Dynamic ridesharing brings together individuals with similar itineraries to travel together and share the costs Centralized (binary integer programming) and decentralized (dynamic auction-based multi-agent) optimization algorithms are formulated to match passengers and drivers An application of these methods on a large scale case study shows that the decentralized approach provides near optimal solutions for single-driver, single-passenger case, with much less computational burden The decentralized approach is then extended to accommodate both multi passenger matches The results indicate higher user cost savings and Vehicle Kilometers Traveled (VKT) savings when allowing multi passenger rides

Agent Based Model for Dynamic Ridesharing

The coronavirus disease 2019 (COVID-19) outbreak has a substantial negative effect on the global transportation industry. Ride-sharing is an innovative means of transportation that is also affected by the COVID-19. How and when individuals adopt ride-sharing services under the COVID-19 context should be explored to reduce the influence of the COVID-19 on ride-sharing and promote the development of ride-sharing services. This research investigates the effect of ambiguity tolerance and environmental concern on potential users' intention toward adopting ride-sharing services and further examines how the COVID-19 affects their intention toward adopting ride-sharing services. Data from 964 potential users of ride-sharing services suggest that ambiguity tolerance and environmental concern directly and positively influence potential users' intention toward adopting ride-sharing services. In addition, both indirectly affect consumers' intention toward adopting ride-sharing services through perceived ease of use and perceived usefulness. Moreover, the perceived health threat negatively moderates the effect of ambiguity tolerance and environmental concern on consumers' intention toward adopting ride-sharing services. This study enriches the research on how and when ambiguity tolerance and environmental concern influence consumers' intention toward adopting ride-sharing services. Furthermore, this study highlights the moderating effect of perceived health threat under the COVID-19 context. Based on the empirical findings, practical implications are proposed for the providers and facilitators of ride-sharing services. 

https://doi.org/10.1016/j.tra.2022.03.004

Exploring non-users' intention to adopt ride-sharing services: Taking into account increased risks due to the COVID-19 pandemic among other factors

Challenged by urbanization and increasing travel needs, existing transportation systems call for new mobility paradigms. In this article, we present the fleet management and charging scheduling of a shared mobility-on-demand system, whereby electric vehicle fleets are operated by a centralized platform to provide customers with mobility service. We provide a comprehensive review of system operation based on the operational objectives. The fleet scheduling strategies are categorized into four types: i) order dispatching, ii) order-dispatching and rebalancing, iii) order-dispatching, rebalancing and charging, and iv) extended. Specifically, we first identify mathematical modeling techniques implemented in the transportation network, then analyze and summarize the solution approaches including mathematical programming, reinforcement learning, and hybrid methods. The advantages and disadvantages of different models and solution approaches are compared. Finally, we present research outlook in various directions.

https://ieeexplore.ieee.org/ielx7/8784343/8891891/09924240.pdf

Fleet Management and Charging Scheduling for Shared Mobility-on-Demand System: A Systematic Review

Extensive usage of private vehicles has led to increased traffic congestion, carbon emissions, and usage of non-renewable resources. These concerns have led to the wide adoption of vehicle sharing (ex: bike sharing, car sharing) systems in many cities of the world. In vehicle-sharing systems, base stations (ex: docking stations for bikes) are strategically placed throughout a city and each of the base stations contain a pre-determined number of vehicles at the beginning of each day. Due to the stochastic and individualistic movement of customers, there is typically either congestion (more than required) or starvation (fewer than required) of vehicles at certain base stations. As demonstrated in our experimental results, this happens often and can cause a significant loss in demand. We propose to dynamically redeploy idle vehicles using carriers so as to minimize lost demand or alternatively maximize revenue for the vehicle sharing company. To that end, we contribute an optimization formulation to jointly address the redeployment (of vehicles) and routing (of carriers) problems and provide two approaches that rely on decomposability and abstraction of problem domains to reduce the computation time significantly. Finally, we demonstrate the utility of our approaches on two real world data sets of bike-sharing companies.

Dynamic Redeployment to Counter Congestion or Starvation in Vehicle Sharing Systems.

Transport-sharing systems are eco-friendly and the most promising services in smart urban environments, where the booming Internet of things (IoT) technologies play an important role in the smart infrastructure. Due to the imbalanced bike distribution, bikes and stalls in the docking stations could be unavailable when needed, leading to bad customer experiences. We develop a dynamic repositioning strategy for the management of bikes in this paper, which supports dispatchers to keep stations in service. Two open datasets are examined, and the exploratory data analysis presents that there is a significant difference of travel patterns between working and non-working days, where the former has an excess demand at rush hours and the latter is usually at a low demand. To evaluate the effect when the demand outstrips a station’s capacity, we propose a non-linear scaling technique to transform demand patterns and perform the clustering analysis for each of five categories obtained from the sophisticated analysis of the dataset. Our repositioning strategy is developed according to the transformed demands. Compared with the previous work, numerical simulations reveal that our strategy has a better performance for high-demand stations, and thus can substantially reduce the repositioning cost, which brings benefit to bike-sharing operators for managing the city bike system.

