A Decomposition Algorithm to Solve the Multi-Hop Peer-to-Peer Ride-Matching Problem

In this paper, we mathematically model the multi-hop Peer-to-Peer (P2P) ride-matching problem as a binary program. We formulate this problem as a many-to-many problem in which a rider can travel by transferring between multiple drivers, and a driver can carry multiple riders. We propose a pre-processing procedure to reduce the size of the problem, and devise a decomposition algorithm to solve the original ride-matching problem to optimality by means of solving multiple smaller problems. We conduct extensive numerical experiments to demonstrate the computational efficiency of the proposed algorithm and show its practical applicability to reasonably-sized dynamic ride-matching contexts. Finally, in the interest of even lower solution times, we propose heuristic solution methods, and investigate the trade-offs between solution time and accuracy

Stable Matching for Dynamic Ride-sharing Systems

Dynamic ride-sharing systems enable people to share rides and increase the efficiency of urban transportation by connecting riders and drivers on short notice. Automated systems that establish ride-share matches with minimal input from participants provide the most convenience and the most potential for system-wide performance improvement, such as reduction in total vehicle-miles traveled. Indeed, such systems may be designed to match riders and drivers to maximize system performance improvement. However, system-optimal matches may not provide the maximum benefit to each individual participant. In this paper we consider a notion of stability for ride-share matches and present several mathematical programming methods to establish stable or nearly-stable matches, where we note that ride-share matching optimization is performed over time with incomplete information. Our numerical experiments using travel demand data for the metropolitan Atlanta region show that we can significantly increase the stability of ride-share matching solutions at the cost of only a small degradation in system-wide performance

Sustainable Passenger Transportation: Dynamic Ride-Sharing

Ride-share systems, which aim to bring together travelers with similar itineraries and time schedules, may provide significant societal and environmental benefits by reducing the number of cars used for personal travel and improving the utilization of available seat capacity. Effective and efficient optimization technology that matches drivers and riders in real-time is one of the necessary components for a successful ride-share system. We formally define dynamic ride-sharing and outline the optimization challenges that arise when developing technology to support ride-sharing. We hope that this paper will encourage more research by the transportation science and logistics community in this exciting, emerging area of public transportation

The important of information & communications technology in Uber services

There is a growing interest and concern towards the concept of sustainable transport due to the low functionality of public transport, unregulated taxi pricing, lack of parking space, insufficient availability of taxies and the growing number of traffic congestion during peak hour in urban and sub-urban area.Uber services appear to be cost effective and a sustainable way to travel for public user especially commuter.The study aimed to explore the relationship between information & communications technology (ICT) and the effectiveness of Uber services.The factor that had been found that influence public ridership is information & communications technology.The application of convenience sampling with the usable data from 408 respondents who at least has the intention to use for future ride has been conducted by using online survey.In this study, there is a significant relationship between monthly salary & occupation with information & communications technology.This study contributed to city planner and local planner in developing or planning in order to have a smart city

Improving Urban Sustainability of Transportation System with Shared Mobility

The current transportation sector in the United States is heavily relied on private automobile, consuming a large amount of fuel energy and producing a large quantity of greenhouse gases. Shared mobility, such as ridesharing and bikesharing, could potentially improve urban sustainability by decreasing the total vehicle-miles, saving fuel energy and reducing greenhouse gases. This research project utilized the real-world private vehicle trajectory data of the City of the Ann Arbor, identified the potential bike trips and sharable vehicle trips, and applied optimization model to obtain the sharing scenario with the maximum vehicle-miles avoidance. The results indicate that 1.06% of total-vehicle miles can be reduced by shared mobility, including 3,799 vehicle trips that could be replaced by bike trips. Shared mobility could reduce multiple types of tailpipe gas emissions (e.g., 536 tons of CO2). Although the sharing potential is low based on the results, it might be due to the limited vehicle data and the irregular travelling pattern of private vehicles. The ridesharing potential is sensitive to the passenger’s time tolerance for dour of their trips and the number of potential bike trips is sensitive to the acceptable distance from trips’ origins and destinations to the shared bike stations. Policies and incentives to encourage longer time tolerance for ridesharing. Also, more shared bike stations could be built in the future.Master of ScienceNatural Resources and EnvironmentUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/136564/1/Shi,Rui_Master_Thesis_2017.pd

