55 research outputs found
Travel Demand Forecasting: A Fair AI Approach
Artificial Intelligence (AI) and machine learning have been increasingly
adopted for travel demand forecasting. The AI-based travel demand forecasting
models, though generate accurate predictions, may produce prediction biases and
raise fairness issues. Using such biased models for decision-making may lead to
transportation policies that exacerbate social inequalities. However, limited
studies have been focused on addressing the fairness issues of these models.
Therefore, in this study, we propose a novel methodology to develop
fairness-aware, highly-accurate travel demand forecasting models. Particularly,
the proposed methodology can enhance the fairness of AI models for multiple
protected attributes (such as race and income) simultaneously. Specifically, we
introduce a new fairness regularization term, which is explicitly designed to
measure the correlation between prediction accuracy and multiple protected
attributes, into the loss function of the travel demand forecasting model. We
conduct two case studies to evaluate the performance of the proposed
methodology using real-world ridesourcing-trip data in Chicago, IL and Austin,
TX, respectively. Results highlight that our proposed methodology can
effectively enhance fairness for multiple protected attributes while preserving
prediction accuracy. Additionally, we have compared our methodology with three
state-of-the-art methods that adopt the regularization term approach, and the
results demonstrate that our approach significantly outperforms them in both
preserving prediction accuracy and enhancing fairness. This study can provide
transportation professionals with a new tool to achieve fair and accurate
travel demand forecasting.Comment: improved the methodology; updated new content
The impacts of ridesourcing services on the taxi market: Empirical evidence from England and Wales
Ridesourcing services have emerged as a major competitor and potential substitute for traditional taxi services. However, research investigating the effects of ridesourcing on the taxi market remains limited, with a focus on specific geographies. This empirical study aims to fill this research gap by examining the impacts of ridesourcing on the taxi market in England and Wales. Using biennial Taxi and Private Hire Vehicle (PHV) Statistics data from the Department for Transport spanning 2005 to 2019, we investigate the impacts of ridesourcing on the number of Hackney Carriages (HCs) and PHVs, as well as the employment patterns in the taxi sector. Our findings indicate a gradual decline in the number of HCs following the introduction of ridesourcing. In contrast, the number of PHVs, which are restricted to pre-bookings, gradually increased. However, we observed no statistically significant change in the number of taxi drivers on average. Notably, our analysis reveals heterogeneous effects across different areas, including rural, urban, and metropolitan districts. Furthermore, we explore the role of regulatory environments in the evolution of ridesourcing and traditional taxi services. Our study highlights that regulation change allowing PHVs to operate across borders may lead to a dramatic increase in the number of PHVs and taxi drivers in specific local authorities. Our research has important implications for policymakers and transportation authorities, particularly in terms of maintaining a competitive taxi market. Furthermore, our findings can inform authorities when planning environmentally sustainable mobility services through the implementation of appropriate regulatory frameworks
Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System
The rapid growth of the ride-hailing industry has revolutionized urban
transportation worldwide. Despite its benefits, equity concerns arise as
underserved communities face limited accessibility to affordable ride-hailing
services. A key issue in this context is the vehicle rebalancing problem, where
idle vehicles are moved to areas with anticipated demand. Without equitable
approaches in demand forecasting and rebalancing strategies, these practices
can further deepen existing inequities. In the realm of ride-hailing, three
main facets of fairness are recognized: algorithmic fairness, fairness to
drivers, and fairness to riders. This paper focuses on enhancing both
algorithmic and rider fairness through a novel vehicle rebalancing method. We
introduce an approach that combines a Socio-Aware Spatial-Temporal Graph
Convolutional Network (SA-STGCN) for refined demand prediction and a
fairness-integrated Matching-Integrated Vehicle Rebalancing (MIVR) model for
subsequent vehicle rebalancing. Our methodology is designed to reduce
prediction discrepancies and ensure equitable service provision across diverse
regions. The effectiveness of our system is evaluated using simulations based
on real-world ride-hailing data. The results suggest that our proposed method
enhances both accuracy and fairness in forecasting ride-hailing demand,
ultimately resulting in more equitable vehicle rebalancing in subsequent
operations. Specifically, the algorithm developed in this study effectively
reduces the standard deviation and average customer wait times by 6.48% and
0.49%, respectively. This achievement signifies a beneficial outcome for
ride-hailing platforms, striking a balance between operational efficiency and
fairness.Comment: 31 pages, 6 figure
Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling
The emergence of on-demand ride pooling services allows each vehicle to serve
multiple passengers at a time, thus increasing drivers' income and enabling
passengers to travel at lower prices than taxi/car on-demand services (only one
passenger can be assigned to a car at a time like UberX and Lyft). Although
on-demand ride pooling services can bring so many benefits, ride pooling
services need a well-defined matching strategy to maximize the benefits for all
parties (passengers, drivers, aggregation companies and environment), in which
the regional dispatching of vehicles has a significant impact on the matching
and revenue. Existing algorithms often only consider revenue maximization,
which makes it difficult for requests with unusual distribution to get a ride.
How to increase revenue while ensuring a reasonable assignment of requests
brings a challenge to ride pooling service companies (aggregation companies).
