359 research outputs found
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
A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers
We propose a ridesharing strategy with integrated transit in which a private
on-demand mobility service operator may drop off a passenger directly
door-to-door, commit to dropping them at a transit station or picking up from a
transit station, or to both pickup and drop off at two different stations with
different vehicles. We study the effectiveness of online solution algorithms
for this proposed strategy. Queueing-theoretic vehicle dispatch and idle
vehicle relocation algorithms are customized for the problem. Several
experiments are conducted first with a synthetic instance to design and test
the effectiveness of this integrated solution method, the influence of
different model parameters, and measure the benefit of such cooperation.
Results suggest that rideshare vehicle travel time can drop by 40-60%
consistently while passenger journey times can be reduced by 50-60% when demand
is high. A case study of Long Island commuters to New York City (NYC) suggests
having the proposed operating strategy can substantially cut user journey times
and operating costs by up to 54% and 60% each for a range of 10-30 taxis
initiated per zone. This result shows that there are settings where such
service is highly warranted
A stochastic user-operator assignment game for microtransit service evaluation: A case study of Kussbus in Luxembourg
This paper proposes a stochastic variant of the stable matching model from
Rasulkhani and Chow [1] which allows microtransit operators to evaluate their
operation policy and resource allocations. The proposed model takes into
account the stochastic nature of users' travel utility perception, resulting in
a probabilistic stable operation cost allocation outcome to design ticket price
and ridership forecasting. We applied the model for the operation policy
evaluation of a microtransit service in Luxembourg and its border area. The
methodology for the model parameters estimation and calibration is developed.
The results provide useful insights for the operator and the government to
improve the ridership of the service.Comment: arXiv admin note: substantial text overlap with arXiv:1912.0198
A general equilibrium model for multi-passenger ridesharing systems with stable matching
This paper proposes a general equilibrium model for multi-passenger
ridesharing systems, in which interactions between ridesharing drivers,
passengers, platforms, and transportation networks are endogenously captured.
Stable matching is modeled as an equilibrium problem in which no ridesharing
driver or passenger can reduce ridesharing disutility by unilaterally switching
to another matching sequence. This paper is one of the first studies that
explicitly integrates the ridesharing platform multi-passenger matching problem
into the model. By integrating matching sequence with hyper-network,
ridesharing-passenger transfers are avoided in a multi-passenger ridesharing
system. Moreover, the matching stability between the ridesharing drivers and
passengers is extended to address the multi-OD multi-passenger case in terms of
matching sequence. The paper provides a proof for the existence of the proposed
general equilibrium. A sequence-bush algorithm is developed for solving the
multi-passenger ridesharing equilibrium problem. This algorithm is capable to
handle complex ridesharing constraints implicitly. Results illustrate that the
proposed sequence-bush algorithm outperforms general-purpose solver, and
provides insights into the equilibrium of the joint stable matching and route
choice problem. Numerical experiments indicate that ridesharing trips are
typically longer than average trip lengths. Sensitivity analysis suggests that
a properly designed ridesharing unit price is necessary to achieve network
benefits, and travelers with relatively lower values of time are more likely to
participate in ridesharing
Congestion Mitigation for Planned Special Events: Parking, Ridesharing and Network Configuration
abstract: This dissertation investigates congestion mitigation during the ingress of a planned special event (PSE). PSEs would impact the regular operation of the transportation system within certain time periods due to increased travel demand or reduced capacities on certain road segments. For individual attendees, cruising for parking during a PSE could be a struggle given the severe congestion and scarcity of parking spaces in the network. With the development of smartphones-based ridesharing services such as Uber/Lyft, more and more attendees are turning to ridesharing rather than driving by themselves. This study explores congestion mitigation during a planned special event considering parking, ridesharing and network configuration from both attendees and planner’s perspectives.
Parking availability (occupancy of parking facility) information is the fundamental building block for both travelers and planners to make parking-related decisions. It is highly valued by travelers and is one of the most important inputs to many parking models. This dissertation proposes a model-based practical framework to predict future occupancy from historical occupancy data alone. The framework consists of two modules: estimation of model parameters, and occupancy prediction. At the core of the predictive framework, a queuing model is employed to describe the stochastic occupancy change of a parking facility.
From an attendee’s perspective, the probability of finding parking at a particular parking facility is more treasured than occupancy information for parking search. However, it is hard to estimate parking probabilities even with accurate occupancy data in a dynamic environment. In the second part of this dissertation, taking one step further, the idea of introducing learning algorithms into parking guidance and information systems that employ a central server is investigated, in order to provide estimated optimal parking searching strategies to travelers. With the help of the Markov Decision Process (MDP), the parking searching process on a network with uncertain parking availabilities can be modeled and analyzed.
Finally, from a planner’s perspective, a bi-level model is proposed to generate a comprehensive PSE traffic management plan considering parking, ridesharing and route recommendations at the same time. The upper level is an optimization model aiming to minimize total travel time experienced by travelers. In the lower level, a link transmission model incorporating parking and ridesharing is used to evaluate decisions from and provide feedback to the upper level. A congestion relief algorithm is proposed and tested on a real-world network.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201
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