Multi-stakeholder collaboration in urban transport: state-of-the-art and research opportunities

Transport systems are undergoing a change of paradigm that focuses on resource-sharing and collaboration of multiple and diverse stakeholders. This paper aims to present a state-of-the-art on the main research issues of multi-stakeholder collaboration in urban transport and address the main contributions of the Special Issue on Collaboration and Urban Transport to the field. To that end, it seems necessary to identify and address the complexity of the relations of the stakeholders in the field, beyond the traditional classification of private and public stakeholders. A functional classification of urban stakeholders related to the different land uses is proposed a refer to space users and space organizers, each with several sub-categories. Furthermore, the collaboration among those stakeholders can take different forms and can be developed at different levels: transactional, informational and decisional. Thus, the main research topics regarding multi-stakeholders' collaboration are defined as: partnerships, resource sharing, resource pooling and Mobility-as-a-Service (MaaS) systems. A set of papers in this special issue focus on Urban Consolidation Centres (UCCs), partnerships in transport under a general perspective, multi-stakeholder cooperation and its barriers, collaborative decision-making, traffic prediction and urban congestion. In the papers, which deal with the field of multi-stakeholder collaboration in urban transport, there is a predominance on the use of surveys, but also a focus on data-driven techniques. As a result, this special issue contributes not only to the theoretical aspects, but adds value to technical and methodological issues

A Tabu Search Based Metaheuristic for Dynamic Carpooling Optimization

International audienceThe carpooling problem consists in matching a set of riders' requests with a set of drivers' offers by synchronizing their origins, destinations and time windows. The paper presents the so-called Dynamic Carpooling Optimization System (DyCOS), a system which supports the automatic and optimal ridematching process between users on very short notice or even en-route. Nowadays, there are numerous research contributions that revolve around the carpooling problem, notably in the dynamic context. However, the problem's high complexity and the real time aspect are still challenges to overcome when addressing dynamic carpooling. To counter these issues, DyCOS takes decisions using a novel Tabu Search based metaheuristic. The proposed algorithm employs an explicit memory system and several original searching strategies developed to make optimal decisions automatically. To increase users' satisfaction, the proposed metaheuristic approach manages the transfer process and includes the possibility to drop off the passenger at a given walking distance from his destination or at a transfer node. In addition, the detour concept is used as an original aspiration process, to avoid the entrapment by local solutions and improve the generated solution. For a rigorous assessment of generated solutions , while considering the importance and interaction among the optimization criteria, the algorithm adopts the Choquet integral operator as an aggregation approach. To measure the effectiveness of the proposed method, we develop a simulation environment based on actual carpooling demand data from the metropolitan area of Lille in the north of France

