14,485 research outputs found
A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage
A key aspect of a sustainable urban transportation system is the
effectiveness of transportation policies. To be effective, a policy has to
consider a broad range of elements, such as pollution emission, traffic flow,
and human mobility. Due to the complexity and variability of these elements in
the urban area, to produce effective policies remains a very challenging task.
With the introduction of the smart city paradigm, a widely available amount of
data can be generated in the urban spaces. Such data can be a fundamental
source of knowledge to improve policies because they can reflect the
sustainability issues underlying the city. In this context, we propose an
approach to exploit urban positioning data based on stigmergy, a bio-inspired
mechanism providing scalar and temporal aggregation of samples. By employing
stigmergy, samples in proximity with each other are aggregated into a
functional structure called trail. The trail summarizes relevant dynamics in
data and allows matching them, providing a measure of their similarity.
Moreover, this mechanism can be specialized to unfold specific dynamics.
Specifically, we identify high-density urban areas (i.e hotspots), analyze
their activity over time, and unfold anomalies. Moreover, by matching activity
patterns, a continuous measure of the dissimilarity with respect to the typical
activity pattern is provided. This measure can be used by policy makers to
evaluate the effect of policies and change them dynamically. As a case study,
we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin
United We Ride National Dialogue
The Coordinating Council on Access and Mobility (CCAM) asked the National Academy of Public Administration and Easter Seals Project ACTION to develop and host the first United We Ride (UWR) National Dialogue. The goal of the Dialogue was to help shape future policy direction and provide input to the next CCAM strategic plan. The National Academy also assembled a small work group with representatives of the Federal Interagency Coordinating Council on Access and Mobility, Easter Seals Project ACTION, and the National Resource Center on Human Service Transportation to help guide the process of design and implementation.The CCAM includes 11 federal departments, nine of which are responsible for providing transportation for people with disabilities, older adults, and people with limited incomes. CCAM officially launched United We Ride in 2004 to (1) provide more rides for target populations while using the same or fewer assets, (2) simplify access, and (3) increase customer satisfaction.Key FindingsThe process used to create coordinated transportation plans needs improvement. Significant federal policy barriers still exist to strategies that would facilitate access to transportation services. Mobility management strategies are underutilized in communities across the country, and missed opportunities to bridge gaps between transportation and other community services still need to be addressed
Quantifying the benefits of vehicle pooling with shareability networks
Taxi services are a vital part of urban transportation, and a considerable
contributor to traffic congestion and air pollution causing substantial adverse
effects on human health. Sharing taxi trips is a possible way of reducing the
negative impact of taxi services on cities, but this comes at the expense of
passenger discomfort quantifiable in terms of a longer travel time. Due to
computational challenges, taxi sharing has traditionally been approached on
small scales, such as within airport perimeters, or with dynamical ad-hoc
heuristics. However, a mathematical framework for the systematic understanding
of the tradeoff between collective benefits of sharing and individual passenger
discomfort is lacking. Here we introduce the notion of shareability network
which allows us to model the collective benefits of sharing as a function of
passenger inconvenience, and to efficiently compute optimal sharing strategies
on massive datasets. We apply this framework to a dataset of millions of taxi
trips taken in New York City, showing that with increasing but still relatively
low passenger discomfort, cumulative trip length can be cut by 40% or more.
This benefit comes with reductions in service cost, emissions, and with split
fares, hinting towards a wide passenger acceptance of such a shared service.
Simulation of a realistic online system demonstrates the feasibility of a
shareable taxi service in New York City. Shareability as a function of trip
density saturates fast, suggesting effectiveness of the taxi sharing system
also in cities with much sparser taxi fleets or when willingness to share is
low.Comment: Main text: 6 pages, 3 figures, SI: 24 page
Optimization of Scheduling and Dispatching Cars on Demand
Taxicab is the most common type of on-demand transportation service in the city because its dispatching system offers better services in terms of shorter wait time. However, the shorter wait time and travel time for multiple passengers and destinations are very considerable. There are recent companies implemented the real-time ridesharing model that expects to reduce the riding cost when passengers are willing to share their rides with the others. This model does not solve the shorter wait time and travel time when there are multiple passengers and destinations. This paper investigates how the ridesharing can be improved by using the genetic algorithm that gives the optimal solution in terms of passengers wait time and routes duration among passengers’ start and end locations. The simulator uses the Google digital maps and direction services that allow the simulator to fetch the real-time data based on the current traffic conditions such as accident, peak hours, and weather. The simulation results that are sub-optimal routes are computed using the advanced genetic algorithm and real-time data availability
Testing demand responsive shared transport services via agent-based simulations
Demand Responsive Shared Transport DRST services take advantage of
Information and Communication Technologies ICT, to provide on demand transport
services booking in real time a ride on a shared vehicle. In this paper, an
agent-based model ABM is presented to test different the feasibility of
different service configurations in a real context. First results show the
impact of route choice strategy on the system performance
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