21 research outputs found
Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services
This study develops an online predictive optimization framework for
dynamically operating a transit service in an area of crowd movements. The
proposed framework integrates demand prediction and supply optimization to
periodically redesign the service routes based on recently observed demand. To
predict demand for the service, we use Quantile Regression to estimate the
marginal distribution of movement counts between each pair of serviced
locations. The framework then combines these marginals into a joint demand
distribution by constructing a Gaussian copula, which captures the structure of
correlation between the marginals. For supply optimization, we devise a linear
programming model, which simultaneously determines the route structure and the
service frequency according to the predicted demand. Importantly, our framework
both preserves the uncertainty structure of future demand and leverages this
for robust route optimization, while keeping both components decoupled. We
evaluate our framework using a real-world case study of autonomous mobility in
a university campus in Denmark. The results show that our framework often
obtains the ground truth optimal solution, and can outperform conventional
methods for route optimization, which do not leverage full predictive
distributions.Comment: 34 pages, 12 figures, 5 table
Modeling Censored Mobility Demand through Quantile Regression Neural Networks
Shared mobility services require accurate demand models for effective service
planning. On one hand, modeling the full probability distribution of demand is
advantageous, because the full uncertainty structure preserves valuable
information for decision making. On the other hand, demand is often observed
through usage of the service itself, so that the observations are censored, as
they are inherently limited by available supply. Since the 1980s, various works
on Censored Quantile Regression models have shown them to perform well under
such conditions, and in the last two decades, several works have proposed to
implement them flexibly through Neural Networks (CQRNN). However, apparently no
works have yet applied CQRNN in the Transport domain. We address this gap by
applying CQRNN to datasets from two shared mobility providers in the Copenhagen
metropolitan area in Denmark, as well as common synthetic baseline datasets.
The results show that CQRNN can estimate the intended distributions better than
both censorship-unaware models and parametric censored models.Comment: 13 pages, 7 figures, 4 table
Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand
Electric vehicles can offer a low carbon emission solution to reverse rising
emission trends. However, this requires that the energy used to meet the demand
is green. To meet this requirement, accurate forecasting of the charging demand
is vital. Short and long-term charging demand forecasting will allow for better
optimisation of the power grid and future infrastructure expansions. In this
paper, we propose to use publicly available data to forecast the electric
vehicle charging demand. To model the complex spatial-temporal correlations
between charging stations, we argue that Temporal Graph Convolution Models are
the most suitable to capture the correlations. The proposed Temporal Graph
Convolutional Networks provide the most accurate forecasts for short and
long-term forecasting compared with other forecasting methods
Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes
Transport demand is highly dependent on supply, especially for shared
transport services where availability is often limited. As observed demand
cannot be higher than available supply, historical transport data typically
represents a biased, or censored, version of the true underlying demand
pattern. Without explicitly accounting for this inherent distinction,
predictive models of demand would necessarily represent a biased version of
true demand, thus less effectively predicting the needs of service users. To
counter this problem, we propose a general method for censorship-aware demand
modeling, for which we devise a censored likelihood function. We apply this
method to the task of shared mobility demand prediction by incorporating the
censored likelihood within a Gaussian Process model, which can flexibly
approximate arbitrary functional forms. Experiments on artificial and
real-world datasets show how taking into account the limiting effect of supply
on demand is essential in the process of obtaining an unbiased predictive model
of user demand behavior.Comment: 21 pages, 10 figure
Context-Aware Sensing and Implicit Ground Truth Collection: Building a Foundation for Event Triggered Surveys on Autonomous Shuttles: Artikel
The LINC project aims to study interactions between passengers and autonomous vehicles in natural settings at the campus of Technical University of Denmark. To leverage the potential of IoT components in smartphone-based surveying, a system to identify specific spatial, temporal and occupancy contexts relevant for passengers’ experience was proposed as a central data collection strategy in the LINC project. Based on predefined contextual triggers specific questionnaires can be distributed to affected passengers. This work focuses on the data-based discrimination between two fundamental contexts for LINC passengers: be-in and be-out (BIBO) of the vehicle. We present empirical evidence that Bluetooth-low-energy beacons (BLE) have the potential for BIBO independent classification. We compare BLE with other smartphone onboard sensors, such as the global positioning system (GPS) and the accelerometer through: (i) random-forest (RF); (ii) multi-layer perceptron (MLP); and (iii) smartphone native off-the-shelve classifiers. We also perform a sensitivity analysis regarding the impact that faulty BIBO ground-truth has on the performance of the supervised classifiers (i) and (ii). Results show that BLE and GPS could allow reciprocal validation for BIBO passengers’ status. This potential might lift passengers from providing any further validation. We describe the smartphone-sensing platform deployed to gather the dataset used in this work, which involves passengers and autonomous vehicles in a realistic setting
Machine learning methods for transportation under uncertainty
Motivated by the prevalence of uncertainty and the widespread use of modeling in Transportation, we develop and study effective methods for modeling Transportation under uncertainty. These methods are Machine Learning-based, i.e., they extract patterns from data and leverage them for better modeling. We study them through several case studies, including: quick adaptation of traffic models upon road incidents; estimation of mobility demand from limited observations; and predictive optimization of dynamic Public Transport. Our results yield several positive conclusions about the effectiveness of the studied methods for current and future Transportation.Doctor of Philosoph