EVALUATING THE SUSTAINABILITY IMPACTS OF INTELLIGENT CARPOOLING SYSTEMS FOR SOV COMMUTERS IN THE ATLANTA REGION

Community-based carpooling has more potential to help alleviate traffic congestion and reduce energy use during peak hours than ride-hailing services, such as Uber or Lyft, because community-based carpooling avoids deadheading operations. However, community-based carpooling is not fully exploited due to communication, demographic, and economic barriers. This thesis proposes a top-down computation framework to estimate the potential market-share of community-based carpooling, given the outputs of activity-based travel demand models. Given disaggregate records of commute trips, the framework tries to estimate a reasonable percentage/number of trips among commuters in single-occupancy vehicles (SOV) that can carpool together, considering spatiotemporal constraints of their trips. The framework consists of two major procedures: (1) trip clustering; and (2) trip optimization. The framework tackles the problems associated with using large amounts of data (for example, the Atlanta travel demand model predicts more than 19 million vehicle trips per day) by following “split-apply-combine” procedures. A number of tricks and technologies (e.g., pre-computing, databases, concurrency, etc.) are employed to make the mass computing tasks solvable in a personal laptop in a reasonable time. Two different methods are established to solve the carpooling optimization problem. One method is based on the bipartite algorithm, while the other uses integer linear programming. The linear programming method estimates both the systemic optimal performance in terms of saving the most vehicular travel mileage, while the bipartite-based algorithm estimates one Pareto optimal performance of such system that pairs the greatest number of carpool members (i.e., maximum number of travelers that can use the system) given acceptable (defined by the user) reroute cost and travel delays. The performance of these two methods are carefully compared. A set of experiments are run to evaluate the carpooling potentials among single-occupancy vehicles based on the output of activity-based model’s (ARC ABM) home-to-work single-occupancy vehicle (SOV) trips that can be paired together towards designated regional employment centers. The experiment showed that under strict assumptions, an upper bound of around 13.6% of such trips can be carpooled together. The distribution of these trips over space, time, and travel network are thoroughly discussed. The results are promising in terms of finding carpooling and decreasing total vehicle mileage. Moreover, the framework is flexible enough with the potential to act as a simulation testbed, to optimize vehicular operations, and to match potential carpool partners in real-time.M.S

A two-stage approach to ridesharing assignment and auction in a crowdsourcing collaborative transportation platform.

Collaborative transportation platforms have emerged as an innovative way for firms and individuals to meet their transportation needs through using services from external profit-seeking drivers. A number of collaborative transportation platforms (such as Uber, Lyft, and MyDHL) arise to facilitate such delivery requests in recent years. A particular collaborative transportation platform usually provides a two sided marketplace with one set of members (service seekers or passengers) posting tasks, and the another set of members (service providers or drivers) accepting on these tasks and providing services. As the collaborative transportation platform attracts more service seekers and providers, the number of open requests at any given time can be large. On the other hand, service providers or drivers often evaluate the first couple of pending requests in deciding which request to participate in. This kind of behavior made by the driver may have potential detrimental implications for all parties involved. First, the drivers typically end up participating in those requests that require longer driving distance for higher profit. Second, the passengers tend to overpay under a competition free environment compared to the situation where the drivers are competing with each other. Lastly, when the drivers and passengers are not satisfied with their outcomes, they may leave the platforms. Therefore the platform could lose revenues in the short term and market share in the long term. In order to address these concerns, a decision-making support procedure is needed to: (i) provide recommendations for drivers to identify the most preferable requests, (ii) offer reasonable rates to passengers without hurting driver’s profit. This dissertation proposes a mathematical modeling approach to address two aspects of the crowdsourcing ridesharing platform. One is of interest to the centralized platform management on the assignment of requests to drivers; and this is done through a multi-criterion many to many assignment optimization. The other is of interest to the decentralized individual drivers on making optimal bid for multiple assigned requests; and this is done through the use of prospect theory. To further validate our proposed collaborative transportation framework, we analyze the taxi yellow cab data collected from New York city in 2017 in both demand and supply perspective. We attempt to examine and understand the collected data to predict Uber-like ridesharing trip demands and driver supplies in order to use these information to the subsequent multi-criterion driver-to-passenger assignment model and driver\u27s prospect maximization model. Particularly regression and time series techniques are used to develop the forecasting models so that centralized module in the platform can predict the ridesharing demands and supply within certain census tracts at a given hour. There are several future research directions along the research stream in this dissertation. First, one could investigate to extend the models to the emerging concept of Physical Internet on commodity and goods transportation under the interconnected crowdsourcing platform. In other words, integrate crowdsourcing in prevalent supply chain logistics and transportation. Second, it\u27s interesting to study the effect of Uber-like crowdsourcing transportation platforms on existing traffic flows at the various levels (e.g., urban and regional)