Ridesharing and the Use of Public Transportation

We investigate the effects of mobile-sourced ridesharing via platforms like Uber, Lyft, and Didi Chuxing on the use of public transit systems. Our study combines trip-level data about Uber, taxi, and bike share usage in New York City with turnstile data about subway usage. We find that on the surface, ridesharing and subway usage are positively correlated. Exploiting a series of exogenous shocks to the system–the closing of subway stations–to better isolate substitution effects, our preliminary results suggest that the average shock results in an increase of over 30% in the use of ridesharing, highlighting crowd-based systems’ potential to smooth unexpected demand surges. Our ongoing work studies how substitution varies with socioeconomic indicators, and how substitution towards ridesharing compares to traditional taxi and bike sharing. We hope to lay a data-driven foundation to better understand how sharing economy alternatives relate to existing and future capital-intensive transit systems

Multimodal Transportation with Ridesharing of Personal Vehicles

Many public transportation systems are unable to keep up with growing passenger demand as the population grows in urban areas. The slow or lack of improvement for public transportation pushes people to use private transportation modes, such as carpooling and ridesharing. However, the occupancy rate of personal vehicles has been dropping in many cities. In this paper, we describe a centralized transit system that integrates public transit and ridesharing, which matches drivers and transit riders such that the riders would result in shorter travel time using both transit and ridesharing. The optimization goal of the system is to assign as many riders to drivers as possible for ridesharing. We give an exact approach and approximation algorithms to achieve the optimization goal. As a case study, we conduct an extensive computational study to show the effectiveness of the transit system for different approximation algorithms, based on the real-world traffic data in Chicago City; the data sets include both public transit and ridesharing trip information. The experiment results show that our system is able to assign more than 60% of riders to drivers, leading to a substantial increase in occupancy rate of personal vehicles and reducing riders\u27 travel time

Drivers’ Tactics in Ridesharing Economy in the Philippines

Increasing mobility needs, coupled with the lack of adequate public transportation has led to the popularity of ridesharing services in the Philippines, which in 2015 became the first country to implement a regulatory framework for ridesharing through transportation network company (TNC) apps. This study aims to explore the participation of drivers in the ridesharing economy in Metro Manila. Employing thematic analysis, the driver tactics are viewed as falling under one of six categories: surge chasing, request skipping, dual driving, colorum, operator tactics and focusing. While many of the themes confirm the findings of earlier studies, some ridesharing tactics found in the Philippine context provide nuances that may offer insights on the drivers’ use of ridesharing platforms. Specifically, these strategies suggest that users find new ways to use the technology to support their motivations, resulting in a notion deviant to the original purpose of ridesharing

Analysis The Potentials and Barriers of Applying Flexible Ridesharing Method in Southern Expressway in Sri Lanka

The ridesharing arrangement means the transportation of persons in a motor vehicle when such transportation is incidental to the principal purpose of the driver, which is to reach a destination and not to transport person for profit. Internet based ridesharing is the method that allows individuals in need of transportation to access a pool of drivers through a mobile app. The research aim is to find out the potential of applying internet based ridesharing in Sri Lanka under three main objectives; to ascertain the problems of the current transportation system available in Southern Expressway, to analyze the commuters’ perception towards the internet-based ridesharing concept and to examine the potential barriers and constraints of application of internet based the ridesharing system in Southern Expressway. Accordingly, data is collected using three samples representing 30 commuters from each group; the public bus users, personal vehicle users, and arranged hired vehicle users who are frequent commuters of the Southern Expressway. According to the research findings, the majority (90%) of the commuters use Southern Expressway to travel their work place and perceive travel cost as fair cost in contrast to time. The majority of the commuters’ view is the availability of buses in Southern Expressway is not at a satisfactory level. More than 90% said Southern Expressway is much comfortable to travel. The majority of the commuters (85%) willing to use the ridesharing if it is available for Southern Expressway. More than 90% have internet access and therefore the application is not much difficult to implement in Sri Lanka. Even though, people are willing to use ridesharing application, there are some barriers have identified through this study such as gender issues, social status and fear to travel with strangers. Anyhow, research findings have shed green light to implement the ridesharing methods in southern Expressway despite of the prevailing barrier

Autonomous Cars, Electric and Hybrid Cars, and Ridesharing: Perceptions vs. Reality

