21,936 research outputs found
A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data
The increased availability of large-scale trajectory data around the world
provides rich information for the study of urban dynamics. For example, New
York City Taxi Limousine Commission regularly releases source-destination
information about trips in the taxis they regulate. Taxi data provide
information about traffic patterns, and thus enable the study of urban flow --
what will traffic between two locations look like at a certain date and time in
the future? Existing big data methods try to outdo each other in terms of
complexity and algorithmic sophistication. In the spirit of "big data beats
algorithms", we present a very simple baseline which outperforms
state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs
permit large scale experimentation). Such a travel time estimation baseline has
several important uses, such as navigation (fast travel time estimates can
serve as approximate heuristics for A search variants for path finding) and
trip planning (which uses operating hours for popular destinations along with
travel time estimates to create an itinerary).Comment: 12 page
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining
In this paper, we develop a reinforcement learning (RL) based system to learn
an effective policy for carpooling that maximizes transportation efficiency so
that fewer cars are required to fulfill the given amount of trip demand. For
this purpose, first, we develop a deep neural network model, called ST-NN
(Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS
trip data. Secondly, we develop a carpooling simulation environment for RL
training, with the output of ST-NN and using the NYC taxi trip dataset. In
order to maximize transportation efficiency and minimize traffic congestion, we
choose the effective distance covered by the driver on a carpool trip as the
reward. Therefore, the more effective distance a driver achieves over a trip
(i.e. to satisfy more trip demand) the higher the efficiency and the less will
be the traffic congestion. We compared the performance of RL learned policy to
a fixed policy (which always accepts carpool) as a baseline and obtained
promising results that are interpretable and demonstrate the advantage of our
RL approach. We also compare the performance of ST-NN to that of
state-of-the-art travel time estimation methods and observe that ST-NN
significantly improves the prediction performance and is more robust to
outliers.Comment: Accepted at IEEE International Conference on Big Data 2018. arXiv
admin note: text overlap with arXiv:1710.0435
Net Effects of Gasoline Price Changes on Transit Ridership in U.S. Urban Areas, MTI Report 12-19
Using panel data of transit ridership and gasoline prices for ten selected U.S. urbanized areas over the time period of 2002 to 2011, this study analyzes the effect of gasoline prices on ridership of the four main transit modes—bus, light rail, heavy rail, and commuter rail—as well as their aggregate ridership. Improving upon past studies on the subject, this study accounts for endogeneity between the supply of services and ridership, and controls for a comprehensive list of factors that may potentially influence transit ridership. This study also examines short- and long-term effects and non-constant effects at different gasoline prices. The analysis found varying effects, depending on transit modes and other conditions. Strong evidence was found for positive short-term effects only for bus and the aggregate: a 0.61-0.62 percent ridership increase in response to a 10 percent increase in current gasoline prices (elasticity of 0.061 to 0.062). The long-term effects of gasoline prices, on the other hand, was significant for all modes and indicated a total ridership increase ranging from 0.84 percent for bus to 1.16 for light rail, with commuter rail, heavy rail, and the aggregate transit in response to a 10 percent increase in gasoline prices. The effects at the higher gasoline price level of over 4. In addition, a positive threshold boost effect at the 3 per gallon for bus and commuter rail modes, and over $4 per gallon for light rail, in order to accommodate higher transit travel needs of the public through pricing strategies, general financing, capacity management, and operations planning of transit services
Ambulance Emergency Response Optimization in Developing Countries
The lack of emergency medical transportation is viewed as the main barrier to
the access of emergency medical care in low and middle-income countries
(LMICs). In this paper, we present a robust optimization approach to optimize
both the location and routing of emergency response vehicles, accounting for
uncertainty in travel times and spatial demand characteristic of LMICs. We
traveled to Dhaka, Bangladesh, the sixth largest and third most densely
populated city in the world, to conduct field research resulting in the
collection of two unique datasets that inform our approach. This data is
leveraged to develop machine learning methodologies to estimate demand for
emergency medical services in a LMIC setting and to predict the travel time
between any two locations in the road network for different times of day and
days of the week. We combine our robust optimization and machine learning
frameworks with real data to provide an in-depth investigation into three
policy-related questions. First, we demonstrate that outpost locations
optimized for weekday rush hour lead to good performance for all times of day
and days of the week. Second, we find that significant improvements in
emergency response times can be achieved by re-locating a small number of
outposts and that the performance of the current system could be replicated
using only 30% of the resources. Lastly, we show that a fleet of small
motorcycle-based ambulances has the potential to significantly outperform
traditional ambulance vans. In particular, they are able to capture three times
more demand while reducing the median response time by 42% due to increased
routing flexibility offered by nimble vehicles on a larger road network. Our
results provide practical insights for emergency response optimization that can
be leveraged by hospital-based and private ambulance providers in Dhaka and
other urban centers in LMICs
A COMPARISON OF STATED AND REVEALED PREFERENCE METHODS FOR FISHERIES MANAGEMENT
In this paper, we compare revealed and stated preference methods for anglers' preferences for various fisheries management measures. Using random utility models of recreation demand, we compare the use of stated and revealed preference methodologies for analyzing fisheries management options. We compare parameter and welfare estimates from the two models.Resource /Energy Economics and Policy,
Detecting Outliers in Data with Correlated Measures
Advances in sensor technology have enabled the collection of large-scale
datasets. Such datasets can be extremely noisy and often contain a significant
amount of outliers that result from sensor malfunction or human operation
faults. In order to utilize such data for real-world applications, it is
critical to detect outliers so that models built from these datasets will not
be skewed by outliers.
In this paper, we propose a new outlier detection method that utilizes the
correlations in the data (e.g., taxi trip distance vs. trip time). Different
from existing outlier detection methods, we build a robust regression model
that explicitly models the outliers and detects outliers simultaneously with
the model fitting.
We validate our approach on real-world datasets against methods specifically
designed for each dataset as well as the state of the art outlier detectors.
Our outlier detection method achieves better performances, demonstrating the
robustness and generality of our method. Last, we report interesting case
studies on some outliers that result from atypical events.Comment: 10 page
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