3,929 research outputs found
Operational research and simulation methods for autonomous ride-sourcing
Ride-sourcing platforms provide on-demand shared transport services by solving decision problems related to ride-matching and pricing. The anticipated commercialisation of autonomous vehicles could transform these platforms to fleet operators and broaden their decision-making by introducing problems such as fleet sizing and empty vehicle redistribution. These problems have been frequently represented in research using aggregated mathematical programs, and alternative practises such as agent-based models. In this context, this study is set at the intersection between operational research and simulation methods to solve the multitude of autonomous ride-sourcing problems.
The study begins by providing a framework for building bespoke agent-based models for ride-sourcing fleets, derived from the principles of agent-based modelling theory, which is used to tackle the non-linear problem of minimum fleet size. The minimum fleet size problem is tackled by investigating the relationship of system parameters based on queuing theory principles and by deriving and validating a novel model for pickup wait times. Simulating the fleet function in different urban areas shows that ride-sourcing fleets operate queues with zero assignment times above the critical fleet size. The results also highlight that pickup wait times have a pivotal role in estimating the minimum fleet size in ride-sourcing operations, with agent-based modelling being a more reliable estimation method.
The focus is then shifted to empty vehicle redistribution, where the omission of market structure and underlying customer acumen, compromises the effectiveness of existing models. As a solution, the vehicle redistribution problem is formulated as a non-linear convex minimum cost flow problem that accounts for the relationship of supply and demand of rides by assuming a customer discrete choice model and a market structure. An edge splitting algorithm is then introduced to solve a transformed convex minimum cost flow problem for vehicle redistribution. Results of simulated tests show that the redistribution algorithm can significantly decrease wait times and increase profits with a moderate increase in vehicle mileage.
The study is concluded by considering the operational time-horizon decision problems of ride-matching and pricing at periods of peak travel demand. Combinatorial double auctions have been identified as a suitable alternative to surge pricing in research, as they maximise social welfare by relying on stated customer and driver valuations. However, a shortcoming of current models is the exclusion of trip detour effects in pricing estimates. The study formulates a shared-ride assignment and pricing algorithm using combinatorial double auctions to resolve the above problem. The model is reduced to the maximum weighted independent set problem, which is APX-hard. Therefore, a fast local search heuristic is proposed, producing solutions within 10\% of the exact approach for practical implementations.Open Acces
On the inefficiency of ride-sourcing services towards urban congestion
The advent of shared-economy and smartphones made on-demand transportation
services possible, which created additional opportunities, but also more
complexity to urban mobility. Companies that offer these services are called
Transportation Network Companies (TNCs) due to their internet-based nature.
Although ride-sourcing is the most notorious service TNCs provide, little is
known about to what degree its operations can interfere in traffic conditions,
while replacing other transportation modes, or when a large number of idle
vehicles is cruising for passengers. We experimentally analyze the efficiency
of TNCs using taxi trip data from a Chinese megacity and a agent-based
simulation with a trip-based MFD model for determining the speed. We
investigate the effect of expanding fleet sizes for TNCs, passengers'
inclination towards sharing rides, and strategies to alleviate urban
congestion. We show that the lack of coordination of objectives between TNCs
and society can create 37% longer travel times and significant congestion.
Moreover, allowing shared rides is not capable of decreasing total distance
traveled due to higher empty kilometers traveled. Elegant parking management
strategies can prevent idle vehicles from cruising without assigned passengers
and lower to 7% the impacts of the absence of coordination.Comment: Submitted to Transportation Research Part
Modelling the Rise and Fall of Two-Sided Mobility Markets with Microsimulation
In this paper, we propose a novel modelling framework to reproduce the market
entry strategies for two-sided mobility platforms. In the MaaSSim agent-based
simulator, we develop a co-evolutionary model to represent day-to-day dynamics
of the two-sided mobility market with agents making rational decisions to
maximize their perceived utility. Participation probability of agents depends
on utility, composed of: experience, word of mouth and marketing components
adjusted by agents every day with the novel S-shaped formulas - better suited
(in our opinion) to reproduce market entry dynamics than previous approaches.