/pdf/a-demand-centric-repositioning-strategy-for-bike-sharing-2ts7xobo.pdf

A Demand-Centric Repositioning Strategy for Bike-Sharing Systems

Dynamic pricing plays an important role in solving the problems such as traffic load reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies can increase capacity utilization, total revenue of service providers, and the satisfaction of both passengers and drivers. Many proposed dynamic pricing technologies focus on short-term optimization and face poor scalability in modeling long-term goals for the limitations of solution optimality and prohibitive computation. In this article, a deep reinforcement learning framework is proposed to tackle the dynamic pricing problem for ride-hailing platforms. A soft actor-critic (SAC) algorithm is adopted in the reinforcement learning framework. First, the dynamic pricing problem is translated into a Markov Decision Process (MDP) and is set up in continuous action spaces, which is no need for the discretization of action space. Then, a new reward function is obtained by the order response rate and the KL-divergence between supply distribution and demand distribution. Experiments and case studies demonstrate that the proposed method outperforms the baselines in terms of order response rate and total revenue.

Deep Reinforcement Learning-based Trajectory Pricing on Ride-hailing Platforms

With the popularity of green travel and the aggravation of traffic congestion, Bike Sharing System (BSS) is adopted increasingly in many countries nowadays. However, the BSS is prone to be unbalanced because of the unequal supply and demand in each station, which leads to the loss in customer requirements. To address this issue, we develop a Monte Carlo tree search based Dynamic Repositioning (MCDR) method, which can help operators to decide at any time: (i) which station should be balanced firstly, and (ii) how many bikes should be picked or dropped at an unbalanced station. In this paper, we first employed a Density-based Station Clustering algorithm to reduce the problem complexity. Then the concept of service level is introduced to calculate the number of bikes that need to be transferred at each station. Finally, considering multiple factors, we propose a dynamic bike repositioning approach named MCDR, which can provide an optimal repositioning strategy for operators. Experimental results on a real-world dataset demonstrate that our method can reduce customer loss more effectively than the state-of-the-art methods.

Monte carlo tree search for dynamic bike repositioning in bike-sharing systems

Due to the positive impact of ride sharing on urban traffic and environment, it has attracted a lot of research attention recently. However, most existing researches focused on the profit maximization or the itinerary minimization of drivers, only rare work has covered on adjustable price function and matching algorithm for the batch requests. In this paper, we propose a request matching algorithm and an adjustable price function that benefits drivers as well as passengers. Our request-matching algorithm consists of an exact search algorithm and a group search algorithm. The exact search algorithm consists of three steps. The first step is to prune some invalid groups according to the total number of passengers and the capacity of vehicles. The second step is to filter out all candidate groups according to the compatibility of requests in same group. The third step is to obtain the most profitable group by the adjustable price function, and recommend the most profitable group to drivers. In order to enhance the efficiency of the exact search algorithm, we further design an improved group search algorithm based on the idea of original simulated annealing. Extensive experimental results show that our method can improve the income of drivers, and reduce the expense of passengers. Meanwhile, ride sharing can also keep the utilization rate of seats 80%, driving distance is reduced by 30%.

Driving Route Recommendation With Profit Maximization in Ride Sharing

\n Predicting the bike demand can help rebalance the bikes and improve the service quality of a bike-sharing system. A lot of works focus on predicting the bike demand for all the stations, which is unnecessary as the travel cost of rebalance operations increases sharply as the number of stations increases. In this paper, we propose a framework for predicting the hourly bike demand based on the central stations we define. Firstly, we propose Two-Stage Station Clustering Algorithm to assign central stations and common stations into each cluster. Secondly, we propose a hierarchical prediction model to predict the hourly bike demand for every cluster and each central station progressively. Thirdly, we use a well-studied queuing model to determine the target initial inventory for each central station. The most innovative contribution of this paper is proposing the concept of central station, the use of a novel algorithm to cluster the central stations and present a hierarchical model, containing the Time and Weather Similarity Weighted K-Nearest Neighbor Algorithm and a linear model to predict the bike demand for central stations. The experimental results on the New York citi bike system demonstrate that our proposed method is more accurate than other methods in solving existing problems.

http://academic.oup.com/comjnl/advance-article-pdf/doi/10.1093/comjnl/bxaa086/33559427/bxaa086.pdf

Central Station-Based Demand Prediction for Determining Target Inventory in a Bike-Sharing System

http://academic.oup.com/comjnl/advance-article-pdf/doi/10.1093/comjnl/bxab166/40811590/bxab166.pdf

Longji Huang

Papers

Deep Reinforcement Learning-based Trajectory Pricing on Ride-hailing Platforms

Monte carlo tree search for dynamic bike repositioning in bike-sharing systems

Driving Route Recommendation With Profit Maximization in Ride Sharing

Central Station-Based Demand Prediction for Determining Target Inventory in a Bike-Sharing System

Detecting Urban Anomalies Using Factor Analysis and One Class Support Vector Machine