Shared mobility, particularly sharing rides, cars, bikes and scooters, is having a transformative effect on cities. Understanding what operational considerations affect the implementation of shared mobility services is critical to their success. This poster session explores several factors for consideration in the implementation of shared mobility in the transportation ecosystem.
Free-Floating Carsharing Users Willingness to Pay/Accept for Logistics Management Mechanisms
Chenyang Wu, Imperial College LondonShow Abstract View Presentation
Scott Le Vine, Transpo Group; SUNY New Paltz
Sandra Philips, movmi Shared Transportation Services
William Tang, EcoService
John Polak, Imperial College London
The spatiotemporal flexibility of free-floating carsharing (FFCS) fleets leads to vehicle stock imbalances across the network. One set of strategies for managing fleet distribution involves incentivising users to participate in relocating the vehicles. The objective of this study is to establish FFCS customers’ preferences for each of four incentivisation mechanisms: 1) vehicle delivery, 2) paid relocation, and 3-4) incentivisation for alternate vehicle pick-up and drop-off locations. Survey data from FFCS users in Vancouver and Washington D.C. are employed to quantify willingness-to-pay/accept (WTP/WTA) for these mechanisms. We find that a majority of respondents report positive attitudes toward each of the four incentivisation mechanisms. Regression analysis shows that user experiences using FFCS are generally stronger predictors of WTP/WTA than socio-demographic features, with (intuitively) the frequency of FFCS unavailability the strongest predictor. We performed k-means cluster analysis of respondents based on the times-of-week that they report experiencing difficulty finding an available FFCS vehicle, and identified four distinct segments of users. However, we found generally weak relationships between WTP/WTA and the specific time-of-week periods that unavailability is experienced.
An Incentive-Based Approach to Control Demands for the Operation of a One-Way Carsharing System
Lei Wang, Tongji UniversityShow Abstract View Presentation
Yong Jin, Global Car Sharing & Rental Co., Ltd.
Wanjing Ma, Tongji University
Ting Li, Global Car Sharing & Rental Co.
Ling Wang, Tongji University
One-way carsharing has become one innovative urban transportation mode which provides better mobility. Fleet imbalance problem occurs in the system frequently which requires efficient vehicle relocation to address. Incentive-based approach could influence the users’ demands and promote the operation of the system, which alleviates the pressures on operator-based relocation. This paper presents an incentive-based approach involving vehicle rewards policy and station rewards policy to attract pick-up demands and drop-off demands respectively. A ranking method is proposed to determine the candidate list of rewarding vehicles and stations through a scoring and sorting approach. The method acquires the user-app log data and the transaction data in real operating environment for real-time. Five factors for vehicles and four factors for stations are computed from the real-time data. The ranking indices are aggregated from the weighted sum of the factors. The reward policy and the ranking method were tested in real operating environment of an electric vehicle sharing system in two districts of Shanghai. The result suggests that the reward policy with ranking method could shorten the vehicle idle time and increase the number of transactions per vehicle and per station, and also resulted in increments on profits.
Is Uber a Substitute or Complement for Public Transit?
Jonathan Hall, University of TorontoShow Abstract View Presentation
Craig Palsson, Utah State University
Joseph Price, Brigham Young University
How Uber affects public transit ridership is a relevant policy question facing cities worldwide. Theoretically, Uber's effect on transit is ambiguous: while Uber is an alternative mode of travel, it can also increase the reach and flexibility of public transit's fixed-route, fixed-schedule service. We estimate the effect of Uber on public transit ridership using a difference-in-differences design that exploits variation across U.S. metropolitan areas in both the intensity of Uber penetration and the timing of Uber entry. We find that Uber is a complement for the average transit agency, increasing ridership by five percent after two years. This average effect masks considerable heterogeneity, with Uber increasing ridership more in larger cities and for smaller transit agencies.
A Decentralized Uber?: Operational Analysis of Cooperative Ridesourcing: A Case Study of Arcade City in Austin, Texas
Adam Stocker, University of California, BerkeleyShow Abstract View Presentation
Matthew Takara, University of California, Berkeley
This paper examines the operational qualities of Arcade City (AC) Austin, a decentralized ridesourcing cooperative that emerged in mid-2016 when Uber and Lyft temporarily left the Austin market. Platform cooperatives are an emerging alternative to more commercial ‘sharing economy’ platforms. They are democratically governed and cooperatively owned and aim to allow users and society at large to benefit from the shifting of transactions to the digital realm. AC Austin consists of a 42,000-member Facebook group that has no centralized management structure and relies on the group’s drivers and moderators to coordinate on-demand rides, deliveries, and other services between requesters and drivers. We collected trip-level request data from three weeks of operations in April and May 2018. The analysis reveals that 96% of all requests received a response from a driver or moderator and 80% of all requests were successfully completed. The average time it takes for a driver to respond to requests is 5.5 minutes and the average overall wait time is just under 15 minutes. In addition, we find that requests with driver response times longer than 14 minutes or overall wait times longer than 24 minutes are much more unlikely to be successfully completed than those with response and wait times under these thresholds. These operational results reveal ridesourcing travel behavior insights that are typically not publicly released, due to propriety concerns of commercial ridesourcing companies. Overall, the results show that the decentralized cooperative ridesourcing model can be surprisingly effective at serving rides and other requests.
