An Optimal Ride Sharing Recommendation Framework for Carpooling Services

Carpooling services allow drivers to share rides with other passengers. This helps in reducing the passengers’ fares and time, as well as traffic congestion and increases the income for drivers. In recent years, several carpooling based recommendation systems have been proposed. However, most of the existing systems do no effectively balance the conflicting objectives of drivers and passengers. We propose a Highest Aggregated Score Vehicular Recommendation (HASVR) framework that recommends a vehicle with highest aggregated score to the requesting passenger. The aggregated score is based on parameters, namely: (a) average time delay, (b) vehicle’s capacity, (c) fare reduction, (d) driving distance, and (e) profit increment. We propose a heuristic that balances the incentives of both drivers and passengers keeping in consideration their constraints and the real-time traffic conditions. We evaluated HASVR with a real-world dataset that contains GPS trace data of 61,136 taxicabs. Evaluation results confirm the effectiveness of HASVR compared to existing scheme in reducing the total mileage used to deliver all passengers, reducing the passengers’ fare, increasing the profit of drivers, and increasing the percentage of satisfied ride requests

How machine learning informs ride-hailing services: A survey

In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed

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

Modeling Individual Activity and Mobility Behavior and Assessing Ridesharing Impacts Using Emerging Data Sources

Predicting individual mobility behavior is one of the major steps of transportation planning models. Accurate prediction of individual mobility behavior will be beneficial for transportation planning. Although previous studies have used different data sources to model individual mobility behaviors, they have several limitations such as the lack of complete mobility sequences and travel mode information, limiting our ability to accurately predict individual movements. In recent years, the emergence of GPS-based floating car data (FCD) and on-demand ride-hailing service platforms can provide innovative data sources to understand and model individual mobility behavior. Compared to the previously used data sources such as mobile phone and social media data, mobility data extracted of the new data sources contain more specific, detailed, and longitudinal information of individual travel mode and coordinates of the visited locations. This dissertation explores the potential of using GPS-based FCD and on-demand ride-hailing service data with different modeling techniques towards understanding and predicting individual mobility and activity behaviors and assessing the ridesharing impacts through three studies

MOBILITY ANALYSIS AND PROFILING FOR SMART MOBILITY SERVICES: A BIG DATA DRIVEN APPROACH. An Integration of Data Science and Travel Behaviour Analytics

Smart mobility proved to be an important but challenging component of the smart cities paradigm. The increased urbanization and the advent of sharing economy require a complete digitalisation of the way travellers interact with the mobility services. New sharing mobility services and smart transportation models are emerging as partial solutions for solving some tra c problems, improve the resource e ciency and reduce the environmental impact. The high connectivity between travellers and the sharing services generates enormous quantity of data which can reveal valuable knowledge and help understanding complex travel behaviour. Advances in data science, embedded computing, sensing systems, and arti cial intelligence technologies make the development of a new generation of intelligent recommendation systems possible. These systems have the potential to act as intelligent transportation advisors that can o er recommendations for an e cient usage of the sharing services and in uence the travel behaviour towards a more sustainable mobility. However, their methodological and technological requirements will far exceed the capabilities of today's smart mobility systems. This dissertation presents a new data-driven approach for mobility analysis and travel behaviour pro ling for smart mobility services. The main objective of this thesis is to investigate how the latest technologies from data science can contribute to the development of the next generation of mobility recommendation systems. Therefore, the main contribution of this thesis is the development of new methodologies and tools for mobility analysis that aim at combining the domain of transportation engineering with the domain of data science. The addressed challenges are derived from speci c open issues and problems in the current state of the art from the smart mobility domain. First, an intelligent recommendation system for sharing services needs a general metric which can assess if a group of users are compatible for speci c sharing solutions. For this problem, this thesis presents a data driven indicator for collaborative mobility that can give an indication whether it is economically bene cial for a group of users to share the ride, a vehicle or a parking space. Secondly, the complex sharing mobility scenarios involve a high number of users and big data that must be handled by capable modelling frameworks and data analytic platforms. To tackle this problem, a suitable meta model for the transportation domain is created, using the state of the art multi-dimensional graph data models, technologies and analytic frameworks. Thirdly, the sharing mobility paradigm needs an user-centric approach for dynamic extraction of travel habits and mobility patterns. To address this challenge, this dissertation proposes a method capable of dynamically pro ling users and the visited locations in order to extract knowledge (mobility patterns and habits) from raw data that can be used for the implementation of shared mobility solutions. Fourthly, the entire process of data collection and extraction of the knowledge should be done with near no interaction from user side. To tackle this issue, this thesis presents practical applications such as classi cation of visited locations and learning of users' travel habits and mobility patterns using historical and external contextual data

Assessing the Potential of Ride-Sharing Using Mobile and Social Data

Ride-sharing on the daily home-work-home commute can help individuals save on gasoline and other car-related costs, while at the same time it can reduce traffic and pollution. This paper assesses the potential of ride-sharing for reducing traffic in a city, based on mobility data extracted from 3G Call Description Records (CDRs, for the cities of Barcelona and Madrid) and from Online Social Networks (Twitter, collected for the cities of New York and Los Angeles). We first analyze these data sets to understand mobility patterns, home and work locations, and social ties between users. We then develop an efficient algorithm for matching users with similar mobility patterns, considering a range of constraints. The solution provides an upper bound to the potential reduction of cars in a city that can be achieved by ride-sharing. We use our framework to understand the different constraints and city characteristics on this potential benefit. For example, our study shows that traffic in the city of Madrid can be reduced by 59% if users are willing to share a ride with people who live and work within 1 km; if they can only accept a pick-up and drop-off delay up to 10 minutes, this potential benefit drops to 24%; if drivers also pick up passengers along the way, this number increases to 53%. If users are willing to ride only with people they know ("friends" in the CDR and OSN data sets), the potential of ride-sharing becomes negligible; if they are willing to ride with friends of friends, the potential reduction is up to 31%.Comment: 11 page

Shifting from Driving to Riding: a study of the impacts of on-demand cab services on public transit ridership and vehicle ownership in Hyderabad, India

This thesis explores the effects of on-demand cab services, Uber and Ola, on public transit ridership and vehicle ownership in Hyderabad, India. India has grown to be Uber’s third largest market in the world but still lacks any comprehensive policies at the federal level to regulate on-demand cab services. These services have risen in popularity and have led to the evolution of new ownership and financial models to help populations afford a car to “drive to work.” This research examines the spatial effects of this rising popularity on public transit ridership and vehicle ownership in Hyderabad. This research found that there has been a shift in the proportion of on-demand cabs and cars to all vehicles from 2010 to 2016. This research also found that annual occupancy ratio along bus routes in the city has decreased from 2014 to 2016. This research found that these relationships are localized in the city. This thesis concludes by recommending further studies be carried out to understand the full extent of these effects to effectively incorporate these technologies and plan for the future mobility of city residents