With such a rich representation, we can realistically model a variety of market
entry strategies and create significant network effects to reproduce the rise
and fall of two-side mobility platforms. To illustrate model capabilities, we
simulate a 400-day evolution of 200 drivers and 2000 travelers on a
road-network of Amsterdam. We design a six-stage market entry strategy with
consecutive: kick-off, discount, launch, growth, maturity and greed stages.
After 25 days the platform offers discounts, yet it starts gaining market share
only when the marketing campaign launches at day 50. Campaign finishes after 50
days, which does not stop the growth, now fueled mainly with a positive word of
mouth effect and experiences. The platform ends discounts after 200 days and
reaches the steady maturity period, after which its greedy strategy leads to
collapse of its market share and profit. All above simulated with a single
behavioral model, which well reproduces how agents of both sides adapts to
platform actions
Piggyback on Idle Ride-Sourcing Drivers for Intracity Parcel Delivery
This paper investigates the operational strategies for an integrated platform
that provides both ride-sourcing services and intracity parcel delivery
services over a transportation network utilizing the idle time of ride-sourcing
drivers. Specifically, the integrated platform simultaneously offers on-demand
ride-sourcing services for passengers and multiple modes of parcel delivery
services for customers, including: (1) on-demand delivery, where drivers
immediately pick up and deliver parcels upon receiving a delivery request; and
(2) flexible delivery, where drivers can pick up (or drop off) parcels only
when they are idle and waiting for the next ride-sourcing request. A
continuous-time Markov Chain (CTMC) model is proposed to characterize the
status change of drivers under joint movement of passengers and parcels over
the transportation network with limited vehicle capacity, where the service
quality of ride-sourcing services, on-demand delivery services, and flexible
delivery services are rigorously quantified. Building on the CTMC model,
incentives for ride-sourcing passengers, delivery customers, drivers, and the
platform are captured through an economic equilibrium model, and the optimal
operational decisions of the platform are derived by solving a non-convex
profit-maximizing problem. We prove the well-posedness of the model and develop
a tailored algorithm to compute the optimal decisions of the platform at an
accelerated speed. Furthermore, we validate the proposed model in a
comprehensive case study for San Francisco, demonstrating that joint management
of ride-sourcing services and intracity package delivery services can lead to a
Pareto improvement that benefits all stakeholders in the integrated
ride-sourcing and parcel delivery market
A duopoly of transportation network companies and traditional radio-taxi dispatch service agencies
Transportation network companies commonly enter the market for taxi ride intermediation and alter the market outcome. Compared to cooperatively organized radio-taxi dispatch service agencies, transportation network companies run larger fleets and serve more customers with lower fares, when the fixed costs of the dispatch office are relatively small. The same holds for private dispatch firms, when the fixed costs of a taxicab are not too small. These results are shown in a two-stage duopoly of fare and fleet size competition with fare- and waiting-timedependent demand
The cognitive and affective antecedents to consumer behavior towards on-demand transportation services in Egypt
In the recent few years, smartphones have shaped and assisted in the creation of new business models to formulate and develop some additional dimensions such as shared-economy or shared-mobility. Since transportation is one of the most essential aspects of shared-economy, it is vital to this study to focus and investigate the consumers’ intention to use the new commuting services provided by Transportation Network Companies (TNCs) in Egypt. Consequently, this research aims to examine and understand the cognitive and affective antecedents to consumers’ behavior towards TNCs in Egypt. Therefore, the model of the Unified Theory of Acceptance and Use of Technology (UTAUT2) has been applied to understand and explain the factors that influence the behavioral intention (BI) to use TNCs services. The factors of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), and Habit (HT) tested through surveying 200 respondents thru online (Google Forms) and offline (Self-Administered Questionnaires) techniques. The results showed that consumers’ intention to use TNCs services in Egypt, was positively affected by the factors of (performance expectancy, social influence, price value, and habit). However, the variables of (effort expectancy, facilitating conditions, and hedonic motivation) showed a negative influence on the intention to use TNCs services in Egypt. Thus, upon the evaluation of the gathered data and discovered findings, the market acceptance and share of TNCs services can be increased if these services considered the factors affecting the consumers\u27 intention that mentioned earlier
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