Taxis: Going Beyond the Licensing System: GPS-Based Analysis of Licenses Intensity of Use
Jerome Laviolette, Ecole Polytechnique de MontrealShow Abstract View Presentation
Catherine Morency, Ecole Polytechnique de Montreal
The rapid rise of ride-hailing services provided by TNC has forced regulators to review existing taxi regulation. Most major North American cities control the entry to their taxi market through a license or medallion systems. This system is once again questioned as the best way to manage supply of taxi services in a world were the type of mobility services more diverse then ever. While those systems have been criticized for keeping many cities in a constant level of undersupply, thus reducing quality of service, this paper looks at minimum service requirement and intensity of use of licenses in cities with such systems. How to best assess if new licenses shall be issued without having much knowledge of the current hours of service provided by existing licenses? It also addresses several key questions related to the long-term strategic planning of taxi services. The objectives are to gain insights on license’s intensity of use and to give regulators additional tools and methods in evaluating how many new licenses should be emitted. This paper proposes a methodology to measure license intensity of use based on 1 year of GPS data from three taxi companies in Montreal. A first analysis is conducted on the overall use of taxi licenses. A second analysis is conducted using K-Means clustering to gain insights on the variability of intensity across licenses and throughout the year.
Competition Among Automated Taxis, Transit, and Conventional Passenger Vehicles: Traffic Effects in the San Francisco Bay Area
Joschka Bischoff, Technische Universitat Berlin Fakultat V Verkehrs- und MaschinensystemeShow Abstract
Caroline Rodier, University of California, Davis
Elham Pourrahmani, University of California, Davis
Miguel Jaller, University of California, Davis
Anmol Pahwa, University of California, Davis
Michal Maciejewski, Technische Universität Berlin
At the end of 2017, Waymo, Google’s autonomous vehicle spinoff, announced the launch of its ride-hailing service. Since then, it has been testing its “Early Rider” service with its autonomous vehicles, without back-up drivers, in Phoenix, Arizona (U.S.), areas. In this paper, we simulate the effects of the introduction of a similar service on conventional personal vehicle and transit travel in the San Francisco Bay Area region. We call this service “automated taxis” and use new research on the costs of automated vehicles to represent plausible per mile automated taxi fares. Alternative automated taxi (AT) scenarios are simulated with a regionally calibrated agent-based model using the MATSim framework. This model uses baseline travel demand data from the region’s official activity-based travel model and dynamically assigns vehicles on road and transit networks by time of day. Our results indicate that the introduction of automated taxis may have a significant impact on transit use (reducing it by more than half), vehicle miles of travel (increasing by 18%), and congestion. Automated taxis out compete transit travel in the outer areas of the region and produce more and longer vehicles trips on roadways (including deadhead travel), which tends to increase congestion in specific areas. This research highlights the significant threat of low cost AT services to suburban transit providers and efforts to reduce VMT and congestion.
Evaluation of On-Demand Ridesharing Services
Zhenliang Ma, Monash UniversityShow Abstract View Presentation
Haris Koutsopoulos, Northeastern University
Yi Zheng, Northeastern University
Ride-hailing services are transforming urban mobility by providing more flexibility and improved level of service to users. However, they also raise a lot of concerns for their impact on congestion, vehicle miles traveled (VMT), and competition with transit. Considering the popularity of the ride hailing services, promoting and increasing ride-sharing is an important means to address these concerns. While companies attempt to promote ridesharing with pricing strategies, evidence suggests that shared trips are only a small fraction of all trips. The paper presents a general operating model for an advanced requests version of the ride sharing problem with service constraints. Advanced requests means that all the requests are received before the time of vehicle dispatching. An efficient algorithm for request matching and vehicle routing is also proposed. A large-scale dataset from the operations of a major ride-hailing company is used to systematically assess the performance, in terms of VMT, of the advanced requests system relative to current practices. The impact of various design aspects of the advanced requests system (e.g. advanced requests horizon, vehicle capacity, etc.) on its performance are investigated. The sensitivity of the results to user preferences in terms of level of service (time to be served and excess trip time) and willingness to share are explored. The results suggest that even with short advanced requests horizons, significant benefits with respect to VMT reduction can be realized, with very little deterioration of the level of service customers experience. The proposed model and results can also help evaluate the usefulness of alternative business models for ride sharing services towards more sustainable mobility concepts.