Autonomous Cars, Electric and Hybrid Cars, and Ridesharing are all important new technologies in today\u27s society that can have potentially large impacts on the environment in the future. This study was conducted to determine the differences in perceptions of Gettysburg College students regarding Autonomous Cars, Electric and Hybrid Cars, and Ridesharing and the reality of these topics in the real world. This paper also compares the perceptions of Environmental Studies majors/minors to the perceptions of other majors at Gettysburg College. The primary research was conducted by analyzing questions that were a part of a survey consisting of 16 questions which was administered to Gettysburg College students via Facebook class group pages and the Environmental Studies majors email alias. The study group consisted of 110 students with 31 of them being Environmental Studies majors/minors and 79 of them being non-Environmental Studies majors/minors. It was determined that there were no statistically significant differences between the Environmental Studies majors/minors and students that are other majors/minors at Gettysburg College. From our survey, we found that there is a distinct gap in knowledge on the current and future impacts on the environment from Autonomous Cars, Electric and Hybrid Cars, and Ridesharing. The questions that ask which power method produces more greenhouse gas emissions as well as the questions about the miles per gallon of participants’ personal vehicles were the most accurately answered. Overall, Gettysburg College students regardless of major or minor were found to have mostly inaccurate perceptions on the topics of Autonomous Cars, Electric and Hybrid Cars, and Ridesharing

Synergistic Interactions of Dynamic Ridesharing and Battery Electric Vehicles Land Use, Transit, and Auto Pricing Policies

It is widely recognized that new vehicle and fuel technology is necessary, but not sufficient, to meet deep greenhouse gas (GHG) reductions goals for both the U.S. and the state of California. Demand management strategies (such as land use, transit, and auto pricing) are also needed to reduce passenger vehicle miles traveled (VMT) and related GHG emissions. In this study, the authors explore how demand management strategies may be combined with new vehicle technology (battery electric vehicles or BEVs) and services (dynamic ridesharing) to enhance VMT and GHG reductions. Owning a BEV or using a dynamic ridesharing service may be more feasible when distances to destinations are made shorter and alternative modes of travel are provided by demand management strategies. To examine potential markets, we use the San Francisco Bay Area activity based travel demand model to simulate business-as-usual, transit oriented development, and auto pricing policies with and without high, medium, and low dynamic ridesharing participation rates and BEV daily driving distance ranges. The results of this study suggest that dynamic ridesharing has the potential to significantly reduce VMT and related GHG emissions, which may be greater than land use and transit policies typically included in Sustainable Community Strategies (under California Senate Bill 375), if travelers are willing pay with both time and money to use the dynamic ridesharing system. However, in general, large synergistic effects between ridesharing and transit oriented development or auto pricing policies were not found in this study. The results of the BEV simulations suggest that TODs may increase the market for BEVs by less than 1% in the Bay Area and that auto pricing policies may increase the market by as much as 7%. However, it is possible that larger changes are possible over time in faster growing regions where development is currently at low density levels (for example, the Central Valley in California). The VMT Fee scenarios show larger increases in the potential market for BEV (as much as 7%). Future research should explore the factors associated with higher dynamic ridesharing and BEV use including individual attributes, characteristics of tours and trips, and time and cost benefits. In addition, the travel effects of dynamic ridesharing systems should be simulated explicitly, including auto ownership, mode choice, destination, and extra VMT to pick up a passenger

To Share or Not to Share: Investigating the Social Aspects of Dynamic Ridesharing

Transportation network companies (TNCs) have introduced shared-ride versions of their ordinary services, such as UberPool or Lyft Line. The concept is simple: passengers pay less in fares for an incremental increase in time spent picking up and dropping off other riders. This paper focuses on the social and behavioral considerations of shared rides, which have not been explored as thoroughly as time and cost trade-offs in transportation. A survey of TNC users conducted through Mechanical Turk in June and July of 2016, which had 997 respondents across the United States, found that (a) users of dynamic ridesharing services reported that social interactions were relevant to mode choice, although not as much as traditional factors such as time and cost; (b) overall, the possibility of having a negative social interaction was more of a deterrent to use of dynamic ridesharing than the potential of having a positive social interaction was an incentive; (c) there was evidence that a substantial number of riders harbored feelings of prejudice toward passengers of different social class and race, and these passengers were much more likely to prefer having more information about potential future passengers; (d) most dynamic ridesharing users were motivated by ease and speed, compared with walking and public transportation; and (e) safety in dynamic ridesharing was an important issue, especially for women, many of whom reported feeling unsafe and preferred to be matched with passengers of the same sex

The Merits of Sharing a Ride

The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads