15,428 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
A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers
We propose a ridesharing strategy with integrated transit in which a private
on-demand mobility service operator may drop off a passenger directly
door-to-door, commit to dropping them at a transit station or picking up from a
transit station, or to both pickup and drop off at two different stations with
different vehicles. We study the effectiveness of online solution algorithms
for this proposed strategy. Queueing-theoretic vehicle dispatch and idle
vehicle relocation algorithms are customized for the problem. Several
experiments are conducted first with a synthetic instance to design and test
the effectiveness of this integrated solution method, the influence of
different model parameters, and measure the benefit of such cooperation.
Results suggest that rideshare vehicle travel time can drop by 40-60%
consistently while passenger journey times can be reduced by 50-60% when demand
is high. A case study of Long Island commuters to New York City (NYC) suggests
having the proposed operating strategy can substantially cut user journey times
and operating costs by up to 54% and 60% each for a range of 10-30 taxis
initiated per zone. This result shows that there are settings where such
service is highly warranted
A new approach for transport network design and optimization
The solution of the transportation network optimization problem actually requires, in most cases, very intricate and powerful computer resources, so that it is not feasible to use classical algorithms. One promising way is to use stochastic search techniques. In this context, Genetic Algorithms (GAs) seem to be - among all the available methodologies- one of the most efficient methods able to approach transport network design and optimization. Particularly, this paper will focus the attention on the possibility of modelling and optimizing Public Bus Networks by means of GAs. In the proposed algorithm, the specific class of Cumulative GAs(CGAs) will be used for solving the first level of the network optimization problem, while a classical assignment model ,or alternatively a neural network approach ,will be adopted for the Fitness Function(FF) evaluation. CGAs will then be utilized in order to generate new populations of networks, which will be evaluated by means of a suitable software package. For each new solution some indicators will be calculated .A unique FF will be finally evaluated by means of a multicriteria method. Altough the research is still in a preliminary stage, the emerging first results concerning numerical cases show very good perspectives for this new approach. A test in real cases will also follow.
Optimal Alignments for Designing Urban Transport Systems: Application to Seville
The achievement of some of the Sustainable Development Goals (SDGs) from the recent
2030 Agenda for Sustainable Development has drawn the attention of many countries towards
urban transport networks. Mathematical modeling constitutes an analytical tool for the formal
description of a transportation system whereby it facilitates the introduction of variables and the
definition of objectives to be optimized. One of the stages of the methodology followed in the
design of urban transit systems starts with the determination of corridors to optimize the population
covered by the system whilst taking into account the mobility patterns of potential users and the
time saved when the public network is used instead of private means of transport. Since the capture
of users occurs at stations, it seems reasonable to consider an extensive and homogeneous set of
candidate sites evaluated according to the parameters considered (such as pedestrian population
captured and destination preferences) and to select subsets of stations so that alignments can take
place. The application of optimization procedures that decide the sequence of nodes composing the
alignment can produce zigzagging corridors, which are less appropriate for the design of a single line.
The main aim of this work is to include a new criterion to avoid the zigzag effect when the alignment
is about to be determined. For this purpose, a curvature concept for polygonal lines is introduced,
and its performance is analyzed when criteria of maximizing coverage and minimizing curvature are
combined in the same design algorithm. The results show the application of the mathematical model
presented for a real case in the city of Seville in Spain.Ministerio de EconomĂa y Competitividad MTM2015-67706-
Analysis and operational challenges of dynamic ride sharing demand responsive transportation models
There is a wide body of evidence that suggests sustainable mobility is not only a technological question, but that automotive technology will be a part of the solution in becoming a necessary albeit insufficient condition. Sufficiency is emerging as a paradigm shift from car ownership to vehicle usage, which is a consequence of socio-economic changes. Information and Communication Technologies (ICT) now make it possible for a user to access a mobility service to go anywhere at any time. Among the many emerging mobility services, Multiple Passenger Ridesharing and its variants look the most promising. However, challenges arise in implementing these systems while accounting specifically for time dependencies and time windows that reflect users’ needs, specifically in terms of real-time fleet dispatching and dynamic route calculation. On the other hand, we must consider the feasibility and impact analysis of the many factors influencing the behavior of the system – as, for example, service demand, the size of the service fleet, the capacity of the shared vehicles and whether the time window requirements are soft or tight. This paper analyzes - a Decision Support System that computes solutions with ad hoc heuristics applied to variants of Pick Up and Delivery Problems with Time Windows, as well as to Feasibility and Profitability criteria rooted in Dynamic Insertion Heuristics. To evaluate the applications, a Simulation Framework is proposed. It is based on a microscopic simulation model that emulates real-time traffic conditions and a real traffic information system. It also interacts with the Decision Support System by feeding it with the required data for making decisions in the simulation that emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona’s Central Business District. The obtained results prove the potential feasibility of the mobility concept.Postprint (published version
The design of public transit networks with heuristic algorithms : case study Cape Town
Includes bibliographical references.The Transit Network Design Problem (TNDP) is well-researched in the field of transportation planning. It deals with the design of optimized public transportation networks and systems, and belongs to the class of non-linear optimization problems. In solving the problem, attempts are made to balance the tradeoffs between utility maximization and cost minimization given some resource constraints, within the context of a transportation network. In this dissertation, the design of a public transit network is undertaken and tested for Cape Town. The focus of the research is on obtaining an optimal network configuration that minimizes cost for both users and operators of the network. In doing so, heuristic solution algorithms are implemented in the design process, since they are known to generate better results for non-linear optimization problems than analytical ones. This algorithm which is named a Bus Route Network Design Algorithm (BRNDA) is based on genetic algorithms. Furthermore, it has three key components namely: 1) Bus Route Network Generation Algorithm (BRNGA) - which generates the potential network solutions; 2) Bus Route Network Analysis Procedure (BRNAP) - which evaluates the generated solutions; 3) Bus Route Network Search Algorithm (BRNSA) - which searches for an optimal or near optimal network option, among the feasible ones. The solution approach is tested first on a small scale network to demonstrate its numerical results, then it is applied to a large scale network, namely the Cape Town road network
Comparisons of two combined models of urban travel choices: Chicago and Dresden
Methods for combining the steps of the four-step travel forecasting procedure have gained more and more interest in recent years. A comparison of two state-of-the-art models is presented: VISSUM/VISEVA by PTV AG and Technical University Dresden, Germany, and CMMC by University of Illinois at Chicago, USA. Each model is tested on large-scale networks used by practitioners at Chicago and Dresden.
- …