Reservation Scheme for Vehicle Sharing Systems
Mireia Roca-Riu, ETHZ - Swiss Federal Institute of TechnologyShow Abstract
Monica Menendez, New York University, Abu Dhabi
In the last 20 years vehicle sharing has been a growing trend in personal mobility. Multiple aspects of these systems have been already discussed: different forms of vehicle sharing, user’s preferences and behavior, or benefits estimation. Nevertheless, the management of these systems needs to be continuously improved to remain a competitive alternative. In this work, we propose a reservation scheme to manage in advanced rental reservations of a two way station based vehicle sharing system. It allows the operator to better plan the necessary vehicles at each station, and encourages the drivers to make a better use of the existing vehicles, by showing flexibility in the starting rental time. The reservation scheme is organized with an auction, where drivers bet for their preferred rental start time. Drivers participating in the auction are offered a reduced rental fare, which is then complemented with the reservation fee that results from the auction. The auction is solved under Vickrey-Clarke-Groves (VCG) mechanism for combinatorial auctions, which guarantees the desired properties for the operator and the drivers. The proposed scheme is tested on instances inspired by the Mobility system in Zurich, Switzerland. The results show that operators could decrease their fleets with low impacts on the overall rental fees, especially when drivers show flexibility in their rental start times. Moreover, reservation fees can at least partially compensate the decrease on rental fees provided to the auction users.
Redefining Car Access: Ridehail Travel and Use in Los Angeles
Anne Brown, Univeristy of OregonShow Abstract View Presentation
Ridehail companies such as Uber and Lyft divorce car access from car ownership, redefining auto-mobility as we know it. Despite ridehailing’s high-tech luster, however, it remains unclear how ridehailing serves different neighborhoods and travelers, and who, if anyone, is left behind. To address this gap, this research asks two distinct questions about ridehail travel and equity in Los Angeles: first, what factors are associated with the spatial distribution of ridehail service? And second, what factors are associated with individual ridehail trip-making? The questions are answered using a new and rich origin-destination dataset of 6.3 million Lyft trips in Los Angeles. This research finds that ridehail service is nearly ubiquitous and served neighborhoods home to 99.8 percent of the county population over a three-month period. Findings reject media reports that neighborhoods are systematically excluded from ridehail service based on the characteristics of those who live there. While ridehail trips are more concentrated in the densest neighborhoods compared to overall car trips, their robust presence in suburban and rural neighborhoods suggests that planning for ridehailing should extend beyond dense urban centers. Associations between neighborhood socioeconomic characteristics and ridehail service and use suggest that ridehailing provides car access to communities where access to its closest substitute, the household car, is limited. Policymakers should promote strategies that ensure ridehail access by populations without smartphone or bank accounts to ensure auto-mobility for all, not just some, travelers.
Car Sharing: Impact on Mobility and Travel Choices and the Role of Life Events and Attitudes
Taru Jain, Monash UniversityShow Abstract View Presentation
Geoff Rose, Monash University
Marilyn Johnson, Monash University
Car sharing as a mobility option is growing rapidly in many countries. To meet growing demand, local governments are often approached to provide support to car share providers and car share users. However there is a lack of local evidence about the effectiveness of car share in Australia. Moreover, much of the car sharing research worldwide has been empirical, focused on the net impact of services. These findings have provided insights into ‘what’ happens as car sharing increases but offer few insights into the ‘why’ dimension. This exploratory study, conducted in Melbourne, Australia, was informed by a theoretical framework informed by the mobility biographies literature. Qualitative methods were used to investigate the impact of car sharing on travel behavior in the form of lifestyle, mobility and travel choices. Focus groups (n=5 groups) and semi-structured interviews (n=18) were conducted with car share members and non-members in inner and middle Melbourne. Car sharers were classified into five categories: car dependents, car avoiders, second car avoiders, car aspirers and car sellers. Key findings suggest that car sharing motives and impacts vary greatly for all categories. Car aspirers and car sellers report the greatest changes in mobility choices (car ownership) and travel choices (use of a car, public transport and active modes). The study highlights the value of a disaggregated understanding of impacts for each member category. It provides evidence relevant to tailoring policy, plans, and marketing measures to encourage the use of car share as a lever for reducing car ownership and dependency.
Predicting Real-Time Surge Pricing of Ride-Hailing Companies
Matthew Battifarano, Carnegie Mellon UniversityShow Abstract View Presentation
Sean Qian, Carnegie Mellon University
Ride-hailing companies such as Uber and Lyft represent a popular and growing mode of transit in cites worldwide. These companies employ surge pricing in real time to balance the needs of both drivers and riders. Surge prices in the next few minutes to hours could be predicted to encapsulate the complex evolution of service fleets and service demand in the short term. Surge pricing, if effectively predicted and disseminated to both drivers and riders, can be used to more efficiently allocate vehicles, save users money and time, and provide profitable insight to drivers, which ultimately helps the optimality and reliability of transportation networks. This paper explores the spatio-temporal correlations among the urban environment, traffic flow characteristics and surge multipliers. We propose a general framework for predicting the short-term evolution of surge multipliers in real-time using a linear model with L1 regularization, coupled with pattern clustering. This model is able to predict Uber surge multipliers in the urban areas of Pittsburgh up to two hours in advance using data from the previous hour within 3-5% error, out-performing the overall mean and the historical average. Cross-correlation of Uber and Lyft surge multiplier is also explored.