignments to select alignments for further study and Right-of-Way preservation. .
Operational Evidence of Changing Travel Patterns
This paper utilizes a traffic counts database covering a ten year period (1976-1985) to identify travel trends for Montgomery County, a suburb of Washington D.C. Generally, travel behavior is analyzed using person based travel survey data. The use of traffic counts to understand travel behavior is a relatively new approach. Unlike household surveys, which are typically characterized by respondent and sample bias, and require special effort for their collection, traffic counts are routinely collected by Departments of Transportation and provide the best available measure of observed traffic volumes. The study provides fresh evidence to support some of the earlier findings: an increase in lateral commuting as a share of travel, changes in work and non-work trip proportions, and increase in peak spreading. An interesting result in this paper relates to a more pronounced directionality in radial as compared with lateral trips. The relative symmetry of traffic flows along lateral routes compared with radial routes results in better utilization of the suburban road network. Non-work trips emerge as the more elastic trips, shifting to off-peak hours with an increase in congestion. .
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