Transit Arrival Time Research Articles

Transit Arrival Time Prediction Using Interaction Networks

Accurate transit arrival time prediction is a critical factor for improving the quality of transit services. A significant factor that can affect transit arrival times is the other vehicles traveling on transit routes, as transit arrival time is significantly affected by traffic conditions. However, few previous studies considered traffic condition impacts in transit arrival time prediction due to a lack of real-time traffic data, reducing the accuracy in predictions under varying traffic conditions. To fill this research gap, crowdsourced speed data with wide coverage is utilized to indicate traffic conditions and to predict transit arrival time in conjunction with General Transit Feed Specification (GTFS) data. Interaction Networks (INs) are employed to model the interactions between transit speed, dwell time, and traffic speed for arrival time prediction. The proposed method only uses limited historical data and can predict arrival time at stops along a transit route. Ten days of data were collected from bus route #4 in Tucson, Arizona to evaluate the performance of the proposed method. The evaluation results show the average mean absolute percentage error (MAPE) of predicted arrival time on weekdays and weekends is 13.5% and 14%, respectively, indicating that the proposed method is promising for predicting transit arrival time while considering real-time traffic conditions. Furthermore, six traditional methods are compared with the proposed method, and the comparison results show the proposed method outperforms the other six methods.

Predicting Public Transit Arrival Times: A Hybrid Deep Neural Network Approach

Predicting Public Transit Arrival Times: A Hybrid Deep Neural Network Approach

Tidal dissipation in evolving low-mass and solar-type stars with predictions for planetary orbital decay

ABSTRACT We study tidal dissipation in stars with masses in the range 0.1–1.6 M⊙ throughout their evolution, including turbulent effective viscosity acting on equilibrium tides and inertial waves (IWs) in convection zones, and internal gravity waves in radiation zones. We consider a range of stellar evolutionary models and incorporate the frequency-dependent effective viscosity acting on equilibrium tides based on the latest simulations. We compare the tidal flow and dissipation obtained with the conventional equilibrium tide, which is strictly invalid in convection zones, finding that the latter typically overpredicts the dissipation by a factor of 2–3. Dissipation of IWs is computed using a frequency-averaged formalism accounting for realistic stellar structure for the first time, and is the dominant mechanism for binary circularization and synchronization on the main sequence. Dissipation of gravity waves in the radiation zone assumes these waves to be fully damped (e.g. by wave breaking), and is the dominant mechanism for planetary orbital decay. We calculate the critical planetary mass required for wave breaking as a function of stellar mass and age, and show that this mechanism predicts destruction of many hot Jupiters but probably not Earth-mass planets on the main sequence. We apply our results to compute tidal quality factors following stellar evolution, and tidal evolutionary time-scales, for the orbital decay of hot Jupiters, and the spin synchronization and circularization of binary stars. We also provide predictions for shifts in transit arrival times due to tidally driven orbital decay of hot Jupiters that may be detected with NGTS, TESS, or PLATO.

Probing Transit Timing Variation and Its Possible Origin with 12 New Transits of TrES-3b

Abstract We present 12 new transit light curves of the hot-Jupiter TrES-3b observed during 2012−2018 to probe the transit timing variation (TTV). By combining the midtransit times determined from these 12 transit data with those reestimated through uniform procedure from 71 transit data available in the literature, we derive new linear ephemeris and obtain the timing residuals that suggest the possibility of TTV in the TrES-3 system. However, the frequency analysis shows that the possible TTV is unlikely to be periodic, indicating the absence of an additional body in this system. To explore the other possible origins of TTV, the orbital decay and apsidal precession ephemeris models are fitted to the transit time data. We find the decay rate of TrES-3b to be ms yr−1, and the corresponding estimated modified stellar tidal quality factor of is consistent with the theoretically predicted values for the stars hosting the hot-Jupiters. The shift in the transit arrival time of TrES-3b after 11 years is expected to be T shift ∼ 69.55 s, which is consistent with the rms of the timing residuals. Besides, we find that the apsidal precession ephemeris model is statistically less probable than the other considered ephemeris models. It is also discussed that despite the fact that the linear ephemeris model appears to be the most plausible model to represent the transit time data, the possibility of the orbital decay cannot be completely ruled out in the TrES-3 system. To confirm this, further high-precision and high-cadence follow-up observation of transits of TrES-3b would be important.

