Overtakes and dwell time delays for Japanese commuter trains

Reducing train delays important in many countries, even in those like Japan, where punctuality is already high. There is a clearpattern across the literature that the delays typically occur at stations and are recovered on line sections. Previous work has shownthat one explanation for this is that trains interact at stations. When trains have different speeds or stopping patterns, overtakes areimportant even on double-track lines. The latter is often the case in Japanese railways, and we can better understand their railway operations and delays by explicitly studying the way trains overtake each other. This paper uses historical train traffic records from three Japanese railway companies, in total 88 million observations, and finds both that most of the overtakes occur at a small sub set of stations, and that only about seven percent of overtakes were executed as scheduled. We also found that the combined dwell time delays decreased in these rare, successful cases but increased in the other scenarios, with a high degree of statistical significance. Looking at the interactions and the resulting dwell time delays, it is also possible to show and evaluate the actions of dispatchers. We found that they often reduced the delays somewhat by shifting the location of overtakes between trains that were either early or delayed. Finally, we suggest that interactions like overtakes can be used to help calibrate and validate simulation models, as they provide another meaningful and quantifiable way to describe the performance of railways, much like delay distributions and punctuality

Structural basis for tropomyosin overlap in thin (actin) filaments and the generation of a molecular swivel by troponin-T

Head-to-tail polymerization of tropomyosin is crucial for its actin binding, function in actin filament assembly, and the regulation of actin-myosin contraction. Here, we describe the 2.1 Å resolution structure of crystals containing overlapping tropomyosin N and C termini (TM-N and TM-C) and the 2.9 Å resolution structure of crystals containing TM-N and TM-C together with a fragment of troponin-T (TnT). At each junction, the N-terminal helices of TM-N were splayed, with only one of them packing against TM-C. In the C-terminal region of TM-C, a crucial water in the coiled-coil core broke the local 2-fold symmetry and helps generate a kink on one helix. In the presence of a TnT fragment, the asymmetry in TM-C facilitates formation of a 4-helix bundle containing two TM-C chains and one chain each of TM-N and TnT. Mutating the residues that generate the asymmetry in TM-C caused a marked decrease in the affinity of troponin for actin-tropomyosin filaments. The highly conserved region of TnT, in which most cardiomyopathy mutations reside, is crucial for interacting with tropomyosin. The structure of the ternary complex also explains why the skeletal- and cardiac-muscle specific C-terminal region is required to bind TnT and why tropomyosin homodimers bind only a single TnT. On actin filaments, the head-to-tail junction can function as a molecular swivel to accommodate irregularities in the coiled-coil path between successive tropomyosins enabling each to interact equivalently with the actin helix

Explaining dwell time delays with passenger counts for some commuter trains in Stockholm and Tokyo

In both Stockholm and Tokyo, small dwell time delays of at most 5 min make up around 90% of the total delays for commuter trains. To understand these disturbances, we use high resolution data on dwell times and passenger counts from both countries over the last several years. We find that trains in Tokyo are much more congested than in Stockholm, and that the exchange of passengers is modest at most stations in the latter city. In both cities, the range of dwell time delays is quite narrow, with between 40 and 50 s separating the 5th and 95th percentiles. Most delays are thus very small, and even small adjustments to dwell times can make a big difference overall. We find that the data on passengers explain about 40% of this variation in dwell time delays, if we account for non-linear and interaction effects, which is thus a ballpark estimate for how much the exchange of passenger contributes to delays for these trains. We also produce simple, linear models which can be used in practice to assign more appropriate dwell times. To facilitate such improvements, key stakeholders and practitioners have been closely involved with the research in both countries

Dwell Time Delays for Commuter Trains in Stockholm and Tokyo

The paper analyses dwell time delays for commuter trains in Stockholm andTokyo. In both cities, small dwell time delays of at most five minutes makeup around 90% of the total delays. Therefore, it is valuable to understandand deal with these disturbances. To this end, we use high resolution data ondwell times and passenger counts from both countries over the last severalyears. We find that these data alone can explain about 40% of the variationin dwell time delays and produce simple models which can be used inpractice to assign more appropriate dwell times. A change of 15 passengersper car, in Tokyo translates to a delay of about one second. For every 10remaining passengers per door in Stockholm, the delay increases by aboutone second, and one boarding or alighting passenger per door correspondsto about 0.4 seconds of delay. We also find that trains in Tokyo are muchmore congested than in Sweden, and that at most stations in the latter, theexchange of passengers is modest. In both cities, the range of dwell timedelays is quite narrow, with between 40 and 50 seconds separating the 5thand 95th percentiles. This indicates further that most delays, by far, are verysmall, and that even small adjustments to dwell times can make a bigdifference in the overall picture. To facilitate such improvements, keystakeholders and practitioners are closely involved with the research

Dwell Time Delays for Commuter Trains in Stockholm and Tokyo

The paper analyses dwell time delays for commuter trains in Stockholm andTokyo. In both cities, small dwell time delays of at most five minutes makeup around 90% of the total delays. Therefore, it is valuable to understandand deal with these disturbances. To this end, we use high resolution data ondwell times and passenger counts from both countries over the last severalyears. We find that these data alone can explain about 40% of the variationin dwell time delays and produce simple models which can be used inpractice to assign more appropriate dwell times. A change of 15 passengersper car, in Tokyo translates to a delay of about one second. For every 10remaining passengers per door in Stockholm, the delay increases by aboutone second, and one boarding or alighting passenger per door correspondsto about 0.4 seconds of delay. We also find that trains in Tokyo are muchmore congested than in Sweden, and that at most stations in the latter, theexchange of passengers is modest. In both cities, the range of dwell timedelays is quite narrow, with between 40 and 50 seconds separating the 5thand 95th percentiles. This indicates further that most delays, by far, are verysmall, and that even small adjustments to dwell times can make a bigdifference in the overall picture. To facilitate such improvements, keystakeholders and practitioners are closely involved with the research