A Deep Learning-Based Approach for Train Arrival Time Prediction

Level crossings have a function to let the traffic cross the railroad from one side to the other. In the Netherlands, 2300 level crossings are spread out over the country, playing a significant role in daily traffic. Currently, there isn’t an accurate estimation of the arrival time of trains at level crossings while it plays an important role in traffic flow management in intelligent transport systems. This paper presents a state-of-the-art deep learning model for predicting the arrival time of trains at level crossings using spatial and temporal aspects, external attributes, and multi-task learning. The spatial and temporal aspects incorporate geographical and historical travel data and the attributes provide specific information about a train route. Using multi-task learning all the information is combined and an arrival time prediction is made both for the entire route as for sub-parts of that route. Experimental results show that on average, the error is only 281 s with an average trip time of one hour. The model is able to accurately predict the arrival time at level crossings for various time steps in advance. The source code is available at https://github.com/basbuijse/train-arrival-time-estimator.</p

Monolithic near infrared image sensors enabled by quantum dot photodetector

status: publishe

Determining charge carrier extraction in lead sulfide quantum dot near infrared photodetectors

Colloidal quantum dots (QDs) based on lead sulfide (PbS) have acquired scientific interest for infrared optoelectronic devices with potential bandgap tunability and ease of fabrication on arbitrary substrates. In this work, we show how device analysis data feed back into process optimization, towards the realization of high performance QD NIR photodetectors. Using the combination of transient PL, carrier transport and CV measurements we obtain the carrier density, lifetime and diffusion length in the layers. From the measured short diffusion length of the minority carriers, we deduce the need to achieve a wide depletion region to minimize recombination and thus enhance the carrier harvesting. Process optimization lead to a depletion region of more than 150 nm, resulting in high photon to carrier conversion. Furthermore, the complex index of refraction of all layers is characterized using ellipsometry and reflection/transmission, and these values are used as input for a transfer matrix method. Using the first interference peak, we show that a maximum EQE of 25% can be expected from optical modeling, a value that we almost reach experimentally (20%). Combining all of the above, we demonstrate 1450-nm photodetectors with dark current in the range of μA and specific detectivity (D*) of 10^11 Jones

SWIR detection with thin-film photodetectors based on colloidal quantum dots

Image sensors operating in the short-wavelength infrared (SWIR) wavelength range typically use epitaxially grown III-V semiconductor as the photoactive material. To realize a two-dimensional focal plane array, the detector chip is connected with a CMOS readout chip using solder bump hybridization, imposing a limit on the pixel pitch. One way to realize higher resolution and finer pixel scaling is to use a monolithic approach with the photoactive layer deposited directly on top of the readout chip. In this paper, we describe a pixel stack based on colloidal quantum dots that can be monolithically integrated. Colloidal quantum dots are an interesting material group as their optical properties can be tuned with their size and their electrical properties can be adjusted by the organic ligand selection. For SWIR detection, we are using PbS QDs with diameter larger than 5 nm, with the cut-off wavelength reaching 1600 nm. The QD film is deposited by spin-coating directly on top of the substrate and the process temperature is kept below 150°C. QD active layers for infrared detection are not widely explored, thus we are focusing on optimization of the QD film and development of the multilayer pixel stack. Electron and hole transport layers are selected to improve the photodiode performance while top and bottom contacts are optimized to allow top illumination. Optical interference simulations provide optimum thickness of each layer to enhance the cavity effect (and thus the efficiency). Furthermore, optical design is used for the semi-transparent top contact to improve the light in-coupling effect, supporting top illumination. Devices realized until now have dark current density in the range of 10-6 A/cm2 at a reverse bias voltage of -2 V. The external quantum efficiency at the wavelength of 1450 nm is above 10%, even though the active layer film thickness in only 150 nm. The stack is compatible with integration on top of silicon substrate. In summary, colloidal quantum dots provide a way to realize monolithic infrared imagers in a cost-effective way. Thin-film active layer enables scaling down pixel pitch beyond the limitations of traditional flip-chip hybridization, as the resolution is defined by the readout chip. In this work, we present a QD-based pixel stack design for a next generation, monolithic infrared image sensor. It enables uncooled detection of SWIR radiation up to the wavelength of 1600 nm

A Deep Learning-Based Approach for Train Arrival Time Prediction

Level crossings have a function to let the traffic cross the railroad from one side to the other. In the Netherlands, 2300 level crossings are spread out over the country, playing a significant role in daily traffic. Currently, there isn’t an accurate estimation of the arrival time of trains at level crossings while it plays an important role in traffic flow management in intelligent transport systems. This paper presents a state-of-the-art deep learning model for predicting the arrival time of trains at level crossings using spatial and temporal aspects, external attributes, and multi-task learning. The spatial and temporal aspects incorporate geographical and historical travel data and the attributes provide specific information about a train route. Using multi-task learning all the information is combined and an arrival time prediction is made both for the entire route as for sub-parts of that route. Experimental results show that on average, the error is only 281 s with an average trip time of one hour. The model is able to accurately predict the arrival time at level crossings for various time steps in advance. The source code is available at https://github.com/basbuijse/train-arrival-time-estimator