WTS-2 b: a hot Jupiter orbiting near its tidal destruction radius around a K dwarf

We report the discovery of WTS-2 b, an unusually close-in 1.02-day hot Jupiter (Mp=1.12MJ, Rp=1.363RJ) orbiting a K2V star, which has a possible gravitationally-bound M-dwarf companion at 0.6 arcsec separation contributing ~20 percent of the total flux in the observed J-band light curve. The planet is only 1.5 times the separation from its host star at which it would be destroyed by Roche lobe overflow, and has a predicted remaining lifetime of just ~40 Myr, assuming a tidal dissipation quality factor of Q'*=10^6. Q'* is a key factor in determining how frictional processes within a host star affect the orbital evolution of its companion giant planets, but it is currently poorly constrained by observations. We calculate that the orbital decay of WTS-2 b would correspond to a shift in its transit arrival time of T_shift~17 seconds after 15 years assuming Q'*=10^6. A shift less than this would place a direct observational constraint on the lower limit of Q'* in this system. We also report a correction to the previously published expected T_shift for WASP-18 b, finding that T_shift=356 seconds after 10 years for Q'*=10^6, which is much larger than the estimated 28 seconds quoted in WASP-18 b discovery paper. We attempted to constrain Q'* via a study of the entire population of known transiting hot Jupiters, but our results were inconclusive, requiring a more detailed treatment of transit survey sensitivities at long periods. We conclude that the most informative and straight-forward constraints on Q'* will be obtained by direct observational measurements of the shift in transit arrival times in individual hot Jupiter systems. We show that this is achievable across the mass spectrum of exoplanet host stars within a decade, and will directly probe the effects of stellar interior structure on tidal dissipation.

A Multimodal Trip Planning System With Real-Time Traffic and Transit Information

Most transit trip planning systems are based on static schedules and generate trips that do not dynamically respond to delays in transit operation caused by traffic congestion or accidents. In addition, the driving-parking-then-transit travel mode is common in metropolitan areas. However, very few transit trip planners incorporate real-time transit data into this mode. This article describes a multimodal trip planning system for multiple modes: driving, transit, and driving-parking-then-transit. The system considers the real-time transit arrival time, which is estimated by a prediction model. Both Web-based and mobile phone-based clients are used to access the system. Case studies show that the multimodal trip planning system works well in real-life situations.

Modeling schedule recovery processes in transit operations for bus arrival time prediction

AbstractMany existing algorithms for bus arrival time prediction assume that buses travel at free‐flow speed in the absence of congestion. As a result, delay incurred at one stop would propagate to downstream stops at the same magnitude. In reality, skilled bus operators often constantly adjust their speeds to keep their bus on schedule. This paper formulates a Markov chain model for bus arrival time prediction that explicitly captures the behavior of bus operators in actively pursuing schedule recovery. The model exhibits some desirable properties in capturing the schedule recovery process. It guarantees provision of the schedule information if the probability of recovering from the current schedule deviation is sufficiently high. The proposed model can be embedded into a transit arrival time estimation model for transit information systems that use both real‐time and schedule information. It also has the potential to be used as a decision support tool to determine when dynamic or static information should be used.

Dynamic Bus Arrival Time Prediction with Artificial Neural Networks

Transit operations are interrupted frequently by stochastic variations in traffic and ridership conditions that deteriorate schedule or headway adherence and thus lengthen passenger wait times. Providing passengers with accurate vehicle arrival information through advanced traveler information systems is vital to reducing wait time. Two artificial neural networks (ANNs), trained by link-based and stop-based data, are applied to predict transit arrival times. To improve prediction accuracy, both are integrated with an adaptive algorithm to adapt to the prediction error in real time. The bus arrival times predicted by the ANNs are assessed with the microscopic simulation model CORSIM, which has been calibrated and validated with real-world data collected from route number 39 of the New Jersey Transit Corporation. Results show that the enhanced ANNs outperform the ones without integration of the adaptive algorithm.