In this paper, we propose a framework for vehicle dispatching for ride pooling
tasks, which splits the city into discrete dispatching regions and uses the
reinforcement learning (RL) algorithm to dispatch vehicles in these regions. We
also consider the mutual information (MI) between vehicle and order
distribution as the intrinsic reward of the RL algorithm to improve the
correlation between their distributions, thus ensuring the possibility of
getting a ride for unusually distributed requests. In experimental results on a
real-world taxi dataset, we demonstrate that our framework can significantly
increase revenue up to an average of 3\% over the existing best on-demand ride
pooling method.Comment: Accepted by AAMAS 202
Future Aware Pricing and Matching for Sustainable On-demand Ride Pooling
The popularity of on-demand ride pooling is owing to the benefits offered to
customers (lower prices), taxi drivers (higher revenue), environment (lower
carbon footprint due to fewer vehicles) and aggregation companies like Uber
(higher revenue). To achieve these benefits, two key interlinked challenges
have to be solved effectively: (a) pricing -- setting prices to customer
requests for taxis; and (b) matching -- assignment of customers (that accepted
the prices) to taxis/cars. Traditionally, both these challenges have been
studied individually and using myopic approaches (considering only current
requests), without considering the impact of current matching on addressing
future requests. In this paper, we develop a novel framework that handles the
pricing and matching problems together, while also considering the future
impact of the pricing and matching decisions. In our experimental results on a
real-world taxi dataset, we demonstrate that our framework can significantly
improve revenue (up to 17\% and on average 6.4\%) in a sustainable manner by
reducing the number of vehicles (up to 14\% and on average 10.6\%) required to
obtain a given fixed revenue and the overall distance travelled by vehicles (up
to 11.1\% and on average 3.7\%). That is to say, we are able to provide an
ideal win-win scenario for all stakeholders (customers, drivers, aggregator,
environment) involved by obtaining higher revenue for customers, drivers,
aggregator (ride pooling company) while being good for the environment (due to
fewer number of vehicles on the road and lesser fuel consumed).Comment: 8 pages, 2 figures, published to AAAI-202
Towards More Efficient Shared Autonomous Mobility: A Learning-Based Fleet Repositioning Approach
Shared-use autonomous mobility services (SAMS) present new opportunities for
improving accessible and demand-responsive mobility. A fundamental challenge
that SAMS face is appropriate positioning of idle fleet vehicles to meet future
demand - a problem that strongly impacts service quality and efficiency. This
paper formulates SAMS fleet repositioning as a Markov Decision Process and
presents a reinforcement learning-based repositioning (RLR) approach called
integrated system-agent repositioning (ISR). The ISR learns a scalable fleet
repositioning strategy in an integrated manner: learning to respond to evolving
demand patterns without explicit demand forecasting and to cooperate with
optimization-based passenger-to-vehicle assignment. Numerical experiments are
conducted using New York City taxi data and an agent-based simulation tool. The
ISR is compared to an alternative RLR approach named externally guided
repositioning (EGR) and a benchmark joint optimization (JO) for
passenger-to-vehicle assignment and repositioning. The results demonstrate the
RLR approaches' substantial reductions in passenger wait times, over 50%,
relative to the JO approach. The ISR's ability to bypass demand forecasting is
also demonstrated as it maintains comparable performance to EGR in terms of
average metrics. The results also demonstrate the model's transferability to
evolving conditions, including unseen demand patterns, extended operational
periods, and changes in the assignment strategy
Towards Fair Allocation in Social Commerce Platforms
Social commerce platforms are emerging businesses where producers sell
products through re-sellers who advertise the products to other customers in
their social network. Due to the increasing popularity of this business model,
thousands of small producers and re-sellers are starting to depend on these
platforms for their livelihood; thus, it is important to provide fair earning
opportunities to them. The enormous product space in such platforms prohibits
manual search, and motivates the need for recommendation algorithms to
effectively allocate product exposure and, consequently, earning opportunities.
In this work, we focus on the fairness of such allocations in social commerce
platforms and formulate the problem of assigning products to re-sellers as a
fair division problem with indivisible items under two-sided cardinality
constraints, wherein each product must be given to at least a certain number of
re-sellers and each re-seller must get a certain number of products.
Our work systematically explores various well-studied benchmarks of fairness
-- including Nash social welfare, envy-freeness up to one item (EF1), and
equitability up to one item (EQ1) -- from both theoretical and experimental
perspectives. We find that the existential and computational guarantees of
these concepts known from the unconstrained setting do not extend to our
constrained model. To address this limitation, we develop a mixed-integer
linear program and other scalable heuristics that provide near-optimal
approximation of Nash social welfare in simulated and real social commerce
datasets. Overall, our work takes the first step towards achieving provable
fairness alongside reasonable revenue guarantees on social commerce platforms
A Systematic Literature Review on Machine Learning in Shared Mobility
Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels
The Governance of Risks in Ridesharing: A Revelatory Case from Singapore
Recently we have witnessed the worldwide adoption of many different types of
innovative technologies, such as crowdsourcing, ridesharing, open and big data,
aiming at delivering public services more efficiently and effectively. Among
them, ridesharing has received substantial attention from decision-makers
around the world. Because of the multitude of currently understood or
potentially unknown risks associated with ridesharing (unemployment, insurance,
information privacy, and environmental risk), governments in different
countries apply different strategies to address such risks. Some governments
prohibit the adoption of ridesharing altogether, while other governments
promote it. In this article, we address the question of how risks involved in
ridesharing are governed over time. We present an in-depth single case study on
Singapore and examine how the Singaporean government has addressed risks in
ridesharing over time. The Singaporean government has a strong ambition to
become an innovation hub, and many innovative technologies have been adopted
and promoted to that end. At the same time, decision-makers in Singapore are
reputed for their proactive style of social governance. The example of
Singapore can be regarded as a revelatory case study, helping us further to
explore governance practices in other countries. Keywords: risk; ridesharing;
transport; governance; innovative technologies; case study; Singapor
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